diff --git a/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_results.jsonl b/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_results.jsonl deleted file mode 100644 index 5d7938281fa604e600bb42906c5abce226588980..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_results.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 1, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 1, "actual_rollout_group_seed": 1, "initial_attempt_group_seed": 1, "final_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 72.9056, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant_preview": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35738 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6095, "output_tokens": 478, "reasoning_output_tokens": 391, "total_tokens": 6573}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6095, "output_tokens": 478, "reasoning_output_tokens": 391, "total_tokens": 6573}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant_preview": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35738 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9274, "output_tokens": 69, "reasoning_output_tokens": 12, "total_tokens": 9343}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 15369, "output_tokens": 547, "reasoning_output_tokens": 403, "total_tokens": 15916}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35738 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36354 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9216, "input_tokens": 12641, "output_tokens": 66, "reasoning_output_tokens": 10, "total_tokens": 12707}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17024, "input_tokens": 28010, "output_tokens": 613, "reasoning_output_tokens": 413, "total_tokens": 28623}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The two assignments are distinct and the order protects the crossing lane. Execute the volley once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35738 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36354 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}, {"role": "assistant", "content": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20378 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36370 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28918 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The two assignments are distinct and the order protects the crossing lane. Execute the volley once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12416, "input_tokens": 16603, "output_tokens": 411, "reasoning_output_tokens": 367, "total_tokens": 17014}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 29440, "input_tokens": 44613, "output_tokens": 1024, "reasoning_output_tokens": 780, "total_tokens": 45637}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | TEAM_S1\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: range...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The two assignments are distinct and the order protects the crossing lane. Execute the volley once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | TEAM_S1\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-bff0ebe7f503", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0001::codex-gpt-5.6-terra-medium", "job_index": 0, "case_index": 0, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 2, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 2, "actual_rollout_group_seed": 2, "initial_attempt_group_seed": 2, "final_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 91.2733, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant_preview": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35702 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6095, "output_tokens": 2092, "reasoning_output_tokens": 2033, "total_tokens": 8187}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6095, "output_tokens": 2092, "reasoning_output_tokens": 2033, "total_tokens": 8187}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35702 chars]"}}]}, {"role": "assistant", "content": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36238 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10860, "output_tokens": 59, "reasoning_output_tokens": 15, "total_tokens": 10919}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 16955, "output_tokens": 2151, "reasoning_output_tokens": 2048, "total_tokens": 19106}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "assistant_preview": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35702 chars]"}}]}, {"role": "assistant", "content": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36238 chars]"}}]}, {"role": "assistant", "content": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36266 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 14176, "output_tokens": 61, "reasoning_output_tokens": 10, "total_tokens": 14237}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13696, "input_tokens": 31131, "output_tokens": 2212, "reasoning_output_tokens": 2058, "total_tokens": 33343}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The complete plan is configured; execute it once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35702 chars]"}}]}, {"role": "assistant", "content": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36238 chars]"}}]}, {"role": "assistant", "content": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36266 chars]"}}]}, {"role": "assistant", "content": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36202 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The complete plan is configured; execute it once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13312, "input_tokens": 18087, "output_tokens": 34, "reasoning_output_tokens": 0, "total_tokens": 18121}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 27008, "input_tokens": 49218, "output_tokens": 2246, "reasoning_output_tokens": 2058, "total_tokens": 51464}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T0\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The complete plan is configured; execute it once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T0\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-cd5b7deee66a", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0002::codex-gpt-5.6-terra-medium", "job_index": 1, "case_index": 1, "model_index": 0, "worker_index": 1, "parallel_slot_index": 1, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 3, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 3, "actual_rollout_group_seed": 3, "initial_attempt_group_seed": 3, "final_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 77.085, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "assistant_preview": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35502 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 1007, "reasoning_output_tokens": 923, "total_tokens": 7106}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 1007, "reasoning_output_tokens": 923, "total_tokens": 7106}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant_preview": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35502 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 9804, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 9862}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 15903, "output_tokens": 1065, "reasoning_output_tokens": 934, "total_tokens": 16968}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35502 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13147, "output_tokens": 66, "reasoning_output_tokens": 9, "total_tokens": 13213}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 12672, "input_tokens": 29050, "output_tokens": 1131, "reasoning_output_tokens": 943, "total_tokens": 30181}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The final assignments are distinct and the phase order is set; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35502 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}, {"role": "assistant", "content": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30682 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The final assignments are distinct and the phase order is set; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 17097, "output_tokens": 55, "reasoning_output_tokens": 12, "total_tokens": 17152}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 24960, "input_tokens": 46147, "output_tokens": 1186, "reasoning_output_tokens": 955, "total_tokens": 47333}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S1_T3\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The final assignments are distinct and the phase order is set; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S1_T3\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-29d1a31b834a", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0003::codex-gpt-5.6-terra-medium", "job_index": 2, "case_index": 2, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 4, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 4, "actual_rollout_group_seed": 4, "initial_attempt_group_seed": 4, "final_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 81.4085, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant_preview": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6103, "output_tokens": 1749, "reasoning_output_tokens": 1676, "total_tokens": 7852}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6103, "output_tokens": 1749, "reasoning_output_tokens": 1676, "total_tokens": 7852}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 10539, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 10584}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 16642, "output_tokens": 1794, "reasoning_output_tokens": 1676, "total_tokens": 18436}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "Set S0 as phase-one shooter.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36138 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Set S0 as phase-one shooter.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 13858, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 13897}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 30500, "output_tokens": 1833, "reasoning_output_tokens": 1676, "total_tokens": 32333}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Set S0 as phase-one shooter.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Set S0 as phase-one shooter.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36138 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}, {"role": "assistant", "content": "Set S0 as phase-one shooter.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36214 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 17754, "output_tokens": 46, "reasoning_output_tokens": 7, "total_tokens": 17800}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 31360, "input_tokens": 48254, "output_tokens": 1879, "reasoning_output_tokens": 1683, "total_tokens": 50133}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T0\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T0\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-0078d2212f9c", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0004::codex-gpt-5.6-terra-medium", "job_index": 3, "case_index": 3, "model_index": 0, "worker_index": 3, "parallel_slot_index": 3, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 5, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 5, "actual_rollout_group_seed": 5, "initial_attempt_group_seed": 5, "final_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 67.3655, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant_preview": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35506 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6101, "output_tokens": 682, "reasoning_output_tokens": 609, "total_tokens": 6783}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6101, "output_tokens": 682, "reasoning_output_tokens": 609, "total_tokens": 6783}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35506 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35806 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9470, "output_tokens": 172, "reasoning_output_tokens": 115, "total_tokens": 9642}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 15571, "output_tokens": 854, "reasoning_output_tokens": 724, "total_tokens": 16425}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35506 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35806 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35822 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9216, "input_tokens": 12926, "output_tokens": 226, "reasoning_output_tokens": 163, "total_tokens": 13152}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 15232, "input_tokens": 28497, "output_tokens": 1080, "reasoning_output_tokens": 887, "total_tokens": 29577}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "All required assignments and the order are set; execute the volley exactly once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35506 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35806 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35822 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}, {"role": "assistant", "content": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30386 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "All required assignments and the order are set; execute the volley exactly once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 17041, "output_tokens": 40, "reasoning_output_tokens": 0, "total_tokens": 17081}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17024, "input_tokens": 45538, "output_tokens": 1120, "reasoning_output_tokens": 887, "total_tokens": 46658}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T1\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "All required assignments and the order are set; execute the volley exactly once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T1\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-11d59a0f7f86", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0005::codex-gpt-5.6-terra-medium", "job_index": 4, "case_index": 4, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 6, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 6, "actual_rollout_group_seed": 6, "initial_attempt_group_seed": 6, "final_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 79.119, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "assistant_preview": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 974, "reasoning_output_tokens": 916, "total_tokens": 7073}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 974, "reasoning_output_tokens": 916, "total_tokens": 7073}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "assistant_preview": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36354 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9745, "output_tokens": 359, "reasoning_output_tokens": 306, "total_tokens": 10104}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 15844, "output_tokens": 1333, "reasoning_output_tokens": 1222, "total_tokens": 17177}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36354 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13369, "output_tokens": 185, "reasoning_output_tokens": 138, "total_tokens": 13554}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16896, "input_tokens": 29213, "output_tokens": 1518, "reasoning_output_tokens": 1360, "total_tokens": 30731}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The plan is complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36354 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}, {"role": "assistant", "content": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30370 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The plan is complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 17408, "output_tokens": 34, "reasoning_output_tokens": 0, "total_tokens": 17442}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 29184, "input_tokens": 46621, "output_tokens": 1552, "reasoning_output_tokens": 1360, "total_tokens": 48173}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T3\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The plan is complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T3\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-78fcd0207d36", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0006::codex-gpt-5.6-terra-medium", "job_index": 5, "case_index": 5, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 7, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 7, "actual_rollout_group_seed": 7, "initial_attempt_group_seed": 7, "final_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 92.6701, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant_preview": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2203, "reasoning_output_tokens": 2141, "total_tokens": 8302}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2203, "reasoning_output_tokens": 2141, "total_tokens": 8302}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant_preview": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}]}, {"role": "assistant", "content": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36314 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 10978, "output_tokens": 58, "reasoning_output_tokens": 14, "total_tokens": 11036}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 17077, "output_tokens": 2261, "reasoning_output_tokens": 2155, "total_tokens": 19338}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "assistant_preview": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}]}, {"role": "assistant", "content": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36314 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36398 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 14296, "output_tokens": 63, "reasoning_output_tokens": 9, "total_tokens": 14359}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 31373, "output_tokens": 2324, "reasoning_output_tokens": 2164, "total_tokens": 33697}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The complete distinct-target plan and firing order are set; execute once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35778 chars]"}}]}, {"role": "assistant", "content": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36314 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36398 chars]"}}]}, {"role": "assistant", "content": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36174 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The complete distinct-target plan and firing order are set; execute once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13312, "input_tokens": 18215, "output_tokens": 46, "reasoning_output_tokens": 6, "total_tokens": 18261}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 31232, "input_tokens": 49588, "output_tokens": 2370, "reasoning_output_tokens": 2170, "total_tokens": 51958}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S1_T3\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The complete distinct-target plan and firing order are set; execute once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S1_T3\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-31eff80e800e", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0007::codex-gpt-5.6-terra-medium", "job_index": 6, "case_index": 6, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 8, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 8, "actual_rollout_group_seed": 8, "initial_attempt_group_seed": 8, "final_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 90.0401, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant_preview": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35786 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 947, "reasoning_output_tokens": 858, "total_tokens": 7046}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 947, "reasoning_output_tokens": 858, "total_tokens": 7046}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35786 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36070 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9749, "output_tokens": 844, "reasoning_output_tokens": 792, "total_tokens": 10593}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 15848, "output_tokens": 1791, "reasoning_output_tokens": 1650, "total_tokens": 17639}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "S0 takes the clear first-phase shot; S0 then ducks before S1 fires.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35786 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36070 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36094 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S0 takes the clear first-phase shot; S0 then ducks before S1 fires.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13888, "output_tokens": 61, "reasoning_output_tokens": 11, "total_tokens": 13949}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16896, "input_tokens": 29736, "output_tokens": 1852, "reasoning_output_tokens": 1661, "total_tokens": 31588}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0 takes the clear first-phase shot; S0 then ducks before S1 fires.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "Assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S0 takes the clear first-phase shot; S0 then ducks before S1 fires.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35786 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36070 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36094 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}, {"role": "assistant", "content": "S0 takes the clear first-phase shot; S0 then ducks before S1 fires.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36234 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28714 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 17836, "output_tokens": 37, "reasoning_output_tokens": 0, "total_tokens": 17873}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 30336, "input_tokens": 47572, "output_tokens": 1889, "reasoning_output_tokens": 1661, "total_tokens": 49461}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | TEAM_S1\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: range...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | TEAM_S1\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-e09a99957ef2", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0008::codex-gpt-5.6-terra-medium", "job_index": 7, "case_index": 7, "model_index": 0, "worker_index": 7, "parallel_slot_index": 7, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 9, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 9, "actual_rollout_group_seed": 9, "initial_attempt_group_seed": 9, "final_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 92.2219, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant_preview": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36010 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6101, "output_tokens": 1313, "reasoning_output_tokens": 1232, "total_tokens": 7414}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6101, "output_tokens": 1313, "reasoning_output_tokens": 1232, "total_tokens": 7414}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36010 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36298 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 10109, "output_tokens": 73, "reasoning_output_tokens": 26, "total_tokens": 10182}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 16210, "output_tokens": 1386, "reasoning_output_tokens": 1258, "total_tokens": 17596}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36010 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36298 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36322 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13464, "output_tokens": 1171, "reasoning_output_tokens": 1121, "total_tokens": 14635}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 15104, "input_tokens": 29674, "output_tokens": 2557, "reasoning_output_tokens": 2379, "total_tokens": 32231}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "Both targets are assigned and phase order is set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36010 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36298 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36322 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}, {"role": "assistant", "content": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=20482 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36454 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28634 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both targets are assigned and phase order is set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 18509, "output_tokens": 48, "reasoning_output_tokens": 7, "total_tokens": 18557}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 27392, "input_tokens": 48183, "output_tokens": 2605, "reasoning_output_tokens": 2386, "total_tokens": 50788}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T1\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Both targets are assigned and phase order is set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T1\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-451c8e2a581b", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0009::codex-gpt-5.6-terra-medium", "job_index": 8, "case_index": 8, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "CrossfireDeconfliction3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "CrossfireDeconfliction3D", "requested_group_seed": 10, "requested_env_id": 0, "actual_task_name": "CrossfireDeconfliction3D", "actual_group_seed": 10, "actual_rollout_group_seed": 10, "initial_attempt_group_seed": 10, "final_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}, "duration_s": 84.847, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant_preview": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35874 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6101, "output_tokens": 2190, "reasoning_output_tokens": 2109, "total_tokens": 8291}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6101, "output_tokens": 2190, "reasoning_output_tokens": 2109, "total_tokens": 8291}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant_preview": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS0 ASSIGNED T1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35874 chars]"}}]}, {"role": "assistant", "content": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36158 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "action_extracted": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10986, "output_tokens": 246, "reasoning_output_tokens": 192, "total_tokens": 11232}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 17087, "output_tokens": 2436, "reasoning_output_tokens": 2301, "total_tokens": 19523}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant_preview": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "CrossfireDeconfliction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nS1 ASSIGNED T2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35874 chars]"}}]}, {"role": "assistant", "content": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36158 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36182 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "action_extracted": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 14521, "output_tokens": 57, "reasoning_output_tokens": 0, "total_tokens": 14578}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13696, "input_tokens": 31608, "output_tokens": 2493, "reasoning_output_tokens": 2301, "total_tokens": 34101}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant_preview": "The complete assignments and phase order are set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "CrossfireDeconfliction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nPHASE 1 SET TO S0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35874 chars]"}}]}, {"role": "assistant", "content": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS0 ASSIGNED T1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36158 chars]"}}]}, {"role": "assistant", "content": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nS1 ASSIGNED T2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36182 chars]"}}]}, {"role": "assistant", "content": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nPHASE 1 SET TO S0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=19802 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36322 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The complete assignments and phase order are set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13312, "input_tokens": 18468, "output_tokens": 37, "reasoning_output_tokens": 0, "total_tokens": 18505}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 27008, "input_tokens": 50076, "output_tokens": 2530, "reasoning_output_tokens": 2301, "total_tokens": 52606}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VOLLEY | EQUIP_S0_T1\\n', '\\r\\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\\n\\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\\n\\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- AssignTarget\\n kind: method\\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\\n signature: AssignTarget(int shooterId, int targetId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - shooterId (int) [required]: Shooter ID 0 or 1.\\n constraints: range=[0, 1]\\n example: 0\\n - targetId (int) [required]: Target ID 0 through 3.\\n constraints: r...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "Coordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targe...[truncated]"}, {"role": "assistant", "text_preview": "The complete assignments and phase order are set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VOLLEY | EQUIP_S0_T1\n\n\r\n\r\nReset context:\nThree current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-cd97641a7291", "output_path": "CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "CrossfireDeconfliction3D-seed-0010::codex-gpt-5.6-terra-medium", "job_index": 9, "case_index": 9, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} diff --git a/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl b/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl deleted file mode 100644 index 6a5d75d99fc82b98a5984e43f1412ff9ec931fe6..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0154-CrossfireDeconfliction3D/codex_gpt-5.6-terra_medium/CrossfireDeconfliction3D_seeds1_10_trajectories.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-bff0ebe7f503", "created_at_unix_s": 1788842374.966752, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 1, "requested_group_seed": 1, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [1], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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mn18rJSHR4uIdL+RTAs7HculrQI44QImA5i1Htzwfy2HvbECOtut+8SeQKfKjBjCuTxq7ekaoBQKyrKTQ4agBRJMTayqL3R4fg1wiQzQNisw6P6Q4jqcoiueFyrJC+fC6ofdxLCu17yLlkbG0QizQfFbG7GRBA1qExrhCH1d4dOVGf3+Br8Opr0tH4fYoUEDHMt9Yd4ogujV7fIMT3NvuPls4Zl4NIeR5geOFIqu0vqahjwKCWKFUnZIXL3V+IRYpFYCcgghoLsliksqIxykDihKj2JBCg0SoscqtxDKKCRdTeU9VTW21USPilJf6O2BZVDf0bpljt2DVhVa+wxrbxwAAu3FKut8DTVO0KNPQVM57Tazgnu7Z1Fm0Wunr1PMj1eL76SlcnvwrUlAodh+yeI+LRUqrQU5BBDQrZDG6MuJMHYoCEC2y0DpL9q8FjwkEQ7FK0kglxVhq4GmUYra9qJxUTqyqe7qCESel5srbCxqyIoKUEnhKP2SeXeI6UGHfLoZTjBxt1vM2CqIJtTxtGjLPyuT76Sj+Uq3tI5Z3TBh6i6cNHl0VLXhNfukPMlCw0BsW0Sp37AJgl2onpNi2krPlHyOl/XcUrwW5BhHQMVNG1NxVDC+IeTiiPRgQOBU0Le2WHw4/LPhc9f8r9sT+pgljhN04xaWvq3Ds0HPDjOBhBA8OJYmVSGk3P8PpLDrd6O8vc+5hBHeBtxXvdOsq+6wnpeSCghTrpwuchvpUZeNnMoJEBDRxWRx1NS0IMFQO0XJZuUO0DtEOBuljstciEce8gacN3YUr4nqKZtwpnI7iLyVwEo+uPPoTMYPmuYNazaISeJ85xLiwQFOsjBBitULraKyR8hpZy2zMfh765fpMBuLDUa7Nx4Ovk5A3sONHFkdVRuQ7hJDjBbyWlhbF4coYviJONMuPkIy2EghjDpsHshhdGXG4GYtjMBNEy9uI9+vYsR+fQCAQcgI2h5VRlDzUVAJCihZjzXL5h0IfJTsygmgScoWC8jrGaLJ3NqO/N4yhSJtASHMEKQUCmpgsxp7SiL4rECB5FH8MycXB/xXX2tIWiaXkLL++877oB5hNxpvvuQsKfEnDnKFmdedNAmFMYFMui3Eoo4AsR6UnEWd1h4SqKYrB8pjMGxr3nLLmjO72tuYjh7L2N7H64st2vvM63p5cXUIBsKepW3mAy+355zPPH967BwBw+8N/vfvaayKdqnvI+eCDv0z/WyYQAJuqZG8ZKHYYQ25HXOuCVTFMHJEyYmkMhQhlOti64T2Q3SxbMHXnO9I2vqsunIqqvyEAW/YfXz57IlqCOHoWTKkBAN581bfxHlxJBQHYG6q2BEJGLdBRlVEKwuDiFnzN4rWzqJLKIykAxE4ESjEkidxjwOb/vSQIgsXI2j38qnO/AnITXEsVWmKgyJHAPwJQaJYm4p7ztW9Org82co+XP/7qd9pvg2FvuOVHCZ+WkOU8+9jvvvX9GxN4IoulM1AGiFpSowRGuQpQdjWGFLtoVLkQxpD3/vMPnKRfaJF0xOPjAAC9Np+lwGwtALs2vKL5RC+XpUOGPm9uFALtfCuK2CMdIXPJjQZU2qjJlAlJNTN3i783LbhRvbRo0JuejVdtKZoBUAhkiBDGgD3NtmQtUNlElDp7E1XMTUYcXuWPxSZmcAgNepTpHkbtfpUwdAp7iidL73Cv/F8ZTlQldLHqUMdywPusRkYpqfJtoLHT9sPrfvzIQw+AMSKgvzGpLcZsMspqWzZ94cDRz9P39giRSMz8zMY8UEICnHHRN2QjNPqSwKBjvH7JfBNLSLNrFQEBDK13Vd0VgrcH+T13DaFbAs5hYxnazyWV4bT+lp88/eeHVTun1JQio7ixC6QBl9sTi9reeufP0vHqhCQhApo/NHZpr0Sm1arnSTA0dbw7OL1nxdwacNQBsgMIEunfzqKAZAruAwUm0cjVAge1lGw5cHz57JCG7Zh4pXbhKcs/37olrqcQsgQioPnDnEml1dVi/CSwJBf/HwoC6BtAFeVeL+pnbjToAaOf3lDAMjRDU+JI9+wa43Wsc3SflHxXUN4MTpxe0SMapJlBUz0BAE6v7wQpkKXSdBR7PdzeJz8RS+2o6tk9hIYFZTjrdpwEzfYk4QAlApqnBLok4wyy8Im/JRaDGCQM9IcSsktAv3SyYrhQyM1A2uzpC94PdDrd9IYag44x6BkjSOfEi0RQepc1bGSVVdvUNRh+iiuu+6ElkGOQEjheWHbuRdve+k/yjgWM2WQEAOS04F65Bg0m+duGuAs0iAWap2h1mqcoUGrRD7n4IZu9rCQ43CZ3bgYIrJ5yh2l0P7AapFyRLK7wbB+Q1vUmgyBHwOwefvHUadGfWGBKpXribJo50+qm/+B7rz3ztLyzoboUUCgdAKff4GaMKJtbgJ8cbLUY9dHVFsRg1WatbfvWM48m/FwioPkLtj8B6O4bAjlNmIaGPZ41UbAwem1SRoGepeXcsl4bspSn1BZbC4AqQyucP91/J5ban9+ZmvIqhqYqS63D9pBBKVI5dCCuGAwwig7pBVNqcHwP9XLEwoqaOlJ4p/hf6U8zakUDVtusAicRj7hCMlhihAhoXqMlOUN2T4nVqIp3nzTZvKNFY/RQtn2QHq2bwcDQSOYN6kgRJBU2h0el78Umqd2XMsNMTuBFqVq82hHh5aCepX9zzx2Nnba/PPLHVLx94IhZL5bPQR8TfwomOO8AbXxyoHX57ElS+of4J5rfUK38uC3dIX+v2aefM2NSFcg+89NsMppNxivXzIl3FU8ENG/QtsJk87OqomTrnua6MrPRbAZAGBwKXcVniw0Xechd6CNVFSVtHWg4j9fnN+iDo8kff+xBkINET9UadHi9fkFpGCYPz6fe30EpbFhNTlk4rW7OksGmLwyWEp/DVjRhGq0zjGHqq+x2SNguDosvEPIU+bo2GTTummOeS//rO+8bdQ0l3wy2Hcilyndk7ItD/OL9pyKF6gkAeOPvz8rbHQMh1RZp4tqbf2wUayIgz3ts/SVT5gy1HBxq2gfGjkKzQfnvha2N8Z6BWKB5guZ3q7s3aH7KO6HP5eOAQc8qV/Fjnkv/lauuHB5xvfaPv0Q6QP4sAICLVmV0OGXyGHTM/uMaEfbo6bpKcLYWythNA6fNnZSkpyIBbO1HAQCWqokjnU0gC/jrBwcSeBYR0Dzk0h9cLm8/fBcadoR7YZ26oKFVXPbWVpUODI0oV/FjboFOnYDGoL+utYC/9AeXv/Loc/KPypuB+FnKBoaCRQG/vvO+7CzaCc/Nwp8UV8F/uOOoMvt1TgOq6N/f3CepKsWYCozzTigCIFKpfs7AuaWcVt6DMnZ1BTmSDRIBIqB5wtqLvr75rRdV6onWTXesv/z/3XpRZUnQhyj+1+vjDIEa82ywQDWRPwveUN4MIrHygktB7iRI4GgMBGDN0qC8yoGyOQ0VONeVoiirWY9W8EJqBFScpJ12mrTSWrHhiaxya0lB1YRcr/0nPtD8IVInoede/LW0hVsRKh7K5iZAqjsBvhlUV4rmZ6jce31+2Qg99cTpYEzZcuB4TMcp2/PFcHhqZ7x6vKkpnViexOreMzIwtur58G9S0HSGWKDjFGkVPxxcxT97x2Xyo9+66yWQ5UQQlCRN6ZTcUAJ1nKOhMKUVZQKRknYpnZCyDEqfn0utRH7eLIVfeAGOWhqQTxABzSeo7u7+2A5E31d5Fe+njQCExGFlMY1dSRecdfWedx4HmSUNnge1gnYM2HE3pkShTHomRg1VlTw0dQ7XlZlD9nSnJlyekpXHlkB8SVZPQRCsBeYj3R3hqawAgAfvQxUBuCgAAJCquoAxjCARAc0rIomJMgIjAYNGKJ68EumcY2GWxi2KqjhSMqTJoyE1eYmqoSr1rKooaeqUilZTzvBITEUTsYTgtx08JFcBAEA7nC5cZ4WZUlssbSkk1WpklHoaiRQWX6UPYoHmG688+ly491ArM1GadIpX8VOrrUeP92DpmhahViQL1/gsTfNRY0ppTfFJGWEfQZVpkPIyAZZlUmViQwDD23jL27jaSqGwIY2xo5+5QM/85p478HYKS7BSCxHQPARHq5Hhg0Mu4eDxflRILF42/I4dR6lOYKzFNPxOEG5Ky6YzjiNVF0XreRELXOR+zG+2SA+dNzn1oVel+fnpwe4LK0ogAPXlZghRGRJFp/h7+t/nnsnkHWIkVGGV1VYySpGdNLG2o73z4wM9p8xEyW0AgJSrZ0oiSERA85nqCFZMXZnZE5BLvIqPIj1Hj3fLyfZYTNu61G7We69Z/X9//TBVb1vW8e7eIXwniHgbSIMTNMr5VlbxTq/fjO43scr0sY62WA5T1ggAAC5cicoEsjKvLI45AsmI7L6DqMHKgknFLne29SdUQyzQfCP4bYx8SQsQ0vjh0S575RcjXDrTh0pTIpHyca9C5OiK1aSzmoJF98nX5yiTW2XQ3UL1FmBaqr/lOaYpn4YwLWpJVQIMqg3YbIkgEQHNW7Yd6ELJ86EEBqyiJo8g4KdSZdRnG5HMz0AWeorjSJnpjKd0TVx7x3pZRoNZrhB8sqe5vjwkBJ88fi44zVTuPKA5gMRsMs6oLRn1JrH14EG8MWdSqVyrGn2gQALyyvHZm62cvd8cQjJcdPps5Y8hqkABZR1KbXXpwOBIt82nqDTUxuvjDWEZOfv2H6Jp6qvLUY32r+++/dbb786A+Rkcqh2+KzkyUJGlGeK79o71Lz+i8PAq5of7eGiInAcVF36OX3PJV3x+juN4XIxEUYCm6fDu9C63J/pkJ7PJuHz2JFlA1bWqIrI1v2H7kdjntYQr7ITyApCVDlAioNlCMetcsWCy/GNJkWR6NLUPoGySI45kzE+VJqCCwOQq+bBo4m2apgwGtBgs0KFzPvHHe5VH9o0gMydhVQ03P6mogtetSKBJjMcfU4/kTC1REiQyoN1mo/7k+VPwNhQvA0EQvD5uwQnXjzjcHq9/YNjB8QJNUwxD+/08L6C42advvxZ+qoDCUlFcDNInguBLikJVtX9CTEmlxG4AuANAeA7srXfeA7IVYoGOPafNtE6uRWulIZuUnTc8Ik1Gu/H+5wEAF198QVwnPGtxvduO1rMma6HqW9ne76ovN/OQvmpF0ZObJVvA6+P+ed0Mvc7X76Je3h908yk5evSoUjRRit/kWvxjRxdSeaefqqspUz5luL+/ohBpq5yMcrxn5OE/P5Ay9Qzdo2oMmihjFrxR9UyRwd6JFJYhYSixwxNDMzqU0mQoLSoI1zWfn4cQzp1+LcfzA8MOnhccLi+WTI/Xb9Sz777yVEyvpFBNleJSgSRkpXpmkmQcoERAs4JNh+w/ve95RTZ7yE06LvX0+LkHL69f/2zLnZdMeOzDASyjmNm1xgOd0pew3ML8Y0v/KZNNu4+7KQCm1lghBBf8du+5py9RnXDPF1KDbnmu2ZRJtaq3WVdd1tE9EG5dDDqQPdgoLtnEscPxFUq67SMma2F0YUu51eYa9piy1SPcmKIypFjAuobbd5oM6LZUW1GsDLVBiOpB331ltFKrCBqquljCC1hxClcGIkhJkqXXynjjoovO725rLCsu6Bl0Do24PD7OqGc7eoe9EfqDROKP35y8/tkWAMCdr6oTaLqHXGvnlRabK492OSgBBRNe2dqt6NRboylGEEoiJU+A2CummKhUFa/iO7oHvF6vLJpYN3Eb4OZO27yp5UN2392Pv3v71WeqXuW+u2/Hhq3RiE1I6Pb4ikxm5Q1AxqxQ1dY+Z0WFwUih1hhTajLXGC0dXTI1zU8l2dP4Bc1JoiRhjZWwtX4M5f+ImpIUR9JUJDDGI1kB3fI/lDhN01SpRY+dXACAFWcHM6uTZPP/Xkrh2XKFy740D290K9qD//Gfm2M/w73fWfLG0IwzLlr+wWto4R/Ohn3oSqUocPfF1V9/+PA/r5t5+SOHn/vhCWig60fdr904/80jQptNnSWuY2m5A/zSmdXKh9wev3KjczBYIEiLNkxzp23+zAk6lq4oL2EF75Dd53bab/0jslsAAEUje/CFhH0CWIUld6q49pfBl5nb46suMbtCVdUkqieWbNnJkDegUqvA9pN/yd5pJdVRalWjaqjmxMOmzuGUZyDIxD6uOQUCKmtl+BhoVM+wve38pfV4+/AnrycmpqqXMBgMFYWGhM+WoxRRNgA0rpgpdXHU2H04NGMuvWdLh3nBkqXK/SdVu5944wv5RwjBNx4+zNLU5Y8cXrN80VNIxABVMvWxHRHPvGx2dVVlOYTA6+chyptGRuvbWw4qVRVCqGNoH8cb9PrG9oG5MyboWKqsrESckov0VJ5IIeum3KMNu1P/+/HBRdNR5UlxebmmL1VWVewc6LV5aIrC0pnacRfpa8X08F1/wnlLMZqf2dmnFXP7bbfG/ZxQDdVUz7Q2AQgngTEeEQU0XC7l7SmT65CJIaZSf7AbLQ8vP/fEy2uC18pwf19FoQFf2Vvfffl4j33I6f/B969RvV6kl5Ctjw272y4/90Rxsw/vOfzJ67INkq96euKsiROqi1vapY8MAJg1BWmKyycAgCysDnvE5VJ/xzF5+yOgkfCxo9uklFTB45Bs3XjQscgy1etoUT+RC+yi0+dClBkjjIwgkxm3/tXr9TRNLZoziaaQXSlNawTARHMAMNPqioBjb1A3Q92p558267+bkIZ2dA3UVQfjUcVl0mXWdLwTG5vYo+rnoEEnjooUT/jwQynLTUmYLQeOqwRU6R/E6Vk45VOW0UjSiU2wriF3RbkJ5BOUdvOUSE0AYg/B7z7Y6u34DG8vW3tJmhRTBbv13ZcjyGXwTtHZNSBL5+XnLrxcCrbCphZ0QSsCBcHVH4SwwEBHVeSQGG5AOvHJodIMwa/S0o0WbixDvfDc3756+ZUgXzh9djDrbXI9CkSWFFnlPYtn1u881I4CNdYQGVWKZlpBeYh+AelmcBegxMo9hkHG7IConjJYy+x2Z2lJ8FOgPU5vuDGFBVEppuefNgvHozq6gxoq66bsVIUQtHTZ6isL/ZxQMuM0tC770aVZvrxVJbeO6vHMZyjtVF+snlv3BJ3sgw7vnmYNa3RBQzCihRkYdrz3yb6V6W/8ooJ1uLxyKECha5J6dgbU7YPdbctnVy6fXdnU0imHCAKiCVmGRnE50ctdZjVQVDC8EHraELSkU5JLVSAC/xciywdwvPDcU09e/p2rQB6p54Rq9QURrqFYRvcciq3huRbPbpTuvd88efTZ3H5eaOyI4N1HGopmUvT2Swds3d+5aBqSCXOB2eVEntDBIbtKQyFEVwtOLQwfTS7HpuQrR6mb8gXQ3Gmbe8IEPUvXl5tbepxYPZGdEvCrKjEVWMMDVmOCSi+iVfeL4HuNQZeiBPp0mpKmUUPwYWiqJ27dgHVHdoBGiiCpVFUQ4CtvbXZ7fWBSpl0erPI6tpgNsn5h5Mu3utggbyv9Tc2dwd/FvBMmMgyFGqVxbuV3Qz6tvGSTsl7Qug8dtnx25XufBEsa8Esg3YQAfdsgaO6Sfl8r5ta29DqVDgGYs+v6WNQzXEP79rxZseC8BERTyd8/7YkioxDAg8cHe4acDE1VFFd7h7VmCFOgR/E1uHDV7I5O5H/gBdjSOTxZ7AIpDq0L0VCKAmXlpSxDv7XpgGZUShmYghB2DblF2aVaumyzptXpdcySshJx5C+F+leMViWuDFhlj6pGavIiQwGw5XNUxyl/zbw+f+wdTLKc7sjqmXAW7ic7D7i9vs7mPWDhwuhHLjjrapBS2MqKsre3HMTXMb528YWrOg6LZktAyFBe4XQUXV1YUsSIy3Iojm2BEC3SeT/YdbRX/m64PX5sd+w92IztC6UNq3oJ/EuUFXP+zEk0BRaXod9vAYNyepAfTXSzGgwGkxjOKtDB3A06jaqemMUz6++/6zYBgsHipRUJiWYkGVUqKS8INqdvb1OfIP69eAG4vZy4AIhGZXmxvDwXBLh4zoSd+9uwhqqgKFBo0kMALlo9D0Do4wQBQl5A0SdeEN755BA+bOnMaryaQQt2hi0oKFhcVgzESwsn0CASzenJmKrKrZiUkhHd9gyvs6qoSKYZfjaF4GNQz8Roae9pakX3+BsuHkU90wE7PDR8yZp5Xr/g9fN6lu7vRx0BdAwNRetySn05TYFjbf2zptUzNL2guAinp+CsbxxglbZD//inzK4pLStxe3maBu9vO6wU0z6bR7r+xe+AaF9IcjlzWr2eRa+AvjCAEhUTFZYBgHM5gpF6VcQJh7ByK+h06N+/oShQNDHEsig8F7Xp7vhvsCBSEgoIBoqWAgpIXZSS0M0oSlqr9+IXZGjaqGecHr/Pz5uifhMqyotpca4kBl8Li+ZMwJ10BwJGKDIZ8UGiAIpuGQp5fgLzJSBkLlg1ZzDQEASHpHBGN8sg3USXQdhHP9QS9JdhZk5uSEpVLVKlI1pI+lpB8tAxrXCzONie4tLVh8O7T0W+HZ57xY3Rz+/y+DZvTzyLMzVLeJqmTAbGqGcghHq9Tr52p9SXY6tw+sQKzue1Flt5AaI9geTq6H92k4E1GVgIwfkr5/h5gaIo+4gdn5xlaJoRv0MUZTCZFhQXMTRlYbFKQhpFLALftgCRgk6qiBPOehl0+PA7b+y0tT/3Nz8Hs8pnikNAQu356B1ORm04Gpqfxh/V9uad7TYeQNA68SuqZ1HNB1Iompp0+tBNaHoh7/JyTnE54ucFU2T1LC9Dfzj03gJfALfDYTWXUpAqLbEqNLQwmi81sCDvi9BOaSQsJBWFcElNWFXb9RPRf6XLTcJUYD3JGK3LRsKdpcLx+DgqYCtkG3eMlsN0aYSq/5DuU7LtGfDHfKKIIAEASsurJ1g9kVyfEIB3PvosSgfCDKCaDI4zivQAoIrX5i7btLrgQmxoOBAWiKqbFAA6UwHgPB2dfXW1FRSFfOF6HQ1hcG6ZWNYHiguYQMQAmSCacjlq0AkTlg8gnQo7UtH/xprocfPmhm8rf1Tm0oe3rT18vP/TllFa2sTCllVdyzci+VZxdIQBYre7Wr2H4wW58Z1KPbfu77x4teILAEB5oaF/BF054o9KDR0pMtHR41E9gXgUsmrLits7e5VHTakwN/VJWfraFa+ZUlW30/6x0wKq5nw8EJ+qaqonlZV1nMlz6WhDZTR+IWG/iwG7d7C/e7A/GHNXBd//88Eukw6YitEtvndY7XXMgANUnQcqfwT8TWioslisBQ57MGiDvw9KcyCmK0BKfEEXvMGgLy8E/SPeJkmdg/ErtXqGfj/kuBNSz3MWhgfrsVwqa67FSpjh+sqizDR5jFc0Q+4YUQ7S2Jf4/WDLqjisp04fcjGXmu2a6qnqmCdV+AXAyws5oMSaLJwrQk8p6dKQ2Lq/88JVyC2ALYuWzqHJtSWfHelRxaOSX/amXlVRJs6cA4GJ63qAfKBFgQFBo5alagOBkc72ruwJcO0d66MnwEIIakujFSM1tfcdbgmGN+3twQhSlCTQtAgoNhU1L8nPD3UsmlXP0tTgcPBbNDhkhwCUlVhDDw/5ycfxesXgKjFoG2J+N1RZCgst0OdSlVorzUkcuMfSieP1OB/gva3qkL0sRs1iDcPcEybqGPR1ri01H1dE7dNNvOmZsiNY6wGNnVCpNKnQzUhG6KgRgAtXzQ6f5Rm+mFo8ZyLuczwwGNkIVeRUi+o5W3neRXMmqtypaSVVTlUMBLB7SGP+ZXUgO0dVlhquqibajy7t7CmDD8WklcMUi/mZJB6v/5V3P1N+Ta5cNwYRJO1Szq37O5dMD0Z6OV7QMWxpiXXXgbbJNZIJTUlJKnG3b5ATBqUGvQDoWaakrOStTQeUgSa8wQtC0/GQtFMMRDXCSEtoRbx+3gkTWYZeUir19EZOFfH321Blae6Jr59mXCSe0w7jPCTR6HNcVmc44fZ7ZXmxsjhC3govRacoUF5a2D8YrVd8mFUbzE9CEUpAlRZb8f1boaGZW1IkbKhGWSjEqKr9I96JRn1jp42mqSeyciZlBgjPmYcQPvXvTUn2tE0VrNJU1Ewy4HmUwE4BavHsiRBAbA5gsHGhKaOs0QI4d9C21Vro7W/sWjQVZWkM9g9eePpcvzgQUYDwf4G0KoamXW6fjqH9vGTcKEP2c2ZMoCjqxBKUQ4MWjGKBCs4KCPStBBww6gVkuqY29z6FhUCxG6BpcuTGaITK10ZVBcrE1NQw5RJeSVlp4UAEDdWwasVG6cEj0B+Xwgt56ecsIBZV9VeeJG8b+qQSwygovXhyL+HGTlv2lPknmcM0Kp983hxLCH7DpwdHHCnujpowrOYVOaG+UoA92A0q5uoBSlx+UYAqKymMXUbDvydyxrWE3gygl6IoHUvrWBRoEgR4wao5HA8FCB12hxy1Zxik8hRFzZ85EcXwaeQjwwk0UiA4zZdZaqsnn93YeMO6uXHJwYWr5ry2cX+GzU8My0olpFUV0u1Kk0hfdQq5MocXiDfLSOoZtGrDHEOQwq4AKR6VwLpnzFXVW6FutBqjquJfaeG0FXLu6pgXAmSGAbs33PZESRHdg5/tQw0b4yUdEST01Tjjoq9vfOMFpamIgwPYmoAAHG7uWTSrXv5uiJFgJKM7D7TKK/rglR1VENAabVWxrM6ieSt4eV4uWaModMXQNM0yoLc/6DOVkqbEt+R2oRt1abGVSfMEsJTXm2ulHMVtVaZkdFpiRigOu0f4lVORusnh8odVC2qHQ60GbfWMdGoqJKZfVpqlGhqXUzW6qkKIGrXItapZVV4VPYfp4Qgdp5REjyChDBCtCJLH63/h7e0gm1D7QHEMVGXNcbygF5vxKAfJLp4zEUItUzRwcavjSPh54vcEq7No3kKWihiyD9kd+p4G5ZwqLaisFk2JB9/Yt3Jq0fzpGglboaBfxc4DrfJvuHEko+anOAAH3e0uPLVB8Aadd4wBX+LB25jXx6t0k6LAoTZ0I2QZqsxq0FRP0arV+IvhYiQZmlIu5HOY6Mt/paqWxDzkIhtUVdVxKoqGJtxI5YW3PuX5YC8Fmc93b1s1eeyCSFjp5GtaeTVTig7+qosc56mUlxailjwKswh7u8pLC+UvlnL9rnp5ng/O19UM2ctutWMdw0tm1w/bguEgrN0lxdZILrksFM1wnnn90zlT1TXpPYNpjHrFa4TKsXXcEEtmsrqKXcLCSl30sXSK2RCgk6krA/0a6onKQEP+dFFCUovnTsJX18DgSFWRPkv8oSkhhTlVY6iq3Yq/bLydT5Uo+7rJbN51tGdA23aIpYjz0i9f9srLqDlc6gVUtRLG1/PE+koIevpsyA2KokiBLO5wUIeI0kIQKqP9gyPlpYXAj+JIDMsGYqxq8+FQc/fiacg1phluUiWl+nmhtNi662AwGUCUUbSgi/xNojQD8WMrmjIfN9pWTo11TPaeIyF9XjJmfu5zh0QJFloGsKC29dnlXzvHo3p2pW5i6exiURtZDKW1cscrEuUBUTyqlOhqx5dZT9IDOLOfLFdVY2gOU3gI+uG7/lRdWSJnNUWXTmUEiaLUbUA7e4e37D4ay7vKZBLoKB3pZcWkKCAI0Yp6cfGJfHFj+gdHKqwhbYCVpkZFkVEqitehOJLmaXFWYFuHVJTCC5CigCoZgKIovD1qgkuWiGYsVJVaNI3Q2Bfvzzz4HfDqr9LhCf3cETJ6c6FlIJCKi26xfnGF1auX5hSEQsnBKKmIHkf/IryQZkyfoqTUejBeSW2maqpUtVuzS4hYa5TAgr1rMKSsyM/xL779aTJZKHc//i5In4CuFeNIylW2fOXWlpo6B937jnYtmlkPRmvLg5P+YGAVr3KDXnT6HOkw0bxtC9TqCQL08SHeUuUfQ7m+QwlVOFFJLBNU5qVGqbn200Y8Evbtdzeec+YqkGWi+XGj7eNG2w/ODA7ODmfX3iNoBe0Ta12ii2aAaXGqZ8Ko9BToY+oehKNG0a8nTceoGI+q27inY9WCukNt+W+EjpWhGklVsbBG7xUNYmh4KtPdOyiVzNQql2IhFtVTr27y+YPu9exxgKotULzKprQuYpQMGhsUAJMqzIACx3tdFKDk9bsqgMuKLUMaqiyoQYnWebbu77xg5Ww8TgfvOdjUvWS2lA8g5aVCqCyRiiWhKtuMTcyj7yKJnKxXFk1BhWKOIp2YK24IDunektCdIq7CpISpLDIAv5uSYlARVz7hPtAChsNRqfpKS2OXTaeOPxLSrqpICpo/VP7Ynah6ytKJMemRIOAv+/S6oj/df5fTGxRNunYZyEokAfVzAiNuKi/aifVVPT2S4x+3m49ywRYEQgc4q/1Q25BYlh7URtVzcdq89D2JcHcRGzaFIEDpHeNsa4qKnFCl+arZJJrhtPg0JholQKq8n6lFgBBH81vc3snVhbwioC/G9AtUfyz5UsS6ib2rqN2B2A+sU+FdJYyVqnaHq2cMzT1V0imrJ8WgXrEYSvAXGBSW1QAaeZiYpO74QuNTp94Cdbm9F57aIF/W+ILGVkBDFbKAIBQbj4UqpuwnlUMHuJ0HHlyD7lccx7Dshaeq/kiqwCslx5HkP8kFK9Uhe1zcKRZwKp4vJlSpQliqhKpsFs1sI31G6D53OXaYcjxUBfQ1Y/r4YsMxfTmgj+IJRDqzxKlKMwnYnpqGJ2bLPhQmXT4XJfZBWnKXU0KgOwsAKknFejq/Cnz4yeerT9Vexbd1D364/WBi89tjgXr2YSna9e6//yHvnVwtSU9Hn6NXDPWUlBYXMJxySMuhtuDNR+6BhAVWOfQNn1Y+oSzNrW09+MzNPY6T5kxgebQtCygOH8mLuLb2XnxwQaG1wChOYgwDQqAyRbGGova94smbexyaPtA8E83kzc8MrOIxWE9xRxWlwxPH9PFfHEelVAF9QuaRVVXfK6X6G436uNQzinRi6upqXnpfmr+NZVRGKaNRUJqot95+t8fHPfrPD97Y8FmB2VRZWbZj22aQaoLCfObF30B1pq8/DyFo77NzPFSqXqkR1UwqRVO2CCgqRDHVL8BQSotDXLshT19dhQV/Q/DM23AfsWZpCjJAI7gSKAosQbn9Gkmp1ShnMJ9FM61G6ANXzti0C02ye/XzoGtfcLrogmjdxmJBM6CPOsUwYo9QUT2JdGaboTrfrJHDVB1VPUeVzvp6SS6/csZ8AMCL7+1VWqNKg7SupryzI6KJoDRR/3T/XWLxEr92Ko5KDSMvQPosUBVYSSkK9Nm8siGA2yBRAHz1W3EPFt7w+vNyE0yszrJ56+eFqmKTnqVxxElV20cB0BqwQE0Wi8Woi1L2h/lsv9oUrS7S41TQPoNmhk3+kBIH6B2dtXUVlqmTyh54TXKCT54ydfe+PYI4cVNTOnEOgDKQlSR1fGcHM2qZFiHTzDcPSAJKS2qFe56atHqbRnJ3Kqmv117xvPDuXhxTUlmjoowGb72dHVpDD2MwUV/Y0pVGAcVseP35PpvXzwssQ6VqGjtWUuz67LWhgXNil0+TLiCgKGR/+hxlfn9rey9NU91D7uYex5LZE+SG81FQZlNh3B6U9UIENBbW7ZheP6G2va0TQtjX17dixYp33nqzuLYmevpUCtWTkM3qiZv1VUeYOSzT3GPHClhWpJ4LoxczKCJJp5IX3/sCf9ujy2hiepq8pI4ioGkC57Tv3bm9c9B99hnIL/n5ju0hArp6jlIjW9t7GYbqGkQCumgWGjwX4wspV/TjREBTYoSuHzwLUIzbhezNr52Efm88L7C8e1ejTV7LK9NOiXSOKwGN1Oq0OlRSZQFVEa6n8sBzhJZ19OK7X8hnUimppozGK6kev7rE/h+bYir8S1dwKpYqoPmLlyKHRyhiLmqIemLk/iOqHhOjplVZKs2HxfQXHUv5uaxowpr9+HzcN0+WklI/3StdSRwvvP6FP00LdkKuEFejaEHsOanaOWALKTQqKzI1He+MqKci/++MechCfO8LmgoJ1suj0uqqI8pobW0gw4PS0FOOh1xYb+Y+rY7XmbZA4y2d3L/7M4ZlUZZ1sNNPAIrq7elv7UNf6cLiIs1BAmG5qChPQFrsi39znH/fyeR/MDd5C3TblJVbdgeH+vo4AY+wlgWUMJ5ZUBCcqCdENmjcge5coxWdxWOfAnC81/HpXimipbGuj6yk4bi8XHd3yPhCcaSjk6Gpd/eFjCDKkAWaTL25188DP6/KskZKKv72PYE/hiqlPzQdFSXwq3JRUSsgkgETJ1g9UbGD+JvN7p7ohEyzx1kWRVKTJBb7dNLaeQ4P99bmgyprVJ4+GYuMJq+eF52zOgUCmqomHYuWnrx35/awnCcNW9pMc4pWPZJoYuT948HSTB9+ThCHo0irtcCUU6KjhEQkVYDwQAsKx8+fWh7LLCOlhRRFT5dML6MA2H50IAEZHXH5+/sUozFEugeco2b4qEhEQFPe1khm/uKleGPvzu0QSOmoqjz8ygJpea58Imo6OT6W55kBSyVOcUe1XxT1+hekcwchDvY4y6axPfKPK+bWbN7XtbdRyorDYqp6iqytUTQsXE+XTpdU0icO7tUbDKPK6DNv7T5tTpWm7QnihB1z0YyQ3I4+80LLAF6Mt/cFO4bI0gkhWqoT3UwHt5S23jcwkaKoz/a3dUL1pUYgjMp0tkc2NUstSNSWnFCpM1lGXL79R9qRkaQQU6ynUay/SHarSk/Li0xYRmX0BoNSRqGoniu11FN+AQ6V64AUBJEyLpoRwTUqOCMVuziJsZnuNiJf+ZjuINJJSJTpovkJFQIKALAWFXE85AXBz0Mfx/s5wevnsZ6qCLdPtYGoUVwk3S0Pi0dtOtCzYra2eqImNQCUWg29NncLRAmqTQcPR3nli85ZrRbQDCgmhlRSpoOUN2HKWFF8qiAp/dksoIXF6imbAoSCAL0cz3FIT71+Ye+hYO5H3GKKCS31VpquSj2FEdQTALB9WGMaqIysqlhAWSKaeUCa+tdlpj1oSiDSmZ1QUQfP0hRFMxTL0NAAIdRBCCqXzRAE6OcFr19wefz7Iiz2R3lJGShmUAX2DNpD5sKWWo1donrix7F6jsqUWSdkLpGeWJrpJt2tP7NfQ4l05gEUTvFAU80ZCKABMGYDKC7QV508g+MFLrDe//xga3x6SkWzTwdH3PgnhqZqa8o9DhRo6Rl2AxDNApVxDPVYSqpSL6D5J5ov/eZaefuymx8G2cTyjTVEQ0lBVD5BiQpH00jcGIbSs2gLAB0vwNVLp2M99fqRnn5xuE2lp6Mv9rF6BpL/ZfUMPh7dZg4wpUiq5aH+cNu3QdLkn2iqdDOc7FHSzPSfz3I7lJCdPlAAQEkgghTuA42jZlS5KQ4JXjp/miDAj3Yc8ot6yvFCeDBKQ08VdVM8GtcOZPXEFmh0B6hKQJOyQPNSNEfVzfDDxlxJM2CE5sRanpCF6pm0KaqwFWUZpUDfMPJm0jS15uRZ8vGVxSY/JyAT1Rvi6/zskPTtmD9FnRCqUs8EiE9A81U0Y9fN7FRSoqGELKcwTvMzAKqGCxqN4vp6cMTT2qse+r3ji0aDjrGPaAwDXzIT3fh3HVNXbbK6kKHrCeAY6hldQIloxsIHm3d+/4Jlj72+DeQ1xA4lZBxKqaEuL9fW7+TD8+oh6B9EvRYjMW9SyaiN3A6224ClOHYHaEQLNI9FMxljM5wPNu8E2UFmjFCyiieMBZRoekKfX2jpsct9hZREV89dx3rnTQoZOsILkA3t6SZAaLdMivedseNENNOqm7wgNj3M91YbRD0JYwjHwabukRFX6hsqBhKYPAl8hVmim4npJhZNCCFNUwn0IMg5I5SoJyEOKCngI4fgk0SAsL3fMeyQOtpcd9kpqf1zDDt97QMuEFvFU5ONl1fxGepIn7vGpko3IUTFYTRNofZPUvpvkHx1gxL1TC2kn3+89Nk8fYEhvvESvn5XRZC8fr6xx+HnhATUMK8ENK26iT3QyN6MYaQdgRAJ5SypvGS6rjdFIXiJQYe3tdcuB46c4mjYWLBYLahNexgqB+ixbofPL7DV08H4FND06aYgNrSHAFKAirfTat6s4on5mSrGyRg+KqZanljx+Li2XgfuCxyXehYVWXUsvXF3U6TvbUVZEWqP2dSdjF81hwU0fbqJB6gxWDIpQOd9eIiQfkjNfgL4eaGp2+7yctGl8+2Pdil/NBcUGPQMS1O8ELHTnUy3mJM/jgQ0rbqJXJxiXIhlYp2cTCDETh4bnilHgLCt12Fz+lTqecu3vxTlWYWFFoNOWp8r00WVSaCH20fmNKCqpNZeB94f46zf8DgSOw5FM9y/iX+DtDgXhCGTfwhpYBxKZ0lyIfjeYXffiCf2NbvZUmA2sMpuIGgqophCrzxMqaq9gYb2iTlAs11AM6CbmPB4OoFAGEMG7ahe025Hk8xHRWD0xRaDQRdYNQY0dPMeafqxCmx+dg44NUs/c15A06ubYpNqPKiaaCaBkFpmpCIE7/FxR1r7HG7k+oyFqhITrmMJNnCiwKiTjboGY7Vtc0NA06qbOKQOAKSo9Oa952sqKIGQGXwcf/h4n8OjrZ7lZSWRWojiDVlD+dEUNCWpAmyeiWYk/yZFUVJMPd/JTFE8gZAOevqHB0a8/SPeSPLm4OgYm+BFcoDKESQZpmpaAm8Vx5HYvNRNpXoS/yaBkP3wAhwYtDk9/h6bO0pM/IaLF8ZwMqShmz7XdoBiNDuSJACbZ7oplQyJ9iZxcRIIORGC7+0fxiWVnYNuPpAznwyjZiW19jl9/lh9rGMsoBnQTakfkkhOlAwRCOMhgjQqEMC+ftSJjhNg+4ArimFYUGDW3L9zX1P4To2GoQpQ4/qoB2SFgGbG3sTg1h4EAiGrKIwcgpelE3+Re4c9TrHiKIp06ozWGF8XO0BVYAcoAOB4XzA7Kt4UeiVNNp7NftHUiKcL6EPjFpxENgmEnEOAsH8goJ4A9Ns9A3aPZuBIaXh6OA2FrSqz9gyoJxp9vBuZpeFNmDA+hbwmnEIvPR3kjm7KS3WKpkihJYGQbZX+9954f+weTxmnh+u1ecINwUhrdiUPvrD50lUnVJWFWKbdA8jMzAxslusm/rXygkCjQks0xBlkPSQVlDCueCbmBn0cLwwOhaibx8e3DzjDA0ea6imH4L0+3uPnrSbdpatOCD+suqzwivMWyz/u0nKSjqWAZlY3kXeTtPaIC5IKShhbSgoMoxqeeEHZOeTy+YV4DU+Pj/v4i87m7pFLltWOevCiuVPk7aULwHNv7kqJAzQ+AU2TaGqu05GHEwKKtEQiEHLH/Lw38vodQtgX8HjKCBB2D7lV9ZpR1JNnTfK21887PakfjpR6Ac2oboqJBci/OQ4GtBEI4yQE3xtmeGIG7N4BhzcuwxMDIbS5fB5fCvJFk4wgRRTQDOumOF2I5G8SCHkFJwiDg9rxHLvb32uTwu6xSycGQuDycD6OT6acXeDTUIlEdJNAICTW53RGcEobwsMBTwT1FANHLhw4il09LazkKhUgdHr8aMiHN9lmdMnDpk80ib1JIIxPPJGLJMXAkdvPCfEanledvwhvCAJ0eTkBjtaubjSSjyClqxIp4jpddHES9yaBkMdFnMUF+kgCKkDYMehyuP3xqqcygsTxghsJKMgGUpmQ/sHmnfhfSMkQ6u4h6qbY3WOcqOf3L1g2tm+AjNIkZCFDDp8fsvGqpwofJ3h8HIBAMwk0kxGk1FigGv2QAn03UQ7nOJFMAoEQFZvLZwtG3ePDJM9xh8Dn5zWnvY8JbMp0U5ROSTfJtAwCgaDA4acH3aN3mYuEG8XcERAAt4/3JbGCp1g2VQ7QRARUs28xbsFJdDOrwKt40p2ekEkghEMOb0N1oThyHRmc1cUGl48/eGTA4eFKiyyJnVYu4oQQur2cn08qgpSqHKY4BDRsvhBq6iE2RUIdkUgLzqxFdoYSJSWkGxgw65q7pQSmhupCAYIjnXY842jQJiUexaWkyggShNDl9YspUFkRRWIT67+JIRH1/FZSEokixMIMXW+kFXFz98iQ098z7FGNI5OVNG4xFaDD40emWxJJoH4BGGump1FAw9fp6K3zUKyzRNvjJpw+9iw9cYnyx+27P8vA0p5IJyF2jvgrAQAz9b3hfeDdPr5/xAMhZORhmWGZP3GZpRwPXR4uGQ+m18+zVMqsVzam+nQx/4hhiGiOpXQqd6ZKRsOVlEgnITEO+ZCMYiXFpqKPEzqH3KhkSAEDhPiVNKg8fo53eblk9C+F6ikJaKS4kJiERLq+j0FXUE3p1DwgtUpK1JOQQiU12VpQ73esfmGqJStpuJhGVFKITFo0Nym5JNCUZIBKp1LlvaP/k+LpJIVzDDhp4eK4fu+pMkiJ7UlIOe6iyWwR2uB6job4/CKLabiSFpVV3nDxguA5vZw/kNKUDeCUKLSFTU7i3RwrnCP8KScvTOyulUKDlEBIOWyVZPEhJQ1Zkav1NFxJjXIKPW4j4vVHn7g5KqfMqgJgZMcQSrRKHlaaMoTqLImLc2ywD6PWsAvmzzQa4xilHc7x5qaq4tKe4cHUvTUCIZ1KitEyTmUl9SjsTV6AHi/P4/TJRCmz6gEAZ1s9A3afy8sd6rDzJZMSPhubE1OG8ls6AQATJ9aWl2lPEIyF481pnPpCIGRISUE04xQVcXK8l+MlT2PSlFh0HC/oWMrT0+hRjBWx1k/PirnwhFikEwBQVGSdPnVSktIpeWAIhNxUUhAupmF6yvPQ5vR6OR541XOMY6esEsW4Bnp6bU7/0S673eWHABh0Qd+rr6fRG7OeEgEdS+kEAOj0uoXzZyZvdRL1JOSfWWoprVYe4ON4pwf1YUqGjq7+upryssoK94AT6vysETWC9nucymOUempvPxpFTMeXgD754juZfDkdAM1dQY+kH93qJMqrJqD/o6gT589ixe4GCahnLFZne1MzyALqpzRk+BXH2wfPks8LUvGRg2ap3y6H4CEALi/n9UXu1RyXhlaXKauBdMYCeTtcTAVewN9de8dRa9308SugY4VSOpWcML3Bag3+5eIwPMWOgcTqJIwfoDjJA4fgk+wEijS0eyDQAUmNUkxR5ZIzmruACOjYSCcAoKqyvL6uKsE1O/F2EsYBlsAcJNyIHnURSS4EDwDYfRQVSp04vZICoK6I2TfsZfWJZ78QAR0bzGbT7FlTUx5kJzlMhHzCwQV9kYIA/ZzAJdfIDgCw7rRZAIA3Nh2EANRVWCkKuN1uVDdlCvZ8UmIosCqNUNUqngjoGEBR1ML5s2h69HkqRDoJBAwUx3km2QlUZt1ps/psng07jrJA8IkLOrfbHUlD0bqeApqLSSKgY0Ch1WIyGWLOT0KzpKJArE5CXmJURJAABH5OGHH5UtRIHkFRVFFJSV/fgJGBHl7S0CimqCZEQDON2Ww0GEJHaEdN7STqSSAAHIJHo5BgMkmgqEXpcan9WFFZKUNTlB5NuDP6XKjqKX4ZJQKaUXQ61hJ5JGFwwR5DkJ0YnoTxgwChFEFK2gK1uzwoo9NstA0MNpRQLE8d67abzJbEZJQIaHqZMbHsSOsA3qZpuqjQmnyQnUgnYbwBIeQFwZdsIxG1jPo56Pb4AAQOR9CqLdAzWENlGY0SrCACmiEoChQVWjSHRxF3J4EQHTSM08ujcfBJJ4HK3T88fqHf7h12+gyo35O008VBpy+kXR7DsALN0gKnGYgnApohLAVmnY6NLp3E3UkgYIz+EEcnz8MRly8l9uf2w71LT6j0c7B/xNs/4lUmllIAmNkQE8fFQZ5H0hmpBSkR0ExgNOhNJqNyD5FOAiEKFp1w1boT5R99HM+laPl+zqkznV5u+85j+EeaDUZ0w89vYoNWjUtrkggR0LSj07FWa3AygcsZUmlLrE4CIRyHX+F4hEAQ05iSz6LHTUU7B1xlZWUCgFgyhwYHw8VU8eISplDjFEMENO2UFBdqSmd0SKSIQJDxc7zd7UOdQL12uxMFdjDWgjhyNjHN3fYhhxepJ160Q1BSWio/Gl1Mw+O7RECD/Of1V0b97V90waXy9imnLvve96+ZPmNab2/vP59/8ZWXXuV5tatk2A2zWTpfe+W5+rra6Mc89ew/H3rkCbw9b+7sO35x0yknL2k53nbPr37/9jsfgNzkh9+/8qpvfyP6MV3dPesu+rpqZ31d7e4dH1bUJtvMIvNMqK/9z8vPjXrY4mVfkrfPX3fWL277aV1tzUcbt9x2+73Hj7eBsQAC4PMj8zPcAkxATAfsXsnyxIgaKiOL6dCIUy+anD6PRxZT/AaYqmny8URAg9SOJiUyNE1v27FF+kUPDdfU1Nx4009uvOkn6869sLenN2HpzLzhWVtTPXFiffRjiovFwWAAPPf0o2efuQYA0N8/MHXK5GefeoTj+JqJs0EOUlxUOOoHpxl1+opOx3627X2QmzAsO+pHVnJ437bS0hKe5+0O55lnrD7zjNV33/PbPwdupZmEokBtWcEps2s+2N22ZG7EwQ0ximmIemppKJZOrJ4AAL0RRS/8Pr9m7TX12P03gXFDXP1At+/ciua1LT4l/KENG9+zWCzvv/fBbT/7Od6z7vzzbr8TbZ+85NQhl+SpiaKec+fNy4x0xtUm8t8vPbti+cnfuXr9G2+qf1Fnn7nmuacf7e3tn7NwOd7z89t+esN114yM2KfOXJLr/UC/fMn5jz70uw83bv7K166KdIzFUnB4/6d6HTJGYrdAs7YfaEGBueXo7ki3wP+8/NzyU5f+/fmXfnwjuqpZlmk5uttgMMxftLKruyfdH9kohuCVwzglIDjY2BrXqcKV9D9bpPBR+MnRbEcXyqUPh1eMZlJaoKP3syCo0Ol0FosFQiirJ2ru8t83W5pbAAA1k9Av1+V0xm579gwPZr/H87mnHwUAyOoJALjnV79Hdf2RSwPyicsuvaD5yC69Tuf3p6Chb/az/NSlAACsnqiVHMefsvIcAMATf/ljZt7ADeHqKZqKs6ZNjOs8dqdb/hf9yKERZyT1jAIR0LgxGAxDQ8PHj4fcCYfd8L0Nm8RFcVWeSSdm0dLVj/7lKTAuuffu/3vkz7/1+XynrjynezT7Kw9YsfxkAMDjTz6r3NnW1oEWZCctysAbKEiPZzGSjA7ZnE6nS16zh+P3+TXNTyKgieBwOM5ae85XLv2qvAe7O9euWQkAaInZ0Z4r0olpa++8/a77lHssFtS4WxBS014sm/H5fPf/7k91k+cdPTYupp/+8HtXAgBefvW/qv04iKTXo7HAacWZNiv/g8871K8VVToLzGaleoZDLNCkGHbDYTd0OZ08xzU0oMmae/fuH/VZXr8vt9RTCU3TJcVF13z3W81HdkEIp806CeQ7d93z29/94WEwbpg2DTkxB4eGVfvb2jsBAFOnJD5FPavAhmeUAwrMUt+fKK17SRQ+QZRBdpqmjxzcDgC4/IofRH+W1+8DOc60qQ1bNr6Ft2+57S673THW74iQYnRioMznVV+rThe62ouKpKyMzBRxqjh4LL4gkiZDtpA4ezgFuGUaBMPDNuV+1fqdWKBJWZ2yejYf280w9Av/+veHH22O9Cyv35cH6gkA6Ovrr6g9oXrC7NbW9t/8+s7ejkNj/Y4IKQb3UQzvHJfCZsZRsLCCchRSyhllzV5gltQztsljZAmfuHRi9Wxt3sMw9JN/+/uNN92e39KJGRLvyTzPL172pR2f7aYo6s3X/jnWb4qQSjgx00CnV5fi4H4O0Ze9yePg6KvOD1bBp5CXNx4ZZc0euVdvJIiAxiGdqtTOGdOntjbvAQBc+d31d9x1v+YT80k6w7n4sisyFpklZIye3j6UQWkJNnDAVFVWAABaW9tz9G+hj9HwDCX67DIioHFIp1I915135ob3/4NK35Z+6d33Psxvw5Om6UsvPv+B392j2i8Ikbp8EXKYp555Xrwvqs3AE2YgD6BtZATkEQWRpXN4aBQHKBHQmAjPij/hhGmPPYLSyGfMWtoTqN3MS+nECILw2MO/++bXL1Ptr4u5+JWQQ+AEpnvu/j/lToZhKIrq75fGK+S3dMYOsUC1wVanZjkmw9AfvPtvJKOzT3a53PktnTI7du4GAPzr+SflPSzL7PjkPQBAwwyyhM83Oru69TrdmtWnyXt2bEVNAL5/7Y1jGIJPhte3HFX+mLx0Ykgak5pRm4DcsP77eOPwgU/DH73ltrv+9jRaAeUZF1z8ja7WA6tPX9F0eOfBQ0dqqqsmTKgDAPz+gUccjvgaphCyn5OWre04vu/FfzzR1d3T3d07a+Z0o9G4dduOjZs+GZsiztQRo3Ti9bvsANVcvxMBDf2VxdY/6UfrvwfGHxzHV9XP+vltP73+h9/FUSOPx3PqqnNxhR8hz/D5/ROnLnznzZdmzZxeU10FAPjJTb947h//ArlMQYqsTiWkG1PireeSCbJnbZOeNJHl3ZjSx3j7Q4PkPnI5GLlq3cJ0ZNGHF3FGh1igyfo6o5Ovvk4CgRBGxBKC8esDJdJJIGQVRr/dEUv1zxiYn9IQ43DGo4AmLJ3E8CQQCONXQIl0EgjZzA1pDsGnnPEioLvapBaDxOokELIWm01q7lVUpC4kzTCjJjCNIwHF6nm8uYlEigiE8aakHyTkAI0RdpxIZwLPJXF2AiHDPL2hETWCYuiGmsJTZ5QplTQ7YceRdMbczZBIJ4GQYYyKIk6WpYrMeorVW816ipI0tLMvG4c4sHns7lQbnjEkSBDpJBDGCpamOAFSADA0TVOU2L0E7S8qsiSTQj84LNmwpcWxegOit7DLWwENl04IofRHGA0inQTC2HL+sobjvfbeYRcvwCKLXsekoNXRqxsPyaaTrKRRxDSWFnZ5KKAaVidEv7dY1JNIJ4GQDUyssk6otHK84HD7/Rwfo+kzKgwlOe94ZN0mbpbmp4BGtDpj++UT9SQQsgeKAjqWLrEa0nFyWUmVYhqLWZqfApqA1Tl33jx5O3cHC2d5F48sYbx98Jz+vMa0tQFlq6ZxPcdiN0vpeByguSqgmmGi2N2d40Q6CYQc4ttrpuJoewpT6F8/hISCDfgxY1FSgWLoQL7OqA7QnBTQ8PwkLJ2xqCeRTgIhC7GwwtMbGr+9Zmpai5FiVNL4zglyVjrFKdWxSidRTwIha3FwaNX89IZGhqbqygrWzKvKjJJqiqnoCIz5VCBn3Z2ibBLpJBDyB4amrGa9DzJlhUaaprCGJpMEmqRZOvrTQQ4G2VGwLrZ7BFmzEwjZj4UVBEbn9vEUBWiKMugYWvyGY9szsRok7ABNTElhqJUa7Vkgl6QTWZ1kwU4g5BNGv/2KCxbYHL7jvfa2PrtRL6knJq3mpyYxSqd0MMidWszY82qJ4Ukg5BAsQ5cVGcsKjfMaypweP8OkpS99OmBzIT+JSCeBMA6gUBZ9sSUtWfR5LqARO4DEbHgSq5NAIIxHAdVK7SRWJ4EwXrClOmMp3ghSrgpo5Kz4mJ5OrE4CIacxikWcGciizzcBjdQEJPYWLEQ9CYS84dkPG8sKjctmVZeZmexvRD+WAho5yE6kk0AYX1hYwcXTAkTpn1aTXs8yFksBjsLbbI7s7EI/ZgKqmdpJ8pMIhHEMPH1BfWOnrWfYhVb0eoamJUOqqMiSmRT6HBDQyAv2WM9AFuwEQl4yb3LZzAklXYPOjn6HnxNATsGOzYIdBdnjyJUl6kkg5CVXnb8IjeFk6YmV1royCy8Icl/KzNcgZZeARuvaSQxPAmHcYwzto8wwFMOgCFIOwWY8K55EiggEgoTN5khtxlImHaBpEdAk+yeRBTuBMK6w5VruZ7oElKR2EgiEuHh6QyOe55GjSpoaASWRIgKBEC9UoP37q9taF0+vnDWx1OV05VAWfQoElESKCARCAhj99nkNZT1DrmGHt8CoMxt0StszMQ3NsAM0KQGNOJCdRIoIBAIYnZXz6zw+rmfI1TPkNhkYVtEGNPtrkDD/H64A2bwK5CFCAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant": "CAM A shows S0 has a clear right-side lane to T0 while S1 is holding centrally. CAM B shows S1's lane to T3 crosses S0's standing position, so S0 should fire first and duck before S1 fires.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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xEXlldWJMfSuvqrpeOXFwXGm13BPX3MqrarsItTmJbi1C7atABQJBWDgMIb35lpFky+AhQy5eaKrRoUhLTQUAjBmjL/buqMs4zZs7FwAwoHec+Xe8fSuta1yPHj37fNPovNtcP/Xt2UWj0QiFQk9Pz5oakzV9Hh6w1pIgmFU8URHwhISGhcXHRS1a9vzIMRNCwyPKSktadzCMaodWljFhuE9UP5FvGE8k0ar06Sy5SkN1HtG3EKJOJuo6pPu/tLyVVY3OmvxOKBYLdBmhnDxjdVcARAV7ogdqtT4iMbKXRXEY26LREv8cTZIpmFGRhGHj0pIvYhrFwJ4dr2aWiTH1zdyqerlqXP/OJ5ILZgxt4QfilPLPRPfNyFunQGW6ZHG3bt1MbTB9+ozR999P/fnq6tXowYyZM+kLWvn7rj9M7Wff3r1CHXDc0UEoiKRSqSVHeOIIrOGI7Q47LJji6eUrxWLjleQtIiBhXWeNrs5/0CBzqfxRo0frkhXGw5o4jqfczt+y+dPrVy95eEq27jTuepuCOn6GAW0lJCHNu6aqL8d4gqBek41soHfum/5Cq2xiPR1MkI9Ian14Fy2OPM4LyXfLKg2ueQzcTb9aU68cM7DrrbxqoFYM6BGVV1736NhuaTmVIkydka8fgcIqEerGWKdA//7778cee+zFl15+ZtnTRjdYs3ZtYGDgKy+/9PffcGTuk08+BSVbn95yObMy+XbmXRzHg4KDGcN1EP/buGHa9OnfbtmyZNEib2/vc+fOWTjmvboKVtUAkuQLBBpdCT2DqI7Rs+fOnzV33rTxI0GrrKeAVKx5Zdm32//7/IvPC25fvnqrqTqPDiokmDNLf7dgoNXA8SRpdwuXPv3M5atJAoHwrQ8/ffihphioXKkmSTK3uDKNVv1H8eC8+SRJKhQK6lld20ryZlaRYek+xeuf/YkefP3tZPqfCAz766tvoLdVLdarmJKS8lB6vg62uGx6SKdDsF9eQRnjXfQIo+h/Fei+H0sJ7ZkOzxP9CKinOpw2uiths7qx1z+D2Tk61fWqMB9dpYH1vonh3uyESq3OvJuj1TLdFxzH/ERKD1xzM6eysk4xfXDs2ZvFPK0yPbeqrEY+bXDs0Wv5VXWKAO+mscIsGX2U6KYi1DoF+tGHH0BDNnGin1/T+FyKsLDwwMBAAMDevXsBAD169ODz+VKptKqqSm7A3cxMmFDa+r3RN7pz5w6Mk44Y2btPHwDAow8/ZGHHEIIgcrLhnjd+/o3RDT785FNYbXryOGgDJcWFCoWcx+PPfeSpgT0i6IvE20vi7fXCSy/iOK5Wq4tKitEaibf+h01/jA74gdmwciA2Lq5b9+6WvLtYLH79DTiw/YE5s4HtIEny/fdgpU7nzjHB/eEhmZeiJeV6sdMh2MjFwNrZkTS6Mfu6CG8HuEQ2W3TRXn3At2NkSGap3PERXhKQBQUlhtYT3SZFmLpHl/DCygbkvFfXKcYM7Hq3qEaMqdNzq+RKzaxn9OGymB7dzC+ArWS6TjDUOgOqUCh++AGavMtXrvbv35/+VEJCwlmdTvzkk3UqXRPl996H1nbTZ9BgGTJ50kT4qoED+XzjKvifv/8Wi8Xbtu9Qq9XjR1rRMnbFskUkIGNju6398BOsecbpk083BwWFaDSaddbX9CH5SbH0USgtn1jy3PIXXqev7xHp+9knHyW+8iqOYc89BeO2LZKXl3fiBDTo27bDyifzjJ8w4fRZeJ5zcu6VlrQycmqKosJCFIB+/Q3jAwT0YE3Ws7FJMFZYCcM7JbUqF5tnrrkMpbWZxo4l5fbuqO8R40DIkpLy+gbjAyvCPBvu6xWdVVQrIFVD4zvll9d7wNxRdZ1cPaJfzPAJ0x4f33354qUolbR6eoz5d3KwDU20pluoq9hQzIc2aM9Ctm79fuy4cfBmrtFcvXIFw7BBgwcjh+jA/v0rVjyLNsu+lwMAiI6KNEykIDKzssVi8crnn/vn779jY2OPnzyVkpw8fZq+AjQ6OvrseZhi+u/ff3fv+hGt7J8weGXiWxfOnPhuMxxRagjKvPv4+u78cw8OM8ugqKiwsCAvLDwiMhKOq9ESxJMPz6lCnr413ZgYBhR2kIqJ/fTb7agIKS83u6ykKCQ0vGM0vGpJQL62cknGTVh9RfHfsasESXbvbiTMz+Pxbt68BTs2Xbr45JNPAAD27NkXG2tyGGhGRsbs2c2CA+npt8w3E6mtrZ37iD5EcOwgbFYwbvJEo0dyeB+MI9+6evqXDS83c+FplJTpDWhosL6xy/mUexGBntUybR5p9RXlMDQlmQRBJHQN9BDyQ2GVEo3m1piE15L+M2YX1UQEwsxSSgN0sJzFoAAYEvUUtiB6Ev/38/qXnqT+VGrgB7tQYXyUcPat2w7uHrLe4qmTXMKRb00Z05Ili+fPezA7K4vP5w8ZOnTwkCEYhhUVFT700HzKei5dCk/TvXv3TFlPOMPE53C2y8+/+NLos7m5+gnNKetpOdLa2ukTRh05tF9LEOHhEfcNGhoZ2ZEgiYMH9syYOMq89TSKofWEGjA7c86EwXv/+U2j0XSMjhk4eETH6Bi1Rr3vv12zx93HsJ4IHMN6RPpSC7Veq9XOmwfl6uDBQwICjMyaqysUVRUXF+3bt3fq1MkM62lDtFrti4mvAAB6DRwl8mAOqJ27/HG0rFiz0qiCU6qMxJ1dg+YfpNQgQOFc60mNoZKpCMAT6hdj0K2nXKW9kAFj00ODjNcss9uR/xW4pQJ15JxIVjVLttW4I0MFqmzQDwXx8rT96EmKWwW27AxiSGRMZ8s37uMJ68k7dwyl1sxdzmyx8dfX2/SPSL0CLalRlAqMdyxkA5qSzP4xUDIbUaAUJD28Cze+oPtozjWgqiIoFUf0DoPyv1p/OzfbIgui0RK/HU4Z1iPEjAiN76Qfq+ow1lsxfycWO5w5AIRVsKtTTlsoLa+y36SSIokvsqH1sma1LFbZU5wnIBpbdRiFrkkdYE/NQ0u5m2Tu8sf1NrTxtAt42CC/6ss1LTdsdQreHtQAYpMfrnl4ly3gOE45c6MSupLG+mMZGlM+DydJcCGjbGh3FsVVEt1oDmS++8zVYYea+Rah21PKmJaUwRABz8qjIXQ/6Q7BgUbtqbOMKbOSycWpk+tvYC3Gdunh3cggT2fNQkgB06EEcTateETvsDvZBbExkfRxqMaHooYFARLMGNVz7xnYw2xoUJ0pEcpmMs/9ymYRyl4D6hTn3RIRyqRx8m+6MYX17Y2TnQDTUWBD7uWXdo4yMu4lOsgjt8KhXX5vyIKQF28Vof6tHKHgXGXNiE5sfvfz0BB/KjQRGaQfm+RcfHy8pVKT7RdMtW1Fo5lNidAYX5zFIhRjvw1lrwF1ovX8+stPZbpWIBbRXGhSQ480Go1STUg8hKCxlMpDbLKckCRJmVzRIFebqe52vA1tUUI3xUABGNavc25+KYY5rJLJZhjGdlesWckITTgXVdFtXmO139m04rH9InJNjEOFQ58bh6JqGoeijuqtvyWPCKk/W2akQS1bbSjJli/AiQZUoBuOaS2ZZ62oAjt04iKwMQTw9ej2JLO7R1lWy02e7mTeZXQjZkyJER4aqLOSOhnUqIWydfNhZBXVdgyWTJ8Ex4DSyW90yr4/Y/JnY29Kyqo3v/t5U/LdBKgmFLAVVBfsIbR6MnrnZpBwHA/2k8hUhI+Pd2yIUFpvdLAZRhkdU3dhxqdA8rO+usTLvylVyBow+h+sFaFsVKDWWk/GREn2I6RLf/M2lLKepnyrmHC/Il0Ld123NOZvkodjGp74y1/2PffENEPr6USoyGCTb2sCDAMRAeI8q+vEOFqgTq6mZsBFwszENc/eu5eVIpR0CRvKRgNqFSRwkPW0CsZkGLrDBCMHwGbMZ65l6YP9Oksa1iEQtQXKKqrl6SZKjwn1NmU9F48Md7AIlTef7MiM9UTfgUKllXg4J6xmCYYXytGrORYNF3O2/67SEB48HoyBhtBGARhEc8nm/1PdXpRKF4uruAq4q8tPnc/oOEK6NBvASudO5l0BHxfwcWOTYRTn5OsXxnwYeQXFaDIMkiT9/PRp98zckszcEjZoT6x5YtrMZgAANJrTtTA6WJMe23U69Aa4CV2bey2YuaGolPWEk5cAMHmMyaxsfbWNxwTbbXwn60rrcZe2nmaGOTmeuNiuLU6DYaphmkyuwjBMJND7aAdPJWG6ecABmzAqP1kd8myOvChTo2uCRYHuDZvfhSPiTFnPyCBPId/qmKnN8RA1TYHDHAJgzIZSI6nQRwAsJtGVbSjfRa2nJd3pHRkJ1Ub2UUx8pVOjB1V75PuaQ1sAybTv/MCIoIfeFnWKl9++UPn7h9p6ff0ACYBIoHPgAVDjoi66mL58xtsKD1+f/e9hjU2OKSQhEYOeey9z386CS8fo63lCUa/5yzuO0Pf01KqUFz97rTobjrKnM+a970mCOLnWZA50zHuwZYwHDv13GCEhtHhDhTrjjDb1KKkwN5GJLhYhBUIfwD54zaeobxrOH+KPjKa3mDdvSPAbc/Thl9Ia1Ws/XBMJTFb0zh/o++RQv2lf6Mccm+K3pVFXcuUbDxvxJ35bCnv9Pbwln7F+WBfPjx8I1XfNJzptPZz9x4XS6hop8t/RNjMSAiMDRF8fKWqWQALgaFIOktW+noKX5sQ1yFWogbS3Fz42tuvB1Jp/kqU5FdCjz64lqDIm9qWSMFNPsCoYyiIDahV2yLy3nobHvwUieJMnGqSkVs338vcev8h7/KKLL02Sl+SOvS8W2VV81EJ8EBweStaWSnqPkvQeVf3be/Jk2C5aoVT5e4kqq6WB/k2mh/QKJiT+usk2mGFQnM+XhETwPZuNVe8y4cEec5egaEBDaQHfQyL2DRj+6qcapeJI4sNaVVPqVhISQZqdj0QSoh+OiVNRteBoQacEweQX5BnnS75rmqCFAscw1HGDzQxsdIHp1hM9GNPLb2gcPPkkCWRKrYeQ18FPuHXV4KuZVav+MVL/G+TFXzkusFZm7jQiIgME93QGy+hTjDX+nrx/V0Qj01ku1cjliqhgzyWTuiyd1OX1n5Or65pGsnmLef5ezX+/GAzpUkGJ0GA/fy++jwcPx+CtAxneuQk+cxN8CAJM+PQe6jPCBhJddmwS33Wdd6fITzhJd2GzQmXlqKeByFOQsld45XdYKZKa7iHkhSbcjz+0bsRnh4n/zdDfTH1C8EEPklUFxI/LdK/DeCt+83vobWRALRk3aZ6Y8Q8g63n8zadklU0hrR4PLO4ycd6Uz//du3yKtfND7X1mMr2WXhgaE/Hq7x7dh4Uu3lj2A2w4YhT4e2U5uqmb6XiKeEPjoFn5ZHcedZJ4OPby9MhBcQG9w5VpRc0qhxKiPTY9DIel2xYRH9vzPJzdZMOhin+TYe8lvDJLKPKIChT9tGrguqf6bfrvjgyYHKpQUlZNs57wxqDVkvuuFO27XDR2xAChAH4vXx7OWTszZGgXz8Mvdh69Pps9IjSxmQ1t4RJijwhlRQzUVZz3pncXNyXKNXGwsz2yngi5SluSdJLMuQaLeoL1s/3gM2ELZPKX5xqzXmTx+7BWyf+J/6PSLyShrayGPxs1LjpwMsmK4+ELej4I5wjYu3wK3XrCMaB/f5+5H57e8R+3JjGSKguilqRs6e7lUwmAiXqNzsC7pDQEGl2K+CxtJiIMZ3YeouRnnyio5X88UUy/xWgJcv7HsPXqixOapW5+Wxq16eEwe4Tf/10Br5bndhYh66lrTQ/fJqukYeSrRwAAq2bFmTItRgehIgrq8H/OZaMmoc9N7JT4Z8n5uzIeDtbOYNEA+eaQrhIMZYUBtQpU+Omsd2fIT0P69umJHpCHN5F/rwE1xfo8SwddNagWJjEwaFkxUqsBJBB1GUhdL/5ecDQ9sqFdwqwYMdJ9Fpw65cpXa41qzNu7fyFJUuxnovmQVZDkxc9gA+neD+v7Frouvh48eZ0ULRqVApAA0yhkdVL6olRrfzp6b+0e/WwlsHVmP5/IAMGhtPpR67NtezwCHuYtxlUaMjlfQe/A1CCT4zimUGlPpsLD6BvtZarBIBrmAO8KukvuanoecgYWTh9QUi1bMqOpK/mrf8G77PiecFfZtQQb0vHAymwSS8BdTn7qJv9xvpNIiVD+7VPwKl/4IxEc00yE3s0gc5KABgW/dAdczZzdiNRqMKG4Q7DfsL6d0EcyzLwbrlk8ktl/LGYCrGUsu3nF1NFWZiQDAPyiWz9lY9OubsM+p2EJo4BrQgIgVyjlCmVJtYxaDiYVkSS5cEKnruGS8lp5WY1+iQj0vHi7Mr+qKfJ4s1gxYl32+/uaTKqtGBgNffMdl5rm6cNx3MsLSmM+jzewa9A/5wswHJs6oKldrEAgaLlSggRn02Ci6XqWfvIx1KleqYb3Wj9P5xcYGMOiHzgbRCjftaznoRMXUdWk0+XnL6dgPTzk0kcPrI8j/cLks96FfyplQVf21xz5AchoWVcBHM9KVhdR2VJ9zzRCAwAfBUALK2XhgZ5CHtyisloa6itskCs9JUD2+NeG1ZWjdRP7MDCTFyq8ciKoR/+Oo6bVbIOTTbUF0qC0wIWgnzV90YMOmQIqzafGd35ibKcnxsJiiox86cnUspRs5rSjmaWtqUgfGSc5u7qFCTYeug+GLyn5aXjsjEENYZ0igbrWZIPBRuK6RH56NHnVnL5f/nejf9emHlSX78lGxkmCvXg1Mi1bI6Et4/RgKFuSSGwbtWkVfyc+5RkU2m/hqwFdegpEnn4jHvQe+gDQqsH2laC6AG6B6e7zmsZ2TfQpgjGM6mKKAeDvLSqtUYiEfDWun77cJsiroGLyDHTC/OasIlVmMo6RkgR+TM556D7fZaOhxIuL8o2LghbtUFp9ih30piGdAuFdtsZYWr9xAJIVavF88r34zrD9QkmVjCRBbmmdliAraxWBvnDCztXTY6S6cjSB7obNKhKhDTU+6S8L4btU7siZ1tN89FNWUXJ+/UvosX+XniMTNwKeAHvqa3LLU6C+EpC6XwVPQG/8w6jfHta3c25BKew20iGgqLSqslqKcjGe25aLDSYU2ZUlGP3Ot5YfvGcQ1BSyCjgnBIcpVFpy28WabReh6uTj2A9PRcQECyf19pKI8Nf+blNw8Mydhtf/gV8uA7osLaxRB3nzvET6u6amJBP57ziP37+THwAkvYfp0as5Ly8Y0OL78vm87OJaCU9z6GquhKf593w2qZYvmQmDoWG+8Lcv1znyDNhXE8peEYq7kPPOlqI1uv+ONGTzUs3qrPTdz0wp+Q5G9PEFuklJdY3FAuN60zdbsWYljnqUNd4UCitlGADlFdXhHfRxLo0G+sstDklCWTWcZ/J2GDFoLEwXHP0HtBmeUDfnuNM7DNsahhbTEOQTPxSMXAczRSNiPR1w3956Bo6qGNTZeC0tCYBYAC8zkmxWrsSA4b9rCfg9PTS+j1ylfXJq/3qF+qlp+rHIA3Qh15Ja/aVFTyU5ncQPvrNqeycGQ52fRLLCeQesk59B3ftN//pAaP9hhk/JM+CUokCCTCE0N1KcOX86X+wJ1ArGiEgkspUqjVZLGi3WMuwncnPX17Bh6P2w5tQogbF9oLIoyQNtJmo4nMvz9t7twI04uzrm8AudDNeTOvHomGRLSgG8Eh4bor9IdPVLMAWvVsL1HkLexHiYIPrmgD77n3PnnlHrien8dzQCVaslhRjx7/lsCU9z7Hq+hKc5cDl36+4kDAMiXYBIjt7GAOem44H1NrR9GdBWFH6yJ/RJl581uZlwsuXFrxluhgvEJAm09foshD9PV+lJuwegx6GiphTRsH5w0rcAXTETEqGEgc4z2o0p9+ReOI/mvGVGRWi/hYkAw8rTrSgsNQWG470fggVMWYd2AXeCBAI+FuJt5Ozd1wlKwlp5yyOO2ghBguQ8BQ8Hi4b7owKmaim03QO7writr0TQu6OEJEFGvn4orUgoUKvVf329jVoYfQkCQmEQqH+3sKo65aLpA24X1CyZkXCvRLpkRsKEOPjD33K6WZsYVolQa3GWCMVdw3l3qsNoJvqpkTdIC7JxHn/yZ828YwzDIz48CfPp3y5HaxJkZwAAJ72aROIRb1h7NKThKP2FqKK+vAJe2ehTHzmXTHnxpnrZkSRxfgMcGjR1816fKNg0j2LUW19FDh4HiwW+eAu0jahhE6dt3gdnpf/vJ0LjstMXG2PRz7DC7O9nOw7v2syD/npBuFiI3SxUGt7J7MHK3+D3u2iE/9bEMfSR+92jvN9/Ip4kwcd/3CZ1tUqMciWjLV0u3ioBGHkzB0YGLmVARZlZVOPjyZ/UnfQRAw0BQgPNtXZ1ORGa6QwbyncJ68lO+Yk4/cGz4/9vh9gvcPo3BxlPVe/fXJefqRHyQkODD2/+Iurl/qX8iD0+j4lIhRKDkcTuyuS/v/7F1HvxdE2AYjroC6fNdwKtupt26v3lo9/6atSbmxlPKetqjiQ+zFiJ8XiGB4yGb1KPjW5wZ+/2uwd+A+7FnVLlZ0crXxgfuG4uM3lSI9Mu284s4LUTBAlGrMve93x0fKT48qeT0AhfNDSWIMh1f94pqVYwTCfV+oRCqyXfrq2WK4lqBRDweA8OCnx0WLBKQwAsFADs5Ym+sNNdjfJMLrOGlNFehA0kfvCdVRl5xyeUXKCMybnWs8WhRzAl+toCkY9//IJV3uHRfA+JRt5QeTct5eeN8c0nZbuyes789z++KU5QYmI/beUA+TkJIb3RfFdoZqHwAA81APJb5wQeEhwQ2y8xy2jUsoaiq6caypqZ1LrCe3uXT4kaNqnT6Oli/yBCo5bmZ6ds/1RVx6wWLLoKi//NwNiA1Grl1eXF187W5sGQhVvyZ1Ltn0m1z94fMKizZ6CER5Agr0r96dGK7HKThZ/HbtXLVC1L02O36m8VK009Zbhy2he5wd78V+8Xdw718hLzK6WqUzcrkrPhl0i3nrkVSrnKiNNdVlWvUhPJpXiol+rM7Vq5UhsW4FlcJesS7nPtbmVIoP87v6YtGNMt0Bca0NXTY9btNTmkyrXS8U4B8wlw3HhYV5SfxovnLSbes0Is5IV2CKZPOE7NLGS0cW9ufqmHkKfEYCRUjKmzi6WlQjiHLUd7Q1V0GwVAxbrpCH09LEpkldfCmQczqoWLJ/f8/mB6fIdmJr5I7iFvqBd5SpSyBlTPZGhA6SKUDQZ0vZVloY4UoXw2J47YOV2HtcBhnaXl9A5A5hueF1bKIgI9K+uqg4P8YYUU+5sbcdgTEpByBRSwclNjlJqPrUK/GAlPs+9SjrS8CHSAXaMkIl6DEubBwj3kABpiBfDknTgPw+uDAsDlKjb2b201jnTkWRTvMNLx09m1hm2Un2h2dYb9Y3TKYYABMFyXi48M8hRjMFHDmc/2DJxLiwaOYWYW/UtIKD9njepZUt1U4EEQpKeH2NNDLBHxGIvOhkrpSxCvhlqcnkoC7C5pcpAL3wrnnSBIp4uvthtQyounFGiL87LBEr98OGpF4iHMKqrl/HcOqxCr6yQ8DckXy5QaaXnRiN76vqWeHuKmK41ouYgCKVYGCp/uzvo61rPSkeez1Hkn2WU924hCpRWL9adaXqdv9diiF59XVudsCc7hktyf0GXf5Vzov9M4l1Y0vLe+jxeJCyLCmrUFKCpkDvNF4pSOQk1I6m83eDHbqrZnRx5nqfPOMse11fITefEoA4AWS17Svwvs4BvoLWLZaeBwARQC70NJeRKehocGCjcyf0L82bSic7rWdvAmXVxRSJv2NTwizOiCntUS5NEUJzdSSGSlI293F74VzruWIHhObThvKD/bYkCRF29KS9KbqhlSXqtgbYN3DtYiVtdJy4t4fL5Wo6Fc+NgYfS3HriM3KCkK+yQ0l6JG+WnfNQyAkb303bycKELXs8yR57PNedfNRcMuXdxG60mJUDet0+VgHRKeps70jXn+hPjfD6dgGIbMKNKh5s3ovHF9/jiaClyQTDs78uwyVU4ftWnz6CcHh+OpxP1baN48se/8CfGUO0+ZUSakfoFzKbMjnJTIMkfejgbUFcvm7SE/OTgcDOpF4iGGYzHMMH9CPC70MB4VRaazeaDpbLqRlqbtfIw8zirnnQ13OU5+crg6cPwFj6dQaX18vM6mmUv+hAVKDKWoUTXKgp9m67GfDcVZYj2d3vGzdfLz+panJ/ePctThcHBYASMWVpBvshnN/PHxgYF+zcxoiTGPnjUkWu/I28mGsiVJwZJRmxbKz+tbmlKBS2cNWjprEOwxnFv+xo+n7Xl0HBwtoy7WzxsoaSw9tsTKigX8+ePjdx2FzW30ySWdDY0I1SeXSJoIdaFqUHuDs0R+Ot92WiY/j2987PjGx6qlhhNlcnCwCVrvBTPZIQbzx8fPGdPbQIrqN2VBfpd1IhRng/XUagk2xFhalJ/HNz5G//PfY0n/Hkvasfci7Et25W5BGbNrHAeHU9JHOI85XoOH4yotyM4p0i+5Jt15AQ+fPx5GRZuSSyWVhSWVj0zsyyb7yRYbyooyJh6PFYfRImeSm2ah+WYXbDgPAOjTNRTZ0Hnj4gd00k8Gx8HhFIzGwWrq5JW1DZW1cIIQiuzcIsNF9wzUm/PH95k7rg9dilZUVGOsScS750ik1slPgiTNj8Zhz9Cjdx8fjh4Ul1QADHQI0PeKv3mvYsVDo57+cBdfKOodoz+ff5zjikk5nBMAhQoU56MYaKQf/JdqLNEYymz2iwv0NT4VKIVIBBuSQk/ret6InnA8EntioOutn0TehqX1NjOgrc+8s8B6WmhA/3nvweTbRTiOIQN6t7C2oLwWA9hXr83dsP3k9GFxMoNZtl/+9ridD5yDQ4+q6DaGYd7eXg0KNbKeUml9z45wms/0nKr4mCCCZHSwp3569BSRcatKf5oEJHsMKHCqDXV2Fp4VxtPS6qU57/yJzGh8bFincOitP7gKTkC090zatGFxAANHL94aP6QH/SWDon0u57bcfomDo+0YapFeHf3QLX1E71BvXz8tSSpUWpVaq1Rrr6fDOa7juwSZ+h1W18kpu0rQJtUL8vXEAMYl4m2pQF3aebeqdUi/jv4Aw7pH+skU6lmjewZ4e6D1JVVwstkeMbDxR1JGAbX90d+/OlDjtBaKHO1RfsrkyH+XSuuRAQ3wEvr4GQ7u1CXVSUCQpEZLKlQahVqrUGlv3ilotKom0L1M5RHKhtk+nC5C+c4adESyxnoyMGM9J/WLGtANlsihAjvKeiKQ9YQzGHePpMzo+IeePdC+vfhBAVYIcBzHLlZ42/Nw3BZToTDTvzE0AQjJ000bIxQIfXQqM9i368gE6J5rtMS5a5lXbxf3jIQTflzNKIaGlVWlTCzoM8J3WsdP1mBh8fzrDw2mHvfrEQ0ASL6VG98V9grrFBnSKTKktr7ZnDWUGR3cyedSTrv24nkYEIlhO3SJwOTPjyRBVYPJ+S/NW2RqSh/GU2421U+LeHvrs5ooANpLF/0kATAmPyng1Mm66ZPhw8zC2kqp4nZh0pKZCXweLmtoQNYTADCwe9i1u2W9dftUeYSybc7ORCsnQLYVuHOcd4ItuaNWtA4Z0ieaMqPIdKI/fb2apkygCM38mTAy9axxft608OdNCwELsNNhNKgxv8Bg3RLEWHwDgyw5IchEBvuI6Mu0Tkq0MNYPCYJxFZvAnu/F6JGg8k96Cb1Uqp8wOcBLn0A3DYZ0alJmRaUUioBaqf68Tbl/AH07ZD1ZS6IzykJxJzjvwPnTdTimdUjhng8JEsy5v9egaEul0JOrfgTuCNVusnFkS5toUGPQFgcFNy16u9xkmk+kOn82NJtj5vJQE/A3BQOgOpD8BC3ITz0aLXHxVplao62WyrR8ybRRfbfuTqJvcO1u2bW7Zegxkp+IehZMOddG2mhDcQdbTzbMtWkT+WkUuggt3PMhSYL8qPnwD8xlNI69joSEo6qvZZZSI1sYz1vlkCBz3NQ3SH9FGRucaDtY/gXRTyCUn7o//VuWn0AqU125U14tbWhokAt5mISnOZacj6aMRyLUwxPWM/WJ1hvi7FqLXSoX6RbaFhvq0CFAKPPOHufdKvn58e+XzFtPug1F1jMPWU8AZo3qBdo70LrNGNkzIhRO94TMaLOnydZKWn3vNbbelh0F8wRadj4KKxtu5lZXSxuEPPir9PAN6iCBSfmyannTbHQ3cqjHGSpm0U49y0Sogzsu4w5t90kbEeHGMKyn7komLffi3Z6I0EBDKcqYAN2y/VCStsJQ0rZTcL5WpeitU4v+xquXmkjPq76RVdrQIEPWE12rER07QhF6PZ/uxfeJ9idJ0tB6ug2tFqG4I5133XQdbLGgrZg27sSlW7f/WXfn308Mn5IeWCvdv7ZWtyj7LW5uPSGzRvd0oQCovY+EIUWhOrXKfjY662YkLcJWeSSX+GqoAGiL+pMkydM38ovLawTNFY2Epzl3s3jJzISyWvlT0/QZpDeXTGa8nOHF17uFCG2dDXVQGROrRm22ms/2pL4wYyb8rV66FV60G8MwvyghulSzOz8FDAJxzf4nXSO+5sgi0IjQQGT1ikoqg4NbznU0p+mERoQG7jmTPiC2A9obZVLdmJ83LTRlSXtH+yM5b0p+nk3N7xwiUak09JUSiaeW7/nk1D5f7UktqoRtR67cLr15t5AKhrqW/Ex0VFWT1SORWi8/YbkZWwxoq2ctXjWjD/0zxMfq54w1tI/FlXUM6/nmNhZVvzrSgPIwENOJOTkzdcL26mwfnNBcqQQAmC+kRzuM7Wx8qmdKhIaHBmq0ZF4BnM2iPVTmq4pua0hcJMC1Gg0yoP4SI/772dT8uDBvqVydU1pPM50eAGAiAU9GirpH+ecXlYs8JUpZg5zgPz+7L7XZur3ZjL3F+DbzX9lTE4ponQG1trQed5jzzh7r2RY27UklSehvot//jcxiw9wvU4Pq/km6BYcet0N4OAYwzLBz2r3GpVdHP6UOy6+P7JxC89HVopLKsvIq0G4xuJ2fTc0vq6iJC/MuqpJR1lMi8dBZTyAWCTU8DwlPczu/evGMhKo65ZNT+xMkbJfDqGdyIRId4sg7IgvPNue91fITNDeLyJJSq5o3+SZDA3XDQtq39ZTJVfUNSsNFrlAZX+QtDEZCU6QRJKnSEhqCVGthylire6D7l5gyvIf10QA3AclPAEh6+ogyndA6FEsrpEq66UQoVSqVhnhwbG8SgOxi2Bf8UkaphKc5kVJAufCrp8e4ViTUMfAdUfjpzrd4LCWzuMmRN9Sijdbz76sme4C7NyQAMR1D9ZJdl0RsuuvoHl3JKO4UDCsNlUqVpZIWYAUFLf9cWXTTtj86/50wdNiR6YRda/NqtLqmSnTTqZOfouAg//KK6r2XcpDRlPA01+6WrZydsOmflHq52stDAFyTxFZFQq0aI4/b23oS7iY/waa9abS/6NKTZj11f4UGeielt2vrmSaH1ZoFRWUYAtf/o3PrYeuQ8spaZD0DA3wtvEzqGhQtK9nGRdaSpHUnUPUSCcCN/HpKdSLBfiO3WkuQDOGJBnEqlKqc0rolMxIqpYqF0wY0KDSPTuxLkuBuEZSiR681q2dyORGaaGdH3r5ZeF3LJeB+bNqbNrKLT/8eHdGHu4FEKM1+Xk3P1T9yx4/fGugFbLouQBgAJWXVaEWHYD96x8mWwDpHN+Yr6HOdNQamyypq0AqVqh1ZT61Gn1X3EIniwkTUepVGm1EgNbCbja/ie47qE346tSg1B+bfkrPKAQAHruRKeJqTOimaW1b33Cx9PdPq6TGGqaR2Dm5/5529JqQV8pPiTJb0+q28a7oF9VW8mp5LLfQt2638BIyvnybTL94spKxnaIh/6xOMUELp0LVzu3yriLKeQQG+oD2B5CeD2gZVfrW60XqipiFGCj91/xZJeJqLt0okPE1hRf2i6VCKLpgEs/AX0ostF6EsxK4iFLej804QrHLebd465EyW9EwWrKq5fivvpz2XGM9+ffjO14fvtHPrCTG4BErKqpHb3iHYLzRE97NH9q9t71JSXkPttkOw3+FLmQCAlAb3rwml4ydpilcmZVfdKa6rqm2ogtPJGTm9Wr5nVJCHliAfnhCvVBOPTuqrIciZI3uSANwugPeho9fzJTxNclbF4hkmU0kMWOjF29WG8u00ZNNMh1c3A9lQw8ccRikpbxSezXPl6GJp8ZJJ6BpoWKNz8WYhMp1Iz7bbkfEkSVbX66MW9QoNPSpSVdtU+IkI8PWS8DQl9UIJT3M8GXrrR69Bc3koCfrvZ9OgIM0ulj4/a8AX/93IK6uL7uD+5bStSCjZpYzJJeRnW/x3o3DW0zwX0wubWU+dN0l5lehqEbQ0wbXhJWXEerI6bmQXhOHd6MmG4mpFfoWMBwjGQn9JVW19UVlVTYNq0fQBOaXSJTMS7pVIURHoU9MGyJSaRyZC/z0pswym5pMLKC/eReuZEu3TZAS3U+gTx1k11Xu7FCRsoqS8mdsOY5bNrRx6bFGjWMxINCA0xJ+ynvo9tzMbmqEKydR0QEu9dzQ/NNZwG7ox9Q0MGdQ3DlZK5FSSJLhXAp2nW3lw9EFSJmz9eTgpT8LTXLldumRGQq1MheKhLk2iHRx53B7OO6rvY1UC2N7yk8MSQkP8YSGT4RO6qiZYCqrWWngmGWmoxt00T/W3b/ihsYYL9Wx9ZUlmYY2Ep7mgSxwdvgbN5VldQumaTnXmldUtmj5ArSXLamGPkrNpRS5dz9QWzNhQG+vEQycualnkvFO1mawy6O0OlYaQyZUyuTI7tyTL6JJTcjfHip8cQ3gayS5zmIBuTAkSzBzVU6HSLpjUV6HSPja5n1ypeWxSXzQqSdeIvhy2ttPFRjN0ozwtTCWxFps78nzbyk+SJHlscd6bjGZFYTO9yclPR5IqMztHbhsI8RURSpmddt4ekPA0+y/DZsnHrucDAM7fhMNkj+jM5f7LcFTSlduly2fC8UjPzuj/1Z7UW3lV51PuUYM7GWTXEvT2Iqyacs5+2STchtaTNaM27Tu1A4dzIUmA9GxOiZSx5JfVUUvPKFbPgMYGKnH/2gbVczPj75VIJTzNzdxKCU+DUknltYqnpvZXaYgq3TRzKCl/6kZTd7t2KEIzjTnyfPfq+GnEbnLy0524YTc9227JyK8mSDBtePddp+9OHtrtr7NZt/NhcPmqLhJ6RFcKmppTuXJWwqZ/U0qrZR389TUPritCbdgtFLdp4SdwKpzq5OCwDglPcz4dJo4OXYVJJJR5P6sblXQts3zJzISSKtnCaQNIEmQUQKt66GqemXomt8dQhOK2ct4JZ07XYdJn5+QnB4cZKnF/mVLzxJT+tTLVk1P718nVT07t36DQPDa5n1pLoGniL9yCFva8LkFfXa80X8+U7SLpeFs58jZI+CDriTtNf3LCk4OjTZxIKdD1AIXG7uodWASKBsUf1snS63fLl8xIqJOrH5kYDweM0OqZVruyCLVJRh63jfMOAAutJyc/OThaRCHwvlNQLeFpbmRXUEWgaTmVjVPLwdb0uWVwfppj1+GIz9v51dNH9AAAyJWaLftviusLFwz0oS9DY72Gxnql3LzNchHaaugiFG+7867VOqXwk0u1c3DYBlgQOrKnWkvOG9dHQ5CjE7poCbKwAk4tdzYNeu77dL2W75VIZVo+QYLfT2Vu3Z3kIeJ7CHmm9ikS4CnpehvKWtruyONtr1viNU4qzSq3nSE/OTg4TCHhaX47eUfC0/x0+JauCBT2Ezl0Ff6bkl2BCTzUWqJBCyt2SN3GcApl3Z8TE6KByOevk7cM97lwWn/qMZtFaBttaFtjoA6f6r2VwpMrnufgMEUl7k822kTq3zq5+rHJ/XSNndRoM7rpRIT4e4gFJkWoAAPsF6FtBG+T8+7oUZuWmk5OfnJwtJ1v96Uho2loOgEAKJU0Oh5OMW1UhC6mjVlyVxHaJgWqm97LBSKenPzk4DAP3VAaGk2G6aQ/1S3KHxf7tlsRirO+8JNLFnFw2J1KHHa0Ynjx9A2MStGtu5MwDPSNCbREhLKZVotQvNWjNh1S+Nka1dnMfyc5+elK/LvQx9RTR5b5HFkGn0X/cjgAM1KUvnJozzBM5NOiCGWzF99qG9pKBWp/42kL4enEsVEcVjK2K5zMZ/aPTXOifLQg5qMFsE57yzwvAMCEb5tNl/Lb4+1xhgm70gr/HehEqICPRwRJTIlQ9wZnpfPe+v3r5Wej+eWin67C6+Po8+5CYJmhCP5Kl/5Rf2SZj5gPLwlKhD68DZZ2c9jVizfvv0tof47rHwVMiFDkxburCMXZ57xzLZDbIwyBCX9scnW9HBbQhPgHT/hWuvqB6LPVARO+laLFSYfZTlNJCIbppOPnJRIJzBgTl/EErbWhLGl+TNEmt72iIIvu+v9ymquld2He/vXe2KLz50bDLr8czk0lGTWdDTQvHkVCTXnxz8wa4MHHMu+4gAi1FtzKuTbt2vGTawvC0QRlOv+Jv8GdF6fTYiqpT6dAXGwylYQBMjauG3A7EWqVAiUxOwrWtlpPrnjenWAIz3Oji/k8jO+EQcPtDvMFoYzNGmgiFMex7lH+pkToE9Nco6jeWnBrop9oHm+2as/m++D8d9fFqNs+tui8M46l3WFhQahRETqid7ipVJJrYbkIxVngvNvAesLoJ4dbYCboydlQx2NGikoMRKiHiN/Bz8OUCB0aC8vRXEWEWmhDLVWg9nHe7TXKiJOfLkqLKSMup+QATJWCmjKdCPTnWNP1TG4Jbpn8JOzgvNvMdHLy0z2w0DhyNtSRXjwDM/FQRIivh6euetco7idCcUs6fmI2bhpi32w7Jz85OJyVSgIYGNkn3G1GJbVoQ1tWoHCyYlvKTzTKiJOfHK3XlZwIZXMqKS7Cj+/p6x4itEVwx071bvdRRpz85OCwExamknAc698luJ2IUNys807aNPFu+xH0XPSTg8NZvUUkpi3pwLgOZorq6biECDVjQ80pUAIqRcyFRrhz8pODw2G9RRqMZeGRCBUK8M6hPpbUM7k6xg3ooRMXNRqt4/rNc7RXzo0uRovDXsjROlpMJVGgZ0fHR5hpEupymBKhxg0oCUg+H7e1/LQxnP/uutjQ/HGW1H5Y2xtUQrOk3p5Cf2+RexTVmwE3NWrTRtbTcRNycP67q2An2cipUccUhFqYSgIA3B8fCdwIoyIUN9Hx0ybvaEfTyclPDg6Wp5Kigr2EEj9Te3ZFEWpoQ00lkTDX6k3HyU8ODuemkiTGiuoTYkPcpp7JKLh9pnq3r/Xk5CcHB9tSSQzQs/ExQZjpeiY3EKE4Y9SmA6d6txmc/OTgYGEqaevuJLGQ16mDyXomF4VuQ5spUNI2s8Vx8pODw31odSoJYb6eyRVFKB28Wcsl1jvvhnDyk4PDkVibSvKVCP28TNYzuboIxfWZd0DibXXeHVGxxEU/3YDhp8KGnwpj/z45KCxvyCQxVs80Oj7CwiahLiRCkQ3VK1CSaEvm3XHFngw4+em62NDecabTiV58g4kBnfRnO4Z4C+DAHDcc2YnrCz/b1HDeQXknTn66GW2XjZzwZPmATqAToRgGUH8m9xOh/EYD2Bb56XqJew4ODquQ8DQNWr6pLHxD41PUn9Sz6PHQnqE5JdKystK6epnhznuH4WnF0At2OXBbTNdBOkV+cv47B4eDvXirsvASWj0TAOCRsXETB0Yb3TnDerqQCMXbMF2H00KfHBwcjqd1Azq7hDV1p+/RtaPhbndnNPP9XQvsl80ftOqFDjWd7JGfgwKkrXshjmEXK71tfThuQuv6gHC5I8cjVtcx1kiaO+yGz8oJ/rJpvYUCHrXy1t08Q+sZ48vMw3j5hwLW07rkESc8rTzLGJZX2krLy8HhoqkkX4kQE3gQJLiaWYa8eHfSnghzo1lZYj3ZIz8RPAyIxGJ4rQgsOhWVdSr0IFiZXy6KsvnxDAqQXq5yn861HGyGrjcbWkol1TaAJyd0//N4WvLdiuUzEsxbz+xagiFC66tL2C9CW2FA2y8M/71BjUWEBtFWGLGnBAkq64oAABGBnjll9da+hYUMCay7W1hTIba9debgoFOJ+4u1dYyEu8TAdMoJvtjTq6FOcepGIQBArSVyS6VHL2cumQnNaI+uHelevEvTpvpPp8Cq5HthSYX5fNp/p9Lg3bW4Ro2LO4V4BSvzW7SePAx4eog9PcTBPiJLFlw3ANem0/9xcLQA3VVvMMgdESTo2yVIwtPkltU9PqUfAODszWJkPc0479m1hHsbUCek3dlWPM/T2Sm68Gy0ocaJ7xQAdb41AxVQfAAA4BcU7BfIWIIYi3dAINq4U4gLD+doRTqIyyA5ixaz8IE+YglPcymjZMmMBJIE1zLLJTxNlVRRJ1NTkdDV02MseS/21zPhrpU4cqL8pJzrmE6RBja00vwpQw80vJZFKABAqVDod1tc0eJXsPvUTepxi3vm4LBTQWgDLR6qa+oGGhSashq5hKdJziqHlhSAs2kwlmUelxOhOJutJ9vkZ3PIiNBAk8/pln9PQv8dABAS7E8NVjAvFRkKlyZvzX0FXIqfgw1ZeE8RjIdW1Skfmwzd9pM3CgAAKg1RVNUg4WnuFtU8oXPn3UmE8l1Fe7IBZN3oJyQiNBDJz8KSSsqeks399+zimvAOeoOo4Yn5WoXRdDxD4V7PLOuvmw6hsKTCjKVGb0ECoIbyFpTXAheFc8ldN5WEkPA0MiVYMK7X9mO3r9wulfA0JVWypVP7frf/5pnUoiUzEzb9k5JVLD2XnE0Phro6OGutJ6uql2jWLYLx1PW7ZehBYUml0SAxSvLQscSRnz6yp/kQAf29OgQHoPfgvHgOZ0lRFRCSAKTnVkl4mrScysU6tz0ps0zC05RWyxoUagDAxVvNUkkWevFsFqGul4VnG9NH9KAeF9EsnZbQ27fgIOi/U0Y0Oa+uVKqvDLU8RMCwoWjXag3zUnPpVBKHS6eS+sbAtHtaTuWSmQkqDZFTIpXwNKn3KlEA9PzNYglPI5WpS6tl1qaS2Ix5A8rJT0P/3QjhNEtH2dA9p2F6J7u4hkdLwVdWS8P8YHduAQ83+xb6M29oQ+nCc9/ZdPrLNTyYvudEKIeDU0leHgIJT3PjXsWSGdB0ltfKAQCnU2HKSKUhCithAPR2Qc1T0wYAAE7dKHQnEcp3y7inbUesm/HfqTMVHhpImc6iksrw0EAUnaRIu1eOTCf9VcbeIpLxJBVmRTaUbqwRMMbarJ6fg8NxSHgamQLMGhb3z7nskmrYqu5sGhSbNQ1g0aT4Hw6ln0mFtfRagrxTUK0bnqSqqlP+fSINmdHV02PW7c12MwXqZOvZxuhnW6xnXqnUcgXHiHga1aE8HL+ZU15RqReeAICgAN/oYEkxP8L0XpnQdSg9SmBoo0ulqu5RzapMODjshISnwQAgeGKCBCo11IynUwslPE1Bed1T0/rrIp4lEh4sZnp4QjwA4Nrd8iUzE5ZM6RXgJZo3ulttbT1aXFqEGjWg7jCoBQ3msXY8D8LQhjL8d1MjCgzloUDAD/MTUU58UIAvvOysPMGkMevcGGKFNhqjKVxuSBKHY6jE/UkABsbBWpHz6VB1llTJHp/cjyDBjexKCU9zp7AapZIu6J6tqVcWVTR8vycJYMDX18vX1+ubM2XfnNHnYF0UvpvJTwQ9wOgXFEz7TMY/nZYgK+tgRzVSl4ehD1rX61mdWcrObbkSWP82uvfJLq4Ri4RoTaC/DzXp6d3CWsD3sjDGSh0xI0oQEqyXnx2CA+jxAaM75+CwBxKe5uodmGevrgfPTO/3zd60O4U1Ep7myp3Sp2ckfP5vSk4pTCXdLqh5dsYAjZbAdVLClOpk4BLtRdwtC2/ov9PG85i8N+TkQeuZXVwTEuTPKDPi4RjP+p7TyFQiMxrg76OznsCCACjTr2dsSdehZeVV6MHNnPJQXxEVHHDt6DWHS1GJ+8tVmgfu7wXLS+7CIZuXM0oBAAqVtqwGxUOh5iAIMrOw5pcDyXwetmRmgntoT0MF6vyfng1rPyPCgijTab4WnYKP4xiGqXGxQAsHU6Jy97oGhZkpT3imc/MESQr5uIenJ6a3p/otMQyYCoDCyicMIwkSw+DLMZ1kJUiAwz/hCzEMhAQHINN5OaM4vlNAdnEN0p5BAfqOdqajqxwcduGsrkQpOauCB0C9Qr1oUp8fDt06p1tZXQeenNgHA4DPw5fMSLBQe7qQCHWrdnb6bkbohJNwQCQ1FJI+UogBFUykbiAanjinrLxBqVHh9woA8OGpUITUw0Pvj1OQzV9ZXafwFOk7b2sJGAVX5UhFIgFlgqcM6JCRXw2EBv67TuTm5lvRmL1v5wCkcFF8QD81oJVauSD7HrADkTGdAftoJx/WwR9TwtPklkpXzBzwxX83Hr6/x28nM5HpLKyof3bGgM27b5y6UVhWXoVy7r6+Xi6dczcEZ7P8bDUx0ZEtDldHkADc00U2s4trggL96E9JRHwhASvapFohobNVcjmzAB6qS53ZwjCgVKkp6ykSCjzE+qCkUqlu2t6YgWtFiIDaGw/HGfEBDg5HUon7EyTILoHa5XRqkW7Me+3CaQMIEqTnVXnyNKPjIx7XDY23FaxKx7NagbYtfcS8JTBEKONpHMNgxXujViQwHk5qJSI+UMpVuAdyyQ0HZSKqpE3CE84bIxKgBxgAQgGuUhP019Lr6hF1DQoS3sow8zEBQ3S7pd4KagGj8tZVGNR/oIVbXr5+1c7HwmEdF/RZ+AYkRe8U1qDU/LIZCVQArHXa09CLp/18nS8ccLeZXLMpFRMdwej3QT2mKtKpp6mGSXDAZXMITG8ThYRcqtU774YilG49RUIB3Xp6eojRN6xSqal3NIxRpsmDMQAEfFzAxztGhBguUWgJDymq1Qhp6HoviqhsUlvELAdHq5HAWKfyiSn9CRLc00lRVAH60OhYpVrr3ieWLQrU5tVLFg7moRomhYYYcfbv5LZwz6yuqh48QK+bnlu5YuVLL1FPPfrg/LTUVGhVBbhSTWgJUsQDphQiBjCVRivkN8nY5s9CSipqOgV7ojUdgmC0Ib+oDMUWaOkpFLuwjoQBfQ/u3WV+m8nT5yddS4G3E4Hg9s1LXl4StH7xslW79xwELkV50W3zG9TXN3SOg+MO6Wz76etxY0aFR8OMs0sQHR119cJR89s8s+KVv/7Zgx5v//nrSRPGosdbf9j2+lsfWNWcKTmrnCoIlcrA4snxXh56MdHGuKexVFKpl38HnRBysmJgiwFtIy2OPqLbUArK+OAYBl1sWrpdpYKBy5qamkbFCp/x94MzXNfW1FBlSrW1+v5xF65eDgwKrKmpOXX8hJ+///CRI3b+uevX7Ts+fv8DmVwhFvIUKrWIJzCl9VPlQX08YL6roKg8MjyY8ezF9CLKdAIAQoP1sdqo8JD8orJAH5FaQwj4ra9I02q11TVNjfBEQqGnp4dSqZTJ9a2dYWJNA5tHiESigns3SJK8mpScnJL6wJwZ33+76fry1IlTHwSuA/3DUl8rfaVMBktw6IwdM3LyxLFqtSvNIkkQBP1DCQUCicRTpVY3NDR9OpVa71Hl3k329PTIyys4evz0jGkTlyx6fPzY0fcNm2Dhe0l4muu6MqbqOrB4SjxBkHwc37o7yf7jNZ3sy7PCgNqweknnvxuBbrjQWHU0YRGqBAoObPLf6UXp8T37eYr4DQq1AhNTymVAn/6w3InmLD+58MnAoMCzp08/u3QZtfJKyvVHHluwedPn8Acph25+g0JdJupq8tAxYNQOlpQ3CU/KdKIXYLptcQwrr6ruYExBW0hySlpcz0HUn0889tDGT977dusv73+4gbFl9h3YR6drj/ukUjg/+OtvfXD62J7+/fpEhIcVFrVmbnenQP+w1NfKWEnn7TdfWbliKXA18vML6R9qxvRJP3z3+e49B5c/9wpjy+efXeLp6fH55i3oG1/9xrs/bvl8+rRJY0aPOHHqrKUiVF33yP09fzt550RyYUkZ7MmErKdNTKdpEYpwmhTF3a16yRhUvw9qDRrSQ40l5/NhCudmdjnVLQnhKeLXy1XIehpFJOCJBLwnFy0EAKx8ZjlJaKnlnTfeBACMHjOaJLT07namSJUFoWugoKicEp4l5XoJTLOeupS/rkYUAyAiHA6k6+DnIcbUMD5gZ4QCgUwmR9YT8eaaDwEA8+bOBO5Ijx5x+dkpK1cszclxk1kkjZL48nMAAPr9cuHSlQCAH7bA27/lXM8q9+RpJg/suGg6MwDirvDdXn4y+n3QR0PqNyBBek5FmJ+og248D0IkROEbWM1uuE8YedTCHcu0sNjzmSXLusbG1tbpPSP0igYZLIHiC8VyWhlTi6i1BOp0ZyA89fEFxtHwdUKYz8NyS1vZQsUqgsO7MdZ07QLLA+/l2sC+3M3K69qlI2ATSxc9LhaLX3j5zR2//tli5NR18fCAdSYM5HI5Fem2BAlPk1Vcu3RqfyhHdNjWbWenCHW+AbUJlPykIiItFhYoFEqUPvIQCz10EhPVFwX5+0ATmF/Pw4zvg8rIU1xKzbqUmgVAs/VvrHkHAHDwxMVaTeP6lr7fVFlQH88KtZZoJjxD/M2/Li2vpndHP2vn/rQJQUGBH73/NgBg/4Ejbd9bbl5RVGSoqLF7ABvY8v0vLyXCD+jGCAVQK1y4eIWxPv3WnYQBfX18vOkOhxkqcf/V02JIi4e62wHqB4u1FwPadvnZmD6yrtmHUXTjeZoVu9fLVUpdEai1zJk1LTwstKysIvtebiteHkLTwoSSmdBgMDkhqqC8LthXnFzf+jCotez85dvhwwajXFOvfsNtkl05+N0qC7ec56g60FsZmcDd6dIV+hApN5qmd0WUlcOsppdEYqEBhejcJPsNN2pJhALHq1F3UKCtK37EMOi8Nx8NyRwpZNR/b5HBgxK++/p/AID+g8ZY+9pUWVBvj/L8MosvWScx5v6RySmpAxP6iUSiS+cO9+gzRKNx84o/d8VLAv10aR3zklMqoYvGM1FaZ5Rap2lPp2Xn+a4+dtOwst0oRkf4oH4fGo0mSJeFp7f8MOW/t8hD82Z/uWkdLJyK7q1SWxH9pEiTM8uYWEhYR/2cd48+PHfT/z7Kz06l1nC4FjJdsF7i2VQnh0AjNTTW+BbfOKPBkgkR6iAzyi4F2or0UaocSjb4ffN5hhWUjH4fJABlFU3hRYVSGegjqpQq5UqNRCxg6M1W+O/7d/9238D+Go22/ViTnb/99d7a13x9fIRCoUpl0c2Mg1Vk3oXuds8ezPRgcDCMCNXVs0dUmhnZaR47evS4G7QO0Uk2jF4AZKrfB916hgT5dYzQ37hk9XV1cjVJG9XTCm4mn7tvYP/y8gp3tZ4Yhvn5+kgkTKmSmwt7pwoE7LoZc1gIuu2NuX8EY32PbnFoUBb7z2R9NWxC2t7rQNsy7XuaPAjN8WvUhiLoqe0OwX44Bvts4hgW7Atz8PKGOplSQ5+y1aoA6OXzh0NCgq4np/bsOxy4KQI+P/PWlXOn9jPWd4qGtUec/HRdULiTDoZhXl4Sdn6n2QbTJVkA6VYG1IbykyJVHqTWGrehF9MLGRXplGnsGAEL0YN9YdePeqlUodLZUAyYr59n8PKLz3buFH0nM8u1RjRai0oNRXpEeJi3d9Nw/gdmT0eVLq41zJGDzvc/7gAALF8Gx4MgNn7yHgBgybIXXOVE1TtJhOJuID8pUmVGbOjF9MJOwRIj43kah/RER0JHHunQ2ppapGSt4rVEWH8TF9ulvOg2Y0FPuQ2jx82AEuB20icfr126+IkjB/769quNAIBe/ZgOIIcLsea9dUql8r01r+3+e/uShY+dPPrf4wvm5+UVHDh0DLCS7NaIUHcxoPaQn6ZsKN16hgT5hQb766Oh+n/0sWVkQ8MDYHSvqqoas8Z/R/NktRNuZWT27j+itLR84ZOPfPT+m/369t6z91BYx54KRVPbEQ5XJLJz/OEjJ4YOue/jD9/u1bP79p1/JAwZB1yK+hZEqF28eOyXze8Dlx27aYo+nhUCHs7XDbFAdAj2Q1MMmSG3oFQs5OWXN6i1xLUCuzik7Xz6B/P88ckKC7ec9+pmV/+w9qOdfEwAgGE63nQ9E8L2uXj3zJyiMZEAqJENDfEVkSp5izegyGDvgnK2V7BzcHC0FtvXM/HdT37Sh/SodMNjcuQKw4k0TBHkIy6ubmH0JAcHh9OxeGQnBadAHTOkRxgIgF38IA4ODufh4grUYfLTLSM+9qCdfMx29WHbycdsrQi1Me0og8zBwdHuIV3VgNq1eomDg4PD8UX1zlSgrPXfOTg4XIVspxbVO8iAcvKTg4PD/USo0xQoJz85ODicIUJJFzOgnPzk4OBwSxHqHAXKyU8ODg43EKF2N6Cc/OTg4HBXEeoEBcrJTw4ODvcQofY1oJz85ODgYKUIxVyymYhj5KedOnpx2JVVa7+2cEvu+3VFIu0/xtSaKedsMy6e72byk/tpcXCwkwKdrHH8UH27jo7nxsJzcHC0T0j2GlCj8pNLH3FwcDg+lWS/dDynQDk4ONotpMsYUE5+cnBwuFl7EbsYUK56iYODg1XYyYt3kALl5CcHB4f7iVC+K8pPLy+vQQPvG5iQoNFoL1y8kJJ6QyYzPg1ccHBwQECASqXKy8vTauEEc6aIiYkx/6ZKpaqwsID6MzQ01M/PT6FQ5ObmkmQLkZTPP/9CJpO99tpqUxtERkYKhUJTz+bm5qKDRweZnZ2N1kskkg4dmPUZBEEolcqKigq1Wm24KwzDOneOmTlzZmRkVFbW3QMH9ufk5Fh1HjQaTV5enuF6HMf79OkzY8YsDAN79uy+ceMGQZi7iKOjo19++ZWVK583/3YAgMDgkMFDR3buGltTXXXm+JH8vFzz2/fo0aOsrKyystLUBp6enqGhoeZ3kpeXp9Hop7YOD4+YM2dOdHT0tWvXDh06WF1dbf61n3/+xbVr13766UdTGzC+RzpisTg8PFwqlVZUVKCz2qlTJ8Y2arVaKpXW1tbSVxpeDFqtVi6Xl5eXm78+w8MjxGJRbW2t4Rlr8WLQarW5ubktbllUVKRQKNBBRkVG0Z8iCEKhVFRVVbX4I7JbPVObCkJtPy+8vSc++mnL91FRzb4DAMCdzMzlz6+g14HyeLyUlFQ+v+kO8dprq//99x9Tu83IuGP+fW/dSp8zZ7buIvC6cuUKjvOopx5/fMGVK1fMvDYj405dXd199yWY2uDAgYOdO5u8BIcPH4oubnSQ3bvHofXjx0/48kuTM6QXFxeNGzeWbsXi4+N37frTcMt58+ampqZSh2rmgwAASktLR48eyVgZE9Nl//4DjJULFjyalHTV1H7QwVCfBQAwqHcXxjbe3j6bf/wNN5hRdf36T7Zs2WJ0tz169Pznn3/Xrfu/H3/8wdRbjxw5csuW74FZ7r9/VElJCYZhFy9e9vX1pT91+fLlJ554zMxrMzLuHDx44IUXVpnZgP490unbt+/vv//x22+/rl27BlmcpKTrpvazePGic+fOoscTJkz44gvjF8OtW+kPPjjXlIBAB6NWq/v06WX0KTNUVFSMGDGsxS0fe2zB1avwBzJ27Livv/nG6DbZ2dnLnnvW/E3XbhPHY2xRoPaWn998+VVUVFROTs7aD97LL4B6sHu37u+vWRsXG/vd5q+fXrGc2jItLR3DsHffXfvrrztDQkL27Tvwf/+37saNFKO3fQDAK6+8TD328fF55501ZWVln3yyjlpZW1uDFNzVq0kYhq1atfLQoYOdO3fes2fftm07BgzoZ0oFW85HH31YVVVluL6uztxs9Tt2bN+27RfqT5FINHjw4FdeeTUsLHz//oOTJ09E6/l8/q5df5IkuXHjhp9++lGj0YhEopUrVy1evOSPP/7q37+vXC5nnAcAwIYNGwmCePXVRGoNkhJ0+Hw+sp7UjaRr16579uzbsWNnfHxvlUrVurOBYdjXP+8CAJw6dnj7D9/I5fD03j9+8pJnX3j55cT6+oZff93JeElcXNw///zb4p4zMjLoH3Ps2LFTp07bs2f3qVOnqJU1NfDrPnLkmK+v76lTJ5ctexqtP3Lk2KBBgzZu/N/LL78EHAh1wBgGvLy8x4wZM2rU6O+//2H48GGVlVCrNh7ekUOHDqLHEomkd+/ec+fO7dGjZ3Lyjb59+xiap65duwIALlw4P3TosPDwiKKiQqNvitiwYSNjpVKpMLM9RXZ2M7Nw/sKFE6dOoseenp5xXWMnT5oUExNz4L+9U2ZOJ0jChUSo3Ydy2lB+8ni82K5dlUrV4meebvox3M6Y9+jDh/Ye6NKli6+vL/Kxu3XrhmHYzp070G+srKxs8OD70tMz9u7d37Nnd6M737t3D/U4ODj4nXfW1NfX0Vcipk+fjmHYe++9iy7Te/fuxcf3Tk/POHHi1ODB97XxAx4/fqxAd1ewipqaGoYbfvv27e3bt6enZ3Tq1MnLy6u+vl4ncg8BAGbNmnHnjl4sKJXK9es/uXTp4nffbT137vyAAf0Z54H6zRieBzqLFi0GAGzdupWS4Xfv3p0//8E//vhr164/Zs+eZcmnUKuZHly/hEEAgEN7/932Q5NmOXn04KXzZ77b9tfbb7/DMKCvvroaHUmLlJeX0z9RWFjY1KnT0tPTDT9mZGSkQqGgrKdO6I27dev2tGnTHWxAGcf26687Fy1a/Oqrq8+cOdOzZw9q/Z07t+lb/v47ePvtt3bt+jM+Pv7PP/964IE5jN1u2bIVDqJdtfLy5avr169fsOBRM2/a4sWw1+x1QpGTm3P85An6mv99/tmXn27q0aPHt5u/WvrsM8AJIztbCe5C8jM8LAwAkHQtyfCpbTu2AwD69OqN/vz1198BAO+99y61AUEQGRm3cBwXCARojUQiCQ8P9/Pzs+oY1q37BACwc+cO+p4JQsvw8pwOQRBpaWkAgNhYvZ+IAmSU9aQ4ffo0AMDDw9PynQcEBISHh3t4eKA/p06dBgDYuHE9fRsUEzAM0VpO95594Nd95QJjvVzWQJIkw69PT89YtGhxRUXFgw/ONbq3cB30kE6LSCReKpXq7Fm9j0xRXW3ES3A8P/wAoxD0UJIpHnpoHgCgZ89eEomEvp7P54eFhZeUlEilUoVCkZAwkMdreW924vmXXkCxVE9PKy5Fp6fjXamQvrCwCAAwbOhQyghSbP91x7jJE8+eP4f+FIvFhjHpM2fOAAAoSzdnzgPHj598+eUm59QScJxH5RYozp+HP3LDo3Iu3bp1AwBQ/h1SqevXbzDcsnv3uB494MYW8v77Hxw/fnLUqNHoz9mzZ3bvHsc44cg3vHMns9XHn3bjGgBg6bMvGj41dMhgRgARx/EHH5w7YsQwU0G048dPHj9+MiTECoPe0FAfH9/7ueeeZaz38/MHLgVJkr///hsAYObMZt4A+vOrr77U/Qvjp7Nnz3HiQf63BwrYyRMmAdcBd6GxmwRJnD0HTeTBPfv+2Pnb7BkzTW2J47hhDvraNfiDHD36/lYfALo/l5Yy72BHjx6BOZBBgwFrSEx8FRl0KmM+a9YMAMCMGTNv3ry1ffvO8PAI+727QCDYs2cfAOCpp55o9U5Sk6+RBBncIXT7Xwc3fPl9915QkJqie/e4tDR9HsyuHD58FMfxd955GzibpUuXIlfDko0PHYIBnLFjx9FXfvTRxwCAXbtgoHnLlu8AAB9++BFwHqfPQGdoyODBzhjZSbpGO7s2sub9dx996OHFCxcFBAQ8v+K551c8B0ggrat7asnCWqmUvqXhhVVeXg4A6NixI/pz+/Zt27dvs+rdkQNomCxCgcvwcBhhaAtHjx43XGk0V0tn+fLly5Y1hY0wDKMcsYQEGNZEkCTZu3fP1NSbPB5v4MCBx4/DIJRGo/ntt18/+MC6SowVK5iijE5gYNDZs+cwDBswoF8ba1OemDf1/z77NiKqY2h4xFvvwxABQRA52XcfedRcEtwoLZ5GS7h06Yqvr++FC+d37YIxIkdCVbnxeLzevXuvX78RVWINGNDPkpcjRyQ6Oppagy6SzEy9i0CSZFbW3S5dugYEBBjNZFpCholcvIUnv0aXp0WRuvalQB3ZOmTn77+NmzxxwtTJ5y9cgBleDPj4eP+968+vNkFPxAzIpBrWxFiLoWlGBSIY1tb2gnK5XCZrYCwWvE7/vhiG8fl8Ho9XVVX58MPzu3ePa2ho9nKNRtOjR7fu3eNWrHi2traWIAg+n//YY49nZNyhAppthM/nnzt3HlnPtpclkCS5etXTjz0w+dWVSyvKSgmtFsfxmK5xFy9dfvBBGNdzJMePn/D19d25c8fChU85+K0BADdupKHl+vWUbdt2hIaGEgQxe/ZMw6IIo6CLk34/27dvvy4B2PRZ3nrrLd1d/FirD7LeBBbvwDZNjh0pQl1MgVIQBPH2u7BQDnrlI0e9/fqb3brFzZ4x88tNm9BKQ3OG8kWGDrjlIEMpFosZ60NCQnQ1cSYrty1kxoxprcjCf/31V1988Tn1Z3r6rYCAwG7duicnJ5t6ybFjRwcPPooe79mzLzY29tq1ZKvCoKb46SdYUDV27Ji2W086RQX5LzzzJHq8fNWrw0aN/eCDDw8fPiRt7nbYj7i4uPDwiMuXL9Mzk/aAx4M/ScOazeeeW0Fd+cXFxbdupVu12+DgYN2IjKZqjU6dYF/OX35hOmGens0STVYxcOCAVr8WAODvD4PLhUUw1dGOFKjD5GfPHj1HjxxluP7UmdPzFzwME3krnkNrSJIUCJj3hh49YLXH+cZEUytA6aOAgADG+iFDhgAArl+HMVan07dvPADg3XffGzoUVjgj+Hz+kCFD6WsoZsyYVllZiWFYUFBQ29994MCBupEnzcoJWwGGYd179Rk0dKSRG8amTzZugKmwZ5/V2xQHcP/9Y3R1HWvt/UZo3JHhYKejR4+g5fjxY9ZaTxT71lWJHkZ/9u+vt3SdO8fQF7Ry9erXgDMYrwvRnrtw3oVEqCtl4d99+5133nwrMDDQ8Kmq5hecRqMxLO8YM2YsGlXWxsPw9vY2enW2OMLPMajV6mXLYHrhxx9/oqp2NBrNTz/9vHnzV0ZfglK00dHMIYOtoFRH2/dDkuRb769fmfim0WcvXbqIgq3AUYhEIlt9NPMsWLCASnjaCgzDUMJ99+7daM3WrbAEqnv3OMaCqqQXLlwEnMHkiXDQx8HDMN/lKuAuFP38WZfz+fQTWM3LoGePnnDIWkYG+nPDBphwYAguVChuYczIFNu3w4LT7t2bVePz+XylUglYw6lTpwoLoQb8/XeYYEWQJOnp6RkT08XYiMAlsOLS9LBLyxk9eqThQM/WUa0bvTpzLvQtGCxZspQqfnAMX3zx+ejRI80PCbOcEydgtvCNN5i3Bx6P10tXy9wWP8mQr76Ck03duHGDukolEonR9D1BECj2QuVaHcb7a2Bs5E5mZquHrtlChGLurED37t8nl8sjIsL3/P3fqBH6Xymfz1/+9LIvPv0MAPDmO2/pTe3PPwEAvv/+B0qu/vzzNhznrV79KrW3SZMm7dz5K6oFsZwNG2Ah/Z9//i2ReKE1hw8fbWO9jj2YPn0qAKBXr94DB+rHR6Hh2/v3H9i06XPqtPTt2/f06bMikYgxlsk8L7308s6dvw42Vm6yY8fOHTuYIywtYXh/WDdK542X4MDc+Que+mDDl1GN6tg/IPDN99dPmjxJJpNRYxYtYefOX3fu/LXVYYq3335nx46dAwaY7GZgFYmJr+i+kSe/+uprqrh9woQJqak30YC0Vu85IiLivkbGjBmbmPhqcvKNMWPGajSaRx55CG2zYcP/AACfffap0T2gCq0//virFe9+n2nom4WEhMT3iUfL0MFDli1Zuv/fPcOGDtVotCtWtdxcxp6Qzk8i2bVz3awHH/ht246AgIA1bzEL8RYvW0qvZJo8eeLBg4fPnbug0WiQJ3vlypX//msaKB0cHDJgQEJWlvGh8aZQKBSLFi384Ycfk5KuqdVqVGt58OCB69dNdnxwCnK5/KOPPnzjjTe3bduOskNXrlx55pll33zz7aRJkydNmkzfODU1dd484wN4jNKlS5cBAxKM1pMnJMAYqE2oq5O+uPypT7/+qVNM148/bdaBQtbQYO3AWWT7hEJR6w6mW7fuCQkDrR23Zor6+vpZs2b899+esWPHMXqFnD179tlnm1o6WMvs2XMMi+Fv3UqfN+9BKjE1ZcoUAMB3331rdA979+7ZsGFj60bWbdvWNELPTCXT+LHjULiTTlZW1rOrnrdtMxEHjOxsUzcmZ0185OPtM3zosNGjRvn7+d/Nzvpt1+/5BfmGs3IKBIKHH3545szZubm5mzZ9mp+v3wbh7+8fEhJSW1tbUlLC2D+fz+/SpYtKpbp3z/j0yJ6eno8//sTEiRMzMjK++OJzwz1Y242pc+fOQqEwOzvbaA86xuCi27dvoz+9vLwiIiIqKirp7SQMty8qKqJ8TwzDunTp8vDDj/Tp00et1uzZs3v//n1mPFPGOyIiIiK9vCSFhYWGFSpGt7ekG5NhKyaKoOAOo8ZO6NWnH58vSE1JOnZoX3VV5eU045eZSCTq1KlTWVkZIySNDiwrK8twIFlgYGBQUJDhS+h07NjRw8OjoKCwoaHlopwWuzHRv4vHH3+iZ89edXXS/fv3Hzp0kPFd4DgeGxtrySlFFwN9jUajbWioLy0tZVTjtvgdoQ3Ky8sZBaFmXthN95QZ0Ku8vLwGDIYtDii0Wq1MJq+orHBAOzuj/ZmMtWjC3NyAuty0xi0a0HaI5QbUKKYMKBuw0IC2TyIdPq2xXQ0o7k7Wk4ODg6PNqSTSPZNIHBwcHPbH/gqUk59WkZ+fT58OhAMAoFKpGFFptyE/P7/tw9I4nCdCSfcfyulaTJjAzDlyZGRkuOtpcdfPxWEbBcrJTw4ODtfFhiKUi4FycHBwtBKrXfiKQlYn351bJMHRajatta56fN6rJqcj5eBoXVF98ynnLJpprlUK1C5d+zg4ODhcDNyd5CcHBweHIyOhrVKgjhhzxcHBweFcbOrCN8lPzoXn4OBwU2gi1E4KlAbnv3NwcLiTF28VeGuin5wLz8HB4b4Ym+3DDll4p8vP/r3hNEccHBwc9hGhpDsX0l9Pu+XsQ+Dg4Gi/WGRAueolDg6O9lrPRNpBgbIgEe/fhgmsOTg4ONpu7PBWys9Tzi+er5Y1OPsQODg43F6E2kOBcnBwcLR7cBeVn5z/zsHBAWxH60To/wN2S9c43pArAAAAAABJRU5ErkJggg=="}], 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"}]], 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"}], 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"}], 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mn18rJSHR4uIdL+RTAs7HculrQI44QImA5i1Htzwfy2HvbECOtut+8SeQKfKjBjCuTxq7ekaoBQKyrKTQ4agBRJMTayqL3R4fg1wiQzQNisw6P6Q4jqcoiueFyrJC+fC6ofdxLCu17yLlkbG0QizQfFbG7GRBA1qExrhCH1d4dOVGf3+Br8Opr0tH4fYoUEDHMt9Yd4ogujV7fIMT3NvuPls4Zl4NIeR5geOFIqu0vqahjwKCWKFUnZIXL3V+IRYpFYCcgghoLsliksqIxykDihKj2JBCg0SoscqtxDKKCRdTeU9VTW21USPilJf6O2BZVDf0bpljt2DVhVa+wxrbxwAAu3FKut8DTVO0KNPQVM57Tazgnu7Z1Fm0Wunr1PMj1eL76SlcnvwrUlAodh+yeI+LRUqrQU5BBDQrZDG6MuJMHYoCEC2y0DpL9q8FjwkEQ7FK0kglxVhq4GmUYra9qJxUTqyqe7qCESel5srbCxqyIoKUEnhKP2SeXeI6UGHfLoZTjBxt1vM2CqIJtTxtGjLPyuT76Sj+Uq3tI5Z3TBh6i6cNHl0VLXhNfukPMlCw0BsW0Sp37AJgl2onpNi2krPlHyOl/XcUrwW5BhHQMVNG1NxVDC+IeTiiPRgQOBU0Le2WHw4/LPhc9f8r9sT+pgljhN04xaWvq3Ds0HPDjOBhBA8OJYmVSGk3P8PpLDrd6O8vc+5hBHeBtxXvdOsq+6wnpeSCghTrpwuchvpUZeNnMoJEBDRxWRx1NS0IMFQO0XJZuUO0DtEOBuljstciEce8gacN3YUr4nqKZtwpnI7iLyVwEo+uPPoTMYPmuYNazaISeJ85xLiwQFOsjBBitULraKyR8hpZy2zMfh765fpMBuLDUa7Nx4Ovk5A3sONHFkdVRuQ7hJDjBbyWlhbF4coYviJONMuPkIy2EghjDpsHshhdGXG4GYtjMBNEy9uI9+vYsR+fQCAQcgI2h5VRlDzUVAJCihZjzXL5h0IfJTsygmgScoWC8jrGaLJ3NqO/N4yhSJtASHMEKQUCmpgsxp7SiL4rECB5FH8MycXB/xXX2tIWiaXkLL++877oB5hNxpvvuQsKfEnDnKFmdedNAmFMYFMui3Eoo4AsR6UnEWd1h4SqKYrB8pjMGxr3nLLmjO72tuYjh7L2N7H64st2vvM63p5cXUIBsKepW3mAy+355zPPH967BwBw+8N/vfvaayKdqnvI+eCDv0z/WyYQAJuqZG8ZKHYYQ25HXOuCVTFMHJEyYmkMhQhlOti64T2Q3SxbMHXnO9I2vqsunIqqvyEAW/YfXz57IlqCOHoWTKkBAN581bfxHlxJBQHYG6q2BEJGLdBRlVEKwuDiFnzN4rWzqJLKIykAxE4ESjEkidxjwOb/vSQIgsXI2j38qnO/AnITXEsVWmKgyJHAPwJQaJYm4p7ztW9Org82co+XP/7qd9pvg2FvuOVHCZ+WkOU8+9jvvvX9GxN4IoulM1AGiFpSowRGuQpQdjWGFLtoVLkQxpD3/vMPnKRfaJF0xOPjAAC9Np+lwGwtALs2vKL5RC+XpUOGPm9uFALtfCuK2CMdIXPJjQZU2qjJlAlJNTN3i783LbhRvbRo0JuejVdtKZoBUAhkiBDGgD3NtmQtUNlElDp7E1XMTUYcXuWPxSZmcAgNepTpHkbtfpUwdAp7iidL73Cv/F8ZTlQldLHqUMdywPusRkYpqfJtoLHT9sPrfvzIQw+AMSKgvzGpLcZsMspqWzZ94cDRz9P39giRSMz8zMY8UEICnHHRN2QjNPqSwKBjvH7JfBNLSLNrFQEBDK13Vd0VgrcH+T13DaFbAs5hYxnazyWV4bT+lp88/eeHVTun1JQio7ixC6QBl9sTi9reeufP0vHqhCQhApo/NHZpr0Sm1arnSTA0dbw7OL1nxdwacNQBsgMIEunfzqKAZAruAwUm0cjVAge1lGw5cHz57JCG7Zh4pXbhKcs/37olrqcQsgQioPnDnEml1dVi/CSwJBf/HwoC6BtAFeVeL+pnbjToAaOf3lDAMjRDU+JI9+wa43Wsc3SflHxXUN4MTpxe0SMapJlBUz0BAE6v7wQpkKXSdBR7PdzeJz8RS+2o6tk9hIYFZTjrdpwEzfYk4QAlApqnBLok4wyy8Im/JRaDGCQM9IcSsktAv3SyYrhQyM1A2uzpC94PdDrd9IYag44x6BkjSOfEi0RQepc1bGSVVdvUNRh+iiuu+6ElkGOQEjheWHbuRdve+k/yjgWM2WQEAOS04F65Bg0m+duGuAs0iAWap2h1mqcoUGrRD7n4IZu9rCQ43CZ3bgYIrJ5yh2l0P7AapFyRLK7wbB+Q1vUmgyBHwOwefvHUadGfWGBKpXribJo50+qm/+B7rz3ztLyzoboUUCgdAKff4GaMKJtbgJ8cbLUY9dHVFsRg1WatbfvWM48m/FwioPkLtj8B6O4bAjlNmIaGPZ41UbAwem1SRoGepeXcsl4bspSn1BZbC4AqQyucP91/J5ban9+ZmvIqhqYqS63D9pBBKVI5dCCuGAwwig7pBVNqcHwP9XLEwoqaOlJ4p/hf6U8zakUDVtusAicRj7hCMlhihAhoXqMlOUN2T4nVqIp3nzTZvKNFY/RQtn2QHq2bwcDQSOYN6kgRJBU2h0el78Umqd2XMsNMTuBFqVq82hHh5aCepX9zzx2Nnba/PPLHVLx94IhZL5bPQR8TfwomOO8AbXxyoHX57ElS+of4J5rfUK38uC3dIX+v2aefM2NSFcg+89NsMppNxivXzIl3FU8ENG/QtsJk87OqomTrnua6MrPRbAZAGBwKXcVniw0Xechd6CNVFSVtHWg4j9fnN+iDo8kff+xBkINET9UadHi9fkFpGCYPz6fe30EpbFhNTlk4rW7OksGmLwyWEp/DVjRhGq0zjGHqq+x2SNguDosvEPIU+bo2GTTummOeS//rO+8bdQ0l3wy2Hcilyndk7ItD/OL9pyKF6gkAeOPvz8rbHQMh1RZp4tqbf2wUayIgz3ts/SVT5gy1HBxq2gfGjkKzQfnvha2N8Z6BWKB5guZ3q7s3aH7KO6HP5eOAQc8qV/Fjnkv/lauuHB5xvfaPv0Q6QP4sAICLVmV0OGXyGHTM/uMaEfbo6bpKcLYWythNA6fNnZSkpyIBbO1HAQCWqokjnU0gC/jrBwcSeBYR0Dzk0h9cLm8/fBcadoR7YZ26oKFVXPbWVpUODI0oV/FjboFOnYDGoL+utYC/9AeXv/Loc/KPypuB+FnKBoaCRQG/vvO+7CzaCc/Nwp8UV8F/uOOoMvt1TgOq6N/f3CepKsWYCozzTigCIFKpfs7AuaWcVt6DMnZ1BTmSDRIBIqB5wtqLvr75rRdV6onWTXesv/z/3XpRZUnQhyj+1+vjDIEa82ywQDWRPwveUN4MIrHygktB7iRI4GgMBGDN0qC8yoGyOQ0VONeVoiirWY9W8EJqBFScpJ12mrTSWrHhiaxya0lB1YRcr/0nPtD8IVInoede/LW0hVsRKh7K5iZAqjsBvhlUV4rmZ6jce31+2Qg99cTpYEzZcuB4TMcp2/PFcHhqZ7x6vKkpnViexOreMzIwtur58G9S0HSGWKDjFGkVPxxcxT97x2Xyo9+66yWQ5UQQlCRN6ZTcUAJ1nKOhMKUVZQKRknYpnZCyDEqfn0utRH7eLIVfeAGOWhqQTxABzSeo7u7+2A5E31d5Fe+njQCExGFlMY1dSRecdfWedx4HmSUNnge1gnYM2HE3pkShTHomRg1VlTw0dQ7XlZlD9nSnJlyekpXHlkB8SVZPQRCsBeYj3R3hqawAgAfvQxUBuCgAAJCquoAxjCARAc0rIomJMgIjAYNGKJ68EumcY2GWxi2KqjhSMqTJoyE1eYmqoSr1rKooaeqUilZTzvBITEUTsYTgtx08JFcBAEA7nC5cZ4WZUlssbSkk1WpklHoaiRQWX6UPYoHmG688+ly491ArM1GadIpX8VOrrUeP92DpmhahViQL1/gsTfNRY0ppTfFJGWEfQZVpkPIyAZZlUmViQwDD23jL27jaSqGwIY2xo5+5QM/85p478HYKS7BSCxHQPARHq5Hhg0Mu4eDxflRILF42/I4dR6lOYKzFNPxOEG5Ky6YzjiNVF0XreRELXOR+zG+2SA+dNzn1oVel+fnpwe4LK0ogAPXlZghRGRJFp/h7+t/nnsnkHWIkVGGV1VYySpGdNLG2o73z4wM9p8xEyW0AgJSrZ0oiSERA85nqCFZMXZnZE5BLvIqPIj1Hj3fLyfZYTNu61G7We69Z/X9//TBVb1vW8e7eIXwniHgbSIMTNMr5VlbxTq/fjO43scr0sY62WA5T1ggAAC5cicoEsjKvLI45AsmI7L6DqMHKgknFLne29SdUQyzQfCP4bYx8SQsQ0vjh0S575RcjXDrTh0pTIpHyca9C5OiK1aSzmoJF98nX5yiTW2XQ3UL1FmBaqr/lOaYpn4YwLWpJVQIMqg3YbIkgEQHNW7Yd6ELJ86EEBqyiJo8g4KdSZdRnG5HMz0AWeorjSJnpjKd0TVx7x3pZRoNZrhB8sqe5vjwkBJ88fi44zVTuPKA5gMRsMs6oLRn1JrH14EG8MWdSqVyrGn2gQALyyvHZm62cvd8cQjJcdPps5Y8hqkABZR1KbXXpwOBIt82nqDTUxuvjDWEZOfv2H6Jp6qvLUY32r+++/dbb786A+Rkcqh2+KzkyUJGlGeK79o71Lz+i8PAq5of7eGiInAcVF36OX3PJV3x+juN4XIxEUYCm6fDu9C63J/pkJ7PJuHz2JFlA1bWqIrI1v2H7kdjntYQr7ITyApCVDlAioNlCMetcsWCy/GNJkWR6NLUPoGySI45kzE+VJqCCwOQq+bBo4m2apgwGtBgs0KFzPvHHe5VH9o0gMydhVQ03P6mogtetSKBJjMcfU4/kTC1REiQyoN1mo/7k+VPwNhQvA0EQvD5uwQnXjzjcHq9/YNjB8QJNUwxD+/08L6C42advvxZ+qoDCUlFcDNInguBLikJVtX9CTEmlxG4AuANAeA7srXfeA7IVYoGOPafNtE6uRWulIZuUnTc8Ik1Gu/H+5wEAF198QVwnPGtxvduO1rMma6HqW9ne76ovN/OQvmpF0ZObJVvA6+P+ed0Mvc7X76Je3h908yk5evSoUjRRit/kWvxjRxdSeaefqqspUz5luL+/ohBpq5yMcrxn5OE/P5Ay9Qzdo2oMmihjFrxR9UyRwd6JFJYhYSixwxNDMzqU0mQoLSoI1zWfn4cQzp1+LcfzA8MOnhccLi+WTI/Xb9Sz777yVEyvpFBNleJSgSRkpXpmkmQcoERAs4JNh+w/ve95RTZ7yE06LvX0+LkHL69f/2zLnZdMeOzDASyjmNm1xgOd0pew3ML8Y0v/KZNNu4+7KQCm1lghBBf8du+5py9RnXDPF1KDbnmu2ZRJtaq3WVdd1tE9EG5dDDqQPdgoLtnEscPxFUq67SMma2F0YUu51eYa9piy1SPcmKIypFjAuobbd5oM6LZUW1GsDLVBiOpB331ltFKrCBqquljCC1hxClcGIkhJkqXXynjjoovO725rLCsu6Bl0Do24PD7OqGc7eoe9EfqDROKP35y8/tkWAMCdr6oTaLqHXGvnlRabK492OSgBBRNe2dqt6NRboylGEEoiJU+A2CummKhUFa/iO7oHvF6vLJpYN3Eb4OZO27yp5UN2392Pv3v71WeqXuW+u2/Hhq3RiE1I6Pb4ikxm5Q1AxqxQ1dY+Z0WFwUih1hhTajLXGC0dXTI1zU8l2dP4Bc1JoiRhjZWwtX4M5f+ImpIUR9JUJDDGI1kB3fI/lDhN01SpRY+dXACAFWcHM6uTZPP/Xkrh2XKFy740D290K9qD//Gfm2M/w73fWfLG0IwzLlr+wWto4R/Ohn3oSqUocPfF1V9/+PA/r5t5+SOHn/vhCWig60fdr904/80jQptNnSWuY2m5A/zSmdXKh9wev3KjczBYIEiLNkxzp23+zAk6lq4oL2EF75Dd53bab/0jslsAAEUje/CFhH0CWIUld6q49pfBl5nb46suMbtCVdUkqieWbNnJkDegUqvA9pN/yd5pJdVRalWjaqjmxMOmzuGUZyDIxD6uOQUCKmtl+BhoVM+wve38pfV4+/AnrycmpqqXMBgMFYWGhM+WoxRRNgA0rpgpdXHU2H04NGMuvWdLh3nBkqXK/SdVu5944wv5RwjBNx4+zNLU5Y8cXrN80VNIxABVMvWxHRHPvGx2dVVlOYTA6+chyptGRuvbWw4qVRVCqGNoH8cb9PrG9oG5MyboWKqsrESckov0VJ5IIeum3KMNu1P/+/HBRdNR5UlxebmmL1VWVewc6LV5aIrC0pnacRfpa8X08F1/wnlLMZqf2dmnFXP7bbfG/ZxQDdVUz7Q2AQgngTEeEQU0XC7l7SmT65CJIaZSf7AbLQ8vP/fEy2uC18pwf19FoQFf2Vvfffl4j33I6f/B969RvV6kl5Ctjw272y4/90Rxsw/vOfzJ67INkq96euKsiROqi1vapY8MAJg1BWmKyycAgCysDnvE5VJ/xzF5+yOgkfCxo9uklFTB45Bs3XjQscgy1etoUT+RC+yi0+dClBkjjIwgkxm3/tXr9TRNLZoziaaQXSlNawTARHMAMNPqioBjb1A3Q92p558267+bkIZ2dA3UVQfjUcVl0mXWdLwTG5vYo+rnoEEnjooUT/jwQynLTUmYLQeOqwRU6R/E6Vk45VOW0UjSiU2wriF3RbkJ5BOUdvOUSE0AYg/B7z7Y6u34DG8vW3tJmhRTBbv13ZcjyGXwTtHZNSBL5+XnLrxcCrbCphZ0QSsCBcHVH4SwwEBHVeSQGG5AOvHJodIMwa/S0o0WbixDvfDc3756+ZUgXzh9djDrbXI9CkSWFFnlPYtn1u881I4CNdYQGVWKZlpBeYh+AelmcBegxMo9hkHG7IConjJYy+x2Z2lJ8FOgPU5vuDGFBVEppuefNgvHozq6gxoq66bsVIUQtHTZ6isL/ZxQMuM0tC770aVZvrxVJbeO6vHMZyjtVF+snlv3BJ3sgw7vnmYNa3RBQzCihRkYdrz3yb6V6W/8ooJ1uLxyKECha5J6dgbU7YPdbctnVy6fXdnU0imHCAKiCVmGRnE50ctdZjVQVDC8EHraELSkU5JLVSAC/xciywdwvPDcU09e/p2rQB6p54Rq9QURrqFYRvcciq3huRbPbpTuvd88efTZ3H5eaOyI4N1HGopmUvT2Swds3d+5aBqSCXOB2eVEntDBIbtKQyFEVwtOLQwfTS7HpuQrR6mb8gXQ3Gmbe8IEPUvXl5tbepxYPZGdEvCrKjEVWMMDVmOCSi+iVfeL4HuNQZeiBPp0mpKmUUPwYWiqJ27dgHVHdoBGiiCpVFUQ4CtvbXZ7fWBSpl0erPI6tpgNsn5h5Mu3utggbyv9Tc2dwd/FvBMmMgyFGqVxbuV3Qz6tvGSTsl7Qug8dtnx25XufBEsa8Esg3YQAfdsgaO6Sfl8r5ta29DqVDgGYs+v6WNQzXEP79rxZseC8BERTyd8/7YkioxDAg8cHe4acDE1VFFd7h7VmCFOgR/E1uHDV7I5O5H/gBdjSOTxZ7AIpDq0L0VCKAmXlpSxDv7XpgGZUShmYghB2DblF2aVaumyzptXpdcySshJx5C+F+leMViWuDFhlj6pGavIiQwGw5XNUxyl/zbw+f+wdTLKc7sjqmXAW7ic7D7i9vs7mPWDhwuhHLjjrapBS2MqKsre3HMTXMb528YWrOg6LZktAyFBe4XQUXV1YUsSIy3Iojm2BEC3SeT/YdbRX/m64PX5sd+w92IztC6UNq3oJ/EuUFXP+zEk0BRaXod9vAYNyepAfTXSzGgwGkxjOKtDB3A06jaqemMUz6++/6zYBgsHipRUJiWYkGVUqKS8INqdvb1OfIP69eAG4vZy4AIhGZXmxvDwXBLh4zoSd+9uwhqqgKFBo0kMALlo9D0Do4wQBQl5A0SdeEN755BA+bOnMaryaQQt2hi0oKFhcVgzESwsn0CASzenJmKrKrZiUkhHd9gyvs6qoSKYZfjaF4GNQz8Roae9pakX3+BsuHkU90wE7PDR8yZp5Xr/g9fN6lu7vRx0BdAwNRetySn05TYFjbf2zptUzNL2guAinp+CsbxxglbZD//inzK4pLStxe3maBu9vO6wU0z6bR7r+xe+AaF9IcjlzWr2eRa+AvjCAEhUTFZYBgHM5gpF6VcQJh7ByK+h06N+/oShQNDHEsig8F7Xp7vhvsCBSEgoIBoqWAgpIXZSS0M0oSlqr9+IXZGjaqGecHr/Pz5uifhMqyotpca4kBl8Li+ZMwJ10BwJGKDIZ8UGiAIpuGQp5fgLzJSBkLlg1ZzDQEASHpHBGN8sg3USXQdhHP9QS9JdhZk5uSEpVLVKlI1pI+lpB8tAxrXCzONie4tLVh8O7T0W+HZ57xY3Rz+/y+DZvTzyLMzVLeJqmTAbGqGcghHq9Tr52p9SXY6tw+sQKzue1Flt5AaI9geTq6H92k4E1GVgIwfkr5/h5gaIo+4gdn5xlaJoRv0MUZTCZFhQXMTRlYbFKQhpFLALftgCRgk6qiBPOehl0+PA7b+y0tT/3Nz8Hs8pnikNAQu356B1ORm04Gpqfxh/V9uad7TYeQNA68SuqZ1HNB1Iompp0+tBNaHoh7/JyTnE54ucFU2T1LC9Dfzj03gJfALfDYTWXUpAqLbEqNLQwmi81sCDvi9BOaSQsJBWFcElNWFXb9RPRf6XLTcJUYD3JGK3LRsKdpcLx+DgqYCtkG3eMlsN0aYSq/5DuU7LtGfDHfKKIIAEASsurJ1g9kVyfEIB3PvosSgfCDKCaDI4zivQAoIrX5i7btLrgQmxoOBAWiKqbFAA6UwHgPB2dfXW1FRSFfOF6HQ1hcG6ZWNYHiguYQMQAmSCacjlq0AkTlg8gnQo7UtH/xprocfPmhm8rf1Tm0oe3rT18vP/TllFa2sTCllVdyzci+VZxdIQBYre7Wr2H4wW58Z1KPbfu77x4teILAEB5oaF/BF054o9KDR0pMtHR41E9gXgUsmrLits7e5VHTakwN/VJWfraFa+ZUlW30/6x0wKq5nw8EJ+qaqonlZV1nMlz6WhDZTR+IWG/iwG7d7C/e7A/GHNXBd//88Eukw6YitEtvndY7XXMgANUnQcqfwT8TWioslisBQ57MGiDvw9KcyCmK0BKfEEXvMGgLy8E/SPeJkmdg/ErtXqGfj/kuBNSz3MWhgfrsVwqa67FSpjh+sqizDR5jFc0Q+4YUQ7S2Jf4/WDLqjisp04fcjGXmu2a6qnqmCdV+AXAyws5oMSaLJwrQk8p6dKQ2Lq/88JVyC2ALYuWzqHJtSWfHelRxaOSX/amXlVRJs6cA4GJ63qAfKBFgQFBo5alagOBkc72ruwJcO0d66MnwEIIakujFSM1tfcdbgmGN+3twQhSlCTQtAgoNhU1L8nPD3UsmlXP0tTgcPBbNDhkhwCUlVhDDw/5ycfxesXgKjFoG2J+N1RZCgst0OdSlVorzUkcuMfSieP1OB/gva3qkL0sRs1iDcPcEybqGPR1ri01H1dE7dNNvOmZsiNY6wGNnVCpNKnQzUhG6KgRgAtXzQ6f5Rm+mFo8ZyLuczwwGNkIVeRUi+o5W3neRXMmqtypaSVVTlUMBLB7SGP+ZXUgO0dVlhquqibajy7t7CmDD8WklcMUi/mZJB6v/5V3P1N+Ta5cNwYRJO1Szq37O5dMD0Z6OV7QMWxpiXXXgbbJNZIJTUlJKnG3b5ATBqUGvQDoWaakrOStTQeUgSa8wQtC0/GQtFMMRDXCSEtoRbx+3gkTWYZeUir19EZOFfH321Blae6Jr59mXCSe0w7jPCTR6HNcVmc44fZ7ZXmxsjhC3govRacoUF5a2D8YrVd8mFUbzE9CEUpAlRZb8f1boaGZW1IkbKhGWSjEqKr9I96JRn1jp42mqSeyciZlBgjPmYcQPvXvTUn2tE0VrNJU1Ewy4HmUwE4BavHsiRBAbA5gsHGhKaOs0QI4d9C21Vro7W/sWjQVZWkM9g9eePpcvzgQUYDwf4G0KoamXW6fjqH9vGTcKEP2c2ZMoCjqxBKUQ4MWjGKBCs4KCPStBBww6gVkuqY29z6FhUCxG6BpcuTGaITK10ZVBcrE1NQw5RJeSVlp4UAEDdWwasVG6cEj0B+Xwgt56ecsIBZV9VeeJG8b+qQSwygovXhyL+HGTlv2lPknmcM0Kp983hxLCH7DpwdHHCnujpowrOYVOaG+UoA92A0q5uoBSlx+UYAqKymMXUbDvydyxrWE3gygl6IoHUvrWBRoEgR4wao5HA8FCB12hxy1Zxik8hRFzZ85EcXwaeQjwwk0UiA4zZdZaqsnn93YeMO6uXHJwYWr5ry2cX+GzU8My0olpFUV0u1Kk0hfdQq5MocXiDfLSOoZtGrDHEOQwq4AKR6VwLpnzFXVW6FutBqjquJfaeG0FXLu6pgXAmSGAbs33PZESRHdg5/tQw0b4yUdEST01Tjjoq9vfOMFpamIgwPYmoAAHG7uWTSrXv5uiJFgJKM7D7TKK/rglR1VENAabVWxrM6ieSt4eV4uWaModMXQNM0yoLc/6DOVkqbEt+R2oRt1abGVSfMEsJTXm2ulHMVtVaZkdFpiRigOu0f4lVORusnh8odVC2qHQ60GbfWMdGoqJKZfVpqlGhqXUzW6qkKIGrXItapZVV4VPYfp4Qgdp5REjyChDBCtCJLH63/h7e0gm1D7QHEMVGXNcbygF5vxKAfJLp4zEUItUzRwcavjSPh54vcEq7No3kKWihiyD9kd+p4G5ZwqLaisFk2JB9/Yt3Jq0fzpGglboaBfxc4DrfJvuHEko+anOAAH3e0uPLVB8Aadd4wBX+LB25jXx6t0k6LAoTZ0I2QZqsxq0FRP0arV+IvhYiQZmlIu5HOY6Mt/paqWxDzkIhtUVdVxKoqGJtxI5YW3PuX5YC8Fmc93b1s1eeyCSFjp5GtaeTVTig7+qosc56mUlxailjwKswh7u8pLC+UvlnL9rnp5ng/O19UM2ctutWMdw0tm1w/bguEgrN0lxdZILrksFM1wnnn90zlT1TXpPYNpjHrFa4TKsXXcEEtmsrqKXcLCSl30sXSK2RCgk6krA/0a6onKQEP+dFFCUovnTsJX18DgSFWRPkv8oSkhhTlVY6iq3Yq/bLydT5Uo+7rJbN51tGdA23aIpYjz0i9f9srLqDlc6gVUtRLG1/PE+koIevpsyA2KokiBLO5wUIeI0kIQKqP9gyPlpYXAj+JIDMsGYqxq8+FQc/fiacg1phluUiWl+nmhtNi662AwGUCUUbSgi/xNojQD8WMrmjIfN9pWTo11TPaeIyF9XjJmfu5zh0QJFloGsKC29dnlXzvHo3p2pW5i6exiURtZDKW1cscrEuUBUTyqlOhqx5dZT9IDOLOfLFdVY2gOU3gI+uG7/lRdWSJnNUWXTmUEiaLUbUA7e4e37D4ay7vKZBLoKB3pZcWkKCAI0Yp6cfGJfHFj+gdHKqwhbYCVpkZFkVEqitehOJLmaXFWYFuHVJTCC5CigCoZgKIovD1qgkuWiGYsVJVaNI3Q2Bfvzzz4HfDqr9LhCf3cETJ6c6FlIJCKi26xfnGF1auX5hSEQsnBKKmIHkf/IryQZkyfoqTUejBeSW2maqpUtVuzS4hYa5TAgr1rMKSsyM/xL779aTJZKHc//i5In4CuFeNIylW2fOXWlpo6B937jnYtmlkPRmvLg5P+YGAVr3KDXnT6HOkw0bxtC9TqCQL08SHeUuUfQ7m+QwlVOFFJLBNU5qVGqbn200Y8Evbtdzeec+YqkGWi+XGj7eNG2w/ODA7ODmfX3iNoBe0Ta12ii2aAaXGqZ8Ko9BToY+oehKNG0a8nTceoGI+q27inY9WCukNt+W+EjpWhGklVsbBG7xUNYmh4KtPdOyiVzNQql2IhFtVTr27y+YPu9exxgKotULzKprQuYpQMGhsUAJMqzIACx3tdFKDk9bsqgMuKLUMaqiyoQYnWebbu77xg5Ww8TgfvOdjUvWS2lA8g5aVCqCyRiiWhKtuMTcyj7yKJnKxXFk1BhWKOIp2YK24IDunektCdIq7CpISpLDIAv5uSYlARVz7hPtAChsNRqfpKS2OXTaeOPxLSrqpICpo/VP7Ynah6ytKJMemRIOAv+/S6oj/df5fTGxRNunYZyEokAfVzAiNuKi/aifVVPT2S4x+3m49ywRYEQgc4q/1Q25BYlh7URtVzcdq89D2JcHcRGzaFIEDpHeNsa4qKnFCl+arZJJrhtPg0JholQKq8n6lFgBBH81vc3snVhbwioC/G9AtUfyz5UsS6ib2rqN2B2A+sU+FdJYyVqnaHq2cMzT1V0imrJ8WgXrEYSvAXGBSW1QAaeZiYpO74QuNTp94Cdbm9F57aIF/W+ILGVkBDFbKAIBQbj4UqpuwnlUMHuJ0HHlyD7lccx7Dshaeq/kiqwCslx5HkP8kFK9Uhe1zcKRZwKp4vJlSpQliqhKpsFs1sI31G6D53OXaYcjxUBfQ1Y/r4YsMxfTmgj+IJRDqzxKlKMwnYnpqGJ2bLPhQmXT4XJfZBWnKXU0KgOwsAKknFejq/Cnz4yeerT9Vexbd1D364/WBi89tjgXr2YSna9e6//yHvnVwtSU9Hn6NXDPWUlBYXMJxySMuhtuDNR+6BhAVWOfQNn1Y+oSzNrW09+MzNPY6T5kxgebQtCygOH8mLuLb2XnxwQaG1wChOYgwDQqAyRbGGova94smbexyaPtA8E83kzc8MrOIxWE9xRxWlwxPH9PFfHEelVAF9QuaRVVXfK6X6G436uNQzinRi6upqXnpfmr+NZVRGKaNRUJqot95+t8fHPfrPD97Y8FmB2VRZWbZj22aQaoLCfObF30B1pq8/DyFo77NzPFSqXqkR1UwqRVO2CCgqRDHVL8BQSotDXLshT19dhQV/Q/DM23AfsWZpCjJAI7gSKAosQbn9Gkmp1ShnMJ9FM61G6ANXzti0C02ye/XzoGtfcLrogmjdxmJBM6CPOsUwYo9QUT2JdGaboTrfrJHDVB1VPUeVzvp6SS6/csZ8AMCL7+1VWqNKg7SupryzI6KJoDRR/3T/XWLxEr92Ko5KDSMvQPosUBVYSSkK9Nm8siGA2yBRAHz1W3EPFt7w+vNyE0yszrJ56+eFqmKTnqVxxElV20cB0BqwQE0Wi8Woi1L2h/lsv9oUrS7S41TQPoNmhk3+kBIH6B2dtXUVlqmTyh54TXKCT54ydfe+PYI4cVNTOnEOgDKQlSR1fGcHM2qZFiHTzDcPSAJKS2qFe56atHqbRnJ3Kqmv117xvPDuXhxTUlmjoowGb72dHVpDD2MwUV/Y0pVGAcVseP35PpvXzwssQ6VqGjtWUuz67LWhgXNil0+TLiCgKGR/+hxlfn9rey9NU91D7uYex5LZE+SG81FQZlNh3B6U9UIENBbW7ZheP6G2va0TQtjX17dixYp33nqzuLYmevpUCtWTkM3qiZv1VUeYOSzT3GPHClhWpJ4LoxczKCJJp5IX3/sCf9ujy2hiepq8pI4ioGkC57Tv3bm9c9B99hnIL/n5ju0hArp6jlIjW9t7GYbqGkQCumgWGjwX4wspV/TjREBTYoSuHzwLUIzbhezNr52Efm88L7C8e1ejTV7LK9NOiXSOKwGN1Oq0OlRSZQFVEa6n8sBzhJZ19OK7X8hnUimppozGK6kev7rE/h+bYir8S1dwKpYqoPmLlyKHRyhiLmqIemLk/iOqHhOjplVZKs2HxfQXHUv5uaxowpr9+HzcN0+WklI/3StdSRwvvP6FP00LdkKuEFejaEHsOanaOWALKTQqKzI1He+MqKci/++MechCfO8LmgoJ1suj0uqqI8pobW0gw4PS0FOOh1xYb+Y+rY7XmbZA4y2d3L/7M4ZlUZZ1sNNPAIrq7elv7UNf6cLiIs1BAmG5qChPQFrsi39znH/fyeR/MDd5C3TblJVbdgeH+vo4AY+wlgWUMJ5ZUBCcqCdENmjcge5coxWdxWOfAnC81/HpXimipbGuj6yk4bi8XHd3yPhCcaSjk6Gpd/eFjCDKkAWaTL25188DP6/KskZKKv72PYE/hiqlPzQdFSXwq3JRUSsgkgETJ1g9UbGD+JvN7p7ohEyzx1kWRVKTJBb7dNLaeQ4P99bmgyprVJ4+GYuMJq+eF52zOgUCmqomHYuWnrx35/awnCcNW9pMc4pWPZJoYuT948HSTB9+ThCHo0irtcCUU6KjhEQkVYDwQAsKx8+fWh7LLCOlhRRFT5dML6MA2H50IAEZHXH5+/sUozFEugeco2b4qEhEQFPe1khm/uKleGPvzu0QSOmoqjz8ygJpea58Imo6OT6W55kBSyVOcUe1XxT1+hekcwchDvY4y6axPfKPK+bWbN7XtbdRyorDYqp6iqytUTQsXE+XTpdU0icO7tUbDKPK6DNv7T5tTpWm7QnihB1z0YyQ3I4+80LLAF6Mt/cFO4bI0gkhWqoT3UwHt5S23jcwkaKoz/a3dUL1pUYgjMp0tkc2NUstSNSWnFCpM1lGXL79R9qRkaQQU6ynUay/SHarSk/Li0xYRmX0BoNSRqGoniu11FN+AQ6V64AUBJEyLpoRwTUqOCMVuziJsZnuNiJf+ZjuINJJSJTpovkJFQIKALAWFXE85AXBz0Mfx/s5wevnsZ6qCLdPtYGoUVwk3S0Pi0dtOtCzYra2eqImNQCUWg29NncLRAmqTQcPR3nli85ZrRbQDCgmhlRSpoOUN2HKWFF8qiAp/dksoIXF6imbAoSCAL0cz3FIT71+Ye+hYO5H3GKKCS31VpquSj2FEdQTALB9WGMaqIysqlhAWSKaeUCa+tdlpj1oSiDSmZ1QUQfP0hRFMxTL0NAAIdRBCCqXzRAE6OcFr19wefz7Iiz2R3lJGShmUAX2DNpD5sKWWo1donrix7F6jsqUWSdkLpGeWJrpJt2tP7NfQ4l05gEUTvFAU80ZCKABMGYDKC7QV508g+MFLrDe//xga3x6SkWzTwdH3PgnhqZqa8o9DhRo6Rl2AxDNApVxDPVYSqpSL6D5J5ov/eZaefuymx8G2cTyjTVEQ0lBVD5BiQpH00jcGIbSs2gLAB0vwNVLp2M99fqRnn5xuE2lp6Mv9rF6BpL/ZfUMPh7dZg4wpUiq5aH+cNu3QdLkn2iqdDOc7FHSzPSfz3I7lJCdPlAAQEkgghTuA42jZlS5KQ4JXjp/miDAj3Yc8ot6yvFCeDBKQ08VdVM8GtcOZPXEFmh0B6hKQJOyQPNSNEfVzfDDxlxJM2CE5sRanpCF6pm0KaqwFWUZpUDfMPJm0jS15uRZ8vGVxSY/JyAT1Rvi6/zskPTtmD9FnRCqUs8EiE9A81U0Y9fN7FRSoqGELKcwTvMzAKqGCxqN4vp6cMTT2qse+r3ji0aDjrGPaAwDXzIT3fh3HVNXbbK6kKHrCeAY6hldQIloxsIHm3d+/4Jlj72+DeQ1xA4lZBxKqaEuL9fW7+TD8+oh6B9EvRYjMW9SyaiN3A6224ClOHYHaEQLNI9FMxljM5wPNu8E2UFmjFCyiieMBZRoekKfX2jpsct9hZREV89dx3rnTQoZOsILkA3t6SZAaLdMivedseNENNOqm7wgNj3M91YbRD0JYwjHwabukRFX6hsqBhKYPAl8hVmim4npJhZNCCFNUwn0IMg5I5SoJyEOKCngI4fgk0SAsL3fMeyQOtpcd9kpqf1zDDt97QMuEFvFU5ONl1fxGepIn7vGpko3IUTFYTRNofZPUvpvkHx1gxL1TC2kn3+89Nk8fYEhvvESvn5XRZC8fr6xx+HnhATUMK8ENK26iT3QyN6MYaQdgRAJ5SypvGS6rjdFIXiJQYe3tdcuB46c4mjYWLBYLahNexgqB+ixbofPL7DV08H4FND06aYgNrSHAFKAirfTat6s4on5mSrGyRg+KqZanljx+Li2XgfuCxyXehYVWXUsvXF3U6TvbUVZEWqP2dSdjF81hwU0fbqJB6gxWDIpQOd9eIiQfkjNfgL4eaGp2+7yctGl8+2Pdil/NBcUGPQMS1O8ELHTnUy3mJM/jgQ0rbqJXJxiXIhlYp2cTCDETh4bnilHgLCt12Fz+lTqecu3vxTlWYWFFoNOWp8r00WVSaCH20fmNKCqpNZeB94f46zf8DgSOw5FM9y/iX+DtDgXhCGTfwhpYBxKZ0lyIfjeYXffiCf2NbvZUmA2sMpuIGgqophCrzxMqaq9gYb2iTlAs11AM6CbmPB4OoFAGEMG7ahe025Hk8xHRWD0xRaDQRdYNQY0dPMeafqxCmx+dg44NUs/c15A06ubYpNqPKiaaCaBkFpmpCIE7/FxR1r7HG7k+oyFqhITrmMJNnCiwKiTjboGY7Vtc0NA06qbOKQOAKSo9Oa952sqKIGQGXwcf/h4n8OjrZ7lZSWRWojiDVlD+dEUNCWpAmyeiWYk/yZFUVJMPd/JTFE8gZAOevqHB0a8/SPeSPLm4OgYm+BFcoDKESQZpmpaAm8Vx5HYvNRNpXoS/yaBkP3wAhwYtDk9/h6bO0pM/IaLF8ZwMqShmz7XdoBiNDuSJACbZ7oplQyJ9iZxcRIIORGC7+0fxiWVnYNuPpAznwyjZiW19jl9/lh9rGMsoBnQTakfkkhOlAwRCOMhgjQqEMC+ftSJjhNg+4ArimFYUGDW3L9zX1P4To2GoQpQ4/qoB2SFgGbG3sTg1h4EAiGrKIwcgpelE3+Re4c9TrHiKIp06ozWGF8XO0BVYAcoAOB4XzA7Kt4UeiVNNp7NftHUiKcL6EPjFpxENgmEnEOAsH8goJ4A9Ns9A3aPZuBIaXh6OA2FrSqz9gyoJxp9vBuZpeFNmDA+hbwmnEIvPR3kjm7KS3WKpkihJYGQbZX+9954f+weTxmnh+u1ecINwUhrdiUPvrD50lUnVJWFWKbdA8jMzAxslusm/rXygkCjQks0xBlkPSQVlDCueCbmBn0cLwwOhaibx8e3DzjDA0ea6imH4L0+3uPnrSbdpatOCD+suqzwivMWyz/u0nKSjqWAZlY3kXeTtPaIC5IKShhbSgoMoxqeeEHZOeTy+YV4DU+Pj/v4i87m7pFLltWOevCiuVPk7aULwHNv7kqJAzQ+AU2TaGqu05GHEwKKtEQiEHLH/Lw38vodQtgX8HjKCBB2D7lV9ZpR1JNnTfK21887PakfjpR6Ac2oboqJBci/OQ4GtBEI4yQE3xtmeGIG7N4BhzcuwxMDIbS5fB5fCvJFk4wgRRTQDOumOF2I5G8SCHkFJwiDg9rxHLvb32uTwu6xSycGQuDycD6OT6acXeDTUIlEdJNAICTW53RGcEobwsMBTwT1FANHLhw4il09LazkKhUgdHr8aMiHN9lmdMnDpk80ib1JIIxPPJGLJMXAkdvPCfEanledvwhvCAJ0eTkBjtaubjSSjyClqxIp4jpddHES9yaBkMdFnMUF+kgCKkDYMehyuP3xqqcygsTxghsJKMgGUpmQ/sHmnfhfSMkQ6u4h6qbY3WOcqOf3L1g2tm+AjNIkZCFDDp8fsvGqpwofJ3h8HIBAMwk0kxGk1FigGv2QAn03UQ7nOJFMAoEQFZvLZwtG3ePDJM9xh8Dn5zWnvY8JbMp0U5ROSTfJtAwCgaDA4acH3aN3mYuEG8XcERAAt4/3JbGCp1g2VQ7QRARUs28xbsFJdDOrwKt40p2ekEkghEMOb0N1oThyHRmc1cUGl48/eGTA4eFKiyyJnVYu4oQQur2cn08qgpSqHKY4BDRsvhBq6iE2RUIdkUgLzqxFdoYSJSWkGxgw65q7pQSmhupCAYIjnXY842jQJiUexaWkyggShNDl9YspUFkRRWIT67+JIRH1/FZSEokixMIMXW+kFXFz98iQ098z7FGNI5OVNG4xFaDD40emWxJJoH4BGGump1FAw9fp6K3zUKyzRNvjJpw+9iw9cYnyx+27P8vA0p5IJyF2jvgrAQAz9b3hfeDdPr5/xAMhZORhmWGZP3GZpRwPXR4uGQ+m18+zVMqsVzam+nQx/4hhiGiOpXQqd6ZKRsOVlEgnITEO+ZCMYiXFpqKPEzqH3KhkSAEDhPiVNKg8fo53eblk9C+F6ikJaKS4kJiERLq+j0FXUE3p1DwgtUpK1JOQQiU12VpQ73esfmGqJStpuJhGVFKITFo0Nym5JNCUZIBKp1LlvaP/k+LpJIVzDDhp4eK4fu+pMkiJ7UlIOe6iyWwR2uB6job4/CKLabiSFpVV3nDxguA5vZw/kNKUDeCUKLSFTU7i3RwrnCP8KScvTOyulUKDlEBIOWyVZPEhJQ1Zkav1NFxJjXIKPW4j4vVHn7g5KqfMqgJgZMcQSrRKHlaaMoTqLImLc2ywD6PWsAvmzzQa4xilHc7x5qaq4tKe4cHUvTUCIZ1KitEyTmUl9SjsTV6AHi/P4/TJRCmz6gEAZ1s9A3afy8sd6rDzJZMSPhubE1OG8ls6AQATJ9aWl2lPEIyF481pnPpCIGRISUE04xQVcXK8l+MlT2PSlFh0HC/oWMrT0+hRjBWx1k/PirnwhFikEwBQVGSdPnVSktIpeWAIhNxUUhAupmF6yvPQ5vR6OR541XOMY6esEsW4Bnp6bU7/0S673eWHABh0Qd+rr6fRG7OeEgEdS+kEAOj0uoXzZyZvdRL1JOSfWWoprVYe4ON4pwf1YUqGjq7+upryssoK94AT6vysETWC9nucymOUempvPxpFTMeXgD754juZfDkdAM1dQY+kH93qJMqrJqD/o6gT589ixe4GCahnLFZne1MzyALqpzRk+BXH2wfPks8LUvGRg2ap3y6H4CEALi/n9UXu1RyXhlaXKauBdMYCeTtcTAVewN9de8dRa9308SugY4VSOpWcML3Bag3+5eIwPMWOgcTqJIwfoDjJA4fgk+wEijS0eyDQAUmNUkxR5ZIzmruACOjYSCcAoKqyvL6uKsE1O/F2EsYBlsAcJNyIHnURSS4EDwDYfRQVSp04vZICoK6I2TfsZfWJZ78QAR0bzGbT7FlTUx5kJzlMhHzCwQV9kYIA/ZzAJdfIDgCw7rRZAIA3Nh2EANRVWCkKuN1uVDdlCvZ8UmIosCqNUNUqngjoGEBR1ML5s2h69HkqRDoJBAwUx3km2QlUZt1ps/psng07jrJA8IkLOrfbHUlD0bqeApqLSSKgY0Ch1WIyGWLOT0KzpKJArE5CXmJURJAABH5OGHH5UtRIHkFRVFFJSV/fgJGBHl7S0CimqCZEQDON2Ww0GEJHaEdN7STqSSAAHIJHo5BgMkmgqEXpcan9WFFZKUNTlB5NuDP6XKjqKX4ZJQKaUXQ61hJ5JGFwwR5DkJ0YnoTxgwChFEFK2gK1uzwoo9NstA0MNpRQLE8d67abzJbEZJQIaHqZMbHsSOsA3qZpuqjQmnyQnUgnYbwBIeQFwZdsIxG1jPo56Pb4AAQOR9CqLdAzWENlGY0SrCACmiEoChQVWjSHRxF3J4EQHTSM08ujcfBJJ4HK3T88fqHf7h12+gyo35O008VBpy+kXR7DsALN0gKnGYgnApohLAVmnY6NLp3E3UkgYIz+EEcnz8MRly8l9uf2w71LT6j0c7B/xNs/4lUmllIAmNkQE8fFQZ5H0hmpBSkR0ExgNOhNJqNyD5FOAiEKFp1w1boT5R99HM+laPl+zqkznV5u+85j+EeaDUZ0w89vYoNWjUtrkggR0LSj07FWa3AygcsZUmlLrE4CIRyHX+F4hEAQ05iSz6LHTUU7B1xlZWUCgFgyhwYHw8VU8eISplDjFEMENO2UFBdqSmd0SKSIQJDxc7zd7UOdQL12uxMFdjDWgjhyNjHN3fYhhxepJ160Q1BSWio/Gl1Mw+O7RECD/Of1V0b97V90waXy9imnLvve96+ZPmNab2/vP59/8ZWXXuV5tatk2A2zWTpfe+W5+rra6Mc89ew/H3rkCbw9b+7sO35x0yknL2k53nbPr37/9jsfgNzkh9+/8qpvfyP6MV3dPesu+rpqZ31d7e4dH1bUJtvMIvNMqK/9z8vPjXrY4mVfkrfPX3fWL277aV1tzUcbt9x2+73Hj7eBsQAC4PMj8zPcAkxATAfsXsnyxIgaKiOL6dCIUy+anD6PRxZT/AaYqmny8URAg9SOJiUyNE1v27FF+kUPDdfU1Nx4009uvOkn6869sLenN2HpzLzhWVtTPXFiffRjiovFwWAAPPf0o2efuQYA0N8/MHXK5GefeoTj+JqJs0EOUlxUOOoHpxl1+opOx3627X2QmzAsO+pHVnJ437bS0hKe5+0O55lnrD7zjNV33/PbPwdupZmEokBtWcEps2s+2N22ZG7EwQ0ximmIemppKJZOrJ4AAL0RRS/8Pr9m7TX12P03gXFDXP1At+/ciua1LT4l/KENG9+zWCzvv/fBbT/7Od6z7vzzbr8TbZ+85NQhl+SpiaKec+fNy4x0xtUm8t8vPbti+cnfuXr9G2+qf1Fnn7nmuacf7e3tn7NwOd7z89t+esN114yM2KfOXJLr/UC/fMn5jz70uw83bv7K166KdIzFUnB4/6d6HTJGYrdAs7YfaEGBueXo7ki3wP+8/NzyU5f+/fmXfnwjuqpZlmk5uttgMMxftLKruyfdH9kohuCVwzglIDjY2BrXqcKV9D9bpPBR+MnRbEcXyqUPh1eMZlJaoKP3syCo0Ol0FosFQiirJ2ru8t83W5pbAAA1k9Av1+V0xm579gwPZr/H87mnHwUAyOoJALjnV79Hdf2RSwPyicsuvaD5yC69Tuf3p6Chb/az/NSlAACsnqiVHMefsvIcAMATf/ljZt7ADeHqKZqKs6ZNjOs8dqdb/hf9yKERZyT1jAIR0LgxGAxDQ8PHj4fcCYfd8L0Nm8RFcVWeSSdm0dLVj/7lKTAuuffu/3vkz7/1+XynrjynezT7Kw9YsfxkAMDjTz6r3NnW1oEWZCctysAbKEiPZzGSjA7ZnE6nS16zh+P3+TXNTyKgieBwOM5ae85XLv2qvAe7O9euWQkAaInZ0Z4r0olpa++8/a77lHssFtS4WxBS014sm/H5fPf/7k91k+cdPTYupp/+8HtXAgBefvW/qv04iKTXo7HAacWZNiv/g8871K8VVToLzGaleoZDLNCkGHbDYTd0OZ08xzU0oMmae/fuH/VZXr8vt9RTCU3TJcVF13z3W81HdkEIp806CeQ7d93z29/94WEwbpg2DTkxB4eGVfvb2jsBAFOnJD5FPavAhmeUAwrMUt+fKK17SRQ+QZRBdpqmjxzcDgC4/IofRH+W1+8DOc60qQ1bNr6Ft2+57S673THW74iQYnRioMznVV+rThe62ouKpKyMzBRxqjh4LL4gkiZDtpA4ezgFuGUaBMPDNuV+1fqdWKBJWZ2yejYf280w9Av/+veHH22O9Cyv35cH6gkA6Ovrr6g9oXrC7NbW9t/8+s7ejkNj/Y4IKQb3UQzvHJfCZsZRsLCCchRSyhllzV5gltQztsljZAmfuHRi9Wxt3sMw9JN/+/uNN92e39KJGRLvyTzPL172pR2f7aYo6s3X/jnWb4qQSjgx00CnV5fi4H4O0Ze9yePg6KvOD1bBp5CXNx4ZZc0euVdvJIiAxiGdqtTOGdOntjbvAQBc+d31d9x1v+YT80k6w7n4sisyFpklZIye3j6UQWkJNnDAVFVWAABaW9tz9G+hj9HwDCX67DIioHFIp1I915135ob3/4NK35Z+6d33Psxvw5Om6UsvPv+B392j2i8Ikbp8EXKYp555Xrwvqs3AE2YgD6BtZATkEQWRpXN4aBQHKBHQmAjPij/hhGmPPYLSyGfMWtoTqN3MS+nECILw2MO/++bXL1Ptr4u5+JWQQ+AEpnvu/j/lToZhKIrq75fGK+S3dMYOsUC1wVanZjkmw9AfvPtvJKOzT3a53PktnTI7du4GAPzr+SflPSzL7PjkPQBAwwyyhM83Oru69TrdmtWnyXt2bEVNAL5/7Y1jGIJPhte3HFX+mLx0Ykgak5pRm4DcsP77eOPwgU/DH73ltrv+9jRaAeUZF1z8ja7WA6tPX9F0eOfBQ0dqqqsmTKgDAPz+gUccjvgaphCyn5OWre04vu/FfzzR1d3T3d07a+Z0o9G4dduOjZs+GZsiztQRo3Ti9bvsANVcvxMBDf2VxdY/6UfrvwfGHxzHV9XP+vltP73+h9/FUSOPx3PqqnNxhR8hz/D5/ROnLnznzZdmzZxeU10FAPjJTb947h//ArlMQYqsTiWkG1PireeSCbJnbZOeNJHl3ZjSx3j7Q4PkPnI5GLlq3cJ0ZNGHF3FGh1igyfo6o5Ovvk4CgRBGxBKC8esDJdJJIGQVRr/dEUv1zxiYn9IQ43DGo4AmLJ3E8CQQCONXQIl0EgjZzA1pDsGnnPEioLvapBaDxOokELIWm01q7lVUpC4kzTCjJjCNIwHF6nm8uYlEigiE8aakHyTkAI0RdpxIZwLPJXF2AiHDPL2hETWCYuiGmsJTZ5QplTQ7YceRdMbczZBIJ4GQYYyKIk6WpYrMeorVW816ipI0tLMvG4c4sHns7lQbnjEkSBDpJBDGCpamOAFSADA0TVOU2L0E7S8qsiSTQj84LNmwpcWxegOit7DLWwENl04IofRHGA0inQTC2HL+sobjvfbeYRcvwCKLXsekoNXRqxsPyaaTrKRRxDSWFnZ5KKAaVidEv7dY1JNIJ4GQDUyssk6otHK84HD7/Rwfo+kzKgwlOe94ZN0mbpbmp4BGtDpj++UT9SQQsgeKAjqWLrEa0nFyWUmVYhqLWZqfApqA1Tl33jx5O3cHC2d5F48sYbx98Jz+vMa0tQFlq6ZxPcdiN0vpeByguSqgmmGi2N2d40Q6CYQc4ttrpuJoewpT6F8/hISCDfgxY1FSgWLoQL7OqA7QnBTQ8PwkLJ2xqCeRTgIhC7GwwtMbGr+9Zmpai5FiVNL4zglyVjrFKdWxSidRTwIha3FwaNX89IZGhqbqygrWzKvKjJJqiqnoCIz5VCBn3Z2ibBLpJBDyB4amrGa9DzJlhUaaprCGJpMEmqRZOvrTQQ4G2VGwLrZ7BFmzEwjZj4UVBEbn9vEUBWiKMugYWvyGY9szsRok7ABNTElhqJUa7Vkgl6QTWZ1kwU4g5BNGv/2KCxbYHL7jvfa2PrtRL6knJq3mpyYxSqd0MMidWszY82qJ4Ukg5BAsQ5cVGcsKjfMaypweP8OkpS99OmBzIT+JSCeBMA6gUBZ9sSUtWfR5LqARO4DEbHgSq5NAIIxHAdVK7SRWJ4EwXrClOmMp3ghSrgpo5Kz4mJ5OrE4CIacxikWcGciizzcBjdQEJPYWLEQ9CYS84dkPG8sKjctmVZeZmexvRD+WAho5yE6kk0AYX1hYwcXTAkTpn1aTXs8yFksBjsLbbI7s7EI/ZgKqmdpJ8pMIhHEMPH1BfWOnrWfYhVb0eoamJUOqqMiSmRT6HBDQyAv2WM9AFuwEQl4yb3LZzAklXYPOjn6HnxNATsGOzYIdBdnjyJUl6kkg5CVXnb8IjeFk6YmV1royCy8Icl/KzNcgZZeARuvaSQxPAmHcYwzto8wwFMOgCFIOwWY8K55EiggEgoTN5khtxlImHaBpEdAk+yeRBTuBMK6w5VruZ7oElKR2EgiEuHh6QyOe55GjSpoaASWRIgKBEC9UoP37q9taF0+vnDWx1OV05VAWfQoElESKCARCAhj99nkNZT1DrmGHt8CoMxt0StszMQ3NsAM0KQGNOJCdRIoIBAIYnZXz6zw+rmfI1TPkNhkYVtEGNPtrkDD/H64A2bwK5CFCAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant": "Assign S1 to the distinct crossing-lane target T3, to be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T3", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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xgJ1wlGbfw7+9fK0WpJcfNlrhbaNLKpiPSiFHNE3xeEJvH2PH/Ofnv9Vd/0bXGLeOsvvpyHUAgQIAV8i+iBcQbPswXkDbYFKSbqhETqZ1hrRKYh/Tb+ZCPND9B90FXK1j11y8uM60//1l8FmvwGC+SESrVJKTTXdJmcPg19/Ffxhy7iOXAgQKAFzh0Cq8BuKIJR+37DOYvZ/i8Z/aiodJXt6pacKf3IDjvie/3MOEq3yRqPXA4cq6Ors04RFCeTcuVhXmeQUGT/vxJBnVxBCW1HH6b+cRQjvnTLL9QnyRaODstzqnPINoevuUocilgE4kAOAKsvLSU+tXD3x5yaMf4HlH1UX5FQU5YW06CcR4Be/K/JzTm97XHErTt/b/0uGRSbP/uL3/nVk+wS2GLsBP/TjzUTvezzdPDpz87eHg+MRZh27VlBbdO3vMOzA0of9QYu0j788vuHVZ5yUjl37G1DdhqK+u3Dy2e5MlRb55wpVGgBJAoADAIS7v3Jb6554xqzdHdOjmGxbpGxapHk5Ue3bruss7t7GP/HPNm5WFub2nvTL2vS3kmJ0vTzTYvW4LO6aNiE3uP2zRR37hUR0feYJJIOxbMtMuRe3kNdXlOfduH/71SkNw7aEFlQEAADwNyIECAABYCTThMRNTRj0/LB5xm5j2eGQ1YBHdRkyHTwxwHCBQzM49BxEapf/pvPnieGZbKMDTPJyNgRGBAABwBWjCm+KDL3814zNsXP/A/tCOPT0AALYAAjULk+Gn46NE0CgAcBIQqOsAoSgAcAzIgWpRXI7rdwnE3kG4HCJXHerExOj5gkqKom/+/D2z59k5s+1+lcnPzx+V3Lp7D7ygBULoyoW0KQ64CgDYHRCoFi2CvIlDOcLkF7BZ9Pc7wmI6qFSqZ6cvJF5j1Hb5QprdL6RU2mE8NgA0CyDQpmme/neE5ArNagqMvxot5vhQtLoGlzd3Anx1mQy2nWHoAeAqgEB1aRHkzZEYVEXTK5cv+HvPLqNHOFKjfL7Ga+LIzl5ikVKpEgr5z/bTXZDWDlBo+dL5Aj6/rl7O4/FEIkGrmBb2vwoAOAAQqC7F5TKyIEyzw+fxlEqaCc0GjptE07RSST8z62EnaJTP573z9hvqwuO82nq5UMAnSzg4gqSWEeQd0Pi/EIACLgMIVMPOPQcnphgYS9+M8HiUSMh/aMITtXVyL5Gwtk7O5/N4PCN+sbdGxUJhu1aRNEI8yuFeI4tDQOMdcDlAoE3wwZe/vvPyRNQc8ChebGQIjcHxoGYNLx7Fo4wPPrOjRinsNXImz4kJx40ewn649m1Ta/YmDXja8XcEcBoQqC4tnDiC6UoGrn3bLSGIbJhDt4QgDo52clfmr9psvm3ZgHk9BBCoozDfieYfaRnqhjfgTuaFsJdrgEDdGghF3cu8xuSrq11DfziT+kPCwf6AQDnBth9+f/6ZR+3QeDcIhKLI07Vr1LzvNBHwEkC+xgCBOhBjytNvsxu0p/XGdK9QNDSpW0n6FSdfdNnKt0RCIUJoyeIVyH2Z/27T5jUoXzAvAQTa/PZ0HhCKms3mjdvy8vId+WW4NvY1L2XotYmukHNwT4Gmnf7B/IPbDHgGeQ4mQ9HQpG4IoZL0K94hEbLSAuR0PvvgkxpZrelj3lyx2Al3AvZsXvMiPfmuMy/b4GTzuueicsxHv+btGRa9cPGH3+nvtO84UDuPWLIaIw7lCUUqeb3TWs0fLGtYp1dNt8Sov29m9u+El1ehafT3zcwBHRuXWsnIL5u14DVm1L0dl/SIDaQe7BKFEPIW6o6xVahLnVRUY63zKErVorP5Vwe4gPnmtUK+7ilQ/T9ftpi0uQbSOwM9F/mFx1UVZukI9LMPPqluKjDUYbEZcSKtoimKWr/mY4RQ68gQsvNKhgSLTGR4sgDRGTEaRVHLV2v51xaBsh/2bBPKbLN9TSEEAnVjFs18jNnuN/41z23C67NgFV4723yZvr9oKrO9+MPv3Myhn763hv1QVq8p++RkKDxLVHd+/cnrecx2j6RQnXCzcdInZeC1VsMX8JX1jdWnaBpl5FfoHNOpVVhtXb29rgi4B54iUIMyNTMsZcu0pqoGOZ3QNjg1uWz6c2+s/Wzly7rtkcXLLc4JGjOmClX7eWn9SBy/jjOh/dqHKRXesWGB7KdaBPiwH0ryS5ntlxfOM/dW9NrjjS7jae6Eoqj0HKnOYZ0Twmrr7FlwLyBIK2HycO+2aj03OJpGBUXN1/sHODf8NB9PFKjVJkUI+fj52EWm7y9vou1pEGLP7olRjLBeXmC2qhrBUgjwFe8/d0u/0Voq13Jr15aBFEXV1MoRkt/NlSZG23vxZ7oJlyXE4YImLWPD2cc7wmUB3oIKmea9MxUAaE2JKM0VcdRr9wsDroxHC9QWk9pFpgE+Ypz+iwphNx4bfmf1gzN7jUmiFi9fTDKPhp+2pJfGVkxeilXKhKI1H4pD1oaiEJ1XUunrg8sgVFRUoXD8veg4NCI8KDO70P7XBriCNYOlQaB2MClbpuaYVEWrFi9/c/1Hn+g/ZVn5I9tUwm4s0zSS5Ok2kxGihvZuw8RfdXZtMptALKDqFLrvjWpwKLmZu3llMSFaOQRbUKlonJBlXYy9mV8IjXfPgVb3IL1u5tEgUEeZ1IRMeRRPE081N6SxjGMuhIb1aad7V+pH+c7N/dEqVWlFDQkGdf6QMA51PhHhwbAsKqAPCNRRJjUtUwpRx69nDu5sNKWYmnPP9MmPXs2nKHRkyksDO+Jy7gihN96yeNKhnxe/qlbJviuSRWAeM76S2DXis5oCdTAYER50N6/MWKph+ty3ZqT06DP8cUtPLquTe4vx9E2GCbOmMNu7NjYuTQoAbi7Q3w4cM1E0zMkmZWRKfuOrjUSmX/x+mmwM7BwpEvAqqk01mbsnBBGDVNaox9ZYPqaHwtGe/qvwSZlAz+HhpyV3zehs18btRs+nogcPH9tneH/2zl7tNH1QM1J6zFxzwOALfX28q2tkCGkJlM3fVzJiQ5v/TwjgCBbNTGG2P9y8x/wXuq1AHYctJiW68FXLdP9uPKfto6Wr9A87dT2fPZbbQd07KhVdVF5NGss659VpLEeGB0mMR3y2wIxGuHwXD/9svACtQtqF99nBILO+yBWJrt+LSsqP/7H3+B97ZSrB1GHtyc5zdzSdP+e07UnEOiOlxwdf/0H2/D6v7aDlF5kDAmtyo0uv3Iod3SEbv3D5hJilf9njbQPcg/Hmopkpvx04ZuarQKDNYFJ2b9/ClW+TnTs3buDxKJVCzvTqUHhRDfxIfxQkQ6eEMNy+zi2PsSE4Cgjw099JISqvsAw5kro6+by35gsEPJ56ZWPS257y9Iu+Pt4hfsLSKvVHYYgJs6acfmmlwacuXEtL7vfw7Gc06+4t3HhUJwiNiWzxSO+WJA4lYj235kDLEM3K1aVVWqO4pD7R0aVXOmQfuBU7+qe5VTwe9d9HZK/u16xZcP7v42QCqK/Y1MLXf1xunBpAeLBTRJVv2yY+HcAVAIE2m0l1Bk1MnDV74qzZZHvwI5MRQl5C/o37pUN7t20ZG6EZtNQ4rFtDQZGjHEcivvUrPo8Md9SUfLFI3V7WjmsF6t7wrMIKTT+ShRSVSEc92ptkQhFCQ3omPjWk7bG/r4QFaEYmHb+WcULVGLcSsRYUFpOHT3x+h3kqv7Bsw4rPZy+bixDavfH701cyEiP90kvoGSk9cQPiOl78+uQ1PTmqlXrxbgk+eRu8PvODnTRJ6uLymsaN8st89TsVRuFZEkDzwm62m98FDwJtfpMaHHt2fP8OZvuLtasbjyD/VQ9NVCco7WDPOrlCLNT9O8q0l19W68NEztEmDKUE5EqVRnUs/jifMYH1cLe6P0ckEuzfvo7Po0ZOntetNbZ82r38sCDvG+k5b73zrubQ/KLjXjKKQqdualrxAj7vZqb0gZa+nVril1RW1wn4VKx/yJ2CuoY0KPIWaSLKyPBgci2GmNadiDoJfAFfUVdH8Rs/QKLUzuqTM8bUHKy3oionhmIANgARKCem3psYFD9n/hIDMrXTb56vj7eXkKqVEyVr7oKdbSQ0+1rt5RU1T09aTII7H28s2C6JmrAOIXRi1/oTiPrnVh6FULfWITkFeBL95XvlirpanSCxR5ImsyzgU7Ja3Pl2IbXgwa7x+rNC8w2lL+rkqhzJDVrgNXVY+6OpcpJgCQzGrpSWNUa1FF+govFy0HwedUWiiW116NU+Gp+wHibXuzYgUK6EpYwSTbiKkekXa1czv+GRYcGSXGv6yklAVCJtGHSpdRdNc1q71pzdUak0lZf+OJ9BUZRKUa/TXmZsSGgbjTO5NWotklCP7+3n59XYq15RUUUjOiK8BZ9H8Xl4zWYehR4UiozdQCQe+6n7rcS07oTHkKXi/CzpVatXKEUCPtEocyEKobgYnByIisSteP16htlSFTsVALgoIFCOmtR0yKcVlq5ZbV2AKFeo2FnGvPxi/d/25qp+39CaxjfYOT7o9A2ZQOzFzopKy8poRF9MxQ3zUQM6KJU0j0fx+VSlFE8KqK2ro1UqCil0hiVRiCJKxWMMGuqDXEgvFIi8tC5PCT7uUvlRAUugDeRIblBCrykPN0ag8vp6kcBAulbTXqcouuFLVdH0+dSiGlltpzhclmXV1oOdWwaJ1DnQHMkN5oWmxZp6/TzOmA/WDDAgXC020A0ImMmimSnWdcGDQLlrUvP9M2fBkjkN28ZGmBrA+Jik9Ss+J6lPgk4S0Cxou60g8sf5jM7xQeRm6+RKsVCTnQwMDqYQNWlYN8aGNEL/3MxJCPO9kF7YKS4QD2NAqLpGZrAzikLUmZvZCWG+EWFBVHphaUU12R8S4EvmZeEe/IhM5vgVd4OSE0MLy2XsCFSlonnGut+1J4PS6odF5bVCoTBQKEwt5Q1PjruQ1jiznpyWwFantlh7FJbLUq/j7Z3Hbxu8LIjVybhzBGr3sfRONqkVtQ3ICFNzZEo1VCz29fGW1ckR0vTc/HH+Xuf4oPUrPo/SdL5rrj+gW+v72QXOX8yuczy+DR6P8lHHpGKhYRuSHG5CmG9Yi0CUrhET0ylkkIQwX+beBA25C2JPmV5KdFli+ewLYooYTeA1+eF2x9MUzCWqa+W+rFyBoTvEEx3uF1RWVFZHh/rEhvCqK8v1LekfEDBuQOujd+RGxFrLfJTMX4UZKT1ziqt+OXKJPASxWoFFI+c9SKAuhAmTWq0g0zI9divbYDDaOT4QITpKk/6zU9+RJe+hXoVKK6oF6jQl2RPow1cqNYKrqVP4iAUmZnnyeLqhtcEglGSQmSxntpRmitL3bBO6saeBvh26RWcaoRh1koPYsxGVbq5AnwqZvLi8SsSnKmm/Ud2S7mXlGrQkY0/GqrW0cEi32MwqrXG+zB+GT388yd7PFmtmUeXuPy8bE+vIni3JRkYtHkoMWDGGCQTqMia1MZJjZNpoUtbUT6VCowPtkfP27nk37z1ogkEVrVT3IdXW1tU2LCbSEFE2FFo2LkQczfn7Mi+pqpWze5PO3MCNd60+Itzbo/aIwGtw/3ao/h+D90akpuSLnx7c9nRGw+oi5pFViO2JKCrGpy4zR9MbViQTIPU565FwTJ+Em0VCg1bN1CQVNB8C2ahWqtMHdVUWiXV6So+0nPL9J9WJAIC1aFJhAV6E1aIEKAjUxUxq++rujEn3f7+BDNfXRx1+OmzcUlPvYd8v+I2PmdT4rnVCysaIUnt/eFhjP/i5tBJmepX+JHdN411z9oaX3Ckc1rcjQuhJI/bc2PLCLNSDbLPtaU4rXqGkK6tImpXu16Njdm4+qUIQ5q0gnUgIoZtFSD/2HJ4cL5FqdXAZTEqYtir7Vf/98RSzs2FsLP7vjMfUPVfXPHFIQKFandYBTXjXM6m9VndnD9ef/+orrGccP+rTPI0yjH1ipom0Jgk/NW13I+cU8KmG4p4Nsare4dIyw2M2GYja6pFo0kNJx68Vmt+KP3UtW67AVa/axodl5+Jf1yPn7kaEtyiSISS54evnP35g4jF1x5RO7CmR6vUmif1MBJ5s2IkLg2Id2DGcacJ7pjqRdhkRKwCBuqRJbQ9FdVj738+Y7TXL30TOweSfgpVbDtVWVxp8ip3WzCsso5jGu5Gz+fp4SytrmAP07fnlgtEf7pUEBvK3d29MDeuzZ0jts5dj1fNBtezZZBDawl+UV4pvgGZlTiJ8lILIrmSbsScjyjYtIwd1jTlwA+cvvCru9G6F3++x64XMUlHmN+f1Pw0Sfip4otG9WiHLllsFtACBunxMavdwccHyD8jG/FdwWOrQBT5Wbjkk07OkzgV1Bvezg9AzN7Jb4WymuvGu5US6okJzWjmNxxt4qRd89xaLw4NEqnrNoKV76qU3O8UHfbhXYk4ESo6RqQRj+iTs+P1vY58MmcfJUFAsLa7AlvL19QkLJMqjIsJbFNTg8DM6MnR0r1YH1aJkh581CBF7nv/7eFRk2N1iBbakdxDbkrY050knEpN1ZcQ947GeUmnV6UxT5VEABhCom5jUEZZb+1ljWLrpw7W2nGrxf3daHZhS6q75Eim2XmigL8sC4laabCYexpRXgOt3hAV4jUjGQSLh+DVNeqtrAg5Rq2pqM/KwlQR87NNrNS1CVbhFP6Zr4L7r0j1Dmg7GAoNbBOKx94aqEBhqxZ+8mtUyzFeuUPn64uyztLqeRKkFhcVdWwWTCJTYk1EYJfSa9FAbdpq1sLiUaFFaXh6oXkTAhCWbsqogITJgbJ9WqRKtWJsRt7ot76H27GdhFzz+sgJCGtY7dFM4PhTUvqy1qtizpRCZ3skuZk/llOSXZotsndl5OyPD2FP1ualMMCoSi5RKlVKdVdRMkPdqqEDSoN5o7VlVNE3/dvwG42Xi0zPF/uxjTg/SLa2kT8ox3KUjp0TFeTkUhfz9/bTMxRP4egnjg3FcQlYSLSyWSgoqVZRGrMlJLbxEgot3NUtAt2kVObhrjHqAp2GIVTWWNBROWhR7shnZsyVTYAWvbBgfMahLzOFbTlr2ips5UCsEChGoWzGfVcTEcTJ9adF8ZvuV1d9ptkR+touydwhuU4cFaPqdj1/LZ0wnim6rLVMN7AnyRKYIoaSEGPZpM+7jQZfdW4dUqov88/mUeu4A6uZbMnlCOyFf9eIn95f8JwGVNS3QEp6B+Z0MEUFe7CVSCoulNE1XyRQ+PhqBVtTUe4kEyYkhB86kto70K5Qqeg4a/fPJTUz2IC46bHhy3KGbGpHFtO50/u/jAQF+TNrXotjTtFIHdgxnBoHWIUTsOSBeCe1384EI1P1xTliqJVMLRckmqIVW5Lj72DUeq0HfNSGYCRvrc1P5Aj7NEyhl2AsUX6v5nJwYolNCn6/uiEcI/Xsr/8Gu8WTG55Xq0KKsHFpe265j17190s2MQKuV/IqifAMRKB5Uq+zcMijYT3Qls7JddEBJZV2ZTBM4UxTq0z5i1ssvN3mVhasalUrcV12GRzkZnJZqUeDJPl5OiVL6tb6a76ENdqTXBQ9NeE9vwnPEpCOmN7G8HRNCkpjxdpbRkvsMpHwnqeBJSkn9m1rMF+Bffp0iI0SmRKAXUjXTT0f276BU0QI+xefxKqS4f4kRKDmgpqzy0rgm1IMDtBOaILc+N1VHoMShjEBrFbiMyN1CreZ5XEI7/XOuesPwgFw2U2YtsUiUTR5MOpGYCJTdieQ5Q5oWgUCbBATqBJMWFuhOk5+yZJOJ45WFdxV1tToxI9MAZyZxElcaLEhM0/S5tBKhgIfXThIIvUVayaiKiqoeSSGREWF8PiVU9xcxkIIjRKCKkNaW5kDZAlWQGiEIhbC6tohAvdQ5WblClVnaOCuUR1ExrcxaycOhShV5+z7Wv3VeHv7KPHwe5yKbBQo5UI9OlVpqUn1RWkRDy12MWsUev4ZP1TUh+PglXPSI4gtJNlOn6U2k+e9tTWf6mAc7StUF6+rU9T6UKpoiC0lpC1QNxZo13zD8ktZMQwoNbaw5Yr49mcPUGqUF6kp1eCUl9QgBPMhdPViK4er9Mp11T7VSs8Z5e92OJn36/UZ1dW2zlcpkSKtrZHl5+ENQdyLhD6EOCQd1iemcEHrAeHeW29PPcnuCQD0dgya10ZKE3iEVzDJELPCeoBY46hk3JKxertx36lZIEM5pks6iE1exnkQC3pmbeaMGdKRpmlLXdR8bHkoCyaKScnZFfppGfuphoRRlel1nzVrNpABoeFiQXKGywp7s4wecaMfkIhiTqqsKYONHBuF36sVHdZYvN92kT61VqgB/BiylsjuRsqpRlgfb02ogAgUw81dtWTjDpjltOlAUdepmUXIbA4PkavIax6v3aR/JfirY3yvAF/fSDO4e7+fd8MPZsMLyPzdzWoX5IERfTC8iNYkZqqoN1/1kc/aGpvFOUeiPf9OYsk2W2pN51YATUczYgIasruac9/Ir6hWqOm1N2xEblUoGMMkp0cieARfSy/2CG9dHASwCBAo4BNIeP3TuHsls6owrImTc05R0q6zR5DrbRPmRNTlYC+g1LMlEI7U9UURYMEpvKLyhxmDJJX3IqHsy6ZNSzxe6VGmlPfVhj7JqmJaJBE70ktVKvbBqk840pENXij1BqYtsmwXvKQLlflllt4RWykkfETHpyWt5D3aJ4lMU40oGskIR8/Df2/kDu8bV1elqlCxBiu2phqe3wiWlPRGI0l5oilTqY1dsItwP7d6y5PKylL9X7Omv/y7Ifua/uDT9nv6fDtv52pGJA05ECXwDFNU4q8tNzEmkIoQ+evslnT2HrmyqKtNN43iCUq3A/QUKNAuBwRrTScvKGJMyHUQ6XUOPDOyI1EnMa3cLBnaN0z8bs4KesYpLBoJQ1kFkynxjxSbW08/sLhi2TsuSBk26POU02X+m9ZNlPtGkL57Ys+WkV+7/0jjnlbOYGaIaVOrCVQaUisCqIFDAaSYlq2VcSC0YNaADWVBo1IBgsZBPVsdUj2/PJY10nRTnmRsk9UkKlZpaOFQnCGUgmdOGMvvkUM3/H3j/1xV7+h7uODupCJcB7ZZ9APdKC3zEClw8qV2BpnomWfeZ4F+rlUAISR7iEgK1u1IRQm4TqFrXBQ8CBRwFewG4Rig08eGuJKfJjCtinEgsqQ/ZzxSQN7ZIvaamnN4CHqTxHonb/pTWEkVqLi0ePwBFnUYbyMMJF9/FNy/0JQJNztxL9rOb8Ac7v/rA+79eWjw+etTU4G4DLy0ej9wFUKqlQBMecAgKeb3+AnAUqzieJrlJmuS0JsXJ9BExKU5mlRHt15miqlauH71qxpaaqrnciECpycC2z/uLSJP5L66F/MbjZCP34He5B3Vnr7oZoFTTgEABRyFXKIXqqZYs9Hp+1P89oxmfhONEdgtdp/HO9p9AbViy8JFOEKpuyGtoeHmI9qubwKe+6amlHouZfVOI2w1/u3TBg0CBRj7asse+Q0Hr6+uFgibGZiKdxrt2+5yxKtLznwLP8MFVk5W4Z4gK8jfQ/CfRq0a+rFcra6tH94zLLm565jtgxxAVGVAqtXDVRo4o1To8IgKtKitwle/DbTBzbCa7ka7TR9TYeNesZqQbQoqi2+GCTEiFaFRRUUkqzzPF5xlUdY1LOt8rwP3mfJ7WAUDzKZX+6O1ZOrtcS6keIdAjZ28O68vd78CNMdYtrt9IZ/URaQWhmta3kQY4eyKQsHE+paYiZ0SgOKOgIqNWM/JUwOMRdV6uajgnwMUodZaTlWp1F7ynCJQBQlFOBaGMPcNbBLHMSJm2p7JWU7mDjcFyyxkFFQIwpmskUqeYONh8pTo/SPIsgYJDmzcIpYykPhtmImk9T2JSVZ2WLokTO8XrTijSl+lVA5oFOMrb677X2WOdUq0IVHPTryMb8DiBgkM5EoTqpD71xynRdTWS/ArmIYklIaL0EN62Wamm2/42etOjBQoOdQJb/7ty+qtLdYNQyljq08Aoz4z8CrJ2phOylgNORLGrivSLPqK5z9xhdrxK+0hTgTNgX6WaCFSfHapVSNsWPEWg6n6kjuw9kA91JmEBYjKVU6fxrt+5zij0ukxrcSSnwdhTZ9u+MgXsrtQmfWpQqRm3LttyD54iUHBo87bi7+XjSsaGGu8WTBBqXvpFHzmTO4xZ0gNwgxAVIZTQofvFvZ+z9ySPnWv+Rd1/VU4ddOJQGNvExrqB9B9t2WPwJOOfeyNUvVgQs1xlzzah+RUK1uQiLXuyu9ezi6suVTp1sBFpwrNDToPw/ves1ZeAJnyzY45S9TGhVA+KQAnQlrcRHV2aQEipShoWC2IGtzOpT/b4dv2kJwBwJ0rVCVHZSvU4gYJDHaRLQ1ANg9tphBrNGBEoputqMvIbSxETbzqty8gYTYafeFjV09ttCUIBN0ikspXqiQIlDtVpzkOfkv28aWCaEDM7qEZWlyFrmBrEd/l5QX2Se+nv/Ofiuea4F6AZQlQPFajB5jwZLwbTPe0OMWl9bqpKRQv4PG4a05zwk6B6erti88QHBz1kjlVBpu6tVI8WKDTnnYkouu11zcrk7sDJE7hUKEIosW376Khwc2QqzU1zyq0BzlOqpwsUHAqQoNKiz0Ewc6di80SyfTf19t3U22S7Dq+93khQYEDXLu1EoibqUQGuCwgUA13zgF0Q8xVsk5ZLK/461ZgP7dU+DD5nNwNGjWh1K7ExWOsFAAiCmTtNfBRivoL5B5+YGwMCbQQc6plY2n5nEPmbVW+fMem521rLeQJuAAhUC3AoYD6qp7eL/L3JP3OOF/MVV9PyyD+QqXsAAtUFHOpRWB1+6sCY1FKZgkldGhCoAcChgC3+hbDUcwCBGsbDHVpeL0IegO3hp+lpnRaZVCcshcjUJQCBWuZQz9Foeb3IQzTqBKxo4EMb3yUAgVrmUA8MRV1aozVllYhjQFjqToBAm8CjHGqshojratQn2N+dTAphKdeAmUjWzFPyzOpNxKFBonrkgvgE+xdl5SCEkpKSeDxeTm5BdZU0FHECtkPrK82qF0BMykx5gjlOzQVEoGbhUXGoaZqMRu1SCs9xiMXi6Ni46ippu7btEPeAsNS1gAjUXCAOdfVoNCwuBiF048YNdOMGXjUkN98LcRcmLDUzJoWwtFmACNQCPCoOFfB5HVpHRYUFUZTR1d5cMTfaPbln3wEP4pWaqqTIHXvwIVvqTCACtQwPiUO3rJymv5NW0XlF0rNXJedv3Csuq6Jp2hWj0ZyivKKs3ORefW7fTkWuBoSlXAMEajEe4lB9KB4VHRE0YXjyhOHJzE6lUpWVX/b35fQrt7NKpdWuotGL5/5FCJkb0TU1FN/5qyRZZ1KEENP1BP1OdsHjljW2F/oOdY/lQMiixGERVr6RooLGhMaUJZsQJynKykUIhS78047n5MJKc+bLlA3I1BYgArVzHOoeGrVamq4BTYcuOmrfUzZXKGpjWApdTzYCArWzQ92jOV9UUGAiCHU9Y7IoyswOXXTMQSfngkZtMSm08S0FBGoTbuxQd5ImG8fZk4NYMUSfAAP1zQQEaitu7FC3kabdq3+aoNnDT2NAWOoIQKB2wC0dCvZ0J3vablIISw0CA+kdOMbejYfZuxxOiD1dDiuG6BOgrAkDRKB2wy3jUPfAOfZ0ifDTGBCWWgdEoPYE4lDA1bEuJkWeGpaCQO0MOJSDOCE2dOnw074NfORJa5OAQO0POJSDOFRw7mdPHSAsNQYI1CGAQzmI22uOyyZFbhqWgkCbwaHQNd9cgEM50sAXu0u2FATaDA6F4U3NCDjU7og8OCyFYUzNM7YJhjcB7ofI2iH6+jPxXWWhJ4hAHQ7EoYCnIbKhgc9u43M/LIUI1BlAHAp4LCK3DkshAnUSEIcCHo7IhpiUs2EpRKDOA+JQAEA2FNnjYFgKEahT4X4cyvFV3TkIdOtzISxtrhFREIE6G4hDAcC+qdJmLAINEWgzwP04FABctAcfOTcshQiUi3GoB65MBwCuGJZCBMrFOBRCUQBgY2NM6riwFCJQjsahMFUJAOzeg68XlgrVYWkL684DAm1+wKEA0HwNfDlrRJQ1MoUmfPMDbXkAaN4GPpGpmC9XN/CLz90uNvNVIFBO4JYO/X71S819C4BnIbK5B59tUnNkCgLlCm7pULcHRtF7eFhKBYSEI49BXl+PuIFQJDK430SfknVjm06dvWjpS9omRCE7cf6GhGzEtk5AziVbkmHsKTqul72uQmWdM32A0964iffrZGKd/l2bidWpUh3Y2VKIQLkFxKEAwOUGvk5YCgLlHOBQR9Nk2Ai4PSI7mRQE6pIOhZQoANiF7OxM8s+6l4NAXdKh0K0EALYjuXVH36QWyRQEyl3AoQDgICS37rDtqYP5JgWBchpwKADYHRPq1KHJsBQEynXAoQDQLPbUwaBJQaAuADgUABzdbLfOpCBQ1wAcykFgOJQLYRd16pCdnQnl7NykbhNCdFVZvl9wpDNvyTMBb7oWEgeokwEE6npxqLZGafYB4FCHAup0OSSOtCcI1HVD0Q7GngWHmq9CMyfFgzddEYmD1UmAHKhLcuTsLRPPVpXlO/FeXJgmzUhlnQN7uiISp9gTIlAtbl4/0+Tn1bFzP2Z7+PAhS5fM79KlU05u7hfrt27Z+p1CoUDO4sjZW6bjUHX1piZSoj/88FV0tO4xQgGf/XDf7/t2/ryTbCcmJb4w44XOXTrn5+Vv27rt7JmzyDWZ/dLzLzw3mXlIC8TsZylFHUIoL79g7LhndF4YGxN96dyxsOh2yNWIi43+bef2Jg/r0fdhZvvRsSPfeeuNmOio4ydOv7V09f37WYjzSJylTgLkQBtp1TLezE+Nx+NVSTU/TMXFJS3j49ateXfdmnfbtO+Zk4OXB+CCQ80JRaMiw+NiY0wf4+/vTzaWrVzWt19fhFB5eXlMbMyylcuUSuXYUWORCxIUGBAfH2v6GIqn2z4TCgXnzx5BrglfIGjyLbO5c/1sSEiwUqmsrKoeMXzIiOFDVq5a8/mGrYjDSJxrT6gHaoqayhyEkI+/Ab/k59wOCPDftfv3Z6dpiq4/O/mJLzd9ghDyDYilaa2OHYvqgVqBaYeevy4RCL3MPNWO77f07dvr5TnzJXdu6DzVb0C/pcuXlpaWTn5SE7g9P/35SU9Oqq6unjhuomvVA9Vn4oRHN36x9tjxU08884KxY/z8fO/c+EckxLUgzY9AOVsP1M/PNyP1olyuiG7ZSf/Z33ZuH9C/9/c//PL6/LdxnCXg30u7JBaLuyY/lJdfwMF6oBKnq5MAOVCLEYmEAQH+NE0z9kQIbd/x8+07aQihrl0N/Dg2Yz4UIaSQ19p+laXLlyKEGHsihLZt3Yb/YPj6Ig9g0uMpGakXRUKhXO68LE0zMqB/b4QQsSf+EVIo+z00GiG09ctPEfeQNJM9QaDW4OXlVVxckpp2V2f//gN/IITaJLVGTscJDn1uynO/7voVeSSrVy7Z8Pma+vr6/g+Nzm8q/nIDBg7ogxDa8tV37J1ZWbhB1rtXMnLHyUVWAxGoxVRUVMYndH2gxyCd/SmP4j/Rkoz7qDlwtEMLCgo2b9rM3uPtg4vRmpOvcHXq6+s/XPtZTKsuaemajIR7M/vF5xFCO3f/rrOfdCKJ7Jd9spHmVScBOpHsg5eXOCkRp34uXryCmokm+5QU8lrz86HGoCjKz8/v4eEPvzjrRZqmTSRA3YYVq9YgTyIpCf8kl5aV6+zPys5t2TIusXXLW7dxtsrD1UkAgdoBHo9XlI9/pMY9/ixqVpzg0PiW8Zu2bCLb6z9fX1NTY8vZAA4iVHeU1dfprsBYXVONEAoMDETNioQz9oQmvD0+QR5PWnqPz+d/u/3Hw4ePoubG0W350pLS0cNHjxk5pqCgYM4rc/Yf3m/L2QAOQlGUweQMF7I1Ei7ZEwRq88enHhDK5/PXb9g6a/YbiBs41KGVlZUIIZVK9dyU527euElR1LpP11l9NoCDKNQjDYQiHIey8fbGbZfq6hrP7C8yCHQiWU+H9m3JcPpJT/5nwaJliEuY41Dbu+YXL1yMZ2d1MlAjqrZaWlsttfH8QLNQUFiEJ1D4+ensjwgPQwhlZmY7/5Yk3FMnAQRqJRPGj71w7hie3dg2ed/+w4h7NOlQ80NRiqKGDB3y6uuv6uxXqVQGvQnqdGm+/vYH9YilB3T2t2ubhBCSVlQ482YknAw8GUCg1tCxY/vvv/sSz0iJaJOXx92BgfZyKE3TCxcvHPXIKJ394eHhzDZ4020gA5hWrVzC3snn8ymKKi4uceadSDisTgII1GL4fP75f/7E+ohsW835PuiiUpyytN2ht25iF7+7+l1mj0Ag+OrbrxBCY4YNg5DTzcjNyxcJhUOHPMjsOXcGFwF46eX5zrkBCbcDTwYYxmQxby56jWwU5qfqP/vavLc2b/kWcYmi0sqwEE1BEKuHNy2Yt2Dvwb09e/fc9X+7MiQZoaEhkVFRCKHvvv4aRjK5H736Dsu5f/2nHVvz8gvy8ws7tG/j5eV15uy5Eyf/dsLVJa6gTgII1GIWNwjUhTDToSaeVSqVj4x4ZOq0yU9NntypM57vX1db+9zkyfl5zqs+BTiNerk8PrH7oX2/dGjfJioyAiE0b8E723f87OjrSlxHnQQqIKQxjeX2yOt1xwY3F3asxmT+W27SofgvqjoObZuAo0sGq1vo1++VuEQ1JsfB2WpMHHzLElezJ0SgnoUVbXlIbgJOQOKC6iRAE96zMNOh4E3AaUhc1p4gUE/EHIcCgBOQuLI6CTCMyRMxZ2wTADgUievbEwTquYBDgWZE4hb2hCa8R+PotjzTBQ8A7qdOAnQieTSOcCh4E/AQe4JAAXs6FNQJeI46CRCBArY6FLwJeKY9QaCAYYdKckpbx4Q0+emAOgGPVScBIlDA4jgUvAmYg8Td7QkCBSxwKHgTMBOJB6jTE4uJAOZA1vV86fF+zJ6F207DRweYicRj7AkRKGBqbeSJC7a17tAOPiPATCSepE4CRKBAE4BDAXOQeJ49QaCAWYBDARNIPFKdBIhAAXMBjQL6SDzYniBQwDLAoQCDxLPVSYBydoAFwO8MAD8JbECggGWAQwH4GWCAHChgDdCW90xAnTpABApYA/wieSDwpesDEShgPRCHegigTmNABApYD/xeeQLwLZsAIlDAViAOdVdAnU0CEShgK/Br5pbA12oOEIEC9gHiULcB1Gk+EIEC9gF+69wD+B4tAiJQwJ5AHOq6gDqtACJQwJ7AL6GLAl+cdUAECtgfiENdCFCnLUAECtgf+J10FeCbshGIQAFHAXEolwF12gWIQAFHAb+inAW+GnsBAgUcCPyichD4UuwINOEBhwNteY4A6kT25v8BaRrx8l1nRPsAAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "S0’s clear shot must be phase 1; then S0 ducks to protect the crossing lane for S1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The two assignments are distinct and the order protects the crossing lane. Execute the volley once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-cd5b7deee66a", "created_at_unix_s": 1788842393.682935, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 2, "requested_group_seed": 2, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [2], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant": "Assign S0 the central target that needs S1 to duck, and give S1 the left-side target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "S1 takes the distinct left-side target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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QaHHxjhfyqoDzsRzaWpAjDlAioHnLwQ0vx7PbB2uRo+3G/34UZIr8qAFM6JXGr54RaoGAJCsqOhzDANHkxNqqEqfLwyCXyChNg+ICjRdSHMdTFMXzQlV5kbR7/ejHOJal7lmoHhlLK8QCzWdlzE4WNKNFaJwr9AmFS1Oh9w4Verrt2vp0FG7HgAIalvnhymMF0a3Z7xlpdG68/7vCoYIVEEKeFzheKDb51tc09FBAECuUalR58jL7LrFIqRDkFERAc0kWU1RGPE4ZUJQYxYYUGiRCjVduJZZRTKiYSluqa+tq9GEiTnmpv8PGRfWjH5bbtgkmTXDlO6y1fAEAsOqnpPscaJqiRZmGhgrebWAF53TXup7iFXJfp5YfqxHPp79oWerPSEGhxLnf6O4Qi5RWgJyCCGhWyGJ0ZcSZOhQFIFpkoXWW5F8L7OMPhmKVpJFKirFU/8Mo2Wx7UTmpnFhV9/cGIk5yzZVuL2jOigiSKvCUdrRgdqljb6V1kxhO0XN0gZa3UBBNqOVpw2jBrEyeT3fJyXWWz1je1jj6Hk/rXJpqWnAbvL4PZLhwoTskolVh2wrAVsVGSLGHS78r/Rkp7b+75BSQaxABHTdlRM1dxfCCmIcj2oN+gVNA077N0t2huwUeq/y/bEv8J00YJ6z6KQ5tfaVts5YzM4KLEVw4lCRWIqXd/Aylp/hEvXeo3L6DEZyF7k680ampGjQdrcoFBSnWSxfadQ1qZeNnMoJEBDR5WYy5mhYEGCyHaLks3yBah2gDg/Qx1WuRiGPewNO6vqLjE3pI2LhTKN0lJydxEJemIvoDMSMFc0fCNYtK4jxziAlhgaqsjBBitULraKyR0ho5nNmY/Tz2m5szGYgPRb42nwi+TkLewE4cWYypjMh3CCHHC3gt7VsUhypj6Io42Sw/QiraSiCMO2weyGJ0ZcThZiyOgUyQcN5GvF3Djv/4BNU543sXAwDWvPbKGd+7eM1rr4z36RAIeQKbw8ooSh5qKgEhRYuxZqn8Q6aPPjsygmhOENa89kqBEeUnr3ntFa1O53G7Qe5QPm0B/vkbaVH2VSMQxjGCpIKAJieL8ac0QggFCJA8in8G5eLg/4prbd8tEkuJzJwjj9q87rNjTzrlq7WogCTb+MO9D0S59+7H//HH226+5Y5bM3hGBEKyApqKLCagjAKyHOWeRJzVHRSqpigGy2MqJ0QAYPM61MQsO9UTALDivAu3fPAWvj25ppQCYEdrn3wHh9MVXWQl+kbtjzzym/ScJoEQBKtWsrcEFDuMIbcjrnXBqhgijkgZsTQGQ4RyYrJ0wdQtH/hu41/VhVNR9TcEYMOejv/86e75U1DJIC6d2rC3c9nsJvFywl4csDNYbQmEjFqgMZXRF4TBxS34msVrZ1El5XtSAIidCORiSBK5x4H17/9b8Dd0NOqDlhpWF7/8zItALoBrqYJLDGQ5EvhPAIoKfBNxz/jBjyY3BBq5J8rDv/9j+NNgWOJAyGOef+KPl/3ktiQeyGLp9JcBopbUKIFRqgKUXI1BxS5hqlwI2cDHbyrXE0VGpCycADoHHVPqxJQg3mPSM1vXrgYAuDnfJ790KmrisLEFNWTMBra3taD+Zlr0S6zXoULGOJnSmFIzc6eHi3APF48DwaBlk1Pb8qnzgdhU33L4YHHj9OGD2xN6OCEVdrQlP5KPVZiIvs7eRBVzEwhgcLkTGLP5Au4lBmbUjIY+YnpHnbJHoWooCoAlzQWb2pLsFZ8i99x1J75RZNTtPWzRsrTLw7X2o0LGKXUlB/q6AR/o5FZZDA50t0l/Sj8DhdpxThr1629caospMOhvuePW4ZadhVUN9oEuQ0kVUc/Mk5z5mY15oIQUaem1TKuLt/+5WEWaXdIvKT4WfVQjOBrQfYkio44XYEuPz3ZgaJTqO7cpVQG9+Y6fP/uXxxUbp9SWIaO4pRekgTiDY3fe+6t0PDshRYiA5htzJpVVV1es3XQgyj7T6oq1LN3qVx8AwMypdWU6oWPABrKG6M4hq93dM+KQNRNQZ9VUaEBjecKCg1pyNuztWDY7qGE7JhWpvfvxfwAA7r/h2qSPQMgkREDzEIoCJx8jjpcQl7byfp/9g6Nozev2UABMayzX6PRaltawNEqKgOOcXQ8BbO0NM5QtkkGNzlmUzo4+9Ki6ikx3Mg+rngAAu9sz0xfIUqg6ir1+2zUoPTBUasNKZ9+oyr7pmDYvdiyACcCOFBygREDzHZxC5tdQrJ4YnFJWVCAzuMansXIQPt0Pln5cgvvpZjQueE4zihG53cgfqtNqWrpGpjXXzpxq1GuZQdmryxrk72mYnlsKq7a1dyT0EJff+FOjP8dAFTheWHrmqo3vvRlln/izboGotgCAnBbcK09Cg0n+uXZPog8kFujE0tDA5mxxfsY4bfE2+v9JS2ZIvwFSk+ij5k5CxnOWvxaRrmGfsWnQCVtbDioSyxSwdMDoLjSoqZ7Ycp8zrX769df957lnpY3NNWWAAhD3YKQo3IwRZXML8Mt9nUa9Nrragjis2qy1bd977m9JP5YIaF5x6qoffvTmS9HECIDqytLOLn9T8dGx8tLAmLDswq/6YS1oDCOm/mQ/h7oPK7ZwYsITapE5iNIepAwz371CYF356IP3KpINWnosf//rw0mfDENTVWUmszUo3cJXDu0PKkrRRVwruGBKLY7yoV6OWFhRU0cKbxT/6/uMYlY0YLXNKnAS8ZgjGRcWEdCJQp9SfZRmm5fWQxAm3p1VGqpgHH8AIkWQkkgvi5RpIKXxijMLEDjrQJXcCVvcerFsDnqZ+PyZwLwDdONLVBI2Cd+FP6P5zTXyF9reF3TVzT7xjBmTqkH2mZ8FBn2BQX/lSXMSXcUTAc1D+vqGamqCq3H81/TGvb3nLi/F3wK3h9MhOyh0wNK4EEEk/Zsl83Pj3r5F01Bekcvt1es02eTCTZIoLgirPSBzPSNOikLq6eV8BWapwPMqHEQBWv1HFvef/emxAoOe9zidI/2cKytKNiS3Q9J2MRHQCUHfgE99Vi2fLW2srS4dGbUOj1qzdxWPoQLnDwA4d/msw939sv6uCI04Oyib0WuZPR1BMaL403UxWD07+sbefe3p1M/nnRefl253D1txrmta8fS3BGohsBsbAENplXN0AIwTUgUw5h+f7E30CERA85kLrr8U33j8PjSxY+Pe3lXLS0Eu22w1laUw107/YLdvFpuUWyZA+Onmg4dkebiRkIts14D1iKl1y+aqPzn9O3MnqeKpiIm2sMhjFzPVRPUsbpxhORwtYTmTJKGeREDzEkqhngCAG+65+fH7Hl114my58AgQ+lfxPqbWmDaPUylnlPn0cvNT7rutqSobGwvK/B9zuP9w7wPZXrRDoXZTSExlLzj4taO/+gd9w6BwztaIzT13RoNBx1Ljna6bCqa6KfJCVcvhAxTNQCGBXgfZBrFA8w0qRD0xN9xz8+q/vRDYAwkoqKsuGx4dy85YvPwlYAu6pqpUrjT4dUgnDyFYvup7qP92riD70Qj+/aDk+Qb+xS4oxJlParw+KSqVVlrDpbViyqcvHD64fXzX76pABHTiIggQMNkY/gqr/pd+/65VVaVBSkMBt8er0wbiSMctnCbzi44PG/Z2qKGhUR6gAi63V5XjLEtwdS+Zn/jG+Krn4//zUOoHyY00OoLqlBm12AxxBzdwe/6eC5+/58IsfMNXnTjLd0vqqajcBb2cFJPqVdFffx2nRNRzkjc6jZDuKuZjsiomS3i8kbr2KVHRAZqXEAs0P1n9txdC7Tg5LEPzXpReH3YVL2noZff9O85nXHD6NTs+eBJkCrSwDdmmxoGVR81EhFpmfCrUs7XH3FBRoPoTyhMYkmaDP76Em7eKaxpBqrCyupBns7TA99bdcPvPQBaTXASJCOjEhaLQ5R5zN7k1Gr+YJn1Sie5fV10+PBqm/0jSjJsDQNRQRbFDdWVpa48vlITRCK6WPhWKHcxjcYUK4wnBb5epJ1L8fl/fblMhKqySSqoeecBXTyUHi+yv783hAVbEAp0QRqgvfOSna8jRXGP0RRKQJzEQiz/U0T8tQq1IZsU0NlJBzvDoWE2xNk3qFyXFR11C1TPsbqqY2SzLqGJib9y3X1E01VTJcl4Prk/F+KpUZYWqEiY9E0lb5dWrHk7IWpElApq39A2MBoLXIVCA4vyhWGkVL8oQ0lBpt3ER05j+B58LQrTapDiSKvLJRS7yebfdd9dZk9MeOaiJoJ4AgCefeCT147/9wnOq/EJAWYlqaCdsjFSlihVWgtVoQyVVzme7+489ogplj7J0dkaQiIDmPyh5PqyAUrJclmAjVM7Bjj7pG4LF9HDvkGKf31274r/+8Wnqp0oFS/8N99wc1nxW7CzbooJxFiUGdUI1b3d7C9C7pE3smHGIuzzXdeO+vlWRBTTbgCFl/pGQK6xImIRWuchi9cxyiAWab5yy6pKP33w5ULuJk+dlUAAsW9jc0dXP8m4AlAGKNZ9/c8byo2Q7B74bodKZVh6/79GwtnNw+yCVn1SIHF0xGTQmg0bF8LTCysbLBbHcVsw3kJ3IiM0tRuHVrP5WVDGOCxQVrfY/7PyurIogEQGdcEQSnLqasuGRsT5LtCVVZlDUHcV8IerGkVQxYyNN65QI66DA1WKBxbtouKobgvdygZofqXtb2AEkBQb9jLrSOH8k9CmX+efK5K5QiAWazygsuOiXYaRVvGwHXqdVZt7v3rM/TZHrSOYnpRysjf6jlvSnrzezsj9WOALVYr6z8d9Q6S32cvxJ51/k8XIcx2MHDkUBmqZDu9M7nK7ok50KDPplsyd9tW9f2Hujl/knKq9/fUwdf6XqDlAioNlCCWs/fsFk6c/SYp/d0do1jLJJDqgw601FZdi9Zz/tNwoM4jiHm85AJ//Uw7/DGwfHkHVz5933J3xomokSRaEi3FbUI6XCk08oR3KqTsz4mMSX29uwBQoB8HhUqB0q0GuPmT8F34ZiKZogCG4Pt2DmTWM2p8vtHTbbOF6gaYphaK+X58XMpK/X/Cf0UA6n6ytZCD4wiwUoRR//Xz7lMGYXFXUN2LRCLNDx5ztHmCbXobXSqMWX/GEe8zl9bnvwZQDAeeedk+gxi8UwqNOqXNsWmHzZ8lefWPXAW7426XrK+4cL6wCobB90/+JFX1qfgoMHD0qiiW9MmVyHkmB6kcTbvVR9bbls9yG5no7YfObh7b++L2m9pzJkK2bvalFdKHFpzNCMBqU06cqKg5o8QTHV3uPlIYRzp9/A8fyw2cbzgs3hxjExl9v7xdsvxHgCUT7x/08WJ7KEGtNiZ3u0EQIwOBRIevV4PNgdPaIMPWWRA5QIaFawbr/1Fw+8LJvsE3SNJaSeFAAXVG39CMCfrCi/9/WgMRJHTirsHYMOUVLLjMxTn9mOm1HUZfbZNTwPL3lsv8LZtGPXHmmcmVw0/cPe0H/qa8q7+4YVZ11SjparrR09UhN1lqHiL34JXbyHibnLNtXVlA+PqOMGdZhdCn9lNlBZUdbTL77JmQIPmtKLb4VB7FpdV1kiD7VBCL54O44Ug5D7FRvwZU8BMBCcBitdLbWl6hdiqUjWXSsTk1Wrzu473FJeUtg/Yh8dc7g8nF7Ldg+YFYXqMTm/cttrA4toav/9b3SJw2wC7Oh0LG4ubBt0j9i4ATMSoC8PjMl6m9di9dTJcu4gVA5+2Lm3DQAgqeqUSaKeinT3DdfXlMtFE+smPmxbj+X4eXV3PrzaUGi6+5rTFKf9wP13G/QarNGoSN9lx04AdFpV4XO8nW6O0vlOQ0+p0xojfuLvkhk6DSlKlmvYhK0sbBqNLq34LXUqhoZGKv/PDEmM8UhVQDe8jxKnpQVdmRGlxUlX/PHfzcZWFNnPhSfPwzf6hgO1eg//a338R1hRemD14JFouXTuJXjLUPchfGPHN5sAAFvafHMUHvrRpJ+92PHc9TMu+cv+l288Ag10/azv/26d++4B9rDFJ6AeMWKrYemNe/uWHFEjfyKnyydYe77twG4yrKoffRUIKdAUGu7Q1mNZcEQTy9KVFaUAoIW802698+HVeJ/isR34QpKuJZ1OZxePXVnkE0f57AfpGsMYZLqJm2bmBJHSDCKpJyoZ8N9WpY5TXQzxGOxRNTRUPVt7zPXl6TI84x/XHA9sQlqJoWlK7//x/2Tb4UvPPNI8NChd8d9++VZCYip/igmrxcWUJTQlE5l49QnU2H06OmMuvWNDd4FXCPq8jq5xgqOWSH8KLts/xaZil/xl/6nfWfwMEjFAlU59YnPgIe39lrZeS6mopUfPrC4vL/VwAssgy+P9DT6VxKoqzcXs7EffbZqmOnqR+Tl3ZqNew5SXl/pCvbLmH5JuAv915fMMAPD2F/sWTa8K51TF+BJRR2zogYd6LCxD8TxEQQ86UFg1voS0YgoDTvmUKgVi2p4S496sL3ko8b/B7VxhHLbnnff+Nq3n9cpX4Z3+yQhoqFxKt6dMrsc3evzZ1Fg6L62tAACWVPgumtZ2tIJjaKoFXdz0Ky884+WES398VfSn0Pnl+O1Nh89e0pC0FucuR85qaqwpae8alLbMmoI0xeERAEB2Vrc1YpRZMjNR9RsIM+9hc58hdOO8ubN9Fq8MCGHXkK1vxGZ1BAw6hqEKDZpC37cXrloxF0IUXsBfCavYEx4bgDqthqKoI2dPosXJtzR2Ivg/apeb0wxsRst/6aKSPAD+79XZ35n19jqfhob61nAwSnKqsgzl4QQt41Pwp9SocUyRDXs7oguo3Py87OK7zsU581GhKUqyQJ/++/i/xuQytMKaomHVEzegSiKCtG1fp7v7G3x76Snnp0kxFbBfffhaOLkM+LZCdRMAcOmZCy+tLW9t78YbZT4vfDVDhkaTo5uqTACACE+BFLnbf9i1SI7RMeVPah4aqizS4a/Ni88+zTKUl4NyOc4DTpwdyNiY3FAp5jChNw2z+IiGLfu7UKzGFCSjctFUBQhhz7DtULcZL8lpmkbTI7zA4xW0Gp9CITmEFEtT0B8UGpCFTSVsNntZqSncU6AjyJ2qO/chj6rcqSrXUOxUVQSjpLRq5FSdW9s+YC+adjxal916Aci1xXs86umfdQmyjbvvujPJR/o1NGTOdpgGVLWlBTvawlxjC5oDES3MsNn20Ze7T8h481JWfkFL13Fre4+koT1inopoQaA9l82uaumxfPRlwNsl2gI0hLDcpDMVhq8PC1XkSNKJbVi5oSE9Cy68Bfmono01ygsiVEOxjO7Yn0jD82Ce/9z32/ujY4JahHh5/qs9PZw455ZhaL2GdXq8dqfH39pRBq6fRB8DNTCk/AJ09Fomi613RkatCg31LeAg2HoQNSEP61TF2r1zX9ukCv3w6JixQNfa0SPpJpbO9l7L3BmNWo3kVPVdD5JfVU7YgFWWqGe0KtXIGa95AhXeERypAVUoClUVBLj6vfVOtwdMyvS7xeJrGl/QTpcXX8To2hWFTH75ypHyXdp7A6/EVNQUVo6D1BOicC2+uXbb4WWzq5bNrmptD/qe4OMjxUTzC1HT/DbxWRqqihXBP+wQqCjS5eJKPx71DNXQwR3vVi44KwnRlPPi1/1yGd24txerZ0Vxgdnmsrv86/eCSmAPeBUCUEhDJaoqSrp70G5HTK3d39IraWh5ODv0mFk1ZeVlHI/ctAxNrZF5VCV3Ksbp8uK+vwKE3QPIwbpgVtPi8lLsH8A/p9GRB6yySlXjVE+cRd897KisRNUK2VBoKxHfAJIwfLKlY05jUfR5q4ny5Za9Trenp20HWLgw+p4LTr8GqAqyMY6dXWsqKfZywqebDkhKCiEckgU9IUQ2oFwuZ09v1DD0gpJilkFJDRA5a4DgFoOGogpLYrpzb5t8jSbpck2JTmFf4BuUXzHF7wz6TTmqvFQL3biJodwhgMtg7F6Qu27TmOqJWXxEw4P33SVAMFKyBK3zExfNSDKKUvy0yMlG09SQP5MfM2J1lUUYmiStv6oqSqQvvM06tmhOo9S+LCwU5RuOhp1c562Yx/GCACEqgKEoXoAalrZYrAq/6uI5ZVC8QoISaMRaRJAg46Oq/gqr6OqZh8ZmMH0Do5HUE7/8L3f4vDqYMy+/DcSivau/tbMPAHDLeTHUMx2wvlCA2VJfV3n+SfM8nMDzgtVqw5cvy9AMgz57/JOP5IwCLE2L4QSINuIAK/7sKSCIV3lJWRlD02vW75VWaniNNmhBJq5ClOVm7KxpjRoWuUkXi9HbQoYDABWWAcAr4l1S3Al7ABRuU3z72y/fyuaY/v43/oeiQHFTUG+0ojNRc9nut301PAEzC4Lh4iXo3Y78LYtTN0Pp8ej9SuqKZ/CDtP76ak/PeScGDAdkGgKqvNQ0LGrocJARii4W+fFwlirSRCQuEEAGlaNAMDgc+JEeGnNTDLpExqz2shJTuJcO97cHfeWOmNyc+BsgU1Wjr9IRGYAe3zz3jBX4x3is2K01qzCE9EZIIn8rUvPTeCJIDpdn/abkszhThz1t1Q8/f+eVQEBcwwANbZXZEKJp6ft8HXZHaYkJQiDqXsRkWqvZUldbuWrFPIfLK0Dw8cb9S46owd9GFi3AQFuPeVpjxaHDQ7OnNzI0tbCkmKJQJkpBkGIGXVdyPwAuHwxVT1QDI+YDyB2pkhf1hWeezpIAFA4BCXVnozOcXAsAaG57Fp+l5d17uyw8gKCz6SLFo6i2vSqKZhQlVchoNPVcMQe7RLEjBS1ZxEBTWalpJKChUQcm+7wBlNhUEoZGpTp7zZPrxC8YFZfiKPQ0FVXt0jah/wZXABkKTUfro3XZSIKwr2WDvxC+sjLNE5nSxgUh9QJSyz7lL4rsl1XegKqsoqbR5Irk+oQAfPDZN1E6EGYAZNa5vTzSTRHRTBArUnRaAFDda2uPZVp9YJk5akZfjLBhVnQ4QyHg0AtGhipDFRVoIYTnrZg3OGz2it0QKIpq67dNa6ykKDC9qdLrdhUYaCBIDtPwihkqnQH1FN89HKhV1MD4/isGnkQf33iukKLHzduar5D/Kc+lD+1c+23H0NftMdoxxMOG5b3LPkfyHadBqjAizlk+O+xcSfEsqfYeM3aGxh46L7rTKOQWCHw9KstLunpQuGnOjHq7zSEPTMk/xUFdYzyvVEVVddqtX9iNoHrOF8Pxqqpey+DWBKHdCaTWBNHRU1nk/Ywnh+mCCKVWOPXVNyihUhxSHdmlWltaMDLUNzIUiLkrgu9vfrLVoAGGEpSlN2CO3TNUdQeoT0AVbRmkP7FN0Vxt1Or1B9r6JtcGzn5k1FpSbKLDrSflciwaqZSY9wI0Wk1FEfKrNlcb5c9ocQrFBjo0+oStSEWxYMzoky9YD31JM21iSsSyuXUdA4GCliwRTRiPoof7cU0lFWHD8gSspx6PvpwN3wiqurJEVryPkJYp+H+L5zZt2d2JNTTyIOLwYdmqCvQov2uIau8ZxUZopMBUcqivquh9mbPXXx5lKDQ1e9AF4PRwzpCS3BqxxBu3JlCgUFWsnlNri9r71Byflwr3JJ3DJHXt++sLyqshwbBUa9fgt+3I9YmxdgUiSFGSQNMioKfKVvESX+3pWTQN/bZgz9T8mQ0sQ8njA6PmMYpCDi//Bv/3J+R9QcuzYIqKjGNiAjZGygRQRJ9wIovisS09ltDokyRGWDHnzmjSsOgLWFGOPHSUWESYARJNzxRdyZHuCLMRyoPfauhmJCM0LJLGVVYo1VNMQgo6NwqAxXOacKvj4ZGx8rIivZZ1RSjtl6tnZXmJ4tiL5jRt3dPpW8inmVBVTU5SsaoeGjgc6deubzTMXEy5qh45tXzQgqwqCqCylGxuKpxMv75wr0YRQYqCy+1d/eE38rf2ypXjEEEKqkTq7hmsr6uUX81SdGhfa9/8GXUahsVrKCyj+CuEwwXR12ihGbMeL19aYtq277Bk1R47u7bAVMQy9Adf7lOEnkKjT1hzGYaSMpzE2sEmhqYWlaHvNko9FkMWvtvpdJIkn9Mex1kpNSmp3roJWZ0KJD+3dFVUVZTIC8mkW6HfcIpCF4akodURYiAK2xN/ZEENewC1aE6TzKlqyqQ3JhVD1VMVGI6iG/QVyURBsQ7FPySRUglzmguuvzR69eqIzX3VrXeHzZmHED7zxjohOyp3420mwvNoWSxGUtG3YsveTvmKXu7nYvVGwDklOZb4ak/PUdN9W3Yf6l08q2Hx7CYIoGTVOqxj9bWV5544142yUZFn+KOvUPTJl+mJNBTKU6lmT2+kKerIUp/BQlO+VCpfkiDa5H9uCJqrjW39KrQlVr0QKH4DNE01BPEYoYqwO76t0DBFw4RQDe23eIoNdHT1lLKU/L95vsHLFAws5GMHprJSVd2VATGNU1KxGwr/OD2udlf2NIXgL4i7XXR0okSG1n69b8yWLY2pfALq4XitbFQ0ChGcMJthKAH2Y68lJ0qaaM6hexfPaYIwoH1YQ2OaonJ4AbIM+mqUlZp4qeMOhdr/aBiUJsULwsoT5pjN6LBut4eiKC1L0QwzvcmnwnotQ4sFo8gSxd+7dNol6lZPPv95yy0r5yZ0xucun/Ofz/dk0vzEsKxGGXYPt1tYAcUaKgWUoiBXT/FhysDU4jlNWzK1kM+MqoZKalhVZWiqaNrxUu5qNhQCpI8Gfwi+rqwg1PZESRF9I9/sbk/iyOmIIAUENNQNKpl1Uvd/XkAV7rIdqPKyIpSfKBvphW8XG2hJjiUT49zls2kqSJF1lJhhCijWYARepyK1haFpJmTtLXe9Wa245jq2BnlpnUZIuK+16vXm4VKOErYqVRyglqgndNWJc6K816icIhyFDLd8AYoEtqAuTbrIGVFhlVEWmELp9E3+DFPkVAW5QKJO1VBVLarI3vKqmvjbiAQTZ/cpBS6395U1qDFj9hC0hO/uGWRYVrFSw5fvwY6BBTPqQPCEe2z0Yasz+Iut/C59tafn3OUlUta9zycAoSTQQbF7v4bC4O8kRO1pzfKcKmwCh+ZURfqex0wFzYho+njknd0nTC2ePz1MtlaoyG7Z2ym9yS1jmTM/kW1Y7FM96HHwADC64LZ7/s/I7Q1MfPQXQaA79x82S6kRkdRz1YnIsA06qvjfiiKdvFUyRQUW8niHSnfXoK4B5BQqRv+zUFUfv+9ReZs+dXnlva/54FgIZvu2jcsnj3cQCcQSIUEMKIZqE/4GSa4uMTOJrzTRCjeoYom3r7Xv6DmNPodX2Ak3FOgPCWch87bYaLEEeTNRhmCJKekZOZkUzVCee+vrOVODWnsgd+GIau7aFI1QAUJF9szkoDYggNEXKt54I+sLtWPpRKsWCHpY1Hyrwt/QU6Ge8s+OiuoWWDx30pbdHZPrSociR6VykRxS1Sg5TH3ixxpTQ8Oan19uD7wDLEOHtgFdv/Vg/3B42yGeIs4Lvnfh6tdQ64x0CaiXEzTBBiYAoKmhCoKBQQty2SKLEZdvhgPlDJUVQTHeGnpvIOlahhDsE5BnAkSKPmHTtbzUBP3mJ2ZETO8PlyRIZaFoSnzRYjlharwDCHccUGZ0ZcD83O30rdEWGlESLgTg8KCVQoUJUK6n9ZXG7kEbBNDIcvsPoyifOLCM6mF8bWTlH4Y8cHTeiXOiZImiDs4hncwXz52k1gSkLCeHVBUEf6xRNDSexXvfqDIrvmfAvGHbwXhOI5NJoBEt0FWi3uELGq+/mquNqNRdkHdFCAMFRBlFpT9O6T2VOwSaGqqwGxRJIYRSl2CNwg0a/Hk01FcKcAA7T8V8Q6TiUr2gRDzxWRWlU90ySjnVZcawRmj8i/fnHvkxeP33KnpCt9uCWrViPQUAHB6w4p9UXAi//7CZoale0d4MByWFpKQyvigrB3kf+8AhAJAW8hMQFTNVVVTVvpA698fve7SmqlQelI8pnfIiTjlejn91zdepZKHc/+SHIN0CesqqS3AcCeuddFnzPKwvL+geduw+2LtoVkM8QRuKCrFn/Ut13OEcKzKML5Pr7BNmyavueZQOAP19K4rkiVDRkwFUyWRSXTS/aLF80WK5/rSQsdoytu5EM7XbPWKtS3TR9DMtQfVMlFA9FYu/4IA2Xo8kqmWKdj+OG0WKStVTVOzx4hOEdBiqkVQVAID9JnpZDlNfuC4h+Ncxpmj2DYzgG1Nks+DrgpuAPfP6OjQBIQLj6ACNaIFGKu6MM3m1kOXkl/aqEwP2LFoGCqCuzNAz4sTDoKXtoalU4aNPOKHKL844ESrEFI1Vf501xibmbx8iiZysldebQplixpBOzOW3PCPd3rA8mdNIqDApjJ4yCYWknPg7oYxKyZrUKAaLFjKcPCoFIMi5CFKuq2oofZHVM07pxBi0tOwTZx598D67OyCadN1SkJUECaiXExgWnHtcM+92+C9rqqmxur/f5/sXx0GHN0EL/aED8fIP7KKwZ337+L8YkZyqQclPwX7Sb9v6F89qCHKeUpFNUX+yi5fRa3jkyV3z4ednnLY8G0QzlHZPmIlGSaCK9zMdCBC29Y3JP87JNUW8O6iokdGjNwHv0zloLyktxrqJo1K+hl7+qBRhHFW1pqYiOfVUSKeknhSjDbRGFLyFOtkP8jAaeZicpG7eFW+FqAoWqMPpbne6pcsaX81YwsQmIJQkeVgxJWsBX9zydh51ZT7LIlQh8e/MzgM9UiBeOpQ8juQzP0NAbXdlf/p6TgCUl6oIL+Aq7PjfjsyLZraRtBGaTEgKopAU749H+fRUNscY/QQa0L1SYIqigCIwRcikU1U7EMjz71NDPbF0YjbsRmHSZXNRYh+kfV9xSghksikkFevp/Grw6ZfbVxwXfhV/uG/k0037kpvfHg9Bxz1l1SVr33oZmUL+zBUcY3W6fNZliU6QFtmSYmKwSYi18uJLrwQAfPjGS5PLC7E9G1ipUcikHfCbtKGBeAVy87OqWD9gcTVXGyN1AJRCWEG5/aKkVkSW0TwTzaw1P2OGpLCeymqRKLl0Rg5MEcbDUA0XTa6Jqp5RpBNz4Snz/v3xLrmMSkoql9Gwerppyy5son76pTizG4A7777f5eFe+2DzB19sKywwVFWFTslWAer5x8OPXV771svSap0C1ICYyaTo6CGJJlZMBR++8ZJ0e3JNQL9YfeHg4HC7GM8pKS3WaZhClsPfFrsTtU3CyfyKvhUUAJ1dA/g0Ck2mQoMmiu6GzaaqKdbiINIZpy3PM9FUXUDlRuhDV85YtxVNsnt9e6CaS7A76MLwMdPUkUL8gxbUFyH+wBQh3cwv8H00er0yCTeKesaUzoaGoHKSVz/aib/dkoxK1NdW9HQncIXLV/2vbOjNnIBKYJsUX8p4C0tTF18WRjGjCzHOHMRK2j1ow1I4VQy97T/sWwtUl+i1LCNVQ606cTZ2F/g8Yn4BLTCaCg1iu7qoKExRSUPzO/KgioDe01NXX2mcOqn8of/41gqTp0zdtnuHYEeLibDSiXMA5IEswsQR0JoI6hnJ3SmnoSG8y+iVD3fi73g4GQ2Ykz3dga6gmdfT2AKKefHZp390RUrzMLAQgwgmLYRAw1AVRXrpgwltXdHZNcAwVO+Is63fdvQcNAsk5pNCCOSNo4iAxsnKzdMbGuu6DvdACAcHB48//vgP3nu3pK42evoUUc8JIqAohymOMXl7D3VjBSwvRk3j5WhZKop0ynn1o12RrdEwq/KE9DR1SY1XQFVEYdJ+99RATHz75k1YQHGFn0IhO7sGWJbuGXa09duOmt3AMsqfskgUMlzHIDKdJlUV7O9EPjVihEbn5pHTAcU4HehN+8HRyK/C8wLLO7e2WKS1vDztlEjnhBLQ6lKfIFqcvDStRI7BVEQBsMcvoApC9VQ+eCJsls+rH+6SjqRQ0rAymqikurzKEvuX1sVV+Jeu4FSU+p/5i5dE2ZPnOLyED2tfShtjJqTK06qkzMGWHouGpbxcVrRizWY8Hu5Hx/ii4V/v9F1JHC+8tcvnyycL9gmrnhBAeTt9pyvQSz+w0T+qRPA3DJIzLHbalygvNigGTwTpqcj3T52HLMSPdtFUULBempNWXxNRRuvq/O0bqDB6yvGQC1GTwXDzAjJtgSZXNLl109fyiJMsyxrFmQb6hzoH7VL0SfHYkMyqUflcOexMnQhJMKm7QTdOOWHDtsBQXw8n4IYvkoASJiDzC1C9WZw715QWtPX78rJjhisSsE8B6Biwfb3TlycQZl0fWUlDcbi5vj40u1BO37CdoakPdysHxGbCAlWl0lyWRBXIssYZqS6Pz2EhJTJJEXwRSopH4T5AuFkeSbpOFKyeXk7AP0e5M4+HkEYSmmbYF7cRl4R9OumUeTYX9976fQprVBo9GY+Mpq6eq85YoYKAqtvZaNGSY1CO/ZZNEAYraXB+dZke0jSvEM1AZhWYEJZm+vByAuo14G/lgX+uIjXiIkwQdjmU7ZMXFMqGO0dGgHBvOwrHz59aEU85uLzcMYqeHjW9nAJg08HhJGR0zOEdGhxUbOwbtkcarBCJZARU9V5wURKtUZcKCLoG0VqA46HGHzg60K1M40eiSXKtVQJLJR4aKohLsLd2Ze9ocsJ4scNeHqekHj+3dv3u3p0tvqw4LKaKfSRtjaJhoXq6ZLrvHDxuFNvU6nQxZfS597Z9Z051WNsTJAibPaIZNrldrqQMGvGB3mIcvocQrdNJbV86uKOs84HhJoqivtlzuAcqLzUCIaakTmf7pY1lRiRqR82s0hiMYw7PngNdaJUpE1Osp1Gsv0h2q0JPK4oNWEYltDqdXEahqJ4nhFNP6Qk4cbi6CkGk8RLNKCw0Dg9a3BBAskJPdx3nRV/Q3UQ6CckyXRRQKBNQAICpuJjjIS8IXh56ON7LCW4vj/VUQah9Gh6I2gpH0t2KkHjUur39x88Or56oSQ0AZSbdgMXZDlGCauu+b6M886ozVigFNAOKicnjSsp8qoJPX1eRNEFS+rNZQItKlI2BBAgFAbo5nuOQnrq9ws79gdyPhMUUE9wvTm66yvUURlBPVFZvjjZBVlJVLKAsEc08IE0NRNLamUldiHRmJ1TUwbM0RdEMxTI01EEINRCCqqUzBAF6ecHtFRwu7+4Ii/0YTykBxQwq/5YRa9A0+TKTvldUT3w/Vs+YTJk1M3OJ9MTSzPXeS9mvoUQ68wAKp3hQgKEZCKAOMAU6UFKorT5mBscLnH+9v31fZ2J6SkWzT0fGUE93SgxB19VWuGwoUt1vdgIQzQKVsI32G0ur1RfQ/BPNf//PDdLtC29/HGQTyz6vJRpKCknzCUpUOJpG4sYwlJZFtwDQ8AJcsWQ61lO3F+nprm8PK/Q09mKfCkojl9QzcH90m9nPlGJfFQ/157uuACmTf6Kp0M1QskdJM9MANMvtUEK2Md0fhS/1R5BCfaAJ5O3Lb6I74JL50wQBfrZ5v1fUU44XQoNRYfRU1koYTVeDQFJPbIFGd4AqBDQlCzQvRTOmbobuNu5KmgEjNCfW8oTsYboshyk1U1RmK0oySoFBM/Jm0jR10jGzpP2rSgxeTkAmqjvI1/nNft+3Y/4UZUKoQj2TIDEBzVfRjF83s1NJiYYSspyiBM1PP6gaLmA0iuvrkTFX54ByvO7mXS06DWMdCzN296gj0A//1kPKqk1WIx8MlAy20f7YAkpEMx4+Wb/lJ+csfeKtjSCvIXYoIeNQcg11uLnDQ3Y+NK8egqGRaGOu500qFUdiRmNflwUYS+J3gEa0QPNYNFMxNkP5ZP0WkB1kxgglq3jCeECJpif0eIX2fqvUUUhOdPXcemhg3qSgrs+8ABUz1AUIrcZJiZ4ZO0FEM626yQti08N8b7VB1JMwjnAcbO0bG3Oo31DRn8DkSuIrzBLdTE43sWhCCGka9c0D+W6EEvUkJADlC/hIIfgUESDsGrKZbb6ONjdeeKy6H4fZ7ukadoD4Kp5aLby0is9QR/rcNTYVugkhKg6jaQqNKPel/wbIVzcoUU91If38E2XQ4hoUp6glQej6XRFBcnv5ln6blxOSUMO8EtC06ib2QCN7U5wOQiAkh3yWVF4yXTOgUgjex4jN3TlglQJHdnE0bDwYTUa3N4zDVOEAPdRn83gFtmY6mJgCmj7dFCBei0AKUIl2Ws2bVTwxP9Vigozho+Kq5YkXl4c7PGDDQ9ETUs/iYpOGpT/f1hrpe1tZLs5Ub+1Lxa+awwKaPt3EA9QYLJkUoPM+PERIP6RmPwm8vNDaZ3W4uejSueazrfI/CwoLdVqGpSleiNjpTqJPzMmfQAKaVt1ELk4xLhT/zGQCIX7y2PBUHQHCwwM2i92jUM87rjg5yqOKiozSuEl5uqg8CfTbrrE5zagqqXPAhrfHTBGNFEdiJ6Bohvo38TtIo8AQYMjkH0IamIDSWZpaCH7A7Bwcc8W/Zi8wFhboWHk3EDQVUUyhl+8mV9UBf0P75Byg2S6gGdBNTGg8nUAgjCMjVlSvabUGjZKMhMBoS4w6nca/avRr6PodvunHCrD52TNsD1v6mfMCml7dFJtU44HHRDMJBHWZoUYI3uXhDnQO2pzI9RkP1aUGXMcSaOBEgZiTjXpHkhy8nKUCmlbdxCF1ACBFpTfvPV9TQQmEzODh+G87Bm2u8OpZUV4aqYUoviFpKB9LQVVJFWDzTDQj+TcpivJPjM9zMlMUTyCkg/4h8/CYe2jMHUnebBwdZxO8SA5QKYIkwVRPS+JUcRyJzUvdlKsn8W8SCNkPL8DhEYvd5e23OKPExG85b2EcB0Maum57eAcoJmxHkiRg80w3fSVDor1JXJwEQk6E4AeGzLiksmfEyftz5lMhSlbSMbOqbS6uc9Du8cbrYx1nAc2Abvr6IYnkRMkQgTARIkgxgQAODqFOdJwAu4YdUQzDwsKCsNu37G6V/zk2NlZUVBSmYaifcpO23KSdVFnQZ3ZtbR21uzgTyEoBzYy9icGtPQgEQlZRFDkEL0kn/iIPmF12seIoinRq9NG0bmjMuWn34ePn1oyNjfF8mAgSL8CpdcXSvHirwwsh1GloT7+vk6fb63uUqSGutNBWC89mv2iGiacLSDlxC04imwRCziFAODTsV08AhqyuYasrbOBIbni6uDAKW11u6h+2jlhdbX2BuUZ4FRrahMnF0/XiHCS7yzt62E1rC3hXINVUyiTFkhqPnrI5pJvSUp2iKVJoSSBkW6X/7257MH6Pp4TdxQ1YXKFey0hrdjmPvLL+guUzq8tN1eWmWZMr8caDbd2o9GvlUVt3BTWJHxjz1leD7l40BrmiolQ0w4BGXyjfxyvTUw0NvLH8sWyW6yZ+W3lBoFGhJRriDLIekgpKmFA8F3eDPo4XRkbH5FtcHr5r2B4aOAqrnlII3u3hXV7eZNBcsHxm6G7Tm+vR/yBcNHcKuiHTjHe+2AcAOHJ61dDQ6KRSdmDIotHp5Y+V66nb7jNprd0HTfXTVRPQzOom8m6S1h4JQVJBCeNLaaEupuGJF5Q9ow6Pf6Ucv+Hp8nBf7Opp6xs7f2ld7LORifPK7/jGIL+9bt+kmmKGoZxOVA5vMBhAUrDjK5ph1+nItIaAIi2RCITcMT9/F3n9DiEc9Hs8JQQI+0adinrNKOrJswGNc3t5uyul4UinHztzw94+WuCxgZq0jLJZpJti5gHyb06AAW0EwgQJwQ+EGJ6YYat72OZOyPDEQAgtDo/Lk2S+aFuHr05vUjHoHi4woGmfqCjexSdjjbLZoJvidCGSv0kg5BWcIIyMBHk8JaxO74DFF3aPXzoxEAKHi/NwfHLl7FaHr4MyhKDSANsH7FL5fKGWUciohooRRwoSUKKbBAIhuT6nMwJT2hAuDrgiqKcYOHLgwFH86mlkfa5SAUK7y4uGfLiTaUZH05QgQAGCoTH3kNllQBLoW/DaZWn8NpuVYVhaiFGtxKZPNIm9SSBMTFyRZUcMHDm9nJCo4XnV2YvwDUGADjcnwFjt6iJAiSnkZrun3+zigguWDGI7ZgkHh6zc6IF4NqPrdNHFSdybBEIeF3GWFGojCagAYfeIw+b0Jqqe8ggSxwtOJKBJnmp5eVnviKOlx3fCNBtkOcuPamBja5WaAkp0M3tSQUkmEyELGbV5vJAtLExJdjyc4PJwAIKwSaAxGba6e0YcpeVlcskcHRkJq6cYKq0CGqYfkv8pUQ4nMTgJBAIAFofHEoi6J4ZBmuMOgcfLh532HidtfVZO3mtZnP9RWubXUwDsDl+neo/Lxfj1NFLPUFY13RSl06ebZFoGgUCQYfPSI85oXeai4/R7IyEATg/vSX4Fj3IDlJuwbEEwOmbXypbtWr2eD3hBgToCGrZvMW7BSXQzq1j2eS0AgHSnJ2QSCOGozd1cUySOXEcGZ02JzuHh9x0Ytrm4smJjcoeVijghhE435w3XbClF7A6HXD1RXbzHS9O0OgIaMl8INfXA1fgUoEgLziyXUaKkhAwA/RZmW58vgam5pkiA4ECPFc84GrH4Eo8SUlJ5BAlC6HB7xRQoVWYaIUYtQYZnKFFmfrDJ9d/EkIh6fiup9BACIQozNAORhnC09Y2N2r39ZpciEiMpacJiKkCby4tMt6SSQEOx25WGZ/zmZ0QBDV2no1PnoVhniW6TWsuMseTIo+R/btr2TQaW9kQ6CfFzwFsFADhCOxDaB97p4YfGXBBCRhqWCZSqlJBZyvHQ4eKiDE1K3fBEKVYQmD3K4v3YAhqxPl0stWQYElAfT+mUb1RLRkOVlEgnITn2e5CMYiXFpqKHE3pGnahkSAYDhMSVNKA8Xo53uDmYNsPTl6Aat9SxUeJCYhIS6fo+DqmgYaUz7A7qKilRT4KKSmqwtHs4QYpxK5CUNFRMIyopRCYtmpuUbBJoDMPTj3nUEufQY1auntje9MfTSQrnOHD0wsUJve9qGaTE9iSojrN4MiuOIOL6DwbZdJHFNFRJi8urbjlvQeCYbs4bK7UoOjHVExOPA9RngWJ/AjY5iXdzvLCP8cceszC5Xy0VDVICQXXYal8VOVLS0AUyjKakeimFHrcRcXujTNxMgkSLShWwvilDaIo6cXGOD1Yzag27YP4Ren0Co7RD6WhrrS4p6zf7itIIhGxXUkw441RSUpfM3uQF6HLzPE6fTJt0ytfvMWFzYspQfksnAKCpqa6iXDlBMH462oJGYxMIOamkIJpxioo4Od7N8T5PY0YMz+gO0PTOhSfEI50AgOJi0/Spk1KUTp8HhkDITSUFoWIaoqc8Dy12t5vjgTswwTgd0hmnA5QI6DhLJ2p5rdUsnH9E6lYnUU9C/pmlxrIa+Q4ejre7UB+m7GFiWaBPv/pBJp9OA0Bbb8Aj6ZUNB6iobkT/o6gj589iWTY59YzH6uxqbQNZQMOU5gw/40R74VnyeoEaLzlglnqtUggeAuBwc25PjBbxKRJ/AtNEFNDxQi6dcmZObzaZAnOoEzA8xY6BxOokTBygOMkDh+CTTgJVHSKg4yOdAIDqqoqG+uok1+zE20mYABj9c5BwI3rURUSlELwqDlAioONGQYFh9qypqgfZSQ4TIZ+wcQEtEwTo5QQuDY3sUoFYoOMARVEL58+K54eOSCeBgIHiOM90dAJN2gFKBHR8KDIZDQZd3PlJIHqgiFidhLxEL4sgAQi8nDDm8KR9AZ8gxALNNAUFep0uzOCqSKmdRD0JBIBD8GgUEkwlCVRdBygR0Eyj0bDGyHm8gQV7HEF2YngSJg4ChL4IUpZZoInJLSFRZjSVB95rmi4uMqUeZO83jxD1JEwoIIS8IHhUbiQSkTgdoMQCzRwUBYqLjGGHRxF3J4EQHTSM082jcfApJ4GOmP3NRkuMSfcQkSA+0AxhLCzQaNjo0kncnQQCRu8NcnTyPBxzeNS1PyUllcQ0UQcoEdAModdpDQa9fAuRTgIhCkaNcNXKI6U/PRzPqSSfDIVnCfvgILo5YrYl580kFmja0WhYkymwWHDY7fJ7idVJIIRi88oEDQJBTGNSMYseBuup+E1MRkKJgKad0pKisNIZHRImIhAkvBxvdXpQJ1C31Wp3SttNhYGR8XHCigEirv+QYjvWUSqRCBIR0CDefGt1zPdr1TkXSLePPW7pdT+5dvqMaQMDA/96+dXV/36d55XTWsxOmM3S+Z/VLzTU10Xf55nn//XYX59SbLzwgnMefeiB2qbZIDf56U+uvOqKH0bfp7evf+WqS/BtrUZz043XXHPlpVqt9qV/vfbA/z5itztATtHYUPfmay/E3G3x0pPxDZZlbvjJVddcdZnJZFz9xtu/f/DhoaFhMB5AADxeZH6GRmCTFlMso5hQMU3gOEk/Mv+oiyUlEjRNb9y8Ad8eHTXX1tbe9suf3/bLn68889yB/oGkpTPzhmddbU1TU0P0fUpKxMFgMmqqq/76l//lUpvtNb6UFBfFfOE041vTlZYUH9i7CU00czohBD+59oqfXHvFkuNOa2vvALkDw7IxX7JERUX5vp1f4pfsdLkv/eFFl/7wol/91/1PP/MSyDgUBerKC4+dXfvJtsNHzY04uGFcxJR64sFfgglDQv1AN235Cs1rW3xs6F1rP//IaDR+/NEnd/3q13jLyrPPuvtedPuYo44bdfg8NVHUc+68eZmRzoTaRL7x7+ePX3bMj6+5+Z13I75Rs46Y/sXad9ClxvHxW6BZ3g/0e+ef/bfH/vjp5+sv+sFVofd2HNpeUGC44KIrvliPLokF8+d8/P7rgiBUN8zK3X6ghYUF7Qe3RfoQu9t3abXac87/0VcbNyPxZZjO1h1ajWbm3KUjI6Ppfsl6MQQvH8bpA4J9LZ0JHSpUSV/aomYhE0mkTxiNRmM0GiGEknoCAN55+932tnYAQO0k9GvmsNvjtz1zJTGeZZm/PPzAF2vfEYTs6oiTVo5denRBgWHNB59g9QQA7Ni55/cPPkzT9OWXXgzykRnTp2q12h0792D1FLOI+Lt+/VsAwK03XZeZc7glVD1FD+WsaU0JHcdqd0r/QBogApowOp1udNTc0RH0S2h2wo/WrhMXxdX5J52Y3s69F1903tDQcDyWV95wy43XAgAeeuRv8o3PPvcyAOD6634M8pEDB1uu++kvrr/xNvnG9nZ0wdfWJtDBNmkK0+NZTIeMEh9owthsttNPOUO+Bbs7TznpBHSddRyO8zg5JJ0Yq9X2/Uuu3rxlG5hIHHXUQgDAtu275BtHzahqZeqUySBPef1N5KWR8/Nbr0dOnjffzcCz29M2tuOt/SofmghoSkiRIp1O19yMJmvu3Lkn5qPcXk/OqScAYMrMxWDiUVzky0KbmJhMRpPRuPbDN8rLyw4cbHnv/Y/H+4yyCyKgSSIPstM0fWAfitJeejn6lY6C2+tJ9gkJhHFg744Nej0qomtr71h57g8yX8SpYN+hxIJI6Yb4QBPG7IRmJ5TCRDRNtx3axjD0K//3xqefrY/0KLfXQ9STkHMcMe/YyrqZl1x23eRJTQf2brr26svS/YxGVpCPQspyiIAmL51YPTvbdjAM/fQ/X7ztl3eHfRSRTkLugusFPvr4s5pGlO30u/v/K93PaOPoq84OVMFnswOUCGhi0qlI7ZwxfWpn2w4AwJVX33zPfQ+GfSCxOnMdt9s93qeQFQiCsEP0759wfJjM6AkLsUATkE65eq4867S1H7+JSt+WnPzhR5+GPpAYnvnB9h27AQD1dbXyjVoNGsrS29cP8pGZM6bdd/cdZWXKmp9Rsxln4I/TeWUjREBjE5oVP3PmtCf++idkhM5a0u+v3ZQg0plP/OvV1wEAl1/6ffnG008/CQDwwYdrQT5yz69/+dOfXHnJxYG2D5hlxx4DAJCy6wlEQCOCrc6w5ZgMQ3/y4RtIRmcf43AE5eUS6cw/XvrXawCAn91yvWR5MQzz1BMPAwBuv/M+kI9c+uOfYhktKAjUQf75f3+j0bADA0Nmy9h4heCzzQFK0pjCELMJyC03/wTf+Hbv16H33nHXff98FpWpEPKG7//w6ldfeqrtwNa29k6O46ZNbaZp+he33511E85Uguf5O//rN3/43X93HNre3t45MmqeMX2q0VgIADjy6BPHp4gzWyFL+GhB9rDcenOGyoEJWcLaT9ctXnqy0+ma0jxpxvSpHM8vP/mc5198FeQvTz3z4uKlJzsczsmTmxYdOd9oLPzLX5+qaZzt8XrH+9SyC5JIH9HqbJikbJiEaWpekK9B9vMujDfLr7IupcFe2cZrr7/92utvR9mhs7Nr0jRU05k32O2O6B/iuLxkowbmUBY9EdBovs7o5Jl0EgiEJJi4FiiRTgIhq9B7rbbAtLcciCBNUAFNWjqJ4UkgECaugBLpJBCymVtyKgQ/gQR062GfDU+sTgIha7FYbPhGcXFgEng2w04c9exoayWRIgJhoinpW2lzgOa/gErSmcRjSZydQMgwz65tQWPHGLq5tui4GeVyJc1O2AkknXGXjRDpJBAyjF5WxMmyVHGBlmK1pgItRfk0tGcwG4c4sHns7lQannEkSBDpJBDGC5amOAFSADA0TVMUw1CU+J0tLjZmYQp9HgpoqHRCCH0fQiyIdBII48vZS5s7BqwDZgcvwGKjVsOoUGieVgdo/ghoGKsTIpMzHvUk0kkgZANN1abGKhPHCzan18vxcZo+4wubt1ZnfG8+UU8CIXugKKBh6VKTDuQI7ISyOufOC/QHycXBwknQMKUZTEgm2gvP6derT1sb0HTD5k2YKH535wSRTgIhh7jipKk42q5iCn26HaA52Q9Uyk+Sr9lF4z+2evabR4h6EgjZhpEVcAYoTvzE/0AuwOZuaqcom8jqJIYngZDT2DhkyT27toWhqfrywpPmVedKWSebu+5O0eIka3YCIX9gaMpUoPVAprxIT9MU1tCsTQLNAQENG2SPb72OIAt2AiH7MbKCwGicHp6iAE1ROg1Di99wbHsmV4OUAQdoVgtoOOlEVidZsBMI+YTea738nAUWm6djwHp40KrX+tQTk83mZ5YKaKRazPjzaonhSSDkECxDlxfry4v085rL7S4vw+RACn02CmiE/CQinQTCBIBCWfQlxpzJos8iAY3YASRuw5NYnQQCYSIKaGjrOWJ1EggTB4vaGUuZiSCNv4CGk06c2hnXw4nVSSDkNHqxiPPZtS1XnDQ1V3I/s0JAIzUBib8FC1FPAiFveP7TlvIi/dJZNeUFTPY3oh9PAY0cZCfSSSBMLIys4OBpAaL0T5NBq2UZo7EQR+EtFlt2dqEfNwENm9pJ8pMIhAkMPHFBQ0uPpd/sQCt6LUPTPkOquNiYzSn0GRXQyAv2eI9AFuwEQl4yb3L5EY2lvSP27iGblxNATsGOz4IdpXYmkCtL1JNAyEuuOnsRGsPJ0k1VpvpyIy8IUl/KLK9BSruARuvaSQxPAmHCow/uo8wwFMOgCFIOwWY8K55EiggEgg+LxaZuxlImHaBpEdAU+yeRBTuBMKGw5FruZ7oElKR2EgiEhMCN6HM0i141ASWRIgKBkCiUbw4keH1j5+LpVbOayhx2Rw5l0asgoCRSRCAQkkDvtc5rLu8fdZht7kK9pkCnkdueyWlohh2gKQloxIHsJFJEIBBAbE6YX+/ycP2jjv5Rp0HHsLI2oNlfg4T5f53pcF7WIPuoAAAAAElFTkSuQmCC"}], 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "assistant": "S1 fires first while S0 remains clear; S0 then fires after S1 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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lsNYSx82reNpEwQ8kPCIirnObxcueHTFmfHhkVEmxrdp1G5h5zU0sY0JQ3zZ9xH4RmFhm0MJ0FtVGqLEWZXhoIMcXv5v+4DKhWEK3FeWyyaw34LuOXFWqzXvxxA8dm3T9AqJX9+8ecyWtRILobmdX1Kq0Y/u2P349b/qQBi4Ql5R/JnhuVVPjLFAlmSzu0qWLtR2mTZs+avRo+s+XV6+m7kyfMYN5ozb+9vsf1sbZv2+fiATOOzp4CAbOq6vtOcLjh2H9UKeusMOCNZ58+jmJhL2SvEGEBKzrrCTr/AcOtJXKHzlqFHTza9nDmiiK3kjN/XbTp4lXLkq9ZN9tZ3e9rUEfv2PCjgRenXNNW1uKYMLgHpPWvg9dBIkIaxsdRt9ijLdQs5tYLKJuMFlcLv9gzeuA2+g08BuUiATtYsLZ3mCYtXfXwpy/nl5SbnHOIyA9+UplrWZM/453cuRAp+7XrU1Oac1D93VJyioXI7qUXOMMFE4ZoR5M4yzQv//++5FHHnnhxZeeWvYk6w5vrVkTFBS06qUX//4bzsxduPBxaLL16qlSGZMeNKlp6SiKBoeEmE3Xofjk4w1Tp03b8u23Sxcv9vHxOXv2rJ1z3uUVMKcBCEIgFOpNs8kUbWLazpr7wMy586aOGwGapJ5CQv3WqmVbtv3z+Ref56VeunKnvjqPCVVIMHum8dfCDIMeFkgmpec/8eRTl65cFQpFr7/36YPz62OgKo2OIIjswvIkRvUfzf3zHiAIQq1W04+SbSuJ2xkFlqX7NK9+9id15+stk5h/UiDIX19tht6WXFJnxdD2GUHfpVpesrvQgd5iy2GBqA3zrzzy+7GX8O7JJoWkDIMxPOwU61Aik7qxVz+D2TmrGPugsr5XorjUxL3QhPRvYDTHodXp0tKzDAZz9wVFEX+xRorqb2eVl9eopw3qdOZ2IWbQJGdXlFSqpg7qdORabkWNOtCnfq4wR2YfJXioEdo4C/T9996FQjZhgr9//fxcmoiIyKCgIADAvn37AADdunUTCATV1dUVFRUqC9LT0mBC6bvvWV/o7t27ME46fETPXr0AAA89ON/OjiE4jmdlwpE//nwz6w7vrfsUVpueOAaaQVFhvlqtwjDB3AWP9+8WxbzJfLxlPt7Pv/gCiqI6na6gqJDaIvMxXtjM+9QBz5kFKwc6de7cpWtXe15dIpG8+j84sX3ObKtTEpoAQRDvrIWVOu3bx0a27WjymKmna+xsCRAziXGL1ZHiO8JT1ARj8qz+L1h/drV+MT6qVrTFIACRl1dkqZ7Uz6QY0XXrEJlfrqCcd3mNekz/jukFlRJEl5xdodLo0/ONKfvYbl1s3wBXSXOfYGjjBFStVm/dCiXv0uUrffv2ZT4UHx9/hrQT1637SEs2UV77DlTbjZ9BwbJk0sQJ8Fn9+wsE7Fbwrr//lkgkv2z7VafTjRvRiJaxy5ctJgDRqVOXNe+tQ0wzTus+3RQcHKrX6z9qfE0fZX7SPPEQNC0fW7ri6edfZW7vFu332br3E1a9jCLIisdh3LZBcnJyjh+Hgv7LNlj5ZJtx48efOgM/56yse8VFTYycWqMgP58KQK9a/6tKa7CtMsWl9f0KwkJYflC5rKTh4bAQzQTGuzt6NatnjHGWV1iIf3QwzMu3FERRUWmtgn1iRYSXYkCPthkFVUJCOySuXW5prRTmjuQ1Kt3wPrH5ZbAsNL2gikolrZ4Wa/uVWlhDExrTLdRdNLTRSaR333mnXdt2940d+9ffu/R6/ZXLlxEEGThoEBWP//fAgc112fP4eKh6333L/qlptVq1Wi2RSKbPmLGL9PfN+HjD+tlz5gQEBPyzG04AtRMq875g7rTtf+4dOGjo/v9OFRTk5+flRERGRUfDpjUGHH/8Ibt0zTbVVfKVTyz4dMu2SdPnTpo+Nyc7s6SoIDQ8MqYtPGsJQLzy3NKS4sJu0cbrMDAqFkUQvC5kWauERU40y5c/c/v2HRRFN3399cKFj1HHSRAENXHWkjt37syaZRYcIAiCOHOOJSlHUVVVNXeBMUSAE9C6ScnKstzt+dWr/tv/r0AgXPa/z/7bBoOh1nxemrCQAOYm1mHdA7gMB/yfoZ71bWtSsoz1Zy2AyJs96loORCm58mqFdubQzhdTijU6w6BeMZdTi71QfWqeHDVoMgqrho0ZO3/ejAt/svT0cXn3kASPWzqpKWVMS5cueWDe/ZkZGQKBYPCQIYMGD0YQpKAgf/78B5Yvf4ba54kn4Md07949yzQ0zRefw9UuP//iS9ZHs7ONPtSe339o7BFWV1VNGz/y8KEDBhyPjIwaMHBIdHQMTuAH/907fcLICipO2gzzkyIrM232+EH7du3U6/UxbWP7Dxoe0zZWp9ft/+f3WWMHpNyGtas08kL4djAU7RbtR9/oRw0Gw7x5UNYHDRocGMiey9ZqtYWFBfv375syZZKFejoMg8HwQsIqAECP/iPlVVYmmCKgiGF+1nn0HGlKZwszj8T2WwsPCUAAOH+j5XTTHsqrVfNHd0rOgUHPB0d1onSzX7c2hRXKGSO7P/vmusN7d2MiKTeN0EaRdnYH4DyIL9k2grNrIjWqWbKj5h1Z9gPVKIxxJW8vu2ZPFpVAjcYaWSWDkxJUoXFWbVl0bHv7d+7lVSYVYeFsk76KSurVM7zORsvOKyYAqFTqcwiXlZ03CF6S0bsd/N1qHxPOugP91uj3de7GvehgL4IANxQWwdMWZ2AgzMt7iRr4GUj45Kf1Ly6k/9To4Xl1voy9VU1cO1hX15Ksb4QRinQaZj4BhFN4zqqcxaUVzltUUizzozS0VmlSZWlDT2GBUV23aWDdDK/fv86AE+GqDuTlfSePW1PL5z4NW0bRbHr78/BQUmXIA88vV0YFtWSssOlIRYIG35pZsIIL6qktSAWBEfAexnDwDSwGClM9j90sRlEwpKtz7aRW68gLPGetDifUzDcIU09pMaXMT1gLrddrdLhMKgJ1nqNUYrWiUKGElV4KlY5e6ILp43NNTGHc9q3n/vr6F2bokGr6C9ibHHAKwoZ6kjMC6kKfHCu0P5NUOLxnhFKlLpLDmFJspD8tplERFmkxkhkBwXtPwVl8Q4JrrBmhXCbt7A4uG6HcXRPJJc67DcQyP9sXY61SQ93o+nYfb6/gAG+pRKRQaRUqaCmo1FqVWmtS/G6aT0YQRCpiaSzfNriJlf+OwlJlTDZyTGisIRKJkEa9Ly6BMepVRtruI8XATyYiCHA+hb0jX6yfaxQgwa6MPML9YCinLVBXqefXX36qJFuB2IXpFck69Sg4wFj4WSavlUlFao3R7ZLWTXGhsvMKlc7G5MK2wdLsMvP5CM4DAcC8ksn90bEVV9qmZWuY7DVC6Z/gEfHGit3TV016SLKapZwyQhMaduTpObeg9QqokJyO2VjSzjSiCuzQ8QvAweDAT9ploXl3j5KMhps83U2D5zFuwLt0Mi71RWIyeef0tQyj/1UHNYE9o6AqMNBfKsJyC8pWPGbs3QcAyK0zK74/zd5AiDsIMWSgv/xSZcOLVrU8+qI0sibEvFe3Dc5dhxkkYV1LUBeiLUgViiUyL2l1NewBlpaZ1ynWZM1BWklpMY2N9KcM0vv6tjmWmGvN/KyVF3kHsKfUXAriFo48Fy3Qxqqn2UJJziO0Q1/bGno3LR22SoOfq9VuaawN01AUofqNS0j/PTbcx1I9XUJRUSmViN/09ufL33rO7FFjDNRtOXIlq8iO98WFliNikQAwml2JBGi29RMshjzB9IwTbGRdRz73MUIJt9BQLgpooyBAC6mnPXTu1PHmrWSzU4D+l063sK6HTu/AxEw9l4yIbDEj9KYyOM7L5NXNNJRVPaOCvLjcVrk/Yx7nkSvG6UYNvi8uGNQ6PR7mJ1JoYAhCpdZa7XfAgPW64EI5gSfh9DpQd3PeG8a2EXrjVjJ92jZK2QkA5LWa4CB4rQpxTWZRzczxLN2eWtKLj/Mqk5CloCaFn3SG2oLs3GKJCMMw9FyZL+AeqoK0QR3hwbcjq8TqSz6tvCMEgLOkC3+91sWioy1IxQQCFBNIxcLq6pr4jkFNO8fMBJSZQXKhF7/eqhHK8ta4ZoQK3Fo9cRy33V/ZJRCw0hALDzOWoNdZoAy7jGGOFpcZG1WoNdoAb3G5vDoowFeHimPDYUkQdNxcjZ3qaeywCdwDe9STO1AnuVRcXyAmEQngdP660DqjkIyi/i+64YumLnXJNRIaUxbKNUfe9ddn09STwiXqyRoJNUT3Uk9YBVC0I7kKmOLYDyDpH4DDLDZi7JZGXpJ+4cjElUhkdyLrKv7fRqC01eZHNf0NtdTP98BahGxyzEQWGjVwxdq0/dvzLsJViGkwkbjHA0/HDJ9UV2StufDZK/LMO2ZPH7P2ewLHT6yxetaOWQtbxkhRA2nfIAA3GKpLxblXiJSTQF1rQ2tUWkORvBaIuGiBYnVL1FtTz+7Rspn9g4wmHQGO3JKfu2FLSE+/HDtyXabtgMXOJ0y6+RkIUFKtP56qOHqnVqE1KQl4eJD/9DifZdsKqlQsxQ+/rx6KosjCjdeoblJUMxQURfrHeo/rFUB9B1o9sfNsSV65hu4+WFwqf31+V43O8PSXl6OCvCaNGXBjXyb1UPtg0fuzw0QY8t6eQrnCwL1UEuIWGsohAW0ULnHeraF4dAsQk8UuWhVi0AORTHbfImTsIuKHZaCygDYPkJGLkAFkH5OqYqTDQOzpX+U714JS2C6aDo8SBkO5vDrr9PjB004R3iG4LIBcbMM8DIoKBLLQKIGXSUK5w/j7u81dSlWhKorzBFKZxC9w2Muf6jXqwwkPGhhxM1loFGFzPRJZaBRZJEytSgK3CELbIp0GIPc9Tdy7Sux6y8Zp3qILezQDM/X832xj1YRaC9N5UhE6Li5gbM8RL3yXCNiaYn/7WJQ93nN0oPm8grZBwgHtpS9PCj6cXPv23vryTD8pGh1ozEFa0iZEShAApuBDjT54x3DpA0OMXo68Vi/AEB8p9tioMI0O/3hvHlHXLivET6zWGqjZqDQ9IyWbH4UzOFdtz5MrXF+sluC2c5MEbuq8G3Acc5HzXpZvUnCnGfkkEHsJb+wTXf6N3piqEEc++w26aAv+6XT4NwKAbyhUz4o8/IenyF0QdPkO//lvqK4bBTS/XBkZ5EUL0oV9I2ec19k/qSd23BxKPY+99riyvL7NXbc5SzpMmDf58937np7c2PWhsl6EcxkkEjjDyk+KoSHtJU//BNrF41Nf0eyEDUnNcIvZnFSdAzSWa+r7vS+f1hEQYP/lwgsp9Y1m+sT6zxka9fGSvsPWmfcT2bakTbvgRsy4Gv6R0e6j6BQm+uHx6PHdvbV64oN/WRqKM9EXwf62CPRkjF+fVCSICBBB9STA5iMFFbV1TacJ8MiIsJgQ8cKRIR/9BTvqUr9/egOsM540xjgzpVuEmFLPqZ9nV6kMdCTUtUZogomGNvDTxB0jFHVT591V6kmBSuoLQfSdYWd7pnpCKyYzUZlyAZ7vwcbVftDpUHHwX1bU7UIUvgMrPQMegy3jwkL8h/RuhwAQ4C2GsyGpQSLtnX2ECoTd74drBOx7ejJTPeEc0L+/TzsAP95xHzSx5Eil1qjUmiK5suDu7cwXBxAEgXYaVqqTFMmV9K2kUlVSqaLUs0AArVcOIoqEnYdUWgP9juhbmL+YIIjd53OZ7+i/a4WFcjVqDL4Y6REpPvNybLtgYWZp00OKacXakeugpE6N8xEJGhALDEW8vWWUdtL++6JRUOk+2pNbr56k7Gw7XazWGCKDpBIhSrfjo3wCqj/T2jmx3z4Gv6BJn2Wxxgq4AdHgHhyZnsQJAW0UVOGnq17dzPy0QenOtRVbXwCVhca/w8g6Z4Oe7kxMGPSAAOIO/esjpCSRYYHtRhym7uvEdrnDXWfCpVMuf7WG1cZM3fMzQRASf/a50rZhDociMJpQvHk5/F2YmwD/rLsBN8H87Zgev+WWn4/ee3ZnEfNZWx6N0hqIiZ9m7b1ha1HrBsEJsOEQrBJ77r4GUvwYilB9Emg6hksBAsprdAaLFeuLSuXf/pe191KhWodT7fgMBuM+S6fHe4nA5G4oQYCxH9+rJSuiYL/9qvpQbK3cwS26nddxmSMI3M78xHEC5UCUDZX44Gp4CQlST+q7jFIs+kG67x201Oip9e7V/catZE1qBTC2gyMPWF6/uhHVdJIw6BGR5MKeYdTG0P5ZUUFeZWVyrYGAGrrtTao9h9kCN0tGRP4OpzLVEzsehlZLbl+2drTlKdeDu/X1b9u5Mtvo2dnDTSWL5iI3sqYCRNJ7/A0FXPTJvbjF9o4AAMlF2i7h4ncX9Vv5WyHTtLwBP2cT8Xry5/zkQsesb7znRvWqicGz+vpu+M/qXAltQSqKogh5xlNqKRUJhnSCObpfz5hPb6eSY2kFtWkFtXQ7PorUSlG/ispn7gvECXDgjrHBHYdB7NmJC468wL3U89DxCy5UT6b5+fPJOg27+P6c9Z0J/wjVzLfhnxqlIPOC6Po/xpQ0NZNHCOezEvICsy4VO31RjKhvHVJyZWrUxOMBPmJM7FUuN0bolI9+bdnWeFSdWcHERl4o//Lx4G59Y0ZOrfylEQLKCkF2s/cwlvyUf/iFdgEy7OfFcH5kpdLwX3LtT+cqLZ1cR6knZYQ2CIqi3t4yygIVkGd+eHhwgAxetrVqQ4NtBs/duIfOgmUAax/pPYwMJmn1LK+bWYVzMhLqBhrqTi68a513G/yd8PihVxcW372tMxBA7KXvdp9ywcaOn1wUhtYtd46QKqk3v/ZMikNJEg+NoX4fNFq9MFdpvxdvG1UFtFa8grjb6tjljP8065Hv8u6VQfPT3wt7oL/f/ufaHn6hnY+Ec9eIAEMa1YtPKMBGxcI/1DpCIgQ9I5AGO9W7kAS3cuQFbpU7cuWsTdvRT2VZ0bn1L1L3Azp0H5bwCQBY9Ct/ln4wg9RJaCy06dIJKE3WKPbTCxUWvesSD42JGXowMiyQ+rU42PbNha/nmO3ze4Zw1Jtb7D94r2BoUyjL6gKyPGxklWsf/T6Puj+so9dHc8OlIvTfle3McugtBuW/w/PEW0o1fKBaHap1uIRsSm+tFx81G/XcdWPxAIrCRPyMD68hQuk/q7p1DcXSS20ZItyrCeWuEYq6kfPOHeOz3n+nJtOZlmrKM5L3PTXp4hevw7Xklm8tKioFZFsHJWqyZDkAoEbA7nSH+cOwJyaAV0v/BefovLw1KKlFMas/h1ED74Oe2pFdoNlgIjIky0lXoMlY/iyfTVcO/yhzy0nYKfG3J02K4R0FZdvSH6RCA+8FemNm/js5BwnFUERb14uvuEoH1/p+0uraiH5eAmYvPgGKjn3v5sIpfWvVuqOwJgpM62EeCWOmklxOwrvfNGp/F2bkOeee2Gq5BDhnfgZ37TPt63/D+w61fKj09hUcIJgvlbWA10Y1ar72L0II9AhLNczlA6OhFx8JpaqkWtvgdPjbv8OVUNuOJmtO2Qjq1AtaFkXmlmwTaDMMrkedum8b8BSeHhV4enWsn5Slj/UvF+BUsagApzTZf3wo9LXpOtD0UhjhmdLTvFWSQqnqHg03XkuXU52xjtyCySLfTLi6tSXeusrlEyMfHGrscYEDUKM2yDD90cRcGabfeTLrUhp8U4dfrG9/Z4lr0/Gg8RraugS0CYWf3Gm5xDQ/K7PhD3rfJa9Y7oaJYP05rqg0tiUuz9XrdPQcO1JTEakBqxSzNGoEAFzdU99J5N8TV2w3E8k+sQ+WKM5bxmqE9lmUABCkNBmWATYTBEV7zocLr2Yc+h14Cruuw3zd7meY/VuN+HtBVS2tbsADaAISITJ/AOwFdTDJWA51PgOGvOf1Z1n44OGRMLW165zxVKlS6vV6PMBbJMChKWpGRClcDvbVrcavW4hhOE4sntYvNa9y6fT4e0XVOQpfnAC+EliFylkjtLG4yghF3cN5d6nDaCP6qVcpqvMyUUww6TMT7xhB0Imf/AUAKNy0jOqqie+DBfN/1A5+NP4qdTvsA12wpwovz1pa3wHPkogAqViIUV68tVZMBIGf2wDXIp6yaZ9vmw7Mh0a+/lX0oLGwWIAMKTSHNkMnTN20H66Q/s+PuJ7l0nVTiqr0lUqDUID8s7wu6UfiJ8WoLXM3O8ByZ3J/vN+RF+AKqWv3ltA5cZwAFbXwt5aqBNAWpOpIQXt+RufuZNs9DfU3Wa702i/wnOkivyzT1c+ngjPc806gKCiu1CnU+uhgr9xqVE86/hdToEWZVlBJAHDzXtn+ZLhxzfQwGyUtbmeEprlCQ1u6nV3T1NO15id79RKDcR/+KvFnKYdO2f1D+sHf6KZwyKy3QHtYNq/0VokNMB6KHf2RuMhiyu3+rjtcFSr7TYnB92DbN0OG/TV2WN9tF+vr/nwi2456c0vSb19lHd9TvzGq/ajXv7Js/6upqTyc8CBzy7TNB6292X1PTbK9w9192+56kP9Oc+SF9hLzBZMgL/1edPGeZSEZmBfvt3Jc0IiPGmgmcma1lXw3ATb8V7abNH6ZnFjV3phkJ1dyRRByBicBnt9yLTIylNkJJSbEa9WcTpYDVyr0X/9XQDXiGzmk7/y+AgRFx76T6IXpNUAohvF4oQjoNi/v3y4QUWgMozeYTFTlSI87mvWvwyl29tPCCSXXF9I3CHfU0xpHXnlY7BsQ9/BKn8i2AqlMr1KUpyfd+Km+zpwqCAXfPC/uNNBnygqJT6A8Sn4tdHvtE6WzLrKrJ7xUZLeFuBRHDPkXJv8MADq8XkB1SkXBlZOKEhODtCb/3r6nJ7cZOrHdqGmSgGBcr6vOzbyx7VNtjfmKngVXTtp+R2Y7EAaDSl5aeO1MVQ6Zg/BExn16LyZQ+NzYoHZBIqkQqVbjFzKVG4/WT403I1euO3qHrcuIKWb76A2guEZ/9E5thpWZoKM33BvRSfbwIL8wLzhhpKRK88/53JwSlUSE0h2gqVolLQDv78oZ2tm3W5TMW4IacFBSpd13rVylxSn1BADsvXAvVBwS7CclAJgzpscvR1IXTor7/mDy/Pt6XMklyJVK0Nl9fXclmuu4O6bjXUKLWqAeaX42CNXXHfawQAXMSj1aKFnpO/F44qExZhvR4TebcAA8bgosY8IEuEHfv2NweomKqZ5me5pdIZSADhvU5/uDyXFhJiZygUqqUtSKvWQapWLpjHg4ob6uwR0Nb4RyMQbahMQRp5braDKsEyIBALOWJrNGP2cvTZ5Nbu878bjZQ/iZOOccIw9HQRDQvyM8f6ypZ11nBRb2X8yqLjX6KDKxscYgUqrqEIxFe6k7BGPHz10/fu76wEB289N9SWvBYGjLWaCt0/w0MULr+sLZ85TEjPKoIC+ZVJRVVJ13YbLZo7wp2hrQFqQaADqoYyAsmyfdF3tOnsSMcsp/T5GLqksLhveMgJWkQhQRwPnEiEXiXqGx1ZOpzODPBS9+PVcjoS0koE1QTy40DXGUgFIa2qhKAqohUHmNJg+NZLU9eQ31eLQFqeRCeMGWU35NdtPhOpyQioydrFE4swPkKL2UGj0toHA2mrS+KY2ljFrCKqxq367ARaznpIYKOOq8E9xSTyfRYCO4IB9xnoLcc/hNMw3Fz8TxGurZiCK7xHmVW6on87S5W1hTo9IjcCPwlgj8vUU+UqFYgFLqyXzW2aSCYT0jjdcXKoyKMIksFeSbT/OlvX4atQ6X1aYqvGFbVe6T1iJTPFvCAm3SrE1XzjtyuPnpQHhTlKdBvPFaMdCVlFUY9HraAu0UG/3b4ZsIALSMwklWpjJqCSWsBpw4fqtoeHfYjMaFArqee0YoykH1NOA4p9STU1hanXxmiccMvYEoLDLvFgoAmD8+7oHxcWeT6i3T/EKrrUgpIqMiIqMijt4soueyyGphYMElJHCvtB7lmvPu8uU6LOGO+UnBayiPbWSY3kb29YHxcWdu5dMyml9Y1qCMzhvby027x6Q5WUO5VUjv8sy7YiDs96GsrgBd6yaQ4EQnf1HR1fyaPJNqj+DuoVFDYkpvFxdcYJnM3n5CR982/vf+S6vONS9ih/3ql8D5SDe2XrGRG5AGefV9eqBfjD8iQNVyVfaxzIz99b/8VEg0DkShtKl+Jhr5wdzoSPol0UDNxG+IgS8N11Zrrn9rtac9jxtRjgYAYGutuvkTesOmiIdv0u58fmEZiztfd35KRQJX+4RGEt79prGOvLsKqDvWLSn7wMUSCKK+75zeQMQPJ9tMEMTJ1w4XXjL2iwzsEtxtQRy2K5lVQGNGx0YMjC5NKrYU0P7PDek4oxtsUZFUXHCRvZPIlK1zfGPqWzf5xviH9o4Y8MKwE6sPFV01dhRFh9/sfqAv6stoFLSIvBjU9S0hkn+7aY+A+kT7xU7uXJ1TyQuoZ6AtSIXNQyXiWuaScxY8MD6usFxx+loGJaOUHWqUUYIlbXUmuZgKg7qXhqY5M5sk4JTzzoVfOYLA0c313Y8OXc0TSIU9H+sT1jdy1PsT/pq5Tado+nKMcPX2KcYY/Mj3xu8ct9Vyh3Ebp/rG+Gsq1Yef21dbAM1egUTQeU6PuMXxoz+a+Of0X/QqRg2KGj8sSdUDhkpKALrQuG6HXtlwtUpAh8CJW2Y15x3xcA3YoRbD1FqDr6/3maRCOo9kSUSQ7IHxcWamKJTR8GDuXZpNx3kainJEPV3e8dOEolrq9vNvN+Tp5aW3io4nHMw/B1vyDH4ZLmJM89iEuEl9G9FtVygTIQI063C6qhz2p0AsS7UQENwjjMCJXfdvp9QT6qBan7z9xg3Svx6xFrZWYlIz6bIcKJm38p+i5RkV8owKoqFld4a9MYZXT0/F7LvPyy2waoqOiwsK8jdJLhU1EBV1u26hac4JhnIlXcORWZs2Vky79dM1AEBILzgrI/HbJ199eDgMDAHwxMyBf62Z+9eaue8vGtng+BM3zwQAXPn8/IHFfwMAZuyYb7aDQAzH1NWyrFx29284vzO4O0vZGTr8pllmCT8T12B2/sHDi9qMaq9X63+b8EODR87jLugKjf6HTGKHf0kYhVYiFDwwDibo65NLRWVMGWXKsQsT8VwD5Yj56XrtbCj5TlmL4b5exz5+RF7N0t/MHrwjYAtbvUpHxQGkQXDKHRO9BgatRL4SKTkbj4lBZ9g5busfU392WHYeQQ4/u/fPaT83aKjyuB91nWts6abF1/7AuLjZY3pamKLGXTmViE/ghhGKckE9DbDnq+sl1HbtZ9+nBklEgsjq+mijHsfTc4p/3XcBdi27nJ5XwpJwZxI7uTNcfuPX69SfeaezAACdZ5v2ZCLA9S2XAAAzdz44648FfZ4cgAob8R01SkN3jttafsdWrpbHTWd/opj5JCIMRbUGkJlVYLxlW3XnhRhqYYqW5xeVL5jQm0v6yRUN5UQZE0Y2JuQQVKNEUrkwsSCsT0S/ZwZ5hXobcHxwljGDdPpaGpjQoapXiGzD5F04gYwagGHoQy/1x61bc/2ehqt0JP8KV1yAOc23jz14ZHG/5YPv7jLpyZTyR5KiuHbYm/dJAqRdH+jV9QG4nJGyVJH49cXcU1BzzZiz+xGTv9XkvxI0G5SfBRm0hvLzPlsDrHGwyhqVcWawH1yijoJVQ2PbRlLG5gPjehkI8NfRW3RyqaxMjnAmEc8dBC43P3GCaHBKeAubn/gbI/UG4gFgki+Cq7b9mXG2CgpoYVEZMgCeWJISpTS3uqRSObBn2z0nb6IYFhpItprvFWIgV9ZkIvCCJUcmdUUEARAEE2FmxUa5p7J2jtvqHeHT49E+7SfAruNeIbJhb95n0Oj/mPazzc4SdahxYPr6/Nz51kn3GH+cgDPlYX1olYIw+nomV1yQnxerqvbvFKzRwBNeLIadnMb1izma6ODVTVq+LNSxGXmHzYVveuadA+pJCyi+Bi6HiRzNvJphbEWO6/Gq7MrK9ApVhXLX2vuvpxagKFJYVKYbEKkeF1v8b2r+T9e+emXuhm0npg3trNQZte3eqIjqSK/2Jwvffgsu9wYAGPvJlJC4cHlambK0Pn7qE+3rG+OvU2j/mmlrkQwEQwM6BE74agZMETB2fuDAQlSE/T1rm7ZW28yJ8w8eWVydU0mltnjcuvzTx8dbodZRGaTq6truZEFxclZFXGwwbp4jpS+9Ol1lU1WzXamUL6daiqxvfGm9ozTU1S48J8TTnF/eNe9kTDH7zT/h4ohr74/rFFHZI/CUAOA6jVZRte900tShnQECjly4M24wLJKnGdjW91I2rEYKiYPp+4BOwQEWy9gIZfDnnUYgEejVJvXPhAGvuFu2c/zWBw8vFspElharNVh7OPHuvKdiaYv0iPGnftKH9wz38fM3EIRaa9DqDBqdITEZ2pJxHYKtXYfyGhWtq8zAVLCfFwIQN2rL5FQcI6Bu7bw3qnVIn5iAt78/1jXaX18eIZvTffSALr38omDlaAVcnHb14+PhisQpefU/41WXAOga1BUu562uUP7z4G9mA87+6yGRj7j/c0OufH4eBjR3PSzyEe956HdlicV6OwTIP5cTNTTGN8Zfnm51rR5Wk5NvhdeKzE+likrBV1fXAn9ofgZ6i3z9YR97AYJ4S1AggaGkiOFdycw6wAlCbyDUWr1aZ1BrDbfv5tWpar2uMntLVlSrACC00vD66Xqt2JFHXTXpiOCMepqhN1gNMU7s02bykM6TB3fq1i4kJrx+qiVFt1iopACA+K7R/j7GAOQ4chX14WvuAwAcfm4fgRNmt90PwLRgx+nGPrVn3joKX4j01i2JHAyL9uV14QX74fuPeDzWQmHWrzEEIZ+EoYhIiPrKRKF+0pgQ79EDOs4cP2jm+EFT7xvg7++fXqwSCYUiofBmBlkTCkuZuJaNbyIOyci7xoU/dByW/nAEO83PV+fDHDpFn25tM8LFpXV1ce2iQ9tFh1bVUvlvIyiKdGoTXFyQM6idrzQYZj8VRSyLOOLUYt8IIg32UpUpS27CxbjF/pI5ux7+e/avzD2n/jgXQZHq7MqmncCucuftX3KHMnMulPk4ZDRqQNujeRg+PkajkAqA9iCjnwQAlPlpBQTA3rvkOYWAtPyq8mp1av7VpTPiBRiqVCi6R/saE0pdI66ll/Qkx9RKw7m2ZmeCi5qMCFzjvHNguY7Gmp80g3u1pe6gCOLr7dUu2hhr9/OWmGkoLLxP+yn4YVhmRM0EZeX2tus9HukzZeucGfegb7V48k/z/l0o8hE/eGQxFQCFNfykfWHQ6A8s/buBMiYGt368dnvb9Sa48z9tXLRwpcOmJ2EIEEugSS4TWv14CQJUWDQZ+GnjIgCA5ZFQq1PYGA0AcPxWUVz7wOYduLM+kOYcBusHQvnvxj8s/PeGRqWeSFxNK9PpYWy9qhrGowAAk0f3+/cEnIBHQaknZ0lwhSOPusB5BxxST6c2Ti67uBMnQPDYjhiKXP7kjLXdkn5OhKkkL3iiL1z5AzXj6OgLB6hHEQwFCEIY8MMr9sJpSM32n1zozit0iH9QiH9QsOXNL6iB1uhMrqUVU6PBAYNDyDHNhwWeiA0R1+HwmoIBUBLK/AQNmJ9G9Ab8wp0Snd4gr1YaBLKpI3t/t+cqc4dr6SXX0o3NEinzk6JWDh2m1uzIC1pYPaHzzlIywZm5mw31TqbNzw75mg75mpgwk99k2ggdl67L37uGIEBOmwe6/Ji8+9RtdaW5cUpD4MTOcVsp44Km9FYRa68mJr9P+QkAx7jz2yVXQGfjz6nZkTiEqIjgvadu9+sURk2vjgoPMtuhUfHw6SN70H2DYCNL2DrI6YE5jhih1o6E+QFC8zPAHxAgoGHzE1Qrtbez5fJqhQhDRBgiw/RHr+cuJ5eMp4zQE5dSAAC92gYQZMwqswpnLhzPKZrmyDfHDm3RD4LKvHOk8LOx5ucHv12k1dMGft7Qu8zf+x6lntTGmeQFzylYQ59ONEUJMH1Ed1o384vM82BNmGcdFR5MmaJk24tGJ9Y8DPMP0L7PM79cQasnbM7gFxwmg0n5ErmqfjW6m/Xz327m1miLM1LupqXcTbuRnMpBI7QJ8zubA9qi7T7JtQM9HjP1JM9kYmBbYzCeO1j2cHKmhhovaGsaanvlXpbByN1ti3JrBBUYtOqebaHbHlBXvWSN5Bz5zYxihUJJqSf1uUbFxEAjNDGX6cVT5uedUkIo8RZKZEKJDIf9KzyHJjvyaEs676QLwFHz056Fj45fvJO666O7u9dZPlT975rqA2uqyJumzxJT9YTMHGXaNIQNl3iIlhr6aPxVpx5JVHhQneUIu1RQG5vU6YewMSDF4GBjPqSZcMR5t30kdAC0QfuTIIhTN3MLSyuFphaNDNOfvV24dEZ8SZXq8an9qI2vLZ1kOYJY5iNEgCcZoU3T0BYqY+LUrM0m89neW89PhxWa+RfvRBbsQRDEv42IOlUz2z9u3IlRJ2fyv81T2hlhRy5UOFmrOpo+Av6cUGKXX1QeGR6EN05BiQYHBB6NjYBsz7YBlDlvzfw8cyu3fahMqzWZ7SaTeRkEXgun9Ppq762CcgUA4HJq8e30/KV1wdDwEP9SrbKsiqVTLQdpsaqmRs+Fb7r5yYGl3ptjflKsnN6L+R7iOtUtlmBx+ReW15ip52u/cKj6tWUWnacEFENAbDvjLAMmBAAFDINRrdHYWQfaqT3LaBR7Tyf36xRm/4CegbYgVU+gYiFq0OspAQ2QsfjvZ27ldo7wqVbpsorrS5JlMikAiFiIKglJ1zYBuQWlYi+ZRqlQ4YJnZ8G15yg2/36qb6fQE0nFBFq/DJdGUaMjQO/ucE4nd2pCKZomoI3NJqEt5rxzRz2bw8a9twgC+puUYN5MK7RsTGtug5L/XL3DrTY2rDgpJNqeVE+C7RZZ5303isysfNbRCACmjeju8eZnw1j8nJ+5lVtSVtk5wqegQkmrp0wmJdUTSMQiPeYlw/SpufIl0+MrajQLp/TFCZCeX0VHQqeN7B4VESwTECoSwHkSWsSRbwkXnmvOe5PNzzrISK5p1sPijCXCg7yLymvcSD2d5M5jKHLPevteqmJRQ5qKxvkwNjmTVDgqDjYStD0mhT0DehKU+QkAwUwfUVZnZ3IphLTCapUGlspTukmj0Wr1mGDB+J4/HU7JLIR9wS+mFMsw/fEbeU/UufDR4cF5RWWDe8ceScyjNRQlH7qRnNq7exdOTUxqMdCWKPz0QOqXRLiRVkhYWyqBoZ5/X2n4gve87LxSpa1VaJg3lVpr66ZqeMVTswEbGNOOAT0DscXKBbTVSf15O6eSVT0lYnF0ZJgM0++7mEWJpgzTX0svWTojXqnR1zKXgAUgLECKooiUBACAowIhh0wjFxih9sZAm6ye3Gm55CDzE7JyWk/AGgmlqZPQ/advA8Sd1NOxIdGeUljrHhsTTluDRsOdkVcrKaukdqZ6995Q2HLAtQWp/ToEMQZk5J6IpgzoGWgLUjGBoFsU1EoCELkVWlo34fdIEEk5sIWCmXTSJTEGgdfKWb037r6xYkbcl3tuPjG5x7f/3p46qN3+i1ltQ33KyuVUKun0ReO3n5xblVtu5sUT3IyEOjsY6txCerLlEvA8Nu5LunYnh750YSTUNMh3JTmburl14JfVDm2UKZqkqptSSRWwkf1/6v6Bk2dosQsK9LXnd7ZfB1INqT3hgCRkYyHzAQPsGtBjMOiNWXWpWMxUT63ekJRdKfMyhjtJyG+C/BANAph8h20TsmA273oGXCPr38vZMkx/gjRFs0tqFk0z1jONGBRXo1DXKNTlVSqpADBvlHq2QlDnO+/cPYubZn5SnM6oTryTc428UX0VadGEusnATc1PB05YyisoMfmBIS/ei8kFxaVGsQsLsbdLBbsmsg14+FJ6KzE/KajieTOqFNpcua5OOut1k0aG6RFj+WeBDNNfuFMkw/T5ZbWLp/VTqPUPT4RZ+PPJhXQqKbeSyCw3+Pr6+Pn64AAVYBiBk5PwQSt15FEnOu84zqnckcNbh5zOqD6dAatqEu/k/Lj3otmjX/939+v/7rq1ejokJMpyBhCgqETeLgQaPmEh/uGhAWSFRrNOFXrA8NCAugFbI/6y+hqjq5kVdwtrKqoUFVUK1o/XIPDSaLUEAA+Oj9Po8Icm9tbjxIwR3QkAUvPgT9GRxFwZpr+eUbZkujGVtGwObOoor1bQ85dQFO3UGS43S8G1onpna6jASVM2bXR49TAoDbW872E0PTtvcRYUlcqpO+EhdUYTYpzja88pY7bLhdv5lHRS6lm/U6s4+0wgCEJetzpWrVrPXIejosq8F22gn7cM0ysArF46dh1660euQbk8dBX672eSoEGaWVj97Mx+X/xzM6ekpm2YMSxAS6dM5lVZWYWiHG0s4hAa7DPilDfvFuZnc/x3VjxYPR3lzl9IzjdXz7olIqmzpcEzxmynesMzBBqeJru1JkSRXZjJhkK5OrdMiQHc7MZ8SkVVbUFJBVzucGCnrOLqpdPj7xVVU0Wgj0/tp9ToF0yA/vvVtBKYmr+eR3vxqx8fK5N50eqJhXXMrDIZ2ZOM0AZBnRT65NjvUqsqB+SoO19UauK2G3NKdWJHlWoIMHtPmwu384tK6rSYGs00yNfaNDRFG5qmD6NutT5tBeEWixcCwBRTv6BQvyBYgVOj1BIEuFcEf/7v5EBJvZoGY9b/Xc2RYfrLqcVLp8dXKbVUPJSGVk/gPjjDkUed4bxTfQM5A0xbONv8bFU0ITtfb3iGBpB5c3Mnm3JXMPuKNixCqK3TZW8AQXgnyxv9aG15EYrA9NF5MnH03zUol2duF8IiUNLqzCmpWTytn85AlFTCiqUzSQVmXZaZuIUR2mRsaKiD7cRDxy8YOOS80zXtnBL01uvOh/qJcY0S1ygNbDc4e11n13LN9GiEVmVjNB5LmGKKE2DGyO5qreHhib3VWsMjk/qoNPpHJvbW6vH774OVztfSS6nWdjJMn0LO8qQGWTFviNuZn05y5AWONT8JgsC44rzXi2ZZvom9yZufDsH+FZa0elyrh5M1s1TsvXxow9OeX16CAEpVw6N1b8PpBXy4gAzTH7gEmyUfTcwFAJy7DcuZD5OppAOX4Kyky6nFT8+I37jrxjPT+36199adnIpzN+7R/ZnMMOtU72EzO61lk1hmIjVZPTnTs87c3uQF1KmwGp7OXuyTxyFIdDUrZsR9tfemFNWrcIEU1SsNsAnT57tvLJ/ea9PeW4+O7fLL0dS49kEZOcU6RPT09F70cz/al2k2mtlSH5wV0CZ3urPUUIdZi9xQT5ZFq3n19LTVQXgcSkquHCfA1GFdcQJMGtIFFoHmwoD1FTISepj0329llS+dHq/V48VyW7GRTDeJhDrQkUcdWvgJXAof6HQZvIa6KTJMfy4ZJo4OXYFJJCrzfoaclXQtrXTpjPiiCuWiqf0IAqTkQVU9dCWnvp5pWixoZVhmk1BHOe9kR3GEO4YnBW9+eugKSzyOoRwNUGr0j03uW6XULpzSt0alWzilr0Ktf2RSH50BL6+GS8mevwMV9hyZoJfXaszqmVqbEWqmoQ5w4V3dcok3PD2q/whPy3P8Rh7ZAxSK3ZW7sAiUmhT/H2mWJqaXLp0eX6PSLZgAv8fTjHqm1e5shDrEkUcd47wDwEH15M1Pl8C78+6FWuhzN08uw/Q3M8voItCkrPK6peVga/rsErg+zdFEOOMzNVc+bXg3AIBKo//2wG1Jbf7D/X2ZtyGdvId08ua+EdpkmEYo2nzn3WBwSeGnVbedx+Xw7rx7AQtCR3TXGYh5Y3vpcWLWqB4GnMgvg0vLnUmCnvt+stfyvaJqpUGAE+C3k2nf7bkqFQukIgy4M8135NFmqicsu6tbVLoFaVg6zcxPnpaHd+fdBbKfSC6dRDp1K59MK8GuIjcyyxChVGfAFQZYM06QO8OWIuSfE+LbArHvXyfuWI7pLkZoMzW0uTHQFl/qvYmGJ1887xJ4d94tKEcDiitVS6bHF5QrFk/rl1tau4QMej4yqQ/Z2Mm4qgdTOilCA6QSoXsboc0EbZbz3tKzNu2VTt785A68hroLVDv6pCzYT+TWPbgWy5b9SZRoWkonAIBKJY2Kg0uutlojtFkWqJ2tH1we8eTNT26GRPnsPHegu4owi5Zo0TSTTuZDXdoEoBI/0FpBipOPcLt0qSm6yeffuYm7T/rc52/uruoIUIwTJ3TEn2rCuCYRyTZf1B9FllYbinCr48ypNFguGbpQgsyTQLNmWqXVvipeCHjVC50krr/69miIdUpcQzRwtGaYvYREV7N0UvfvDiY/OaXHNwduh/lIpuJgilQUiCJZevxPtfKA0gB7MDPEVIbpl86IP3UzPzEZTuucOxpm5804n1bL8ZmdzZnfiTZ51qZ7qCfBm5/u5M7vXuRr7bmHl/keXgYfpf51CWGo+S0aA/FC5CUv9HwgNoixwm8wioShwJqAUc9lzR0skaLUo+2sPLmXADkZgFHqWYWDalKgZ4iRMwFYXwFi+2jNbpacIfuJXLhT1AZD//SSLPKWBKBIBY53EmKv+vgcCvFT1akn0wgd0j0CETf8pXDZi2+yI99EF9754umIKiUXzo3iaaQ7P/p32Btt1g8sXf2/nQdDaeO3mDy089H6hSdbmAEVBuZtYIVhN2n7fenT3JRsEAqkCPhYCUXxI2/20b7zhdufq8EHVBjGVRrGVsJjeE8Bn7KFfMj20TJvZnvKML2xIPRe+S9B8ONdVlEztqTq/rKaEcWVpzU6LxT5PAAuBMJ06r/bc1UoQKOCZdYioZ4NyslZm00f32h+1skvH/3kJpYa+tp1ubWQ6BN/1B5e5ishzSvaCH3wF1jazQUIAN5T4EryfJvBcKubwNsyeD2e0BLVOIjFEJHFDtEovGKT9MR5nYl5sVtDJOsJBIAHJU0/gHI0gOoq0lsowBDkmFqbXNeeVYbp/1epIAiit0hAS6eMoaRj+7YBVoxQd0klNc0IRbnnvPMtkFujho6dApO51jR0/JZqtZ4Yv6WavgGO8ZMa2oBjGF58E6CCAEU4+FUDR5tvoYZRZM21nC2u+lwNflJLSJrz8qQm/nEqvZ2QSNHpD2tUtEpS1JKXpsB0I4W/t1gs5Egj4BbVUK6952a57WV5GUzX/+dTfC09p/Gk7HwcKW332KTNTiaJ4Ajb1PD03aqC/z7nZX55XiQNzxEihBlvpagiwKpa/Efy6U2mHA0gAPhHaXiiovYMORQzC+9DVt3Q2qlgePFUJNTd65maQAPLGlustUmgTixd4qdmtkYsF0y21tmes0gQMIyUv81k+JLmUQlaY/dJTcnlDyrjCPcMRHsM6YQhaQaTIf5UE/dLECremqgj/tMSe7QEXNzdCt1MM0s0OQZC0ZgLbkMAXITqmBoWDjC9eIXB6NT3ahd06qYvruacc9BYEt79xv6MfCPKmA4dP0/GJhFuqieMfpqOwVugboRbVDhdDoSp8R0MK0+KgC6YUaRu6YnFVFIcgIP+WFBD3t0wuYEWPhEAZwMxFQFGyo1hx24C5GdfVI6DCRb1TAOFyCfeKDPcSpDCus5UvqmjtcaKGpyyZ80IwuUKg4BSRvrfyWLZSh8pLJsvrqReg7We6fDVnOS07AbrmTyppEnQmOincfFujtqevHp6oh3KQRldwJao+U1NbDDVLwDAyhq8hM2p32G69AW0esj0EZVMp7ijJ3AAAlAgQ4CZqXhJRwyXG1AARomQmSJkmAhelvMkyBwxNrhOf2ksj4oi29SwpSlHAySGGqbnPlfi/YQ3DK5OLIHNPplWJzD9c3jPyOQcOdC0IiPULgvUyc67A9QTRj9N4c1PN4XLpihl091fVS9SegKU4sCyHp6yQGdVGvJxq+MwLdCLgRhK1nUy8UEAioCdaoIqbLJBOAp+88O8EFCM15fHU69iWa7UIBJdfYXDe/7SkWIx7BtSUoWiOjOrU2FhhO48fre4uNgDjFA77VB7k0gI6k4t6Xj1dF84opU2yDbU3/LZ1LOx9BQYLy8/1ORGWSzMyqRnpOi7bPWhRTgYQ9qerOXxjYWe/749yGekWFyF42OKK1UEwVrAREP9eZ/1eiaPBLXP/MSdEPp0mHRamp88bg33NdSxrPaCF9eyalgbb3ajbNIedYmghyXIRBFC5qvMaUb+35xyNADGGQID2wiw61r9tNJqOvPOKp0yRlVTqJ/US2w1MOhe6Xh7SppQezp+Ig523p2bbefNTw+rcPJ4Pe1K6uM1Pct18TWZlN9SN8fpMpn2+c2XJTvkQ16jZPmTA9gR7BOAokfV6mfltTYaMsksZiUBBIzoFekxs5Ia1NCGk0iOXqyYmmXEm588DePx0kl1D4HlQVYKkf7SEK/IgBgBvgioJsDztfjFACwaAycCsA8U+CHyWQgAK73Qh8lxHqs2j3gOtl7bf11PsJaNjhUh0RiqJ4gDKv1AkQCqBFRs6l9wRau3nUrqHOV/1MtPr6xifdEhnbzpSGitvIj7kdCmJ5GcsNS7421PM/+dNz89hk8XdwYAvLD1rkJeKwuod/1ciP1pGfuTSGcCMDEC5lQZcq2MutMP7YAhp7TES7VwLBECTvhjrKr4Qg1+hlGZZLuMCQAwu9KQx3Z4+/2xUOuu6czy8go9ZjuVdO524eVb6Z6RSrKdTRLYdN4Jh5YtOdj25KOfnk2tytgIvX1s+xJ56VsPtHv79yzXHtKzNfZGGl+rxYUIKCNsjUO57KtIWbSmngCAJ6vx7oxieC0BhsoN0SgYL0IGC5EAFOQYwB8awrKos8GjtXZ475DVVL54jRrHJKiB/pd6tLJOPWXWjdD+ncOuppXYU1TvFkaojaomWy48TgDHTXtviRnuvPnpYbyx4x6s0QkLLZHDZuku5wJb5TkrV9kCmqzjNDhmNcGyTx4OflATP9icuGn/0Vp5onddPROVI6rPFJlJJwUzErp0Rnz7cN+MrOq/TtyxNEKZXry7w26pHzp+Qa83tFy/eR4eNs6OKjw7Cnao5HE5DaaSaKhHR8VF2dMk1N2zSewCSgBCILDS8bUpOMXw5KuXPJv7Cs5Rd74OOxIT0UaAIe8saO/qg2rtomk7Cy9jKKmPlyjAR+zx7UVQa7M2HaSeLbd6O++/ewx+YqmZ4bmj8yXXHU7rhSoIZRVNVulUmPZnGh0XDTwIViMUtdLx0yGv6ETp5M1PT+XsqMIDg+ECO2bcV3COtkl5WgymXLKaojLrStomxFsk87c2sjsaoZYaaq1aAXGv3nS8+ekZNBjx5EOiLjRC7fHfFaZF9fGdQj2mqJ4V1DlLvTtXPXnz0yOxUxx5DeVmKskM6tG42GBE4uvBRihqNmuzBZd6dxi8+cnDw8FU0nd7rkpEWLswXw8zQpkaamKBEo5ZLY43P3l4PIcmp5IobNczuaMRygQ1abnEeefdEt785OFpSRqbSvKTify9rdYzubsRihoz76D5/ZJbomKJj37y8LQ89jdkkrHVM42Ki7KzSagbGaGUhhotULh6QNOd95Yr9jSDNz9b54wjfoYSd7x4hZUJncxHY0J9hHBiTsNF9W4H8vOmd8mWS80sXWoJAeXX7fAwHJVPH3YSLqjL41SoefHMxeaAKawb6f5M55OLLt1Ms6c/k7u0aKIg33Cz1NMheSee1oVjS5Go0XgZdSrMBiKWpqjCoi0T/Sh1f0j38Kyi6pKS4ppapeXgPSPQpEIH9tRvOVBHLNfBm588PK3Ci29UFl7GqGeCq5ne13lC/7asg5uppxtFQtFmLNfhstAnDw9Py9O0CZ0dIvzoEbp1jLEcdk+KSTMn9wJtqvnZotLJRz95eNw0lXSvqOqxyX2saSilnplmCzq7jxHa8JpIbLRew3NgoNUm25cqfG3sQD3anPEvppYiIR30xelDu4U2eXwenubDTCUp2OKh1H0/mchgEOJq3ZW0kuT0gqUz4j3J9qRAua+e7mJ+DgystiZ/KIKEaHKbOf6gLiEDA6tZ1RNFkEh9fjPH5+FpEGbHT0VDBaFVCu3cER1kmP56etmS6Q2op5saoU2zQFsplD5iCBBLJPAUETb8W0IQoKJW26hXadT40LGqgePjZA9XHh6nUo4GSAw1Zgl3mUUWXoULJF7eihr1yZvwd11nwLOLq49cSqOM0G4dY+6k53jGN+V+Aupy85NSNwCAf1CI6SMsErbrRFJcu8DMwsp2od6gJLdU3MbGyGYGrEKHRIUH234JAgpoAey9GCwzNDQ+dxh2MsKxlUx8DVMLw3TVFWzryvXuEHzxVlZ2Sc2yqX0270s6c7uQ6cKzOu+ZVXisXxN8YleCcjztzrW5mxgCoiKMopZfVNbgJxPXLhD+TKFNPC0YL8Eu0DjurobnsJMRDlE9R43DYycNZuGDfCUyTH8xpWjp9HiCANfSSmWYvqJaXaPUUfVMAIDV02LteS3ue/H2X9hEKzc/681DwkzgrH4y9AMEADpU0i7U285IKNPwrHsJ9lfZc+o2dYcaH7gbzdQ+Xjo5UhCqYMRDyaZuQKHWl1SqYAA0oxQqKQBnkqCrZBvLSKhnCKhr1JNr5qcVgSu3ttvuE0nUndCQAKparEGNw5DGvQRFZmElNX7zU1UtT5NFkFdPrvUW8RLDeGhFjeaRSbBu6cTNPLiQvR4vqFDIMH16QSWznskzjFCBu9ieXIBWN1Lggmhdyy8qjwoPsvzIKP89s7AyMgyqoR6TCAzqEA17pJK2cGPbGZfiigoPpl142y+B1YUI2oV6l1YBt4OXQrdOJVHIML1SAx4e22Pb0dTLqcUyTF9UoXxiSu9vDtw+fQvWMG3cdSOjsPrs9UzLeib3BeWsenKqeslU3YwONVPRmEai2UeGmnZZtduRh69i50uEhQQipEC7qRHK4wGmqBaICACSsytkmD4pq3wJ6bZfTSuRYfpiuVKh1gEALtwxSSWx4l71TG6W8+IAJtplJnDMUKWhLr0TEgz9d0pEKY1r1KvYeAmd3s0CRjwenErqHRtMSefSGfFaPZ5VVC3D9LfulVMB0HO3C2WYvlqpK5YrG5tKcl8X3l3NTxvzeWyDIkhGQSWri83035kwfXkme0/dpgqYKP+d5npOTZ8YH9aSJgwBX6WUgJQS6s8Nk/pavkRBUXlknaTuP5NMufD0dFxqcHf04nnc0YsHJN5SIaFV3bxX9vT0+I27b5RWqQAAp27BlJFWj+eXwwBoal7lU9P6bdpz8+TNfHuMULN6plp5ETd73KEe3CgkxFfcqBvta5t5wQz/PYr1hRLTjJJXwFBSo7SZUi6vjvAXF1drhVj9J59Ymj4wsHpLaglUTwarDiauOphI3TcLgJpnkEiS7pVSgzf0wfDwOAwZpleqdRMGd9bq8SI5bFV3Jgkam5UKzfxxcQCA07dgLb0BJ+7myanpSRU1Go8xQlkF1MXS6RDzE0NhIbpCh/gHh8BbEPMWbHk7fgvGWYwV742BAGDqiPoesZSG0tWZVHoHIdWtXF5vFxcIjFqcWJoOANiSaiKdTFYdTKR+zWjDk5ZppkZT6gkAaBsia9Tx8/A0DRmmRwDAMQlOAK0ORpNO3cqXYfq80prHp0Ln6cKdIhkGi5keHA+V9Fp66dIZ8Usn9wj0Fs8b1aWqqpa6uXUklFVAPaFBslhsjDbmF5YxfhHsqni3zPOw+u/MsWh1owRuz0ljAVNYSODtLCidlLpRAtcuFGpcYmk6pZ4NklBnhyamFdMvQWt0ZFjw7Tr1hF+eJ3x7PG5AORpAANC/M2zOcC4ZWp1FFcpHJ/XBCXAzs1yG6e/my6lU0nny0cpaTUGZ4vu9VwEC/Py8/fy8N58u2XzaqungFqBcc94dknzHUKDRqOk/64qBrL4vOuFDVbxT9ykNtea/W47F1FC6gKmisl46AQDBgbA3Ynp+lZ3SaaahU0d0p8MFRcVGO5R+ieBA37YhsvR8PgLK00LIMP2VuzDPLq/VPDq5DwHA3fxKGaa/fLd4yfR4vYHIKoappNS8ysXT+j05pUdogBcAwLbt6V5GqPvNhW9EvRFRPxUyv6jMRhgxKwfOy84srAwPhfvoMEm7UJBVUv8F63GiUK4SCVC11iAWGn91cAImnQw4LsBQrR6XijBU7IVr6lcsYPb3CArwpVeNbtoPVFm1GgHIwLj2OpXC8tHgQF/KdXD70DWPe6WStDWPju3xy9HU6+lwyualFOgkqbWGkkoqHgpTSThOpOVXnk6EFaBUBumjfZnAI2AKqOsvPUfWfhLmteg2d4RgKJlGqis5ahfq3R6rxowd+xFVtRwmFwHQWBmHepQCQaB6ikRCSjrJLUb1RBBwwDhSI3imWxiQV1huzyyslIhF5EsYxy+si67y8LQMZ8gSpesZZRgAtWrd4om9th66c5bcKK8BCyf0QgAQYOjS6fENWp1ul473KAuUTh+x/iRYTuYxBiwYe9H9+Y/drkAJA+3a2w8dgiQAjKyLxSKQW0uHJSf3C9uY1BQ3pFZhottYXVBWiKGUQJu9up3kZd4DTiA6tj3gHq3kzbbw25Rh+uzi6uUz+n3xz80HR3fbeSKNks78stpnphvrlkpKKyjD08/P22NsTzMB5aL52WRi21LzhWzNuaTf8L1s6GVkFlaGhZgUHuEIhqFGDUURRCoVMR+tD5oykNeovcQYGVQ1hm8MOE7Ps2wOP+cYa5UAAMu6hFF31BpNkK+4pLQijIw88PC4qiA0swjaLqduFZBz3quentbvyz03k3MqvDD9qLgoiZBlKaQmwykjlNMWaNPSR6zzhcw01PLnAkUQqHQWS0RhKEJpqEqlZWponUNu/JMAQKPRUeoJuypIxCq1RiREtTo9JhbS+31+2wFR8C2pxcu6hIUE+eUXljL7KMswfUquHIjcrycTxcC+/e3c81LiFScfC0/jOG/MwisoU/RuPvy9P5dcuGx6PH2NNM32tNIklDrtXV9xgro87e749FFbliAg03kn2BomwQmXplQrNGodrtbhGIpQOqVSsdeoV1SrNRo41ZdCIhYCBEglYurrZT7kKLakFqMoEh0ZiiJIkK9YZ4AGL4KYz7vn4WkBZDDWqXlscl+cAPdIU5SqAJ0/qpNGZ/Dsr4ArFqijqpfsgTkbkq43ovLvNFqtTiJEcwqybA9VKZf37N6XMjyXPfP0Sy+voh966P55ly5dEQtRjQ7HCQJauIgjf6jWnkl9a3gXynYuLZdTXnyR0NjJqVHE9+t9cN/vtveZNO2Bq9duMLegKFqcd2fpU8//s+df4FaUFqTa3qG2VtG+cz/qfofYdudPH6RygBqNJrZLf63WPeZ6tW3b5sr5I7b3eWr5qr927YVCIMBSki74+Rrj6S+/uuaHn3Y0you/nlFKF4RWK8GSSXHeUphEbX7OnS2VVOwdEEbaQi62GLgioM2kwcnv1Eweeg4PpaG0+0uQBUmU/86cL1RZWYkYTXSYV/f39yer2OoLLSsrqyj1PHflYnBwcGVl5cljx/0DAoaNGL79zz92bPv1jf+9LhKgWq0OZpMcDoIkZVf2bAuPqjkYDAZ5Zf2bEotEXl5SjUajVNXX0ur1JmswoCianW4s73c7mG8WABDg72e2Uak01qLNmzvjqy/Wa3W6/Qf+i46OHBDfNz/rVmyX+JqaxmWTXQKO48w3JRIKZTIvrU6nUNRX2ml18MdAJBLlZ92C89lu3zlz9uLc2dPWfbDmiSWPDR052c7XkmH6RLKMSV4DlkyOw3FCgKLf7blK5Y5WT4t1Wu7Ixb488vOmd4D7m590/p3Vf2cafszp6pdSCql+H6EhgQIUvZ1VP58nMUMuEgkFpM2oUOvUiIS2XDrFxFLBUHWdb/7wo4+89c7bZ06deuaJZfTgl28kiiWS2NieqF6BIogeoCIUpDhURdeM6AoAyM4rRhGkuFLVIdLvVLF38zO2jz0y/+N1az/f9O07721gfZavr0/6ncuUUcZqgTYtMe3sGKi19DT1tYZEQnOeCYIgJfkpBoMhvE13aoufr296yuXi4tKefYe7XRZ++rSJW7/5/M+/9z69ot5Poki8fDw6KrJLj0EVcmOucvefvwwbOpDpdjT4NiW6mgWjO+88cTc2wq+oBPZkorY7SjotI6GkEUrjGg1FPbF6yQSioflCAIDUnHKzKUNikZD6QmpVWko9mVRUq2n1BAAseXIpAGDlMyuY+7z1+hsAgAXzpsE/EKDTOj4SSnE7p5IK0cIMkvP54L03MlKuIAiybfsfwKN57JH5AIDHl9R/rVXV1ZevJoaFhUilUuBBREdFwrO6Tj0BAK+/9T5pgM9s1DiJGaVemH5S/5jF04wBEI8H9aTi+fCIcI0epn3UOoNGj6u0BrXOoDPgaq1Bq8P1BkKtNVj20CQIwJTOoABfasIlBV0AT4MTBJ1tFwsxsRB7/pnlq194UatRE7iBvuk0sHLTSyqVigTU0m+1VnJQTcagURo0yonxsC1e25ZaEGnpokfSM+61ie198NAxx46cnsGtpW4XzJ8DmwmdvcDc+PY766HPseB+4EGERHYxM8AjIqBxV1jYiKIRGabPKKxaOKWvQICi5PSTj/ZlOtBzZ5vZaWwNQeKaTLiHxECp+UJFhcwP1C4yCyvF5HwhxpShujGt53wMZNbbS2J8YmpKSmpKitk+L7/2P1jecfYsNaJYCKeBAqeh1LCsE+sMevUdUVTslAYQ2TkFbaLDnRIsbhJ9+/SCARxGxBCGgEhN6R/f+7utvwAPxdfX58fvvwQAfPN9I95jORqwemosQU51B66BaHl3XuAB0c+62ZaNBkHgTB4cNwT4BwjIuT2k1tWPVqvSalBzZ63aYLzCq1mmpBuZPXNqWHh4SUnZxdvZ5IDkU1DQN6R9Y9uIWKNvSEdQH+wi4xKiliind5J6AgAOfrPSzj3ntUgdKMo2A4LS0/AwZvTNc/h+y8Yxo4f7+Hhrdbre8aNUqkbOOUbgue686UbW0/FmtFx23u0t0EsVvj2lpbThSM9xtAecIIJ8xeXVGpVWL5MISCE2ebql/24PgwbGf/P1J1DjBo5pwtN5uIxBD90IEZwc4YFMmzrh6rUbA/r3FQmFZ07s79prSKNqtqpcZnu6LDsv8IC5m0mqkJ7SUqEAxiWjI0OtRUOorcWl9ZHykCC/3AJoT6lqa1DEVyYR2OO/22b+vFlfbvwIlu637anVOStxBM1PHleAkclKjdo9SkEbS1i0sTX42PtG7tz2bU7GdboCwR42u6K5pxUjtIVk1PVJJIekj5JUIVoyO5RHCiIriKl6hoX4oyhCzeSBX0NNtUJtEkZkzb/b5sCenV9u/EivN4REdrGmns3XPl49XYjMGzbDLixqdLTdvTh67FRRcQmGYSEhJit6uZxMi1SSHRAeKKAObB1CkaQKpmY05hXAGRGWFJmqJ+Wux0TB2ZAhfhIEAEVNtUrb9FTM7etnB/TvW1paFhHTiB/txsKrZ4uReB3WlouEJt56eChswH7NdFKWW4MgiL+fr6+vj9n2a4k36VkGHKfWJB3fonDIAm3+su+3lFY1lKme4aR61oHEREP7P8QPGpvVlVU6PU4t2dqoAOilc/+FhgYnXr/Vvfcw23s2J4nEq2dLsvP3v2FEe5DJEpKvrn4eALCDfMgzwDA07c7l65dPmG3v368PNHTKWFacdS2ZXDJCUc8wP21o6IXkfDP1JP9HYNoJgSKJANCW1NAwfwndia5R/vtLLzzTvl3bu2kZE6Y0UB7Iq6cbQU0G/2PHVjod7+vrM2L44MqqarPaJrdGrzdotVofH29/v/rGsmNGDQ8NDVYolMzqei5T6yIjFPUY85NVQy8k57erW6UyNNg/PCSAqZtMC7NtdBhOgKggr6rKqsYGnV9JgPU3nTt1KC1INbtRD1Hcp1E0zajsG9KRtz1bHoIg1r67HsOwguyk559d9vabqzNSYPnU8NFTgGcxfDScL5d25/JnH7+3dNEje/7e9vuO7+GJN2A04CSZTTFCPSUL7zzzk+aWMriXV5kQQ+vV01cEdCrbn3p0iE9eaU3bUO/Mgir7/XfWakFLbKinmTgmlqbzcskRvvjqu8x72d9/s/G1V1+kGo6MGTezuJg9yO6+3MvK7tx94LHDu+kZVkePnXps8XJ36TtlRzreWcWhLmgm4siFj2zSy6usbVi9V2IneaU1pVXqi9mOPHVo9fwtuD4swAWV5NQqF3+sW27nnvNe3uTub9Z5tJK3CRpuL2KJ4wXU7QvpbXBLGXyrKedSEIAVpQ47C5vsufPw8DgUxxuhqKeanxyBV08eHifRUHuRlrBAuZJE8kh49eTh4RKEe7vwbmR+Nj/iAyuWfCQc99w5GNhyHq3kzbaSt9nI9iLOgrdAnQJrvSfX1JOHp/VBuKuAtkD1Ekfg1ZOHp5UU1bvSAuWs/94cePXk4Wk9RfUtJKCtxPx0VLNkHh4etzBCXWaBeqT5yQof+uTh4ZIRSriZgLZm85NXTx4eDzZCXWOBep75yasnD08rNEKdLqCtwfzk1ZOHp3UaoS6wQD3M/OTVk4en1RqhzhVQjzc/efXk4XFPIxRxy25MLWN+Oqmjlxn3aRTzLTYeE8tATUu8uuexcs3XnPp+edxujmmmxcxOZ3dmEniY+dlilxZroxConjw8PNavzZafqu/U2fH8XPimwKsnD4/7Q3BXQFnNT89IH/HqycPjXqmkWqel43kLtHHw6snD40EQbiOgHmB+sqonDw9Pq20v4hQB9cjqJWvqySeOeHi4j5O8+BayQN3d/OTVk4fHjchsKSNUwDXz08/Xd/CgwX3iemu0mjNnzybdvq3WqFn3DA4K9vPz1Wg0BQWFOGHr84qNjbX9orW1tSUlJfSfkZGRfn5+CoUiJyenQfX8/PMvKisr33zzDWuDx8TECARWP+esrCwcx+mDzMzMpLYHkJjtjOO4Wq0uKyvT6/WWQ6Eo2rFjxxkzZoWHh6em3jl06BB1/PZ/DjqdLjc3l/UhHx+foUOHjRo1ymDAT548cfHihZqaGhtDBZHcvXvX9iu+9trrYWFhzz33rO3dEASJjGozfPTY4JCwvNzsktptWVlZtp/SrVv3O3eSbewQEhLi4+NjexD66wAAyGTe4eHhAgFWWVlZXNyAOSOTeX/wwQdvvPF6VVWVtX0svw6CIDQarVxeoVKpmNv9/PyCgoJsvBx9FpkRFBTk5+dXWFhIDYhhWNu2bW2Mk5OTY3lqBQYGDh06bMiQITU1Nfv27btzJ9lgMNh4F2bgOE59WdSeYdHRgA25vKJWAS80gUAQER7BfIggcLVaU1FRYfsyb2o9U7MKQh2/LnyTFz5CEOSvnb/7+fmZbb969erLr73K3CIRS/b+vRvF6s3nV17/3+UrV6zVgaakNHAZ79+/76WXXqROuNOnz6Jo/cgfTp+Uk3TLhueeknK3uLh41KgR1gY/ceJUeHi4tUf79+9XW1tLH2TXrp2p7c8++9zy5SusPSstLW3GjGkEUR8CHz169ObN31juOWXK5MzMDDs/h+zs7IkTx1tu3737n65du5ltzMhInzp1Cus4AoEgKSlZLq8YMmSw7Vc8cODf2NgO1Lse2LMD6z5duvV8470NzC04Dt/4rFkzUlJSWJ/y8surFy9eQn+YrKxbt37GjJm2D48e4dy584GBJhI2ffrUtLQ0a08MDAw8d+7CyJHDmT/MZtj4OgiC6NWrB61ljz++6JVXTC4BMwYMiGf9PVu9+pVFixYvXrzo3Lmz1K/y+fMXbYwzfvxY5i9ocHDwyZOnMQwu883ko48+/OGHrQ2+C4qampoBA+LpPRGUXa0+++Lzvfv3AQDCw8J//eln1n1KS8sefvxRpnw7aOF4hCsWaHPMzx0//+rn55eSmrr2vXeLS+AvfJ+43mveeDM+Pv7jD9e99MrL9J77d+8BCHjvww+OnTgeGRH5/ZZvPnz3/WmzZ5r9btOsWvUSfT8iIvyllxKys7O/+OJzemN+fj6l4GfOnEMQZNGix8+fP9etW7ddu/55be/BFV1jdRpNM+Oeb775ulLJcnhqNbt9TfHee++cPn2a/lMqlQ4fPnzlyhc6der0559/zZ07h9oeGhq6efM3BEG8++47O3fuMBgMUqn05ZdXL1jw0IED//bu3UtDHj/zcwAAbNjwsVKpePPNN+ktlJSb8euv27t27ZaVdW/FiuXp6bBlX7du3b/+enOHDh3//ffg5MmTzPYXCoVXryYCByEUCqF6EmD7T98e3LcLx3GhSDRiwqzHH1+0e/eePn3iLD9AUjKWNDjyjh3bT506Rf/5xhtv+vn5mX1EFOfPXwwICNi16+9XX32FVJnxX3yxae/e/YMHD6ysrGzOu1MqlXPmzKL/FAgEPXr0fOWVVwMCAm7eTOrevStz5z///PPChfOs41g781lJS0vbsmUz60Pl5eX0/djYDgcO/AsA2LdvH3n2KsmfjekffbRu9epX/Pz8PvvsU9aTymyjTqdj7vDehx+wvnSqqbOSmZm54/ffqPtSqbRDbIcpEyeFhAQf3LN/4vQprOa2S4xQp0/ltNP8FAqEISHBapV6+cp6b+76zRuz5s09vP9gnz59xGIxpQLDhgwFCPjmu2+PnTgOACgoLJg6c8bhfw/u/uPvidMmsw6+b99e+n7nzp1feilBLq9gbqRYsWIFgiBPPrn0/PlzAIA7d+680KPT57fTEv7Y/f6Myc3MGh06dMiGN2eNkpJSM1/1zp07P/zwQ1JSco8ePYVCIXV2/vPPHsp8yMvLo3ZTqVRvv70mMfHaunUbtm/fQUmt2VvesOFjjUZr+TkwwTAsPr6/VqudNGki4xiSR48emZSU3L59bGhoKNPI6t9/wLZtvzb2bZKXGXtByWdboDGyeuWT+XnGcIROq92yefPly5c3bfrq8OGjI0YMo3dGEOS33/6Ii4uz5xUTSeg/ExJe9vPzs/w0goODAwICMjLSKfUEABw+fPihhxZs375j3boNTz65FDQDgiDMvt/09PR//tl948YtsVgcHR1Nf6EAgJs3b9j+suykvLzMnnH27dtvaWjv3bv3v//+u3Hj1lNPPf31119pNBrLk8ryTGNCXbYNH2RFhdmen2/6Yt17H8THx+/4+df5jyxw9MzOJoJyxPzs2BG6b0ePH7N86ORpaCaEhoRQf655HVpMv/35B70DTuCKWoVAUO9o+Pn5RUZGymTejTqGp59eDgCgrZL7NAoN+avbtlccp3Luer2eCm4GBgZSWwIC4B3mxUaxdy88jzt27GT/4CEhIZGRkXTQNiwM/lxfu3bVcs+1a9cAAMaMuY/ecvLk6W3bfsVx3Jpr3wT8/GEgmFZPmqNHj5Ahl0Cm1t+5kxoXF7d3754rZDzHkkiSRh3AU089TTrRC5kbqQ9k5MiRwDns2fMPAGDevAeAi3jxxZdQFD1+/JhlmEKj0Wzbtg1eiWvebuGjokJ5wcFBvj6+HEnHc6WQ/i75PU2dMgVBzG3pdz98f+ykCbl16oBiqGUQ5GriNcrdo/589tmVx46dmD/fsteHLVAUpYxcZuLo3nU4MkaOzAX1pIiKimI6boWFBQCAF16AYVwmBEF07dq5d+9e9o/8008/Hzt2omNH4wrMRUVFAIDBg4dIpVKzPX///feuXTv/9ttOektgYOBnn33avXvXjAyHrQ1VXFQIAFjy9ErLh7p27dy9e31kFkEQuVw+cuTwhIRV1kY7duzEsWMnGnUA7777TteunUtLS5kbvb3hb3Mz/XcbjBw5CgCQxBZ8bxkeeeRRAMDrr7/G+ui7767t2rUzbZK3JN98/x0A4MklzTL8uSigzZy7aTAYMjLgzkf+PfTbth2TJtQ7jEwoebU8cS9evgQAiO8HY9VNgxKI9PQ0s7T7nbNnAABtuvXgjnquX7+BiutXV1dTW6ZNmwYAWLbsqVu3bn/77fdBQcGOei0cx//660/S4b1x9uz5hQsft7Fzr149Nm+2t6OSnaxaDqOZY8ZP/un3/a+tXRdo/a3p9fohQwbZSNo4kO+++96GvjST8ePHU4b/4cOHgYvw8vIyC4lyhGPHoV/ftYtJdNgRMzsJ92hnZ4Mnlz/97DPLZ82YGRwclPDiSwkvvgQIIJfLH13yOG1qUQKq0WjNnkuVlYSTpx31C/nuu2sb9epeXlAf5fJKs6IlOWncZUZEg1SrKVd7uHjxsuVG22liAMCnn36K45/Qf6IogqLGSEVcXE96u0JR27dv72vXrguFwhEjRpw9C2O4er1+y5bNzFyZPUyZYh5Hfu21/yUnJ7/xxptBQUGvvvq/V1/9H5VanTRpYnl5GXAyBIE//sD0H3/biwmwbj3jPv8WOo86nf7nn3/66KMPGztagx+4PcybN69Pn74ajebIkeYKnEwmS0qqr7VCEIROeQ8fPtRs57Vr31m7lqVm5uTJE8uWPWn/iw4ePIQ1dV5eXj5s2BDgZI4e/I91+9hJE+x5OuUjBgT4A0+yQB3VOuSLrzaNnTRh0vSpV69d02l1AAEBgQH7dv3z4bvvMwWUWb5DQRWIoUjT3w5KVlcE6c2lmcBxQ93rNgclRGF2s//pCIIIBAIUxQoLC6ZNm9K1a2et1uRQVSpVt25dunbtnJDwUk1NDUEQAoFg+fIVKSl3xWJxMw/+11+3de3auUePbocOHaTOYB8fn7Nnz+3bdwA4H71e98jcSY/MmbRx3TuK2loCh29t0aLFKSl3LQMLzmbOnLnvvPMejhsaFRix8/vFMEytVickvNS1a+eyMvMfJ41GU8sGlR+3H4PBwDoOaw2Gw1EolKy3Rg1iIQAuM0I5ZIHS6HS6l/9nDK9MmzL1+RXPDejfv1/fvtcSEwmyAFAoND/sADLVUF7RdI9DpVJhAEjI2BYTH9JnbEIC3YwxY0Y1YZAXXnjh0KGD1H2pVHr1amJERGS7du2pciJW9pJQ948fPxEREXnt2vUePcyrOJuAwWBYufI56v7o0aO/+urrjh07rljx7JdffgFahMsXzl6+AOsZ0/Iqtu/Y0b597NWriWa1Pk7lvffenzv3frVa3aePXYn+BlEoFPHxfek/qWrToUOH0d+g6au/+3tdZU9zuHz5kllOrCWZMbe+bKsJSCTQGpBXyoHHWKAOMT97dO9x3+gxltv3Hdj/6BIYd1v//ke0pWk5gSSuJzQHrt+8AZrKwHL4ixTStp3Zdu/4ATDHdTcVuBqVSkW5dV9+ualbt3pBFIlEQ4cOY63dGTNmtFqtxjDMcnqCnfTt23fixElsswNOUEXyK1Y0MImoOWAY1jOub7ceLG9t8uRJSqUSRVHbU3QcyNmz5+fOvb+oqMhR6mnJ0KFDtFrt7NlznnxyGXApcnkFVXhvbYcff/ypf394abQwUyfDAo9bSUmNepbzjFCuZOE/WPvua6+8KpFILB8qLIR5WGahq6XjNmbU6ObYiXTc0y8klLn9mFg2ihy5ZbybBqmoqHjnHRjb3bXrHzpYhqLo1q0/rF37LutT9u6FJaIRESZz4+znk08+27jx82i26XfNt8obxGAwvLLmg2dfglFXS3bu3AHze23aOPswqBxaUFDQ+fPnRo92VukSxYQJ46gqoiZEJ06fPmsW3BSJRA1O1mCFKlR68823WB9dtuypwYOHrF+/HrQ4jzz0MADg260wF88FUI5EPw8ePgRrZT+GcxvMGD4UVkpfumxMwvxOVoB26WSSDfD19aW8++aoZ+IhOO8iMDKKVk8MwxAEaZnErp38+us2Kjf6zTffUluooGTXrl0j646cCVVLaG3KY4N8+CGMPv/88y+WD3Xu3Lk5I9uPr79/ONtbW7RoMXQ7rl939gEcO3ZcKpVu3/7rokW2ihAcQlFR0bFjRwEAV67A+rlGkZ2dTVX+01t6kSXMTcj1bdr0JUEQkydPoeoBmEgkEqpgbqrjqn3tZNXz8HVLS0sbNfOqMUYo4q4W6FdbNuu0ug4dOvzz59+DBgykNorF4hefe/5t8mdwTV1WfeuPP8J002cbvWXGuqKtW74FCHjx5frqv4ULH9++fceUKVMbfF1mzv3n1XD+2TvHzwjFYqpo6cwZmM5euhReqNxhyhToUw8bNpxSTIIgEhLgkR87dvzDDz+i4xsDBw68fBnWe585Ayux7GT9+o+3b98RExND/Xno0KGamprIyKhr165Pmzad2ohh2KpVCXv2wJnLTzzR8KRJexjW11h5asaHa2Dt9IYvv1/x4qs+deXTU8cNP3fuPIIgKSl3GvUq27fv2L4d2q32M2XK1MjIKJ1O9++/BwZYAJzAM888jeMGDMNeesmkoLVdu3YDrECdCSvJWXzHj5+kipDmzr2fCuxQwkrj4+NjbZzu3XvQu1GTrE6ePP3WW3DGBMXMmbOuXYO/WL/9trOxySuKuF5x1m5mB0lvHzxw0JLHF+35a/fkSZMInHj48ceAsyBcn0Rqcue66XNm/fPn397e3u+/Y+6NLnjsEbrEXafXvbDqpU83fPzPX7v0Or2ATCidO3/+JqPquE2bmH794o8cgZNVbGBWsaSsqtr64nOLP/l8Y0qmRqOhkte//PJzgy2FWpiqqqpt23555JFHjx491q1bFypxJBKJ33vv/VmzZs+aNZu58+nTpxulcT16dI+N7UBdgRRDhgw6dOhwVFTUhg0fUxP1aGbPnmVWYe5wkm4mblz3zsqX3xg8fNTg4bC8nALHiRs3rs+f37i5Ov0aXyn8+uuvU3M0fvnlVyfVRVkyfvz4o0ePPfHEk5988jFdc7J48RJrc/y//PKLL7/8ory8PDMzIza2A6VxFJb5oh49erK+FxhbvHVr3ry51P3Tp08vWDB/x47fFix4aMGCh5i7ffHF55s2fdm0t/bpepO+MNYqmbp26WK5Z2lp6aOLHre/mUgLzOxsVjcmZyx85O/vP3L4iOFDh/n6+qampv7x9595ZKcPM8Ri8ewZs0aNHHkv696PP/9UUncZU92YQkNDAwICSktLKyoqLJ/Yrl07lUrVMY3deLkUFLZ06RMjR468efPmF1980aD702A3ptjYDkKhID093fYX36ULlMLUVGO2KigoODg4qKCgwFrXOGr/rKws+qcFRdHOnTs/+OCC7t17qNXqv//+6/Dh/xRkizBrIxgMBrOEfvv27UUiEXNYCj8/v9Gjx0ydOjU0NDQ1NfWbb76xPd2oS5cuer2hwSlJdDcma62YKBAEjWoTM3bi1NgOnXU67ZmTR7/67icbgemYmBipVEp/mNY+ZMuvydpTrME6lJ3dmFg/f7PXVSqVubm5gYGBIXVTmVkpKyunT9S2bds+//wL0dFtDh78d8eO7Uw7USAQdOhg63NWq9Vm5irVhu6RRx7t0aNHRUXF9u2/Xrhwwaw/iJ2fMPVQWDRLNIYi8x68eAUCQYxpXNtgMKhUqrKy8ma2s2Ptz8TWoglxYwFtmWWNHdgguUEB5Wm+gFpyKYnTLbrtEdDWSXSLL2vsVAFFPUk97YRvL8/D0zrJtCuVRLhfEsnlcGeqOw8Pj0txvgXqYeZnc9QzNzeXaobE0wSKioqsLSLi1hgMhtzcXIOh5ZaH5HGcEUq491RO91rVffz4sc15eitn8eJFwBOpqqriT4zWANp6zE9+VXceHh7gUCO0tcRA+cQRDw+Pw2m0C1+Wz2nzk7VIIrE0HfiwzLLvG9KRfYlVnhZn4xq4cob9zHt5k9OOhcfzyWQrqjddcs6uleaaZIE2tzdmiwLVk42+IexzB3l4eHjsBPUk89MSXj15eHicFwltkgXaxLZHLQ2vnjw8PM3AoS58vfnpDi48r548PDxNgGGEOskC5bz/zqsnDw9P07z4RoE2JfrJbReeV08eHp7mwLbahxOy8C43P/v2NF8rjVdPHh4exxmhhCcX0icmmfT05NWTh4enJUE9pnqJV08eHh4n1DMRTrBAOZCID/BquAkIXy3Pw8PTPJBmCSi7+XnS9eanXKlo0Pzk4eHhaZ4R6gwLlEvwzjsPD49LQN3U/KT9d149eXh4QLNpmhH6f1oYDkkupf+uAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The complete plan is configured; execute it once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-29d1a31b834a", "created_at_unix_s": 1788842380.421449, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 3, "requested_group_seed": 3, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [3], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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HLPx62+bXHq3Yb1+iYtJpTbzxLTHpcKWvwFDVt8IC5OT+g33wN2LGjlnNZhQxkOTrW2cu+mn/sGsW+0RA4fWjqSEsNj5zxLjyvUHTJry7kkiB5trTlbXNTkeOBBkEN/9DMqr4xQcfOtjRXS6jFEnKZRRtpUmSlFEkRcG7lq6tjAnYhfAr541c9PP+yJS0uW98AYeBa9vrio/88eqjwtSQVMDRwrQ0U0ZGu36JBZ9Ld9rlI1OGwS69Da4Gxhg1cNKEKioG+IjSbb8OnrsgYzgWUL8giMH69hdPKOSBfy1xF7mMqlT1lnxowczR6SlxEg90oYwmxEbcc/0lNE0TBEESqBEVETgVAt2LSaf5cEr/uD595735ZVhsvEIdkTbkwhu+2mq1mNfefXX1sYMunmu1wh49pMwH32SCuQabDboOByuQMjjEwSfomQiGIsLLQejdggef9abff+WHQbsbH4dKYyLV6zeVodqaIPLWJYmLDjdbLCazVaWQw1Z5gFCHKUxmS5hKERGu7PaQiXhAHkZA45mSTy+HLWBkqrDscZdc8sSbJCW78oM1394+r/bkIecfLNMlsqMhBa7rQBGoVJ6Su/q2EMylz2LyrJulC2Iz4K+vraYSBA8eX6weffuHNx68AoScm58UH1UwGgbmXDD34uDIDy6YNZogCZqmSbujRwRpxWgPx6zXFW/6sXjTj7kTZ0x/5v05r362cu5wZztTSqh3+rbmzr8uirdGJrtqa6tkYgW6Zu9HMwjIvQj2XTy1HU5RDBZCx2/t5K8/NSnm4HHGmWQCczRNy5g4nVxO0TSQUSRBElqdzy62XWDiiUfJewBevOQGl824CADw+jKnM2D45I61lxw5Y8nmUkAQgrVAiNI/f+EHHJsrTsdkZJGUjJ/kUUVARas9cRh0GqvZBGiaIEl5mJqfvOJz07cwx7VjudMZ497RdLYUBA8hJqDey2iftIT/mz1aDpWSUCkVFqtVKZdZaZoRU5g4hpLao+pvsIa6x4MvfOLObqU7v+5wnw2UoVin6nfxnOJNP0oGJWlbJ/IzuzYPybglrk9u/akT3D6jFz8MC92/Eg5w9Y41S+df8f53C7/etvKyEeJHVdGxlEJBW61l2ztOSbnDpPueBwC0nD8LgoqQFFBv/Hq1SqlWuYwP9kCwO9+1OkupI3Mf/mjei8unP3dnBm13jWlArKWGNxJhBdaK+2xC/LYRXP3xTx9M7Y/MVUqhyB431WIw+MSFBwBUHd2PKj0XfrP9PzdczC+ZSswdePWKnwEA3y+5yv4E1uPxeMENpVCMvvXBQXP+D9D0F9dNBiEsoAGWR/JGBs61Kxvq2Gpk9ynM8lmtRvCBTdGuwqJtq9rwedzF1/4I+sO7bU3mlgZFSm9CJgcWYG6p+u7DR79j9nx92eJhquaTZK/Hdp2bajmqA/JtFHzKVcojD4hMXXeiB5Ksunrcgs9/i83MuWPjcW1j3ZldW8Ki47PGTEZauenlB2uOC6sCpj31LtffhMOoaftk1pAOW4qsmj8O9JChckUbV/jhZJy81mnv16UVZkV38gi8Q/VgDQWhllnySR0oioH6HCo8KunKe5Sp9mYrtNnU9Of3rf8IneWYcXNjxl/G7VO1+gVjTTl/B0F89mtqVDuhusqyJ4aWCGu+bYR5f3EENn3YmCmPvBqR1IvbcmbXlvVPLBY0tZt473OD5y6QfEecgIql06TVNJ8/c+K3H4q+/wwEIQEqoD6RPERhVjQWUJ8RKhoayALa9XSQBxP90XPHeGnShh4hHAPF+AEcFQ059XQ/D/b6k6zOlv7VcUIM9AydxQKK8Rwsoz2SB5/3i86CYJZaLKAO0VIegpRiqPiuPgTLqCMvvvw0uvHEY960Ge6xOguCWWq9jIEGYBhUpIAOVLSpGutrPHw6Fk1P8MWnFZ8DmwE3nTkW0zuvscwHBeH+i4FKuvAvvvx0D1dPn8PXWffpMp0NXAvUtSCKdbYjwfUiK4XNTw8/rU5raMOpQ8rI2LdffE2j0z/1wSfP3SX88XCv8OizsFslJuR50A17Viyykiat5Nczp3NSG7gCGkhgZ9UHGjrnmiWj+6d1eIxh4yZodMIhlENyetEAnK5ma8tvu9+dEbDdADY/Azlo8IaUMXvK7eiBpNQGvYD6qkopkFvlBxNuW+0F2Smwhx0Bn0ID8Nexs2PzYQs+c2VxYU6v/qPG//jm00Ny7LWH/OPJZLDtEAbjEQ90QmedSW3QC2g3EeLe/bv/ektsBiIee+Yx7z6hl59/lCCILWv/i+7aO0UxKgobg6J7zL/Fu7cH1yeraDkRoXSQ9a1H2Jj7mLyk9vB+3XReGP/qrPcCWjhtUVeuR+rGMGsgePfxfdnhiw0lRfF9C816XUtFsa80sWv8+vy+6TX1HvgK+0+VRqjg93PrkZrRAxIH95YYg9wtpEfD9xPWxk5FV4cxLRTCVfx9TEbTuIHJAIBWjb5dawDaw9y1gU4Y1D3njfGDzoaIBeoLKQxoGW0oKeLfRnr69kuv8ffRGZ2Or+lmbB8PxbQUatM1AUAUnW4OU9i7WyVEg+LzZ/hPQuq5+XA17IZFEscq4KOtGngNeOaJx5558eVuVE8AwPbDDtN3R/SVnggjbCoo6lOHCWpCQUC7Wz39IqMCcexiiUTBxzJb0objjgfv9f6gNJDLoZO7/Ui14JHJQ1JbNQbpM+kDWxC0agzs6A+kR4EnQzTtMKZIshnrqcqWnF5BNnUSE/oCGmD4JtfkQiuf+gD6DuISnw5JT5S+0iREqd08Akl0qiOq2Sw9Oa6l3SDZ/blTPaH9BiWjLEZHuSfh7+hUVauzr8NFI3Pr6ltUSvxzCzU69RcNgTCom7z7r7c7DCA+9syjPsw1/X3yvLOiH4F0cqnqncfKxw7MlHzKzmPl/dMTQHfDdaSWK5UmnQbpjjPRLD0vDJjmZyXC1j5GY7fbn/Kw8DClfZhaa2s7AODiCySmYaOvQE2db3p0YkJBQFNi3DVYApZ3//U2nNrqu6TKg8se8q13/81nb9A0WPXeB+KHsntJjdXsBE6V19ehXa78yGoxR8Uwvnkzqyw0DcrsFhx84YtG9oPz8HhXoUCWIYI3WZSpy+I90O16jwkoAVXLieC1E91HozeOye/N/BrgAFeSRNE3+H8EILiibjhsbuGNvPFtLvBAkFRKueCHZ7ay+lJ8Xugq0jT951FYNBMVzjr+vsxZ+66hsnj4O5JRxJRR/Rykx/bq6MWrA0k9mUlETsf5EoDgv5FA1n1M97jwSrnPhkH7im17T7qjngXZKdztv46Vo+Jt9G2nef+QJPHX0bO2ieW8qeUOd1jSk2M9OVNpGZ05f9G4Qey5qRXuFoqbrewPtaA3VCINM/aOIMDukydG9R/gyVl5ccoewx8qFami2vTshB9n0mN/0WAz4rg3smnvmcGZ8E+TnBhjMBqtuIaphwtoSoxaa6K1JmOg2aHRkeqR0+YAAE46jkXtzVM3ZEXaxZCLtzH/sMpoe5jVVn8hER7dwaSnxw1K0RqhsihkUG6cpacF2AfS+O/P0mkV4yfP9SaheiKQ1y54Kc78TE6MqThfC7oV2molSFJnMPHDoM40FKknuo8JPTxOqobLSbWc4NRz3nWdqGvxKQOyel0yZfycuTME2ymS4P5jAlV2nKVcuhba9h/LjiPVOr1JbzC1agwu1BPGB3n/2bbyjhp4UCgMwqBnLhJOtAUFPyXojArduvRx9N/u39d88pDwe9JZpM5s0162sjU5KeavotM+fkVMMFqgp+ra81IDcTpQREysSdOg1TQ88dFnL95+k0+O6SKvvfVI+aRB7EMfvPrWXQ/f18nXWv/tJzOvvBklpveVNgzPjXewlvk3Rb9VQcI6TClPi1fvPnFi1ADfefG+gLK58LTVqjcYw9Vh7njB1bWs+ZmSFOPNhYGm3//qjxGDsla+9xK3rfCSW0f2Zxs5LpozHACw+DU4eN193JFyu/mJCVG8iYEer2wOKA1VxSTom+tN2jZ0t+68w1wtryXSNTdPHVJWJawz7ySUQskWGNrqe05VurX2ceKIviQBNu8/OygTVnqWVQZo1sJT9SMA8fve04N4MvTXodPp8eHO9q9oUzaKRq4aDMYzVS0Hd336IZBfNjbn8mtuQNv3nGRDAXts0okk1U09tbjMIwEALr/jOnRj7fL/SCTHMCFBKFT2hiem65vr0e3YrPzTh/aAoEURFq5SylFdIf83d/EF/ZxlVLh7SD2dcfJcffeWgp6vbVz94cf8LRRBW2jCSVyYjeZy6gnNTycUlbm6YFRU1dE0/dB9S/tnswWzty59fGT/JI2FunN2wZK37BFzJKmSespJKgqjh6vDNFqdO+qJbq9d/h8XO2OCl1AQUI74vkNoizl76BgA7D/UsqpGnxdO+g+2WMrGxaOkuvg4yigSmz/2nXEhoGMHZu485oFh7g+aW7UT5lyhM5h2HINFuACAdr2ZzcMw7joPWqBBHzz7nteve7y0AgBwumhL/+z/Q1tWvvcS6ki/5K0qTig1Fur2WYPvfvs313paU1sfEcGawM7ySPwz57bsvP05r98CJpQF9JqbHvrmM4mF211GQ8lB/g1r4C2UdhOKoNVKGX+5Y3V1fUqKE7ORV6FNdGR+BgL5uWlI+996q+OdL7/jev7du55eCgBYs/wLT19UbzDISUtGUuTFsy5GticsPo2OevfeSzit5Hz5u9920NNbZ+RHqOScL492y4y1511RmQSmJxNSFijCvZr2QERGkQoZaTZao6Ii3HqCzRStrmvitiUnxpZVNhMEAav/AwuHv4vNC/agoJhffyYoTS3MikHlT6i7dmE26+9v+LMI0MBgMtc3tfdKiuHySPwkUkZqwvQRvQVCCQC4/z0HPV04NS82QvnSp3YTlcuDOWvF5BNUrSd5HarYAuHfD1aNz4ft8nCn0aAU0EDLIwmYc8PCn1Z/jm43aRv3nWqMtH3zmOaS7C9ncO9s1xmkLq5z0hsthmaN69y0BI7XCx9ePfiVBgEFRcJuePwtgrsIq9VafAaW1spl1JPPvwpD5LHRsy7MQranPYl0snbtlmOcUF4xLjcjMUKsp4+UQj3tl+zNEhLJGGhY60naURalET3624FKGoA/j1QztgJMmmEx7S6C2wJ94Zkn0ZoWF/s4PEoDqxX8faIWaejhs2WB06aXo8MKbQHVtXbzMyXRozVR3UY40+sTdgYxmRVy97+E7NVh9k0Pu7N3ZW2zwQTXtprNlkceuLtvH3ah18hfDnJG5SPLNwuE8hgjlJye3n1Z4e2v/8odU2+0RCiAizzS73tPX+7kfCpaCdB6dNGcEejuc5/YO4qOH8QmuNRqh8bMAIC0FKF5uzCVfSOfr9+PJPjPI9WwGpjAYtrVBK6AvvDMk6juWs1ruyvAtXRypKWnAgDOllcCACYMYvqEtxvYJY8nTpitaheWppuloD7BaqW5tY5e9qDw6mnOsvP+KNVCaA2WcOarlxIb1thu8sdLAAAOHi9Hn0Zx0c43Tx6YP7HvRbOuFhuVSCtNhOLO2YPFhuftr9v30VllrfUOzUxVckJvgq+hsq2+HZwZ8+Gz76UkxfLLmNCNtOx8GFU4AmX3fNlR/nG2H3EUUwLWArOXiyPnnb3BqcOFi+UMBnblRYSGHVhQ36xVpQ3x7IPDdLGA+iOPxK1aadebI8LVrssbMxMdGkQhfeRoPGZfBIICpD4Jk/L1xVe19LOvuQ14MjDNwfxMYszPYIgAc/HZ+lYDybSp51PFe1McriuBYAyUg/kELGYL8t9h/mrExKULpshkFEoiFfRLmzIs481vdgm0cvGJc5zhOXd0dlZKlFhP+UkkGCBqN/Ldhd/3nuaK512fcFp2flX1VtpiJijhb5AT0/GDWbO0b1Za2Vl4+RdwvqapwxVf/NuYHmSB7jhSjZprkCRpNhk5lRSII0JyIyLoUkqZieF1rUxPkM4dx6NuxF4vKOgMyAtmY76OJ/vBs+8yyfe7JcXIQS7FMOJ8vrbZZGZXi5rNFiTZKIlUeMmth4qhWYfkMjMtcdrwTKSVko68ziq7+7JCydJ6ri1Tr2TYqxSpJ3slE4GsTo2FGj8obf2WfQQBKKUSkDKzTgM/ALGS2qaGNLW6qjl1LZSBlkkMMbwX0C7II+n1rHPnUYu6blHMVq3h5WdecdJW2TPOVLeEq8P2ljaMyHWa273Cscrng2ffdfajDTo487NXUuxatm7Jm7/ovqP2CUtx0eFymezWpY9rrbLx+akfPzTjNp5c7jlZu2bzUaSVWovsnnl2rbQbp69VjuyfVFVdJ34huOohWSkwP8Wkx4dbE6ALD4tJ+Zd7qzk6ln1WSxPrYwnEVDAvpEPTsqiMXVdSkN39XbRDm8C1QFFjOWdNJbxQzNJKiQXj7Ot0RHFlk7YLR1rqDOyVQ8fruOFMPTlLjV8mea5ek57gdMljQMG8WWawpTTeqKfJbDlVYW/a1NCs0eoMyPw0Gs0jZ92O5DIrPWnKsAyB7Ym0EpmKd8wqWPo2u1Rpz8labqIcQpBHcmF+7iwqc7EC1WyxyCgYuOlQSWH5gU0cJRnUL0Mlp0YOgHkumune7/JzwgSMgPo8DIrW5DjTUEnRFEikJGgsBD+iKiN1LvQxXCk/eMph/iLHBdPnkiShVilMZovZYrFa6ZGDs4CfEaunBEHbBZ1vfjL/eulNVNY2WSz2BQnFRTvuLtqhjoi4cnxu8oDxSA2vGduLMjYufu0IkkuZUn3TtDyBmC59u4oT00F94v/a45D5cRaMFmJ7E1ziiFQ4VKqZzLStVT8Lp6RoUS8swIpQbD9SlZebPiSvN0USSjkll5EkQcgoGKohCbauq6HNtGgOOwF7w9Z9nnxmmNCyQH/+5uOnHn+McV+ghhKEW/qImDyyL/OtFf78angF59mpMQYDe312pp4XTJ+jVMi1emNMZBh3tJhItUxGxkaFUyQRplIQBEH5NFBPW60e5ZE4rrjjei/W6njEB6++edfD94Pu5rXnlz305AsudthVdIq7ba3clZMAP9DEKLr4ZEnxyZKlM9jr3NbD1YPTw/Qa2IamIEm2+LVfkFwagfyuOQXiqKjAAhXjOpCCsvCI8+X2lbVWi6nDX2JkdMyl42Icroy8ayTNVPUTBDCbjL/8uR+6IC3o+iHjqqZQ9h8TQALq7zAo6uqGNPRUpXCOxeQL+tl7TogQW2Di1eLccp2Jl12JDBYZRREESIiNpCgyJjKMoqgwpRxJZJflt2UUyW+q4Wo1pxPGFmbtDJLuk3wv2H3zU2uQbsaMeOX5p9CNhEgmMhDFVlbGJAg/xssuYres3Xz40Okmu5hmxXJiKoiKjvgU1pD+ddOQlNwBWfev3H/3BFiov+A18P0ytIOuXWI257CchNpm3aWDVBuOOFyqrRYLyTjvGr0pXOWyPTMhaoTAU1NkWwzOite2a2zz68l9JbWDesf87/ddil6FnPGL9bSnWKBodiPzL/ym5PeOk9QR+4+so0kQLlaLXzA427dWpNeEq2RGs9ViMcPVnF554oHwLnyB9++DS15zlUAnKpDvItHKflhfqJIX5rEFQwiD0XjdBHYkqlZv5MS0qrqONpv3LBomA6DxdHHdXeNlFDxPss8w6HerKaSedTnTZDPuMTw36bQxSt5eHT9gqKr2JEmAX87HgKtuPv+vBRqLB7+7/N4SXVDZr4btG1LfolcpFQfKdb2ToueNzfnlz/37SmrQZ6joVcg3fsVVqFhSu01A/T2e02o2efBzkmr1xm3jO++Issrm9AS1eFZP96I1mGHKlZkYIZlaWbP8C2dh0DXLvwgW9TRaQWOrJi5KIq/CmJ/291FV24BuJEbBT0NnpEuq2guyYl994amHlwn7Gz23cqNO00YQBEmRFqaGSSymuVnCYdGnbSWWbbz+/9z1lKbp7Lz+DQCUHT+ZHg32LBrGGY8ErDZl9qkt0+kNOj0gYnqRmYMsBzYQMckmi7Xp/Fmop7t3URShVshyE6vBdy9lP7fOsOqRvXXwxCIjI1gD3Oqyu6jU35X9bhPsNNOquiYFRShjLSt+2sv0wE/UWGTtpOq6QWFi550fTBBIqpt6WpDABmcFHKp3r41DqNApAY1QkAaTfRnJvOvu/eE/bKcynyDzwih0LqNiztVr0xPUAdVzw2KhBR3tBFTXNn3w7LtcgSQf8dOKykoKs6WGlXc3MkCbAdHYqlGgQSss8C9RVduItBLBvx2TkHjZRYk/bDly6DS8HF53y9KCLOjsN0UWiF+CVCgjVPKWpiaCYoUJien2w1XjB/eiSIIvlwiBE/LPiWq1WpWfER1vbYqLi3n0oRmvrCsb+vIPAIDdC/Mv+KwI7nMTtO/M699kn9NSZT1cRQBAjb7GsHXtwIsmmM8XtzTBvLnGIpsyQKGQkRtWPSIZxOjQi5cELoZv0jXBT5JQKmBGKT2aSIiP3rirBADQJyIG6Sk6gYQo1XUXD5BURmcmKqenAkn9fusJwRGunDRAUlgDTVLD2k5y36sth9mlFoVZcd51ZiGi4tjOGt6RnehgRPhWQAEATB4JEsYslfMsGmhTRn6zIkRyYuzfTJQQCegzL74MAobZ19wWERGuM5gKMyMADdcI8t81l+3la6jY9txZBDu3K+UkX0DrW7XOXvTkuXrJQnrxUk6fJJFmzl/Ev4tsQ7VKaHGn9nL15167+TCnvkhJobmnGnh8z2aScSn4bVlamuDnxonpsJw4tNAAieY/J9gf0iVj8pAqadqhEOwvrcvPiC7SsAW58dYmjYUqyEqYPCT92+3QuoTxgePHR/aNb5MlcUI5Nr/XyH5J3A4chFzZXFcJaFpjkbXWVREEtEDhU5ARSsrEAtra2g5deAD6pEv/TqubdGVVrRoN/MvOmjR0UJ/4vUdgAu3XXSUFfWKtNI1ceA5OGTUWWbRacdO0ge4klwQu/6HT9ZsPngMGdgaEM5CkivFUUpHkCVTPfckTPJ2Di4lbrPSPW4+KC3vceYnOCqiAoo0rfHg05pe2mMkjQcIcpcRdHLu9oY4bSFr/LjrNWaCBo6EuBFRi4aYT23PnwdPpCUIB3Xms3Nmy9+4VULGXvW7bsQ4PgsKXaGgHt7FNa6BIYl8Z/KBISh6mlPCxWlvbh+XE7S+pnT42z2S2KuQwcyinYFUQ+hXV1rMlbkhAZ1w08pV1ZfwjxFvh8TUW2agByaPzUsRayYlpn+TIy8Zki3c4cfy4TwT0r2M1SD0hBBEfywb6NRbZokvzw5Vy1/rI19NwlWzRjHxBmqtjPZ07Qmcwr9hwxKr3pZ46Vb14+AVes/kw/FsRHevd3r+2ohsTbOtiw8JE7Vp6iVas0GDN5iPsaEPC1UsEdBIJDqBXCd+tp4jNz6DItDBrBL0+UduE5kBdx7r+2xWPvfM9/PHs3EjJKLNBj2xDvpctHa88Y18SXt/UJva+KRJWVlitVpIkXZUHEeCKiwscPh7b3KLdx873YRYN7y+tG9w7xmqlV/y0L56RmKVzC17bAB2XBpK5epHgz1PaIyWw3FJjkV2Yl3LhgGSkldGx8HceDYDRAlb8ZKvHlKsWzcgXiymL1azRA7GGukglag1m5Lyju1NH9EF7mi30pn9K/9huqNGyBXF6Wr5kbgEBCIGe8oOhANhPVWORxYQrbrxkoGQwlL984O2vt7PGPmPvL5o7AtBg9abjjfXCgn93vP5/2wIOSO/CwlQak0jjaHDF5MGCQ63ZfPhgWSPzB93F17tJhalbi+B3ZhsvGs5/CVgx+afE1VrQLXt4v2Te+2cbtbRrDIEuoCY4ZM37FTV/7DuTn+GQfBc3fIPNeQMpDMqxt6SB36nXbfNTenRwd4ESO673kSlV4eowvpftEK8kiDatRK8DcchyXEEGa43a/qByGWEyS/x1Cdh4234XZbTRyg2knnw4uXxtw2lke6LV8cgs5R7dWqo5XMyK6cwL+vRNi+aLKeLb7WeRccrH5YQlV1/NrQcrOPWEP/L8nH1Hof++6Z9StCUxzMy58Ct54hgfqbx+Sp7r5BKjp/b4KbexX9/+4jPR2M6f01MufrJo7ojjZxs37isXu/wCSeWbqAK9mzCYtYuPswUVQi4cCDuuSTLtgiyuSRX80Bg9lZRUTlVzsx2v3DTgN3NpZ6LnFEVQFOFjAS2ctsjnXnxnEKhnMK7SQSK4aa/0yCOnFmZ3vMfnVmzU2Wajuo/VYiUpsl1vio5lrwoCJeViOHzR5KKWl44bSANwuLRmXEEGWsBmtdKcKjW3at1pUM2lHjl/JTkxFpTCle/n2sgwS9s984Ry+cq6MiSmMpX6pkvyBI+uOdAQvw+qmMYimzM6KzslijM8kZ5W10gsqxfALUOSxGKlU2NVdS2sskREqDm9S0pNW3TpoJ83/cMPgLoQx+F9E8cPSutQT5HnDv9XGcluMkj/uQUmKtRTCiATNTeR1ZzEKGVDO7zaHTjdKtBTvlOCcGE/it0UDk71UMIQ1ZxFRUWgLxiC/yrcC/FfbsLgXiivi3QTSSf0VI5XjxyQEugWqG09EotHVeVi0WHNT6lMfaARrg7j/374b4QzP50Xt3iMJwM74doF77RSErbBIK+Oh6+kBCWnaXp/CSzhnDFuoNkCR15RJDFzfDxFEiTJTnDpk+S0oq5db4rg+cW2T1Xig9p1lHXe4Sds+3pATeTJZa/k+Fmj+jjIpZF9VGORDc1JmFiQJhDTb/fWx1tLJMVUiJNipjjUw9mRv46c0xuYOA8A4eHqvMzYmHB2t1/+Lln5kwEACpQd1Vhk00dkDsiIc+G8tznq6XUX90+IclX8VHxkL99t13D2qZSeFva2qa0ooFlU1ggrFnhbJhZCQ3LH0RqZEsbuOL/EmdLxbyBJ5RexcIUWSDqZgWkOXzCODiUVdfahKGL38eoZ4wYePlUzvjBTbzAGuoDy1iN5jITJRghvo0qmQA0Vskianz5Uzw45p7Tnlx57Z01nDnXitHCJFEUSaNWhyWKRM8tyOKJjY5HezZ6Yz6z7ZtaD2XwI7grIFfmiqCX3dGSE8qXZBX/b1BOtg3IYt8yABLGhzlplC4nePG1glFpul0sS/F1uKD7DPnr1xL694tSSYlpVXSf4yrkuZoqKEa73236oIjU2rKzaLlicerKfSW19QZ9YWQos8CprAdsPsPpooOV3dRQM/accnC8TOu9iz13jGHbg62lhirRiclkgxOW2lWDwxEyW9TuOHyqDScuoMHZWa2FWFMiK2m8zUd1ROhjKsMkF0s3dx6svHTewrQ1+kfS8fCMfgaQKVHt4bnx8QhzNHHDe5MQ9xyuzksL3l9TmDRsXBALKud06owUVM7lGXGeOmksGa8M3QsL87Fg9CXCuQeOiA5Ake/S9hHalMsKHWimJlabDw9Uarc5oMMol3W0CqOT2vzu7Agewk5B32XI+SQkxyOnu0AiVRBD6JJxfjjg1/HBzRby1KZ5J0SydWyDQys//qkaPaiyyJXMKZBTB26Fj/x0xqLfEl9ZipfulRqJiWGR+EgTYf4otDEiMDlt26/QXVv7qTB/5wdChuYkTB3fgvHOeu+sFVBqbnra2tm/nvT+uLbSaSX9rqtmVEWJG5fXi5pdYafDD1iNFp6GeUvwkeFYUvFJKSSqnpzQAcsZUnDYmjyDA3EkJ+05WIcnTMwHxDqM6SE9b29q5q3Sk2nZwGmQl2X9WvhfQB15f88aDV/jwgK4Ly/k4W6Ij7vYW4DS1amCzEkGLHifqqW2X8JuG5iQcOOWq79lpRY5QK+F3tc2vWilGkdrPVF3S1qZBlZsGk0XJ00qE4P3yF4NzTreg5CAyMtyFEToiN552aX6i47kT42kgY63XfkF+fT3nxRdmJ1xUKPTiX//lNCemjNPvHDfs5frGFrMtMxbODGugaTAsx37mK37am5yUUKMF2pKTd0HtJvkSyRdHDc9511lld80poEiHnaUjoW547hz8xWCCNvs/i4rV9hfDsU4IBW994DAmCc7lgoZlRbVpDCRJnKzWc8YjX++SkxOuSE1ChtfuY+f5ksdk2AmjVGrRGdm9U2xOD42Otp/pM2AJ/DImH/d5c2TMkKy/DrK//GeeeCxASkGZHAgBy1NEY8d1UlrJUdPI1gMiJUmLUwvtypSBx9igGQDmrtZKp9B2pTObjEq52DqQ6mfI/C+nnh1O03MwQh0TiZLq6dE7sF4Lr83I6tr99fWlZ1kv/pbpAyPD5AIxZWDXcXqxJGn7oYp+qZEnmd46SD2hn2C/6hCc/04D2qBM+Gw9bM6E9O7ioemD+yS40Md/r7Pv7IXnDligs8xd/gXpIE5P0Q2+nrpIBCHp5OeCZBRxvMqxZNX2N83JhBEEpHjcZRBJ3l7GTm9yL7WIDsEGBJjvzK6jdi1G4VTfC+im338FPrVAfZU8d93tLaDCoBTzXo1me0dLpleFTCCUHDRTlCN+CzRNb2uM8Nqu9ItcdoQ77rage5MgOLO3tJGzQN2JhIrqllCAwC0RRdIp2IKUlPz6+g/+YH18A1OGKajG94JtRRX90yJpGvC7ncKTJan9p5q44s1f/oaFigZlCr+IKhrWALXvOgQb9WssstgI5cKpeR0am7YxJG7paTijSoJuVFRYBFpTKxm4lNRToqNcEKdfYrgAKKMYxK5j5wSSx5221/PD95XUwoMnDAqCQnqUiJ85fzE330IyEe+F+RmwrP/2k5nzF3Myyow2MXL/S/P+lIIWZwIMSfZVzIGplQKQ0snda+zCmY3um4smi1V8cOkeer64miLXnjU8mRp1t1x45148TYP+aTCjfbqmnW9+ovZRo/PYxA3nv+s1Lah4/tvt9ukmXIU/8NB5d6eGSSNVzYouXa6zNJL+Pj91zKWDZo7Pb21tc5EOcnhpwMYr9xbXoGoNfsmta0sfBgQc+fsI1GLo6zA1IeyJgZ6EpBGKEvEgIDVUsjGK2NIMOq10gRMvXginnjy/2ynoZ2M0GARJKpHzbldP78xPF4hdeGfn6ey3veMwdN6hka4z8dWTaXkHlxgiBmVG/7H31ID0aGt4Apcv0lpld84ukFOkWEyRnnbovHtUw0RAuYJtxZyZe27qqYzJBV0yJo9xsAh+Omig4/gpsd4xf18oecmJsUQxlLyT1Y4uv9WsNxIqhccaSMDkHgyA+ktAfZ5HigqHHyvhdiI+SHluxW9cYqdg7LRDO9lpPHxjM5S00lnOp0MvXmQ2uhuGUcnJVmnn3cGY91U4BxmhPjkUCn3CdfrNwrXqKqWitArKh4GW3zmngCSIP/aeKqlsy+nbiy+Rq2zBUK1Fdv2UAXGRKmdiitby89Pu7njuQB1m27+NIkhAg/Bwu7lnpWl+3y0BfD1FhWvDc+OTkqAFOntCvFwGuxQAqXSQC5B6sn9N3ivzjVCL2QScCShjuIzoG5/FRFRZ81N0tQ4OC9Rs6SD+6aJLZuBrpTMKxk7zdCRYUMhlh7iu3OTMRrhYiPckzlinaWtDi4YiiZhIteBn09DCNmwnHFQ4zgv19Mj89BjHT8BsgXVL6HZ1k05gfuoNxvgoVf90aJF9+jM0OZOTEvS0/KpxvQnC7r/zJXLNliPotsYiu3J8bnpCBF9MBZ4+ElPBgk7XnjtBALNjkFKn03sacwxXyQhYC0EL6odQCFJoTnJ6l2GrQeVlF5HNyCWdHBx594KhnHCv33kS9oGNH+RHAXUnj1T012/uH9BXa7rF/vvYIVk7bYn4btRKLwgNrXSGMyOUH/rkpRmEaR85YQU0aG1tM9HQZ46Pdmq2sOppe6ZVzyosAGD68PRz9a4WU3bGCDVZiUZGzeMcz437bXPr0GIiFA1N7AJwjc4sUE9Ev3S2Unh4btz6nSdhUpEkV/7M9uo1AmiZ8oOhfDH963h9S9MJTh9zekXpTA6RYv7OcCG/yxqmViZAyamnF1LFYeuFxGooZ34K1NC1+Qlf1zbjlg//xMRfNkFAwJn56UsBXf30VV7rY4e8cPcVa9dtcW0dOGsz7EI9BUd75dlljz7talRZh2Ct7DzipDnhKvQpkUdTpEJ/01hZzCopAMjwlFGEvUU3829ytNJqsCsmk5+BLr6MJP1nfjInRsuY+hozTUrKKJ9D5W1287NNwpKAwzyYFDyz9il83U62ERyXv+KK5zUWmUpB3T5zsAt7s7qWbcSHHkpNiHHbcwd8MY2McAjLoNt6o9l1zFEylEkAghPEuNgoydEsAtDO0PwkwJa9p0hCVPPEP77V3K4HzqJGSD1tZyIUDZk7ahgI7C1tcJ2Id62hHaqnb7TS2Yg7twltu1KMsbLYWc+9xChlXatBum6Jdd5dBS4VqWzTRmNlsRxVA1qtegspZ/ZTKRXJ0XCV4elqe1BURpGcdB5st6dl/BcJlTH6zsmoQEkHMctSOfV0FsjSG4wDU8NiI+RVFWfW7dD1Sk7UWmR3zhksp6hX1p0SpLD0NJt8tzUAHeTaeW9pqndHTIEtrYQsUGe4ijm6QXJizIadJwmCENQw2WWX2Y7UFqmni6aOfGV3FjXKykypPnJOUCrHZZDgXzBAtLL0rH0Fgmsk80hcqze0alPQrV18EMJPdqWH6tnT5NJN0Df7THUz3+NzqFvi5wU6ClwiJWXsPoJVUjjI2nBah6qyPVZML8zPz9+5aeE9n/HPh7ORGWuULepESqqUS9u/B5h14pKUkuaRjHkxvHfYhr0VABCr1sMlE/EAGAnFXbMH80tQ7WJqsYtplFpx87SBHYqppzVMHjvyttr1bCZ1I0oHsdXO+8uaJA+SlZlid97diGg7PTFbRJXvvEseShYImoiAkQ5owXEDBx1OmCusdUejXK/aFB+BaynSyU4ZLsBa6QyU80G3BcFKDeo2RIidd4cfhztfCb5sWa20jCJ9aGN2yPW7Jn/xjv0uElOesrMgJTVZCDmvYhyRiEY086BssgutCjLqZJPs2kn9V/+4nV2MVNcgS84VlKCmxYdfNaEvZ5Y6iKnJLqaS9iYSU835CokaJoODmMIeWS6lSnLBrsMRnPnjPBKilPWcg2LTO3HuqMOYqWMzVihATsIIkHWOGSTPBNRTQUSayMSAUXcbeMPFd525vPDP1l34nYY7hHCdquq0D4610iMUqf2MlSdRpJILVsZHh6OvNW3rdO1Yt2RXT37Ox820jyK1nxtDgHwf/eQsUPH58GQUviuSZscRINACijKNnq+YNtgva3tjTZ+IaKiARFhEXOTl43IyEyPF/vupVqiSSExHD0wZ1T9FUkzRzBI3nfdwYUFom69KfcXpICsNm2ShFZlnq5t7p8TYNVSws01LLM5WLEmh0QpLBZD5yUmCWBtk3skiZyq6XvDGmykiKMbyGRI9551XVkuegEMiHvvgXY6znI9KTjKRKTu9kmKtBuEyVhTEDJzB1N7VhIr8eiFlNdylQuI7KkvOPWGwVemT4KvdtfHWk/HMEqMlcwtJghCL6ZYSzaGTrJheM6lvSmy4W2KqjBTYmxqemHKVA1wGySNHnjP9aJEgIotyw18n+cWkZx2DPHZblacAv/xVTBDAZLZ7OWJfR3Bi3PYwZsohej30xZsxIqOiziGRx15huGIrd1YAe20qdoYb5447dNjeu4XLI0n0nHeCO+ca23KwKXqIs0exG+4/+IYYE6mkDSaglJN8v8xq0ApyPt4FMb3Gv7WfDGK/HsV8GUuK4LzyDuEUEFWGqiyy3kmR88bmSKrkz9uPo4ApZ3Lq5ZHi3VTtlcjedLMPk6fFQ85w6IMOAN8Z12h1FotDZYIwXsnc2n+qkQt/I8R6apdOXkCAqdNgr9mnq1vhEG5HHWE/CPc6JwRQIt6Z8+7M/HTz7XGfDtbKboFviMkI2mJmfxtaJuHjddonQHB/YRL6HIp59YuyZC+n53Lyd7LJ7r/Pn5ibGhchNEuZ3VDjfWCyKyNfTDmVcV3D5A4SiW+bcqHIDd95F1T6Sk6RQgYj2tdiUz1oM9a2WZnGH9yETttF2m7sanVOi5zQNZsiCTROhh8ADZqVSII8kt5oUXmyoNP9i4PWYMbSGQjwDTE046iL0z7dYn76G05M1+84weXo75w1mJDy8bnpzXwxdV0Q2upyiJP7GXmUgufXcvKDgI2tGhlF2tNsPIMxiWcwnmEMRnR7bKZlZzk1Yswk/qswesruYDY5BIv4pRroIEiCxQSTgApw0/wMAtMa4wRFar9DDp6WD/j8nZv4KR1tU5s61m5h9RAcauxtPj5a3KWVSTjvrJg6sTc1PDGVTMG70NBwFaXRS6wOkKxGQhmk3SfrAeObUDIJ+TrjaDAi4dtZLnEaUnpqB43esiYMEmfuQkRA3aFD9dS2tbH928vq0+MDqycTxh8svOezz9+5iZNRTj17ppKKVTKM6asvGQlVtVe6cN5bXWaQnFHb2C42Qu25I15A03H2OONN2yI8PINRj5ZCODMYXcDXU7r2MJE02LV0sufx+v1XguCByyNBF56kXJufhJRWuqCuBRr/B9u9GWCHCTqQhrquMfKtC++rzkxdAHLe+d1D9PJIJKB8wm3ap7HIWutgjz4TbxqfixYE4iAAN5BjRN/46lazw1IiZh+j2fr77pJBvWMy0+B4j8NV1hPHYe4LMTwXTmmtaIX7ai2yay7qlxyr3nC4EwVr7hEiFqh4ooOuI7msbmJjJfyRSwHVlx7jVzjRRAapQEPDZUoZRbYYdANz+/+5ZUtiBhy323OQjoSiZUhOnHcGmp/s5me6xWIqyMjzH5JcSkQb2R9sXt/Mb7fBFVMD8vK4p6BXimbOOhqAn2zTlhbNHdHS0r6z3F89MIPMAv33D9u5RLxKZZ/jGh0mc1MuES7m1BEENkIxoK7iPDdHpl+/fucra1R3/tBzLFAxbA0Tg4vMeyvryDv426gnllhM+fqL/PERfeNRMp1z3rl8OgCgoraNIEB5C2tmXjkhNy0hAompayJiodHa0y1QmqYtUlFNgXoKtJKPO/M9HUMtmJ6KrZ96eER0embv4uJiOJO3B6OPSAU8Fx6JqUa0ND4qyj4pj4uKcivNkJhylilJEAqZ9G+Sv2KCn0/PTEvOTIP+ezQAG3ae4AR98pD0guwEZ2La3lTjJzENDgGlabpZY6hp0nJjgjjdrG7S6pi1bl6LZpEGBz0xQtRRcQRt0bS3atpbNm/6HX9ArIYyqNorxas5NSKz1ImY2gswSZmcGVQl/IkmMY2yzlS3wBkftnz6iWr9tBG9kRqIG5XuPnxm92F2Xl5eZizaU1JPfSumgS6gZqu1XWusbGiva9FZLHSfPpko3EF7a2liucS4Q3gMW5KhaRdMmcTYlRQ4iqmL6nqRmDI1RmYzCZ13h49Uq9OfYcraKSaffqKaXZ8erg7bdKTx+il5MZHKI9VCZeSLafn5uhXn67gA6MYi2OFUEr6YeqengSugVppubNVV1LW1ao2CIa5u6ubgQQPh4l1eyf2n24VpRAzGBZr2NhQJjX94M/6gPDZLgYSPzxdTceE9DYGLJk7wJnagwyKj8rvNh5GZSZHETdMGRoTJxWYmJ6buhEc7aZwGqIC26YxnqlsaWnRWXkiSIAg4qtCkoaVEE8klBuMHfFOcgQqhgjqV5FezdETf+PNt0OqEeyrlkkmqAelROysptOT069+KODG9dUZ+mFLmqWJ2XkwDKwtPA2A0Wc7VtVY2aEy8KlmKItVKmRWaoTRBEGHmpjpTOMX0D5dR5LB+yS6GiWMLNNDqLt0pugwQ6iqgyxL/8B++PWyoaqgYQemoO3l88Z5Th2Xk94k/XgKXE206eE6jEsoZW2jF7CyjoGUarpKwTH0FX08DRUAfWDS37FyDePsfe0rhTNd/Dml0Jr3RolbKDGYL8ugTotUms0VrMA/vl6xG16uONBS78N1OcMloXXlF/CMOK/x8SM+RURdi6k4vEk5MuQr5V36295RyJqYdBkB9QncK6AOL5vLvuhBQAMBVFxcAAJa8+EVTmwGl8HrFR9A0XdOkLcxJjIt01Z8VC2igESwy2gUNRHqajIrF1B2zFAVA/z5ebWFKSjUWmVJO3jwtX6mgXIgpIjvaX1X03RMDvXn+1NhoiVXn2enxAg0VqOd1j62MDFOgtvsEAXQGc1ykiqZprd4c1xMXMQcxSDf5iykDkJ6pnt+/dhd3+8qHPujm1BNwXPjEjHiBLjwJwq1Nq385gNqgxEYob5g6APaUkhLTshaL/8S0qwX05vlTAQBNLVpJDZVk0bxRza26BY+uUMplWmbEs0JGGs1WvdGskFMEQbTrXNWBYgKWQDY/Q6B5nde6Kd7YBUoKJFNPjJjmpkbn5WYCwiEAyu9hatQ1obnNYRZZYU7CpIJ0QICuEdMudeGRenIINPTHTfsPlcJ+BACAJ2+fiW5ceQ/8y+lM8G3HRqqa2lBxA6wsoSiyf3pc8bnGiDDFkNwkfq9/AdiFxwSgegaC+Smpmy7wn5K6mXqaOyY7KyXKnRjo/EkDlm9zd16R12LaRQJqpenmdn2/fn3R3Tnj2S4A2/bAxViFA/ugu31S4/i6iUhNTzt1upwARHSEsrldDwDBWKAWAhD9MmIr6tpomh7eL0Uho1wLKM4gYdz6rvYA9fRUN7tLSYFNTKeN6P3jCRO/TZT7MVBP8UhMiTcfv9Gq77iPtNeYLJamNkNNk6ahVW9lCpGUFHjzsetgEumVr4RnI7IiU1J7Deuf2jcj/s3Pf7PQtMViJQmC6VAO/793SnSb1tiiMYzsn9JhIh4LKCZwPPduFNABKXDE5gsPLPDVAa/sKjF1VrrULy3m0lHQCOt6MfWjgJot1qY2/ZmaVo3eiKYyIEiSUMpltOMoKIGGXjp5hFop79MrxmyxErAOlFjx3dbqRq2gy0tijJqmQVObfnR+qgsLFADwn90eDx/F9ExCPneEBJQj2JUU8MQ0MSH24qEZkWqFz5XUmZj6S0CtNH2wtLZVayAAYWEMTwRFkmqVzEwoZRQlo8hJw3PMFqvFatXqTSXnGnLT4kYMzEiMUUeq2flQsF1IA7sY+fmP1wteJSJMQRKE1mAa1i/5f0UeTIfHYLpRQwNKQENJSYFNTOdPGgAAcD8G6rWY+ktAa5o0R8/AElalnDIwKSAERZEWC61SwyVcSjkVGaG2GZVEXp/E7NTYgr69BIfiBPSO93y8IASD6XoN7fbckTMBDTEl7RqggMKvi081lAb0nhPVLqqLVOpIiiIsVnr8kGwZRcooQq1SxETAjotiAUUampedhm5Pum+1D081NBAMSsMEsoYGpvnpbyUFISqmfqkDNZosWl6P/kqjsBdtthr2q5VTZFZqjDsj6Tn1BABsfesGdAMrqQDxXAoMpjMse+NLHyrp9/6vKg0+C9S75EzfeKXFalUpZFdPKRQ/yhmhfdITuY0tbAWoECyj3o1Iw3SLERr4/rtrsE3qmYD6KXmdnxozaVjWlj0nJAV0zkVw4aYYZxqKwEoaRGvMgwUsoM7ASurgwndZlc+YfiljBmfImZKjuZMKh+Rluv/c6EiVCxn9cnLFgs0ZoGeDhp5391lgQh/s3XfDWvhH54/il8pfONgD9eSIjlRJaiieBYfAticmBJQUBEmotOsE9LGrL/TVocQaev7nF7GCYjAho6QgSJJOsu5STxfmZ3l1c2ZHoW6+hp7/+UWaBuWZ819aCB7//G9fnC8Gg/GSZT1JSWXBZXuKQ6LHvnoSqSfchN14DMZvKXhPWdYDlBQK6Oo/fb9u1LV6ehf95NO64Rn4Dw3rSbF6YoKCbq9h6i6W+U1Ju11M/WuB+lw9oW7SDrYmVk8MpscqKehus9SPAjqEPAqAb5x3pJt8B70sC5avQhra+A+8dMPox1fjMCgGE9AsCxUl9a8F+st3nwIAZlx1i6fmJ8ojudJNGynxkdX19qkpOAqKwQQRy4JcSYkhY8f77+jZMbKo1iJ0G8moQEBL/veqM9UbmaGA0+Oc6CYfTkDRcZ7AFigmIFcidXsMtIuTSIGwxsnfSur3LHxrFFysGdVa9Mt3ny4YFl4sla+indiNp13qpu3Jgn8xGEwQs8yfNqnPxbSL6kCRjH65v+j/hoZzGytTZ7t4SlmWRF87O071sgMhxavFMZgeq6TA1w6+3114dCOeaIRqTUGPfNToMe48V7IxqKQ8Vje08TbDm0+s3uXiyFhDMT3QhQ8K/73rvftOKqm/LNCRsWwb+fhIBfOvokVr7xDqDodKquwa6tKsTI6PrG5AYVDsx2MClG4PgIYAywLPJvWZBSpSTAc27q+8cADb3NNNCxQaobmuvHi+WDICao+GPvGFKwsUG6GYrrdAu11AQ8AC7TKb1H0xlXVSLvk2pni3+ERWNGlwHvgIJ0YmziVhMD2OZf6xSW1mKQw5XvnQ+z4QUJFiSsglfCgpUVLn+Hn2ndu3jRk/wc3XtR/B7T33HS/39OAYDCaoWeZHJV2CbkgqqazTipnE/CvI4vgMzw5GQ/Vcu7eywx1x00wMJiRZ5ksl5SrRnSqptIBeEMeqJ221hoer9faxxHbSeiXYXoP3ck6YOS6vqaEB3T5XrxWepBOK+Hkkl6TER1XXt2LbE4PB+FRJHTRUoKRuufAHSmqH9kVmpgPnq+DYd2ekpcTz75Ik4eDO+9JKxZl3DAbjPyWV0FAPBLRfagQyNtdtOyZ+VFJbobxWs/YmBwEItK6ddrbwyDOEB8HmJyZgwSn4UI2TuiGguezq9VkTBjpz2H/eflws0fmZ0fy7bVoDxdihKbFhf+3YNmacx3kk4asy7D161qvjYDCYHsoyj5XUqRHqfhmTq1jn7PF56Mbps/YETpvWgG4g3UT/66316a5uupNBwmAwGOALm9SVgNJWK/qXt0m4j6RiIpBiuji8O+d3qKTSIY9Eg73HnJqcWD0xgUa3O+8Yvyqp2xYoLaGY7ovmPydrYXK/f7LXOR/XrnpNY7t3h8Vg/AHWzR6ipBJLOfk1TAAAuYxyoZgudBOJJgBg3OB0dGN/aW1+BlxPVt+qv3LuNBendeB4BXc7Pye5w7dR09iOzU9Mty/oDFjdDMl1nF0DT0kJDwRUZtNEScWU1E1OMedMGGi10iRJ0AA0NdkL8gEABoMR3VDIybD4Ph2ePQ3oQTkpHe62/LfiDvfBYPwno76SzlFDR/Lv7j6wxyeHxQLaeV544Dq3BHRMQpsL0eTrJqeYqFSeZh4iCYLboba+WfBco9GEypgUclLFCqgrp54AhGsLFEsnphtl1Icmp0A6fSujWED9pKQSAjooDFbIEyJzFckiJ5ozx7GZd4okGd10tHAZVaxxFND9pbWDMmNFAuqA4zFogtkg1tBfth44Y7T3ZsZgugzrtV90jW76SkaxevqJFx64TrqdHV9D95XUcdtnjR9I0zSUS8bSJMQqy7Moa+oc1DMpIebXXcUAEIMyY2iabtJa+mT37fAUkYbyBRRLJyY0cF867RBg936PZRQLqP9wWcZEQzvx0rF5JEnYfXNC2ufmbYOqumX/2YEZUfwdCAJcckHf3/4pRXctZpOu4YzrMKhtIiftqJvY8MT0MN3kP30YfLoXMorxB64sUORoAwDSU6XXa0KciGlNXRN/S1JCDEFARf51VwmyQNHBG60xaYnC/GB5ub156BlThKdvCYMJWIxtOgDA+AneLcNj4Hl9bsootkD9R6dHeojinkCknoz5Cfdj/gfuRBAE0tBTrcSp1hbRQbFoYkKZ7du2eS+jvFWF2BrtdqCBKeaIDrWqA0YTWozkHiiPJIqLpiTGSm0GNS2eTUnCYEKJ7du2ISXtJKOGjURKigkUAe0M1bUO5mdyYix3GxmgR8phcokgiLPVYtsTg+lZeCOjUnEzLKPdgtOhcoPV9ajxR8dhUB6b9wlzRylJdgEFNLBY6Y27YRgUAFDXokuNV+9p5u3gZ86VnQaBQXp2Vo96y132fgP8jaMYqCQmoEyIj+mTmRYZGY6q9zrAcZfNm37nbjc1242YCJX0LAn/ceGwfNBjcBoDPaxN4DTUfYTqyTM/IYRDZ+WEKJVnR8dgQhc5MLQ01PxT24juqlSK9LSU1JREhULhaZ/f2JhYgYxiuieJRBAwDIqMUE+dd+b5Egfk3SZwGBSD4aOkYGLAYJHr9cbSU+Wlp+CERIIAMTFRfTJTY2OjSYJ0r1c6K6MmvQZ/wgGchXeOg/PuAH2kvBl58WerW4hkt4IDGEwIoIgMc+HFi2UU3aVp2FOCayshl8tSkhMy0lPUYWF+Pl9MpwUUXeHOVda6DoMKzE+h887H8bKZaKioU2Z0eBoYTGigiISq54WMcphM5opz1RXnquEdAkRGqDPTU2madrIyEONHXPnmh7VMMZMbf5TN+0TNOp08iwBgxuh+3N30BHXfNNxoC9PjUESGISXtECVlQkoqDQ3a2rRHj5fW1TfV1jXW1Te1trabzGZfniumky58h2HQDnJHjqLK9B1h6RWnxhdNTI/FTY/ehTUqgKZpvcGotzWNpCgySt3BUzCdwa3skGvPwJ3cEX8b/0GsnpgejvumaMfWqAiLxZOFMBi/FtKfq7R3/3SBpPkpEkr6qK2c3v0TwGBCFU9l1M09YyOVnTgpTKcFlA2DerDuKEaidEn0LC4MajRbz9Q4tKzHYHosPguMYgLNAjWZre7kjgQWpeQSeMFuGQm4PR0GY8d/Hj0msOpABbkj/rJ31/FNhzAo9uIxGG9LnTzy6DHd3EyEHwYV544cVhl1dChbs2SgM1p6me0NQDEYjBeBUYqgKTjFERNgAtphGFS661JHzBjdH+WR4JAZK/7DYzDSYBkNERfeZLbKZaTTdUc21XSnjYxZ3+5wElRA5OL37fqjw32GX3gxd3vyReMffeie/PwBVZXVH634fNXqr8xmCwgqflzzRXpaqut9Plv99fsfrkS3Bw8a+PSTD40eNeLM2YrnX3z91982g+DkzttvvuVGbt63NFXVNbMu+z90WyGXL12yaNHN1ysUii+//v6V197RaLQgUJ16ZIpaaKKpzcAl4tPSUr/+6t8dPnfCxOnijaUlRXff89CGDb95fuIhjo/XwrsQQrOoqcElw9LO18ONidFh5x0UtXvIzEx3c0+SJGvOHUe3GxoaMzLSXn5h2csvLCscPrGyillgFySk9krp8F3HxESjG1+sWj79kskAgPr6hpzsPl+sWm42W3plDgRBSEx0VIdvnCBZcyE2Jrr42D8w3KTT0TS4ffGNty++8YIxl5w+I1qAFzCF95yMNrXBEeWxkUoZRWVkpHnxoj/+72uK8n3j4B4qoOcqa2UyuWTTEMIN0YQjj2rbSEGyPjDySImp/fl36ypPijciSpif048//XLr7feiLddcffl7b71ctO/PpLQBaFpJUDByzFT+3R++Wz1u7KibFt29bv1GwZ6XTp8y/ZLJNTV1g4aOQ1uefOLBu+9adOrE3pwBI0Cw8dK/3n7pX29zd6+8fPby91/fsnXH/P+7Rbzzwb1/AgCumH/jth1/AwAKC/I3/bp2145fk9PZyd4Ba4oiGUWmaNPR0qzswdz28PDwI4d3mc3mvv2Gunj6up+/zc/vhrcZLHh8YSmukvjL8cuVzHoN9x+3Q3ltG/rvXF27QD3R+jMQPCjk8qioSJqmOfUEAHzz37XFJafgPL4Q/bZ9/u8P4LuzqScA4PkXXwcAREVFgpBm9IUj1eqwXzb+gdQTAFB06OhL/3qbJMmF118TXIFRKKOMQeoOffr0Li0pys/P0+n0nTjTEMctAeXySCazVdxwnnAimnzdJAk4FVkgnUWaeO4/EDyowlQNDY2lp4QNzzf+vgUAkJPtalBz8DJs1OSPPlkFeh73LFkMAHjrneX8jas+/woAcMdtN4FuxX8yKpfLt2xeR1Hkc8//67NV//HFyYYmnYqBJkYpLCInvbymDf7DSKXY2AwurZSktbVtwODR4u0zZ0B3+MxZ2AE39KioOP/kMy/zt0REwBUQVmuIr7YeMWIIAODAwcP8jU3NLYFzsfQiMGqhieb2DjR07979V81fCAB46KF7fHGaoYnHAmo2mwQxULto2uDP7QgN0ewQpVKZndUbxsuKjoCQhiTJ6KjI+Vdd9sKzj9M0nZsX4vMgo6McXK7ARBEZRtaRVqnlgq6tUWfBM5PJhNQT43sLNDEKDmkpr2kr19kvYjQAVM/TTQRJkmdLD8BU0nWLQKjTr2/O9i3r0O1HHn+2rS0A6icwDCRTZei+jCK4ND3+FP2YRLKHQU1WKJ0ikxOpZ5CGNTsDSZKVZ49QFPXVN2v+2OyDMd8BTk1NbWJq/5SMgRUV5199+Zna8ye6+4wwDpAyEimpR9aoR/kljPdZeL7JT5IESRKHdQk9TTQFBaEURX2ycvU99z8OegAo/GexWIaNmrxn7wGCINb/+HV3nxRGCJbRgHThaZgcIkmiB2qlmP79cndsXQ8AuP7GO4J3TU5nmHfVwnOnD10wchgIXQwGg1IZrO4tKfMgMMovGsVOve8t0MPahMM6+B9WTwDAnNnTkXoOHjo+5NWTJMkr5s1+87XnBdvpUE/Bc1nBtNRe/I0KOcyjVlXXgBA1RT0tGu2x4BVa3pA3oO+nH78DAOidO6S6xq1G/UGN1Wr96IPXr18wX7A9raN19CHA1/9dCwBYeP3V/I3TpsElrRuD58LZGRnVG4Ksw0NXggXUYyiK2rYZpqH79B2q1bpbfxfs7NkHywy++c8KbotcLvvnL9hdIqtfKLvwX379PQDgvnvuCA9Xoy0URa38CC4DffixZ0FQ4bWMYrqomUhP4IF770Q3zpRATRHwyOPP/nsVXKYSYsyZt6Cq/NjFkyeUndx37PjJ1F4pqDPFG2992N4u0fEglLh6wa3//XLl6eL9p8+Um83m3JwskiQfePip4FqC7HVglGQ0VKMz83s7YRBYQD3mgftYAe1RmM2W5PS8Jx9/YMmdt466YDhqTTR20syKitBvhr15y/bhF168ffM6tFbCaDJNnXrZseOw10yQ4qkpimQUVTvh/BIfYsjY8aDHcK5MuIC9u0jPzupRb7nL3m+PfeNev19PC+/RSlAXMnrhsHzQY8AxUAymR+OFNYrT9BxYQDEYDK528hIsoBgMxiYHnpuiVM8uGsVJJAwG06mOJBRvBFNP+yixBYrBYHwTGKUYJd1zon7Pifoe8pliAcVgML6UUSVlQjLaEz5W7MJjMBhfFt4DAPgaOnIA2wkzJMECisFg/NKqWdkDZBS78BgMxl8ePeDJaEg69VhAMRiMf2VUSZlCVUaxgGIwGM+FA8soAxZQDAbjJaRXHn0oWaM9q5kIBoPxB0a3B9PzMVjYAenBm2LCFigGg+ksisgwRWRYD7RGsYBiMJhAkVEQbGABxWAwvkThuYZyMhp0pigWUAwGExCmKAjColEsoBgMxi8oekBgFAsoBoPxI4qQllEsoBgMJqADoyCAZRQLKAaDCfTAqDJQZRQLKAaD6ToUoSWjWEAxGExQyigIAHA/UAwG0z0oGA31Yhlo4HQaxRYoBoPpThRemaIBUjSKBRSDwXQziqANjGIBxWAwAYEiCGUUCygGgwkgFEElo1hAMRhMSAVGlV0oo1hAMRhMSJmioAtlFAsoBoMJXBSBLaNYQDEYTIjLKGDwh4ZiAcVgMMGBIvDyS1hAMRhMMKEIJI8eCygGgwkyFAETGMUCisFgghJFAMjo/wMkdgVVeCSM1wAAAABJRU5ErkJggg=="}], 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"}]], 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r+ry9Ya0lQZhW8XSPgm9IeEREckL3xcufHDtxSnhkVFWltdp1K5h4ze0sY8Jwv+6DRP4RPJFEp4bpLDRGyF6LMjwsiOWL36Xes1wg8qLGirLZZNbqiO0HL8qVprN4UkZNzrxyBtMqh/aPuZBX5YVpsgrrmhXqyYN7HLlSkjqyjS+IS8o/0zy3qsk+C1SuTxb36dPH0g6zZqWOnzCBuvnC6tXoSurs2fQL2vjbtt8tHWfXzp1CPbDvaO8+GDiXSm05wyMHYP1Q775wwoIlHn38KS8v5kryNhGQsK6zQV/nP2yYtVT+uPHjoZvfzBzWxHE8Pbd4w1efXr5w1lss+X4Ls+ttCer8HRN2JAlp0SV1czXGE4QMmP72+9BF8BLyYqO7UZcYwyXM5CISCdEFJotr6z9481XAbjQq+Bf0EvLjYsKZXmA3S6+ukzl95UZVrdlnHgM3si80NKsmDu11rageaJRD+nUvqm66d1KfzIJaEabJKTZ0oLDKCPVg7LNA//rrr/vvv/+ZZ597bPmjjDu88eabwcHBzz/37F9/wc7chx56GJpsSYkKhWn7Sm7eDRzHQ0JDTdp1EJ98vHbmrFnfbtiwdPFiX1/fkydP2tjzXl8HcxqAJPkCgdY4m4zoHhM7d/7COfMXzLx9LGiXegpI5RvPL/920z+ff/F5Se65C9daq/PooEKCeXMMvxYm6LSwQDLzRumyRx87d+GiQCB89b1P77m7NQaqUGlIkiwsr82kVf9R3LVgIUmSSqWSulc/tpLMullmXrpP8dJnf6ArX387nX4TgWF/rv8Gelv1Xi1WDGWfkdRVNPKS2YUO8hGZHxYIu9Nvlej/PrYS3j/bqJCUZjCGdzvGeCihUd3YS5/B7JxFDHNQGV8rWVlt5F6oQoe2cTTHodZo8m4U6HSm7guOYwEilTeuzSqorW1Szhre+0RWOU+nyi6sq2pQzBze++Cl4romZZBva68wS7qP0jzUCLXPAn3/vXehkE2dGhDQ2p9LERERGRwcDADYuXMnAKBfv358Pl8qldbV1SnMuJGXBxNK3//A+ETXr1+HcdIxYxOTkgAA995zt40TQwiCKMiHR/74828Yd3jvo09htel/h0EHqCgvVSoVPB5//qKHh/aLol8kvj4SX5+nn30Gx3GNRlNWUY62SHwNX2z6dXTCd86FlQO9ExL69O1ry7N7eXm99DJsbL9znsWWhHZAkuQ7b8NKnR494iNjexndZ+zpGiZbAsxEYtxidaSUXvAjaoQhedZ6C9afXWxdjA/VinYaJCBLSirM1RP9TIowTb+ekaW1MuS81zcpJw7tdaOswQvTZBfWKVTaG6WGlH18vz7WL4Ct5LlPMNQ+AVUqlT/+CCXv3PkLgwcPpt+VkpJyQm8nfvTRh2r9EOW334Fqu+4zKFjmTJ82FT5q6FA+n9kK3v7XX15eXhs3bdZoNLePtWNk7Mrli0lA9u7d5833PsKMM04fffpVSEiYVqv90P6aPmR+Uiy7F5qWDy594vGnX6Jv7xft/9lH76c9/wKOYU88DOO2bVJUVHTkCBT0jZtg5ZN1bp8y5dgJ+D4XFNyqrGhn5NQSZaWlKAD9/JrNCrXOuspUVrfOK+gWyvCDymYlDQ+HhWhG0F7doYsFiTGGLq9uoQHRITAv31mQFRXVzTLmxooIsey2AbE3yxoFpHpkclxxdbM3zB3VNyk0YwbFl9bAstAbZY0olbR6Vrz1Z+pkDU2zZ1qou2io3Umkd995Jy42btLkyX/+tV2r1V44fx7DsGHDh6N4/J7du79pyZ6npEDV+34D87umVquVSqWXl1fq7Nnb9f6+CR+vXTPvzjsDAwP/+Rs2gNoIyrwvmj9ryx87hg0ftWv/sbKy0tKSoojIqOhoOLRGRxAP32uTrllH2li/atmiT7/dND11/vTU+UWF+VUVZWHhkTGx8FNLAvLFp5ZWVZb3izZ8D4Oi4nEMI1pCls1yWOREsXLliqysaziOf/X11w899CA6T5IkUeOsOdeuXZs71yQ4QJIkeeIUQ1IO0djYOH+RIURAkNC6ySkoMN/t6dXP79+1h88XLH/5s/2bYDDUks9L0S00kL6J8bDuAVyGA/5PU8/WsTU5BYb6s05A6MMcda0FwpzieqlMPWdUwtmcSpVGNzwp5nxupRjX5pbU4zrVzfLG0RMnE5WGkhW2TQ9J87ilk9pTxrR06ZKFC+7Kv3mTz+ePGDly+IgRGIaVlZXefffClStXoH2WLYNv061bt8zT0BRffA5Xu/z8iy8Z7y0sNPhQ/277yd4zlDY2zpoy7sC+3TqCiIyMum3YyOjoGIIk9u7ZkTp1XB2Kk3bA/EQU5OfNmzJ85/ZftVptTGz80OFjYmLjNVrNrn+2zZ18W04WrF2lqC+HL4eH4/2i/akLda9Op1uwAMr68OEjgoKYc9lqtbq8vGzXrp0zZkw3U0+HodPpnkl7HgAwYOi4+kYLDaYYqKCZny0ePUuG0lnDxCOx/tLCQwMxAE6nd55u2kKtVHH3hN7ZRTDoec/43kg3h/TrXl4nnz2u/5Ovf3Rgx9/3P/s2O41Qu8g7uRWwHsxPPzaCtWsi2TUs2VF9R+bzQFUyQ1zJR2xT92RFFdRonp1VMoRegupUzqoti47vYfvOSeIabyEvnKnpq6KqVT3DW2y0wpJKEoAGubaIdFnZeZsQVTcHxsHfrR4x4Yw7UC+Nel2n0m9Fh4hJEqTLzIKnnc6wIJiXFwvts3tUWvi5Ol3DPKomOQ7W1XUma+wwQrHeo00bQFiF56zKWVld57xFJUUSf6ShzXKjKksregoLjFqmTQPLZnjr/i0GnJBQ9NR/va+VsKu1fP7jcGQUxVdvfR4eplcZ/YmX1sqjgjszVth+vIX8Nl+aSbCCDeqpLssFQRHwGo/m4OvaMFAOX63EcTCyr3PtpC7ryPM9Z60OJ9TMtwldTykxReYnrIXWalUaQuItBC2eo7eXxYpCmRxWeskUGmqhC7qPzzYxhXHbN5768+uN9NAhGvoLmIccsArSinrqOwJaQp8sK7Q/kVk+JjFCrlBW1MOYUnxkACWmURFmaTE9swNDdhyDIdGRIU2WjFA2k3dyK5uNUPauieQS590KIom/9S9js1yFLlR9u6+POCTQx9tLKFOoZQpoKSiUaoVSbVT8bpxPxjDMW8gwWD42pJ2V/47CXGWMNrJMaCwhFAoxu14Xm+DR6lXGWZ8jRcNfIiRJcDqHeSJfvL9rFCDNpow8xv5gKKstUFep59dffirXjwKxCeNvJGPrUUigofCzpr5Z4i1Uqgxul3dLiwvKzssUGivNhbEh3oU1nbecBgaAaSWT+6NhKq60TufWMNlqhFI/wWNTDBW7xy8azZBkNEtZZYSmte3IUz23oOsKqEDfjmkveSfsqALbd+QMcDAE8Pfu85DpdI+qm20PebqeBz/HhI7o09uw1Jceo+ad45duGvyvFlAD+82yxqCgAG8hr7is5okHDbP7AADFLWbFD8eZBwixBwEPGxZQf66h7UWrOh9tRZ6+JsR0VrcVTl2BGSRBy0hQF6IuyxWIvCRib6kUzgDLyy/pHW+05iClpJSYxkcGIIN00uDuhy8XWzI/m+srfAKZU2ouBXMLR56NFqi96mmyUJLzCOs52LqGXs+7AUelwffV4rQ0xoFpOI6heeNeev89PtzXXD1dQkVFNUrEf/XW5yvfeMrkXkMM1G05eKGgwobXxYaRIyIhH9CGXQn5eKHlD1iM/gOmpX3AxrVM5HMfI5R0Cw1lo4DaBQk6ST1tIaF3r6sZ2SYfAepfKt3CuB46tQMdE/VcMjay04zQq/KQZLHRs5toKKN6RgWL2TxWeSitj/PgBUO7UZuviw0GtUZLdPMXylQwBKFQqi3OO6DB+L1gQzmBJ+H0OlB3c97bxroRmp6RTX1s7VJ2EoD6ZlVIMPyuCghVfkXTnCkM054604tPFtd46UtBjQo/qQy1GYXFlV5CHo+Hn6rxA+xDUZY3vBc8+Th9lVhryaeFV4QBcFLvwl9pdrHoqMtyeXw+zuN7iwRSaVNKr+D2fcZMBJSeQXKhF7/GohHK8NLYZoTy3Vo9CYKwPl/ZJZCw0pAX3s1Qgt5igdLsMpo5WlljGFShVKkDfUS19dLgQD8NLooPhyVB0HFzNTaqp2HCJnAPbFFP9oA+5N6i1gIxLyEftvO3hNZphWSI1lvUwBdVS+qSbaTZUxbKNkfe9d/P9qknwiXqyRgJ1UUnKac+D3C8l34VMNnhn0DmP4CAWWzMMC1N/5X0D8emrcIi+5MFF4n964Dc2pgfReprSm9/v91vY/ohx3QkYVHDnng7b9eWkrNwFWIKnlA0YOHjMWOmtxRZq8589mJ9/jWTh098+weSIP570+KnduLbcGSMN67T2zcYIHQ6abWo+AKZcxQom61ojUKtq6hvBkI2WqC8liXqLamnrxdvwYjQ8EBD2rOyQX39ljV7f+FQ/4dGBsz8onVuEyO/Lut+vlDx8X6GWPavy+Csv3s2mGZ4RvUUf3BnOJW72rBH98vhQvo0qfDwkNSU4Ogg0dcHylAXLf2vgG75evHuGRUCADQt1Rodn4d5iyV7Mxq2X5EW1EAxzW8kKCOUfakkzC00lEUCahcucd4tIXvgWyDSF7uoFZhOC4QSyaRHsMmPkD8tBw1llHmAjXsEu00/x6SxEus5jPf45vpf3wbVcFw0FR4ldTpkhMKvgU8oIQnUL7ZhGgbF+XxJWBRfbJRQ7jnlrn7zl6IqVFllCd9b4uUfNPqFT7Uq5YG0e3S0uJkkLIq0uh6JJCxKXySMViWBW/hhsVjv27BJj5O3LpLb37DyMe/UhT06gIl6ThwQMDJB/7aTQK7SeQt53QKEPz49/Nz1ulXbGboYQnz4T00ObpS3XekVHSS4pRcsxrtMtgSKeX+vjEXSWS3VKrVk90DBsmk9l03v+e626/+eugXCDD64rxcv0Kfl+2tsgqJbtfXSUH/4d4ToA9N8Ppif4jc/xY8gwJRPb6EWTzaQ5ra9SWwRUDdy3mtKjQruVOMeBSKxIH2n8Pxv1MZcmSjyye/wR74lPk2FtzEA/MKgetaVED89pt8Fw1duDbj7NcUVg4CW1sojg8WUIGlwkVqjsb2pJ/72O5F6Hn7lYXlt65i7fncu6Tl1wR2f/73z8TvsXR+q4FnYy+DlBTus/L15eGgPr8d/AXEpxMwXVb/CgaQmuEU3J6pzgMZyU+u8d4kXf2TvGEJHvrUlm5qYxcOxN+7tPywhKDFSlVlmlLZJifVed4++q9KhiPjYjifh6iZr99X8fcVwetqKvN6RvpueH/nawj5VdU0aLWneimqioQf14/gC9SOdjl9r/Oi3K1HB4ukTbwOA+HJ/wZuzw0b2FO9/psf4NfnsMULTjDS0jd9g9hihrAgguovz3vrsXq2FINoEONmerp4woJl/WZ5zBn6eQwyr/eCpUHGIjU+07EKWvwMrPQMfhCPjuoUGjBwYhwEQ6CNSqbW19VL7zocv6H8XXCNg5+N30NUT9oD+9UPebvj23v5BO0uOFEqVQqmqqJeXXc/Kf/Y2kiTx3qOrNV4V9XLqUtWgqGpQIPUs47dYPSxDGAknDynUOuoVUZf4cFg88PbWzLI6GfWKyuvkT30LV/B+ZopREunXZd3X3RNhw3gDu/l7Jfy0PLGljFJPpOPlUmLSa8dhwcDjQzHGYaY02UHqadiAtfYCoPlMT0yNS/uj4tQNOQ8Hb6ayK4FMo+1fepa0J7FCQO0CFX666tlNzE8rVP/6dt2Pz4CGcsPtbvo6Z52WmkxM6rSABKKeQ1sjpHoiu9m9NFvfOXDplPPr32S0MXP//R9Jkl4BFr51VqEfDsdgNKHym5Xwd2F+GrzZcgFugunLabmgsIOvt8DkFak1xFu7at7c0doHOXeQX3SQYF9m87g1+Y49NwEP8/XC1VrySrGRtcvDMZlcoVTr/jkHP0uj+zPMx6KoqK6n1DMsGF4pLDf06S1NbZ1K/sKf8Ff29v6wQS6/sfWnoLnewSO6nTdxmSXw3c78JAgSZ0GUDffyJZSwJ4Sfe1TbZ7zskZ+8d76DVxu+VAOT+qdnZKty64BhHJz+hOtbVzdCQydJnRYTeqHJaRjAkAtcU1Ov1pG19dIoOI6EUKm1JgvcLBkbuQ22MrUSPwWGVquyzls629qcKyH9BgfEJjQUwrVSbOSqnEFzsfSCmQDzGjglXQYXfXIvMpheEQxWXCDmjMJWzev74d7q3RnNOn1TAyI9E/6VKbLKlWM+dLB0IobGwnEHm88a5RXVZbk4jmM47i0SfPZX1rzhEQ9OjvvgH4a2IktjBhG5DcJuN6sH94KfxtWz4j/cma/SkCIBFiDmNdgQxu10MFt2YoMjz3cv9dx35IwL1ZNufv7vaIuGnX3/zjUJZECEYs5b8KZKzs8/I7zyjyEljTp5BDAaRdaXmUyp+JPQmv8JAn1FPJG4tl6q05FCHMgf+Np8rPF4HYOlaSUvVHr+SEi/wTHjZjZstENAGSH10+w9jEaF7t1d1a/ODF09HV4ACS4UKv68JD2eZzoSIa+yPcVAYxMkJ1a3Mdv47tugwWhifuI47uMjQZO6+uqnc2Fmn3+LYwZhQQiJhomsmjfwy3+uIgFFnLslH5sgCfWBAsrWSKgbaKg7ufCudd6t8Ffaw/teeqjyepZGRwKRWNtvknzRul6fnBWEtSx3jukTF1rTFdvFPmJYCN3i1I8aCAceY9RcOMehqINOqDiYvaOOXc7ezKZJa299e1Tv8GJgaJz3B3d2O7E6/rWZnRQojAuGv7L22oO2zOLLbRAWVjbpCLK20aDOq2fFS5Xwh1DAc70z59aOPN+tckeu7Nq0Hv2U11ScWvMsuh7Ys//otE8A4EW/+Ef1B7P1RfTwW9G9T28gN1qjmASALxAYTBr9K0NefHVNfWS3IJIkCRKcfmxk6ijT1Tq33RSMf/1b209eHAJtCnlNS0CWgwm1jtx4pmHjGehE83Hsx4ej4kOF0xJ9JCL8xb86FBw8fl320vZK8+10s7S0QRPiy/MR4Sb+OyyB8PGua2iiCpgoBAKGMo35jz+AulEvZBWB22GpgISn3XehUMLT/n0qn9Qols6GwdAIf/jdV2gYLBL21YSy1wjF3ch5Z4/x2eq/o2Y641LN+pvZOx+bfvaLV+Facit/rKioBvqxDnLcaMlyquDZ3IjoFgDDnjy+frBIN582rVFkmOM8iz+HUcMmwXTBwe2gw/CE+pAsK12BjmBii2kJ8sEfS8bqw51jeos74Xf7++PQ+B3WQ2ziv6MVBof2CvEW8ehvfESc0TQmRpC7f/ftSQq17qEZg5uVmodnGhbTHaIPuVY0Gj5a9FSSy0l79zu79ndhRt49XHjDyCXAOvMzpO+gWV/vCR88yvyu6qwLBMB4fihrAT/1Utx07V8C8PhAS59eMWoQ9OK1tBBnlVTdZjt81ja4EmrsBH3NKRPBvZOgZVFRBDpM99FwPercnZuAB3Fidfz+p+PMt5N64xEAECBmmHLtWNJLoH99/wjTD4lMrkBJrXvGxWI49tvparS94LpNq901KrG/T+VLeNpDl4slPO2ec4Xf/3sRw4BIPzZMoWHWTdem44H9Gtq1BLQdhZ/sGblENz8bCvMAAIOXvGi+G08I688JWYNhLHFtsVajoXfcoesBOtMlQktrYdKougZmVA2LRf93wfowkcL/dsJ1NBcsZzRCBz2SBjCsOhuWAXYQDMcT74YLr97ctw14EiQQ8LEwX4Z377Y4aBI2KpyeqiZIcKVIycPB4tGm7fnNzfIAiWBEX+jC36pUmCTcTUC/x6euGOR1cJ+IuibV4llDcksalqam3KqQLk1NmZIAv/gbjhkdhFVGqL24ygjF3cN5d6nDaCX6qVXIpCX5OI8//TMj7xjD8Gmf/AkAKP9qOZqqSeyEBfO/N4+g9jngC2uPRsgOMh45KsjgykUEeosEPOTFWxrFRJLEqbVwLeIZX+30696Tfte4V9dHD58MiwX0IYWO0H3U1Jlf7YIrpP/zM6HVAA9i8S8wNv3XipjRvYw6qb6+L9JLiGWVqmh1TU7kqV/h33fxmMD190Wqy3Ip6/C2hOAPFw8EAHx3sJy0QT3Rr3REkDcBQFYBjAyczYEWZV5Zg5+YP60v6ecFtAQI14/+soTbGaF5rtBQvluoJzvNT8Sxd1fc/n+bvQKCZ32z1+SunL9/upEvTUZfydpCMv88v8fQv0B3XXM93zcIaAF58oe/zpqacqMG9SgsrqSHK0gbBtnV3cg8+s7j419dP+6Vr0zuUjU1HEi7x2QjxuOZnzDsZXrMMIgEAMC4w/Wdm27s+RV4FtcrVZ8drH369uAP55smTxrkuuWbjFJ/zoMgwZgP83c9GZsc7XXu02nIBSEBycMwkgQ//FdR06Shq6dGo+lTcZR+hJfnwXUQ1u8zfFQEPN5dw4LvHRWq1hIACwcAe26qPwBYZYPqeCFDAopez8QG0t79bs2rsMWOtQkl1xfSt4lr1dOW1qODL94n8gtMvm+Vb2Qs31uiVchqb2Sm/9JaZ44KQsF3T4t6D/Od8QTPNwhU5BF71tJL6+mgXHzdpUN+QcGA0GIAbDprtCiYRi4ru3BUVmUkqU2lt3Y+fkf3UdPixs/yCgwhtBppcX76pk/VTaazMMouGH3rzDHZgdTpFPXV5ZdONBbBkIVH8sfFxj8uNq6YEDSshzhYwiNIUFSn+fRgTX61xcLPQ9ea5eq2TdND15qvlass3WW+ceYXhaG+/BcmePUI9xHw8PI6xdmc2qyixswSGdVlFB4WWCEldCWtJcL0L8mJKwXRIWK1hjie26hQ6SKCxOV18p6Rfpdu1IYFB76+NfO+iX2C/QVUUb2lM3evdLznD1R2R/OTuXjeHtBcdzjDAue3OVWTmuMbFSyubVLFhPneLGusFLadb+XwPGAZE49P6LRDe4XcqFLQ1dNkT5NvCJoDPXr4oB/2Zid3M5L4MoW3QtYsEktUchmqZzIXUJYMWqawywjt5KHLOJsTR6xarqPdMDZEWgK92tH6XHywr2GheY4uC4aBob3g58eSerY0YTCw62yBtNrgo0j0JVBw0oK3omcIL1qs7BnCO3LqypFTV4YF2Te5hv3kdWIwlM/qiZ+uXta04+YnhVKt8/KC77a/N48+SM0S1Gg4dxmvyeEMYOwSui8GQ8eWD8/lm7Vo/FJFfauDTxCk2BsW8GKEafZPptJZ0dCaetcboWl2RkI90IVvh/POhqEhDhTQZHGNXYlcNBCotklVgkd25Hk53Bd1Wa5+IbwQo/VgzHfTEBqC9BYafmpx2NkBiuRiuUorrS4bk2iYW4oEFGEuo+bIVAyVW0o/06a4Lu7I81nqvJPsUk8n0eYguGBfUYnpOAuOroIwsk+yuNZcPekfm+vlTU0KmGbEMeDjxQ/wEfp6C0R8HKkn/VEnM8tGJxp+jElcEBVhFFkqKzVt86W8fgqlhpA058p84FhV9pPXKRn5zrBA29W16cq+I4ebnxwcnYkP0SwCmqqaOp1WS1mgveOjfztwFQOAklFYbmwso+YgYdUR5JGMijH94TAaFwroGvYZoTgL1VNHEKxSTw4O90KrI8srjOreEHdPSV44JflkZqtlWlrOsNQdncioiMioiENXK6heFkkzDCy4hDT2ldbjbHPe9WkTFpXycuYnh9sh4WmtlK8snJJ8IqOUktHS8po2ZXTB5CQ3nR6T52QNZZdUubxrE8GZnxxuTS1urdAYmqJTB9pkipKGC1zGjh3FIGksGzLiRAF1x7J5c7joJ4ebpu+99cupWmHhlGRc6M1siiLpNE5bnchmGGnaxR15nFXOOxt+5Tjzk8PdgRNqeTylWufn53Mi09oU7YhgibkpymiNsuCr2X6cp6E4S9TT5RM/22d+Xt7w6PTB3TvrdDg47MAkFlZSbHEYzcLbk4ODA4xktKKNqKjbOfJ5ztFQtnQisaRr00bz8/KG1nKKZXOGLZszDM4YLqx++adjzjw7Do620ZQb1g2U6DvfbFRZLwF/4e3J2w5eBS11TkhDo1rWoKd3BbpRNaizwVlifrpeO20zPw9/fP/hj++vl5ovlMnBwSZaJtcw05IdMmHh7cnzJiaamaKGXVmQ32WdEYqzQT11OoINMZY2zc/DH99Pv/n3oYt/H7q4eecZOJfs/I2SKtOpcRwcLkkf4TzTJiIejqt1IL+gzHAptOjOC3j4wtthVLQ1uVRRW1pRu2jqQDbpJ1s0lBVlTDweK06jTY63LJMAAPhm23/oSlKvcKShCyYnD4kLct3ZcXAwD89taFLUNspqG42agvMLy8wv+nugvbnw9qT5k5PopmhNTT3GmkQ8e3BkK2f7zE+CJNtsCe9885PRf3/rgdHoSnlFDcBAtyDDKptZt2pW3j3u0fe28YWixHjD+/n7Sa6XicM1AVBogeJ8FAONDoD/UoMlWkKZRt+4YH+jhUzMEYngmvXQ07pc5PKGThPaMajJgS2eDhPQ9mfeWaCeNgro9rfvupJbhuMYEtAbpY0l1Y0YwNa/OH/tpv9mjUqQm62y/dy3h5184hwcBtRluRiG+fr6yJQapJ5SaXP/GLjMZ3ZBXXJ8CEGaLBtHffUYBkeaqCr9bhKQ7BFQ4FINdXUWnhXiaWv10rzX/0Aymtw7Ii4Seut3rYILEO08njlzVALAwMEz124f0Y/+kGGxfucKPW1gLQc7MbdFBsQEoJ/0MYnhvv4BOpJUqnVqjU6l0V3OhmtcJ/cMsfQ9rG9SULpK0BbVC/EXYwDjEvGOtEDd2nm30fxEDIoJBBjWNzpArtTMGd8/yNcbba+oawIA9IuPAgBczCmh9j/42/o9DS4bocjRFc1PuQL571JpMxLQIB+hX4B5c6c+qU4CgiS1OlKp1io1OqVal3W9pEVVLaB/mNo73OWDltlghPJd1XREskY9TbCintMGdR/SB5bIoQI7Sj0RSD0BACl9oykZvf3uFXs4L57D+VgKhVn+jukfgMElP3EcEwqEfnorM9S/19gU6J5rdcTJS3kXcsv7R/sBAC7klENhZVUpEwsGhvJdtlwHa7CxeP6lu4dT1wf1iwUAXLlWmNwLDluMiw6Liw5rbFbS96dkdHic39mCruvF277kDhqhfabG1yFHQwe0fjQPw9fXkNVEAdAB+ugnCQCT+UmBATh7V29WYiCvtLFWqswtvbh0dgqfh8tlMqSecDB+34hLN6oS9cdUe4ezbc3ONBct+4G7xnkn2JI7asfokBFJsZSMIulEN/19WpdMoAjP+4UwCdxb5pd1j/yy7hHAAhx7GjwMrich9vYK9RNZuoQwraBn6Q1p82ihfqLMwtb10zsOe/4ujGeCyj/pJfRSqWHB5CAfQwLdMoaF6S7m1dRKoRHQKIXxKADAHROG0PdD6sla0lxRFoq7wHkHrl+uo3NGh5TueI8gwbwJA4bFGn7G2+ShVT8Bz0WmwQKCQwOCQ8wv/sF2rF16KQ9WI8o0GDxgSKj+mKaHBZ6IlY+HhoDfKRgA1YPMT9CG+WlAqyPOXKvSaHX1UrmOL5k5buD3/16k73DpRtWlG4YJzcj8RDTXVwA3p4MaineyehrW2mQldpmfjNCN0NId75EkKO6+EN7A3MbGcdKZREWEINUrragprag138EuhyR13ABqLYqW0UFO/1Sx/A9EfwOh+am/Gdi2+QmkcvX569X1UplMphDyMAlPe+hKMVoyHhmh3mJYz5QUaxDi/EabXSo3mRbaEQ3t1BYglHlnj/Nul/n5wW9nrasnXUORehYh9QRgzrgBoItDgtSx/aPCg9Etcw1tR3IiKrwNUe5SmL6Btr2fpbWyrML6eqlMyIPfSm//kG4SmJSvqle0rkZ3tYC6nqM2LdppZpkR2skTl/FOHfdJ64jwYEzUU/9JJm334j0UwxfakoZaX7mX4WD63a2LclcE5+vUykS9tRjIXL3USnZR/dWblTKZHKknel+jYmKgEXq5mO7FJ8UGkiRprp4eQ7uNULwznXf9ch1sUVDbaz8pjpy9lrv9w+t/f2R+l3TPm9LdbzbqL6pBS4zVEzJnfH83CoA69UyiwoNbLEc4pQJtbFd5DGnlgIgRIYZ8SAdxiz8NFQBt0/4kSfLY1eLy6gaBsUUj4WlPZpUvnZ1S1ah4eKYhg/TK0ukmDzfx4ps9wghtn4Z2UhkTq7o2281nOzKeTp0Nv6hnr0WW/YthWEB3Ifqo5vd42LATbUqY0f+ke8TXHIulqqPUsfDnBIldaUVtZHgwYZ+Ckm0eEHg0v6x7xJKSJsYGInPekvl5IqO4R5hErdbSN0okYh1f/NCMpPU7Mspq4diR87mVWTdKqWCoe5mfaZ1V1WR3J1L7zU8WLPXeEfMTsSo1if4aknsbFt0218fy2iYT9XxlI4uqXztTQHkYiI8zdBnQIQEooxmMSpXKxjrQ3j0YjobYcTx7SO9uth/QM1CX5WpJXCTAdVotEtBACYP/fiKjOCHCV6rQFFQ206TTGwBMJODJSVHf7oHFZdUisUQllykI/pNzB1K7fbgz3+Ro8f5G/it7akIR7RNQe0vr8U5z3tmjnh1h3Y4MkoT+JhLMq3nl5oNpTW1Q/T8Xr8HW465JD716kkyXyBbv2y7yC0oZj0YCMGtsf483P9vG7Of8REZxVU1DQoRvWZ2cUk+JxFuvnsBLJNTyvCU8bW5x/ZLUlLom1UMzBhMkHJdjUs/kRqR1iiPfGS4825z3dpufLegjucZZD7NPLBke7FNR28SpJw/Hblke34sqFlV6U9HQD2OVE5nl45NhN631YyJsOaAngcxPAEh6+ghZnQkR0AzPK5cqVLoWq7MVlVqt5fEXTUn85UBOfjmcC342p1LC0x5JL1nW4sKvnhVvYoTmNxJ0I5RVjUmdBr8zCj9ZybWrl9GV26wWGhdUNFaLzJeNo76YWHpeeasjb26Lttief11o+wvvecgVapNfTl5LwpcRWwKhzTKotjYe04Nat9tA778b5Xbo0gmn1hY16PRDlUzU00skCg0JrK6p33m2AImmhKe9dKPqqbkp67anNys0Pt4C4J6ktSsSalePPN/Z6smekUt08zMn4zKm7y/0wuEPMiMkAFKFlm82LX/dzsxVsxJpezEaoPCf8GDfXcezANZF1RNBAhAfE05ZgwbDnZZXq6ppQHuqVGr7D0hTSLKdB/QYUPUSCcDV4uYELUZJJ0GSmUUNgDSVTlQSo1SpCyqbVs1NWfd3+hOzh3z579VldwzcsCfrRhk0RQ9eKq6pradSSSbks94IdbaGOreQXj9yCbATUt+jriR4wd266S9hwWFGF6lCe7OsMTpYHKoqNnnsup2Zl64VUV9dGAk1DvJdyC5EF48I/LafTEVLSyUqYMPg0KCWf2DzDCV2wUF+tvzODumpj2+iPeEB9cAjmR0w0KYDegw6rSGr7i0SUdIJ80taXWZhg0RsCHfSmt/hu6Pji0cP7gUAyCiA2bwrN6sBAHvOF0p42v/0pmhhVdMjswz1TKtnxXf+62I5uPOdd9Z9inMyDM47ageEvYBUVqgFNEAWxzA17sW8PtJN6eVrRZf0FzRXkRJNqJs0urL5iSgpqzL6gdF/ec9ml1VWG8SuW6itUyqYNZHpgAfOQVcjXRbcpcxPExpl6uJ6TYt0tuqmSeGn/t8yCU975lqFhKctrWlePGuITKm9bxrMwp/OLreSSspncWdnJ2STcCc67wTBqtwRPX1EEkRCrxgASNTHYt7EUlAEVQ/ZNnHdfMyNUKShx2/CqprL14p+3nHW5N6v91//ev91Tj0ZPgEkqKiqjwuFHdbdQgPCwwL1FRod+qhQBwwPC2w5YFckQNIar7yYX3e9vKmuUVYHl5NjeEN0fHH3EG8dQd4zJVmlIe6dNlBLkLPH9icByC2BP0UHLxdLeNorN2uWpLamkqyfQDPLiuqdraF8J7VsWpnwyhZaomdR4cGog4VqCkR33CxrjOgG1+3QQCO0tW7OBKSh5tc5DJh9CiqqDYPmwkNbjCbM0ONry0fGZJczWaVIOpF6tu7E7k+fMyBJsr7ZEPZtVmrp63DUNZp+gIP8fSQ8bUWzUMLTHr4CvfWDl6Bc7rsI/fcTmdAgzS+XPjlnyBf/XC2qaort5vnltO0IhjolBspm8zMn4zI0P3saJdYpOxS5mKhEBkfhNf030ZIRSodTzzY5k11qqp4tS0RSb7V1THZqNTxDoeFptFtXQhjZh55sKK9XFtfIeYAwudAfUtfYXFZV1yBTL541pKBSujQ15VaFFBWBPjxziFylXTQV+u8X86pgav5KCeXFmxuh+ezu7HTqkBHcSaFPHGfVUu9tF7NczoNxOqox5mZZYzfKPgJAayESymE7FdVGbrshp9QidqhUw7zmwRJnskorqlq0GB3NOMjX1TQ0Rx2Wp+2GLs2+sfzw3ub70MXUPzhs2MAEmOgrqCVJcKsCOk/XiuqQaAIA9l8skvC053Mrl6amNMrVKB7q1qQ5wZHHneG864eGsAeYtqCbn+Z3kwDMHNvPRENNbCJbjFAOS7QanmGByLA3cbKRu8KzrWjDLITaNV32NuCH9za/UPc211bklTZIeNrT+sTR/ktQLk/oE0qX9FZnUVXT4llDNDqyqhHOKDmRWWZ7KqmZlUZou7GioQ62E/cdOaNjkfNONVUaBJ2PYwI+T8Dn3Sosoy4FLZcBMf5qlUqtUt0sa/QSCuoapPRfAi3uFdfNsOYMR/sI8xcRKjmhkuuYLrCqTKOz62ikWmHlaBzm0MWUIMHscf2Vat190wYq1br7pw9SqLT3Txuo1hJ3TYKVzpduVMPRdvrYaI6+y9Pd65nSHO3I8x1rfpIkyWOL896qfTWlhmbNJrlpEwvlPNKJCPSuk2kxAOobWoehBQfCgZ6hqmKmxiQOa6i1hFoL3/kCBcP7Tzc8bfnlJUkgV7R9tP7dWb2ADxuQ8LS7z8FhyYcuQ9fqVBYsZz6gl8vd52BX0vncysdnw36kFamD1+/IuFZUdyr9lvsW1Tsjm8Qwjand6smamXWmAQRKQC+dg8VGQj4eHRnWmoan1YCiMmyVSl0jVZbWymO6mU5B7uYvPN/Q9iIzHBxugZem6YnZyet3XPXGtQqC741r5To4hOnzv9NXpiZ9tSPjgcl9Nh7KTe4RfLOoUoMJH09Nct/5TBTtnnRnrqEOsxbZoZ6kFfWEfSzDWpcmbqnyhBE53HDNcEdWcUOIn9eAXpHBgX7oUlRpqE+qbFRzkVAOTyKnuJ4gwczRfQkSTB/ZBxaBFsOA9QV9JPSAvhQ0o6B2aWqKWktU1luLjeS7SSTUgY487tDCT+BSbMpcZSpC1FrC0BvTCtRPqo/ljhEJGAYa6g03AQCD+0YjJe0eLOZceA6PQcLTnsqGiaN9F2ASCWXeT+i7ki7lVS+dnVJRJ39k5hCSBDklUFX3XSiyUs/k8Zhnk3BHOe/6ieIYewxPc/OTmlxnXei7hQZgGIiKCA3x86qqhlUdFN64Nl9f7cHB4RnU4oFylfbBOwY3ytUPzRjcpNA8NGOwTKm9f/ogjY5Ay8SfvgYV9pQ+QV/frLJez5Tv6UaoiYY6wIV39cglu0umMuQhGp2pEUrV2aDaRB7O8IJcHtzl4HAGR9JL9DNAodhduA6/F6gpfr/eLL18o3ppakqTQrNoajJsGKHVM612ZyPUIY487hjnHQAWqiej+dmqoQyOPG2qBQZiosJC/VuNUG9ce6OssZxvcTEJDg53RCnwvV5SL+Fpr+bXUEWgmQW1LUvLwdH0hVWwHOXQZdjxmVtcP2tMPwCAQqXdsDvLq7n0vqF+9MvI3j4je/uw3whtN3QjFO+4867TuaTw06LbbiuYJfOT6mWB/4UHGoaAYbC9irNBOTwQWBA6tr9GRy6YnKQlyLnjB+gIsrQGLi13IhN67rv0s5ZvVUjlOj5Bgt+O5n3/70VvEd9byAPuTMcdebyD6tnmjHHn0LZ0mpiftjjysDu7paMF/R8b3U1HkJXGkVAODg9DP0+kmEoiHcso1aeV4FSR9PwaTOCt0REyHawZJ/U7wyWU9TenpsQCkd+f/10zP6a7GKEd1NCOxkA7fan3dhqeVhY+osxPunTS6R4i4fx3Dg+mFg+sbFAsSU0pq5UtnjWkuLp5iT7oef/0QfrBThq0G106EWGB3l4C9zZCOwjeIee9s7s2bZXONs1PuhGKrof6CXVKmU4p0xpfokJ8VBpdQUsdKAeHp4LG0WcWQH8r41YNAODbXZlINM2lEwCAUknjk2FWoMsaoR1aldPG0Q+OoEPhTivmZ4Y8JImEn5Uipr5AKugZ5u91pbmrzDbn6IJQU0XoRUsyHR+Jpol00u/q0z3wgJc/oYQLKHVBcNYXfnY4WdQWGYoQS5d0WTC6cOrJ4dnU4oEaLbFoSjKaLaLSEl5io9E5jKbo9/9exDAwMD7YFiOUzbTbCMXb3bXZKYWf7ZFOI/+dbMey7xxsZPAH2wd/sB1dcfW5eCwn9PNEzlyD7nZtk9JcNCnppG8c2T8CE5lOjTCHzV58uzW0nRao88XTEYanC3ujOOwkcNA4dCXu3udN7ur79Do48fqlefSNiS//wL3HjkXC0xoKQm/VMoomtZuJESrg41EhEktGqGeDs9J5b//xDeZni/xy5qe7EHf3M8i6LNiy1uSunM9WDf5gOy6EiwJQRmjm+0tcdKYeSy0eiKaKWBJNE+mU0G5OHtwdWDBC3SWV1D4jFKvMPsiykUsdMjxbBVTP/45x/jsHh60EE/VyHV+szxFJzP6ldjPZuGoe7I7/ZmeGqrlh/gTYpGTC6bxm9s+4a9+wO5YMP3aQepbcpLv+nHq6O8SijcSija4+iy5ELR5IGnvujKFPhIzmxaNIqLvXM7UD3M61Np1qfrJqJSUOF0NJJ6ehbKDNVFJSXDDu1XYqif3Y5cjbZYGSmBMN1o6qp43F8xxugYlochraadDDnVaq6M1TSTiO9e0e2NWMUNye6GfLrA122p7Gx+D8d/eFUS45De0cavFAS/47wkoqaUxipKVUkqcaoTgLnHcHqCeMfnJ4BFaEktPQzseKKSoxM0K9RfxuAd6eYYTaqKG2WqDOcd6d1WXEmZ9uSpsSyWloJ2Cpft6SdCLQzUmW65k8Etw285NwgvPuMOnkzE/PwEZx5DS0M714E6zEQxFh/t5ikcUJG55nhOK2TPyEq1a6T7adMz85OFyVSgIYGJsU6TFdSW1qaNsWKKycd6T5ibqMOPOTo/12JWeEsjmVlBAVwBf7e4YR2iZ45/Ydkc62QDnzk4PDSdiYSsJxbHDP0C5ihOJWnXfSoYl3x3fQc9FPDg4nYcl/tySdEtrNoQndbCyqdwsj1IqGWrNACWgpYu5ie3LmJweHU714CitZeKA3QoUCvEe4n7sPCbUF5nzZviNntFodn8+2TnkOT6Pd0UzqgfjWBxx6RhzM0AeIMA6oBy2g6+OTo/LLpaTKQ9bCSXv3O8YhI8wCSgJSr56ONT8dDOe/uy8OzAJxStqZomllbQ9grKS+YmGgr6hOBY1Q8xFNI3v7UCOamusr2D+iyRK4pa5NB6mn0xfkoODSR+6Ck3LoXGq+cwpCbUwlAQAmJEcDD4IxEopbWK7DIc/oROnkzE8ODpankrqH+gglAZ5Uz2SuoZainJh7zabjzE8ODtemkiRMRfUpvcM8pp6JEdw5S707Vz0585ODwyVY6UoyAd2bHB+CWa5n8gAjFDfp2uzEpd4dBmd+cnA4Dyv+uxVTFBmhXkJeXDeL9UxuCl1DjSxQ0jGrxXHmJweH59DuVBJifHKUlUWP3dEIpYMbjVxivfNuDmd+cnB0JvamkvwlwgAfkacaobgh8w5IvKPOe2dULHHRTw8A3/qAw6vfnXFMDgrbBzJJmOqZxidH2Tgk1I2MUKShBguUJDqSee+8Yk8TOPPTfXGg3nHS6UIvXmahoZN+b0yYr0Df1uh5nZ24ofCzQ02bnZR34sxPD6PjZiNneLLNFKWgG6EYBtB8Js8zQvktAtgR89P9EvccHBx20b6GTtCy88j+4QUV0qqqyqZmufnBEyPwzHLoBbsduCOW6yBdYn5y/jsHRyd78XZl4SW0eiYAwKJJCVOHxjIe3EQ93cgIxTuwXIfLQp8cHBydT/saOntGtE6n79crxvyw/+YY+f7uBd5e87NTpZMzPz2YdodBudyRW6SSblU0PnjHIEsaitQzv9HUf3cXI9RaM5ZlOMOTg6P9aCtvjOoHm8TNOZtbjYX2tLTDuTpbVwweFmRxECc6COMOGQX1Cj9mL7sds0H9JUKdTkAoNRfyqrJvlC2dneJJticCZ796cuYnh4dB6HSWUq/D+4QOC5JaktcRIU22HF9dlmvFsxwWJLUkrwPjg6wfmZ5ql7VVENooU88f21PC0165UbMktQ31dFMjtH0WKAcHR8fBxN6wRUcisMkoOZJRmdwj0I6jA8zb/uOPDGk6XeNraZ9aPNBL12SScJeYZeEVBN9L7CNrUh69WgoA0OiIwkrpwXN5yAjt1yvm2o0i4BG4n4ByyXcOt0ZdlsvjG0cMNVhURIi1x5CAgHMqKm0//vHMsrGJkSqlUuTlJdNAa7SNp4DYdHxzV11mrKfo5sCeIWczCgqrmpbPHPTNzswTWeV0F57Rec9vJOL93WwZIZzlaXeueL4r0I50kGdkkChRKy2vKS2vYd5J/53DW+ZU2OjFIwgSu5RnkEWLx29h9vgBV2/Vt3nMNrPwwX5eEp72bE7F0tQUkgSX8qolPG2dVNkk16B6JgDA6lnxtpw/+7143L0SR5z5yeHu8HmtX7r8WyV0w5BB40j7voPQ/KQdHwCQOm6ATTKtr8hBjLSq0YwFoTJaPFQ/1A3IlNqqBgUMgN6shkoKwInMsjbP3zwS6hkC6hr15MxPDg9DXZar0y8XTleTqIgQZjvR7GvXpoWIYRihPz4u9EJb8m+VWHsK+mNh3onsSEOnWATjoXVNqvunw7ql/67Cp1ZribI6mYSnvVHWQK9n8gwjlO8utieHZ+MZLnmbmEyM1JGGm6njBlDShv6NCjcNWc4Zn/jP0SzkxZ9hyvPA5DuG+fpKZHKlkI+jMChBe4odx7KG9O7W+hT0qChJ7ZM9sK1UlUkqCSHhaeUqcN/kAZsO5Z7PrZTwtBV18mUzBn63O+t4BqxhWrc9/Wa59OSVfPN6JvcFZ616ctVLHB6GuiwXrvfrC4cP+fkxjCAyshMrTO1EKgxq6eB0dfb1Fpjvg9x5BlO05VvOb5kqZN2LZzRF1UBIApBdWCfhaTMLapfo3faLeVUSnrayXi5TagAAZ64ZpZIYca96JjfLeXFweDap4wbQNdRMRtswaJA6Q9uWIBk12vQprEZF7UolDYwPQdK5dHaKWksUVEglPG3GrVoUAD2VVS7haaVyTWW93N5UEpvB/vfVu5bv5cxPDg6HoSm/jgROJldIxF7JMbAjiIeR8T2izb9zdOmk3Pm/jmRgGJbcI9DEhW9x3g2KKZMrMJ5ALOJJpc1jEyNxk6dogS6d1FNoCYLy4k0KQtVluWMTI8l2yQIPx0bdlvzVv1cjgsQLx/emtn+4M59xf/N6Jp/AcOA+Fig3KISDw8GoCSMfnCErTZOmqHAGd37uhERLxUwmBxeLeNR1KgxqgslToGehvHjG+APKNYX6iey68PQJ/usl9ag9qa5J5TFGKJ+FWSMu+snRdaDySOZfu9Sx+sySXtfQv5FmmSWEMLKPpjTPvicmTZ8CXTFPXlFQ9fl1TSqRF8zy21CcDxQqbXpGzsAegTXlxUtnw8pQDIAF4/s0NjZbeZR5UX1zfQULjVDGXxtuQDIHh+MR8Iy+WUYxSstGC13RyswyS5YOTj3Fcb2di4qZLD2diSk6rG94ur5eikolmWSoKJO21IYQqrew1Uorq5H9sOMiwIC/v4+/v883x6u+OV4F3Bmcbc47Z35yeCyErrZRBsVOKKRvNlU3M+gCN6xvOHM1aMvBmZ+Z7sUzfb9Txw6gK/WwvuEmzjvfWKDbqP+nAx9HIkUOCxQDABobm9EFuH863v164Tk43BcUDVRrCaF+kTWK8nq5iI8r1DovIYxdqrUEn4cp1TqRAN4kSTA8GcYKNYpWiTSvBqVpHINGVjUo9KufwUp7/To8mI4gvIR8hUrrLeITBMnjYUKx5Ex6PqoVpYNhmFZH4jxYn4+8+Bv5pb3io3YeyxpsqbCUxtwJiX//BytYT52/isqYLOWO3A4+qwrmOfOTw4MRRvaRGUcqT2SWjUmMBBimkNYr9FtUxg/RMIXUBvaAQ+dMsuHmB6cHCnAca25gsFuV6ElptuCAmECVSg1anndkSNOxcngF1eebnMosWv0/usKooXT3v03D070ioZwFykxJ/i1nvN3R8T0Am+giL5PNL1ahMNHMtuExhTthZAAjahtljTKtTqfjNei1FwCtVnu9qC5AYhQ0aBN6zX59s5rP5/vqjV0cx3RmO0dFhLRpiqaO64+qo9Kv3bQyLs/t4LPZ/OTg8EiQOkmlzUN6BpOAjI+JoH8NW7+K+mvo5rlrZXGhMIAIALh0o2pAd/90WdsZcARST4nYy/RZ0Iq6tK9+fZOSqn8iCIJST2oHAQ/31iemKC8e0ExRuoyaaKil6qh2wCojlNUWKDd7qUsxbPBQG/c8d/kCcE+QkRgWKEEjRfTQzUn6CuOw3ocE4Fw2lE66eibGBKCJR4zodDpvkUCthfKn1WrN1JP2LPr0DsBAndSgm+jfhmZoFPuJBSbqaZ5KMmHWuAFWTVGYSmqz0d62IaEkS+qFcJen3Tk4uhqClolzGADxMczG1JnssjPZZZXVDUg6w0MDuoUGXMqDRT8kSTKan8LIPugKklet1jC02Fg9W6mTKuqkSqVKg3TTSyQQiQR09bR0/q3VUQWm9QOz9O32l5kmP80Zn2hXo71bwBYLlJ3po+qW7gtL1NXV90kcga4//eTyV156lrpr6oy7Ll/JAO5AypCBe3dus77P9FkLL15Kh99SgSA366yPjwRtX7J81b879gK3os0/a3OzrEfCEHS9Z3zc6eN7URpEpVLF9xmqVltUFhupaVIRamVKr2BGO/JMdind5AwPNZhsWh0BMGDd/BRg0PBUaQkcw+LiYi9eOmX9TJ5b9cyOf/71EhkipJW1TRiGVVYXr/2/jz74aB29+EkmRwknIwjj9idrpmh4CBqG4hAjtLm+0iewW0sMwpWwRUDZSX1DI/1mYIC/ycaGRsPiXNnpJ0NDQ+rqG/bvPxwUFDhxwtj9u//4/qdNL73yDmA9Op2O/qJEQqFY7K1SqeSK1u8MMmdEIlHJraskSV64eOVKesad81J/+Hbd5cczps64C3jQn1Uul6MrC+bPXv/FGrVGs2v3/ujoyNtSBpcWZMT3SWlqsiOVbKVliF7MdCYbLh9ESSelm0giMAAOnM1D19uMfhIkSeh0PiJeQ0MDlQEXCARisVij0chlLbVQGAZIklJPlC4vKIS/LkoC2qRm45/scFVnmURF9T1Oc8YPQBP5rK+8ZA8u9uVZIaDsND8BAAn9h5lbLiYbAQCLH743NDTk0OFj99y/jNpYnJ++9JH7P/xoHSWyrOVKeib9RT14/90ff/T2t9//75331prsmX8dtjD36nebVAq9sJdefffYoR2DByVFRUaUlpUDN8HGPyuGYeu/WKPT6aJiDb6nv5/fjZzzp4/tTRw8piMnQKiVQ3sFkwBER4YxmJxhBumkqwKMmerNzyvNwTY+S0lJybjhI5GrDgC4c96sz9d/uWfX7ueffpYumhT1zeqKqiJ6yZGxXhqZe8iLH5sYeaugpEccw6QSxKyxA3Yeb9VQ0F4sG6EM59aZcOPsHMCK5Yuh7jyygr7x6edeBQBMmzoJeBBCgUAuVyD1RLzyxnvIUgMex4P33w0AeHjJE9SWRqn0/MXL3bqFent7t/uwyMtGkdAz2aUV1fV06YwIC8T0YmCiB3tPX7dFIYSRfXQEdK1R7qhOqlCq1GIRj8qt83g4o3ouXfFEcelNDMO+/vYntAX38sEBqdborFugOgtePLX7LH2PExUVNe8TdWtcL6CsNT9t56ElTyxf8ZxaY6i8Q6g1MFImkRi+G55BaGSf2F6tqzJAa7QnrIK8VeiAVWpv3GTXUreL7r4TAHD85Bn6xrfeWQMAuG9Rh0IWQ3tBK5IvECDpDPMXoQuhkussXDAMDLDN/MQxg8zxeDjSTZGAJxLwDJkrErr35penVq2sKK9I7DNg67a/7H05Ko1Oqdap9f+qNDqNllCqtSqNTqHWKjU6tY5QqLVTRvS9dL1VQ+1+y5g7O+nLiJJd14V3d7Kyc7Kyc0w2vvvWy3BZmKMngecSEhL8/juvAQB27znQ8aMVFpV1jw4XMdlHLmHwoCSYPJEZ4qGIsnLYjj00ZeD3P27s4PFRiDkuHEZg22RATIDtR0bmpw7aokDsxTCa3pxpEybezC9o1Aphk5MFSNzoUH4tXnxpqU3rIQ+IpXqcUHzC4ZLHWBnm0QLqAeYnI/PmzIyMCK+qqsm/VQg8kS3/+3b0qOEo1zRg0GiNhmGZb3vZ+90qG/dc0Cl1oDhT7TfS0/Bupt3itiOM7HOVpsmZtrZH2Rr6xL18pDS3W9qSMarXdzwptFiFjEFfKq6XAWD604V7+QBls1qjE+pb8k2QSmEmDacW87QbbGRI8+ka5rH57YqEgs4PiXIWqOMZPizlu68/gSbMsInAQ5k4YeyV9IyhKYNEItHZk/v7JY3Qas17/DwQnf5lCkU2mXUehheTjDbJFPrlPK31mDJCkiBdZuuvAmuz864UUI/s3bx7wdwv130IC99iE02iop5EREx/dOXee+av++T94vwMaotng1ZdVyk7WgrqjgT6CJsUGrr5OaRncHxsuAVP2lCtejYbltzTO6mSYgIIx6mnBSO0k2TU9UkkT/Lfd//765frPtRqdaGRfTxYPels+fXPRqmUz+cJjWdceioSfQdBeYVNUT8PAObiMUMunqpaReqZ0tOCAuprCM5kl51t6aSKCxV3Cw0ICwm4dAN2UnVEPc1TSTZAeqAF6nnmZ9aVk2FhIdXVNf0HjgaeCIZh/n6+Gq3WJK9SWFicnDRAIOB3vEWHVVy+kjF4UJJQIKD/FoaHwcrNS/qmrK4Dqg1FzZ0G9dS3UcWbmZ9nsozbAVpqWnefzEWGYFJMwGWbS1kdYYQ6FxZZoG5tfp47tT8sLOTylQxPVU9Yt8jn5107f/LobpPtcbExsGzLs9QTAPCrvqBn+HCjdcxfWv00AKAdtT5uDerCbFJoKPWEC2fGtqrnmazSM1mlFVWwpjUuVBweFoguhnS7vgvAUerJKiPUNQLqYebnc8+s6BEXez3vpnt1NNqLWqMhSTIqMoJaPhf2t8yd5efnK5U2OSQRzyp++mUrAOD3rT9S6Xg/P9+xY0Y0NEpNbHDPBvfyIUmoWTq1kipi7aGfgXKGSTfpvQAo+rjnzHUn2Z4WakI7D7Zk4d3a/HwxDdbfJPTuaT6l4uNP1//fmnXAUxg/OfXY4Z35uRd/+mVr3o38hXfNGTQQtjkOGNSh1kZ2QpLk2++uef3VtLLCzP/7aF1gYMCKx2DL2ZgJM0BXBXWgVjbpKrNa208jWvx0RnadysUAbEJ1oHpamHHXNQTUw8xPxmpBT+VaTl7i4DGH9m5/5KFFaMuOnfseXfGMp9YwfbH++/xbhT98tw7N2apvaJx4+5zKymrQ9UD1W3CVTS9RnH48Xpi/yHCXyqI9vvdiscPVs72RUKcUh2L/+6qzxwW5RfE8O5d/8OyX+ftHK23cc8ELX7n7i3UeTnqZYV7qob2CY7r52fvA0uomJ6mnuRHaVioJ81gXnoODg82gBqqr7RHnYMAWHG+Edrb76RbmJwcHB/vJb2O8SGdYoF0ofsfBwdG1Id3bhXcj85NtgS0n0UVeZpd6sV3kZdo5XsRZcBYoBwdH14F0VwH1sOolDg4OdtKZRfWutEBZ679zcHC4C/nt6ex0NwHlzE8ODg7PM0JdZoFy5icHB4crjFDSzQSUMz85ODg80gh1jQXKmZ8cHBweYIQ6XUA585ODg8NTjVAXWKCc+cnBweEZRqhzBZQzPzk4OFhphGJuOY2pc8xPJ0304nAqq9782sY9ub+vOxLt/B5TewYtO2YyE9/DzE/uq8XBwU5K9GZN57fqO7U7nuuF5+Dg6JqQ7BVQRvOTSx9xcHB0firJeel4zgLl4ODospBuI6Cc+cnBweFh40WcIqBc9RIHBwercJIX30kWKGd+cnBweJ4RyndH89PHx2fY0NuGpqRotbrTZ06nZ1yVy5lXpg4NDQ0KClKr1UVFRTqdtbXL4+PjrT+pSqUuLS2hbkZGRvr5+anV6tLSUpVKZf2xn3/+hVwuf/HF1ZZ2iI6OFgqFlu4tLCxEJ49OMj8/H22XSCTdupnWZxAEoVKpampqNBqN+aEwDOvRI3727NnR0d1v3ryxZ8/ugoICu94HrVZbVFRkvh3H8aSkpNTUORgGduz49+rVqwRh7UMcGxv73HPPP/XUk20v6hgaNnzk2B69ejfU1x0/fKC4qND6/v369auqqqqtrbW0g1gsDg8Pt36QoqIirVaLrkdGRs2bNy82NvbSpUv79u2tr6+3/tjPP//i0qVLP//8k6UdTP6OdLy8vCIjI6VSaU1NDXpX4+LiTPbRaDRSqbSxsZG+0fzDoNPpFApFdXU1SVqL9EVGRnl5iRobG83fsTY/DDqdrrCwsM09y8rKlEolOsnu0d3pdxEEoVQp6+rqrJ+kM+uZOlQQ6vh14Z298NHPG37o3t3obwAAuJ6X9/iTK+l1oDweLz09g89v/YV48cXVf/+93dJhc3KuW3/ea9ey582bCwCYMGHiN998S78rOzvrzjvnWXlsTs71pqam225LsbTDnj17e/Sw+BEcPXok+nCjk+zbNwFtv/32KV9+aXGF9PLyssmTJ9FVLDk5edu2P8z3XLBgfkZGBnWqVl4IAKCysnL8+LEmG+Pje+7evcdk43333Xvx4gVLx0EnQ70WAMCwxJ4m+/j6+n310684buonrVnz0YYNGxgP269f/+3b//7ww//76acfLT312LFjN2z4AVhlwoRxFRUVGIadOXPO39+ffte5c+cefPB+K4/Nybm+d++ep59eZWUH+t+RzsCBA3/77fdff9365ptvIMW5ePGypeMsWbL45MkT6PqUKVO++IL5w3DtWvZdd823ZECgk9FoNElJAxjvskJNTc2YMaPa3PP++++7cOE8AGDSpMlff/MN4z75+fnLn1hh/UfXaQvHY2yxQJ1tfn7z5fru3bsXFBS8+e7bxSXQHuzbp+87b7yZ0Lv3d199/ejKx6k9MzOzMQx76603t27dEhYWtmvXnv/7vw+vXk1n/NkHADz//HPUdT8/v9dff6Oqquqjjz6kNjY2NgAAevTo8c0336rV6kWL7snKyhQIBJs3b01OTt6zZ+8dd0zv4Kt7//336urqzLc3NTVZedTmzZs2bvwfdVMkEg0fPvz551+IiIjcvXvv9OlT0XY+n79t2x8kSX788dqff/5Jq9WKRKKnnlq1ZMnS33//c/DggQqFwuR9AACsXfsxQRAvvJBGbUGmBB0+n4/U84EH7jt/Hn5PevXqtWPHrs2btyQnJ6rV6va9GxiGff3LNgDA0UP7N/34jUIBnYwJt09fuuLp555La26Wbd26xeQhCQkJ27f/3eaRc3Jy6C9z0qRJM2bM3LHj36NHj1IbGxrgn/vAgUP+/v5Hj/63fPmjaPuBA4eGDRv28cefPPfcs6AToU4Yw4CPj+/EiRPHjRv/ww8/jh49qrYW2qotp3dg37696LpEIklMTJw/f36/fv2vXLk6cGCSuTz16tULAHD69KmRI0dFRkaVlZUyPili7dqPTTaqVEor+1Pk5xvJwqnTp48c/Q9dF4vFCb16T582LT4+fs8/O++YPYsgCTcyQp3eyulA85PH4/Xu1UulUi957NHWL0NuzoJ779m3c0/Pnj39/f2Rj92nTx8Mw7Zs2Yy+Y1VVVcOH35adnbNz5+7+/fsyHnznzh3U9dDQ0Ndff6O5uYm+EbF9+z8AgJEjh8tkMvS7vXDhXdnZOVbsR9s5fPhQif5XwS4aGhpM3PDc3NxNmzZlZ+fExcX5+Pg0Nzfrjdx9AIA5c1KvXzcYCyqVas2aj86ePfPdd9+fPHlqyJDBJu8D9Z0xfx/oLF68BADw/fffI/UEANy4cWPhwrt+//3Pbdt+nzt3ji2vQqMx9eAGpQwDAOzb+ffGH1ttlv8O7j176vh3G/987bXXTQT0hRdWozNpk+rqavorioiImDFjZnZ2tvnLjI6OViqVlHrqDb3J167lzpw5q5MF1OTctm7dsnjxkhdeWH38+PH+/ftR269fz6Xv+dtv4LXXXt227Y/k5OQ//vjT3E/asOF72ES76qlz5y6sWbPmvvvutfKkbX4Ydlr9nFAUFBYc/u8Ifcsnn3/25afr+vXr9+1X65eteAy4oLOzneBuZH5GRkQAAC5eumh+18bNmwAASQMS0c2tW38DALz99lvUDgRB5ORcw3FcIBCgLRKJJDIyMiAgwK5z8PLy0mg0SD0pDhzYDwCw91BOhSCIzMxMAEDv3gY/EQXIKPWkOHbsGADA21ts+8GDgoIiIyO9vb3RzRkzZgIAPv54DX0fFBMwD9HaTt/+SfDPff60yXaFXEaSpIlfn52ds3jxkpqamrvums94tEg99JBOm0gkPmq1+sQJg49MUV/P4CV0Pj/+CKMQOM5rc8+7714AAOjff4BEIqFv5/P5ERGRFRUVUqlUqVSmpAzl8do+mpN48tmnUSxVLLbjo+jydLw7FdKXlpYBAEaNHEmJIMWmrZsnT5964tRJdNPLy8s8Jn38+HEAABXPmjfvzsOH/3vuuVbn1Bb69k0wDxWNHTsOACCVSgGb6NOnDwCA8u+QlbpmzVrzPfv2TejXD+5sI++88+7hw/+NGzce3Zw7d3bfvgkmbzjyDa9fz2v3+WdevQQAWLbiGfO7Ro4YbhJAxHH8rrvmjxkzylIQ7fDh/w4f/i8szA5Bl8mak5MTn3hihcn2gIBA4FaQJPnbb78CAGbPNvIG0M3167/U/wvjp3PnznPhSf6zAxqw06dMA+4D7ka9mwRJnDgJJXLvjl2/b/l1bupsS3viOG6eg750CX4hx4+fAByNWCwmCJ1j498dJC3tBfQzQ2XM58xJBQCkps7Oyrq2adOWyMgo5z27QCDYsWMXAODhhx9s90EyrlwiCTK0W/imP/eu/fKHvgOgQWqJvn0TMjMNeTCnsn//QRzHX3/9NeBqli1bhlwNW3betw8GcCZNmkzf+P77HwAAtm2DgeYNG74DALz33vvAdRw7Dp2hEcOHu6Kzk3SPcXYd5I133rr37nuWPLI4KCjoyZVPPLnyCUACaVPTw0sfaTQ2AM0/WNXV1QCAmJgYdHPTpo2bNm3s+CllZmbDn83pDvjZPHjwsPlGxlwtnccff3z58tawEYZhlCOWkgLDmgiSJBMT+2dkZPF4vKFDhx4+DINQWq3211+3vvuufZUYK1eaGmV0goNDTpw4iWHYkCGDOlib8uCCGf/32bdR3WPCI6NefQeGCAiCKMi/sehea0lwRtp8G23h7Nnz/v7+p0+f2rYNxog6E6rKjcfjJSYmrlnzMarEGjJkkC0PR45IbGwstQV9SPLyDC4CSZI3b97o2bNXUFAQYybTFnIs5OJtfPMb9HlaFKnrWhZoZ44O2fLbr5OnT50yY/qp06dhhhcDfn6+f237Y/066IlYAUmqeU1Mu8FxPDMzm8/nb9jwHWNppL0oFAq5XGZyseFxhgQihmF8Pp/H49XV1d5zz8K+fRNMYrVarbZfvz59+yasXLmisbGRIAg+n3///Q/k5FynApodhM/nnzx5CqmnpeJc2yFJcvWqR++/c/oLTy2rqaokdDocx+N7JZw5e+6uu2BcrzM5fPiIv7//li2bH3nk4U5+agDA1auZ6HL5cvrGjZvDw8MJgpg7d7Z5UQQjGAY/JPTfs127dusTgK2v5dVXX9X/ih9q90k2W8DmAzhmyHFnGqFuZoFSEATx2luwUA565WPHvfbSK336JMxNnf3lunX0TwwdlOSprHRMCNnX1/f8eZjO+uSTj7/7zqgstN2kps5sRxb+66/Xf/HF59TN7OxrQUHBffr0vXLliqWHHDp0cPjwg+j6jh27evfufenSFbvCoJb4+WdYUDVp0sSOqyedspLipx97CF1/fNULo8ZNevfd9/bv39dpceeEhITIyKhz587RM5POgMeDX0nzms0nnlhJffLLy8uvXYN+j+2EhobqOzJaqzXi4uBczv/9z9QJE4uNEk12MXTokHY/FgAQGAiDy6VlMNXhLuBuZH7279d/vD5dY8LR48cW3ncPTOStfAJtIUlSIDD9bejXD1Z7nGpJNHWEpKQkpJ5Llix2lHo6ioEDkwEAb7319siRsMIZwefzR4wYSd9CkZo6s7a2FsOwkJCQjj/70KFD9Z0nRuWE7QDDsL4DkoaNHMvwg7Huo4/XwlTYihUGTekEJkyYqK/reNPZT4T6jsybnQ4ePIAuhw8fslc9UeybKhcBAAwebFC6Hj3i6Re0cfXqF4EruF0foj15+pQbGaHulIV/67XXX3/l1eDgYPO76ow/cFqt1ry8Y+LESairrIOnMW7cuN9//1PfCDSJ6gNhDxqNZvlymF746aefqaodrVb788+/fPXVesaHoBRtbKxpy2A7qNTT8eOQJPnqO2ueSnuF8d6zZ8+gYCvoLEQikaNemnXuu+8+KuHpKDAMQwn3f//9F235/ntYAtW3b4LJBVVJP/LIYuAKpk+FTR9798N8l7uAu1H08xd9zufTj2A1rwn9+/WHLWs5Oejm2rUw4WBicKFCcRtjRpaIjIz67rvvCYIYOnRIO9ztzuHo0aOlpdAG/O03mGBFkCQpFovj403bJfV29FJYcWm57dJ2xo8fa97o2T7q9d2rs+dD38KEpUvhL8TBgwdAZ/HFF5+PHz/WekuY7Rw5ArOFL79s+vPA4/EG6GuZHeInUaxfDxebunr1KjW0QSKRMKbvCYJAsRcq19ppvPMGjI1cz8trd+uaI4xQzJMt0J27dykUiqioyB1//TNujOFbyufzH390+ReffgYAeOX1Vw1S+8vPAIAffviRMld/+WUjjvNWr36BOtq0adO2bNmKakFs59Ah+NF/6aXV/fr1u80YR+VhHMKsWTMAAAMGJA4dehvagtq3d+/es27d59TbMnDgwGPHTohEIpNeJus8++xzW7ZsHc5UbrJ585bNm007LG1h9GBYN0rn5WdhY+7C+x5+d+2X3Vus48Cg4FfeWTNt+jS5XE71LNrCli1bt2zZ2u4wxWuvvb5585YhQyxOM7CLtLTn9X+Rh9av/5oqbp8yZUpGRhZqSGv3kaOioqgP5MSJk9LSXrhy5erEiZO0Wu2iRXejfdau/QQA8NlnnzIeAVVoIR/LXm6zDH23sLCw5KRkdBk5fMTypct2/71j1MiRWq1u5aq2h8s4E9L1SSSnTq6bc9edv27cHBQU9MarpoV4S5Yvo1cyTZ8+de/e/SdPntZqtciTPX/+/D//tDZKh4aGDRmScvMmc2s8I0FBQSg39eGHRl03iNmzZ5n3+bgKhULx/vvvvfzyKxs3bkLZofPnzz/22PJvvvl22rTp06YZte1nZGQsWMDcwMNIz549hwxJYawnT0mBMVCH0NQkfebxhz/9+ue4+F4ffGo0gUIukw0fbvSdbBOkfUKhqH0n06dP35SUoY5qNmtubp4zJ/Wff3ZMmjTZZFbIiRMnVqxoHelgL3PnzjMvhr92LXvBgruoxNQdd9wBALAUu9+5c8fatR+bzE+xkY0bN1u6i17JdPukySjcSefmzZsrVj3p1GJqZ3R2dmgak6sWPvLz9Rs9ctT4ceMCAwJv5N/8ddtvxSXF5qtyCgSCe+65Z/bsuYWFhevWfVpcbNgHERgYGBYW1tjYWFFRYXJ8Pp/fs2dPtVp965bR8sgCgcDK2K6CggJLc+3anMbUo0cPoVCYn5/POIPOpLkoNzcX3fTx8YmKiqqpqaWPkzDfv6ysjPI9MQzr2bPnPfcsSkpK0mi0O3b8u3v3LiueqckzIqKion18JKWlpeYVKoz72zKNyXwUE0VIaLdxk6YMSBrE5wsy0i8e2rervq72XCbzx0wkEsXFxVVVVZnkYdCJ3bx5kxpSRxEcHBwSEmL+EDoxMTHe3t4lJaUyWdtFOW1OY6L/LR544MH+/Qc0NUl37969b99ek78FjuO9e/e25S1FHwb6Fq1WJ5M1V1ZWmlTjtvk3QjtUV1ebFIRaeWAf/V1WQI/y8fEZMhyOOKDQ6XRyuaKmtqYTxtkxzmdiGtGEebiAut2yxm0KaBfEdgFlxJKAsgEbBbRrEt3pyxo7VUBxT1JPDg4Ojg6nkkjPTCJxcHBwOB/nW6Cc+WkXxcXF9OVAOAAAarXaJCrtMRQXF9fUWFxQhIP1Rijp+a2c7sWUKaY5R46cnBxPfVs89XVxOMYC5cxPDg4O98WBRigXA+Xg4OBoJ3a78DWlrE6+u7ZIgqPdrHvTvurxBS9YXI6Ug6N9RfXGS87ZtNJcuyxQp0zt4+Dg4HAzcE8yPzk4ODg6MxLaLgu0M3quODg4OFyLQ134VvOTc+E5ODg8FJoR6iQLlAbnv3NwcHiSF28XeHuin5wLz8HB4bkwrfbhhCw8Z35ycHB4tBHahrXIFdJzcHC4nuqiji5E6BJsElCueomDg8OphMYYTYNmUz0T6QQLlEvEc3BwOBRcbVh0hGVgHRJQZvPzKFc8z8HB4UiaZB1aMddpRqgzLFAODg6OjhElbIgSNrx/v2E1F0mgYYFSN6INAeXMTw4ODuexciFcKEzeqEA3k/sOcNW73T4j9P8BQUPfsC3EF1cAAAAASUVORK5CYII="}], 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HLPx62+bXHq3Yb1+iYtJpTbzxLTHpcKWvwFDVt8IC5OT+g33wN2LGjlnNZhQxkOTrW2cu+mn/sGsW+0RA4fWjqSEsNj5zxLjyvUHTJry7kkiB5trTlbXNTkeOBBkEN/9DMqr4xQcfOtjRXS6jFEnKZRRtpUmSlFEkRcG7lq6tjAnYhfAr541c9PP+yJS0uW98AYeBa9vrio/88eqjwtSQVMDRwrQ0U0ZGu36JBZ9Ld9rlI1OGwS69Da4Gxhg1cNKEKioG+IjSbb8OnrsgYzgWUL8giMH69hdPKOSBfy1xF7mMqlT1lnxowczR6SlxEg90oYwmxEbcc/0lNE0TBEESqBEVETgVAt2LSaf5cEr/uD595735ZVhsvEIdkTbkwhu+2mq1mNfefXX1sYMunmu1wh49pMwH32SCuQabDboOByuQMjjEwSfomQiGIsLLQejdggef9abff+WHQbsbH4dKYyLV6zeVodqaIPLWJYmLDjdbLCazVaWQw1Z5gFCHKUxmS5hKERGu7PaQiXhAHkZA45mSTy+HLWBkqrDscZdc8sSbJCW78oM1394+r/bkIecfLNMlsqMhBa7rQBGoVJ6Su/q2EMylz2LyrJulC2Iz4K+vraYSBA8eX6weffuHNx68AoScm58UH1UwGgbmXDD34uDIDy6YNZogCZqmSbujRwRpxWgPx6zXFW/6sXjTj7kTZ0x/5v05r362cu5wZztTSqh3+rbmzr8uirdGJrtqa6tkYgW6Zu9HMwjIvQj2XTy1HU5RDBZCx2/t5K8/NSnm4HHGmWQCczRNy5g4nVxO0TSQUSRBElqdzy62XWDiiUfJewBevOQGl824CADw+jKnM2D45I61lxw5Y8nmUkAQgrVAiNI/f+EHHJsrTsdkZJGUjJ/kUUVARas9cRh0GqvZBGiaIEl5mJqfvOJz07cwx7VjudMZ497RdLYUBA8hJqDey2iftIT/mz1aDpWSUCkVFqtVKZdZaZoRU5g4hpLao+pvsIa6x4MvfOLObqU7v+5wnw2UoVin6nfxnOJNP0oGJWlbJ/IzuzYPybglrk9u/akT3D6jFz8MC92/Eg5w9Y41S+df8f53C7/etvKyEeJHVdGxlEJBW61l2ztOSbnDpPueBwC0nD8LgoqQFFBv/Hq1SqlWuYwP9kCwO9+1OkupI3Mf/mjei8unP3dnBm13jWlArKWGNxJhBdaK+2xC/LYRXP3xTx9M7Y/MVUqhyB431WIw+MSFBwBUHd2PKj0XfrP9PzdczC+ZSswdePWKnwEA3y+5yv4E1uPxeMENpVCMvvXBQXP+D9D0F9dNBiEsoAGWR/JGBs61Kxvq2Gpk9ynM8lmtRvCBTdGuwqJtq9rwedzF1/4I+sO7bU3mlgZFSm9CJgcWYG6p+u7DR79j9nx92eJhquaTZK/Hdp2bajmqA/JtFHzKVcojD4hMXXeiB5Ksunrcgs9/i83MuWPjcW1j3ZldW8Ki47PGTEZauenlB2uOC6sCpj31LtffhMOoaftk1pAOW4qsmj8O9JChckUbV/jhZJy81mnv16UVZkV38gi8Q/VgDQWhllnySR0oioH6HCo8KunKe5Sp9mYrtNnU9Of3rf8IneWYcXNjxl/G7VO1+gVjTTl/B0F89mtqVDuhusqyJ4aWCGu+bYR5f3EENn3YmCmPvBqR1IvbcmbXlvVPLBY0tZt473OD5y6QfEecgIql06TVNJ8/c+K3H4q+/wwEIQEqoD6RPERhVjQWUJ8RKhoayALa9XSQBxP90XPHeGnShh4hHAPF+AEcFQ059XQ/D/b6k6zOlv7VcUIM9AydxQKK8Rwsoz2SB5/3i86CYJZaLKAO0VIegpRiqPiuPgTLqCMvvvw0uvHEY960Ge6xOguCWWq9jIEGYBhUpIAOVLSpGutrPHw6Fk1P8MWnFZ8DmwE3nTkW0zuvscwHBeH+i4FKuvAvvvx0D1dPn8PXWffpMp0NXAvUtSCKdbYjwfUiK4XNTw8/rU5raMOpQ8rI2LdffE2j0z/1wSfP3SX88XCv8OizsFslJuR50A17Viyykiat5Nczp3NSG7gCGkhgZ9UHGjrnmiWj+6d1eIxh4yZodMIhlENyetEAnK5ma8tvu9+dEbDdADY/Azlo8IaUMXvK7eiBpNQGvYD6qkopkFvlBxNuW+0F2Smwhx0Bn0ID8Nexs2PzYQs+c2VxYU6v/qPG//jm00Ny7LWH/OPJZLDtEAbjEQ90QmedSW3QC2g3EeLe/bv/ektsBiIee+Yx7z6hl59/lCCILWv/i+7aO0UxKgobg6J7zL/Fu7cH1yeraDkRoXSQ9a1H2Jj7mLyk9vB+3XReGP/qrPcCWjhtUVeuR+rGMGsgePfxfdnhiw0lRfF9C816XUtFsa80sWv8+vy+6TX1HvgK+0+VRqjg93PrkZrRAxIH95YYg9wtpEfD9xPWxk5FV4cxLRTCVfx9TEbTuIHJAIBWjb5dawDaw9y1gU4Y1D3njfGDzoaIBeoLKQxoGW0oKeLfRnr69kuv8ffRGZ2Or+lmbB8PxbQUatM1AUAUnW4OU9i7WyVEg+LzZ/hPQuq5+XA17IZFEscq4KOtGngNeOaJx5558eVuVE8AwPbDDtN3R/SVnggjbCoo6lOHCWpCQUC7Wz39IqMCcexiiUTBxzJb0objjgfv9f6gNJDLoZO7/Ui14JHJQ1JbNQbpM+kDWxC0agzs6A+kR4EnQzTtMKZIshnrqcqWnF5BNnUSE/oCGmD4JtfkQiuf+gD6DuISnw5JT5S+0iREqd08Akl0qiOq2Sw9Oa6l3SDZ/blTPaH9BiWjLEZHuSfh7+hUVauzr8NFI3Pr6ltUSvxzCzU69RcNgTCom7z7r7c7DCA+9syjPsw1/X3yvLOiH4F0cqnqncfKxw7MlHzKzmPl/dMTQHfDdaSWK5UmnQbpjjPRLD0vDJjmZyXC1j5GY7fbn/Kw8DClfZhaa2s7AODiCySmYaOvQE2db3p0YkJBQFNi3DVYApZ3//U2nNrqu6TKg8se8q13/81nb9A0WPXeB+KHsntJjdXsBE6V19ehXa78yGoxR8Uwvnkzqyw0DcrsFhx84YtG9oPz8HhXoUCWIYI3WZSpy+I90O16jwkoAVXLieC1E91HozeOye/N/BrgAFeSRNE3+H8EILiibjhsbuGNvPFtLvBAkFRKueCHZ7ay+lJ8Xugq0jT951FYNBMVzjr+vsxZ+66hsnj4O5JRxJRR/Rykx/bq6MWrA0k9mUlETsf5EoDgv5FA1n1M97jwSrnPhkH7im17T7qjngXZKdztv46Vo+Jt9G2nef+QJPHX0bO2ieW8qeUOd1jSk2M9OVNpGZ05f9G4Qey5qRXuFoqbrewPtaA3VCINM/aOIMDukydG9R/gyVl5ccoewx8qFami2vTshB9n0mN/0WAz4rg3smnvmcGZ8E+TnBhjMBqtuIaphwtoSoxaa6K1JmOg2aHRkeqR0+YAAE46jkXtzVM3ZEXaxZCLtzH/sMpoe5jVVn8hER7dwaSnxw1K0RqhsihkUG6cpacF2AfS+O/P0mkV4yfP9SaheiKQ1y54Kc78TE6MqThfC7oV2molSFJnMPHDoM40FKknuo8JPTxOqobLSbWc4NRz3nWdqGvxKQOyel0yZfycuTME2ymS4P5jAlV2nKVcuhba9h/LjiPVOr1JbzC1agwu1BPGB3n/2bbyjhp4UCgMwqBnLhJOtAUFPyXojArduvRx9N/u39d88pDwe9JZpM5s0162sjU5KeavotM+fkVMMFqgp+ra81IDcTpQREysSdOg1TQ88dFnL95+k0+O6SKvvfVI+aRB7EMfvPrWXQ/f18nXWv/tJzOvvBklpveVNgzPjXewlvk3Rb9VQcI6TClPi1fvPnFi1ADfefG+gLK58LTVqjcYw9Vh7njB1bWs+ZmSFOPNhYGm3//qjxGDsla+9xK3rfCSW0f2Zxs5LpozHACw+DU4eN193JFyu/mJCVG8iYEer2wOKA1VxSTom+tN2jZ0t+68w1wtryXSNTdPHVJWJawz7ySUQskWGNrqe05VurX2ceKIviQBNu8/OygTVnqWVQZo1sJT9SMA8fve04N4MvTXodPp8eHO9q9oUzaKRq4aDMYzVS0Hd336IZBfNjbn8mtuQNv3nGRDAXts0okk1U09tbjMIwEALr/jOnRj7fL/SCTHMCFBKFT2hiem65vr0e3YrPzTh/aAoEURFq5SylFdIf83d/EF/ZxlVLh7SD2dcfJcffeWgp6vbVz94cf8LRRBW2jCSVyYjeZy6gnNTycUlbm6YFRU1dE0/dB9S/tnswWzty59fGT/JI2FunN2wZK37BFzJKmSespJKgqjh6vDNFqdO+qJbq9d/h8XO2OCl1AQUI74vkNoizl76BgA7D/UsqpGnxdO+g+2WMrGxaOkuvg4yigSmz/2nXEhoGMHZu485oFh7g+aW7UT5lyhM5h2HINFuACAdr2ZzcMw7joPWqBBHzz7nteve7y0AgBwumhL/+z/Q1tWvvcS6ki/5K0qTig1Fur2WYPvfvs313paU1sfEcGawM7ySPwz57bsvP05r98CJpQF9JqbHvrmM4mF211GQ8lB/g1r4C2UdhOKoNVKGX+5Y3V1fUqKE7ORV6FNdGR+BgL5uWlI+996q+OdL7/jev7du55eCgBYs/wLT19UbzDISUtGUuTFsy5GticsPo2OevfeSzit5Hz5u9920NNbZ+RHqOScL492y4y1511RmQSmJxNSFijCvZr2QERGkQoZaTZao6Ii3HqCzRStrmvitiUnxpZVNhMEAav/AwuHv4vNC/agoJhffyYoTS3MikHlT6i7dmE26+9v+LMI0MBgMtc3tfdKiuHySPwkUkZqwvQRvQVCCQC4/z0HPV04NS82QvnSp3YTlcuDOWvF5BNUrSd5HarYAuHfD1aNz4ft8nCn0aAU0EDLIwmYc8PCn1Z/jm43aRv3nWqMtH3zmOaS7C9ncO9s1xmkLq5z0hsthmaN69y0BI7XCx9ePfiVBgEFRcJuePwtgrsIq9VafAaW1spl1JPPvwpD5LHRsy7MQranPYl0snbtlmOcUF4xLjcjMUKsp4+UQj3tl+zNEhLJGGhY60naURalET3624FKGoA/j1QztgJMmmEx7S6C2wJ94Zkn0ZoWF/s4PEoDqxX8faIWaejhs2WB06aXo8MKbQHVtXbzMyXRozVR3UY40+sTdgYxmRVy97+E7NVh9k0Pu7N3ZW2zwQTXtprNlkceuLtvH3ah18hfDnJG5SPLNwuE8hgjlJye3n1Z4e2v/8odU2+0RCiAizzS73tPX+7kfCpaCdB6dNGcEejuc5/YO4qOH8QmuNRqh8bMAIC0FKF5uzCVfSOfr9+PJPjPI9WwGpjAYtrVBK6AvvDMk6juWs1ruyvAtXRypKWnAgDOllcCACYMYvqEtxvYJY8nTpitaheWppuloD7BaqW5tY5e9qDw6mnOsvP+KNVCaA2WcOarlxIb1thu8sdLAAAOHi9Hn0Zx0c43Tx6YP7HvRbOuFhuVSCtNhOLO2YPFhuftr9v30VllrfUOzUxVckJvgq+hsq2+HZwZ8+Gz76UkxfLLmNCNtOx8GFU4AmX3fNlR/nG2H3EUUwLWArOXiyPnnb3BqcOFi+UMBnblRYSGHVhQ36xVpQ3x7IPDdLGA+iOPxK1aadebI8LVrssbMxMdGkQhfeRoPGZfBIICpD4Jk/L1xVe19LOvuQ14MjDNwfxMYszPYIgAc/HZ+lYDybSp51PFe1McriuBYAyUg/kELGYL8t9h/mrExKULpshkFEoiFfRLmzIs481vdgm0cvGJc5zhOXd0dlZKlFhP+UkkGCBqN/Ldhd/3nuaK512fcFp2flX1VtpiJijhb5AT0/GDWbO0b1Za2Vl4+RdwvqapwxVf/NuYHmSB7jhSjZprkCRpNhk5lRSII0JyIyLoUkqZieF1rUxPkM4dx6NuxF4vKOgMyAtmY76OJ/vBs+8yyfe7JcXIQS7FMOJ8vrbZZGZXi5rNFiTZKIlUeMmth4qhWYfkMjMtcdrwTKSVko68ziq7+7JCydJ6ri1Tr2TYqxSpJ3slE4GsTo2FGj8obf2WfQQBKKUSkDKzTgM/ALGS2qaGNLW6qjl1LZSBlkkMMbwX0C7II+n1rHPnUYu6blHMVq3h5WdecdJW2TPOVLeEq8P2ljaMyHWa273Cscrng2ffdfajDTo487NXUuxatm7Jm7/ovqP2CUtx0eFymezWpY9rrbLx+akfPzTjNp5c7jlZu2bzUaSVWovsnnl2rbQbp69VjuyfVFVdJ34huOohWSkwP8Wkx4dbE6ALD4tJ+Zd7qzk6ln1WSxPrYwnEVDAvpEPTsqiMXVdSkN39XbRDm8C1QFFjOWdNJbxQzNJKiQXj7Ot0RHFlk7YLR1rqDOyVQ8fruOFMPTlLjV8mea5ek57gdMljQMG8WWawpTTeqKfJbDlVYW/a1NCs0eoMyPw0Gs0jZ92O5DIrPWnKsAyB7Ym0EpmKd8wqWPo2u1Rpz8labqIcQpBHcmF+7iwqc7EC1WyxyCgYuOlQSWH5gU0cJRnUL0Mlp0YOgHkumune7/JzwgSMgPo8DIrW5DjTUEnRFEikJGgsBD+iKiN1LvQxXCk/eMph/iLHBdPnkiShVilMZovZYrFa6ZGDs4CfEaunBEHbBZ1vfjL/eulNVNY2WSz2BQnFRTvuLtqhjoi4cnxu8oDxSA2vGduLMjYufu0IkkuZUn3TtDyBmC59u4oT00F94v/a45D5cRaMFmJ7E1ziiFQ4VKqZzLStVT8Lp6RoUS8swIpQbD9SlZebPiSvN0USSjkll5EkQcgoGKohCbauq6HNtGgOOwF7w9Z9nnxmmNCyQH/+5uOnHn+McV+ghhKEW/qImDyyL/OtFf78angF59mpMQYDe312pp4XTJ+jVMi1emNMZBh3tJhItUxGxkaFUyQRplIQBEH5NFBPW60e5ZE4rrjjei/W6njEB6++edfD94Pu5rXnlz305AsudthVdIq7ba3clZMAP9DEKLr4ZEnxyZKlM9jr3NbD1YPTw/Qa2IamIEm2+LVfkFwagfyuOQXiqKjAAhXjOpCCsvCI8+X2lbVWi6nDX2JkdMyl42Icroy8ayTNVPUTBDCbjL/8uR+6IC3o+iHjqqZQ9h8TQALq7zAo6uqGNPRUpXCOxeQL+tl7TogQW2Di1eLccp2Jl12JDBYZRREESIiNpCgyJjKMoqgwpRxJZJflt2UUyW+q4Wo1pxPGFmbtDJLuk3wv2H3zU2uQbsaMeOX5p9CNhEgmMhDFVlbGJAg/xssuYres3Xz40Okmu5hmxXJiKoiKjvgU1pD+ddOQlNwBWfev3H/3BFiov+A18P0ytIOuXWI257CchNpm3aWDVBuOOFyqrRYLyTjvGr0pXOWyPTMhaoTAU1NkWwzOite2a2zz68l9JbWDesf87/ddil6FnPGL9bSnWKBodiPzL/ym5PeOk9QR+4+so0kQLlaLXzA427dWpNeEq2RGs9ViMcPVnF554oHwLnyB9++DS15zlUAnKpDvItHKflhfqJIX5rEFQwiD0XjdBHYkqlZv5MS0qrqONpv3LBomA6DxdHHdXeNlFDxPss8w6HerKaSedTnTZDPuMTw36bQxSt5eHT9gqKr2JEmAX87HgKtuPv+vBRqLB7+7/N4SXVDZr4btG1LfolcpFQfKdb2ToueNzfnlz/37SmrQZ6joVcg3fsVVqFhSu01A/T2e02o2efBzkmr1xm3jO++Issrm9AS1eFZP96I1mGHKlZkYIZlaWbP8C2dh0DXLvwgW9TRaQWOrJi5KIq/CmJ/291FV24BuJEbBT0NnpEuq2guyYl994amHlwn7Gz23cqNO00YQBEmRFqaGSSymuVnCYdGnbSWWbbz+/9z1lKbp7Lz+DQCUHT+ZHg32LBrGGY8ErDZl9qkt0+kNOj0gYnqRmYMsBzYQMckmi7Xp/Fmop7t3URShVshyE6vBdy9lP7fOsOqRvXXwxCIjI1gD3Oqyu6jU35X9bhPsNNOquiYFRShjLSt+2sv0wE/UWGTtpOq6QWFi550fTBBIqpt6WpDABmcFHKp3r41DqNApAY1QkAaTfRnJvOvu/eE/bKcynyDzwih0LqNiztVr0xPUAdVzw2KhBR3tBFTXNn3w7LtcgSQf8dOKykoKs6WGlXc3MkCbAdHYqlGgQSss8C9RVduItBLBvx2TkHjZRYk/bDly6DS8HF53y9KCLOjsN0UWiF+CVCgjVPKWpiaCYoUJien2w1XjB/eiSIIvlwiBE/LPiWq1WpWfER1vbYqLi3n0oRmvrCsb+vIPAIDdC/Mv+KwI7nMTtO/M699kn9NSZT1cRQBAjb7GsHXtwIsmmM8XtzTBvLnGIpsyQKGQkRtWPSIZxOjQi5cELoZv0jXBT5JQKmBGKT2aSIiP3rirBADQJyIG6Sk6gYQo1XUXD5BURmcmKqenAkn9fusJwRGunDRAUlgDTVLD2k5y36sth9mlFoVZcd51ZiGi4tjOGt6RnehgRPhWQAEATB4JEsYslfMsGmhTRn6zIkRyYuzfTJQQCegzL74MAobZ19wWERGuM5gKMyMADdcI8t81l+3la6jY9txZBDu3K+UkX0DrW7XOXvTkuXrJQnrxUk6fJJFmzl/Ev4tsQ7VKaHGn9nL15167+TCnvkhJobmnGnh8z2aScSn4bVlamuDnxonpsJw4tNAAieY/J9gf0iVj8pAqadqhEOwvrcvPiC7SsAW58dYmjYUqyEqYPCT92+3QuoTxgePHR/aNb5MlcUI5Nr/XyH5J3A4chFzZXFcJaFpjkbXWVREEtEDhU5ARSsrEAtra2g5deAD6pEv/TqubdGVVrRoN/MvOmjR0UJ/4vUdgAu3XXSUFfWKtNI1ceA5OGTUWWbRacdO0ge4klwQu/6HT9ZsPngMGdgaEM5CkivFUUpHkCVTPfckTPJ2Di4lbrPSPW4+KC3vceYnOCqiAoo0rfHg05pe2mMkjQcIcpcRdHLu9oY4bSFr/LjrNWaCBo6EuBFRi4aYT23PnwdPpCUIB3Xms3Nmy9+4VULGXvW7bsQ4PgsKXaGgHt7FNa6BIYl8Z/KBISh6mlPCxWlvbh+XE7S+pnT42z2S2KuQwcyinYFUQ+hXV1rMlbkhAZ1w08pV1ZfwjxFvh8TUW2agByaPzUsRayYlpn+TIy8Zki3c4cfy4TwT0r2M1SD0hBBEfywb6NRbZokvzw5Vy1/rI19NwlWzRjHxBmqtjPZ07Qmcwr9hwxKr3pZ46Vb14+AVes/kw/FsRHevd3r+2ohsTbOtiw8JE7Vp6iVas0GDN5iPsaEPC1UsEdBIJDqBXCd+tp4jNz6DItDBrBL0+UduE5kBdx7r+2xWPvfM9/PHs3EjJKLNBj2xDvpctHa88Y18SXt/UJva+KRJWVlitVpIkXZUHEeCKiwscPh7b3KLdx873YRYN7y+tG9w7xmqlV/y0L56RmKVzC17bAB2XBpK5epHgz1PaIyWw3FJjkV2Yl3LhgGSkldGx8HceDYDRAlb8ZKvHlKsWzcgXiymL1azRA7GGukglag1m5Lyju1NH9EF7mi30pn9K/9huqNGyBXF6Wr5kbgEBCIGe8oOhANhPVWORxYQrbrxkoGQwlL984O2vt7PGPmPvL5o7AtBg9abjjfXCgn93vP5/2wIOSO/CwlQak0jjaHDF5MGCQ63ZfPhgWSPzB93F17tJhalbi+B3ZhsvGs5/CVgx+afE1VrQLXt4v2Te+2cbtbRrDIEuoCY4ZM37FTV/7DuTn+GQfBc3fIPNeQMpDMqxt6SB36nXbfNTenRwd4ESO673kSlV4eowvpftEK8kiDatRK8DcchyXEEGa43a/qByGWEyS/x1Cdh4234XZbTRyg2knnw4uXxtw2lke6LV8cgs5R7dWqo5XMyK6cwL+vRNi+aLKeLb7WeRccrH5YQlV1/NrQcrOPWEP/L8nH1Hof++6Z9StCUxzMy58Ct54hgfqbx+Sp7r5BKjp/b4KbexX9/+4jPR2M6f01MufrJo7ojjZxs37isXu/wCSeWbqAK9mzCYtYuPswUVQi4cCDuuSTLtgiyuSRX80Bg9lZRUTlVzsx2v3DTgN3NpZ6LnFEVQFOFjAS2ctsjnXnxnEKhnMK7SQSK4aa/0yCOnFmZ3vMfnVmzU2Wajuo/VYiUpsl1vio5lrwoCJeViOHzR5KKWl44bSANwuLRmXEEGWsBmtdKcKjW3at1pUM2lHjl/JTkxFpTCle/n2sgwS9s984Ry+cq6MiSmMpX6pkvyBI+uOdAQvw+qmMYimzM6KzslijM8kZ5W10gsqxfALUOSxGKlU2NVdS2sskREqDm9S0pNW3TpoJ83/cMPgLoQx+F9E8cPSutQT5HnDv9XGcluMkj/uQUmKtRTCiATNTeR1ZzEKGVDO7zaHTjdKtBTvlOCcGE/it0UDk71UMIQ1ZxFRUWgLxiC/yrcC/FfbsLgXiivi3QTSSf0VI5XjxyQEugWqG09EotHVeVi0WHNT6lMfaARrg7j/374b4QzP50Xt3iMJwM74doF77RSErbBIK+Oh6+kBCWnaXp/CSzhnDFuoNkCR15RJDFzfDxFEiTJTnDpk+S0oq5db4rg+cW2T1Xig9p1lHXe4Sds+3pATeTJZa/k+Fmj+jjIpZF9VGORDc1JmFiQJhDTb/fWx1tLJMVUiJNipjjUw9mRv46c0xuYOA8A4eHqvMzYmHB2t1/+Lln5kwEACpQd1Vhk00dkDsiIc+G8tznq6XUX90+IclX8VHxkL99t13D2qZSeFva2qa0ooFlU1ggrFnhbJhZCQ3LH0RqZEsbuOL/EmdLxbyBJ5RexcIUWSDqZgWkOXzCODiUVdfahKGL38eoZ4wYePlUzvjBTbzAGuoDy1iN5jITJRghvo0qmQA0Vskianz5Uzw45p7Tnlx57Z01nDnXitHCJFEUSaNWhyWKRM8tyOKJjY5HezZ6Yz6z7ZtaD2XwI7grIFfmiqCX3dGSE8qXZBX/b1BOtg3IYt8yABLGhzlplC4nePG1glFpul0sS/F1uKD7DPnr1xL694tSSYlpVXSf4yrkuZoqKEa73236oIjU2rKzaLlicerKfSW19QZ9YWQos8CprAdsPsPpooOV3dRQM/accnC8TOu9iz13jGHbg62lhirRiclkgxOW2lWDwxEyW9TuOHyqDScuoMHZWa2FWFMiK2m8zUd1ROhjKsMkF0s3dx6svHTewrQ1+kfS8fCMfgaQKVHt4bnx8QhzNHHDe5MQ9xyuzksL3l9TmDRsXBALKud06owUVM7lGXGeOmksGa8M3QsL87Fg9CXCuQeOiA5Ake/S9hHalMsKHWimJlabDw9Uarc5oMMol3W0CqOT2vzu7Agewk5B32XI+SQkxyOnu0AiVRBD6JJxfjjg1/HBzRby1KZ5J0SydWyDQys//qkaPaiyyJXMKZBTB26Fj/x0xqLfEl9ZipfulRqJiWGR+EgTYf4otDEiMDlt26/QXVv7qTB/5wdChuYkTB3fgvHOeu+sFVBqbnra2tm/nvT+uLbSaSX9rqtmVEWJG5fXi5pdYafDD1iNFp6GeUvwkeFYUvFJKSSqnpzQAcsZUnDYmjyDA3EkJ+05WIcnTMwHxDqM6SE9b29q5q3Sk2nZwGmQl2X9WvhfQB15f88aDV/jwgK4Ly/k4W6Ij7vYW4DS1amCzEkGLHifqqW2X8JuG5iQcOOWq79lpRY5QK+F3tc2vWilGkdrPVF3S1qZBlZsGk0XJ00qE4P3yF4NzTreg5CAyMtyFEToiN552aX6i47kT42kgY63XfkF+fT3nxRdmJ1xUKPTiX//lNCemjNPvHDfs5frGFrMtMxbODGugaTAsx37mK37am5yUUKMF2pKTd0HtJvkSyRdHDc9511lld80poEiHnaUjoW547hz8xWCCNvs/i4rV9hfDsU4IBW994DAmCc7lgoZlRbVpDCRJnKzWc8YjX++SkxOuSE1ChtfuY+f5ksdk2AmjVGrRGdm9U2xOD42Otp/pM2AJ/DImH/d5c2TMkKy/DrK//GeeeCxASkGZHAgBy1NEY8d1UlrJUdPI1gMiJUmLUwvtypSBx9igGQDmrtZKp9B2pTObjEq52DqQ6mfI/C+nnh1O03MwQh0TiZLq6dE7sF4Lr83I6tr99fWlZ1kv/pbpAyPD5AIxZWDXcXqxJGn7oYp+qZEnmd46SD2hn2C/6hCc/04D2qBM+Gw9bM6E9O7ioemD+yS40Md/r7Pv7IXnDligs8xd/gXpIE5P0Q2+nrpIBCHp5OeCZBRxvMqxZNX2N83JhBEEpHjcZRBJ3l7GTm9yL7WIDsEGBJjvzK6jdi1G4VTfC+im338FPrVAfZU8d93tLaDCoBTzXo1me0dLpleFTCCUHDRTlCN+CzRNb2uM8Nqu9ItcdoQ77rage5MgOLO3tJGzQN2JhIrqllCAwC0RRdIp2IKUlPz6+g/+YH18A1OGKajG94JtRRX90yJpGvC7ncKTJan9p5q44s1f/oaFigZlCr+IKhrWALXvOgQb9WssstgI5cKpeR0am7YxJG7paTijSoJuVFRYBFpTKxm4lNRToqNcEKdfYrgAKKMYxK5j5wSSx5221/PD95XUwoMnDAqCQnqUiJ85fzE330IyEe+F+RmwrP/2k5nzF3Myyow2MXL/S/P+lIIWZwIMSfZVzIGplQKQ0snda+zCmY3um4smi1V8cOkeer64miLXnjU8mRp1t1x45148TYP+aTCjfbqmnW9+ovZRo/PYxA3nv+s1Lah4/tvt9ukmXIU/8NB5d6eGSSNVzYouXa6zNJL+Pj91zKWDZo7Pb21tc5EOcnhpwMYr9xbXoGoNfsmta0sfBgQc+fsI1GLo6zA1IeyJgZ6EpBGKEvEgIDVUsjGK2NIMOq10gRMvXginnjy/2ynoZ2M0GARJKpHzbldP78xPF4hdeGfn6ey3veMwdN6hka4z8dWTaXkHlxgiBmVG/7H31ID0aGt4Apcv0lpld84ukFOkWEyRnnbovHtUw0RAuYJtxZyZe27qqYzJBV0yJo9xsAh+Omig4/gpsd4xf18oecmJsUQxlLyT1Y4uv9WsNxIqhccaSMDkHgyA+ktAfZ5HigqHHyvhdiI+SHluxW9cYqdg7LRDO9lpPHxjM5S00lnOp0MvXmQ2uhuGUcnJVmnn3cGY91U4BxmhPjkUCn3CdfrNwrXqKqWitArKh4GW3zmngCSIP/aeKqlsy+nbiy+Rq2zBUK1Fdv2UAXGRKmdiitby89Pu7njuQB1m27+NIkhAg/Bwu7lnpWl+3y0BfD1FhWvDc+OTkqAFOntCvFwGuxQAqXSQC5B6sn9N3ivzjVCL2QScCShjuIzoG5/FRFRZ81N0tQ4OC9Rs6SD+6aJLZuBrpTMKxk7zdCRYUMhlh7iu3OTMRrhYiPckzlinaWtDi4YiiZhIteBn09DCNmwnHFQ4zgv19Mj89BjHT8BsgXVL6HZ1k05gfuoNxvgoVf90aJF9+jM0OZOTEvS0/KpxvQnC7r/zJXLNliPotsYiu3J8bnpCBF9MBZ4+ElPBgk7XnjtBALNjkFKn03sacwxXyQhYC0EL6odQCFJoTnJ6l2GrQeVlF5HNyCWdHBx594KhnHCv33kS9oGNH+RHAXUnj1T012/uH9BXa7rF/vvYIVk7bYn4btRKLwgNrXSGMyOUH/rkpRmEaR85YQU0aG1tM9HQZ46Pdmq2sOppe6ZVzyosAGD68PRz9a4WU3bGCDVZiUZGzeMcz437bXPr0GIiFA1N7AJwjc4sUE9Ev3S2Unh4btz6nSdhUpEkV/7M9uo1AmiZ8oOhfDH963h9S9MJTh9zekXpTA6RYv7OcCG/yxqmViZAyamnF1LFYeuFxGooZ34K1NC1+Qlf1zbjlg//xMRfNkFAwJn56UsBXf30VV7rY4e8cPcVa9dtcW0dOGsz7EI9BUd75dlljz7talRZh2Ct7DzipDnhKvQpkUdTpEJ/01hZzCopAMjwlFGEvUU3829ytNJqsCsmk5+BLr6MJP1nfjInRsuY+hozTUrKKJ9D5W1287NNwpKAwzyYFDyz9il83U62ERyXv+KK5zUWmUpB3T5zsAt7s7qWbcSHHkpNiHHbcwd8MY2McAjLoNt6o9l1zFEylEkAghPEuNgoydEsAtDO0PwkwJa9p0hCVPPEP77V3K4HzqJGSD1tZyIUDZk7ahgI7C1tcJ2Id62hHaqnb7TS2Yg7twltu1KMsbLYWc+9xChlXatBum6Jdd5dBS4VqWzTRmNlsRxVA1qtegspZ/ZTKRXJ0XCV4elqe1BURpGcdB5st6dl/BcJlTH6zsmoQEkHMctSOfV0FsjSG4wDU8NiI+RVFWfW7dD1Sk7UWmR3zhksp6hX1p0SpLD0NJt8tzUAHeTaeW9pqndHTIEtrYQsUGe4ijm6QXJizIadJwmCENQw2WWX2Y7UFqmni6aOfGV3FjXKykypPnJOUCrHZZDgXzBAtLL0rH0Fgmsk80hcqze0alPQrV18EMJPdqWH6tnT5NJN0Df7THUz3+NzqFvi5wU6ClwiJWXsPoJVUjjI2nBah6qyPVZML8zPz9+5aeE9n/HPh7ORGWuULepESqqUS9u/B5h14pKUkuaRjHkxvHfYhr0VABCr1sMlE/EAGAnFXbMH80tQ7WJqsYtplFpx87SBHYqppzVMHjvyttr1bCZ1I0oHsdXO+8uaJA+SlZlid97diGg7PTFbRJXvvEseShYImoiAkQ5owXEDBx1OmCusdUejXK/aFB+BaynSyU4ZLsBa6QyU80G3BcFKDeo2RIidd4cfhztfCb5sWa20jCJ9aGN2yPW7Jn/xjv0uElOesrMgJTVZCDmvYhyRiEY086BssgutCjLqZJPs2kn9V/+4nV2MVNcgS84VlKCmxYdfNaEvZ5Y6iKnJLqaS9iYSU835CokaJoODmMIeWS6lSnLBrsMRnPnjPBKilPWcg2LTO3HuqMOYqWMzVihATsIIkHWOGSTPBNRTQUSayMSAUXcbeMPFd525vPDP1l34nYY7hHCdquq0D4610iMUqf2MlSdRpJILVsZHh6OvNW3rdO1Yt2RXT37Ox820jyK1nxtDgHwf/eQsUPH58GQUviuSZscRINACijKNnq+YNtgva3tjTZ+IaKiARFhEXOTl43IyEyPF/vupVqiSSExHD0wZ1T9FUkzRzBI3nfdwYUFom69KfcXpICsNm2ShFZlnq5t7p8TYNVSws01LLM5WLEmh0QpLBZD5yUmCWBtk3skiZyq6XvDGmykiKMbyGRI9551XVkuegEMiHvvgXY6znI9KTjKRKTu9kmKtBuEyVhTEDJzB1N7VhIr8eiFlNdylQuI7KkvOPWGwVemT4KvdtfHWk/HMEqMlcwtJghCL6ZYSzaGTrJheM6lvSmy4W2KqjBTYmxqemHKVA1wGySNHnjP9aJEgIotyw18n+cWkZx2DPHZblacAv/xVTBDAZLZ7OWJfR3Bi3PYwZsohej30xZsxIqOiziGRx15huGIrd1YAe20qdoYb5447dNjeu4XLI0n0nHeCO+ca23KwKXqIs0exG+4/+IYYE6mkDSaglJN8v8xq0ApyPt4FMb3Gv7WfDGK/HsV8GUuK4LzyDuEUEFWGqiyy3kmR88bmSKrkz9uPo4ApZ3Lq5ZHi3VTtlcjedLMPk6fFQ85w6IMOAN8Z12h1FotDZYIwXsnc2n+qkQt/I8R6apdOXkCAqdNgr9mnq1vhEG5HHWE/CPc6JwRQIt6Z8+7M/HTz7XGfDtbKboFviMkI2mJmfxtaJuHjddonQHB/YRL6HIp59YuyZC+n53Lyd7LJ7r/Pn5ibGhchNEuZ3VDjfWCyKyNfTDmVcV3D5A4SiW+bcqHIDd95F1T6Sk6RQgYj2tdiUz1oM9a2WZnGH9yETttF2m7sanVOi5zQNZsiCTROhh8ADZqVSII8kt5oUXmyoNP9i4PWYMbSGQjwDTE046iL0z7dYn76G05M1+84weXo75w1mJDy8bnpzXwxdV0Q2upyiJP7GXmUgufXcvKDgI2tGhlF2tNsPIMxiWcwnmEMRnR7bKZlZzk1Yswk/qswesruYDY5BIv4pRroIEiCxQSTgApw0/wMAtMa4wRFar9DDp6WD/j8nZv4KR1tU5s61m5h9RAcauxtPj5a3KWVSTjvrJg6sTc1PDGVTMG70NBwFaXRS6wOkKxGQhmk3SfrAeObUDIJ+TrjaDAi4dtZLnEaUnpqB43esiYMEmfuQkRA3aFD9dS2tbH928vq0+MDqycTxh8svOezz9+5iZNRTj17ppKKVTKM6asvGQlVtVe6cN5bXWaQnFHb2C42Qu25I15A03H2OONN2yI8PINRj5ZCODMYXcDXU7r2MJE02LV0sufx+v1XguCByyNBF56kXJufhJRWuqCuBRr/B9u9GWCHCTqQhrquMfKtC++rzkxdAHLe+d1D9PJIJKB8wm3ap7HIWutgjz4TbxqfixYE4iAAN5BjRN/46lazw1IiZh+j2fr77pJBvWMy0+B4j8NV1hPHYe4LMTwXTmmtaIX7ai2yay7qlxyr3nC4EwVr7hEiFqh4ooOuI7msbmJjJfyRSwHVlx7jVzjRRAapQEPDZUoZRbYYdANz+/+5ZUtiBhy323OQjoSiZUhOnHcGmp/s5me6xWIqyMjzH5JcSkQb2R9sXt/Mb7fBFVMD8vK4p6BXimbOOhqAn2zTlhbNHdHS0r6z3F89MIPMAv33D9u5RLxKZZ/jGh0mc1MuES7m1BEENkIxoK7iPDdHpl+/fucra1R3/tBzLFAxbA0Tg4vMeyvryDv426gnllhM+fqL/PERfeNRMp1z3rl8OgCgoraNIEB5C2tmXjkhNy0hAompayJiodHa0y1QmqYtUlFNgXoKtJKPO/M9HUMtmJ6KrZ96eER0embv4uJiOJO3B6OPSAU8Fx6JqUa0ND4qyj4pj4uKcivNkJhylilJEAqZ9G+Sv2KCn0/PTEvOTIP+ezQAG3ae4AR98pD0guwEZ2La3lTjJzENDgGlabpZY6hp0nJjgjjdrG7S6pi1bl6LZpEGBz0xQtRRcQRt0bS3atpbNm/6HX9ArIYyqNorxas5NSKz1ImY2gswSZmcGVQl/IkmMY2yzlS3wBkftnz6iWr9tBG9kRqIG5XuPnxm92F2Xl5eZizaU1JPfSumgS6gZqu1XWusbGiva9FZLHSfPpko3EF7a2liucS4Q3gMW5KhaRdMmcTYlRQ4iqmL6nqRmDI1RmYzCZ13h49Uq9OfYcraKSaffqKaXZ8erg7bdKTx+il5MZHKI9VCZeSLafn5uhXn67gA6MYi2OFUEr6YeqengSugVppubNVV1LW1ao2CIa5u6ubgQQPh4l1eyf2n24VpRAzGBZr2NhQJjX94M/6gPDZLgYSPzxdTceE9DYGLJk7wJnagwyKj8rvNh5GZSZHETdMGRoTJxWYmJ6buhEc7aZwGqIC26YxnqlsaWnRWXkiSIAg4qtCkoaVEE8klBuMHfFOcgQqhgjqV5FezdETf+PNt0OqEeyrlkkmqAelROysptOT069+KODG9dUZ+mFLmqWJ2XkwDKwtPA2A0Wc7VtVY2aEy8KlmKItVKmRWaoTRBEGHmpjpTOMX0D5dR5LB+yS6GiWMLNNDqLt0pugwQ6iqgyxL/8B++PWyoaqgYQemoO3l88Z5Th2Xk94k/XgKXE206eE6jEsoZW2jF7CyjoGUarpKwTH0FX08DRUAfWDS37FyDePsfe0rhTNd/Dml0Jr3RolbKDGYL8ugTotUms0VrMA/vl6xG16uONBS78N1OcMloXXlF/CMOK/x8SM+RURdi6k4vEk5MuQr5V36295RyJqYdBkB9QncK6AOL5vLvuhBQAMBVFxcAAJa8+EVTmwGl8HrFR9A0XdOkLcxJjIt01Z8VC2igESwy2gUNRHqajIrF1B2zFAVA/z5ebWFKSjUWmVJO3jwtX6mgXIgpIjvaX1X03RMDvXn+1NhoiVXn2enxAg0VqOd1j62MDFOgtvsEAXQGc1ykiqZprd4c1xMXMQcxSDf5iykDkJ6pnt+/dhd3+8qHPujm1BNwXPjEjHiBLjwJwq1Nq385gNqgxEYob5g6APaUkhLTshaL/8S0qwX05vlTAQBNLVpJDZVk0bxRza26BY+uUMplWmbEs0JGGs1WvdGskFMEQbTrXNWBYgKWQDY/Q6B5nde6Kd7YBUoKJFNPjJjmpkbn5WYCwiEAyu9hatQ1obnNYRZZYU7CpIJ0QICuEdMudeGRenIINPTHTfsPlcJ+BACAJ2+fiW5ceQ/8y+lM8G3HRqqa2lBxA6wsoSiyf3pc8bnGiDDFkNwkfq9/AdiFxwSgegaC+Smpmy7wn5K6mXqaOyY7KyXKnRjo/EkDlm9zd16R12LaRQJqpenmdn2/fn3R3Tnj2S4A2/bAxViFA/ugu31S4/i6iUhNTzt1upwARHSEsrldDwDBWKAWAhD9MmIr6tpomh7eL0Uho1wLKM4gYdz6rvYA9fRUN7tLSYFNTKeN6P3jCRO/TZT7MVBP8UhMiTcfv9Gq77iPtNeYLJamNkNNk6ahVW9lCpGUFHjzsetgEumVr4RnI7IiU1J7Deuf2jcj/s3Pf7PQtMViJQmC6VAO/793SnSb1tiiMYzsn9JhIh4LKCZwPPduFNABKXDE5gsPLPDVAa/sKjF1VrrULy3m0lHQCOt6MfWjgJot1qY2/ZmaVo3eiKYyIEiSUMpltOMoKIGGXjp5hFop79MrxmyxErAOlFjx3dbqRq2gy0tijJqmQVObfnR+qgsLFADwn90eDx/F9ExCPneEBJQj2JUU8MQ0MSH24qEZkWqFz5XUmZj6S0CtNH2wtLZVayAAYWEMTwRFkmqVzEwoZRQlo8hJw3PMFqvFatXqTSXnGnLT4kYMzEiMUUeq2flQsF1IA7sY+fmP1wteJSJMQRKE1mAa1i/5f0UeTIfHYLpRQwNKQENJSYFNTOdPGgAAcD8G6rWY+ktAa5o0R8/AElalnDIwKSAERZEWC61SwyVcSjkVGaG2GZVEXp/E7NTYgr69BIfiBPSO93y8IASD6XoN7fbckTMBDTEl7RqggMKvi081lAb0nhPVLqqLVOpIiiIsVnr8kGwZRcooQq1SxETAjotiAUUampedhm5Pum+1D081NBAMSsMEsoYGpvnpbyUFISqmfqkDNZosWl6P/kqjsBdtthr2q5VTZFZqjDsj6Tn1BABsfesGdAMrqQDxXAoMpjMse+NLHyrp9/6vKg0+C9S75EzfeKXFalUpZFdPKRQ/yhmhfdITuY0tbAWoECyj3o1Iw3SLERr4/rtrsE3qmYD6KXmdnxozaVjWlj0nJAV0zkVw4aYYZxqKwEoaRGvMgwUsoM7ASurgwndZlc+YfiljBmfImZKjuZMKh+Rluv/c6EiVCxn9cnLFgs0ZoGeDhp5391lgQh/s3XfDWvhH54/il8pfONgD9eSIjlRJaiieBYfAticmBJQUBEmotOsE9LGrL/TVocQaev7nF7GCYjAho6QgSJJOsu5STxfmZ3l1c2ZHoW6+hp7/+UWaBuWZ819aCB7//G9fnC8Gg/GSZT1JSWXBZXuKQ6LHvnoSqSfchN14DMZvKXhPWdYDlBQK6Oo/fb9u1LV6ehf95NO64Rn4Dw3rSbF6YoKCbq9h6i6W+U1Ju11M/WuB+lw9oW7SDrYmVk8MpscqKehus9SPAjqEPAqAb5x3pJt8B70sC5avQhra+A+8dMPox1fjMCgGE9AsCxUl9a8F+st3nwIAZlx1i6fmJ8ojudJNGynxkdX19qkpOAqKwQQRy4JcSYkhY8f77+jZMbKo1iJ0G8moQEBL/veqM9UbmaGA0+Oc6CYfTkDRcZ7AFigmIFcidXsMtIuTSIGwxsnfSur3LHxrFFysGdVa9Mt3ny4YFl4sla+indiNp13qpu3Jgn8xGEwQs8yfNqnPxbSL6kCRjH65v+j/hoZzGytTZ7t4SlmWRF87O071sgMhxavFMZgeq6TA1w6+3114dCOeaIRqTUGPfNToMe48V7IxqKQ8Vje08TbDm0+s3uXiyFhDMT3QhQ8K/73rvftOKqm/LNCRsWwb+fhIBfOvokVr7xDqDodKquwa6tKsTI6PrG5AYVDsx2MClG4PgIYAywLPJvWZBSpSTAc27q+8cADb3NNNCxQaobmuvHi+WDICao+GPvGFKwsUG6GYrrdAu11AQ8AC7TKb1H0xlXVSLvk2pni3+ERWNGlwHvgIJ0YmziVhMD2OZf6xSW1mKQw5XvnQ+z4QUJFiSsglfCgpUVLn+Hn2ndu3jRk/wc3XtR/B7T33HS/39OAYDCaoWeZHJV2CbkgqqazTipnE/CvI4vgMzw5GQ/Vcu7eywx1x00wMJiRZ5ksl5SrRnSqptIBeEMeqJ221hoer9faxxHbSeiXYXoP3ck6YOS6vqaEB3T5XrxWepBOK+Hkkl6TER1XXt2LbE4PB+FRJHTRUoKRuufAHSmqH9kVmpgPnq+DYd2ekpcTz75Ik4eDO+9JKxZl3DAbjPyWV0FAPBLRfagQyNtdtOyZ+VFJbobxWs/YmBwEItK6ddrbwyDOEB8HmJyZgwSn4UI2TuiGguezq9VkTBjpz2H/eflws0fmZ0fy7bVoDxdihKbFhf+3YNmacx3kk4asy7D161qvjYDCYHsoyj5XUqRHqfhmTq1jn7PF56Mbps/YETpvWgG4g3UT/66316a5uupNBwmAwGOALm9SVgNJWK/qXt0m4j6RiIpBiuji8O+d3qKTSIY9Eg73HnJqcWD0xgUa3O+8Yvyqp2xYoLaGY7ovmPydrYXK/f7LXOR/XrnpNY7t3h8Vg/AHWzR6ipBJLOfk1TAAAuYxyoZgudBOJJgBg3OB0dGN/aW1+BlxPVt+qv3LuNBendeB4BXc7Pye5w7dR09iOzU9Mty/oDFjdDMl1nF0DT0kJDwRUZtNEScWU1E1OMedMGGi10iRJ0AA0NdkL8gEABoMR3VDIybD4Ph2ePQ3oQTkpHe62/LfiDvfBYPwno76SzlFDR/Lv7j6wxyeHxQLaeV544Dq3BHRMQpsL0eTrJqeYqFSeZh4iCYLboba+WfBco9GEypgUclLFCqgrp54AhGsLFEsnphtl1Icmp0A6fSujWED9pKQSAjooDFbIEyJzFckiJ5ozx7GZd4okGd10tHAZVaxxFND9pbWDMmNFAuqA4zFogtkg1tBfth44Y7T3ZsZgugzrtV90jW76SkaxevqJFx64TrqdHV9D95XUcdtnjR9I0zSUS8bSJMQqy7Moa+oc1DMpIebXXcUAEIMyY2iabtJa+mT37fAUkYbyBRRLJyY0cF867RBg936PZRQLqP9wWcZEQzvx0rF5JEnYfXNC2ufmbYOqumX/2YEZUfwdCAJcckHf3/4pRXctZpOu4YzrMKhtIiftqJvY8MT0MN3kP30YfLoXMorxB64sUORoAwDSU6XXa0KciGlNXRN/S1JCDEFARf51VwmyQNHBG60xaYnC/GB5ub156BlThKdvCYMJWIxtOgDA+AneLcNj4Hl9bsootkD9R6dHeojinkCknoz5Cfdj/gfuRBAE0tBTrcSp1hbRQbFoYkKZ7du2eS+jvFWF2BrtdqCBKeaIDrWqA0YTWozkHiiPJIqLpiTGSm0GNS2eTUnCYEKJ7du2ISXtJKOGjURKigkUAe0M1bUO5mdyYix3GxmgR8phcokgiLPVYtsTg+lZeCOjUnEzLKPdgtOhcoPV9ajxR8dhUB6b9wlzRylJdgEFNLBY6Y27YRgUAFDXokuNV+9p5u3gZ86VnQaBQXp2Vo96y132fgP8jaMYqCQmoEyIj+mTmRYZGY6q9zrAcZfNm37nbjc1242YCJX0LAn/ceGwfNBjcBoDPaxN4DTUfYTqyTM/IYRDZ+WEKJVnR8dgQhc5MLQ01PxT24juqlSK9LSU1JREhULhaZ/f2JhYgYxiuieJRBAwDIqMUE+dd+b5Egfk3SZwGBSD4aOkYGLAYJHr9cbSU+Wlp+CERIIAMTFRfTJTY2OjSYJ0r1c6K6MmvQZ/wgGchXeOg/PuAH2kvBl58WerW4hkt4IDGEwIoIgMc+HFi2UU3aVp2FOCayshl8tSkhMy0lPUYWF+Pl9MpwUUXeHOVda6DoMKzE+h887H8bKZaKioU2Z0eBoYTGigiISq54WMcphM5opz1RXnquEdAkRGqDPTU2madrIyEONHXPnmh7VMMZMbf5TN+0TNOp08iwBgxuh+3N30BHXfNNxoC9PjUESGISXtECVlQkoqDQ3a2rRHj5fW1TfV1jXW1Te1trabzGZfniumky58h2HQDnJHjqLK9B1h6RWnxhdNTI/FTY/ehTUqgKZpvcGotzWNpCgySt3BUzCdwa3skGvPwJ3cEX8b/0GsnpgejvumaMfWqAiLxZOFMBi/FtKfq7R3/3SBpPkpEkr6qK2c3v0TwGBCFU9l1M09YyOVnTgpTKcFlA2DerDuKEaidEn0LC4MajRbz9Q4tKzHYHosPguMYgLNAjWZre7kjgQWpeQSeMFuGQm4PR0GY8d/Hj0msOpABbkj/rJ31/FNhzAo9uIxGG9LnTzy6DHd3EyEHwYV544cVhl1dChbs2SgM1p6me0NQDEYjBeBUYqgKTjFERNgAtphGFS661JHzBjdH+WR4JAZK/7DYzDSYBkNERfeZLbKZaTTdUc21XSnjYxZ3+5wElRA5OL37fqjw32GX3gxd3vyReMffeie/PwBVZXVH634fNXqr8xmCwgqflzzRXpaqut9Plv99fsfrkS3Bw8a+PSTD40eNeLM2YrnX3z91982g+DkzttvvuVGbt63NFXVNbMu+z90WyGXL12yaNHN1ysUii+//v6V197RaLQgUJ16ZIpaaKKpzcAl4tPSUr/+6t8dPnfCxOnijaUlRXff89CGDb95fuIhjo/XwrsQQrOoqcElw9LO18ONidFh5x0UtXvIzEx3c0+SJGvOHUe3GxoaMzLSXn5h2csvLCscPrGyillgFySk9krp8F3HxESjG1+sWj79kskAgPr6hpzsPl+sWm42W3plDgRBSEx0VIdvnCBZcyE2Jrr42D8w3KTT0TS4ffGNty++8YIxl5w+I1qAFzCF95yMNrXBEeWxkUoZRWVkpHnxoj/+72uK8n3j4B4qoOcqa2UyuWTTEMIN0YQjj2rbSEGyPjDySImp/fl36ypPijciSpif048//XLr7feiLddcffl7b71ctO/PpLQBaFpJUDByzFT+3R++Wz1u7KibFt29bv1GwZ6XTp8y/ZLJNTV1g4aOQ1uefOLBu+9adOrE3pwBI0Cw8dK/3n7pX29zd6+8fPby91/fsnXH/P+7Rbzzwb1/AgCumH/jth1/AwAKC/I3/bp2145fk9PZyd4Ba4oiGUWmaNPR0qzswdz28PDwI4d3mc3mvv2Gunj6up+/zc/vhrcZLHh8YSmukvjL8cuVzHoN9x+3Q3ltG/rvXF27QD3R+jMQPCjk8qioSJqmOfUEAHzz37XFJafgPL4Q/bZ9/u8P4LuzqScA4PkXXwcAREVFgpBm9IUj1eqwXzb+gdQTAFB06OhL/3qbJMmF118TXIFRKKOMQeoOffr0Li0pys/P0+n0nTjTEMctAeXySCazVdxwnnAimnzdJAk4FVkgnUWaeO4/EDyowlQNDY2lp4QNzzf+vgUAkJPtalBz8DJs1OSPPlkFeh73LFkMAHjrneX8jas+/woAcMdtN4FuxX8yKpfLt2xeR1Hkc8//67NV//HFyYYmnYqBJkYpLCInvbymDf7DSKXY2AwurZSktbVtwODR4u0zZ0B3+MxZ2AE39KioOP/kMy/zt0REwBUQVmuIr7YeMWIIAODAwcP8jU3NLYFzsfQiMGqhieb2DjR07979V81fCAB46KF7fHGaoYnHAmo2mwQxULto2uDP7QgN0ewQpVKZndUbxsuKjoCQhiTJ6KjI+Vdd9sKzj9M0nZsX4vMgo6McXK7ARBEZRtaRVqnlgq6tUWfBM5PJhNQT43sLNDEKDmkpr2kr19kvYjQAVM/TTQRJkmdLD8BU0nWLQKjTr2/O9i3r0O1HHn+2rS0A6icwDCRTZei+jCK4ND3+FP2YRLKHQU1WKJ0ikxOpZ5CGNTsDSZKVZ49QFPXVN2v+2OyDMd8BTk1NbWJq/5SMgRUV5199+Zna8ye6+4wwDpAyEimpR9aoR/kljPdZeL7JT5IESRKHdQk9TTQFBaEURX2ycvU99z8OegAo/GexWIaNmrxn7wGCINb/+HV3nxRGCJbRgHThaZgcIkmiB2qlmP79cndsXQ8AuP7GO4J3TU5nmHfVwnOnD10wchgIXQwGg1IZrO4tKfMgMMovGsVOve8t0MPahMM6+B9WTwDAnNnTkXoOHjo+5NWTJMkr5s1+87XnBdvpUE/Bc1nBtNRe/I0KOcyjVlXXgBA1RT0tGu2x4BVa3pA3oO+nH78DAOidO6S6xq1G/UGN1Wr96IPXr18wX7A9raN19CHA1/9dCwBYeP3V/I3TpsElrRuD58LZGRnVG4Ksw0NXggXUYyiK2rYZpqH79B2q1bpbfxfs7NkHywy++c8KbotcLvvnL9hdIqtfKLvwX379PQDgvnvuCA9Xoy0URa38CC4DffixZ0FQ4bWMYrqomUhP4IF770Q3zpRATRHwyOPP/nsVXKYSYsyZt6Cq/NjFkyeUndx37PjJ1F4pqDPFG2992N4u0fEglLh6wa3//XLl6eL9p8+Um83m3JwskiQfePip4FqC7HVglGQ0VKMz83s7YRBYQD3mgftYAe1RmM2W5PS8Jx9/YMmdt466YDhqTTR20syKitBvhr15y/bhF168ffM6tFbCaDJNnXrZseOw10yQ4qkpimQUVTvh/BIfYsjY8aDHcK5MuIC9u0jPzupRb7nL3m+PfeNev19PC+/RSlAXMnrhsHzQY8AxUAymR+OFNYrT9BxYQDEYDK528hIsoBgMxiYHnpuiVM8uGsVJJAwG06mOJBRvBFNP+yixBYrBYHwTGKUYJd1zon7Pifoe8pliAcVgML6UUSVlQjLaEz5W7MJjMBhfFt4DAPgaOnIA2wkzJMECisFg/NKqWdkDZBS78BgMxl8ePeDJaEg69VhAMRiMf2VUSZlCVUaxgGIwGM+FA8soAxZQDAbjJaRXHn0oWaM9q5kIBoPxB0a3B9PzMVjYAenBm2LCFigGg+ksisgwRWRYD7RGsYBiMJhAkVEQbGABxWAwvkThuYZyMhp0pigWUAwGExCmKAjColEsoBgMxi8oekBgFAsoBoPxI4qQllEsoBgMJqADoyCAZRQLKAaDCfTAqDJQZRQLKAaD6ToUoSWjWEAxGExQyigIAHA/UAwG0z0oGA31Yhlo4HQaxRYoBoPpThRemaIBUjSKBRSDwXQziqANjGIBxWAwAYEiCGUUCygGgwkgFEElo1hAMRhMSAVGlV0oo1hAMRhMSJmioAtlFAsoBoMJXBSBLaNYQDEYTIjLKGDwh4ZiAcVgMMGBIvDyS1hAMRhMMKEIJI8eCygGgwkyFAETGMUCisFgghJFAMjo/wMkdgVVeCSM1wAAAABJRU5ErkJggg=="}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "assistant": "CAM A gives S0 a clear left-sector line to T2. CAM B gives S1 the adjacent clear line to T3, but S1 should fire only after S0 has ducked. Set S0 as phase 1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant": "Complete the paired assignments with S1 on T3.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T3", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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HLPx62+bXHq3Yb1+iYtJpTbzxLTHpcKWvwFDVt8IC5OT+g33wN2LGjlnNZhQxkOTrW2cu+mn/sGsW+0RA4fWjqSEsNj5zxLjyvUHTJry7kkiB5trTlbXNTkeOBBkEN/9DMqr4xQcfOtjRXS6jFEnKZRRtpUmSlFEkRcG7lq6tjAnYhfAr541c9PP+yJS0uW98AYeBa9vrio/88eqjwtSQVMDRwrQ0U0ZGu36JBZ9Ld9rlI1OGwS69Da4Gxhg1cNKEKioG+IjSbb8OnrsgYzgWUL8giMH69hdPKOSBfy1xF7mMqlT1lnxowczR6SlxEg90oYwmxEbcc/0lNE0TBEESqBEVETgVAt2LSaf5cEr/uD595735ZVhsvEIdkTbkwhu+2mq1mNfefXX1sYMunmu1wh49pMwH32SCuQabDboOByuQMjjEwSfomQiGIsLLQejdggef9abff+WHQbsbH4dKYyLV6zeVodqaIPLWJYmLDjdbLCazVaWQw1Z5gFCHKUxmS5hKERGu7PaQiXhAHkZA45mSTy+HLWBkqrDscZdc8sSbJCW78oM1394+r/bkIecfLNMlsqMhBa7rQBGoVJ6Su/q2EMylz2LyrJulC2Iz4K+vraYSBA8eX6weffuHNx68AoScm58UH1UwGgbmXDD34uDIDy6YNZogCZqmSbujRwRpxWgPx6zXFW/6sXjTj7kTZ0x/5v05r362cu5wZztTSqh3+rbmzr8uirdGJrtqa6tkYgW6Zu9HMwjIvQj2XTy1HU5RDBZCx2/t5K8/NSnm4HHGmWQCczRNy5g4nVxO0TSQUSRBElqdzy62XWDiiUfJewBevOQGl824CADw+jKnM2D45I61lxw5Y8nmUkAQgrVAiNI/f+EHHJsrTsdkZJGUjJ/kUUVARas9cRh0GqvZBGiaIEl5mJqfvOJz07cwx7VjudMZ497RdLYUBA8hJqDey2iftIT/mz1aDpWSUCkVFqtVKZdZaZoRU5g4hpLao+pvsIa6x4MvfOLObqU7v+5wnw2UoVin6nfxnOJNP0oGJWlbJ/IzuzYPybglrk9u/akT3D6jFz8MC92/Eg5w9Y41S+df8f53C7/etvKyEeJHVdGxlEJBW61l2ztOSbnDpPueBwC0nD8LgoqQFFBv/Hq1SqlWuYwP9kCwO9+1OkupI3Mf/mjei8unP3dnBm13jWlArKWGNxJhBdaK+2xC/LYRXP3xTx9M7Y/MVUqhyB431WIw+MSFBwBUHd2PKj0XfrP9PzdczC+ZSswdePWKnwEA3y+5yv4E1uPxeMENpVCMvvXBQXP+D9D0F9dNBiEsoAGWR/JGBs61Kxvq2Gpk9ynM8lmtRvCBTdGuwqJtq9rwedzF1/4I+sO7bU3mlgZFSm9CJgcWYG6p+u7DR79j9nx92eJhquaTZK/Hdp2bajmqA/JtFHzKVcojD4hMXXeiB5Ksunrcgs9/i83MuWPjcW1j3ZldW8Ki47PGTEZauenlB2uOC6sCpj31LtffhMOoaftk1pAOW4qsmj8O9JChckUbV/jhZJy81mnv16UVZkV38gi8Q/VgDQWhllnySR0oioH6HCo8KunKe5Sp9mYrtNnU9Of3rf8IneWYcXNjxl/G7VO1+gVjTTl/B0F89mtqVDuhusqyJ4aWCGu+bYR5f3EENn3YmCmPvBqR1IvbcmbXlvVPLBY0tZt473OD5y6QfEecgIql06TVNJ8/c+K3H4q+/wwEIQEqoD6RPERhVjQWUJ8RKhoayALa9XSQBxP90XPHeGnShh4hHAPF+AEcFQ059XQ/D/b6k6zOlv7VcUIM9AydxQKK8Rwsoz2SB5/3i86CYJZaLKAO0VIegpRiqPiuPgTLqCMvvvw0uvHEY960Ge6xOguCWWq9jIEGYBhUpIAOVLSpGutrPHw6Fk1P8MWnFZ8DmwE3nTkW0zuvscwHBeH+i4FKuvAvvvx0D1dPn8PXWffpMp0NXAvUtSCKdbYjwfUiK4XNTw8/rU5raMOpQ8rI2LdffE2j0z/1wSfP3SX88XCv8OizsFslJuR50A17Viyykiat5Nczp3NSG7gCGkhgZ9UHGjrnmiWj+6d1eIxh4yZodMIhlENyetEAnK5ma8tvu9+dEbDdADY/Azlo8IaUMXvK7eiBpNQGvYD6qkopkFvlBxNuW+0F2Smwhx0Bn0ID8Nexs2PzYQs+c2VxYU6v/qPG//jm00Ny7LWH/OPJZLDtEAbjEQ90QmedSW3QC2g3EeLe/bv/ektsBiIee+Yx7z6hl59/lCCILWv/i+7aO0UxKgobg6J7zL/Fu7cH1yeraDkRoXSQ9a1H2Jj7mLyk9vB+3XReGP/qrPcCWjhtUVeuR+rGMGsgePfxfdnhiw0lRfF9C816XUtFsa80sWv8+vy+6TX1HvgK+0+VRqjg93PrkZrRAxIH95YYg9wtpEfD9xPWxk5FV4cxLRTCVfx9TEbTuIHJAIBWjb5dawDaw9y1gU4Y1D3njfGDzoaIBeoLKQxoGW0oKeLfRnr69kuv8ffRGZ2Or+lmbB8PxbQUatM1AUAUnW4OU9i7WyVEg+LzZ/hPQuq5+XA17IZFEscq4KOtGngNeOaJx5558eVuVE8AwPbDDtN3R/SVnggjbCoo6lOHCWpCQUC7Wz39IqMCcexiiUTBxzJb0objjgfv9f6gNJDLoZO7/Ui14JHJQ1JbNQbpM+kDWxC0agzs6A+kR4EnQzTtMKZIshnrqcqWnF5BNnUSE/oCGmD4JtfkQiuf+gD6DuISnw5JT5S+0iREqd08Akl0qiOq2Sw9Oa6l3SDZ/blTPaH9BiWjLEZHuSfh7+hUVauzr8NFI3Pr6ltUSvxzCzU69RcNgTCom7z7r7c7DCA+9syjPsw1/X3yvLOiH4F0cqnqncfKxw7MlHzKzmPl/dMTQHfDdaSWK5UmnQbpjjPRLD0vDJjmZyXC1j5GY7fbn/Kw8DClfZhaa2s7AODiCySmYaOvQE2db3p0YkJBQFNi3DVYApZ3//U2nNrqu6TKg8se8q13/81nb9A0WPXeB+KHsntJjdXsBE6V19ehXa78yGoxR8Uwvnkzqyw0DcrsFhx84YtG9oPz8HhXoUCWIYI3WZSpy+I90O16jwkoAVXLieC1E91HozeOye/N/BrgAFeSRNE3+H8EILiibjhsbuGNvPFtLvBAkFRKueCHZ7ay+lJ8Xugq0jT951FYNBMVzjr+vsxZ+66hsnj4O5JRxJRR/Rykx/bq6MWrA0k9mUlETsf5EoDgv5FA1n1M97jwSrnPhkH7im17T7qjngXZKdztv46Vo+Jt9G2nef+QJPHX0bO2ieW8qeUOd1jSk2M9OVNpGZ05f9G4Qey5qRXuFoqbrewPtaA3VCINM/aOIMDukydG9R/gyVl5ccoewx8qFami2vTshB9n0mN/0WAz4rg3smnvmcGZ8E+TnBhjMBqtuIaphwtoSoxaa6K1JmOg2aHRkeqR0+YAAE46jkXtzVM3ZEXaxZCLtzH/sMpoe5jVVn8hER7dwaSnxw1K0RqhsihkUG6cpacF2AfS+O/P0mkV4yfP9SaheiKQ1y54Kc78TE6MqThfC7oV2molSFJnMPHDoM40FKknuo8JPTxOqobLSbWc4NRz3nWdqGvxKQOyel0yZfycuTME2ymS4P5jAlV2nKVcuhba9h/LjiPVOr1JbzC1agwu1BPGB3n/2bbyjhp4UCgMwqBnLhJOtAUFPyXojArduvRx9N/u39d88pDwe9JZpM5s0162sjU5KeavotM+fkVMMFqgp+ra81IDcTpQREysSdOg1TQ88dFnL95+k0+O6SKvvfVI+aRB7EMfvPrWXQ/f18nXWv/tJzOvvBklpveVNgzPjXewlvk3Rb9VQcI6TClPi1fvPnFi1ADfefG+gLK58LTVqjcYw9Vh7njB1bWs+ZmSFOPNhYGm3//qjxGDsla+9xK3rfCSW0f2Zxs5LpozHACw+DU4eN193JFyu/mJCVG8iYEer2wOKA1VxSTom+tN2jZ0t+68w1wtryXSNTdPHVJWJawz7ySUQskWGNrqe05VurX2ceKIviQBNu8/OygTVnqWVQZo1sJT9SMA8fve04N4MvTXodPp8eHO9q9oUzaKRq4aDMYzVS0Hd336IZBfNjbn8mtuQNv3nGRDAXts0okk1U09tbjMIwEALr/jOnRj7fL/SCTHMCFBKFT2hiem65vr0e3YrPzTh/aAoEURFq5SylFdIf83d/EF/ZxlVLh7SD2dcfJcffeWgp6vbVz94cf8LRRBW2jCSVyYjeZy6gnNTycUlbm6YFRU1dE0/dB9S/tnswWzty59fGT/JI2FunN2wZK37BFzJKmSespJKgqjh6vDNFqdO+qJbq9d/h8XO2OCl1AQUI74vkNoizl76BgA7D/UsqpGnxdO+g+2WMrGxaOkuvg4yigSmz/2nXEhoGMHZu485oFh7g+aW7UT5lyhM5h2HINFuACAdr2ZzcMw7joPWqBBHzz7nteve7y0AgBwumhL/+z/Q1tWvvcS6ki/5K0qTig1Fur2WYPvfvs313paU1sfEcGawM7ySPwz57bsvP05r98CJpQF9JqbHvrmM4mF211GQ8lB/g1r4C2UdhOKoNVKGX+5Y3V1fUqKE7ORV6FNdGR+BgL5uWlI+996q+OdL7/jev7du55eCgBYs/wLT19UbzDISUtGUuTFsy5GticsPo2OevfeSzit5Hz5u9920NNbZ+RHqOScL492y4y1511RmQSmJxNSFijCvZr2QERGkQoZaTZao6Ii3HqCzRStrmvitiUnxpZVNhMEAav/AwuHv4vNC/agoJhffyYoTS3MikHlT6i7dmE26+9v+LMI0MBgMtc3tfdKiuHySPwkUkZqwvQRvQVCCQC4/z0HPV04NS82QvnSp3YTlcuDOWvF5BNUrSd5HarYAuHfD1aNz4ft8nCn0aAU0EDLIwmYc8PCn1Z/jm43aRv3nWqMtH3zmOaS7C9ncO9s1xmkLq5z0hsthmaN69y0BI7XCx9ePfiVBgEFRcJuePwtgrsIq9VafAaW1spl1JPPvwpD5LHRsy7MQranPYl0snbtlmOcUF4xLjcjMUKsp4+UQj3tl+zNEhLJGGhY60naURalET3624FKGoA/j1QztgJMmmEx7S6C2wJ94Zkn0ZoWF/s4PEoDqxX8faIWaejhs2WB06aXo8MKbQHVtXbzMyXRozVR3UY40+sTdgYxmRVy97+E7NVh9k0Pu7N3ZW2zwQTXtprNlkceuLtvH3ah18hfDnJG5SPLNwuE8hgjlJye3n1Z4e2v/8odU2+0RCiAizzS73tPX+7kfCpaCdB6dNGcEejuc5/YO4qOH8QmuNRqh8bMAIC0FKF5uzCVfSOfr9+PJPjPI9WwGpjAYtrVBK6AvvDMk6juWs1ruyvAtXRypKWnAgDOllcCACYMYvqEtxvYJY8nTpitaheWppuloD7BaqW5tY5e9qDw6mnOsvP+KNVCaA2WcOarlxIb1thu8sdLAAAOHi9Hn0Zx0c43Tx6YP7HvRbOuFhuVSCtNhOLO2YPFhuftr9v30VllrfUOzUxVckJvgq+hsq2+HZwZ8+Gz76UkxfLLmNCNtOx8GFU4AmX3fNlR/nG2H3EUUwLWArOXiyPnnb3BqcOFi+UMBnblRYSGHVhQ36xVpQ3x7IPDdLGA+iOPxK1aadebI8LVrssbMxMdGkQhfeRoPGZfBIICpD4Jk/L1xVe19LOvuQ14MjDNwfxMYszPYIgAc/HZ+lYDybSp51PFe1McriuBYAyUg/kELGYL8t9h/mrExKULpshkFEoiFfRLmzIs481vdgm0cvGJc5zhOXd0dlZKlFhP+UkkGCBqN/Ldhd/3nuaK512fcFp2flX1VtpiJijhb5AT0/GDWbO0b1Za2Vl4+RdwvqapwxVf/NuYHmSB7jhSjZprkCRpNhk5lRSII0JyIyLoUkqZieF1rUxPkM4dx6NuxF4vKOgMyAtmY76OJ/vBs+8yyfe7JcXIQS7FMOJ8vrbZZGZXi5rNFiTZKIlUeMmth4qhWYfkMjMtcdrwTKSVko68ziq7+7JCydJ6ri1Tr2TYqxSpJ3slE4GsTo2FGj8obf2WfQQBKKUSkDKzTgM/ALGS2qaGNLW6qjl1LZSBlkkMMbwX0C7II+n1rHPnUYu6blHMVq3h5WdecdJW2TPOVLeEq8P2ljaMyHWa273Cscrng2ffdfajDTo487NXUuxatm7Jm7/ovqP2CUtx0eFymezWpY9rrbLx+akfPzTjNp5c7jlZu2bzUaSVWovsnnl2rbQbp69VjuyfVFVdJ34huOohWSkwP8Wkx4dbE6ALD4tJ+Zd7qzk6ln1WSxPrYwnEVDAvpEPTsqiMXVdSkN39XbRDm8C1QFFjOWdNJbxQzNJKiQXj7Ot0RHFlk7YLR1rqDOyVQ8fruOFMPTlLjV8mea5ek57gdMljQMG8WWawpTTeqKfJbDlVYW/a1NCs0eoMyPw0Gs0jZ92O5DIrPWnKsAyB7Ym0EpmKd8wqWPo2u1Rpz8labqIcQpBHcmF+7iwqc7EC1WyxyCgYuOlQSWH5gU0cJRnUL0Mlp0YOgHkumune7/JzwgSMgPo8DIrW5DjTUEnRFEikJGgsBD+iKiN1LvQxXCk/eMph/iLHBdPnkiShVilMZovZYrFa6ZGDs4CfEaunBEHbBZ1vfjL/eulNVNY2WSz2BQnFRTvuLtqhjoi4cnxu8oDxSA2vGduLMjYufu0IkkuZUn3TtDyBmC59u4oT00F94v/a45D5cRaMFmJ7E1ziiFQ4VKqZzLStVT8Lp6RoUS8swIpQbD9SlZebPiSvN0USSjkll5EkQcgoGKohCbauq6HNtGgOOwF7w9Z9nnxmmNCyQH/+5uOnHn+McV+ghhKEW/qImDyyL/OtFf78angF59mpMQYDe312pp4XTJ+jVMi1emNMZBh3tJhItUxGxkaFUyQRplIQBEH5NFBPW60e5ZE4rrjjei/W6njEB6++edfD94Pu5rXnlz305AsudthVdIq7ba3clZMAP9DEKLr4ZEnxyZKlM9jr3NbD1YPTw/Qa2IamIEm2+LVfkFwagfyuOQXiqKjAAhXjOpCCsvCI8+X2lbVWi6nDX2JkdMyl42Icroy8ayTNVPUTBDCbjL/8uR+6IC3o+iHjqqZQ9h8TQALq7zAo6uqGNPRUpXCOxeQL+tl7TogQW2Di1eLccp2Jl12JDBYZRREESIiNpCgyJjKMoqgwpRxJZJflt2UUyW+q4Wo1pxPGFmbtDJLuk3wv2H3zU2uQbsaMeOX5p9CNhEgmMhDFVlbGJAg/xssuYres3Xz40Okmu5hmxXJiKoiKjvgU1pD+ddOQlNwBWfev3H/3BFiov+A18P0ytIOuXWI257CchNpm3aWDVBuOOFyqrRYLyTjvGr0pXOWyPTMhaoTAU1NkWwzOite2a2zz68l9JbWDesf87/ddil6FnPGL9bSnWKBodiPzL/ym5PeOk9QR+4+so0kQLlaLXzA427dWpNeEq2RGs9ViMcPVnF554oHwLnyB9++DS15zlUAnKpDvItHKflhfqJIX5rEFQwiD0XjdBHYkqlZv5MS0qrqONpv3LBomA6DxdHHdXeNlFDxPss8w6HerKaSedTnTZDPuMTw36bQxSt5eHT9gqKr2JEmAX87HgKtuPv+vBRqLB7+7/N4SXVDZr4btG1LfolcpFQfKdb2ToueNzfnlz/37SmrQZ6joVcg3fsVVqFhSu01A/T2e02o2efBzkmr1xm3jO++Issrm9AS1eFZP96I1mGHKlZkYIZlaWbP8C2dh0DXLvwgW9TRaQWOrJi5KIq/CmJ/291FV24BuJEbBT0NnpEuq2guyYl994amHlwn7Gz23cqNO00YQBEmRFqaGSSymuVnCYdGnbSWWbbz+/9z1lKbp7Lz+DQCUHT+ZHg32LBrGGY8ErDZl9qkt0+kNOj0gYnqRmYMsBzYQMckmi7Xp/Fmop7t3URShVshyE6vBdy9lP7fOsOqRvXXwxCIjI1gD3Oqyu6jU35X9bhPsNNOquiYFRShjLSt+2sv0wE/UWGTtpOq6QWFi550fTBBIqpt6WpDABmcFHKp3r41DqNApAY1QkAaTfRnJvOvu/eE/bKcynyDzwih0LqNiztVr0xPUAdVzw2KhBR3tBFTXNn3w7LtcgSQf8dOKykoKs6WGlXc3MkCbAdHYqlGgQSss8C9RVduItBLBvx2TkHjZRYk/bDly6DS8HF53y9KCLOjsN0UWiF+CVCgjVPKWpiaCYoUJien2w1XjB/eiSIIvlwiBE/LPiWq1WpWfER1vbYqLi3n0oRmvrCsb+vIPAIDdC/Mv+KwI7nMTtO/M699kn9NSZT1cRQBAjb7GsHXtwIsmmM8XtzTBvLnGIpsyQKGQkRtWPSIZxOjQi5cELoZv0jXBT5JQKmBGKT2aSIiP3rirBADQJyIG6Sk6gYQo1XUXD5BURmcmKqenAkn9fusJwRGunDRAUlgDTVLD2k5y36sth9mlFoVZcd51ZiGi4tjOGt6RnehgRPhWQAEATB4JEsYslfMsGmhTRn6zIkRyYuzfTJQQCegzL74MAobZ19wWERGuM5gKMyMADdcI8t81l+3la6jY9txZBDu3K+UkX0DrW7XOXvTkuXrJQnrxUk6fJJFmzl/Ev4tsQ7VKaHGn9nL15167+TCnvkhJobmnGnh8z2aScSn4bVlamuDnxonpsJw4tNAAieY/J9gf0iVj8pAqadqhEOwvrcvPiC7SsAW58dYmjYUqyEqYPCT92+3QuoTxgePHR/aNb5MlcUI5Nr/XyH5J3A4chFzZXFcJaFpjkbXWVREEtEDhU5ARSsrEAtra2g5deAD6pEv/TqubdGVVrRoN/MvOmjR0UJ/4vUdgAu3XXSUFfWKtNI1ceA5OGTUWWbRacdO0ge4klwQu/6HT9ZsPngMGdgaEM5CkivFUUpHkCVTPfckTPJ2Di4lbrPSPW4+KC3vceYnOCqiAoo0rfHg05pe2mMkjQcIcpcRdHLu9oY4bSFr/LjrNWaCBo6EuBFRi4aYT23PnwdPpCUIB3Xms3Nmy9+4VULGXvW7bsQ4PgsKXaGgHt7FNa6BIYl8Z/KBISh6mlPCxWlvbh+XE7S+pnT42z2S2KuQwcyinYFUQ+hXV1rMlbkhAZ1w08pV1ZfwjxFvh8TUW2agByaPzUsRayYlpn+TIy8Zki3c4cfy4TwT0r2M1SD0hBBEfywb6NRbZokvzw5Vy1/rI19NwlWzRjHxBmqtjPZ07Qmcwr9hwxKr3pZ46Vb14+AVes/kw/FsRHevd3r+2ohsTbOtiw8JE7Vp6iVas0GDN5iPsaEPC1UsEdBIJDqBXCd+tp4jNz6DItDBrBL0+UduE5kBdx7r+2xWPvfM9/PHs3EjJKLNBj2xDvpctHa88Y18SXt/UJva+KRJWVlitVpIkXZUHEeCKiwscPh7b3KLdx873YRYN7y+tG9w7xmqlV/y0L56RmKVzC17bAB2XBpK5epHgz1PaIyWw3FJjkV2Yl3LhgGSkldGx8HceDYDRAlb8ZKvHlKsWzcgXiymL1azRA7GGukglag1m5Lyju1NH9EF7mi30pn9K/9huqNGyBXF6Wr5kbgEBCIGe8oOhANhPVWORxYQrbrxkoGQwlL984O2vt7PGPmPvL5o7AtBg9abjjfXCgn93vP5/2wIOSO/CwlQak0jjaHDF5MGCQ63ZfPhgWSPzB93F17tJhalbi+B3ZhsvGs5/CVgx+afE1VrQLXt4v2Te+2cbtbRrDIEuoCY4ZM37FTV/7DuTn+GQfBc3fIPNeQMpDMqxt6SB36nXbfNTenRwd4ESO673kSlV4eowvpftEK8kiDatRK8DcchyXEEGa43a/qByGWEyS/x1Cdh4234XZbTRyg2knnw4uXxtw2lke6LV8cgs5R7dWqo5XMyK6cwL+vRNi+aLKeLb7WeRccrH5YQlV1/NrQcrOPWEP/L8nH1Hof++6Z9StCUxzMy58Ct54hgfqbx+Sp7r5BKjp/b4KbexX9/+4jPR2M6f01MufrJo7ojjZxs37isXu/wCSeWbqAK9mzCYtYuPswUVQi4cCDuuSTLtgiyuSRX80Bg9lZRUTlVzsx2v3DTgN3NpZ6LnFEVQFOFjAS2ctsjnXnxnEKhnMK7SQSK4aa/0yCOnFmZ3vMfnVmzU2Wajuo/VYiUpsl1vio5lrwoCJeViOHzR5KKWl44bSANwuLRmXEEGWsBmtdKcKjW3at1pUM2lHjl/JTkxFpTCle/n2sgwS9s984Ry+cq6MiSmMpX6pkvyBI+uOdAQvw+qmMYimzM6KzslijM8kZ5W10gsqxfALUOSxGKlU2NVdS2sskREqDm9S0pNW3TpoJ83/cMPgLoQx+F9E8cPSutQT5HnDv9XGcluMkj/uQUmKtRTCiATNTeR1ZzEKGVDO7zaHTjdKtBTvlOCcGE/it0UDk71UMIQ1ZxFRUWgLxiC/yrcC/FfbsLgXiivi3QTSSf0VI5XjxyQEugWqG09EotHVeVi0WHNT6lMfaARrg7j/374b4QzP50Xt3iMJwM74doF77RSErbBIK+Oh6+kBCWnaXp/CSzhnDFuoNkCR15RJDFzfDxFEiTJTnDpk+S0oq5db4rg+cW2T1Xig9p1lHXe4Sds+3pATeTJZa/k+Fmj+jjIpZF9VGORDc1JmFiQJhDTb/fWx1tLJMVUiJNipjjUw9mRv46c0xuYOA8A4eHqvMzYmHB2t1/+Lln5kwEACpQd1Vhk00dkDsiIc+G8tznq6XUX90+IclX8VHxkL99t13D2qZSeFva2qa0ooFlU1ggrFnhbJhZCQ3LH0RqZEsbuOL/EmdLxbyBJ5RexcIUWSDqZgWkOXzCODiUVdfahKGL38eoZ4wYePlUzvjBTbzAGuoDy1iN5jITJRghvo0qmQA0Vskianz5Uzw45p7Tnlx57Z01nDnXitHCJFEUSaNWhyWKRM8tyOKJjY5HezZ6Yz6z7ZtaD2XwI7grIFfmiqCX3dGSE8qXZBX/b1BOtg3IYt8yABLGhzlplC4nePG1glFpul0sS/F1uKD7DPnr1xL694tSSYlpVXSf4yrkuZoqKEa73236oIjU2rKzaLlicerKfSW19QZ9YWQos8CprAdsPsPpooOV3dRQM/accnC8TOu9iz13jGHbg62lhirRiclkgxOW2lWDwxEyW9TuOHyqDScuoMHZWa2FWFMiK2m8zUd1ROhjKsMkF0s3dx6svHTewrQ1+kfS8fCMfgaQKVHt4bnx8QhzNHHDe5MQ9xyuzksL3l9TmDRsXBALKud06owUVM7lGXGeOmksGa8M3QsL87Fg9CXCuQeOiA5Ake/S9hHalMsKHWimJlabDw9Uarc5oMMol3W0CqOT2vzu7Agewk5B32XI+SQkxyOnu0AiVRBD6JJxfjjg1/HBzRby1KZ5J0SydWyDQys//qkaPaiyyJXMKZBTB26Fj/x0xqLfEl9ZipfulRqJiWGR+EgTYf4otDEiMDlt26/QXVv7qTB/5wdChuYkTB3fgvHOeu+sFVBqbnra2tm/nvT+uLbSaSX9rqtmVEWJG5fXi5pdYafDD1iNFp6GeUvwkeFYUvFJKSSqnpzQAcsZUnDYmjyDA3EkJ+05WIcnTMwHxDqM6SE9b29q5q3Sk2nZwGmQl2X9WvhfQB15f88aDV/jwgK4Ly/k4W6Ij7vYW4DS1amCzEkGLHifqqW2X8JuG5iQcOOWq79lpRY5QK+F3tc2vWilGkdrPVF3S1qZBlZsGk0XJ00qE4P3yF4NzTreg5CAyMtyFEToiN552aX6i47kT42kgY63XfkF+fT3nxRdmJ1xUKPTiX//lNCemjNPvHDfs5frGFrMtMxbODGugaTAsx37mK37am5yUUKMF2pKTd0HtJvkSyRdHDc9511lld80poEiHnaUjoW547hz8xWCCNvs/i4rV9hfDsU4IBW994DAmCc7lgoZlRbVpDCRJnKzWc8YjX++SkxOuSE1ChtfuY+f5ksdk2AmjVGrRGdm9U2xOD42Otp/pM2AJ/DImH/d5c2TMkKy/DrK//GeeeCxASkGZHAgBy1NEY8d1UlrJUdPI1gMiJUmLUwvtypSBx9igGQDmrtZKp9B2pTObjEq52DqQ6mfI/C+nnh1O03MwQh0TiZLq6dE7sF4Lr83I6tr99fWlZ1kv/pbpAyPD5AIxZWDXcXqxJGn7oYp+qZEnmd46SD2hn2C/6hCc/04D2qBM+Gw9bM6E9O7ioemD+yS40Md/r7Pv7IXnDligs8xd/gXpIE5P0Q2+nrpIBCHp5OeCZBRxvMqxZNX2N83JhBEEpHjcZRBJ3l7GTm9yL7WIDsEGBJjvzK6jdi1G4VTfC+im338FPrVAfZU8d93tLaDCoBTzXo1me0dLpleFTCCUHDRTlCN+CzRNb2uM8Nqu9ItcdoQ77rage5MgOLO3tJGzQN2JhIrqllCAwC0RRdIp2IKUlPz6+g/+YH18A1OGKajG94JtRRX90yJpGvC7ncKTJan9p5q44s1f/oaFigZlCr+IKhrWALXvOgQb9WssstgI5cKpeR0am7YxJG7paTijSoJuVFRYBFpTKxm4lNRToqNcEKdfYrgAKKMYxK5j5wSSx5221/PD95XUwoMnDAqCQnqUiJ85fzE330IyEe+F+RmwrP/2k5nzF3Myyow2MXL/S/P+lIIWZwIMSfZVzIGplQKQ0snda+zCmY3um4smi1V8cOkeer64miLXnjU8mRp1t1x45148TYP+aTCjfbqmnW9+ovZRo/PYxA3nv+s1Lah4/tvt9ukmXIU/8NB5d6eGSSNVzYouXa6zNJL+Pj91zKWDZo7Pb21tc5EOcnhpwMYr9xbXoGoNfsmta0sfBgQc+fsI1GLo6zA1IeyJgZ6EpBGKEvEgIDVUsjGK2NIMOq10gRMvXginnjy/2ynoZ2M0GARJKpHzbldP78xPF4hdeGfn6ey3veMwdN6hka4z8dWTaXkHlxgiBmVG/7H31ID0aGt4Apcv0lpld84ukFOkWEyRnnbovHtUw0RAuYJtxZyZe27qqYzJBV0yJo9xsAh+Omig4/gpsd4xf18oecmJsUQxlLyT1Y4uv9WsNxIqhccaSMDkHgyA+ktAfZ5HigqHHyvhdiI+SHluxW9cYqdg7LRDO9lpPHxjM5S00lnOp0MvXmQ2uhuGUcnJVmnn3cGY91U4BxmhPjkUCn3CdfrNwrXqKqWitArKh4GW3zmngCSIP/aeKqlsy+nbiy+Rq2zBUK1Fdv2UAXGRKmdiitby89Pu7njuQB1m27+NIkhAg/Bwu7lnpWl+3y0BfD1FhWvDc+OTkqAFOntCvFwGuxQAqXSQC5B6sn9N3ivzjVCL2QScCShjuIzoG5/FRFRZ81N0tQ4OC9Rs6SD+6aJLZuBrpTMKxk7zdCRYUMhlh7iu3OTMRrhYiPckzlinaWtDi4YiiZhIteBn09DCNmwnHFQ4zgv19Mj89BjHT8BsgXVL6HZ1k05gfuoNxvgoVf90aJF9+jM0OZOTEvS0/KpxvQnC7r/zJXLNliPotsYiu3J8bnpCBF9MBZ4+ElPBgk7XnjtBALNjkFKn03sacwxXyQhYC0EL6odQCFJoTnJ6l2GrQeVlF5HNyCWdHBx594KhnHCv33kS9oGNH+RHAXUnj1T012/uH9BXa7rF/vvYIVk7bYn4btRKLwgNrXSGMyOUH/rkpRmEaR85YQU0aG1tM9HQZ46Pdmq2sOppe6ZVzyosAGD68PRz9a4WU3bGCDVZiUZGzeMcz437bXPr0GIiFA1N7AJwjc4sUE9Ev3S2Unh4btz6nSdhUpEkV/7M9uo1AmiZ8oOhfDH963h9S9MJTh9zekXpTA6RYv7OcCG/yxqmViZAyamnF1LFYeuFxGooZ34K1NC1+Qlf1zbjlg//xMRfNkFAwJn56UsBXf30VV7rY4e8cPcVa9dtcW0dOGsz7EI9BUd75dlljz7talRZh2Ct7DzipDnhKvQpkUdTpEJ/01hZzCopAMjwlFGEvUU3829ytNJqsCsmk5+BLr6MJP1nfjInRsuY+hozTUrKKJ9D5W1287NNwpKAwzyYFDyz9il83U62ERyXv+KK5zUWmUpB3T5zsAt7s7qWbcSHHkpNiHHbcwd8MY2McAjLoNt6o9l1zFEylEkAghPEuNgoydEsAtDO0PwkwJa9p0hCVPPEP77V3K4HzqJGSD1tZyIUDZk7ahgI7C1tcJ2Id62hHaqnb7TS2Yg7twltu1KMsbLYWc+9xChlXatBum6Jdd5dBS4VqWzTRmNlsRxVA1qtegspZ/ZTKRXJ0XCV4elqe1BURpGcdB5st6dl/BcJlTH6zsmoQEkHMctSOfV0FsjSG4wDU8NiI+RVFWfW7dD1Sk7UWmR3zhksp6hX1p0SpLD0NJt8tzUAHeTaeW9pqndHTIEtrYQsUGe4ijm6QXJizIadJwmCENQw2WWX2Y7UFqmni6aOfGV3FjXKykypPnJOUCrHZZDgXzBAtLL0rH0Fgmsk80hcqze0alPQrV18EMJPdqWH6tnT5NJN0Df7THUz3+NzqFvi5wU6ClwiJWXsPoJVUjjI2nBah6qyPVZML8zPz9+5aeE9n/HPh7ORGWuULepESqqUS9u/B5h14pKUkuaRjHkxvHfYhr0VABCr1sMlE/EAGAnFXbMH80tQ7WJqsYtplFpx87SBHYqppzVMHjvyttr1bCZ1I0oHsdXO+8uaJA+SlZlid97diGg7PTFbRJXvvEseShYImoiAkQ5owXEDBx1OmCusdUejXK/aFB+BaynSyU4ZLsBa6QyU80G3BcFKDeo2RIidd4cfhztfCb5sWa20jCJ9aGN2yPW7Jn/xjv0uElOesrMgJTVZCDmvYhyRiEY086BssgutCjLqZJPs2kn9V/+4nV2MVNcgS84VlKCmxYdfNaEvZ5Y6iKnJLqaS9iYSU835CokaJoODmMIeWS6lSnLBrsMRnPnjPBKilPWcg2LTO3HuqMOYqWMzVihATsIIkHWOGSTPBNRTQUSayMSAUXcbeMPFd525vPDP1l34nYY7hHCdquq0D4610iMUqf2MlSdRpJILVsZHh6OvNW3rdO1Yt2RXT37Ox820jyK1nxtDgHwf/eQsUPH58GQUviuSZscRINACijKNnq+YNtgva3tjTZ+IaKiARFhEXOTl43IyEyPF/vupVqiSSExHD0wZ1T9FUkzRzBI3nfdwYUFom69KfcXpICsNm2ShFZlnq5t7p8TYNVSws01LLM5WLEmh0QpLBZD5yUmCWBtk3skiZyq6XvDGmykiKMbyGRI9551XVkuegEMiHvvgXY6znI9KTjKRKTu9kmKtBuEyVhTEDJzB1N7VhIr8eiFlNdylQuI7KkvOPWGwVemT4KvdtfHWk/HMEqMlcwtJghCL6ZYSzaGTrJheM6lvSmy4W2KqjBTYmxqemHKVA1wGySNHnjP9aJEgIotyw18n+cWkZx2DPHZblacAv/xVTBDAZLZ7OWJfR3Bi3PYwZsohej30xZsxIqOiziGRx15huGIrd1YAe20qdoYb5447dNjeu4XLI0n0nHeCO+ca23KwKXqIs0exG+4/+IYYE6mkDSaglJN8v8xq0ApyPt4FMb3Gv7WfDGK/HsV8GUuK4LzyDuEUEFWGqiyy3kmR88bmSKrkz9uPo4ApZ3Lq5ZHi3VTtlcjedLMPk6fFQ85w6IMOAN8Z12h1FotDZYIwXsnc2n+qkQt/I8R6apdOXkCAqdNgr9mnq1vhEG5HHWE/CPc6JwRQIt6Z8+7M/HTz7XGfDtbKboFviMkI2mJmfxtaJuHjddonQHB/YRL6HIp59YuyZC+n53Lyd7LJ7r/Pn5ibGhchNEuZ3VDjfWCyKyNfTDmVcV3D5A4SiW+bcqHIDd95F1T6Sk6RQgYj2tdiUz1oM9a2WZnGH9yETttF2m7sanVOi5zQNZsiCTROhh8ADZqVSII8kt5oUXmyoNP9i4PWYMbSGQjwDTE046iL0z7dYn76G05M1+84weXo75w1mJDy8bnpzXwxdV0Q2upyiJP7GXmUgufXcvKDgI2tGhlF2tNsPIMxiWcwnmEMRnR7bKZlZzk1Yswk/qswesruYDY5BIv4pRroIEiCxQSTgApw0/wMAtMa4wRFar9DDp6WD/j8nZv4KR1tU5s61m5h9RAcauxtPj5a3KWVSTjvrJg6sTc1PDGVTMG70NBwFaXRS6wOkKxGQhmk3SfrAeObUDIJ+TrjaDAi4dtZLnEaUnpqB43esiYMEmfuQkRA3aFD9dS2tbH928vq0+MDqycTxh8svOezz9+5iZNRTj17ppKKVTKM6asvGQlVtVe6cN5bXWaQnFHb2C42Qu25I15A03H2OONN2yI8PINRj5ZCODMYXcDXU7r2MJE02LV0sufx+v1XguCByyNBF56kXJufhJRWuqCuBRr/B9u9GWCHCTqQhrquMfKtC++rzkxdAHLe+d1D9PJIJKB8wm3ap7HIWutgjz4TbxqfixYE4iAAN5BjRN/46lazw1IiZh+j2fr77pJBvWMy0+B4j8NV1hPHYe4LMTwXTmmtaIX7ai2yay7qlxyr3nC4EwVr7hEiFqh4ooOuI7msbmJjJfyRSwHVlx7jVzjRRAapQEPDZUoZRbYYdANz+/+5ZUtiBhy323OQjoSiZUhOnHcGmp/s5me6xWIqyMjzH5JcSkQb2R9sXt/Mb7fBFVMD8vK4p6BXimbOOhqAn2zTlhbNHdHS0r6z3F89MIPMAv33D9u5RLxKZZ/jGh0mc1MuES7m1BEENkIxoK7iPDdHpl+/fucra1R3/tBzLFAxbA0Tg4vMeyvryDv426gnllhM+fqL/PERfeNRMp1z3rl8OgCgoraNIEB5C2tmXjkhNy0hAompayJiodHa0y1QmqYtUlFNgXoKtJKPO/M9HUMtmJ6KrZ96eER0embv4uJiOJO3B6OPSAU8Fx6JqUa0ND4qyj4pj4uKcivNkJhylilJEAqZ9G+Sv2KCn0/PTEvOTIP+ezQAG3ae4AR98pD0guwEZ2La3lTjJzENDgGlabpZY6hp0nJjgjjdrG7S6pi1bl6LZpEGBz0xQtRRcQRt0bS3atpbNm/6HX9ArIYyqNorxas5NSKz1ImY2gswSZmcGVQl/IkmMY2yzlS3wBkftnz6iWr9tBG9kRqIG5XuPnxm92F2Xl5eZizaU1JPfSumgS6gZqu1XWusbGiva9FZLHSfPpko3EF7a2liucS4Q3gMW5KhaRdMmcTYlRQ4iqmL6nqRmDI1RmYzCZ13h49Uq9OfYcraKSaffqKaXZ8erg7bdKTx+il5MZHKI9VCZeSLafn5uhXn67gA6MYi2OFUEr6YeqengSugVppubNVV1LW1ao2CIa5u6ubgQQPh4l1eyf2n24VpRAzGBZr2NhQJjX94M/6gPDZLgYSPzxdTceE9DYGLJk7wJnagwyKj8rvNh5GZSZHETdMGRoTJxWYmJ6buhEc7aZwGqIC26YxnqlsaWnRWXkiSIAg4qtCkoaVEE8klBuMHfFOcgQqhgjqV5FezdETf+PNt0OqEeyrlkkmqAelROysptOT069+KODG9dUZ+mFLmqWJ2XkwDKwtPA2A0Wc7VtVY2aEy8KlmKItVKmRWaoTRBEGHmpjpTOMX0D5dR5LB+yS6GiWMLNNDqLt0pugwQ6iqgyxL/8B++PWyoaqgYQemoO3l88Z5Th2Xk94k/XgKXE206eE6jEsoZW2jF7CyjoGUarpKwTH0FX08DRUAfWDS37FyDePsfe0rhTNd/Dml0Jr3RolbKDGYL8ugTotUms0VrMA/vl6xG16uONBS78N1OcMloXXlF/CMOK/x8SM+RURdi6k4vEk5MuQr5V36295RyJqYdBkB9QncK6AOL5vLvuhBQAMBVFxcAAJa8+EVTmwGl8HrFR9A0XdOkLcxJjIt01Z8VC2igESwy2gUNRHqajIrF1B2zFAVA/z5ebWFKSjUWmVJO3jwtX6mgXIgpIjvaX1X03RMDvXn+1NhoiVXn2enxAg0VqOd1j62MDFOgtvsEAXQGc1ykiqZprd4c1xMXMQcxSDf5iykDkJ6pnt+/dhd3+8qHPujm1BNwXPjEjHiBLjwJwq1Nq385gNqgxEYob5g6APaUkhLTshaL/8S0qwX05vlTAQBNLVpJDZVk0bxRza26BY+uUMplWmbEs0JGGs1WvdGskFMEQbTrXNWBYgKWQDY/Q6B5nde6Kd7YBUoKJFNPjJjmpkbn5WYCwiEAyu9hatQ1obnNYRZZYU7CpIJ0QICuEdMudeGRenIINPTHTfsPlcJ+BACAJ2+fiW5ceQ/8y+lM8G3HRqqa2lBxA6wsoSiyf3pc8bnGiDDFkNwkfq9/AdiFxwSgegaC+Smpmy7wn5K6mXqaOyY7KyXKnRjo/EkDlm9zd16R12LaRQJqpenmdn2/fn3R3Tnj2S4A2/bAxViFA/ugu31S4/i6iUhNTzt1upwARHSEsrldDwDBWKAWAhD9MmIr6tpomh7eL0Uho1wLKM4gYdz6rvYA9fRUN7tLSYFNTKeN6P3jCRO/TZT7MVBP8UhMiTcfv9Gq77iPtNeYLJamNkNNk6ahVW9lCpGUFHjzsetgEumVr4RnI7IiU1J7Deuf2jcj/s3Pf7PQtMViJQmC6VAO/793SnSb1tiiMYzsn9JhIh4LKCZwPPduFNABKXDE5gsPLPDVAa/sKjF1VrrULy3m0lHQCOt6MfWjgJot1qY2/ZmaVo3eiKYyIEiSUMpltOMoKIGGXjp5hFop79MrxmyxErAOlFjx3dbqRq2gy0tijJqmQVObfnR+qgsLFADwn90eDx/F9ExCPneEBJQj2JUU8MQ0MSH24qEZkWqFz5XUmZj6S0CtNH2wtLZVayAAYWEMTwRFkmqVzEwoZRQlo8hJw3PMFqvFatXqTSXnGnLT4kYMzEiMUUeq2flQsF1IA7sY+fmP1wteJSJMQRKE1mAa1i/5f0UeTIfHYLpRQwNKQENJSYFNTOdPGgAAcD8G6rWY+ktAa5o0R8/AElalnDIwKSAERZEWC61SwyVcSjkVGaG2GZVEXp/E7NTYgr69BIfiBPSO93y8IASD6XoN7fbckTMBDTEl7RqggMKvi081lAb0nhPVLqqLVOpIiiIsVnr8kGwZRcooQq1SxETAjotiAUUampedhm5Pum+1D081NBAMSsMEsoYGpvnpbyUFISqmfqkDNZosWl6P/kqjsBdtthr2q5VTZFZqjDsj6Tn1BABsfesGdAMrqQDxXAoMpjMse+NLHyrp9/6vKg0+C9S75EzfeKXFalUpZFdPKRQ/yhmhfdITuY0tbAWoECyj3o1Iw3SLERr4/rtrsE3qmYD6KXmdnxozaVjWlj0nJAV0zkVw4aYYZxqKwEoaRGvMgwUsoM7ASurgwndZlc+YfiljBmfImZKjuZMKh+Rluv/c6EiVCxn9cnLFgs0ZoGeDhp5391lgQh/s3XfDWvhH54/il8pfONgD9eSIjlRJaiieBYfAticmBJQUBEmotOsE9LGrL/TVocQaev7nF7GCYjAho6QgSJJOsu5STxfmZ3l1c2ZHoW6+hp7/+UWaBuWZ819aCB7//G9fnC8Gg/GSZT1JSWXBZXuKQ6LHvnoSqSfchN14DMZvKXhPWdYDlBQK6Oo/fb9u1LV6ehf95NO64Rn4Dw3rSbF6YoKCbq9h6i6W+U1Ju11M/WuB+lw9oW7SDrYmVk8MpscqKehus9SPAjqEPAqAb5x3pJt8B70sC5avQhra+A+8dMPox1fjMCgGE9AsCxUl9a8F+st3nwIAZlx1i6fmJ8ojudJNGynxkdX19qkpOAqKwQQRy4JcSYkhY8f77+jZMbKo1iJ0G8moQEBL/veqM9UbmaGA0+Oc6CYfTkDRcZ7AFigmIFcidXsMtIuTSIGwxsnfSur3LHxrFFysGdVa9Mt3ny4YFl4sla+indiNp13qpu3Jgn8xGEwQs8yfNqnPxbSL6kCRjH65v+j/hoZzGytTZ7t4SlmWRF87O071sgMhxavFMZgeq6TA1w6+3114dCOeaIRqTUGPfNToMe48V7IxqKQ8Vje08TbDm0+s3uXiyFhDMT3QhQ8K/73rvftOKqm/LNCRsWwb+fhIBfOvokVr7xDqDodKquwa6tKsTI6PrG5AYVDsx2MClG4PgIYAywLPJvWZBSpSTAc27q+8cADb3NNNCxQaobmuvHi+WDICao+GPvGFKwsUG6GYrrdAu11AQ8AC7TKb1H0xlXVSLvk2pni3+ERWNGlwHvgIJ0YmziVhMD2OZf6xSW1mKQw5XvnQ+z4QUJFiSsglfCgpUVLn+Hn2ndu3jRk/wc3XtR/B7T33HS/39OAYDCaoWeZHJV2CbkgqqazTipnE/CvI4vgMzw5GQ/Vcu7eywx1x00wMJiRZ5ksl5SrRnSqptIBeEMeqJ221hoer9faxxHbSeiXYXoP3ck6YOS6vqaEB3T5XrxWepBOK+Hkkl6TER1XXt2LbE4PB+FRJHTRUoKRuufAHSmqH9kVmpgPnq+DYd2ekpcTz75Ik4eDO+9JKxZl3DAbjPyWV0FAPBLRfagQyNtdtOyZ+VFJbobxWs/YmBwEItK6ddrbwyDOEB8HmJyZgwSn4UI2TuiGguezq9VkTBjpz2H/eflws0fmZ0fy7bVoDxdihKbFhf+3YNmacx3kk4asy7D161qvjYDCYHsoyj5XUqRHqfhmTq1jn7PF56Mbps/YETpvWgG4g3UT/66316a5uupNBwmAwGOALm9SVgNJWK/qXt0m4j6RiIpBiuji8O+d3qKTSIY9Eg73HnJqcWD0xgUa3O+8Yvyqp2xYoLaGY7ovmPydrYXK/f7LXOR/XrnpNY7t3h8Vg/AHWzR6ipBJLOfk1TAAAuYxyoZgudBOJJgBg3OB0dGN/aW1+BlxPVt+qv3LuNBendeB4BXc7Pye5w7dR09iOzU9Mty/oDFjdDMl1nF0DT0kJDwRUZtNEScWU1E1OMedMGGi10iRJ0AA0NdkL8gEABoMR3VDIybD4Ph2ePQ3oQTkpHe62/LfiDvfBYPwno76SzlFDR/Lv7j6wxyeHxQLaeV544Dq3BHRMQpsL0eTrJqeYqFSeZh4iCYLboba+WfBco9GEypgUclLFCqgrp54AhGsLFEsnphtl1Icmp0A6fSujWED9pKQSAjooDFbIEyJzFckiJ5ozx7GZd4okGd10tHAZVaxxFND9pbWDMmNFAuqA4zFogtkg1tBfth44Y7T3ZsZgugzrtV90jW76SkaxevqJFx64TrqdHV9D95XUcdtnjR9I0zSUS8bSJMQqy7Moa+oc1DMpIebXXcUAEIMyY2iabtJa+mT37fAUkYbyBRRLJyY0cF867RBg936PZRQLqP9wWcZEQzvx0rF5JEnYfXNC2ufmbYOqumX/2YEZUfwdCAJcckHf3/4pRXctZpOu4YzrMKhtIiftqJvY8MT0MN3kP30YfLoXMorxB64sUORoAwDSU6XXa0KciGlNXRN/S1JCDEFARf51VwmyQNHBG60xaYnC/GB5ub156BlThKdvCYMJWIxtOgDA+AneLcNj4Hl9bsootkD9R6dHeojinkCknoz5Cfdj/gfuRBAE0tBTrcSp1hbRQbFoYkKZ7du2eS+jvFWF2BrtdqCBKeaIDrWqA0YTWozkHiiPJIqLpiTGSm0GNS2eTUnCYEKJ7du2ISXtJKOGjURKigkUAe0M1bUO5mdyYix3GxmgR8phcokgiLPVYtsTg+lZeCOjUnEzLKPdgtOhcoPV9ajxR8dhUB6b9wlzRylJdgEFNLBY6Y27YRgUAFDXokuNV+9p5u3gZ86VnQaBQXp2Vo96y132fgP8jaMYqCQmoEyIj+mTmRYZGY6q9zrAcZfNm37nbjc1242YCJX0LAn/ceGwfNBjcBoDPaxN4DTUfYTqyTM/IYRDZ+WEKJVnR8dgQhc5MLQ01PxT24juqlSK9LSU1JREhULhaZ/f2JhYgYxiuieJRBAwDIqMUE+dd+b5Egfk3SZwGBSD4aOkYGLAYJHr9cbSU+Wlp+CERIIAMTFRfTJTY2OjSYJ0r1c6K6MmvQZ/wgGchXeOg/PuAH2kvBl58WerW4hkt4IDGEwIoIgMc+HFi2UU3aVp2FOCayshl8tSkhMy0lPUYWF+Pl9MpwUUXeHOVda6DoMKzE+h887H8bKZaKioU2Z0eBoYTGigiISq54WMcphM5opz1RXnquEdAkRGqDPTU2madrIyEONHXPnmh7VMMZMbf5TN+0TNOp08iwBgxuh+3N30BHXfNNxoC9PjUESGISXtECVlQkoqDQ3a2rRHj5fW1TfV1jXW1Te1trabzGZfniumky58h2HQDnJHjqLK9B1h6RWnxhdNTI/FTY/ehTUqgKZpvcGotzWNpCgySt3BUzCdwa3skGvPwJ3cEX8b/0GsnpgejvumaMfWqAiLxZOFMBi/FtKfq7R3/3SBpPkpEkr6qK2c3v0TwGBCFU9l1M09YyOVnTgpTKcFlA2DerDuKEaidEn0LC4MajRbz9Q4tKzHYHosPguMYgLNAjWZre7kjgQWpeQSeMFuGQm4PR0GY8d/Hj0msOpABbkj/rJ31/FNhzAo9uIxGG9LnTzy6DHd3EyEHwYV544cVhl1dChbs2SgM1p6me0NQDEYjBeBUYqgKTjFERNgAtphGFS661JHzBjdH+WR4JAZK/7DYzDSYBkNERfeZLbKZaTTdUc21XSnjYxZ3+5wElRA5OL37fqjw32GX3gxd3vyReMffeie/PwBVZXVH634fNXqr8xmCwgqflzzRXpaqut9Plv99fsfrkS3Bw8a+PSTD40eNeLM2YrnX3z91982g+DkzttvvuVGbt63NFXVNbMu+z90WyGXL12yaNHN1ysUii+//v6V197RaLQgUJ16ZIpaaKKpzcAl4tPSUr/+6t8dPnfCxOnijaUlRXff89CGDb95fuIhjo/XwrsQQrOoqcElw9LO18ONidFh5x0UtXvIzEx3c0+SJGvOHUe3GxoaMzLSXn5h2csvLCscPrGyillgFySk9krp8F3HxESjG1+sWj79kskAgPr6hpzsPl+sWm42W3plDgRBSEx0VIdvnCBZcyE2Jrr42D8w3KTT0TS4ffGNty++8YIxl5w+I1qAFzCF95yMNrXBEeWxkUoZRWVkpHnxoj/+72uK8n3j4B4qoOcqa2UyuWTTEMIN0YQjj2rbSEGyPjDySImp/fl36ypPijciSpif048//XLr7feiLddcffl7b71ctO/PpLQBaFpJUDByzFT+3R++Wz1u7KibFt29bv1GwZ6XTp8y/ZLJNTV1g4aOQ1uefOLBu+9adOrE3pwBI0Cw8dK/3n7pX29zd6+8fPby91/fsnXH/P+7Rbzzwb1/AgCumH/jth1/AwAKC/I3/bp2145fk9PZyd4Ba4oiGUWmaNPR0qzswdz28PDwI4d3mc3mvv2Gunj6up+/zc/vhrcZLHh8YSmukvjL8cuVzHoN9x+3Q3ltG/rvXF27QD3R+jMQPCjk8qioSJqmOfUEAHzz37XFJafgPL4Q/bZ9/u8P4LuzqScA4PkXXwcAREVFgpBm9IUj1eqwXzb+gdQTAFB06OhL/3qbJMmF118TXIFRKKOMQeoOffr0Li0pys/P0+n0nTjTEMctAeXySCazVdxwnnAimnzdJAk4FVkgnUWaeO4/EDyowlQNDY2lp4QNzzf+vgUAkJPtalBz8DJs1OSPPlkFeh73LFkMAHjrneX8jas+/woAcMdtN4FuxX8yKpfLt2xeR1Hkc8//67NV//HFyYYmnYqBJkYpLCInvbymDf7DSKXY2AwurZSktbVtwODR4u0zZ0B3+MxZ2AE39KioOP/kMy/zt0REwBUQVmuIr7YeMWIIAODAwcP8jU3NLYFzsfQiMGqhieb2DjR07979V81fCAB46KF7fHGaoYnHAmo2mwQxULto2uDP7QgN0ewQpVKZndUbxsuKjoCQhiTJ6KjI+Vdd9sKzj9M0nZsX4vMgo6McXK7ARBEZRtaRVqnlgq6tUWfBM5PJhNQT43sLNDEKDmkpr2kr19kvYjQAVM/TTQRJkmdLD8BU0nWLQKjTr2/O9i3r0O1HHn+2rS0A6icwDCRTZei+jCK4ND3+FP2YRLKHQU1WKJ0ikxOpZ5CGNTsDSZKVZ49QFPXVN2v+2OyDMd8BTk1NbWJq/5SMgRUV5199+Zna8ye6+4wwDpAyEimpR9aoR/kljPdZeL7JT5IESRKHdQk9TTQFBaEURX2ycvU99z8OegAo/GexWIaNmrxn7wGCINb/+HV3nxRGCJbRgHThaZgcIkmiB2qlmP79cndsXQ8AuP7GO4J3TU5nmHfVwnOnD10wchgIXQwGg1IZrO4tKfMgMMovGsVOve8t0MPahMM6+B9WTwDAnNnTkXoOHjo+5NWTJMkr5s1+87XnBdvpUE/Bc1nBtNRe/I0KOcyjVlXXgBA1RT0tGu2x4BVa3pA3oO+nH78DAOidO6S6xq1G/UGN1Wr96IPXr18wX7A9raN19CHA1/9dCwBYeP3V/I3TpsElrRuD58LZGRnVG4Ksw0NXggXUYyiK2rYZpqH79B2q1bpbfxfs7NkHywy++c8KbotcLvvnL9hdIqtfKLvwX379PQDgvnvuCA9Xoy0URa38CC4DffixZ0FQ4bWMYrqomUhP4IF770Q3zpRATRHwyOPP/nsVXKYSYsyZt6Cq/NjFkyeUndx37PjJ1F4pqDPFG2992N4u0fEglLh6wa3//XLl6eL9p8+Um83m3JwskiQfePip4FqC7HVglGQ0VKMz83s7YRBYQD3mgftYAe1RmM2W5PS8Jx9/YMmdt466YDhqTTR20syKitBvhr15y/bhF168ffM6tFbCaDJNnXrZseOw10yQ4qkpimQUVTvh/BIfYsjY8aDHcK5MuIC9u0jPzupRb7nL3m+PfeNev19PC+/RSlAXMnrhsHzQY8AxUAymR+OFNYrT9BxYQDEYDK528hIsoBgMxiYHnpuiVM8uGsVJJAwG06mOJBRvBFNP+yixBYrBYHwTGKUYJd1zon7Pifoe8pliAcVgML6UUSVlQjLaEz5W7MJjMBhfFt4DAPgaOnIA2wkzJMECisFg/NKqWdkDZBS78BgMxl8ePeDJaEg69VhAMRiMf2VUSZlCVUaxgGIwGM+FA8soAxZQDAbjJaRXHn0oWaM9q5kIBoPxB0a3B9PzMVjYAenBm2LCFigGg+ksisgwRWRYD7RGsYBiMJhAkVEQbGABxWAwvkThuYZyMhp0pigWUAwGExCmKAjColEsoBgMxi8oekBgFAsoBoPxI4qQllEsoBgMJqADoyCAZRQLKAaDCfTAqDJQZRQLKAaD6ToUoSWjWEAxGExQyigIAHA/UAwG0z0oGA31Yhlo4HQaxRYoBoPpThRemaIBUjSKBRSDwXQziqANjGIBxWAwAYEiCGUUCygGgwkgFEElo1hAMRhMSAVGlV0oo1hAMRhMSJmioAtlFAsoBoMJXBSBLaNYQDEYTIjLKGDwh4ZiAcVgMMGBIvDyS1hAMRhMMKEIJI8eCygGgwkyFAETGMUCisFgghJFAMjo/wMkdgVVeCSM1wAAAABJRU5ErkJggg=="}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "S0’s shot is the safe phase-one shot while S1 is standing; S1 then uses the ducked clearance.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The final assignments are distinct and the phase order is set; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-0078d2212f9c", "created_at_unix_s": 1788842384.297153, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 4, "requested_group_seed": 4, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [4], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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nDEx88eYhGYmqayZmkIPbD9fetO6gX5ratXUZi6rbv/pfxYZfQvDdCaihMgAAQLgBOVAAAAAvgSE8EFj2/fAWXq/isiQbsKH81F6B/0wqdAkLlz4T6qsIPSBQIOCAPT0lo/+5SBjsPVrg8DjYkwACBQDAFSYTwzAMhcFfUoiCHZE4QKAAADgHr0eisDZ5OFoqFaZAZgoAAMBLIAIFxJEA/XD9G8NH9CO3Dx04XVLT/Ojq5X55ZptX4W4P+r/rGQaN6qVB4QxEmy4BgQKAFZymUf6fhw6c/ikwpnbtbo4A/Z0A/AUIFBANhw6cRhJFbzBauVvq369kAIEC4mD8ZVeYTIzJxKhVio4u3dWZVu02/MW5F8+hKUohl8tktE5vQMFSmMlJmyUIPwUOCBQIJYeLmhweH9bHdvuK3Mxkhs3IkRXYDptm+k6kKkKr0zMM09bRpZDLzp9z5XXpwdjjjKZp8hdCJqPlMvq3TZ8H4UUB3wGBAiJzq0O9+ovstASzprGpGQqZix8DDcOYdHoj3omIQVqd4dyL5tA0lRgv0PkrqKLnAIEC6PSuT1z8FLb+atXz8Z7H1kn7R0aEiUsfg+NOFoVC3j8nxcTgyJphkMlkotnyS2EuQwI4QKBh6kE372Irqflf+HkRCj+WJCEnd8T+LgmDXUlRMou8ZbTTjTQAQQECFZMEffIguxoP+4/cwJvSkv/DUQ/5ADss+7NegxLwqMihKwM3ZgdcwLARMeACEGhoPOjMd64lyEHydCayQhkhg9FIU2y9OsneWYRIvuIPSLtFic8h5wEA4CUgUJ8k6KMHcf0KOygmpjOZGOI1Ni7E+4hZ9EdZmdESFZKNxvC7KOON+KydKCJD2oeZkh+5CxwIP3tE+gL12oPuS5AbION9E40mWkYjhjGaGBltHikTFZoDP3ZcZPEhvsk5kTbnwLq9F9oWDhddcfWWrz5DoUAZHadJyzk/o62lPD8kFwAA0hGomxL03YPOMoZGdlYUyxE70exKc2yISKLIrEiZjAylKTlrw+5xM/fU3Y/t/i9gg66tqf70oZiMvon9htefPgQ/H0CYyEXhQS8kyFXzkdJrimIHy+Y4kE0gmhiKtaE5PLTEhsSYPPPh/8horEW5RY7WL2V5RskR/PDz6VU9NDmPVKvuW7YYBRdZhNqo7Qzyi4qC025/ohFC/cZfiySHx5vK5bvxI2N8i/5cPbNFhdz0SPcsiaWGjjc1wh0l/+lWKmDPtm8/iYnGe0B26QwIoeioSHzUqLM5ranD8PWf1e7/AA9vfcej9/f1517KTUvg3ujdx0vHDcwm3Xx3HS8ZP6j3oYJK/kNWrAp4rw2KljEmvFY93LCpA1VGJx3/p+iWeeM9epKtFglIsoLY3Qj0f1+/RG7UVFcfOFbky0vyHcewUyikfphClIkx0RRFYkNysll53YEh/pLEjOYuadZpQ7u0d3d4CEPmHt4XxLS0abkvG/Wt3O3KRnP8hZfKIDS6T+RfRR3Ir6x8eAV3u8uAdxuXK5TIqEuOQ/mVxQghrYGJiULf7j4oo6nZY4YXVjYghO56cIm/Yls+aqWcksm5UFcZFaNtbZQplEa97Z+T8GHSuKEIoXFnp7t5/lZe8MQwzL2Pv4qkiB+G8N3pQm5ozLrKaGJHx5z4GMRbWsEdZwfIlvBQxtbiyLrnUszPyf6PVSYRoshAUFCJzcWnb3osQihCISso754QT0+KRoGktZ14XGtjcBlNyWhKZzAFepq4E8fgBo+cG5xYOJjsPVpApOk+W61HnEajiQwdgrmmSwQCHTG4z4FjRaQI0f5ebgUamUXpdp1t5hCEKDgG905ITU3Ct0ghFULVtY34f9p0eZkJCfExpOS+rKLO7y+tUsqOleCg0p6+6bF8g3un7xWrlq9/9qXu1iRpCfx7SXKA+xJnCegOk8kUrcIfkI4uFQoMDh0dE6NZ/OhyuSoqMjE1+BNokdFR5Ib79tzKepO3RoNhk2wUTQYsksb7CJQrQnS2ggUQNd1ZZvIlRXW/44Fh2piz8P+wr8q9dHVto1aLB87nDslhix3QmQDom2PPiZP8Lxt0Rn4sTHLERrb1XEEFjtZlNDU4Oxsh1NLRHS+7SWuHdvHy+zeuW88JnaR3W1pan3joEYTQ4+vfdj8K9iX+5aTpXbxpYmdiyYwrTsrhNanh4gNxlDEBQYGy0ScJP1OS4yvr24L3FpCZP9ah5vgXw7B1EAH/WDIIh07mC6Gc5Ra6Uwq++EsTiXXMZ3heGnd73rKnjHr9MHKEqya2HvaRRLAX7vZamnxvMpaL4f9llfBo3SeBnjd3CTePBIQFIfwg8PTNiay+sTUxPiZAL8gfv9sngm3omx57psY8wzZhSLfy/MumtY/yF1TwVufaEhMZcfdDS4LmTaPJRKr6qDBzpUMgAgXM2Hwaqmqwv1KT48mXnVp9fWMLURgTxKtJSY4vK/egaspvmQTe98nPJ+DxvNaSEcb3eRP92dDS1YiQVU7Wv3gtTX5+k1RMczXRAAEECpiZPufanT/iRujz7ryBHFm/eh0JBhOilUa5uqUleAN5ou89x6vmTDIbnCW4VbzmUjv7fAKGtYllrs1zHn94OZcriImOOF6GSwNLazty0+Psa2/5aA34Jc/uncsdKa/vLjjzizTt480gd0eVpkAPHCsaMbgPfyI+YFcFhAzOngihu1cu2vTGh+RjTip2g/nq61evmzN5IN9Q3FKIoGLJyRJSkuNLz3ARsX+cwpXfxqllDY0ObMi5i+ReaZo6uzvl4Ddp2niTdHT2S7x5jxRL6D0WKJHm6ZLqM9WNDyy4KJBXBQiC9xRXKfuVqk7voChEZw3RHtrFjeKDAF/fZKOLBksalFuM9PSqZ4JRfUmZI2IOLbtYy0fIvJCnj7IpDXrwCZwq9RH+OJ0EmzBOD9QQPjM1oVOr9/RRgDhY9gtCP34ov+IGw1fkgOr0DiyQzMGq+S+kV17cNfpq9Fdo+jOlpyTUN7aQ2ytWLSfbWBqM3UX1fsKB0Th7pibH/+9wUUZipInB+D6q7Z2ZwiD061//9Hhm3/TYhjYdmbyaMCRt3p13+fjSVvEm20IMxumBFeh5c5e89uQiVYSiX++UD7/bNbivu4u6ANGwdsam1KQb7jR/FbPuEnJDXnWi7akLVEp5+9a3VU4i0GvGp5E1FAnRSnKktkX72S6rdeteYz9sJ6+lDOLWF6m94rnroBDV0ITD4YKq1nNyfdi2k82D8gtgbb5ZLvGq1eo2vPsm8gf23jS3EAOCEIFSCEUo5f16e9aCBBAR61ev23O8cs7kQdwRcw8CduhKwsAPVl656ftfuROi2ZLG3N7p3+88cU7fXhmpiYUlFTKaGpsXdftdi8matPWv2pbBPf3E4ysef4J/ZNMbH3JpUDx+5xPcOQx+NtY8n8brt5CemtDY5CBZ6Ql26VzLAUvSAvPks88jP8H3JjEmeDNks/BKBUzfSxMyXLW2J25iQD7P6akJ9Q0teama/JJqhkEHTteQc0YPSEUIHTtV0ic5srGp9VhhNWn9V1aNbTt8YHaM0vTsUyu558zNwcOXSLXqlef+lZGGI7jkOLWxq72hTffmk+sKKpoZBo0blmNzbX7JPPL5odg0K4d2N/z0N2ql3YeIdejdD+JlSH6BX/ROOnzj/CbEm/7DKw9CSzhJg8NPiy9INNSlNzr8VbliOrtcmsGL+YwmxmA0NTe3arU6VYSSoqgYTVRyUjyZJ+no6uI/8MhxXMJBZiqOF9XYzJCUVbdk9opxGHdyaVDfYRhmShpZhdjzyfPuvIEfEQeipOfupf73JuL2ibF0+AZCKVAyEU9+e6CSSeDEydsnsEFcfCzu7Fl4pn7XP24VcnLhJ/eBOzD7x4H/uZwXBiqtJEIhml3/XN9gXsNTUtmckx4nl3cHd0q5LDEpQWcwGow4DlLK6R/+OD5haJZWq1PIaFomi1CrFTKckaMoKjMpqrgaX6rNJ57Ev8hPUBQVGeHu779NPoESnjSt4k3LOks8epB+Q49Q4uVIPNi1eIDn/HvTf/+9idxkm66amMsvv9T1Q7p0RpVK1tmKJbV1ovnBNuSmaYwGu+7CFKpmx/69kuLKymsG9UvvaO+ob2hJSuiecVIpZSolnvNhK0qZCUOzLBeHP+rRKgV5Hgw7tS7Yz71cRhsRpdV5WYtCqjWfX7cuIPGmxZtQ9y5cgZKOVVBIL3BmnD88MTYqMS6y8EyDTEb9nd/zhDiDmM4ubWcXOnzZFpu7EtRMoaUNkoymzurvIHOanBRHxsMMgxLjY+obW7gF7FqcBDDDLuEx65EbPzc0tSbEa7hzzrNLgAYoDcpnz4mTalbx3KyRw9Qnv/nYObmOr9PvJe6u402TCXciD0LHLC+4R7pV9PgX2KOzz5vrbs8CIOSkJWpGDczokxY/uE+vAb2TO7p6jphmzLluxpzrHH4Id834lvQzxtvqWQ6WV9RyJ+w+hqfd8XDe3NMM8YVoQ5WlzxOZ4idnNjS2sv3qMB2tLdw/8hA1pVdT+rw0p8/pR2zK5vlkJkVy2yVwCVlniym5f368tq2/7iH/SLxJSt/xakvhqTMc8DgCJas5k1NSArHlEeAvJg+KzUo1L/eLj8XSuWKGCSF9eSs7UnbJtMuuPegoRzN1/Aj+l9xSbqKby6cMtr4Xj1SKK3AX5MQE3ImZg5yfkhzfnURlz8xJj+NCu5qm7k3cKArpDaaCimYyxRTBS60Ggu6yeevw02Y3hPTURIcJ2UAEmzafNbzOkp0YEmC8GW54mQOtrQ5qgxzAIyYPwvtw2DByQOb+k2cyND079HnmeofHP8h9fn7hUpuDnA35imFXCWFGDcmua2ipb2iJVdNWg/3EOP5nn0Jo5JDs+oYWbqnoqjVPk7tWPbKCrMJ/8/WXURBxZk+HbdxeXr82QJdhP58O/ZAEhU/lnDAR73d2/vQlY9FPVJRaJTMvVaxvxR16zr/oSjftmZUa575D68rzrc7raYmZiWHa2YSATC43x5LdO6Fi+IZJSoips47Udh+ruHyKbW6RQlzatCU1VnnbnYvffuNlvklDiKPNaNAzawN4YTY7spGNFGE+XYBAPbxQ+OXbj7nbmqgIhkFarfYEu28Et3Lxw/ffveGmW1w/z6lvnx2dHcE3VnlT98TLtv3tDEIN8aMH9E52+PD301e5ePIPcp+/8uTispqWhpauSIsN53bb0OxRmxAtOzmyua3LZrBvP/ikKGQZyGNWvGwuA1BHaR6/9QIURGLV5lKESI3VutW7/Fp15NY4nQ14YaAuWECgQkEhp/ecwImR0f17dXTqapu1JoYpq8Ef4yFnZcnwLjNUbZ3TyQ2OM2mzE3Os2qRXcVMcDGooPI4rXXwoQzt4uppBSKNWonbU0akdlptYUFKVk2olGo3cFCXH1j5ZhnOgchmVpFGZOzT36k592kOG/FXNVg0xO9tbOZna4He3kv04O7mq/8aO1PjIpasDNUK396bBaMTrC6AfklQFCnt7BAgKUVdMO5vsflNd24SQViaTjxyMd1IzzxVwU9TOqSvPT4iJdPEal00a9M1vx3y5zvzZn+b955r6lk6GiaIoShOpHNEv5fjxE1x1sEqJ8wN6I/4qLz02IgIvk49SmJ1t7GonfUZwnUCKgzYcvZMjS2rd3XTehVufXjzPzSfhtuRkExHmH/GzrwS8+Ma2fpO9ArkseO1RAN+BCFRg4Ip08/69NE01t7QlOi8Gss1dsrBV6vaHbF+Fzwc7Crq/uKbna9TqzTkBuYxmGKYgP1+tMndgslntjhMIlfUIoXY9Rda8E+KSUGFxRU1tQ3lDh1xGlVXUDcyO48JVhkEKOXWy2HHH7gE55q7ernEgVmW2uRRJV+rwIUGQpm1+07KuQKrz6fdIuggUBCpQUpLjy2tb4+M0jU2tbC26xaGMlTRnz7pw8w8/2T6Y6cmfjJ00PaRCZ94nPR1plQpZe6fO/uN/5HgR16KpvAo7tLC4gtxFNgTGS7MZXJNPJtnzy5trlJn8ZxiTgNMXNE0lRCtJxPpXA04U+ChWnOVQZp/BV4R55dH57kaqPmBT8Gc0mUiZF05vSlSdYYKvEShMxAcC7jNFJqZJxPjw8gfsz5w960L+lw58GgBS4xOrGrGBKnQR8fLoDNSglMt2H6/kd2bC42t2pv7YqRIylUy2o2AYRi6ji9mdL4cOyFLIaDWtL65uq5BnEGny5prZqs+IiHY96peb0VRXNyvGdgc378T6yqPzUVCwmUw3r7OE/KaEgCF86CFBpY5dYF5V00iaIb38wpoeH7h9537uNu7QTiF1TDJieugY0tDkaz8OlSoiJy29trqm3WRo7GjLUOODV0wbihsymUwmE+7MRM78adcJ0jEED6CVCpqiVGq1Qk4nJsThdYfswLVLa+ibEdsXtXLS5Ib/GAaVV9WXV9ZnpCbZXAZpOVpQ0Zwua9UbmNoIqwCWgxPr1g3d/fSCHG+SQTrZZQiQEiDQYOMwcTl71oU2saQ923ce4H/JlgcybKMy/CX32Tx4vOzgibLBeXipGKG6wVapBb4pNP8f8xYUeGFndKQ5XqaQTEbJZDJLDgFfH+kYQlOUeZIEIXWEnL+WnOQA2zq09s3uuD7NJA4tr6rXarU2SQCGTQIUVTRz7e+c8dGaO2xWf5DVdIHzJtl0hKJgH2Ap441AYSLed12681i+MdlFz2QAaD7CnzLm83tB8/l5DlYiEQ7/Y05E2mO6xroDvHPo276h33Ve0k8hCjuUqrEuuiI97hqtm4Z0l3Adr+K+tEkCcDcqGvDsPMNgdZIkwMRhmTRN5aTGRERERCm09uN6L1bTeSpWG2+SuncSX3v0PIAYgQhUiLrkthG26JJMNrj7gfyjoHkILwLlY+hsLdFrfLGn+fxbvqQ/tdr0wgqe2nslxZGeI2flpv5TWJWTHlff0IJXx1s/AmcA2D8SJhNu3WQ0meTs0vctbBJg55Gyc/unyHGEK09PwCkDfuVpbk56eWV9ux4/JTvXj7tG2SdMfV+mzHerM29C3XtYAQIVgi7xZ4/BK52JJM3rT7h7vei/+sbPzjZ69IM9uUfxHGp7haRsfvexijmTzeuUGAaNHJxV39hKURS7QbH5Six/IxBeK8A2Z1bKacayET1JAkwYmtXZpe2dYiXN7tdkUEZqIpnrd5Yt9ct2C5xbSS9HUoSEO0+xf94g4AxD/CDQMJmI97MucU6QG4Sz5rAa8YmjYzVx6NGOpJExDQ4bGl02aRD/7wBCVEKcpqF7OzbLAgE+5Bj74+H2pNx9rGJ4XiI/VWqfJ+Xqpcqr6smudly21BMod8RqMuEENGnBCeIMZyAC9b8u7cfjJpPJKn3ZQ7wSPHtO66fYflpPf3qDfRC6PeWRadVruP/WGWOuqltGviQn0MhksvSTZRzZs1dSnFUekI3UkKVzHddr2UUileOS8weWldcoFeZUKZck5edJSb0UgdtniV815RtW7xdFU7RwW+YLhXukXkUPAnXqSl90iYfj7HicCzBpdvc0l4Qg5Hz64sgVP3aMzpb/VWpw6FDC+RHHiDS3p1i10jAhenvM3TOsT3ZmT9xlmf0euc51pCGxC4eSpsvkqciMNkJo7tSzdbhUitHqjaQ96M+7T5K7OKsyDN52CSFUXNk85KwspYJOSvT3nppk3A6EPV5GoOKdiA+ELkm2zjLng//fDWN2PxyFiBU/4kntNRdFznjLaVnTls5Rf2jNnZLZKgCrq910xKrrB4eNPZOSE2pq6rmgm+1cp6lvZAfyTixkJWKKMllel6YpsrFSZIScZELmTj3baMI16o2W/vBaLV4ZJZcrzhmUjWeizC/MXTmID/AbEh/CB0WX9sNxdz6igshyZsX1IPpP2ifhynlKN0N1aHrNU/y76E9veNPJxBHX7tNcIYrX3lAUbyc6iqLM3T8bWuLUMmf2TEnG9fbOlvNjqbL3ymlUXWc9SGfPb2vrcLStCOzKDfgNSQk0QINxbk7c0p7Rx8sUhDoJZU0mLvy0GcWTYXuFMQHv1skoN3eOdv1UnPXmTLZt90maAdv0y7Dp/mn/PMlJFnta10XxnoI9yKCqOnP7FTw/nl8zOCuOOzs3ObLQQW8nh2+BJ+8rjN8BPwo0JBPxvuvSwWwPrkM00eyaOxKoeDIYF403A0cq253eBnY1joM/PKT7p2N7JsaRPKb5GRwb1HwfR3JSHMrHNacUooorGnPS4/f9U+2inZU1MMYHpBuBBkCX7G7peAU5xrwgkqJoS0NG/6XKpK9Og4GRy1Gv2AiTDod7sohIK/WxQ/j21tZoNY5ncXtQGW6IR1HojFaH1GrXE1Cu3wh+xMqf8x85OHv/sdKc9HirdlYev18OXjwcivYAcQs0ENGl2Zjd9eqkHAUFDOl7k8PEMB2d2uJOrk6zhb9SiFaoO7U4AxrNtqnnd6rnwkzOg/YpVOtbruaarO+kRgzObiBTVf5+E2EeyuuPsMTwXqB+nIgPjC7xdI/RZK5o4c2PB+GXP4zUSfi7s3vxz/BoXM1eVos3ebfQSiFKbzQRb8poSk5TpH8djhyRpTen2Z6Od0yyhz/etymZsswymQfy5hl/ICjUsR/nVW/+GA4/b7lw/i55pEsn3YmQZV7cvBkX7tobbMLOnjYcarPaqIP4lGDTMtmeOZMHO3rDKGdLRa1m6u0egyg0cnBvtqEqECTqwiPw9I9AKwqOpuedHRJdWnWoNTdxcDHREBzCXZ09+FRpToDao9ObdHptTmoMo+vgip3YRKrjt5Q/cnddFUFRvJpTIMDUhZk9/RCBVhQctdyM9stI3JkuEcLjcctGr+YAk20/wREqe4I3feJIR+IQNe6fVFxlFSrmpDoqvnU+cie3zs62rYui8OLRnnczBXykLvzs6c8hfKqqrc6vumQYPDVB6olIlIG3exUcgrPnHZeOffM/Vp3WfGR3xXRyY1z6Nu6gq152vqVQuVH/mdpW/oJJEovyRu7d9VI2fznt09yfbHzRj1cL2FMXlvb0g0D7DBjOu+2rMRk8A0C+xM40B5jdKwAFguCk6Xc4adoc5Ds0aFlUTqlyucJmpt7h7wS/gBQIiT1XhccMkscC3b/Zm/Yq9rpkm4GZA0urnsFI+EjZnmQxkkN7hpZDbYlDI+v5M/VOflWCVmgBmAlne/YgUH/pkoUxGtnxONntgO1/icSElL3Jx7U9gxaEOqRXLG79edl5fUxam3J9q8WeWh1v1b2laJ/w+75Te492l8E/sMDjdBPApy687WklUD/qkmGXkFsmfMwrfWRWFUUiihHCRZ0Cx8QwNrNMNuX6+LdZFcXdtinaJ6iUchlNk20yEUIvvGe7CzQo1X3qwt6e+FfOU286GI+bTBSN90ekuJV7qHuFiWgJR2/ummTe212AQah9rT7DoLKaVvIH2mBkbCbuuaJ9/q+iXCbLSIkrrbTqn88HlOomYE+3cqBOZ3vYAkzzeJydHJdKexope3P0OaPsD/51cB8Sf60+WTFBfEpWPTms22cQykpNKKtscP9t5pQKwakze64Ks2G7U4E6KFZndx43Gk2W9CU+KN2NYCRoz9YmvVKpOH+8A3XyrVpSVPj1sGNuPufuiunjURoSsE+dLnliGKVCHhcT1djSHqSLkxYQeNogt2tQRBmMRhlNk4oiokmZTFwTPuGuztYm8zZBCKF+fXOys5zKrqSoEEkO+yooG3Izk/YfB4F6DNjTHjm3gQ8XU8rZfm6SjDAlr04bJk8cLbN057OXJpeHQQi5H34Sdk2qHL9DQEGom5DvVxOtilAqtLruPzNAj4A9HSJntzwLG1uGkz2nTRnnOtiUaB7GFaRJNoWonPTEU8V4g0/AHcCeIusHGnikrM7YGM2okUPcH6F7Gn6KGfOGSClJMfllNUZ2rsnLpwgbwJ4ukHxyM+zseaDMkJwQWVJUWFJUyGvB4ZTqJqc1Pb7XPAkOyw9ERtPZaQnePomUf39sAHu6JtwiUGn+6h8oMzgMNl2M0TlvitKD/iAzJaG4vJ7f5wmwAezZI2ElUEaq3mQl0D0j5PdgU2JTSQS5jE6Ki66FVqFOAHu6Q/gIlJFwsMltWOKpN8M2/CTkZiV7I9AwmHwDe7pJOAhU9Oo8UGbwombTnXhz/I40Hx3KBaFPXtMHr0BXK574rAIhNOuyyz/69MN4dVR9XS0dxe/6EUps5n8iVcpIlbKjS9fjA+s7qPCZRgJ7uo+0BcpIMrMZzEG6O8RHRiOEXvwOb8jOMEyfvL5KueyDDW8hhBoREo49HZKbmfx3frnb6gzHvsjhvFIznAXKiD3YJNMb7owXQ+JNPrl5uQyiiwryKYra+evW5+4dc1H/gbjIw2R4/EtXego5ifHRCrlMb7DqgNeTOqWpVLCnF0hPoGL1pv0CoR7VGXJvEjKzsnU6/fVjdLohyQihgyf0R06Za9S/PsTtFC9QaIrqk5H0T0m1m1HngWPFktQn2NM7pCdQsWY2+Q2uhC9NPjedpzcYTX8eOcMdMRhN/zkqxIWSDn+yaclx+aU1eJeEMBuwc4A9vUYyAmXEX7MpMm9y7DpYSm7oDCbeHi2C48CxYofHaZpKSYo5VtoanjWhYE9fkIxARTWN7vYsrpC9yUGab1LdGwKKbKnOzKljZ5JbDKpvbt/yx9G9R4vcmZqXAGDPMBcoI7rMJoYSvTT5UOzbYH4n2K1Vv/9biON3e2ZOHWv1NYUS46Kunz32+tn4uNFoyi+t+W3vqcMnyxzOMokdsGeYC5QRUc1m8JcJBRXzltTUf/4WduBGOVGnI2Qyun+f1P59UgvLap/baLt7ktgBe4azQBnRBJs9EUJv+msZkonB6tx8VNjqRB6o04YodYRSIdJPimPAnv5CdL8WAlLnwTJDsefTQQIJNv24iHNFYqkoVsSPGJzj3QPVKqUqQoGkAtgzbAUaenseZEfoxWILNgO9/l1EXUVqq7v7KCen8LbxdE6EQh6lxlvSS1KdsNYoHATKiDTYFI43A906REQOdShTFz6Vy2V3zjsXiRywZ3gKlBFMsIn/62a4KRxpBhMxOtSd4JS/s7xIAXuGoUAZIQSb7i+sFL43fe+9JHmH+jLSFyxgz/AUqCAym+IapPcIONT3kb64AHuGm0CZkGU2LQuEJCbN4COZOFTsgD3DTaBMkINNK8Q/SBdIEAoOFQJgz3ATKBP8aXQ3kYA3gbAC7BluAmWCGmyGtzSDEITCED6EgD3DTaD+tCcEmyF3qITtKfztkMCeYSVQRgjBprTjzSAjYXsKX6Fgz7ASKBOgmk03AWn6HanbU7j+dKZOWKkpSYEy/gw2PSk/Am8GDunbU6iAPcN2CO+PETosrwTCGLBn+AjUs8ATMpsA4BqwZ/gIlAmONGGQDoQJYM8wESjjhTc9nRECbwLBQDAzSGDPcM+Bug42YUYIECCUMAwK9gwHgTJu1h65bctuoAJJLIvipYSzneWDDNhT8gJl3A82oXITANwH7Cl5gTIuVlUyjLtN3Tkg2AQAAthTwgJl3Mtsuvt04E0AcEedsNZICgL1S7AJ3gQAe8Ce0hTowTK9Xe1RtzQh2AQA3wF7Skqg9tLke9OjkBMG6QDgGrCnRATq0Jsc4E0A8DtgT3EL1OEI3VNdEiDYBELFR2tuF+MPH+wpVoG6yGxCBZLEgFp6YQL2FJlA/ZjZFHK8eaawCAmDzNw+AvmW57d0+e216rpC/v3af+NLn3rbX8/Z1NLu6UPuuvHy4NhTr9MhYaBQKlHYID9YpnfWwgMymwAQZCD2FBfy4qJCa2lCAyTAG3bUqeAH5yNgT1EO4T3u5IHQ7t27yY3cgf0DcFUAEF64ViesNZJCHSgnTT6FJ06BQwHAF8CeUhaoQ2/yKTxxCkJRAPAOsKcEBdqjNO2BUBQAPAXsKSmBeuFNPuBQAHAfsKdEBOqjN/nAcB4A3AHsKQ1oP9rTRqOAKIDyo+AD9pQMgdoTCUJRMTp0UpLfViUBzgB7SonA7soJWVExmhQ0GjigVF5iBHxbYwhFJR+QQhLATcCe0oMOzstAVlSM7KhT9ShHsKebgD0lScAjUBGFovv3bO/xnJFjp9kfPLx/h0FvcHiXwPlu04eZGemuz3nvg09fe30DCUg1cQk3PfjYmGkzO9vb//XC6+9/8CkSJ/OumHfJZZe4Pqe+rn7pkqXkdlxc3FXXXHX+pIkqVcS+vfvffvvdwoIicSU9s7Mzf/rxyx5PGzRkHLkhl8sXL7rjrjtv0cREf/HFN6ueeLa2ti7wlykygidQ4WdFs7MzvXjUtVfPS09LLS09g0RIelpqj991XFwsuUENmvbeZxsRQs0tLZr4xOeeWfXMmsdSswYhERKtiU5NTXV9Dk2bx2fzrpi38PaFCKHOzi6jwTBp8sRJkyfu23fg3rsXi8We+KMuk+X0znbz5OTkpJLCwwihjo7Orq6uBTddt+Cm65Y88Mhbb78f4MsUGVRMQq+QvHBINOpRP9DaChwyJ6e7us7bFs5f88QjCKHS0jMeRaDC6QfK55svP5gwfsyCWxd9/8NWm7tUKlVZ4WGDwZidN1SvxztXL3/wvgeW3NXe3pHT75wenzmE/UBHDc7t8eQp06Y8tPyhA/sPPLIcv5t8EhISPv78Y6PROHPGJe3tuB8oRVFbf/leo9G89OK6Lz7/ysXT6jrbr589Njj29LQfqCY6urrylF5viE3obX9vY11RRITyggvn7dy1ByEkk8nqawqUSkVWztn19T10+1WEUz/QIOVApZcV1Wii80/uXfPEIx0dnSgMWP34MoTQwtvvI/ZECD3z3CtarTYqKhJJmiUPLEEIrXx0JbEn6Zl75bxr8E/j1gUuHqjrbBdI7OkpA/r3i4hQHjx4hNgTIWQ0Gh948FGE0IMP3BvqqxMWIRMocah4NVp4an9sTMzhI8d69x2OwoCbb7oWIfTDll/4B//vWjywXbZ0EZIuaelpHR0d+/ft5x9sbm5h/4hqpGdPhNDJU6dvuvnumxdaubKoqATnfNJ7yHuEG8HOgYorK+qC6uraoSMnmkwmFDbo7AaJhw4fxXtm3H/32ufXIYmycAH+I2FDXHwcQoiLSaVkT8IXX35rc2TZQ/chhL786rsQXZFACb1ARTFBb8+QcyagsCE6Ogoh1NDYZHPcYBnOhxuvrX8Z1zB8+70k7cknRqPRxETv3rk1KSnx5KnTm7//KdRXJCwEIVBRh6LhQGSkGiHU1WlbV29iwigA57hpwfy8vFytVvvquvXSticeuRccUqtxLXBBYfG0GXNCfTmCI5Q5UHvEmxKVNjSFf08YxIT6QkLPwlsX3H7HQpPJNHniDMnbEyHUu8/QSE3G3Cvm5/bpXV567O67HCQ0whlhCVTsM0tSpZONPZV25SlErOHDSy8/d8vCBQzDjB83ORzsiRBqY/O8P23dronDNaTPrV0d6isSFgL9AIBDBUVzC550jrGbdJbJBZQCckZXW3NXW7Pvz/PTz9+PHTemq6vLxp66znap2pOPyWQ6ePAIrpmdHEbZf7EKFEJRAaLRRNscSUqMRwht+mYzEolGvTbp5i2bY2Nj8vMLpky6gGG6Uxk9qlOM9hw44Kyn1zyemJhgc5zMIkZF4RlFQOgCJUAoKhB279mLl+Ww5Tsc//nmY4TQ3Ytwjb1Y8EKjX3/3tVwu3/rTLzdcZ1U5L0l7IoTWPPXofYtun3/9VTbHJ55/HkKIq64HRCBQCEUFwt33YUse3Psbd6RXr6TMjHQjCxIb7geki+9frI5Unzh+YtXKJz2154miKiRCrrwK/51Y89SjUZHdy8zWv/qsQiGvrq5pavJDPkQyiCCHRYAip9BSVlb+yWebrr16XmXp8VP/nI6JicnKxG2ccvuPRGKGOFTf5XQ97syLZuJR7aCBu//83f7ekUNHSsyeZOHm/UsfffH5p2qrTxcWlTQ0NPbv31cTjRM4/QeNDvXVCQsRRKAcMEEfWu67/+Grr79VLpcNHjQgKzO9vr4hb8AoKbUCMBkMNjEp15DJU8RrT8Kbb703aMi49o6O3D69R40cromOfvHl1zVx2TqdPtSXJixC1o3JF7yut/eoNVFAEWY3psARwm5MQ3onevcMqmhzHz9CU0u7FyP3oLUm8rQbU+BQQDcmgQOhKBAE7JOkEs57AtIfwtsAE/RAMKebwJ6ApAQKoSggKCD2DEPELVAChKJAyAF7hidSECg4FAgCLjqpgD3DFokIFIbzgDP+LqkP6A8H7BnOSEegBBjOA/b8XVJP/vnyw6EcHQR7hjlSEyiEooALfNcoH7AnIEGBEiAUBQIakII9AWlGoBxQbw+4xn2NtrRr+V+CPQHpC5QAoSjg34AU7AmIrxtTuO36CQQfzqEuls+DPQHRNxPxGnAo4D7PLhjP3f67sEqk3ZGBgBJeAiWARgH3KTxx6qtnb/67sArsCdgTjgIFhwJuAgl0wDVhKlAChKKAC8CeQI+EtUDBoYBDQJ2Am4S7QAkQigIcYE/AfaRfB+oO8JkB4DcB8AIQqBlwKAC/A4CnwBDeFhjOhydgT8ALIAK1BT5IYQi86YB3QATqFAhFwwSwJ+A1EIE6BT5X4QC8y4AvQATaMxCKShWwJ+AjEIH2DHzMJAm8rYDvQATqARCKSgawJ+AXIAL1APjUSQN4HwF/ARGoN0AoKl7AnoAfgQjUG+BDKFLgjQP8C0SgPgGhqIgAewJ+ByJQn4DPpFiAdwoIBBCB+gcIRYUM2BMIEBCB+gf4iAoWeGuAwAEC9RvwQRUg8KYAAQWG8P4HhvMCAewJoADz/07BIDz/fzZUAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant": "CAM C indicates S0 has the clear lane to T0 and S1 has the clear lane to T2; these lanes do not cross the standing teammate. Fire S0 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "Assign S1 to the distinct clear target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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nDEx88eYhGYmqayZmkIPbD9fetO6gX5ratXUZi6rbv/pfxYZfQvDdCaihMgAAQLgBOVAAAAAvgSE8EFj2/fAWXq/isiQbsKH81F6B/0wqdAkLlz4T6qsIPSBQIOCAPT0lo/+5SBjsPVrg8DjYkwACBQDAFSYTwzAMhcFfUoiCHZE4QKAAADgHr0eisDZ5OFoqFaZAZgoAAMBLIAIFxJEA/XD9G8NH9CO3Dx04XVLT/Ojq5X55ZptX4W4P+r/rGQaN6qVB4QxEmy4BgQKAFZymUf6fhw6c/ikwpnbtbo4A/Z0A/AUIFBANhw6cRhJFbzBauVvq369kAIEC4mD8ZVeYTIzJxKhVio4u3dWZVu02/MW5F8+hKUohl8tktE5vQMFSmMlJmyUIPwUOCBQIJYeLmhweH9bHdvuK3Mxkhs3IkRXYDptm+k6kKkKr0zMM09bRpZDLzp9z5XXpwdjjjKZp8hdCJqPlMvq3TZ8H4UUB3wGBAiJzq0O9+ovstASzprGpGQqZix8DDcOYdHoj3omIQVqd4dyL5tA0lRgv0PkrqKLnAIEC6PSuT1z8FLb+atXz8Z7H1kn7R0aEiUsfg+NOFoVC3j8nxcTgyJphkMlkotnyS2EuQwI4QKBh6kE372Irqflf+HkRCj+WJCEnd8T+LgmDXUlRMou8ZbTTjTQAQQECFZMEffIguxoP+4/cwJvSkv/DUQ/5ADss+7NegxLwqMihKwM3ZgdcwLARMeACEGhoPOjMd64lyEHydCayQhkhg9FIU2y9OsneWYRIvuIPSLtFic8h5wEA4CUgUJ8k6KMHcf0KOygmpjOZGOI1Ni7E+4hZ9EdZmdESFZKNxvC7KOON+KydKCJD2oeZkh+5CxwIP3tE+gL12oPuS5AbION9E40mWkYjhjGaGBltHikTFZoDP3ZcZPEhvsk5kTbnwLq9F9oWDhddcfWWrz5DoUAZHadJyzk/o62lPD8kFwAA0hGomxL03YPOMoZGdlYUyxE70exKc2yISKLIrEiZjAylKTlrw+5xM/fU3Y/t/i9gg66tqf70oZiMvon9htefPgQ/H0CYyEXhQS8kyFXzkdJrimIHy+Y4kE0gmhiKtaE5PLTEhsSYPPPh/8horEW5RY7WL2V5RskR/PDz6VU9NDmPVKvuW7YYBRdZhNqo7Qzyi4qC025/ohFC/cZfiySHx5vK5bvxI2N8i/5cPbNFhdz0SPcsiaWGjjc1wh0l/+lWKmDPtm8/iYnGe0B26QwIoeioSHzUqLM5ranD8PWf1e7/AA9vfcej9/f1517KTUvg3ujdx0vHDcwm3Xx3HS8ZP6j3oYJK/kNWrAp4rw2KljEmvFY93LCpA1VGJx3/p+iWeeM9epKtFglIsoLY3Qj0f1+/RG7UVFcfOFbky0vyHcewUyikfphClIkx0RRFYkNysll53YEh/pLEjOYuadZpQ7u0d3d4CEPmHt4XxLS0abkvG/Wt3O3KRnP8hZfKIDS6T+RfRR3Ir6x8eAV3u8uAdxuXK5TIqEuOQ/mVxQghrYGJiULf7j4oo6nZY4YXVjYghO56cIm/Yls+aqWcksm5UFcZFaNtbZQplEa97Z+T8GHSuKEIoXFnp7t5/lZe8MQwzL2Pv4qkiB+G8N3pQm5ozLrKaGJHx5z4GMRbWsEdZwfIlvBQxtbiyLrnUszPyf6PVSYRoshAUFCJzcWnb3osQihCISso754QT0+KRoGktZ14XGtjcBlNyWhKZzAFepq4E8fgBo+cG5xYOJjsPVpApOk+W61HnEajiQwdgrmmSwQCHTG4z4FjRaQI0f5ebgUamUXpdp1t5hCEKDgG905ITU3Ct0ghFULVtY34f9p0eZkJCfExpOS+rKLO7y+tUsqOleCg0p6+6bF8g3un7xWrlq9/9qXu1iRpCfx7SXKA+xJnCegOk8kUrcIfkI4uFQoMDh0dE6NZ/OhyuSoqMjE1+BNokdFR5Ib79tzKepO3RoNhk2wUTQYsksb7CJQrQnS2ggUQNd1ZZvIlRXW/44Fh2piz8P+wr8q9dHVto1aLB87nDslhix3QmQDom2PPiZP8Lxt0Rn4sTHLERrb1XEEFjtZlNDU4Oxsh1NLRHS+7SWuHdvHy+zeuW88JnaR3W1pan3joEYTQ4+vfdj8K9iX+5aTpXbxpYmdiyYwrTsrhNanh4gNxlDEBQYGy0ScJP1OS4yvr24L3FpCZP9ah5vgXw7B1EAH/WDIIh07mC6Gc5Ra6Uwq++EsTiXXMZ3heGnd73rKnjHr9MHKEqya2HvaRRLAX7vZamnxvMpaL4f9llfBo3SeBnjd3CTePBIQFIfwg8PTNiay+sTUxPiZAL8gfv9sngm3omx57psY8wzZhSLfy/MumtY/yF1TwVufaEhMZcfdDS4LmTaPJRKr6qDBzpUMgAgXM2Hwaqmqwv1KT48mXnVp9fWMLURgTxKtJSY4vK/egaspvmQTe98nPJ+DxvNaSEcb3eRP92dDS1YiQVU7Wv3gtTX5+k1RMczXRAAEECpiZPufanT/iRujz7ryBHFm/eh0JBhOilUa5uqUleAN5ou89x6vmTDIbnCW4VbzmUjv7fAKGtYllrs1zHn94OZcriImOOF6GSwNLazty0+Psa2/5aA34Jc/uncsdKa/vLjjzizTt480gd0eVpkAPHCsaMbgPfyI+YFcFhAzOngihu1cu2vTGh+RjTip2g/nq61evmzN5IN9Q3FKIoGLJyRJSkuNLz3ARsX+cwpXfxqllDY0ObMi5i+ReaZo6uzvl4Ddp2niTdHT2S7x5jxRL6D0WKJHm6ZLqM9WNDyy4KJBXBQiC9xRXKfuVqk7voChEZw3RHtrFjeKDAF/fZKOLBksalFuM9PSqZ4JRfUmZI2IOLbtYy0fIvJCnj7IpDXrwCZwq9RH+OJ0EmzBOD9QQPjM1oVOr9/RRgDhY9gtCP34ov+IGw1fkgOr0DiyQzMGq+S+kV17cNfpq9Fdo+jOlpyTUN7aQ2ytWLSfbWBqM3UX1fsKB0Th7pibH/+9wUUZipInB+D6q7Z2ZwiD061//9Hhm3/TYhjYdmbyaMCRt3p13+fjSVvEm20IMxumBFeh5c5e89uQiVYSiX++UD7/bNbivu4u6ANGwdsam1KQb7jR/FbPuEnJDXnWi7akLVEp5+9a3VU4i0GvGp5E1FAnRSnKktkX72S6rdeteYz9sJ6+lDOLWF6m94rnroBDV0ITD4YKq1nNyfdi2k82D8gtgbb5ZLvGq1eo2vPsm8gf23jS3EAOCEIFSCEUo5f16e9aCBBAR61ev23O8cs7kQdwRcw8CduhKwsAPVl656ftfuROi2ZLG3N7p3+88cU7fXhmpiYUlFTKaGpsXdftdi8matPWv2pbBPf3E4ysef4J/ZNMbH3JpUDx+5xPcOQx+NtY8n8brt5CemtDY5CBZ6Ql26VzLAUvSAvPks88jP8H3JjEmeDNks/BKBUzfSxMyXLW2J25iQD7P6akJ9Q0teama/JJqhkEHTteQc0YPSEUIHTtV0ic5srGp9VhhNWn9V1aNbTt8YHaM0vTsUyu558zNwcOXSLXqlef+lZGGI7jkOLWxq72hTffmk+sKKpoZBo0blmNzbX7JPPL5odg0K4d2N/z0N2ql3YeIdejdD+JlSH6BX/ROOnzj/CbEm/7DKw9CSzhJg8NPiy9INNSlNzr8VbliOrtcmsGL+YwmxmA0NTe3arU6VYSSoqgYTVRyUjyZJ+no6uI/8MhxXMJBZiqOF9XYzJCUVbdk9opxGHdyaVDfYRhmShpZhdjzyfPuvIEfEQeipOfupf73JuL2ibF0+AZCKVAyEU9+e6CSSeDEydsnsEFcfCzu7Fl4pn7XP24VcnLhJ/eBOzD7x4H/uZwXBiqtJEIhml3/XN9gXsNTUtmckx4nl3cHd0q5LDEpQWcwGow4DlLK6R/+OD5haJZWq1PIaFomi1CrFTKckaMoKjMpqrgaX6rNJ57Ev8hPUBQVGeHu779NPoESnjSt4k3LOks8epB+Q49Q4uVIPNi1eIDn/HvTf/+9idxkm66amMsvv9T1Q7p0RpVK1tmKJbV1ovnBNuSmaYwGu+7CFKpmx/69kuLKymsG9UvvaO+ob2hJSuiecVIpZSolnvNhK0qZCUOzLBeHP+rRKgV5Hgw7tS7Yz71cRhsRpdV5WYtCqjWfX7cuIPGmxZtQ9y5cgZKOVVBIL3BmnD88MTYqMS6y8EyDTEb9nd/zhDiDmM4ubWcXOnzZFpu7EtRMoaUNkoymzurvIHOanBRHxsMMgxLjY+obW7gF7FqcBDDDLuEx65EbPzc0tSbEa7hzzrNLgAYoDcpnz4mTalbx3KyRw9Qnv/nYObmOr9PvJe6u402TCXciD0LHLC+4R7pV9PgX2KOzz5vrbs8CIOSkJWpGDczokxY/uE+vAb2TO7p6jphmzLluxpzrHH4Id834lvQzxtvqWQ6WV9RyJ+w+hqfd8XDe3NMM8YVoQ5WlzxOZ4idnNjS2sv3qMB2tLdw/8hA1pVdT+rw0p8/pR2zK5vlkJkVy2yVwCVlniym5f368tq2/7iH/SLxJSt/xakvhqTMc8DgCJas5k1NSArHlEeAvJg+KzUo1L/eLj8XSuWKGCSF9eSs7UnbJtMuuPegoRzN1/Aj+l9xSbqKby6cMtr4Xj1SKK3AX5MQE3ImZg5yfkhzfnURlz8xJj+NCu5qm7k3cKArpDaaCimYyxRTBS60Ggu6yeevw02Y3hPTURIcJ2UAEmzafNbzOkp0YEmC8GW54mQOtrQ5qgxzAIyYPwvtw2DByQOb+k2cyND079HnmeofHP8h9fn7hUpuDnA35imFXCWFGDcmua2ipb2iJVdNWg/3EOP5nn0Jo5JDs+oYWbqnoqjVPk7tWPbKCrMJ/8/WXURBxZk+HbdxeXr82QJdhP58O/ZAEhU/lnDAR73d2/vQlY9FPVJRaJTMvVaxvxR16zr/oSjftmZUa575D68rzrc7raYmZiWHa2YSATC43x5LdO6Fi+IZJSoips47Udh+ruHyKbW6RQlzatCU1VnnbnYvffuNlvklDiKPNaNAzawN4YTY7spGNFGE+XYBAPbxQ+OXbj7nbmqgIhkFarfYEu28Et3Lxw/ffveGmW1w/z6lvnx2dHcE3VnlT98TLtv3tDEIN8aMH9E52+PD301e5ePIPcp+/8uTispqWhpauSIsN53bb0OxRmxAtOzmyua3LZrBvP/ikKGQZyGNWvGwuA1BHaR6/9QIURGLV5lKESI3VutW7/Fp15NY4nQ14YaAuWECgQkEhp/ecwImR0f17dXTqapu1JoYpq8Ef4yFnZcnwLjNUbZ3TyQ2OM2mzE3Os2qRXcVMcDGooPI4rXXwoQzt4uppBSKNWonbU0akdlptYUFKVk2olGo3cFCXH1j5ZhnOgchmVpFGZOzT36k592kOG/FXNVg0xO9tbOZna4He3kv04O7mq/8aO1PjIpasDNUK396bBaMTrC6AfklQFCnt7BAgKUVdMO5vsflNd24SQViaTjxyMd1IzzxVwU9TOqSvPT4iJdPEal00a9M1vx3y5zvzZn+b955r6lk6GiaIoShOpHNEv5fjxE1x1sEqJ8wN6I/4qLz02IgIvk49SmJ1t7GonfUZwnUCKgzYcvZMjS2rd3XTehVufXjzPzSfhtuRkExHmH/GzrwS8+Ma2fpO9ArkseO1RAN+BCFRg4Ip08/69NE01t7QlOi8Gss1dsrBV6vaHbF+Fzwc7Crq/uKbna9TqzTkBuYxmGKYgP1+tMndgslntjhMIlfUIoXY9Rda8E+KSUGFxRU1tQ3lDh1xGlVXUDcyO48JVhkEKOXWy2HHH7gE55q7ernEgVmW2uRRJV+rwIUGQpm1+07KuQKrz6fdIuggUBCpQUpLjy2tb4+M0jU2tbC26xaGMlTRnz7pw8w8/2T6Y6cmfjJ00PaRCZ94nPR1plQpZe6fO/uN/5HgR16KpvAo7tLC4gtxFNgTGS7MZXJNPJtnzy5trlJn8ZxiTgNMXNE0lRCtJxPpXA04U+ChWnOVQZp/BV4R55dH57kaqPmBT8Gc0mUiZF05vSlSdYYKvEShMxAcC7jNFJqZJxPjw8gfsz5w960L+lw58GgBS4xOrGrGBKnQR8fLoDNSglMt2H6/kd2bC42t2pv7YqRIylUy2o2AYRi6ji9mdL4cOyFLIaDWtL65uq5BnEGny5prZqs+IiHY96peb0VRXNyvGdgc378T6yqPzUVCwmUw3r7OE/KaEgCF86CFBpY5dYF5V00iaIb38wpoeH7h9537uNu7QTiF1TDJieugY0tDkaz8OlSoiJy29trqm3WRo7GjLUOODV0wbihsymUwmE+7MRM78adcJ0jEED6CVCpqiVGq1Qk4nJsThdYfswLVLa+ibEdsXtXLS5Ib/GAaVV9WXV9ZnpCbZXAZpOVpQ0Zwua9UbmNoIqwCWgxPr1g3d/fSCHG+SQTrZZQiQEiDQYOMwcTl71oU2saQ923ce4H/JlgcybKMy/CX32Tx4vOzgibLBeXipGKG6wVapBb4pNP8f8xYUeGFndKQ5XqaQTEbJZDJLDgFfH+kYQlOUeZIEIXWEnL+WnOQA2zq09s3uuD7NJA4tr6rXarU2SQCGTQIUVTRz7e+c8dGaO2xWf5DVdIHzJtl0hKJgH2Ap441AYSLed12681i+MdlFz2QAaD7CnzLm83tB8/l5DlYiEQ7/Y05E2mO6xroDvHPo276h33Ve0k8hCjuUqrEuuiI97hqtm4Z0l3Adr+K+tEkCcDcqGvDsPMNgdZIkwMRhmTRN5aTGRERERCm09uN6L1bTeSpWG2+SuncSX3v0PIAYgQhUiLrkthG26JJMNrj7gfyjoHkILwLlY+hsLdFrfLGn+fxbvqQ/tdr0wgqe2nslxZGeI2flpv5TWJWTHlff0IJXx1s/AmcA2D8SJhNu3WQ0meTs0vctbBJg55Gyc/unyHGEK09PwCkDfuVpbk56eWV9ux4/JTvXj7tG2SdMfV+mzHerM29C3XtYAQIVgi7xZ4/BK52JJM3rT7h7vei/+sbPzjZ69IM9uUfxHGp7haRsfvexijmTzeuUGAaNHJxV39hKURS7QbH5Six/IxBeK8A2Z1bKacayET1JAkwYmtXZpe2dYiXN7tdkUEZqIpnrd5Yt9ct2C5xbSS9HUoSEO0+xf94g4AxD/CDQMJmI97MucU6QG4Sz5rAa8YmjYzVx6NGOpJExDQ4bGl02aRD/7wBCVEKcpqF7OzbLAgE+5Bj74+H2pNx9rGJ4XiI/VWqfJ+Xqpcqr6smudly21BMod8RqMuEENGnBCeIMZyAC9b8u7cfjJpPJKn3ZQ7wSPHtO66fYflpPf3qDfRC6PeWRadVruP/WGWOuqltGviQn0MhksvSTZRzZs1dSnFUekI3UkKVzHddr2UUileOS8weWldcoFeZUKZck5edJSb0UgdtniV815RtW7xdFU7RwW+YLhXukXkUPAnXqSl90iYfj7HicCzBpdvc0l4Qg5Hz64sgVP3aMzpb/VWpw6FDC+RHHiDS3p1i10jAhenvM3TOsT3ZmT9xlmf0euc51pCGxC4eSpsvkqciMNkJo7tSzdbhUitHqjaQ96M+7T5K7OKsyDN52CSFUXNk85KwspYJOSvT3nppk3A6EPV5GoOKdiA+ELkm2zjLng//fDWN2PxyFiBU/4kntNRdFznjLaVnTls5Rf2jNnZLZKgCrq910xKrrB4eNPZOSE2pq6rmgm+1cp6lvZAfyTixkJWKKMllel6YpsrFSZIScZELmTj3baMI16o2W/vBaLV4ZJZcrzhmUjWeizC/MXTmID/AbEh/CB0WX9sNxdz6igshyZsX1IPpP2ifhynlKN0N1aHrNU/y76E9veNPJxBHX7tNcIYrX3lAUbyc6iqLM3T8bWuLUMmf2TEnG9fbOlvNjqbL3ymlUXWc9SGfPb2vrcLStCOzKDfgNSQk0QINxbk7c0p7Rx8sUhDoJZU0mLvy0GcWTYXuFMQHv1skoN3eOdv1UnPXmTLZt90maAdv0y7Dp/mn/PMlJFnta10XxnoI9yKCqOnP7FTw/nl8zOCuOOzs3ObLQQW8nh2+BJ+8rjN8BPwo0JBPxvuvSwWwPrkM00eyaOxKoeDIYF403A0cq253eBnY1joM/PKT7p2N7JsaRPKb5GRwb1HwfR3JSHMrHNacUooorGnPS4/f9U+2inZU1MMYHpBuBBkCX7G7peAU5xrwgkqJoS0NG/6XKpK9Og4GRy1Gv2AiTDod7sohIK/WxQ/j21tZoNY5ncXtQGW6IR1HojFaH1GrXE1Cu3wh+xMqf8x85OHv/sdKc9HirdlYev18OXjwcivYAcQs0ENGl2Zjd9eqkHAUFDOl7k8PEMB2d2uJOrk6zhb9SiFaoO7U4AxrNtqnnd6rnwkzOg/YpVOtbruaarO+kRgzObiBTVf5+E2EeyuuPsMTwXqB+nIgPjC7xdI/RZK5o4c2PB+GXP4zUSfi7s3vxz/BoXM1eVos3ebfQSiFKbzQRb8poSk5TpH8djhyRpTen2Z6Od0yyhz/etymZsswymQfy5hl/ICjUsR/nVW/+GA4/b7lw/i55pEsn3YmQZV7cvBkX7tobbMLOnjYcarPaqIP4lGDTMtmeOZMHO3rDKGdLRa1m6u0egyg0cnBvtqEqECTqwiPw9I9AKwqOpuedHRJdWnWoNTdxcDHREBzCXZ09+FRpToDao9ObdHptTmoMo+vgip3YRKrjt5Q/cnddFUFRvJpTIMDUhZk9/RCBVhQctdyM9stI3JkuEcLjcctGr+YAk20/wREqe4I3feJIR+IQNe6fVFxlFSrmpDoqvnU+cie3zs62rYui8OLRnnczBXykLvzs6c8hfKqqrc6vumQYPDVB6olIlIG3exUcgrPnHZeOffM/Vp3WfGR3xXRyY1z6Nu6gq152vqVQuVH/mdpW/oJJEovyRu7d9VI2fznt09yfbHzRj1cL2FMXlvb0g0D7DBjOu+2rMRk8A0C+xM40B5jdKwAFguCk6Xc4adoc5Ds0aFlUTqlyucJmpt7h7wS/gBQIiT1XhccMkscC3b/Zm/Yq9rpkm4GZA0urnsFI+EjZnmQxkkN7hpZDbYlDI+v5M/VOflWCVmgBmAlne/YgUH/pkoUxGtnxONntgO1/icSElL3Jx7U9gxaEOqRXLG79edl5fUxam3J9q8WeWh1v1b2laJ/w+75Te492l8E/sMDjdBPApy687WklUD/qkmGXkFsmfMwrfWRWFUUiihHCRZ0Cx8QwNrNMNuX6+LdZFcXdtinaJ6iUchlNk20yEUIvvGe7CzQo1X3qwt6e+FfOU286GI+bTBSN90ekuJV7qHuFiWgJR2/ummTe212AQah9rT7DoLKaVvIH2mBkbCbuuaJ9/q+iXCbLSIkrrbTqn88HlOomYE+3cqBOZ3vYAkzzeJydHJdKexope3P0OaPsD/51cB8Sf60+WTFBfEpWPTms22cQykpNKKtscP9t5pQKwakze64Ks2G7U4E6KFZndx43Gk2W9CU+KN2NYCRoz9YmvVKpOH+8A3XyrVpSVPj1sGNuPufuiunjURoSsE+dLnliGKVCHhcT1djSHqSLkxYQeNogt2tQRBmMRhlNk4oiokmZTFwTPuGuztYm8zZBCKF+fXOys5zKrqSoEEkO+yooG3Izk/YfB4F6DNjTHjm3gQ8XU8rZfm6SjDAlr04bJk8cLbN057OXJpeHQQi5H34Sdk2qHL9DQEGom5DvVxOtilAqtLruPzNAj4A9HSJntzwLG1uGkz2nTRnnOtiUaB7GFaRJNoWonPTEU8V4g0/AHcCeIusHGnikrM7YGM2okUPcH6F7Gn6KGfOGSClJMfllNUZ2rsnLpwgbwJ4ukHxyM+zseaDMkJwQWVJUWFJUyGvB4ZTqJqc1Pb7XPAkOyw9ERtPZaQnePomUf39sAHu6JtwiUGn+6h8oMzgMNl2M0TlvitKD/iAzJaG4vJ7f5wmwAezZI2ElUEaq3mQl0D0j5PdgU2JTSQS5jE6Ki66FVqFOAHu6Q/gIlJFwsMltWOKpN8M2/CTkZiV7I9AwmHwDe7pJOAhU9Oo8UGbwombTnXhz/I40Hx3KBaFPXtMHr0BXK574rAIhNOuyyz/69MN4dVR9XS0dxe/6EUps5n8iVcpIlbKjS9fjA+s7qPCZRgJ7uo+0BcpIMrMZzEG6O8RHRiOEXvwOb8jOMEyfvL5KueyDDW8hhBoREo49HZKbmfx3frnb6gzHvsjhvFIznAXKiD3YJNMb7owXQ+JNPrl5uQyiiwryKYra+evW5+4dc1H/gbjIw2R4/EtXego5ifHRCrlMb7DqgNeTOqWpVLCnF0hPoGL1pv0CoR7VGXJvEjKzsnU6/fVjdLohyQihgyf0R06Za9S/PsTtFC9QaIrqk5H0T0m1m1HngWPFktQn2NM7pCdQsWY2+Q2uhC9NPjedpzcYTX8eOcMdMRhN/zkqxIWSDn+yaclx+aU1eJeEMBuwc4A9vUYyAmXEX7MpMm9y7DpYSm7oDCbeHi2C48CxYofHaZpKSYo5VtoanjWhYE9fkIxARTWN7vYsrpC9yUGab1LdGwKKbKnOzKljZ5JbDKpvbt/yx9G9R4vcmZqXAGDPMBcoI7rMJoYSvTT5UOzbYH4n2K1Vv/9biON3e2ZOHWv1NYUS46Kunz32+tn4uNFoyi+t+W3vqcMnyxzOMokdsGeYC5QRUc1m8JcJBRXzltTUf/4WduBGOVGnI2Qyun+f1P59UgvLap/baLt7ktgBe4azQBnRBJs9EUJv+msZkonB6tx8VNjqRB6o04YodYRSIdJPimPAnv5CdL8WAlLnwTJDsefTQQIJNv24iHNFYqkoVsSPGJzj3QPVKqUqQoGkAtgzbAUaenseZEfoxWILNgO9/l1EXUVqq7v7KCen8LbxdE6EQh6lxlvSS1KdsNYoHATKiDTYFI43A906REQOdShTFz6Vy2V3zjsXiRywZ3gKlBFMsIn/62a4KRxpBhMxOtSd4JS/s7xIAXuGoUAZIQSb7i+sFL43fe+9JHmH+jLSFyxgz/AUqCAym+IapPcIONT3kb64AHuGm0CZkGU2LQuEJCbN4COZOFTsgD3DTaBMkINNK8Q/SBdIEAoOFQJgz3ATKBP8aXQ3kYA3gbAC7BluAmWCGmyGtzSDEITCED6EgD3DTaD+tCcEmyF3qITtKfztkMCeYSVQRgjBprTjzSAjYXsKX6Fgz7ASKBOgmk03AWn6HanbU7j+dKZOWKkpSYEy/gw2PSk/Am8GDunbU6iAPcN2CO+PETosrwTCGLBn+AjUs8ATMpsA4BqwZ/gIlAmONGGQDoQJYM8wESjjhTc9nRECbwLBQDAzSGDPcM+Bug42YUYIECCUMAwK9gwHgTJu1h65bctuoAJJLIvipYSzneWDDNhT8gJl3A82oXITANwH7Cl5gTIuVlUyjLtN3Tkg2AQAAthTwgJl3Mtsuvt04E0AcEedsNZICgL1S7AJ3gQAe8Ce0hTowTK9Xe1RtzQh2AQA3wF7Skqg9tLke9OjkBMG6QDgGrCnRATq0Jsc4E0A8DtgT3EL1OEI3VNdEiDYBELFR2tuF+MPH+wpVoG6yGxCBZLEgFp6YQL2FJlA/ZjZFHK8eaawCAmDzNw+AvmW57d0+e216rpC/v3af+NLn3rbX8/Z1NLu6UPuuvHy4NhTr9MhYaBQKlHYID9YpnfWwgMymwAQZCD2FBfy4qJCa2lCAyTAG3bUqeAH5yNgT1EO4T3u5IHQ7t27yY3cgf0DcFUAEF64ViesNZJCHSgnTT6FJ06BQwHAF8CeUhaoQ2/yKTxxCkJRAPAOsKcEBdqjNO2BUBQAPAXsKSmBeuFNPuBQAHAfsKdEBOqjN/nAcB4A3AHsKQ1oP9rTRqOAKIDyo+AD9pQMgdoTCUJRMTp0UpLfViUBzgB7SonA7soJWVExmhQ0GjigVF5iBHxbYwhFJR+QQhLATcCe0oMOzstAVlSM7KhT9ShHsKebgD0lScAjUBGFovv3bO/xnJFjp9kfPLx/h0FvcHiXwPlu04eZGemuz3nvg09fe30DCUg1cQk3PfjYmGkzO9vb//XC6+9/8CkSJ/OumHfJZZe4Pqe+rn7pkqXkdlxc3FXXXHX+pIkqVcS+vfvffvvdwoIicSU9s7Mzf/rxyx5PGzRkHLkhl8sXL7rjrjtv0cREf/HFN6ueeLa2ti7wlykygidQ4WdFs7MzvXjUtVfPS09LLS09g0RIelpqj991XFwsuUENmvbeZxsRQs0tLZr4xOeeWfXMmsdSswYhERKtiU5NTXV9Dk2bx2fzrpi38PaFCKHOzi6jwTBp8sRJkyfu23fg3rsXi8We+KMuk+X0znbz5OTkpJLCwwihjo7Orq6uBTddt+Cm65Y88Mhbb78f4MsUGVRMQq+QvHBINOpRP9DaChwyJ6e7us7bFs5f88QjCKHS0jMeRaDC6QfK55svP5gwfsyCWxd9/8NWm7tUKlVZ4WGDwZidN1SvxztXL3/wvgeW3NXe3pHT75wenzmE/UBHDc7t8eQp06Y8tPyhA/sPPLIcv5t8EhISPv78Y6PROHPGJe3tuB8oRVFbf/leo9G89OK6Lz7/ysXT6jrbr589Njj29LQfqCY6urrylF5viE3obX9vY11RRITyggvn7dy1ByEkk8nqawqUSkVWztn19T10+1WEUz/QIOVApZcV1Wii80/uXfPEIx0dnSgMWP34MoTQwtvvI/ZECD3z3CtarTYqKhJJmiUPLEEIrXx0JbEn6Zl75bxr8E/j1gUuHqjrbBdI7OkpA/r3i4hQHjx4hNgTIWQ0Gh948FGE0IMP3BvqqxMWIRMocah4NVp4an9sTMzhI8d69x2OwoCbb7oWIfTDll/4B//vWjywXbZ0EZIuaelpHR0d+/ft5x9sbm5h/4hqpGdPhNDJU6dvuvnumxdaubKoqATnfNJ7yHuEG8HOgYorK+qC6uraoSMnmkwmFDbo7AaJhw4fxXtm3H/32ufXIYmycAH+I2FDXHwcQoiLSaVkT8IXX35rc2TZQ/chhL786rsQXZFACb1ARTFBb8+QcyagsCE6Ogoh1NDYZHPcYBnOhxuvrX8Z1zB8+70k7cknRqPRxETv3rk1KSnx5KnTm7//KdRXJCwEIVBRh6LhQGSkGiHU1WlbV29iwigA57hpwfy8vFytVvvquvXSticeuRccUqtxLXBBYfG0GXNCfTmCI5Q5UHvEmxKVNjSFf08YxIT6QkLPwlsX3H7HQpPJNHniDMnbEyHUu8/QSE3G3Cvm5/bpXV567O67HCQ0whlhCVTsM0tSpZONPZV25SlErOHDSy8/d8vCBQzDjB83ORzsiRBqY/O8P23dronDNaTPrV0d6isSFgL9AIBDBUVzC550jrGbdJbJBZQCckZXW3NXW7Pvz/PTz9+PHTemq6vLxp66znap2pOPyWQ6ePAIrpmdHEbZf7EKFEJRAaLRRNscSUqMRwht+mYzEolGvTbp5i2bY2Nj8vMLpky6gGG6Uxk9qlOM9hw44Kyn1zyemJhgc5zMIkZF4RlFQOgCJUAoKhB279mLl+Ww5Tsc//nmY4TQ3Ytwjb1Y8EKjX3/3tVwu3/rTLzdcZ1U5L0l7IoTWPPXofYtun3/9VTbHJ55/HkKIq64HRCBQCEUFwt33YUse3Psbd6RXr6TMjHQjCxIb7geki+9frI5Unzh+YtXKJz2154miKiRCrrwK/51Y89SjUZHdy8zWv/qsQiGvrq5pavJDPkQyiCCHRYAip9BSVlb+yWebrr16XmXp8VP/nI6JicnKxG2ccvuPRGKGOFTf5XQ97syLZuJR7aCBu//83f7ekUNHSsyeZOHm/UsfffH5p2qrTxcWlTQ0NPbv31cTjRM4/QeNDvXVCQsRRKAcMEEfWu67/+Grr79VLpcNHjQgKzO9vr4hb8AoKbUCMBkMNjEp15DJU8RrT8Kbb703aMi49o6O3D69R40cromOfvHl1zVx2TqdPtSXJixC1o3JF7yut/eoNVFAEWY3psARwm5MQ3onevcMqmhzHz9CU0u7FyP3oLUm8rQbU+BQQDcmgQOhKBAE7JOkEs57AtIfwtsAE/RAMKebwJ6ApAQKoSggKCD2DEPELVAChKJAyAF7hidSECg4FAgCLjqpgD3DFokIFIbzgDP+LqkP6A8H7BnOSEegBBjOA/b8XVJP/vnyw6EcHQR7hjlSEyiEooALfNcoH7AnIEGBEiAUBQIakII9AWlGoBxQbw+4xn2NtrRr+V+CPQHpC5QAoSjg34AU7AmIrxtTuO36CQQfzqEuls+DPQHRNxPxGnAo4D7PLhjP3f67sEqk3ZGBgBJeAiWARgH3KTxx6qtnb/67sArsCdgTjgIFhwJuAgl0wDVhKlAChKKAC8CeQI+EtUDBoYBDQJ2Am4S7QAkQigIcYE/AfaRfB+oO8JkB4DcB8AIQqBlwKAC/A4CnwBDeFhjOhydgT8ALIAK1BT5IYQi86YB3QATqFAhFwwSwJ+A1EIE6BT5X4QC8y4AvQATaMxCKShWwJ+AjEIH2DHzMJAm8rYDvQATqARCKSgawJ+AXIAL1APjUSQN4HwF/ARGoN0AoKl7AnoAfgQjUG+BDKFLgjQP8C0SgPgGhqIgAewJ+ByJQn4DPpFiAdwoIBBCB+gcIRYUM2BMIEBCB+gf4iAoWeGuAwAEC9RvwQRUg8KYAAQWG8P4HhvMCAewJoADz/07BIDz/fzZUAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-11d59a0f7f86", "created_at_unix_s": 1788842369.992265, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 5, "requested_group_seed": 5, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [5], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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tfu2xigO2JSpGrcbI274lOh2u9BUEqro2WICc1H+IH/5GzLZjFpMJZQxE+fr2OYt+PjD86jv8IlD4/dHcqIyJyxw5oXxf0LQJ76lJpEAb2tNVdS1OtxwJMghu/w/RrOIX739gF0d3u0YpkpRKKNpCkyQpoUiKgj+au7cyJmAXwq9eOGrRLwciktMWvPEF3Axc01FffGzTq48Jp4bEEo5mpqWZPCLK9VNc95l4p10+ErkSdultdLVhjEENd5pQREYDP1G67fchC67LGIEF2iUIcrD+/cQTMmngf5d4ilRCVSn6iN503Zyx6cmxIjd0o0bjY8LvveEimqYJgiAJ1IiKCJwKgZ7FqFV/ML1/bFbfhW9+qYyJk6nC04aOufGrrRazae2yq2pOHHJxX4sF9ughJX54JxPMd7BJr3W7sQIpgZs4+AUdk8GQhfu4EXqP4MVrvfHP3/lp0J7Gz6nS6AjVuo1lqLYmiEbrosRGhZnMZqPJopBJYas8QKiUMqPJrFTIwsPkPZ4ycdwgDyOg6WzJx5fCFjAShTJnwkUXPfkmSUkuf3/Nt3ctrCs64vyFZbpEutukwHUdKAKVylNSV+8WgvnqMxu962bpgpgM+Olrr60CwYPXX1aPvf3DGw9dBkJumJ8YF1kwFibmXLBgWnDMD143dyxBEjRNk7aBHhGkFaO9HJNOW7zxp+KNP+VNnj1rxXvzX/1k9YIRzg6m5NB3uvaWzj8vyrdGJLlqaytncgXaFt+3ZhCQdyHsu3h6O9xFMVgInXFrJz/9qYnRh04yg0kmMUfTtITJ00mlFE0DCUUSJKHR+u3LthtCPMet5L0AL17ygEtmXwgAeH250z1g+OSNt5UcOWPJ5lJAEIK1QIjSv37jJxxbKs5EZ2STlIQ/yaMIh0arO3UUdBqLyQhomiBJqVLFn7zic8u3cI5rx0qne4z7RvO5UhA8hJhAfddoVlr8tfPGSqEpCYVcZrZY5FKJhaYZmcKJY6jUXlV/gx3qGQ+98JEnh5Xu/NrtMespfbFW0W/a/OKNP4kmJWlrJ/KzuzcPzbgtNiuv4fQp7pixdzwCC92/Em7g6htrll552Xvf3fT1ttWXjHS8VREVQ8lktMVStt39lJQnTLn/eQBAa+U5EFSEpEB9GderFHKVwmV+sBeCh/Pd61lKFZH3yP8tfHHlrOfuyaBtQ2MaEGupEU2EssBScb9VxG8bwFUf/vz+jP4oXKVkspwJM8x6vV+G8ACA6uMHUKXnTd9s/++N0/glUwl5A69a9QsA4PslV9juwI54vF5wQ8lkY29/aPD8awFNf3H9VBDCAg2weSRfNHC+Q95Yz1Yje05htt9qNYIPHIp2F2ZNe/X6z2KnXfMT6A9/bG82tTbKkvsQEikwA1Nr9XcfPPYdc+Try+8YrmgpIlMe331+hvm4Fki3UfAuV8iPPegQ6nqSPRDl06smXPfZhpjM3Lv/OKlpqj+7e4syKi573FTkyo0vP1R7UlgVMPPpd7j+JhwGdftHc4e6bSny6ZUTQC/ZVO7wH6u64GScPNcZ39elFWZHdfIReA/Vix0KQm1myS91oCgH6neosMjEy++Vp9qardAmY/Nf37f9IxwsR09YED3xEu6Y6s9fMNSW8w8Q5Ge/pkZ3EIorzHujaZG05tsGOO/vmIFNHz5u+qOvhiemcNec3b1l3ZN3CJraTb7vuSELrhP9jTiBOqrTqFG3VJ49teGHw99/AoKQABWoX5SHKMyOwgL1G6Hi0EAWaPfjZh7M4Y+eN87HkDb0COEcKKYLwFnRkLOn5/Ngrz/FerZ0l/sJMdA7PIsFivEerNFeyUPPd4lnQTCrFgvULlvKQzClGCpjVz+CNWrPiy8/w11e/sRzaKVjL+QhLz0b1Kr1MQcagGlQBwPaUdGuaGqo9fLuWJre4I9XKy4XNgNuPnsiJmtg42mnaxYDIQcqOoR/8eVnnnz8Wf4FTCfhe9Zzus2zgRuBuhaio2fdCdeHWSkcfnr5anXaoY2nj8gjYt5+8TW1Vvf0+x89t1j44eGe4bFnYbdKTMjzkAfxrKNkRUNa0bdnbudUG7gRaDfP13vgaxyQeoDzF2n+1UvG9k9z+wDDJ0w6sGObwJ5Dc1NoAM7UsLXldz5wr0QCG2cEWgSKCWTe8CmYda3awI1Au7lKKZBb5QcTHkftBTnJsIcdAe9CA7DrxLnxg2ALPlNVcWFuyo9vPF2Ym8J/DP7lTtoT0zt50LPkrDPPnhaLaoNeoD1EiI/u3/nXW2qt+F7kj6943LdX6OXnHyMIYsva/6EfbZ2iGIvCxqDoJ9QftDOdUHoCWeupcLmd1rceY3Pu4/ITO8L69dB5YbrWs74LtHDmogAZxXd1mjUQpqLj+rKbLzaWHPa7E7snNzqob3ptgxdjhQOnS8MV8P259Vjt2AEJQ/qIbIPcI6RHwd9H2c7uiq5SMi0UwhT8Y4wG44SBSQCANrWuQ6MHmqPctwIdP7hnzhvTBZ4NkQjUHyoMaI2KevPtl17j/6g1ON2+poexvjwU01KoXdsMAHH4TItSZutuFR8FiivP8u+E7Ln5aA3shkUSJyrgrW1q+B2w4snHV7z4cg/aEwCw/ajd7rsj+4rvCCMMpXtrbVOoEgoC7Wl7dolGBXLsZkUOzYULn8uskzYcdz90n+8PSgOpFA5ytx+rEdwydWhqm1ovfiZZsAVBm1pvN7QPPA3RtN02RaIpiNNVrbkpQbbrJCb0BRpg+GeuybUrh42bcHCX1zsXpieIf9PER6o8fASS6FRHVJNJfOe41g69aM4zMDOhlIQyG+x1T8LP0enqNmdvhwtH5dU3tCrk+OMWanTqLxoCaVAPeedfb7tNID6+4jE/zjX9XVTpouiHb08ULQIAdp4oHz8wU/T4nSfK+6fHg56G60gtlcuNWjXyjjNpllYKE6aDshNgax+DocfjT6kyTCm3babW1tYBAJh2gchu2OgtUFvvnx6dmFAQaHK0pwFLwPLOv96Gu7b6b1LloeUP+3d0/80nb9A0+PTd9x1vykkR21azEzg1r79Tu1z5kcVsioxmxuYtrFloGpTZIjj4xBeO6gf3w+N9CwWyhgjezqJMXRbvhh73PSagBKqSEsEbJ3qOWmcYN6gP82mAy5pJEmXf4P8RgOCKuuFmczfdzNu+zQVeCEkhlwo+eCYL65fiSuFQkabpv47DopnIMHbg7885a/81VHbc/B1pFDF9dD879VifHT15TSDZk9mJyOl2vgQg+L9IIHsf0zNDeLnUb5tB+4tt+4o8sWdBTjJ3edeJclS8jd7tNO8fkiR2HT9n3bGct2u53Q8s6Ukx3pypuEbnXLlowmD23FQyTwvFTRb2g1rQB5pIzWx7RxBgT9Gp0f0HeHNWPpyy1/A3lYpQUO06docfZ+qxPWmwBXHcL7Jx39khmfBPk5QQrTcYLLiGqZcLNDlapTHSGqMh0OLQqAjVqJnzAQBF9tui9uHZDUWRNhly+TbmH9aM1ptZt3YVIunRHcz09ITByRoDNItMAnXjbHpagG1Dmq77s3TaYvzJc51RaE8EGrULnooLP5MSoisq60CPQlssBElq9UZ+GtSZQ5E90c+Y0MNrgYZJSX6MsPD6+374L8wn9jgDslNGjBoBaItAoBQaeovhbMqlexFqacexmhF5cQQBdC7NKZ4z4K4MyJCN/7fQGcwURTnJE8JfTmQ43zkL3b70CXRh0fwRHz08+47X4EbBfkPszGzhZ2L0rsNn0uPC/PmMmGAU6On6jvzUQNwdSBUebmqDscmi5c+uesHWmbEzuJjX3nqsfMpg9qb3X31r8SP3d/K51n370ZzLb0UT0/tLG6FD7T+WhPPPqmDCWimXpsWp9pw6NXqA/0bx/oCyDuFpi0WnN4SplJ6Mgmvq2PAzOTHaly8Gmn7vq00jB2evfvcl7rrCi24f1Z9to7No/ggAgLc+9UTltvATE6L4kgM9WdUSUA5VRMfrWhrMBpgAjes79PCOLX5RpGtunTG0rFpYZ95JKJmcLTC01vecrvJo7ePkkX1JAmw+cG5wJqz0LKsK0FkLb+1HAOLPfWcG8zS064irOK6iXd7ksOWqXm84W916aPfHHwDpJeNzL736RnT93iI2FbDXqk6kVA99anY5jwQAuPTu69GFtSv/KxpNY0KAUKjsDUtI17U0oMuNJYcGjhr302efguBEpgxTyKWorpD/mZt2QT9nMyrcT8iezig639CzpaCVdU2ff/Ah/xqKoM004SQvzGZzOXvC8NMJh8tcfWFUVNfTNP3w/Uv757AFs7cvfWJU/0S1mbpnXsGSt2wJH6RUUZ9ySkVp9DCVUq3RemJPdHntyv+6OBgTvISCQDni+g5tKj0skTHNHayUVTf5vXCy62CLpaxMGy3Wxcdeo0g2m/afdSHQ8QMzd56w2/C2+2lp00yaf5lWb9xxgk2ad+hM7DwMM1znQQsc9P6z7/r8vCdLKwAAZw5v6Z9zLbpm9bsvoX6gS96q5kSpNlN3zR2y7O0Nrn1aW9cQHs6GwM7mkfhnzl2z867nfP4VMKEs0KtvefibT0QWbncbjSWH+BcaSg6C4IQiaJVcwl/uWFPTkJzsJGzkzbwQ7sLPQGBQXhpy/1tvuT/40rtv4P+4+JmlAIA1K7/w9kl1er2UNGckRkybO42bR4qMinznvos4V3Jj+WVv2/n09tmDwhVSbiyPDsuMsVVioTIJTG8mpCJQhGc17YGIhCJlEtJksERGhnt0B2soWlPfzF2XlBBTVtVCEETgbWpm93exjoK9KCjm158JSlMLs6NR+RPqrl2Yw4731/91GNBAbzQ1NHekJEZz80j8SaSM1PhZI/sIRAkAeOBdO5/eNCM/Jlz+0se2EJWbB3PWiskvKNqKeB2q2LqFPw9VTxwE2+XhTqNBKdBAm0cSMP/Gm37+/DN0uVnTtP90U4T1ncc0l2Q/OUP65LieQermOiedwaxvUbuemxbB/vvCj98e/EqDgIIiYTc8/jWCHxEWi6X4LCytlUqop55/FQAQExM1d0w2ij1tk0hFdWu3nOBEedmEvIyEcEefPloKfdovyZclJKI5UGVbEW2vRXEcbt1wsIoG4K9jNUysACfNsEx7iuCOQF9Y8RRa0+LiGLtbaWCxgL9P1SGHHj1XFjhtejncVmgLqKmzhZ/JCV6tieoxwphen7AziNEkk3r+JmS/Hebd8ognR1fVteiNcG2ryWR+9MFlfbPYhV6jfjvEBZWPrtwsEOUJRpScT5ddUnjX679zj6kzmMNlwMU80p/7zlzq5Hwq2gjQdnzR/JHox+c+snUUnTiYneBSqewaMwMA0pKF4e1Nqewv8tm6A0jBfx2rIWBcjmXa3QSuQF9Y8RSqu1bx2u4KcK1OjrT0VADAufIqAMCkwUyf8A49u+Tx1CmTReUi0vSwFNQvWCw0t9bRxx4UPt3N2ex8V5RqITR6cxjz1kuOUTZ1GLviKQAAh06Wo1ej+PDON4sOXjm574Vzr3IMKpErjYTsnnlDHAPPu163HaO1SNoa7JqZKqSEzgifQ2FdfTskM/qDZ99NTozhlzGhC2k5g2BW4RjUbmXZcf7jbD9mL1MC1gKzXxfHKp39gjNGCBfL6fXs6otwdTG60NCiUaQN9e6Fw3SzQLtiHolbtdKhM4WHqVyXN2Ym2DWIQn7kaDpxhruMEqR+SZPy/eKvWvp5V98JvNkwzS78TGTCz2DIAHP52YY2Pcm0qedTzfulOFxXAsEcKAfzCphNZjR+h/NXIycvvW66REKhSaSCfmnTh2e8+c1ugSvvOHWeCzwXjM3JTo509Cl/EgkmiDoM/OHCn/vOcMXzrk84LWdQdc1W2mwiKOFnkJPpxCFsWNo3O63sHPz6F1BZ2+x2xZeLlXiYUI5AdxyrQc01SJI0GQ2cJQVyRIheiQi6KaXMhLD6NqYnSOcex6tuxD4vKOgMaBTM5nztT/b9Z99hJt+XicrITpeOMHKurGsxmtjl9iaTGSkbTSIVXnT7kWIY1iFdZqYlzByRiVwpOpDXWiTLLikULa3n2jKlJMFepcie7DeZAyjqVJupiYPT1m3ZTxCAkssBKTFp1fAFcDSpddeQ5jZXNaeuRRloM4khhu8C7YZ5JJ2OHdx51aKuR4zZptG/vOIVJ22VveNsTWuYSrmvtHFkntO53cvsq3zef/YdZx/aoIMLP1MSY9aydUu+/EX3H7ftsBQbFSaVSG5f+oTGIpk4KPXDh2ffydPl3qK6NZuPI1dqzJJ7F9pcaQtOX6sa1T+xuqbe8YngqockuSD8dCQ9LswSD4fwsJiU/3VvMUXFsPdqbWbHWAKZCvYLcRtaHi5j15UU5PR8F+3QJnAjUNRYzrF3nM/GLK0SWTDOPo87iquaNd24paVWz35zaJm2TK7tyUVq/DLJ8w3q9PjgaF3B/LJ2ax/s8cWeRpP5dIWtaVNji1qj1aPw02AwjZp7F9Jldnri9OEZgtgTuRKFinfPLVj6NrtUaW9RHbejHEIwj+Qi/Nx5uMzFClST2Sxh+qq4NSksP7DKUZTB/TIUUmrUADjPRTPd+12+TpiAEajf06BoTY4zh4pKU6BIUdC2EPyMqoTUuvBjmFx66LTd/oscF8xaQJKESiEzmswms9lioUcNyQZdjKM9RQjaLuj88JP518fRRFVds9lsW5BQfHjHssM7VOHhl0/MSxowEdnw6vEplKHpjteOIV1K5KpbZuYLZLr07WpOpoOz4nbttZv5cZaMFmL9JbiJI1JmV6lmNNHWVv0snEnRol5YgBUu236sOj8vfWh+H4ok5FJKKiFJgpBQMFVDEmxdV2O7cdF8dgfs9Vv3e/OaYUIrAv3lmw+ffuJxZvgCHUoQHvkRMXVUX+ZdK/z41fIKznNSo/V69vvZmT0vmDVfLpNqdIboCCX3aNERKomEjIkMo0hCqZARBEH5NVFPWyxezSNxXHb3DT6s1fGK9199c/EjD4Ce5rXnlz/81AsuDth9+DR32VK1OzcevqAJkXRxUUlxUcnS2ez33NajNUPSlTp1OxztJkrueO03pEsDkC6eX+CYFRVEoI64TqSgWXhEZbltZa3FbHT7SYyIir54QrTdNyPvO5JmqvoJApiMht/+OgCHIK3o+0PCVU2h2X9MAAm0q9OgqKsbcujpKuE+FlMv6GfrOeGAYwTmuFqcW64z+ZLLUcAioSiCAPExERRFRkcoKYpSyqVIkd02vy2hSH5TDVerOZ0wvjB752Fb4UEgwx8Fex5+avTizZgRrzz/NLoQH8FkBiLZysroeOHLeMmF7DVrNx89cqbZJtPsGE6mgqzoyI9hDemuW4Ym5w3IfmD1gWWTYKH+da+B75ejA7QdIntzDs+Nr2vRXjxYsf6Y3Ve1xWwmmcG7WmcMU7hsz0w4NELg2RTFFkOy4zQdauv+9eT+krrBfaJ//HO3LKWQC36xT3tLBIr2bmT+he+UQX1iRT1i+5C52wnCxWrxC4bk+DeK9JkwhcRgspjNJria06eReCD8Fv7A99+Dm7zmKoFOVaCxi0gr++F9oSXH5LMFQwi9wXD9JHZLVI3OwMm0uqaeNpn2LhouAaDpTHH94okSCp4nmTUcjrtVFLJnfe5Myex79c9NOWOIlHbUxA0YpqgrIgnwW2U0uOLWyn9dpzZ78bkb1EekCyr71rC+QxpadQq57GC5tk9i1MLxub/9dWB/SS16DWUphfzg17EKFSu1xwTa1dtzWkxGLz5OYq3euOv4g3dEWVVLerzKca+enkWjN8EpV2bHCNGplTUrv3CWBl2z8otgsafBApra1LGRIvMqTPhp+z2q6xrRhYRI+GpoDXRJdUdBdsyrLzz9yHJhf6PnVv+hVbcTBEFSpJmpYXKUaV62cLPoM9YSy3be7inc9ylN0zn5/RsBKDtZlB4F9i4azgWPBKw2ZY6pK9Pq9FodIKJTyMzB5oPriegko9nSXHkO+nTPbooiVDJJXkIN+O6lnOd+1X/66L56eGIREeFsAG5x2V1U7O/KvrcJdjfT6vpmGUXIY8yrft7H9MBPUJslHaTi+sFKx8E7P5kgUKqHPi2IZ5OzAo40eNbGIVTolEDDZaTeaOy67T0kPgSFzjXqyPkGTXq8KqB6bpjNtKCjnYCauub3n32HK5Dk43i3w2UlhTlim5X3NBJAmwDR1KaWUfzeL/AvUV3XhFyJ4F+Ojk+45MKEH7YcO3IGfh1ef9vSgmw42G+OKHB8ClImD1dIW5ubCYoVE5Lp9qPVE4ekUCTB1yVCMAj551SNSqUYlBEVZ2mOjY1+7OHZr/xaNuzlHwAAe24adMEnh+Ext8D4zrTuTfY+rdWWo9UEANTYq/Vb1w68cJKpsri1Gc6bq82S6QNkMgm5/tNHRZMYbkfxosDF8M3aZvhKEnIZnFFKjyLi46L+2F0CAMgKj0Y+RScQH6m4ftoAUTM6C1E5nwqU+v3WU4JHuHzKAFGxBppSle1F3Ptqy1F2qUVhdqxvnVmIyFi2s4Zv5CTYBRF+3x+JmUeCKJmlct5lA61m5DcrQiQlxPzNZAmRQFe8+DIIGOZdfWd4eJhWbyzMDAc0XCPI/6252V6+Qx1jz53MDjxyKckXaEObxtmTFp1vEC2kd1zK6ZdJpDlXLuL/iGJDlUIYcaemuPpzr918lLMvMikM9xQDT+7dTDJDCn5bltZm+LpxMh2eG4sWGiBp/nOK/SBdNC4fWUndAUVwoLR+UEbUYTVbkBtnaVabqYLs+KlD07/dDqNLmB84eXJU37h2SSInyvGDUkb1S+QO4CCk8pb6KkDTarOkrb6aIGAECu+CglBS4ijQtrYOOIQHICtd/HNa06wtq25Tq+Ffdu6UYYOz4vYdgxNov+8uKciKsdA0GsJzcGZUmyVRKtktMwd6MrkkGPIfOdOw+dB5oIf5YhcgpTrirVKR8gTW81x5grtzcDlxs4X+aetxx8IeT56iswIVcPiPVX58NOaTdgczjwRR2qvEU+y7vaGOG0itfx8+w0WggeNQFwIVWbjpJPbceehMerxQoDtPlDtb9t6zAnUcZf+67YTbB0HpS7RpB3dlu0ZPkcT+MvhCkZRUKRcZY7W1dQzPjT1QUjdrfL7RZJFJ4cyhlIJVQehTVNfAlrghgc6+cNQrv5bxHyHOAh9fbZaMHpA0Nj/Z0ZWcTLOSIi4Zl+N4wKmTJ/0i0F0napE9IQQRF8Mm+tVmyaKLB4XJpa79yPdpmEKyaPYgwTSXe58uGKnVm1atP2bR+dOnTq0XB9/AazYfhX8rwr3v9u3aii5Msq6LVSod2rWkOKxYocGazccIVEFJuHqKgJ5EghvQK4S/rbc4hp9BMdPCrBH0+UStOzQH6jrWdd+uevzf38MPz84/KAll0utQbMgfZYvnK8/aloQ3NLc7jr4pElZWWCwWkiRdlQcR4LJpBXYvj3Xfoj0nKrOYRcMHSuuH9Im2WOhVP++PYxSzdEHBa+vhwKWRZL69SPDXac2xElhuqTZLxuQnjxmQhFwZFQM/51EAGMxg1c/WekypYtHsQY4yZbGY1Drg6FAXU4kavQkN3tGPM0ZmoSNNZnrjP6WbtutrNWxBnI6WLllQQABC4FN+MhQA26mqzZLoMNnNFw0UTYbylw+8/fV2Nthn4v1FC0YCGny+8WRTg7Dg35NR/3+sCQfkO6VSoTY6OI4Gl00dInioNZuPHiprYv6gu/m+m1KYuvUwfM9s42XD+U8BKyb/Evm2FnTLHtEviff7s41aOtT6QBeoEW6y5vuKmk37zw7KsJt8d2z4BpvzBlIalGNfSSO/U6/H4afd1sGgp0ETO66PkcgVYSolf5Rtl68kiHaNSK8Dx5TlhIIMNhq1/kGlEsJoEt8emX9vNKONVm4ge/LhdPna+jMo9kSr41FYyt26tVR9tJiV6ZwLsvqmRfFlivh2+zkUnPJxucOSq7fm1kMVnD3hh3xQ7v7jcPy+8Z9SdE2C0sQN4Vfz5BgXIb9her7rySXGp7b8KXdlv779Hc9EbT1/zqdc/mTRgpEnzzX9sb/cccgvUCo/RBX4btIQNi4+yRZUCBkzEHZcE2XmBdlckyr4ojE+FVUqZ9W8HPtvbhrwm7l0MNlziiIoivCzQAtnLvL7KL4zCOwZjKt0kAQ37hPf8shphNkTv+Nzq/7Qaty40hGL2UJSZIfOGBXDfisITMrlcPjS5LKWF08YSANwtLR2QkEGWsBmsdCclVraNJ40qOamHrnxSlJCDCiFK9/Pt5NKc/u9C4W6fOXXMiRTiUJ1y0X5glvXHGyM2w8tpjZL5o/NzkmO5AJP5NOaWpFl9QK4ZUiimC10aoyivpU1S3i4ivNdYmraoosH/7LxH34C1IUcR/RNmDg4za1P0cgd/lcewV6lF/9zC0JU6FMKoBA1L4F1TkKkvLEDftsdPNMm8Cl/UIJwET86DlM4OOuhCUNUcxYZGY7eYAj+s3BPxH+6SUNS0Lwu8iZSJxypnKwZNSA50CNQ63okFq+qyh2lw4afYjP1gUaYSsn//PB/ES78dF7c4jXebNgJ1y745kpR2AaDvDoevkkJSkrT9IESWMI5e8JAk9lC01CjcybGUSRBkuwOLlmJTivqOnTGcN642PqqirxQu4+zg3f4ClvfHtCJPF2mJMXNHZ1lp0sDe6vaLBmWGz+5IE0g02/3NcRZSkRlKsRJMVMs6uFsz65j53V6Js8DQFiYKj8zJjqMPey3v0tW/6wHgAJlx9VmyayRmQMyYl0M3tvtfXr9tP7xka6Kn4qP7eMP29VcfCrm08I+Vts6JDQPlzXBigXeNZMLYSC543itRA5zd9y4xJnp+BeQUvlFLFyhBVInfIFpuzcYh1ulos4+FEXsOVkze8LAo6drJxZm6vSGQBcobz2S14iEbITwMqpkCtRUIYto+OlHe7rlvNw2v/T4v9d05qFOnREukaJIAq06NJrNUmZZDkdUTAzy3bzJg5h138x6MOsYgvsG5Ip8UdaSuzsKQvlqdsHfVnuidVB22y0zICE21luqrSnRW2cOjFRJbbokwd/l+uKz7K1XTe6bEqsSlWl1Tb3gLee6mCkyWrjeb/uRitQYZVmNTVicPdnXpK6hICtGkgwLvMpawfaDrB/1tHSxu2ToP+Wgskw4eHccuavt0w58nxYmixuTmwVCXGpdCQZPzGhet+PkkTI4aRmpZPdqLcyOBNmRB6whqiemg6kMqy6QN/ecrLl4wsD2dvhG0vHmG/kIlCqw9oi8uLj4WJp5wIVTE/aerMpODDtQUpc/fEIQCJQbdmsNZlTM5BrHOnPUXDJYG74RIuGne3sS4Hyj2kUHIFH26lKEcaU83I+uFMVC02FhKrVGa9AbpKLDbQIopLa/O7sCB7A7Ie+2zvkkxkejQbfbIFQUQeqTcP51xNnwg80VcZbmOGaKZumCAoErP9tVg25VmyVL5hdIKIJ3gPvxO2JwH5E3rdlC90uNQMWwKPwkCHDgNFsYkBClXH77rBdW/+7Mj/xk6LC8hMlD3AzeuZG76wVUaqtP29o6tvN+P64ttIqZ/lbXsCsjHBmdn8LtX2KhwQ9bjx0+A31K8SfBsyPhN6WYUjmf0gBImVBx5rh8ggALpsTvL6pGytMxCXG3WR3k07b2Du5bOkJlfXAaZCfaPlb+F+iDr69546HL/PiArgvL+ThbouPY7S3AaW5Tw2YlghY9Tuyp6RAZNw3LjT942lXfszOyXKEr4Xu1vUtd6YgstZ+xpqS9XY0qN/VGs5znSoTg9+UvBucG3YKSg4iIMBdB6Mi8ONpl+Ikez5McTyMZY7nmC/LrG7hRfGFO/IWFwlH867+d4WTKDPqd40G83NDUarLOjIUxmzXQNBieazvzVT/vS0qMr9UATUnRYuhukq9IvhzVvMG71iJZPL+AIu0OFs+EejBy5+AvBhO02f/FoVjtQDHc1gkh460PHM5MgnNzQcOzI9vVepIkimp0XPDI911SUvxlqYko8NpzopKvPGaGnTCITS06I6dPsnXQQ6NHO8D0GTAHfhmTn/u82TNuaPauQ+wnf8WTjwdIKSgzB0LA8hSHbce1Yq7kqG1i6wGRSdJiVcK4MnngCTZpBoCpu13pFNpmOpPRIJc6Rgdi/QyZ/3L2dLubnl0Qaj+RKGpPr34DyzXwuxlFXXu+vqH0HDuKv23WwAilVCBTBnYdpw9LkrYfqeiXGlHE9NZB9oTjBNu3DsGN32lA6+Xxn6yDzZmQ76YNSx+SFe/Cj//51XawDyN3wAIHy9zXv2A6iPMpusD3qYuJIKRO/lyQhCJOVtuXrFr/prmZMIOAjMd9DSLl7WPi9GbPphbRQ7AJAeY9s/u4zcUonep/gW7883fg1wjUX5Pnrru9BVQalGJ+V4PJ1tGS6VUhEYiSg2aKchx/BZqmtzWF+xxXdoku3eHJcFvQvUmQnNlX2sRFoJ5kQh3qllCCwCOJInUKrkEmJb++4f1N7Bhfz5RhCqrxfWDb4Yr+aRE0DfjdTuHJktSB081c8eZvf8NCRb08mV9EFQVrgDp2H4GN+tVmSUy4/KYZ+W6DTes2JB75NIyxkqAbFaUMR2tqRROXoj4l3M0Fcf5yhEuAMsYgdp84L1Aed9o+7x++v6QOPnj84CAopEcT8XOuvIPb30J0It6H8DNgWfftR3OuvIPTKLO1iYH7L837UwpanAnQJ9pWMQemKwUg00k9a+zChY2eh4tGs8XxwcV76Pnj2xQN7dnAk6lR92gI73wUT9Ogfxqc0T5T28EPP1H7qLH57MQNN37XqVtR8fy32227m3AV/sDLwbsnNUxqsWpW9NXlepZGdLzPnzrmpoPmTBzU1tbuYjrI7qkBm6/cV1yLqjX4JbeuI32YELDn72PQxXCsw9SEsCcGehOiQSiaiAcB6VDRxiiOkWbQudIFTkbxQjh78sbdTkEfG4NeL5ikchi82+zpW/jpAschvLPzdPbZ3nEUDt5hkK418u3JtLyDSwwRgzOjNu07PSA9yhIWz80XaSySe+YVSCnSUabIp24H717VMBFQV7CtmLNwz0OfSpi5oIvG5TMDLII/HTTQfvspR98xf1+ovKSEGKIYKq+oxn7IbzHpDIRC5rUDCTi5BxOgXSVQv88jRYbBl5XweCI+SHlu1QZuYqdg/MwjO9ndePjBZii50tmcj9tRvEPY6GkaRiEl28QH73bBvL/SOSgI9ctDodQnXKffIlyrrpDLSquhPvS09J75BSRBbNp3uqSqPbdvCl+Rn1qToRqz5IbpA2IjFM5kitby86fdPRm5A5XSenw7RZCABmFhtnDPQtP8vlsC+D5FhWsj8uISE2EEOm9SnFQCuxQAsekgFyB7sn9N3jPzg1CzyQicCZQJXEb2jctmMqps+OnwbR0cEajJ7Cb/6aJLZuC70hkF42d6uyVYUOjSLa4rN7mwES4W4t2JC9Zp2tLYqqZIIjpCJfjYNLayDdsJOwvH+mBPr8JPr7F/BUxmWLeELtc0awXhp05viItU9E+HEdnHv8CQMykxXkdLr5jQhyBs43e+ItdsOYYuq82SyyfmpceH82UqGOkjmQoWdLoeuRMEMNknKbVanbc5xzCFhIC1ELSgfgilIIXhJOe7DGsNKm92EcWM3KST3UDes2QoJ+51O4tgH9i4wV0oUE/mkQ7v2uD5A/prTbfj+H380Oyd1on4HnSlD4SGK53hLAjlpz550wzCaR8pYQE0aGtrN9JwzBwX5TRsYe1pvadFxxoWADBrRPr5BleLKTsThBotRBNj81j7c+M+29w6tOhwWWMzuwBcrTUJ7Inol85WCo/Ii123swhOKpLk6l/YXr0GACNTfjKUL9NdJxtam09xfsxNidQa7TLF/IPhQn6XNUxtTIKSs6cPquKw9kJiHcqFnwIbug4/4fNa97jlwz8xxzebICHgLPz0p0A/f+YKn/3olheWXbb21y2uowNnbYZd2FPwaK88u/yxZ1xtVeYW7MrO4zhpTrhKfYrMo8lS4XjTUFXMmhQAFHhKKMLWopv5NylKbtHbjMnMz8AhvoQkuy78ZE6MljD1NSaaFNUonyPl7bbws10kkoCbeTBT8Mzap7Bfd7KN4Lj5K654Xm2WKGTUXXOGuIg3a+rYRnzoptT4aI9H7oAv04hwu7QMuqwzmFznHEVTmQQgOCHGxkSKbs0iAB0Mw08CbNl3miQcap74j28xdeiAs6wRsqf1TITSkHhiw0BgX2mj64l41w51a0//uNLZFnceE9pxpSOGqmJnPfcSIuX1bXrxuiV28O4qcSlLZZs2GqqKpaga0GLRmUkpc5xCLkuKgqsMz9TYkqISiuTUeajDNi3TdZlQCeN3TqMCkw5mlqVy9nSWyNLpDQNTlTHh0uqKs7/u0KYkJWjMknvmD5FS1Cu/nhZMYelodvLd2gB0sOvBe2tzgycyBdZpJRSBOsNVztEDkhKi1+8sIghCUMNk0y5zPbItsqeLpo58szvLGmVnJtccOy8oleNmkOBfMEBcWXrOtgLBNaLzSFyrN7RqU9Ct3fFBiC6KK720Z2/TpYegd/bZmhb+iM+ubok/L+AucYlMysR9BGtSuJG1/owWVWV7bUwfws/P/n3LTfd+wj8fLkZmolG2qBOZVC4Vj38PMuvERSklTaOY8GJEH+X6fRUAEJ+ug0sm4gAwELLF84bwS1BtMjXbZBqpkt06c6BbmXpbw+T1QN5au57DTN04TAex1c4HyppFHyQ7M9k2ePcgo+30xKwZVf7gXfShJIHgRATMdMAIjttw0O6EucJaTxzletWm4yNwLUU62SnDBdiVzkBzPuiyIFmpRt2GCMfBu92Hw5O3BF9bFgstoUg/xphuuWH31C/+bfsRyZRndhZkUqOZkPIqxhEJaItmHpRVuzCqICOLmiXXTOn/+U/b2cVI9Y2SpDxBCWpaXNgVk/pyYamdTI02mYrGm0im6soKkRomvZ1MYY8sl6oSXbBr9wjOxuM84iPlDdwAxeo7x7kjtzlT+2asUEBO0giQX+1nkLwTqLdCRE5kcsCouw284OK9zny98M/WU/idht1CuJ6q6vQYHLvSK2Sp/QxVRShTySUr46LC0Nuatna6tq9bstmTP+fj4bSPLLWfB5sA+T/7yUWgjufD0yj8rUia3Y4AgRZQlKl1fGNaYd+sHU21WeFR0ICEMjw24tIJuZkJEY7j99Nt0JJIpmMHJo/unywqU7RniYeD9zBhQWi7v0p9HaeDLDRskoVWZJ6raemTHG1zqOBgq0vMzlYsiaHWCEsFUPjJKcHRDRLftMiFiq4XvPH2FBEUY/kNkZ7zziurRU/AbiIej8G7HWdzPgopyWSmbKQkxlj0wmWsKIkZOBtT+1YT6jCuF1JWy31ViLxHJUl5p/TWKn0SfLWnLs5SFMcsMVqyoJAkCEeZbilRHyliZXr1lL7JMWEeyVQeIYg31TyZcpUD3AySVwN5LvSjHYSIIsr1u4r4xaTn7JM8tliVZ4DfdhUTBDCabKMcx7GO4MS465XMLofo+dAbb/bIjIp6u4k89huGK7byZAWwz6FiZ7h5wYQjR229W7h5JJGe807w5FxjWg81Rw11disehncd/ECMyVTSeiOQS0n+uMyi1wjmfHxLYvpM19Z+MjiO61HOl4mkCG5U7hbOgKgyVGGW9EmMWDg+V9SSv2w/iRKmXMipk0Y4HqboqELxpod9mLwtHnKGXR90APiDcbVGazbbVSYI85XMpQOnm7j0N8LRpzZ18hICTJ0G+519pqYNbsJt7xH2hfCsc0IATcQ7G7w7Cz89/PW4Vwe7skfgB2ISgjab2M+Ghpnw8XnaJ0DwfGESeh2KefWLkiQfd8/l9FfUbBu/Xzk5LzU2XBiWMoehxvvAaDMjX6acZVzXMHmCyMS31Vwoc8MfvAsqfUV3kUIBIzrWbLUejBnr2i1M4w9uh07rl7Qt2NVonRY5oe9siiTQdjL8BGjQrEQSzCPpDGaFNws6Pf9y0OhNWJ2BAD8QQ3scdfO0T4+En10NJ9N1O05xc/T3zB1CiI3xud2b+TJ1XRDa5nITJ89n5NEUPL+Wk58EbGpTSyjSNs3GCxgTeQHjWSZgRJfHZ5p3llMjx03hPwvjU/YAk9EuWcQv1UAPghTsSDAJVICH4WcQhNYYJ8hS+x2xG2n5gc/+fQt/SkfT3K6KsUVYvQS7GnvrGB8t7tJIRAbvrEydxJtqnkxFp+BdODRMQal1IqsDRKuR0AzSnqIGwIxNKImIvs7aB4xIfDvLRU5DzKc20NZblvjBjjN3ISJQT3BrT017O9u/vawhPS6wejJhuoKb7v3ks3/fwmmUs2fvNKmjJZVMX33RTKiio8rF4L3N5QySM+qaOhyDUNvcES+hab/3ODOatmZ4eAGjDi2FcBYwuoDvU7ruKJE4xLU62fN4/YHLQfDAzSPBITxJuQ4/CTFXuqC+FQb/hzp82cAOE3Qgh7quMfLvEN5fnZm6ATR453cP0UkjkED5hFndpzZL2uphjz4jbzc+Fy0IHJMA3IYcI/vG1bSZ7JYSMccYTJY/95QM7hOdmQa39zhabTl1Es59IUbkwV1aK9rgsRqz5OoL+yXFqNYf7UTBmmeESATquKOD1p0ua5rZXAl/y6WA6kuP6VI4aaKAVODQMIlcQpGteu3AvP5/bdmSkAG32+09iGdC0TIkJ4N3Bpo/2c2f6XaUqWBGnn+T6FIi2sB+YPP7Zn67Da6YGpCfz90FPVMUc9ZRAPxs3W1p0YKRra0dO8u7qgdmkEWg//lhOzcRr1DY9nGNUko81CXCxT51BIGDUAyor6jk9pHp169fZVWt4p4fek8E6ghbw8TgYua9jR3I2423UU8sR5ny/YvG4yP7xqHJdG7wzs2nAwAq6toJApS3smHm5ZPy0uLDkUxdEx4Dg9beHoHSNG0Wy2oK7ClwJR9P9ve0T7VgeivWfuph4VHpmX2Ki4vhnry9GF14KuAN4ZFM1Q5L4yMjbTvlcVlRbqUZkikXmZIEIZOIfyb5Kyb48+mZaUmZaXD8HgXA+p2nOKFPHZpekBPvTKYdzbVdJNPgEChN0y1qfW2zhtsmiPNmTbNGy6x181mah9U46YkRooqMJWizuqNN3dG6eeOf+AViHcqg6KhyXM2pdghLncjUVoBJSqTMRlXCj2gi0yjrbE0r3OPDOp9+qkY3c2QfZAPHRqV7jp7dc5TdLy8/MwYdKepT/8o00AVqslg6NIaqxo76Vq3ZTGdlZaJ0B+1rpIl1ifGEsGi2JEPdIdhlEmMzKbCXqYvqegeZMjVGJhMJB+92L6lGqzvLlLVTzHz6qRp2fXqYSrnxWNMN0/OjI+THaoRm5Mu0vLJ+VWU9lwD94zDscCoKX6a++TRwBWqh6aY2bUV9e5vGINjE1UNvDhk8EC7e5ZXcf7xdOI2IwbhA3dGOMqFxj2zGL5TXYSkQGePzZepYeE9D4KKJU7wdO9DDoqDyu81HUZhJkcQtMweGK6WOYSYnU0/So50MTgNUoO1aw9ma1sZWrYWXkiQIAm5VaFTTYtJEusRgugD/FGegQqignkrq0rB0ZN+4ynYYdcIj5VLRSaoB6ZE7qyi05PTrDYc5md4+e5BSLvHWmJ2XaWDNwtMAGIzm8/VtVY1qI69KlqJIlVxigWEoTRCE0tRcbwyjmP7hEooc3i/JxWbiOAINtLpLT4ouA4T6CjhkiXtkk38fNlQd6oigdNSTeXzHI2cMzxiUFXeyBC4n2njovFoh1BlbaMUcLKFgZBqmEIlM/QXfp4Ei0AcXLSg73+h4/aa9pXBP13+OqLVGncGskkv0JjMa0cdHqYwms0ZvGtEvSYW+r9w5FA/he5zg0mh9eUXco3Yr/PxI79GoC5l60ouEkylXIf/KL7aeUs5k6jYB6hd6UqAPLlrA/9GFQAEAV0wrAAAsefGL5nY9msJLiQunabq2WVOYmxAb4ao/KxZooBEsGu2GBiK9TaOOMvUkLEUJ0L9P1piZklK1WSKXkrfOHCSXUS5kisiJ6qoq+p7Jgd565YyYKJFV5znpcQKHCux5/eOrI5Qy1HafIIBWb4qNUNA0rdGZYnvjIuYgBnmTv5gyAOmd9vz+tcXc5csffr+Hp56A/cInZosXOIQnQZil+fPfDqI2KDHh8htnDIA9pcRkWtZq7jqZdrdAb71yBgCguVUj6lBRFi0c3dKmve6xVXKpRMNs8SyTkAaTRWcwyaQUQRAdWld1oJiAJZDDzxBoXuezNx2v7AaTAtGpJ0amealR+XmZgLBLgPJ7mBq0zWjfZqVZUpgbP6UgHRCge2TarUN4ZE8OgUN/2njgSCnsRwAAeOquOejC5ffCv5zWCH/tmAhFczsqboCVJRRF9k+PLT7fFK6UDc1L5Pf6F4CH8JgAtGcghJ+i3nRB15nUw6mnBeNyspMjPcmBXjllwMptnu5X5LNMu0mgFppu6dD169cX/Th/ItsFYNteuBircGAW+jErNZbvTURqetrpM+UEIKLC5S0dOgAIJgI1E4DolxFTUd9O0/SIfskyCeVaoHgGCePRe7UX2NNbb/aUSYFVpjNH9vnplJHfJsrzHKi3eCVT4s0nbrbo3PeR9hmj2dzcrq9tVje26SxMIZKcAm8+fj2cRHrlK+HZOESRyakpw/un9s2Ie/OzDWaaNpstJEEwHcrh//dJjmrXGFrV+lH9k91OxGOBYgJn5N6DAh2QDLfYfOHB6/z1gJd3l0ydlS71S4u+eDQMwrpfpl0oUJPZ0tyuO1vbptYZ0K4MCJIk5FIJbb8VlMChF08dqZJLs1KiTWYLAetAiVXfba1p0gi6vCREq2gaNLfrxg5KdRGBAgD+u8frzUcxvZOQnztCAuUIdpMCnkwT4mOmDcuIUMn8blJnMu0qgVpo+lBpXZtGTwDCzASeCIokVQqJiZBLKEpCkVNG5JrMFrPFotEZS8435qXFjhyYkRCtilCx+0PBdiGN7GLk5z9cJ3iWcKWMJAiN3ji8X9KPh73YHR6D6UGHBpRAQ8mkwCrTK6cMAAB4ngP1WaZdJdDaZvXxs7CEVS6l9MwUEIKiSLOZVqjgEi65lIoIV1mDSiI/KyEnNaagb4rgoTiB3v2unxeEYDDd79AenztyJtAQM2n3AAUK3y5+dSgN6L2nalxUFylUERRFmC30xKE5EoqUUIRKIYsOhx0XHQWKHJqfk4YuT7n/cz+eamgg2CgNE8gODczws6tNCkJUpl1SB2owmjW8Hv1VBmEv2hwV7Fcrpcjs1GhPtqTn7AkA2PrWjegCNqkAx30pMJjOsPyNL/1o0u+7vqo0+CJQ3yZn+sbJzRaLQia5anqh461cEJqVnsBd2cpWgArBGvVtizRMjwShgT9+dw2OSb0TaBdNXg9KjZ4yPHvL3lOiAp1/IVy46YgzhyKwSYNojXmwgAXqDGxSuyF8t1X5jOuXPG5IhpQpOVowpXBofqbn942KULjQ6JdTK67bnAF6N2jT854+C0zog0f3PbAW/rErR/NL5ccM8cKeHFERClGH4r3gEDj2xISASUGQpEq7T6CPXzXGXw/l6NDKX17EBsVgQsakIEgmnSQ9ZU8X4Wd5TUumu1Q336GVv7xI06A888qXbgJPfPa3P84Xg8H4yPLeZFJJcMWejinRE189hewJr8LDeAymy6bgvWV5LzApFOjnf/l/3aioPbdu+nPKtBk+Zz/5tK1fAf+hYT0pticmKOjxGqaeYnmXmbTHZdqFEeioGLgEc+OGDXCBaoSsVcOW1nfGntCbtF2sie2JwfRak4KeDkslfjcm0iXzL/ovZMPBqtH9bVXxvnmTP0Avy4blq5DGdv4NL9049onPcRoUgwloloeKSSWd16WoMW03JSSY4d7ubIvpT79cM+aV+90+OJpHcuVNK8lxETUNtl1TcBYUgwkilge5SSWeJ0BdBJh84hIThDJzbrWSH191dntUhgzuHufEm6Jge2IwQcry4DSppJPGjEtMFHGX/U8kaSuez0qOLGakaXe4E/Od8cSb1vtie2IwIcDyrjSp32XqVKAXxLL2pC0WnRnuxewEnricOEzgR+7HqtR5Ls6sLFukr53b53IrUrxaHIPptSYF/g5LiaHjJ7oVaHEV22pkWF8Ub3pBWnIcDcA3Gw5zk0ijx47z5I6ijUFF9VjT2C6w+JOf73bxyNihmB5pJhJEnUADkxf8alKOzpjUo0mkuZMGAntD/brtpOiRjoatrGk06PVjrPbU6Aw7t28bP3GS2yc9UlJtc6jLsDIpLqKmEc0j4XE8JkDptUWgoR2TejULb9PT3En5zmR15pzdns7tGj2zFRLhOt3p7gk9udHTbCjXsgh3IMZggpHlXWxSz2XqoUBdzRGJGhNBkQSnTq/t6dGpOFyNY1AMptewvGtMapUpdNflD7/nu0Bp3oaazM9ujMkPNp1R2ajxwXKe32X/yXLvHx6DwQQxy7vQpEvQBVGTuplEQgJF/Y+dGdOFN/8pqpswJJ37Ua9nt5mTSclxHqRBC/NSPPQmu3MnDe25dp+d3zGYQJhE6vEcaAhMInmFn0wqojW+SSUu7CkhCUBSzowp6s1/iurQhbkTBxIEmJsY39JiW7BEEATtOIx37sjD/HkklyTHRdY0tOHYE4PB+DUm5VbziMSkTgWq0Rp4neNdSZMz5sXj80mSmJcYT5IEvJ1pPV/X0OLqxPwDznpiMJiuM6mIQ90I9Jg2frCyQeBQ5E3OmLPGDSAIYl5SPEUg4JW2uzhoLTYmauPe0kEZUcA/CJ8Ah5+YgAWP30M1T+pqEgmNtg+U1nPXXDSm/8Xj4yQUKaEYVYqHqczdCFBbbxd+kqRtFF/VqPGXN/cdP+frQ2EwmN7Icq9N6jQIdV/GNCY/KSkxniK5KJN5KLFxs/U6eFRtfTP/pqT4aP494iMVu3ZsGzfB/TyS8LHdeRPPIGEwmG6LST2qA5VKHNbCE07E6QyCuZWJaXlTSe7Tl0dKquzmkWiw74TTkBPbExNo9PjgHdOlJnUqUJQGRZfPV9WlpzpfBe8g0xpB+JkQY3vY8pbBmb6UU7geqtc2sav1MZhAAHuzl5jUfQRqMFpkUhfdmOxxiEIT46O566ZdkLdpL9t+tLaV3eFDlIMnK2wnYGCrR12Dw09MF3nQ82pQ7M3QNal4GtSVQJ2kOt1QU9csbAZqfRQJmkhiRvFmk/HAyQo3i5aYXeM8eVJsT0zPatRf6hw9bBT/xz0H9/rlYTH+MOn1XkagTOLSqwXsAnsmo8E7IdJZOT5SoWs8q4jLcm1plDt1wcoNxV6cHwbjV436MeQUqJN/JdZoILD8jf86mtTpUk7EEFUDTcOVlwAAV2lQFwK1F+D6XUWDM9mUqFRCMAJ1kVClkUAH5SYJDvtt68GzhjC354PB+B3LNV90tTdF8VmjvW0RZ7fxwoPXuxcomjyXSUm3AhXYMykhmit84li/swgQBJpHalKbsnL6un5M5FC+QLE6MaGB5+q0QYA9B7zWKBZoUO4L72hPphS/74Y9pejyuZrWrBxPEqC0vTdx4InpZd7k3304M6j3XqOYHhDoUU38EJVwTadn4aetdIkPyXus9HiVtvGs0mEUX15eyV0+awwHAGw73Ya9iQkBDO3a7du2TZzk+RISe6xTwVijwReBuqgGFdgTli6JOZdg55HYWaPkGJVUItl2utXhQChNDCZU2b5tGwDAR43yymmwRnscTws8jSaLFw8q1hsUXcWsRwLHy+EyeU8CWwwmVNm+bRsyaScZPXwUMimm+/G4Qr5zg3e+KqdfwE4cYYFiML5oVKzwD2s0QIfwKA3q+SMKtOgYZUoo9jqjia5sbANUBOguzpedAYFBek52r/qVu+33DdJffPu2bUYgj4+LzspMi4gIc1v+7LguZvPGP7nLzS22mCZcIdtdZZtU6AbGDB8Eeg3ezcI7pkGFhZ+JduGn6LuAJAnuGzQjPqzK7gEwmF6KFOhbG2v/qWtCPyoUsvS05NTkBJlM5m2f35joGIFGMT0vUKPJImjL5Hrw7uw7lLmeVShBECmmympJmuengcGEMHIK9ojQm6U6naH0dHnpabhDIkGA6OjIrMzUmJgokiA965XOatSoU3fPmfdO/FkHyh+8ux6BzBzdb8OekkGZ0TqD2Y8ngMEEOLIIpaFd67lG0Y80DZqb25qb2e3FpFJJclJ8RnqySqns4vPFdJlAXQze3eVv7LqUWCx4UyNML0IWAa3ng0Y5jEZTxfmaivM18AcCRISrMtNTaZoWXbqC6flZ+KOaeO7y+Sp2TyRneGBPQBs0jnNKGEzvQRahRCZ1i5wyIpOKQ4P2ds3xk6X1Dc119U31Dc1tbR1Gk8mf54rxVwTKpUGdhZ8uXGji5WIIAlw0PK2yAV6TEKWsxN2QMb0SD0f0LqJRATRN6/QGnZ5toUtRZKTKzV0w3T2EF7Un4U6aHOV17fwFnXjcgenNeD6i91yjHGazF+tfMD02iUR4IE1uITzfnug70y/ngMH0Ho166NCYCHmnTw3jD4E6K6dPTowhXEoTGVMgTQDAYXWch0+NwfQSOj+/hAn0CNRoskh4dzLz1MkZE4GlicEEQmIUE4hD+IRImaM0aRpQ9p1EcKSJwXRDYhQTNALV6vRQncx/kTStferYbCiWJgbTzRqlmAXSZhoXBQaqQPlpUCRNQDDjdCxNDKbLwBoNwSE8GqeHWKS5f/cmt8eMGDONuzxzxtQVTz+S1Sfz7917Vzz/6pGjx0Gw8dOaL9LTUl0f88nnX7/3wWrBlVddccnbb7yUkjkQBCf33HXrbTej/b6dUl1TO/eSa9HlhIT4e5fcsXDBHKVSsW3H3/967d8nT5WAgNdoc7uem4hPS0v9+qv/uL3vpMmzuMuzZ1/06CP3pqQk79jx97PPvVJefr4Tv0Fo4rVA+auSQozMzHQPjyQIoq7yFKrBamhonDhhzKY/1h48dPSiiy8HQUVqSrLb3zo6OkpwTUpy0nv//pfJFMR9DKKjIt3+4gTJrtNbfPdtK556BACg0WiNJuOc2TPmzJ6xfcfuS6+8CQTq/BKn0eZ2mGqLiZBLKCojw4uuPQf2b4uJiTGbLR0dHVOnTp46dfIr/3rrww/dK7hX4WZXzhDDqx6R9VVFMPRI7e94095df2ZlZf7082+333Ufumbf3xv79Mm4854H1/74a/D2A/3hu88njB99y6Jlv677w9kx+fn9tm36BRb8msyeR6AB3g/08kvnrXzv9S1bd1x57W2Cm5KTEo8e3G40mvoPHt3eDtfMEQRRfOKf6KjIJ556cdXHn/fIL+6o0ZrqKmcHc4lRflloWFjYsaO7TSZT337DHO/y9Vf/GTNm1P/+t+axx1cwK5qo48f2yOXyseOm1dS4Wcw9pjf1A/VDR/rehkRCZWVl0jTN2RMAMHLsdADAhx+8AUIXiYR69+1Xtm36xWLpRetb3nnrZQDAdTfeieyJhh2jx80AADzy0NLAX0qPQlEuGkUBqVvGjIF7hCB7MiuazNNnzAcAvPvu65046xCkC7c1DlVMJvPcS6698fqrQC+juvwEAKChoTG/YBwKz3sDffpktLd3bPlrB//Kpma4qVd0VCToUXxOjKII1NmRY8deAAD49NMv+VeePw8j3JEjRMLV3gwWqC/s+Wf/nn/286+ZMxuGJH/vDuXdutvbO6689rZ9+w+B3sTo8Rc5XhkXF4teEBAA+JAYNdNES4fTUPT222Fu98ef1gmuLy8/n5mZLpPJDAa2WQkGC7RTKCGKle++NvXCiQajceEV3T2r0J3k9B/R06cQKPzw7WcAgC++/BYEBrIIJVlPWjzeOpcb1Is2osjNyQIAtDBRNp/KyqrMzPSsrMzi4lI/nHRIgAXaKf5Y913+AHaT0emzLjWbg3hiGuMh9997d35+P51O98xz/wqoF41kWk16rlEEN03PXSOVwrWheocwU6OBbXyjejpxEVDgSaROcfNtixNS+/cfPEan023b9MuBf7b46w+DCUwefWjZE4/eZ7FYMnIKQUBCSkhkUq+iUf78krXDpDA6xY3THMEC7RRlZ87BKYWm5oycQrVak5GeOmRwsNaWY9zyvy9XP/TAYpqmk9LzA/zl6oxGjUYTF4fyUTJbMGk0nuZbewNYoH7jhZffBAAsW7LIfw+JCSCKju+ZeuFErVYb+Pbk8Mqh3BRTFVPpGR4eLrg1IREuoqmowOuRbGCBeo1Kpbz7zlvmXiycnD1XXiG6bgcTAlSeOxYbE33iZFFm7tDgGsn6EIqiAqb8QYMFRaN983IBAG1tdg3YejlYoF4jl8mee+axVf/3tuD6hx9YAkd53/7gr78NJkA4U3xAJpV+t+bnydNgMXkw4pVGv/v+ZwDAyy88ya+9pyiSIIjGxqYuPtMgAwvUa5pbWtGynPz8ftyV/fvlDRs6BBa4/LTev38hTM/y7lsvh4eH7d138J6lDwf738JzjVZV1chk0oumTeSWMP219TcAwL33Pdr1pxlM4DImX+g38ILiE/9s2/RLTW1daemZfn1zE5n00LhJs3ElU4hx9VWXAgBGjRwmuvhKtFVCgENK3FeMjrhgavX5E99+83F1dW1NTW1+fj+FQrHr7707d+7urtMMDnAE6gvNLa0ZOYUnT5UkJyVOGD86MTH+4KGjffKGlpSW+f0vhOlBJBIqJF9/t6GowWBM71Nw8lRxSkrSsGEFCoXi/geXz1twreer6XsJuBtTzxCY3Zi6jgDvxtSb/9DeFt5z7Z1inGz5ibsxYTCY3oK30/T8TqPNvT4axUN4DAbjY+E96PUaxQLFYDC2aNTbUJTq3RrFs/AYDKZTHUko+06jvQocgWIwGP8kRinGpHtPNew9xW7fG/JggWIwGH9qVE4ZkUZ7w8uKh/AYDKazhfcC+A4dNSBk9/HFAsVgMF3VqlneCzSKh/AYDKarRvSAp9GQHNRjgWIwmK7VqJwyhqpGsUAxGIz34sAaZcACxWAwPkL6NKIPpWi0dzUTwWAwXYHB443p+ejN7LZLwTvFhCNQDAbTWWQRSlkE3HKut0WjWKAYDCZQNAqCDSxQDAbjT2TeO5TTaNCFoligGAwmIEJREIRFo1igGAymS5D1gsQoFigGg+lCZCGtUSxQDAYT0IlREMAaxQLFYDCBnhiVB6pGsUAxGEz3IQstjWKBYjCYoNQoCABwQ2UMBtMzyBiH+rAMNHA6jeIIFIPB9CQyn0LRACkaxQLFYDA9jCxoE6NYoBgMJiCQBaFGsUAxGEwAIQsqjWKBYjCYkEqMyrtRo1igGAwmpEJR0I0axQLFYDCBiyywNYoFisFgQlyjgKErHIoFisFgggNZ4M0vYYFiMJhgQhZII3osUAwGE2TIAiYxigWKwWCCElkAaPT/AXttwyMXnUkOAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant": "CAM C shows the central lanes T1 and T2 as the clear respective lanes: S0 to T1 and S1 to T2. Set S0 to T1 first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "S1’s corresponding unobstructed central lane is T2, distinct from S0’s T1 assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "The two selected paths do not cross the standing teammate; use S0 for phase 1, after which S0 will duck for S1’s shot.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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ma/o8PGCtpV7PrOLpEQFPSGhYWEJsj+WPPjH+9qmh4RGVFZZq1y3A8Jo7WMaE4d49hoh8wngiqU4N01mojZCtijI02J/lk9/NuftRgUhMtRVls2TW6vQHjl+WK5m9eBLHTE67loxplcMHRF3KrRRjmvTC2maFevLQXievFc8Z3c4F4pTyzyT3rWqyTYHKyWRxv379zK0we/aciZMmUS+fX78ePZkzdy79gRb+8ONP5rZz+NAhIQkcd/THMRg4b2y0Zg9P/gXrh/r2hx0WzPHI6ifFYtOV5O0iIGBdZz1Z5z9ihKVU/oSJE6Gb32w6rInjeEr2rU93bbt66byHRPrZPtOutzmo/bdP2JHQNxZdUTdXYTxB4MDpm96GLoJYyOsZGUI9ogyPYMZDJBKiB0wW19S9s/EVwG40KvgXFAv50VGhpg4wxNzRdTHnruVV1hj95jGQl3Gpvll1+/A+mUV1QKMcFtejqKrpnjv6pRXUiDBN1i3DCBRWiVA3xjYFun///vvuu+/pZ5597NFHTK6wYePGgICA5559Zv9+ODL3wQcfgpJtULxCYUh6UGTn5uE4HhgUxBiug3j/vXdnzZ798aefrly+3MvL68yZM1aOea+rhTkNQBB8gUDbNpuM6BHVc/6iJfMWLZ41ZTzokPUUEMoNzz368Te/7di5ozj7wqXM1uo8OqiQYME8w92CgU4LCyTT8kpWPfLYhUuXBQLhK29tu3tpawxUodIQBFFYVpNGq/6juGvxEoIglEol9S7ZtpJIv1FqXLpP8eIHP6Mnez6eTn+JwLBfdn8Eva06cYuKofQZQT1FLS9Nu9D+niLjzQJhD/qrYvLvYy2hAzLaFJLSBGNoyH8mNyVsUzf24gcwO2cWQx9Uk8dKVFS1cS9UQcPb2Zr9UGs0uXkFOh3TfcFxzFek8sC16QU1NU3K2SP7nk4v4+lUGYW1lfWKWSP7Hr9yq7ZJ6e/VOlaYJaOPktxUhNqmQN9+601oyKZN8/VtHZ9LERYWHhAQAAA4dOgQACAuLo7P5zc2NtbW1iqMyMvNhQmlz/aa/KKcnBwYJx03Pn7QIADAPXcvtbJjiF6vL8iHW35vx0cmV3hryzZYbfrP36ATlJeVKJUKHo+/aNlDw+Mi6A+pl6fUy/OpZ57GcVyj0ZSWl6ElUi/DhU1/jnZ44XxYOdA3NrZf//7WfLtYLH7xJTiwfeECs0MSOgBBEG9sgpU6vXrFhPfs0+a9tp6uobMlwBgmxiVmR0rsA3+ibTAkz1pfwfqzy62T8aFa0S6DAERxcbmx9US3SRGmiesdXlIjQ857XZPy9uF98krrxZgmo7BWodLmlRhS9jFx/Sw/AFvJdZ1gqG0GVKlUfv45NHkXLl4aOnQo/a3ExMTTpE7csmWzmmyivOkNaG23fwANljHT75wGPzV8OJ9vWgUf2L9fLBZ//c23Go1myngbWsaueXQ5AYi+ffttfGsL1jbjtGXbrsDAYK1Wu9n2mj4kPylW3QOl5QMr165+6kX68rhInw+2vJ303PM4hq19CMZt26WoqOjkSWjQv/4GVj5ZZsrUqf+dhue5oOBmRXkHI6fmKC0pQQHo57Z+q1DrLFuZiqrWfgUhQSZuqGy2pKGhsBCtDbSjO3G5ID7KMMorJMg3MhDm5bsKory8qllmemBFmER228CeN0obBIR6dEL0rapmD5g7qmtSaMYNiSmphmWheaUNKJW0fnaM5W/qYhuaZEu3UFexoTYnkd58443ontF3TJ78y/4DWq320sWLGIaNGDkSxeOPHjnyUUv2PDERWr3PPjV91tRqtVKpFIvFc+bOPUD6+wzee3frgoUL/fz8fvsVDgC1EpR5X7Zo9r6fD44YOebwn/+VlpaUFBeFhUdERsKmNTq9/qF7rLJrlmlsqFu3atm2j7+ZPmfR9DmLigrzK8tLg0PDo3rCXy0BiBeeXFlZURYXabgO/SNicAzTt4Qsm+WwyIlizZrH09MzcRzftWfPgw8+gPaTIAg0cNaYzMzM+fMZwQGCIIjTZ00k5RANDQ2LlhlCBHoCqpusggLj1Z5a/9yfh4/y+YJHX/rgz29gMNScz0sREuRHX2Rys64BnIYD/k+znq1ta7IKDPVnXYDQ03TUtQYIs27VNcrU88bEns+qUGl0IwdFXcyukODa7OI6XKe6UdYw9vbJdy2YefHXj1nYPSTJ7aZO6kgZ08qVK5Ysviv/xg0+nz9q9OiRo0ZhGFZaWrJ06ZI1ax5H66xaBU/TzZs3jdPQFDt3wNkud+z80OS7hYUGH+r3H7+wdQ8bGxpmT53w17EjOr0+PDzithGjIyOj9IT+j6MH50ybUIvipJ2Qn4iC/NwFU0ceOvC9VquN6hkzfOS4qJ4xGq3m8G8/zp98W1Y6rF2lqCuDh4NjWFykD/Wg3tXpdIsXQ7M+cuQof3/TuWy1Wl1WVnr48KGZM6cbWU+7odPpnk56DgAwcPiEugYzA0wxUE6Tny0ePUua0lmC4ZFYPrTQID8MgHMpXWc3raGmUbF0Ut+MIhj0vHtiX2Q3h8X1KKuVz50w4InXtvx18NfXXniBnSLUJnLPfAdYD+ZNto1g7ZxINjVLtte4I+N+oCqZIa7kKbFq9GR5JbTRPBurZPSkCapVOaq2LDKml/UrD5JUewh5oaYGfZVXtlrP0BaNVlhcQQBQL9cWEU4rO28XfeWNwdHwvtUrKtTkCtShUcd1NuVmZKCEIECKzCh42uWM8Id5eYmwndtA0vtfbn3mQeqlSgt/V+eqTbeqSYiGdXVdyVYbRCjWdyxzAAircJ9ZOSuqah03qaRI6oNsaLO8TZWlBXsKC4xauk0D8zK8df0WASfUK3qTl3dmMbuGli9aDVtGUex6fUdoMGllyB0vqZFHBHRlrLDjeAj57R4aI1jBBuupLs0G/mHwGY/m4OtMCBS69fz7egWOg9H9HauTuq0jz3efuTocUDPfLnR7ShlTJD9hLbRWq9LopR5C0OI5eojNVhTK5LDSS6bQUBNd0H18thlTGLfd8OQve76mhw5R019guskBqyAsWE9yREBL6JNlhfan08rGxYfJFcryOhhTign3pYxpRJhRWoxkrl/gwf/gKL7RgU3mRCibyT3zHZtFKHvnRHKK824BkdTH8sXYLFehB1Xf7uUpCfTz9BALZQq1TAGVgkKpVijVbYrf2+aTMQzzEJpoLN8zsIOV//bC2Mq0WcgyQ2MOoVCI2XRcbIJHq1eZYLmPFA0fqZAgwLks0x35YnycYwGSrMrIY+wPhrJagTrLeu75cJucbAViFW2vSJNDjwL9DIWf1XXNUg+hUmVwuzxahrig7LxMobEwuLBnoEdhNXM8guPAAGBWMrk+GlPFlZbp2homa0UodQsen2io2D11uU0PSZOylFUiNKl9R54acwu6rwEVkMMxbSX3tA1VYMdOJgM7owc+Hv0eZHb3qLzRfpOnnFz4O9br9P36Gqb6ImkzeOfUlRsG/6sFNID9RmmDv7+vh5B3q7R67QOG3n0AgFstsmLvKdMNhNiDgIeN8K27UN/+pFVdj7Y8l6wJYfbqtsDZazCDJGhpCepE1KXZApFYKvFobIQ9wHLzi/vGtJlzkLKklDGNCfdFgvSOoT3+vnrLnPxsriv39DOdUnMqmEs48mxUoLZaT8ZESY4juPdQyzY0JzcPtkqD59VstzSTDdNwHEP9xsWk/x4T6mVsPZ1CeXkVSsTven3Hmg1PMt41xEBdluOXCsqtOC42tBwRCfmA1uxKyMcLzf/AosgfmJb2A5vQ0pHPdUQo4RI2lI0G1CYI0EXW0xpi+/a5nprB+AlQ/1LpFpPzoVMr0GFYzxXjw7tMhF6XByZI2nw7w4aatJ4RARI2t1UeThvHefySYbhRu8fFBkGt0epDfIQyFQxBKJRqs/0OaJi8LthQTuBOOLwO1NWc9/axLEJTUjOon61Nlp0AoK5ZFRgAr1WBXpVf3jRvqoluT13pxSdIqsVkKWibwk8qQ21E4a0KsZDH4+Fnq70B+1CU5o7sA3c+mqwSay35NHNEGABnSBf+WrOTjY66NJvH5+M8vodI0NjYlNgnoGO/MYYBpWeQnOjFbzUrQk0cGttEKN+lrader7fcX9kpELDSkBcaYihBb1GgNF1Gk6MV1YZGFUqV2s9TVFPXGODnrcFFMaGwJAg6bs7GSutp6LAJXANrrCd7QD9yD1FrgZhYyIfD+VtC67RCMkTrK6rhi6oldck2kmwpC2WbI+/867Nj1hPhFOtpMhKqixyknPYcwPE+5Cxgsr+/AGm/AT3MYmOGbmnkJekTit25DgsfQBRc1v+5HcgttflRzHlV6eHjfWQTRjY5piMNjhixdlPu4X3F5+EsxBQ8oWjgktVR46a3FFmrkj94oS4/k/Hx2zftJfT6fzaa/dXevgm2jPHAdaS+wYBep2usEt26RGT9C5TNFmyNQq0rr2sGQjYqUF7LFPXmrKeXmLd4VFConyHtWVGvzrlpSe8vGe7z4GjfWTtb+zaZ5PtVPS4WKt7700Qs+/tVsNff3Z8yMzxjekveWRhK5a4+Par78u9Cejep0NDAOYkBkf6iPX+VolG09L8CeuUl5t09JhAAKC3VGh2fh3lIpH+k1h+41lhQDY1pfoOeEqHsSyVhLmFDWWRAbcIpzrs5ZPd/DERksYtagem0QCiV3vEwNvlh4otHQX0pJQ+wCQ9jt5F9TBoqsN4jeKu/rft+E6iC7aKp8Cih0yERCi8DzyC91I+cbIMZBsX5fGlwBF/SJqHce+pdcYtWoipUWUUx30Mq9vEf+/w2rUr5V9LdOlrcTBocQVicj0QaHEEWCaNZSeASfnBPrO9t2B2riZuXiQMbLPzMu3Rij07AsJ63D/QdHUuedgLIVToPIS/EV/j5UyMv5NSuO2BiFEOgJ//JyQEN8vYrvSL9BTdJg2XyLcYSPwnv1zU9kemsatQqtUQPP8GqO3uvmt77zR9zfj97EwQbfHAvMc/Ps+X6bStB0auausYgH/h3hJCBaT4fLEr0XpTordeDqdtuoiGebCDJZccmscWAupDzXl3SpuBONeERIJIIUg4JL/5ALcyWicKf+AR/+GP9tjnwNQaAdzC0nrXF+i8eI1fB8DXf+S59VXHNYEBLauThARLKIGlwkVqjsX5QT8yUhch6/v3yQ/Ka1jZ3cQtX9J62eMaOXw+tnmHr/FAFz8CxDGIxHGHl48HDg3qJV38JohP1s15QfQ8bkjJwidGcqM4BiuWm1n7vUjF/dN8ovY54fV8G1TGLh2Mb7hkwItY/PlyVVtombZPY02P73eSoSrsi4mMHn4Czm7x7rPrXa4bd05bn9g33+ua50a8u6VdZ26TREsZDURk29DjZjs+PbOl0KrNhyw/XIgIk02+/DQD9h38WbJwbPLq35M+ne03cms8eEZrUxoa2cw9mjwhlRQDRVZz31m8XtxaCaGNhZ3u69YQBzfyr8qxk+HsONMz2g8+BFkf/9dqWVYiyN2Clp98DsGVcSJDv6MHRGAB+niKVWltT12jb/vAFA+6CcwQcWj2Dbj3hGND9e3OPwNM75Z0OlhwplCqFUlVeJy/NSc9/5jaCIPC+Y6s04vI6OfWorFdU1iuQ9Szlt6geliEMh52HFGoddUTUIyYUFg9s+i6ttFZGHVFZrfzJj+EM3k9PbZNE+n5Vj+13h1nR3sBmfl0Dfy1r95VS1hPZ8bJG/R2vnoIFA6uHYyabmdLMDrKehgVY61gA1J9p7bTopJ/Lz+bJeTjYOIddCWQa7d/pWTI8iRUG1CZQ4aezvp0hPy1Q9f2m2s+fBvVlhtchZJ2zTkt1JiZ0WkAAUe/hrRFSkvAQm6dm6z8PTp1ycfdGkxoz+/evCIIQ+5q56ixC3xyOwWhCxUdr4H1hURJ82fIALgLzcFoeKOzg5SFgHJFao3/9cPXGg63jIOcP8Y70FxxLa56wNd+++ybgYV5iXK0lrt1qo3Z5OCaTK5Rq3W8X4G9p7AAT/bEoyqvqKOsZHACfFJYZxumtnNPalfz5X+BddsoAOEAuv6H1VtBcZ+cW3Y7ruMwS+C4nP/V6AmdBlA0Xe+mVcEwIP/tfbb+Jsoe/8Dj0Bl5luKgGDxqQkpqhyq4FhnZw5A7Xtc5uhJpOEjotJhSjzmkYwJALXF1dp9YRNXWNEbAdiV6l1jImuFkxPvxHOJSplZipMLRamX7R3N7WZF0LjBvq2zO2vhDOlWIl1+UmbC6WUjALYOLBU1NkcNIn1yLV1BHBYMUl/bwx2LoF/Tf/UXUktVlHDmpApKTBvzJFeply3GY7m07E8J6w3cG359vkFdWl2TiOYzjuIRJ8sD99wciwByZHv/ObiWFF5toMIrLrhSE3qob2gb/G9bNjNh/KV2kIkQDzlfDqrQjjdjmYNSuxwZHnu5b1PHYy2YnWky4/v/q3xYadf3vh1ljCN0wx73X4UiXn5ycLr/1mSEmjkTwCGI0i6koZXSp+0WuN/wR+XiKeSFJT16jTEUIcyO/fY9zWeKLOhNK0kBcquXgyMG5o1IRZ9V/bYEBNQpDd7N2MBoXuzcNVr8wKWj8dPgABLhUqfrnSeCqX2RIht6IjxUDjY6Wn17fT23jpbVAwMuQnjuOenlLUqas/2Z0LM/r9m20zCAtCCNRMZN2CwR/+dh0ZUMSFm/LxsdIgT2hA2RoJdQEb6kouvHOddwvsT3ro2IsPVuSka3QEEEm0cXfIl23v8/55QXDLdOcYmbjQMmdsl3hKYCF0i1M/ZjBseIxRfeHsh6IWOqGSAPa2OnY6f6Q13fHuzY//JR1eDAyP9nhnYcjp9TGvzuqiQGF0ALzL2qoHrenFl10vLKxo0umJmgaDdV4/O6ZRCW+EAp7znTmXduT5LpU7cuaoTcvRT3l1+dmtz6Dnfr0HjE16HwBe5As/V70zlyyih1dFj359gbzNHMUEAHyBwCBpyCNDXnxVdV14iD9BEHoCnHts9JwxzNk6f7whmPiaDZPeSAKhppBXtwRkOUyh1hFfJ9d/nQydaD6Off5QREyQ8M54T6kIf2F/p4KDp3JkLx6oMF5Ol6Ul9ZpAL56nCGf477AEwtOjtr6JKmCiEAhMlGksWn0/Go16Kb0ITIGlAlKe9tilQilP++vZfEKjWDkXBkPDfOC1r9CYUCTsqwllrwjFXch5Z4/4bPXf0WC6tqWadTcyDj02/fzOV+Bccms+Ly+vAmRbBzneZspyquDZWESE+MKwJ49PNhYJ8WxXjSJhjvPM3g4jRtwB0wXHD4BOwxOSIVlWugKdgaHFtHrigc+Lx5PhznF9JV1w3/7sFBS/I3pJGP47mmFweJ9ADxGPfuLDott0YzIJcveXThmkUOsenDm0Wal5aJZhMt1hZMi1vMHw06KnkpxO0puf2LS+EzPyruHCG1ouAdbJz8D+Q2bvORo6dIzxW1Xpl/QA43mjrAX81TfizLl/9YDHB1p694oxQ6AXr6WFOCsb1e0Oh0//Ec6E2nMSWXNqioC+g6CyKC8CnabHWDgfdfahb4AbcXp9zJ9PRRsvJ0jxCADwlZjocm1fUoqhf33fKOaPRCZXoKTW3RN6Yjj2w7kqtLwgx6rZ7hqU2K9n86U87Ymrt6Q87dELhZ/9fhnDgIhsG6bQmLabzk3HA9ttaPcyoB0o/GRPyyW6/KwvzAUADF3xgvFqPCGsP9fL6g1tiWtuaTUa+og79NxXx5witKQGJo2qqmFG1TBZ9D+XLDcTKfznEJxHc/GjJkXokIeTAIZVZcAywE6C4Xj8Ujjx6o1jPwJ3ggACPhbsZeLs3RYNJWGDwuGpaj0BrhUpeThYPpY5PL+5We4rFYzqD134mxUKRsKdAbofn71mMK9D+4XVNqmWzx6WXVy/ck7izfLGlXMSp8bCC//T/9pshFUi1FacJUJx13DeneowWoh+ahWyxuJ8nMef/kEb7xjD8Dvf/wUAULbrUdRVU38IFsz/1DyKWucvL1h7NEp23OSWI/wNrlyYn4dIwENevLlWTAShP/sunIt45q5D3j1609+a8MruyJGTYbEAGVLoDD3GTJu16zCcIf23/+m1GuBGLP8Sxqb3Px41tk+bkVR77g0XC7H0EhWtrsmBPPk9/PsuH+e3+95wdWk2pQ5viw3YvHwwAOCT42WEFdYT3aXD/D30AKQXwMjA+SyoKHNL670l/Dv7E95ioNWDULL1lzlcToTmOsOG8l3CerJTfiL+e/PxKf/3rdg3YPZHfzDeyvr1i7z8xgR0SdYUEvkX+b2G7wc9dM11fC9/oAXEmb37zzOl3JghvQpvVdDDFYQVjexq89L+fWP1xFd2T3h5F+MtVVP9X0l3MxZiPJ7xDsOxTI8ZGpEAAEyukHPom7yj3wP3IqdC9cHxmqemBGxexEye1Mt1j37TJvXnOPQEGLc5//ATPRMixRe23YlcEAIQPAwjCLD3n/LqJg3demo0mn7l/9K38NICOA/C7mOGn4qAx7trRMA9Y4LUWj3AQgHAnp3mAwBWUa86VWgiAUWvZ2IDSW9+svUVOMSOtQkl5xfSt4tzrac1Q4+Ov3CvyNsv4d51XuE9+R5SrUJWk5eW8mVrnTkqCAWfPCXqO8Jr5lqelz8oz9UffZdeWk8H5eJrr5zw9g8Aei0GwDfn20wKppHLSi/9K6tsY1KbSm4eWj2jx5g7oyfOFvsF6rWaxlv5Kd9sUzcxe2GUXmpz1RnDWIHQ6RR1VWVXTjcUwZCFW/Lz5YafLzc8Psl/RC9JgJSnJ0BRrWbb8er8KrOFnycym+Xq9qXpiczmzDKVubeMF87aWRjkxX9+krhXqKeAh5fVKs5n1aQXNaQVy6hRRqHBfuWNel1xa4kw/SI5fa0gMlCi1uhPZTcoVLowf0lZrbx3uPeVvJrgAL/Xvku79/Z+AT4Cqqje3J67Vjre/Rsqu6L8NF08bwuorzvsYYHz2+2qSfXxjQiQ1DSpooK9bpQ2VAjbz7dyuB+wjInH1+u0w/sE5lUq6NaTsSbjCkF9oMeOHLL3j4yEkDYmvlThoZA1iyRSlVyG6pmMDShLGi1T2CRCu7jpMs7mxBGrpuvoMCYHRJoDHe1YMhcf4GWYaJ6j24JhYHgf+PsxZz1bBmGY4PD5gsYqg48iJUugYKcFD0XvQF6kRNk7kHfy7LWTZ6+N8Letcw37ye3CYCif1R0/nT2taeflJ4VSrROL4dn28eDRG6mZg2oN5yrtNTkcAYxdQvfFIHSs+fFcvVGD2i+V17U6+Ho9IfGABbyYnpn9k6l0FmxodZ3zRWiSjZFQN3ThO+C8s6FpiB0NaIKk2qZELmoIVNOkKsbDO/O9HK6LujSbnAgvsM18MMarafQaPeEhNNxqcTiyAxTJJXKVtrGqdFy8oW8pMqAIYzNqjExlonJL6c0cFNfNHXk+S513gl3W00G02wguwEtUzGxnwdFdEIb3S5DUGFtP+s8mp6ypSQHTjDgGPMV8X0+hl4dAxMeR9aR/6kxa6dh4w82YwAURYW0iS6UlzGG+lNdPodTopc3ZMk/YVpX95HZJRr4rFGiHRm06c9yR3eUnB0dX4qlvFgFNZXWtTqulFGjfmMgf/rqOAUCZUVhu3NaMGoMMq05PnEwtHzcANqNxogHdyj4RirPQeur0elZZTw4O10KrI8rK29S9IZZOTVgyNeFMWqsyLSkzMdUdnfCIsPCIsBPXy6mxLNJmGFhwCknsK63H2ea8k2kTFpXycvKTw+WQ8rQWyleWTE04nVpCmdGSsup2zejiyYNctHtMroNtKLtMldNHbSI4+cnh0tTglgqNoRSdNtgqKUoYHnAaO3YUgySxrMmIAw2oK5bNG8NFPzlcNH3vQU6naoElUxNwoYdpKYpMZ9u01ekMEy1Nu7kjj7PKeWfDXY6TnxyuDuxQy+Mp1Tpvb8/TaZa6aIcFSI2lqEk1yoJLs+M4zobiLLGeTu/42TH5efXTR6YP7dFVu8PBYQOMWFjxLbPNaJZMSQgI8G1jRsvbiYq6nCOf6xgbypaRSCwZtWml/Lz6aWs5xap5I1bNGwF7DBdWvfTFf47cOw6O9tGUGeYNlJIj36y0smIBf8mUhB+PXwctdU7Ihka0zEFPHxXoQtWgjgZnifx0vu20Tn7+/d59f793X12j8USZHBxsoqVzjWlaskMMlkxJWHB7vJEUNazKgvwu60QozgbrqdPp2RBjaVd+/v3effSXv564/OuJy98eSoZ9yS7mFVcyu8ZxcDglfYTzmIOIeDiu1oH8glLDo9CsOy/g4UumwKhoa3KpvKakvGbZtMFssp9ssaGsKGPi8VixG+1yqmWaBADARz/+g54M6hOKbOjiyQnDov2dt3ccHKab59Y3KWoaZDUNbQYF5xeWGj/Id6DeXDJl0KLJg+hStLq6DmNNIp492HMoZ8fkp54g2h0S3vXy06T//vr9Y9GTsvJqgIEQf8Msm+k3q9csnfDIWz/yhaL4GMP5/OkMN5aJwzkBUKhAcT6KgUb6wn+pxhItocw2V1yAT5uJTIwRieCc9dDTulrk9AGdDDrQqMmOQzztZkA7nnlngfW00oAe2HTXtexSHMeQAc0raSiuasAAtvuFRe9+88/sMbFyo1m2n/34bwfvOAeHAXVpNoZhXl6eMqUGWc/GxuYBUXCaz4yC2oSYQD3BmDaOuvRMNI5kWFX62wQg2GNAgVNtqLOz8KwwntZWLy147WdkRhP6hkWHQ2/9rnVwAqJDp9JmjYkFGDienDllVBz9IyN6el8odLeGtRzsxFiLDIzyRbf0cfGhXj6+OoJQqnVqjU6l0V3NgHNcJ/QONHcd1jUpKLuqp02qF+gjwQDGJeLtqUBd2nm3Un4ihkT5AQzrH+krV2rmTRzg7+WBlpfXNgEA4mIiAACXs4qp9Y//sPtovdNaKHJ0R/kpVyD/vbGxGRlQf0+ht6/x4E4yqU4APUFodYRSrVVqdEq1Lj2nuMWqmoH8mNoj1OmNltkgQvnOGnREsMZ6MrBgPe8c0mNYP1gihwrsKOuJQNYTAJDYP5Iyo1OWPn6U8+I5HI+5UJj5a4z8AAan/MRxTCgQepMqM8inz/hE6J5rdfozV3IvZZcNiPQGAFzKKoOGlVWlTCxoGMp32nQdrMHK4vkXl46kng+J6wkAuJZZmNAHNluMjgyOjgxuaFbS16fM6Mho7/MF3deLt37KHdRCO7nayy5bQxu0vDU3w8vLkNVEAdCBZPSTAMCU/KTAAOy9S8pKDOSWNNQ0KrNLLq+cm8jn4XKZDFlP2Bi/f9iVvMp4cptqj1C2zdmZ5KRpP3DnOO96tuSOOtA6ZNSgnpQZRaYTvfTxbJ0ygSI090s9I3Bvni+3P/zl9ocBC7DvbvAwOJ+ExEMc5C0y9wg0NYOeuRPS7taCvEVpha3zp3ce9vxdTO4JKv+kl9A3NhomTPb3NCTQzWOYmO5ybnVNIxQBDY0wHgUAmDFpGH09ZD1ZS5IzykJxJzjvwPnTdXRN65CSg2/pCbBg0sARPQ238XZ5cN0XwH2RaTDfgCDfgEDjh0+ADXOXXsmF1YgyDQY3GBhEbpO5WeCOWPh5aPTwmoIBUBIkP0E78tOAVqdPzqzUaHV1jXIdXzprwuDPfr9MX+FKXuWVPEOHZiQ/Ec115cDF6aQNxbvYehrm2mQlNslPk9BFaMnBtwgC3OqxBL7AXEbjOGhPIsICkdUrKa8uKa8xXsEmh2TOhIHUXBQtrYMc/qti+R+IfgKh/CRf+rUvP0GjXH0xp6quUSaTKYQ8TMrTnrh2C00Zj0SohwTWMw3qaTDE+Q1Wu1Qu0i20Mza0S2OgrMq8U/IzK/Wqv1QAAJhuvq1Sg0L7v2/3f/R/T7e7TR9PcUOzElnPImQ9AZg3YeCFgnOgO0OAOeMHoHGB6N+I0IA279tuACNCAw+eSh/WN6Sl7UWbDXY3mCfQuvNZUiMrqmyua5QJefCq9PAJDJFq8huJyjpFsJ8hR3rmegG1fpaaWbTTzKZIaNcHQ/EubfdJGxHBKpR6HnoEhISQj+CA4NaH1M+2K5NhPclfMmG9F++mGC5oyswxdKjlmXtNbIxcfc74AeY22E3B+Tq1Mp5Ui36mq5daySiqu36jQiaTI+uJzmtEVBQUoVdv0b34QT39CIIwtp5uQ4dFKN6Vzjs5XQfr5Ceh17fOTUgQ5KPNmqWlFTdKG4KD/FYmvZd9YHPOr1uMt9Z4dGPjkY0N5EM1ZEVb6wmZNxHqL1cJgDp0TyJCA1rcedilAi3sUHkMYWGDiFGBhnxIJ3GJPw0VAG1XfxIE8d/1W2VV9YK2ikbK055JL1s5N7GyQfHQLEMG6eWV0xkfZ3jxzSyLhHalI99FLjyrRm2aJCIsoKQMXn4MTxANwcBhq2csOsSzNHwCvFDPZ4aX/o5hmG8PIfqp5vd6yPABWpewNv8TrhFfsy/mqo4Y7nx4aIDeNgtKtLtB4NZ8uf1hc5Y0vqcfkgDm5Ofp1Fu9gqVqtZa+UCqV6PiSB2cO2n0wtbQGth25mF2RnldCBUNdS34mdZUjb/NIpI7LTxZM9W5SfgIAYnsbwp/oCgwPDaB2FLWoket4yPqXlFXfe9dMajsJfQ2Tbhvbx7KaJob1fPlrFlW/dqUB5WEgJtowyoAOAUApTTAqVSor60D79jKxNcTBUxnD+oZYv0H3QF2arSVwkQDXabXIgPpJTfjvp1NvxYZ5NSo0BRXNNNPpAQAmEvDkhKh/D79bpVUiiVQllyn0/CfmD6ZW23won7G1GJ82/iurIqEdG5vUgeFJeJc57+yxngwo60lF6ErLa+gNZ2+UGhp9YgBEh3jSvfzruWXGjWmZGpT853ImHHrcPelFWk/C1CO8xfu2ifyCEpNbIwCYPX6A28vP9jG6nZ9OvVVZXR8b5lVaK6esp1TqQVpPIBYJtTwPKU+bfatuxZzE2ibVgzOH6gnYLodRz+RCJHWJI98VLjzbnHeG/GQQERpQUl5TSrqBN0n5ycMx+v7v++XIPYtmMrIeRr9YIjTAs7ymibOePBxDp9EcA6N8VaRUNIyHscjptLKJCXA0reVtIqzZoDuB5CcABD19hFRnbBiU4blljQqVrkV1tqJSq7U8/rKp8V/+lZVfBuXC+awKKU97MqV4VYsLv352DEOE5jfo6SKUben4rgHvisJPFkOXn5RKQhIGeZcwfRTY6gppcXF0iCd9SoSU3DLC3FQJNO25/1L7F7z7IVeom2Uq+kOhVFt6KNTtbpOxwXa2acUG3QORADenOtHL9KJ6k9ZTLBJFhodIedpD5wuQ0ZTytFfyKlfOTZSrtM0KDXBZkhwvQq2NgXbYerKq8JMuP/lWVFQRAOSV1HuIhf5+3tTafL2yoKL5nkUzTERC6Z8kOXwqHWDd1HrChIYHrNCMiQql1KBBuNPyapXV9WhllQoauxSZJQdcXZo9rHcAbYO03BPRkQ26B+rSbB6fHxcBbSUBiFu1aspukolQIq2oHhBM00mVxOj4knXzB2//NWXt3IQPf7++asbAT4+mzxoZffh8Qc9gr+qaOpRKMg6DAtZHQh0dDHXsXBpkyyXAQvoPGtokVzEecoXa+CHkw1NUW9dYQz4oEXols4i6dGEktG2Q71JGIXqwNfDbRaQpqPoww5BrMhqC/oGDZyhjF+Dvbc19dlhv0hqiNeEGSeCWjDboZ9UG3Qad1pBV9xCJ6NZTrdWlFdZLJYZwJ23wOzw7Or5k7NA+AIDUAuhvXbtRBQA4erFQytP+Q0rRwsqmh2cb6pnWz47p+uNiObjjnXeW/ooJAs6fFd0jBD4i4aNHRHBky0MoEgpbpjEoqmitxUFmtKJBnZWRfjWz6Ar5QH0VKaMJ7SaNbis/KYpLK9vcYMiL93xGaUWVwdiFBFnbpcK0TTS1wb8u5HUT+YlAxfMMGmTqW3WaFtPZajcZhZ/kv6VSnjY5s1zK05ZUNy+fPUym1N57J8zCn8sos5BKymfxyM4ucORxBzrvej2rckeM1iHDRoxUa/UlpVVQwuDkA/5DipmWve4RERzoLY4IkAT4eQf4eReVt1pSD7Hw1I3GUzfgkquZRf87eJ7xXXv+zNnzZw5nPU38AghQXlkXHQRHWIcE+YYG+5EVGp36qVAbDA32a9lgd8SXHJSMuJxfm1PWVNsgq4XTyZk4ITq+pEegh05P3D01QaXR33PnYK2emDt+AAFAdjG8FR2/ekvK0167Ub1iTmsqyfIONLOsqN7RNpTvoCGbFjq8sgfm/pENZjEClFcZOqEhYxroLUayaWhcpKE5TWbx2IGhmvKSMn4EsqEI+nMOM2e59fSGBrWIJswwxteanwxjleT0EmQ6kfVsXYntvz77QxBEXbMhadas1NLn4ahtaC38RPj7eEp52vJmoZSn/fsa9NaPX4Hm8thl6L+fToOCNL+s8Yl5w3b+dr2osqlniPuX03ag6bJDYqDsl5+o91KqIlCj1ReXGPp0GWjZ65BAX4wUoTCyVlVLX2XswNCb5Y1l/Dbl3Jz1bJfkjBKm9WyZIhL9Wtr9xTBWahWeQVB4Gv0NuwvC8H70ZENZnfJWtZwH9IwH/SO1Dc2llbX1MvXy2cMKKhpXzkm8Wd6IikAfmjVMrtIumwb998u5lTA1f62Y8uKNRWg+u0d2dkaEtgvuoNAnjrNqqnez5YCpikC1ts2f/2SaYWwM8ufRhR1EilCOzlBe1cZtN+SUWowdKtXg86z92SSnl5RXtthitLW2Qb7uZkOz1MG52hD0aPbqyQ/ta7wO3Zj6BASPGBwLE30FNQQBbpLhqcwiKBQu50JJ8eflIilPezG7YuWcxAa5GsVDXZokBzjyuCOcd7JpCHuA/reFaeMwDNBFaFwwbkhrtBxEVGQIhoEqmgjFAGDITw7LtArPYD90U2I42chd4VlXtGEUQu2eLns78EP7Gj+od5trynNL6qU87TkycfTnFWguT5MJpSuk6iyqbFo+e5hGR1Q2wB4lp9NKrU8lNbNShHYYCzbUzjrx2MlkHYucd6qm3ZJBT5UHanRMR944CxHi64HuCxJcm9cyuJPDJoJ9RHqVXK+S60w94Oh1jc6mrRFqhYWtcRhDN6Z6AsydMECp1t1752ClWnff9CEKlfa+Owertfq77ognG9FXwdZ2ZGw0ixzl6er1TEn2duT59pWfBEHw2OK8txrN6pIbVnaeR14hlJ8t9hP93zMypLC4orK6NiQITgfPnilJXAK1Vq/WwsGaBQr4rzGU8LTmzksQQK5of2sDerB6Ah82IOVpj1yAzZJPXL0FADibDsuZ/yLN5ZELcFTSxeyK1XMTtx9IeXzO0N0HUzOLas+m3KT6MzFw75Gd5rJJJkYiddh6sqZnHVNvWmNAB0mqBTycL4BVIFQ6gn4kR87CMTAYjt8sa6wQGtLxHBwujVjTtHZuwu6D1z1wrULP98C1ch1swrTj15Q1cwbtOph6/+R+X5/ITugVcKOoQoMJV88Z5Lr9mSg63OnO2IbaTS2yw3oadUayRX4iAr2EOqUMPbQtDwDAzDH9VBpdUWWTbb3TOTjYTdatOj0BZo3tryfA9NH9YBHoLeiHXSIjoX+RpaCpBTUr5ySqtfqKOkuxkXwXiYTa0ZHH7Vr4CZxKp+waioQqFKpblU1FFcxHfmF5wa0KggDBPuJKofm5kzg4XAopT3s2AyaOjl2CSSSUeT9Njkq6klu1cm5iea384VnDCAJkFUOreuxSkYV6JrfHOJuE28t5JzuKY+wRnh2Qn6nywFSF2UeKLCBFFnCtubsMDeToDtTgfnKV9oEZQxvk6gdnDm1SaB6cOVSm1N43fYhGp0fTxJ/LhBb2LJmgr2tWWa5nynd3EcqwoXZw4Z3dcolzqDk4OsXJlGKyByg0dpdyYDkKGhT/JylLr+ZVrZyT2KTQLJuWAAeM0OqZ1ruyCLWLI8+3j/MOAAutp63RTw6ObohS4JVTXCflaa/nV1NFoGkFNWvnwfz7mrlDd/2eWlgJ56c5cRWO+My+Vbd4QhwAQKHSfnMiW9xcu2gSfMngXG6zW6bjGRl5vPPOu07nlMJPs247BweHTcCC0PEDNDpi8eRBWj0xf+JAnZ4oqYa509Np0HM/TPZavlneKNfx9QT44d/cz36/7CHiewh5Ln2qO+/I4520nrDsrmVS6S6kfdPJkJ8cHBzmIPuJ3KKSSP+llpBpJdhVJCW/GhN4aHR6mQ56qwS5MpxCmXw5LbEnEHn/8k+m8TZH9/VkfyS08za0szHQLp/qvYPCk/PfOTjMUYP7VdQrVsxJLK2RLZ897FZV8woy6Hnf9CFkYyfDrB5004kI9vMQC1xbhHYSvFPOe1eP2rTWdHLyk4PDVlA7+rQC2PMh9Saci+Xjw2nIaBqbTgAASiVNTIBNIbqtCO2UArWy9YPTI56c/OTgsAzVVYRetEQZTYbppL/Vr4cfLvbptqcXZ33hJ5cs4uBwODW4n0arXzY1AfUWUWn1YomnsdFkSNHPfr+MYWBwTIA1IpTNdFiE4h0etdklhZ8dUZ1t/HeCk59uwtB3Dgx95wB64ux9cVtOk/1EkjOhu13TpDQ2mpTppC8cPSAME3m3u3E2e/EdtqEdVKCON572EJ5OHBvFYSN+QyagJ9H3PMd4q/9T2+HEUy8uoC+Mf2kvd47ti5SnNRSE3qyx0n8HpAgV8PGIQKk5Eere4Kx03ju+fYP8bDG/XPTTVYhe+jRSlwX73mW8lfXBuqHvHMCFYkp+Dn3nQNrbK5y0p25LDe6HuoqYM5oM0ymlvZw8tAcwI0JdJZXUMRGKVWQcZ1nLpU4Jz1YDSvLVf1w1KAeHtQTo6+Q6voSnlen4UqN/qdUYC9ctgKPjPzqUqmqutzwqic097jrW7I4lzY/tZD2Lb9Bdf856ujr6ZV/rl33t7L3oRtTgfkRbz91k6BMho3nxKBLq6vVMHQC3ca5Nh8pPbmgmRyuU6eRsKBtoN5U0KDoAF7efSmI/NjnyNilQAnOgYO2s9eSK590JhtHkbGiXQQ93WqiiN04l4TjWv4dfdxOhuC3RTxNTrbFIe7bdBue/uy4mzSVnQ7uGGtzPnP+OsJBKGhcfbi6V5K4iFGeB824H6wmjnxxugQVDydnQrseCFJUaiVAPET/E18M9RKiVNtRaBeoY591Ro4w4+emitGsiORvaBZirnzdnOhHo5R3m65ncEtw6+al3gPNuN9PJyU/3wErjyNnQrvTiGViIhyKCfTwkItOTpbulCMWt6fiJ2blpiGOz7Zz85OBwVioJYGD8oHC3GZXUrg1tX4HCynl7yk80yoiTnxwd15WcCGVzKik2wpcv8XEPEdoueNeOOyIcrUA5+cnB4SCsTCXhODa0d1A3EaG4ReedsGvi3f4j6LnoJweHgzDnv5sznVLay+GxIVYW1buECLVgQy0pUD1UipiraE9OfnJwONSLp7CQhQekCBUK8F6h3q7eJNQaTOfLjp1M1mp1fD7bRspzuBsdjmZSH8S/u9+ue8RhGnoDEWN7KqMtQc8nJkTklzUSqkb3OKFJb35issmIaQNKAIK0nvaVn3aG899dFztmgThL2pVG06TplNG8eOq5l0To5yWqVUERatyiaXRfT/eYOB43N2rTTtaz6ybk4NJHroKDcuhcar5rCkKtTCUBACYlRAI3wmQkFDczXYddvtGBppOTnxwcLE8l9QjyFEp93ameydiGmotyYq7Vm46Tnxwczk0lSU0V1Sf2DXabeiaT4I6Z6t2x1pOTnxwcTsHCqCQG6N2EmEDMfD2TG4hQnDFqswunercbnPzk4HAcFvx3C1IUiVCxkBcdYraeyUWh29A2CpSwz2xxnPzk4HAfOpxKQkxMiLAw6bErilA6eJuWS6x33o3h5CcHR1diayrJRyr09RS5qwjFDZl3QOCddd67omKJi366Afh399u9+t0R2+SgsL4hk9RUPdPEhAgrm4S6kAhFNtSgQAl9ZzLvXVfsyYCTn66LHe0dZzqd6MXLzAzopL8bFewlIIc1ut/ITtxQ+NmpQZtdlHfi5Keb0XnZyAlPtklRCroIxTCA+jO5nwjltxjAzshP10vcc3Bw2ETHBnSClpVHDwgtKG+srKxoapYbbzw+DE8rg16wy4HbY7oOwinyk/PfOTi62Iu3KQsvpdUzAQCW3RE7bXhPkxtnWE8XEqF4J6brcFrok4ODo+vp2IDO3mGt3enj+kQZb/b3rDa+v2uBd1R+dqnp5OSnG9PhMCiXO3KJVNLN8oYHZgwxZ0OR9cxvYPrvriJCLQ3GMg8nPDk4Oo62Im9MHBwkbsz57CosqLe5FS7UWjtj8Ah/s4040UZMrpBaUKfwNu1ld6A3qI9UqNMJ9ErNpdzKjLzSlXMT3Ul7InD2W09OfnK4GXqdzlzqdWS/oBH+jebM66jAJmu2ry7NtuBZjvBvNGdeB8f4W94yPdUua68gtEGmXjS+t5SnvZZXvWJOO9bTRUVoxxQoBwdH58EkHnCIjlRglSg5mVqR0MvPhq0DzMP27Y8ObDpX7WVunRrcT6xrYiTcpUZZeIWeL5Z4ypqU/14vAQBodPrCisbjF3KRCI3rE5WZVwTcAtczoFzyncOlUZdm8/htI4YaLCIs0NJnCKCHfSoqrN/+qbTS8fHhKqVSJBbLNFCNtvMVEKu2b+yqy9raU/RycO/A86kFhZVNj84a8tGhtNPpZXQX3qTznt+gj/FxsWmEcJan3bni+e5AB9JB7pFBooxaSVl1SVm16ZXIaw5v6VNhpReP0BPYlVyDWTS7/RbmThx4/WZdu9tsNwsf4C2W8rTns8pXzkkkCHAlt0rK09Y2KpvkGlTPBABYPzvGmv1nvxePu1biiJOfHK4On9d60eXfLKYLQxM2jrDtGoTyk7Z9AMCcCQOtMtNkRQ5itEUbbbIgVEaLh5JN3YBMqa2sV8AA6I0qaEkBOJ1W2u7+G0dC3cOAOsd6cvKTw81Ql2bryOnC6dYkIizQtE40uuzaVYgYhunJ7eNCMVqSf7PY0lfQPwvzTkRnBnRKRDAeWtukum86rFv65zr8arVWX1ork/K0eaX19Hom9xChfFfRnhzujXu45O3C6BipIwwv50wYSJk29G9EKDNkOW9i/G//piMvPtlUngcm3zHMy0sqkyuFfByFQfW0rzj4X/qwviGtX0GPihLUOhmD20tVMVJJCClPK1eBeycP/OZE9sXsCilPW14rXzVz8CdH0k+lwhqm7QdSbpQ1nrmWb1zP5LrgrLWeXPUSh5uhLs2G8/16weZD3t4mWhC10YnlTJ1IhUHNbZxunb08BMbrIHfehBRtucr5LV2FLHvxJqWoGggJADIKa6U8bVpBzQrSbb+cWynlaSvq5DKlBgCQnNkmlWQS16pncrGcFweHezNnwkC6DTUyo+0IGmSdobbVEyZtNPMrLEZFbUolDY4JRKZz5dxEtVZfUN4o5WlTb9agAOjZ9DIpT9so11TUyW1NJbEZ7Ktdb5p/l5OfHBx2Q1OWgwycTK6QSsQJUXBEEA8jYnpFGl9zdNNJufP7T6ZiGJbQy4/hwrc47waLKZMrMJ5AIuI1NjaPjw/HGV/RAt10Ul+h1espL55REKouzR4fH050yCzwcGzMbQm7fr8e5i9ZMrEvtXzzoXyT6xvXM3n6hQLXUaBcoxAODjuj1rfxwU1kpWmmKSLUhDs/f1K8uWImxsYlIh71nAqDMmB8BfoWyos3GX9AuaYgb5FNDx6Z4M8prkPDk2qbVG4jQvkszBpx0U+O7gOVRzK+7OaMJzNLpF1D/4YbZZYQwvB+mpJc276YYH4FemKcvKKg6vNrm1QiMczyW1GcDxQqbUpq1uBeftVlt1bOhZWhGACLJ/ZraGi28CnjovrmunIWilCTdxuuQTIHh/0R8NpcWW1ilOZFC92ilRpllsxtnPqKU6TORcVM5r6OIUVH9A9NIeulqFQSI0NFSdoSK0KoHsJWlVZaLdt78DLAgI+Pp4+P50enKj86VQlcGZxtzjsnPzncFr2upkEGjZ1QSF/MtG5G0A3ciP6hpqtBWzZu+pvpXryp63vO+IF0Sz2ifyjDeee3NdDt1P/TgZ8jkEUO9pMAABoamtEDuH463vXGwnNwuC4oGqjW6oXkJGsUZXVyER9XqHViIYxdqrV6Pg9TqnUiAXxJEGBkAowVahStJtK4GpRm40zYyMp6BTn7Gay0J+fhwXR6vVjIV6i0HiK+Xk/weJhQIk1OyUe1onQwDNPqCJwH6/ORF5+XX9InJuLQf+lDzRWW0pg/Kf7Xf2AF69mL11EZk7nckcvBZ1XBPCc/OdwYYXg/WdtI5em00nHx4QDDFI11CnKJqu1HNKZCaoN7waZzjGy48cbpgQIcx5rrTehWJfpSmhYcGOWnUqlBy/eODmz6rww+QfX5jF2ZTav/R09M2lC6+9+u8HStSCinQE1TnH/TEac7MqYXYBPd5DDZfLAKBcNmtg/PVLgTRgYwfU2DrEGm1el0vHrS9gKg1Wpzimp9pW2CBu1Cr9mva1bz+XwvUuziOKYzWjkiLLBdKTpnwgBUHZWSecNCuzyXg89m+cnB4ZYg69TY2DysdwABiJioMPpl2Hopks/QywuZpdFBMIAIALiSVzmwh0+KrP0MOAJZT6lEzPwWNKMu7dKva1JS9U96vZ6yntQKAh7uQSamKC8e0KQo3YwybKi56qgOwCoRymoFyvVe6laMGDrcyjUvXL0EXBMkEoP9pKilCAldTtJnGIf1PgQAFzKg6aRbz/goX9TxyCQ6nc5DJFBrofnTarVG1pP2LWR6B2CgttFgN9G/9c1QFHtLBAzraZxKYjB7wkCLUhSmktodaG9dk1CCJfVCuNPT7hwc3Q1BS8c5DICYKNNiKjmjNDmjtKKqHpnO0CDfkCDfK7mw6IcgCJPyUxjeDz1B5lWrNTQtbms9W6ltVNQ2KpUqDbKbYpFAJBLQrae5/W+tjipg1g/MJofbXzXV+WnexHibBtq7BGxRoOxMH1W1jL4wR21tXb/4Ueh50rNrn3/2CfT8Rn7BuEkztVrjeBEbSRw2+I9DP1peZ/rsJZevpNCX4DheUZy58rGnfvv9KHAp2v2zNjfLesUOQ88femDZ1v/bSL21YPEDp8+c7+QOVDep9GplYp8AkzoyOaOELjlDgwySTavTAwxYlp8CDApPlVaPY1h0dM/LV85a3pNn1z198LffxSKoUnfu2T3xjtvR8q+++PLpZ1+iFz/J5Cjh1AZ92+FPlqRoaCBqhmIXEdpcV+HpF9ISg3AmbDGg7KSuvoH+0s/Xh7GwvsEwOdeVCyd7RIZXVVUf+O3IqJGJCYMGlhVl9IgZrFSa+NmxDZ1ORz8okVAokXioVCq5onXnKTmDwHG8MO8qcNM/q1wuR0/OnfqjT+9eDY2Nf/71j4eHeNrU2w/89NXRYyceePjxjn01Y8gQvZgpOQNOH0SZTspuIhOBAfDX+Vz0vN3op54g9Dqdp4hXX19PZcAFAoFEItFoNHJZSy0UhgGCQNbz/LUrHh4eRYVFp/79986ZMx54+MHxkyYlJo5tu2EbXNXZjKgoOcZp3sSBqCOf5ZmXbMHJvjwrDCg75ScAIHbACGPlwlgIAJg29fYekeEXLl6ZNW8ZWhLXv+9/fx869Ou+KdMXAtZzLSWNflAP3Lf0vS2bPv7sqzfeetfk+t7eXnmZFxmtLV0IK/+sM+6c3Kd3L/qfFQBQkHt1xp2TQ0KCKiqqOrwDerVyeJ8AAoDI8GATkjPYYDrp5xfGTEn5ea05wMpvKS4unjByNHLVAQALF8zesfvDo4ePPPfUM8hoUjy8coWHh8fOHbvf2PS2t0Tw1AtvfvHpjtmz7rz99omnT51uWauN3ENe/Pj48JsFxb2iTXQqQcweP/DQqVYbCjqKeRFqYt+6Eq6dnR14af1TAIAl96yglmRmQZUxOAHehN2Md9569UbWJQzDvtn3E3Brnn16DQBg2f2P0BcuWPwAAGDVCvhvx0BeNoqEJmeUlFfV0U1nWLAfRhoDhj3441yONRZCGN5Pp4euNcod1TYqlCq1RMSjcus8Hs6wngCA1WvXAgCQ9WzQwncfXvUkAODLLz9Wa3SWFajOjBdPrT6bHONERUWNx4m6NM5XoKyVn9Yzaco80G1Y+fB9eTdu3j51/sTxY+67Z7Edt5x3o6hP7yjAGp54+oV+ffs0Nra5zlFcW+JhOi1jJcP7QBXJFwiig2Db42AfkUGZqgyhA2MwDAy0Tn7imMHM8Xi4hDSbaDiTIXNFQPee8RExeTjeEkFraQAsUFV4epruKMpApdERBJxSCapkDDZ+1un1aHIRDMNg6ahOP3VU/z/PZQ2LhZpxRP9QlUrlHiLU+QbULfnu608AABs2bQZux6Ch48srHNIAorCotEdkqMhIHzmLzMyczMwcxsK9n3wAAPh5/8HObx+FmKNDYQS2XQZG+Vq/ZSQ/dVCLAonYRGt6OgIBXOHyxYsAgCZd68nPyMxJHDbY29tLqTCYdQJvsynvFi++pMSq+ZAH9qTGOKH4hN2Lf0xWhrm1AXUD+ckg7erpkJAgAMCrG9/56JP/AbfDQdYTeqmfrLNyzcVOqgMdcduwXtE9Gxobr1y93uGNCMP7XacJzTRrh0dZG/rExZ6NNInZ2JIxqiM1n0KLlcva2Je4ODjK/uLVdOS8U1RWwZClt5e0sbFJSGpYBo2NcFAmTk3maTPY6MDmc9VWiVzrRCjoejXKKVB7EhwcWFdfDwAICQnatOGFqqqaXw7YQapwsIGoqMjDv30HAIgfMh64EZ5SKbSGTcyIJPKy6YX0YlNmtEmmIKfztDTG1CQEAVJk1t4VWJudd6YBdb+xm5WV1eNvn03+7HhlRRkf7Xr3XPLF0jLW9eDisJUpkyeisExkr4QOxO/YjFwOc/RSiSGRRSEke+5pNJrWei9PYZNCQ5efw3oHxPQMNeNJG6pVz2fAknv6SKpBUb56+1lPMyK0i8wou7Lwru6/U2i1uodWwMzmc8/ATC6HS/PpR9u++/oTgiCCwvu5mfUEAOTmwc5yA+IMo5gogoKggZNpCBwjUC6eqlpF1jOxtxkLSNYQJGeUnm8ZSRUdJAkJ8g0O9L2SB+M/nbGexk1CrYBwQwXqTvJTIvHw8fYuK28TSk9Nz4Tu3sA45+0Xhx34+88Dg+IHNDU1x/Rzn9nM6ajVMLFz+6RxjOVx/WLRoCyqORMa3GmwnuQwqhgj+Zmc3nY4QEtN65Ez2UgIDoryvWp1Kas9RGi3iYG6rvy8kXWZz+eF9hig07VG74cOGQTbXly84tRd4+gUh37dNyh+QGZW7oQ7YGTGXVGpVCKRoZQKgWGYp6cU2VZqSvomhUanhpUDiWQZVgzNepqzm0iP6slRAPaynmbai1jGUWkl57jw7iQ/AQBffwsHkn/wbusE0UKB4NM92wAA23Z85NRd4+g4y5YuGjkisaio2L2tJ6zN+uJbWE7/6MPUkve2bII1v4/CESK42JMgoOOMrCcqYu1F9kBJTi9JTi8pr4RjAaKDJKHBfoyxAMhoHU3OcZD2pGius6qUym0VqOvKTwDA8y9uXDB/1t1LF95227C9X3wb3bPHIyvhSJWNb2ypqal19t5xdJAd295GyXfj5iM//fL7408kuc2Z3bBp84qH79204YUZd07+/eAf9927eOCA/kVFxUePnWCsiUagVjTpKtJbh5+G0fSmMYfPZmMADkK1o/XskAh1FwPqZvIT0TfutnfeenXlw/e9/cbLZJyoadHSh66lpDl7vzg6iOuO9O8Ykb0Svv3yo2lTbx896jYAwDf7fnr6uVfoK+haWot5iEXR5Dis4JYBVDrzA6j+uHzL7tazo5FQh3jx2Fe73gBdi0sUz7Nz+gf3PsyftlhbsbD4+V2ufrCOw0GHGSxWD+8TEBXibesHS6qaHGQ9jUVoe6kkzG1deA4ODjaDBlBd74hxDgBswf4itKvjCC4hPzk4ONhPvomJ4yu6WIGyIhDLwcHB4XgI13bhXUh+si2w5SC6yWF2q4PtJodpY3sRR8EpUA4Oju4D4aoG1C2rlzg4ONhGVxbVO1OBstZ/5+DgcBXyO9JexNUMKCc/OTg43E+EOk2BcvKTg4PDGSKUcDEDyslPDg4OtxShzlGgnPzk4OBwAxHqcAPKyU8ODg53FaFOUKCc/OTg4HAPEepYA8rJTw4ODlaKUMwluzF1jfx0UEcvDoeybuMeK9fk/r6uSKTjx5ja0mjZPp2Z+G4mP7lLi4ODnRSTsqbrh+o7dHQ8Nxaeg4Oje0Kw14CalJ9c+oiDg6PrU0mOS8dzCpSDg6PbQriMAeXkJwcHh5u1F3GIAeWqlzg4OFiFg7z4LlKgnPzk4OBwPxHKdxv5GRwUNH7suB7hkTU11b/+eiAnJ0enM8xkzSAyMtLLy6uxsamkpNjCBsVicXh4uOUvLSkpUalUjIVxcXHFxcVNTU2WP7tjx87c3JydO3eaWyEmJsbCx/Pz8+mrUS979eplPKe5TqdTKBSVlZUmN8Xn8wcOHDh//gKJRHLlypV//jlZUVFh/Z4AABoaGmpqaky+FRQUdMcdk4cPH97Q0HjkyOHU1OsajcbkmgFBwZ6eXkqFoqK81PLXobNXV1e3YcNrllfj8Xj9+8ctXLjQy8srJSXlxInj5eXlltePje2XmZlhYZ2ePXvyeDzL30v9OQAAvr6+gYGBGIbV1NTU1tZa/uCgQYNWrXrkySefMLeCl5dXUFAQY6Fer1epVFVVVVqtlr48Ojoax3Er95Px575165bJvxTjx0AQhFqtqa+vk8lk9OXh4eFisVij0dy6dcvkVwcHBYlEYp1OV1rW/p/bkfVMnSoItf+88F0/8ZFUIj3w08/oN03oW6PCu3btZJinHj16/PXXCfqSKVPuKC42bUaHDUvct+87y1+9cOGCjIx0+pJx48Z/9tnexx9f/fffbb7ImKysnAsXLjzwwH0WVrDw8f79Y9EFn56eSb0EAKSmpgsEAnOfOnjw96Sk5+hLnn32uVWrHmGsptVqR40a0dzcTP8KC+zd+9nWrVsYCzEMS01N5/OZN+lffvn55Zdfoi8JCAg8deo0n99qlV55dk3BTbM/mwtpN7KycioqKiZOHG9hr1ateuTZZ58zvpeMHz/WnCH7+ef98fHx1Mk0yblz5/38/CysoNfrBwzoDwDw9vY+c+Yc/c9BEMTw4YkyGTyxJpk8ecquXbst7MCSJUs3bTJ7zRYVFU2fPk2vN+ivS5eueHp6WthVk1+EfngpKSlLly429267l0NgYODp02cBAImJw+jHi+pA+Xz+sUNHAAAz580xliBdPnE8xhYF2vXyE8Ow3/cfAACc+PvvHbs/zLqeCgCYN2/+5s1b1qx5QqlUffrpJ2hNHMeR9Zw7d3ZOTs7YseP27v38+PG/4+MHMO7biIKCm8899yz1cvTo0YsW3XX8+PE//jhKLWRo2OHDb/vss732PUD6PljPXXctam5ulcCenl6zZ89+6KGH58yZW15e/t5776Ll99xz76pVj+j1uscff/yff04iwbh585YxY8ZeunRlwID+ehL6PvTqFb1mzRM5OTmffPIxtfDGjTzjfcjMzAYAnDp16sUX11dXVyPr8P772xYtuksoFFJ2HMOw06fPYBj2zoYX0lOv9ezV+633dr353q6H756rUatBR5k+fcazzz6n1+uefvqpY8eOAQD8/f3ffPOtO+6YfPZs8sCBccYOykcffRwfH9/uljdseFUoFFEv3333PZN/JgzDLly4RK6w9bPPPgUAPProY08//czFixcHDIgDnWPPnt2//fYr9VIsFo8aNfrZZ5+Lior6/fdDs2fPpK/8+usb2/WHKN5++x0AgEqlGjx4sIXVqOMVCAQRERF33HHHgAED9+8/8Oyzzxw+fAiagurqr7768oEHHjx3LjkhgXlWP9n9EQDgwz27HWc9u0aE2lmBdr387Nc3dvfOD0/8/ffbW/6PPhJJIBCkpqYTBBEX1w8t+fLLr0eOHDl69Mi6ujq0JD5+0M8///LDD9+36wkCAO6+e9nGja/v2bN7+/YPTK7wxhtvLl68BD23owK1rIbMKdARI4Y3NjYyVvb19U1OvkDpI2plY2vyySefTZgwYf365+lXKV2Ynz17Zvnyhy3smEgkSklJLSgomD59GuOtzMxsDMOoHX766WceffSxBx98iJAZ4gYeHpJPv91/Izd7w/p1JjeeW1x7Lvm8ZQV69WqKh4fHsGFD5HI5ffn//d/m+fMXvP/+e/QbgFQq/euv4/7+Aeil5XNu5Z9p5sxZ77+/bdu29z/+GBoLxFNPPf3YY6v/7//e+d//vuiMAn3rrTe+/vprcz8G6qiRAh03bgy6gVlDenomj8dbtmzpd9/98Pbbb3311ZdWHu/AgfG//LIfAIDuu2jhxYuXvby8Nm16fd++b9GSyJheE8dPeO3lV+pq6+66ZylwMI4WobirRz8Thw0DAPx76j/Gco1GQxAEPRo4YsQIAABlPQEAaWlQri5deje1JJzE2Otsl6ysnMWLl9y8mf/UU6aveTZQX1+PlDi1BHmXxlosKQnqi2eesUH8olNHbXz8+Amkt/6T8ZpVVVX0lyiAcPr0GWqJQgEv/t59DXe+juHh4QEAYFhP8j4HFcNDDz1ELQkLC7t8+aq/f8Dbb7/F2DcEjuPo6GzagaeffgYAQDlAiB07tgMAZs2aDRyATqfLyYHWLTIysmNbCAsL4/F4f/31V0pKCjL31n82PT1t//5fAAC7d7f2NJgy5Q4AwGuvbaCW8Hi8115+BQBw38MPAtdPx7t8If2FixdhH4q1JoLucXH96PdJDMMYcW4UM6LblL///ufvv/+JiIiwdTf0et28eXNmzJhOEHaeN9WOGOcTFAoFAKBPnz6M5Q0NDf37x1qOMDJAp87Hxwe9RAGBtWufNF5zwoRx9L8LjuPGftyNXOj+82y/k1Egp3XUqFGM5TJZc//+sWPGjG4r3NKGDRtirLYQnp6e6Ohs2oGpUyf37x9LaTEE+mkxcnR2BGV4GhoaOvZxFIB64ok1er2+sLBQIpGEh9twLWwgPTl070Q0NDR88803SAujJR9/CM3rprfeVKqUwPXBXX3sZl7+Db1OHxAQcOLon1/t/d/o0WNMrubv7w8AOHOmVebQg3eBgYGd3I0BA+Kys+E1z2b+/fcUqbvTqCUrViwHABw6dOTy5atvvPGmheyTrWi1Wp1OJxKJsrJyTpw4OW0a05FHSCQSAADSTXQyUqEC6tmzney/Be6//14AwP/+99WVK9e2bNkqFArNrVlcXLxo0UJjreoI/vzzOJm4s0HZWc9rr21AzlOHDXTv3q230sceg57Bu+8awuXWoCGz9owShTff3KTRaDw9PUePHhMXF9erV3RObq6xy+jskZ2Ea7SzcwTTZs/YvX1nbGxsRET4F1/8D6VB8/Ly5s5tdZRQ5UdpaQnjs+hejS5jW4NfXYPJpGe7+3nu3Hm6FsZxDMfhz1oul99110Jq+ZUrlydNmvDPP/9JpdLFi5egGK5arV679vH//rPtJ24qKBa3Z89Ht99+R0RExI4dH6K/S1lZ2bRpU6igATrzdXXMnHhtDYzZebfo2Q6QlZU1duyYM2fOSiSSuXPnzZ07Dx3a+vXPHz0K87/W09jYaJcfxv79BzAMKyi42fnMyYsvvrR+/YvUSwzDKLM1dCgz+YOy4cZs3Ljh++9b60yWL19Bzw7dvAnTCcOGJXZyVwEAgwcPysjI2rv3cxRSe/zJtcBdwN2gdQhBEKufXDt5+rT7H36ooKBAp9PhOB4bG5uVlfPQQ4ZEB4/HNxnsQx6WceEke5DLZcaPdj9FWU8cx/l8Po7zrl27mpAQP2zYEMaa5eXl/fvH9u8fu3PnDqTChELhJ598lpWVY7mE0BpWr36M9JdHpaamajQaHMcjIiLS0zPfeOMtaveov0Lb/SeXdO7vUlNTjQ5t69Yt1KFt2/ZBVlZOu4Wcdmffvu8GDBioVqunT7/TjpvFMIzP5/N4vKqqqkWLFvbvH4vCMnRkMlmzKRjFJ2vXQrtGv7ugTNeqVas6uZN6vf7zz/fiOI5h2Jp1T3ZxmMuhItQdFChFaVkplfN9882377rrrhdeePHXXw/UQ2DuKCDAkGalQFVySiV7wzHDhg3twKfGjBlFZeH79u178ODhIUOGSiQStfnCoF27Pty1C+rE4ODgw4ePenl5XbhwafhwmKPrJLW1tYsXL0LPn3xy3eOPr1m8ePGuXTvLy8vR1e7p6cX4iJc31J5y8/WSNrF372d7936G6hD++ONPX1/fixcvG99LHASGYceO/RUVFVVTUzN2bGvstTO8887bVBaex+OlpqYHBQX16dM7Pb01PkNx551T283Ce3p6SiRSMp5zmFrI58OQzrPPJn36KSzD6gxbt25ZvnyFRqPJys4CbgTu6vIzYVDCpAkTjZe/8spLKJX88suvks47HO0wZAjzmomKiiKliulRNO5Bbm4uCggmJ1+gFxhERUVNnDhRKoWXDZ3KysrbbkvU63Wenp4dFqGJicPnzYNeM4MdO7YvXLgAAPDTT79QqZ6ePXsyVkMp+FtFhR379tDQ0AkTJgQEMEPb9fX1o0aN0Gq1EonEjgFfC4hEoqtXU6Kios6fT7aX9WSg0+luuw062ps3b+2wx43KP8k63xjq0aNHD7QwLm6ANRsRkKfU3EgzJ+I4EeryWfhtW9999aWXTb6VnJwMAKDywuTINmY37P79YUmzBV3mHly8eBENETlz5hy1cN68+R9//OmcOXNNfqSkBN5yOuzqfvXV15s3bzX5FtoTuik3Hps4ZBisOVNYEawwybhx4z/55LP777/f5LvZpAhCdU4ORSqVXrt2XSwW79y548EHH3DcF8lksmefhVVT+/Z914EiPJhImAYDCyjiQX+gzW7fvsOajWzc+DpZj9FOBbQ7gbt64+SKCji+e9XylcZvPfEErKE5fvxP9LKgoIBx3fr6+gIArl+/DroBSPf5+PigXAFVorhx4+vGVhLHcaQ+OqwmUK7/44/bVEEiVqxYSY+1oestLCyMWgHuD2bII3UMVP//2GOrja0kjuMDB8KBMcYDDewLGomEYdiTT65FsRGHcvjwITTM/6uvmAX27TJxIvThzp0zkWtCw4qioqLEYrHljYSEhCxadBesKVxnonbNRUQo1u0U6MrVsNji7iVLPtvzSZ+WIoyAgMC9ez9fsmSpSqX68ccf0cLly2Ht9MWLl9FLPp9/4gSsVVyzZjW1tX37vtu377vQ0FDgjixYAH3q559fjyymUqlEqfb09Mx1656ivPX58xekpMAhBs88Y0O1DTp1Xl6GaCYaRj1x4qSTJ/8ZPvw2tNDLy2vbtg+Skp6HlYCboFoBALz0Eswm//fv35RPvWsvTA1veQOWW5ukbyQsSrOARqNBRd1Xr6YkJT1PHdqMGTOvXoUFUm+/bchiWYNUKkVHZ/1HAABvvfU2j8c7e/ZMXV3dbW1BFtzuoBGcw4YlMjzuIUOG3mYGVAWxadOb5BCDTSY3e+bMaXQ3oi+ktjB69Jh77rn3u+++R0VySLG6LITzk0hd3LlOLpcvvfeeH77d16tX9Me7dtObiSgUipEjDZcuCoPu2bN79erHs7JyVCqVSASHM7/++kb64BMUQkJvuR+ZmZllZaVhYeGHDx9BueBHHlm5ZcvWuXPnrV79+OrVj9NX3rz5/44cac0ntAs6dXSBP2RIwrVr18PCwr/5xjCMj4JealNfX//iiy9s3rz5ix8OatRqAVmw+cfBA8VF0GPoMC+99KJcLr/vvvtXrFiJNC/Fnj27zdXMm4TH43UgtrhwIcybjRkzdsyYsYy3srOz582bA+xNc3Pzzp07nnjiyf37D1AjmOGQ8w93mfvIggXz8/NvhISEmOvMRKYTXj558t/HHlv9wQfbqIVff838mwIAFi9elJoKb73sxJY5O7tkLLzT/Xc6wcHB0yZPHdg/TiQSJSef27fvW5ONy/z9/R9/fO2wYUPPnj37yScfM/y4fv3gzy4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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "All required assignments and the order are set; execute the volley exactly once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-78fcd0207d36", "created_at_unix_s": 1788842382.529462, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 6, "requested_group_seed": 6, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [6], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "assistant": "CAM C shows S0 has the clearest unobstructed lane to T3; assign that target first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T3", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "assistant": "S1’s clear independent lane is to T0, distinct from S0’s assignment.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "The selected lanes do not cross the standing teammate; fire S0 first.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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pRP+eMUVVjWJMl1NUJ2vSTB6eXClTSXB9TlFdx0Wo00nzahHqWgUqEAiiomEI6ZVXLSRbho8YkX6qpUaH4nJWFgDg1ltNxd7xZMZp/ty5AIAhKcm2/+LVnMs9kvv06dv/02bn3en6aWDfJL1eLxQKJRJJfb3Vmj4fH1hraTQyq3jiYuALEhkVNSA5bsmjT4699fbI6Jiqyor2nQwjX9zOMiYM948bJAqI4omkBq0pnaXS6qnJI6YRQtSLiaYOkf+vrG5nVaO7Fr8TisUCMiNUUGSp7gqAuDAJuqDTmSISY/vZFYdxLnqDcefBDKWaGRVJHTXx8sV0TK8e2jf+XF6VGNNlF9Y1qbQTB3c/fLFkxsg2viBuKf9M896MvGMKVEkmi3v16mXtgOnTZ4yfMIG6+vwLL6ALM2bOpG9o58+//Grtcfbs3i0kgX1H/0BBJJfL7TnDwwdgDUfP3nDCgjUeWf6UWGy5krxNBASs66wn6/yHDbOVyh83fjyZrLAc1sRxPPNq8RebPrxw7rSPRLp1u2XX2xrU+VssuHEYwigvOq9tqsZ4gtB+UywcYHLuW66hXU6xnp1MqL9I7nh4F22deZ6nLl6vqjX7zGPg+pVz9U2aW4f2yCmSAZ16SJ+4ourGe2/rdbmgVoTpcotNHSisEqFejGMKdMeOHffff/8zz6567NFHLB6wes2akJCQ51Y9u2MH7MxdtGgxlGz9U1QqU9KD4mredRzHQ8PCGO06iA/ef2/a9OmfffHF0iVL/Pz8Tpw4YWfPu6wOVtUAguALBHqyhJ5BXHzC7LkLZs2dP23SWNAu6ykg1Kufe/Sz7//8eOPHJVfPnMtpqc6jgwoJ5swy/VowMOhhP8nl66XLHnnszLkMgUD46lsf3r2wJQaq0ugIgigsr71Mq/6jmDd/AUEQarWaupUcW0lk3ygzL92neOmj39CFLZ9NoV9FYNjvmz+F3pZMbFIxFRXVkfR8HRxx2XKRTkRYYFFJFeOvmBDG0a+VkO+PvUT2vQJfJ/oZUDdF/GfxoYSt6sZe+ghm5+jImrRR/mSlgeO+ifmjuQitTpd3vcBgYLovOI4FijQ+uD67oLa2UT19eM/j2eU8g+ZKYV1VvWra8J4HzxfXNaqD/Vp6hVnSfZTmpSLUMQX69ltvQkM2eXJgYEt/LkVUVHRISAgAYPfu3QCAPn368Pl8uVxeV1enMuN6Xh5MKG3dZvEPXbt2DcZJx4xN6d8fAHDv3QvtnBhiNBoL8uEjv//xpxYPeOvdD2G16ZF/QQeoKC9Vq1U8Hn/uPYuH9omhb1I/X6mf79PPPoPjuE6nK6soR3ukfqYvNv0yOuG7ZsPKgZ7Jyb1697bnr4vF4pdeho3td82ZDZwHQRBvrIWVOt27J4YNhqdkW4pWVJvETkSYhQ8Da1dH0pM9+2SENwJusa02MtprCvjGx4bnVao6P8JLAKKkpMLceqKfSRGm65MUXVqrQM67rFF969Ae18vqxZjuSmGdSqOfvRzOMYAJsT69bG+AreR5TjDUMQOqVqu//BKavDNnzw0ePJh+U2pq6nFSJ7777jotOUR57RvQ2m74CBosc6bcMRnea+hQPt+yCt65Y4dYLP7u+x90Ot2ksQ6MjF3x6BICED179lrz1rtY64zTux9uCg0N1+v16xyv6UPyk2LZvVBaPrj0ieVPv0Tf3yc24KN330577nkcw55YDOO2bVJUVHT4MDTo330PK59sM+n22/87Dl/ngoKblRXtjJxao6y0FAWgX3rZcoOACazFejYPCcZKa2F4p6JB62HrzLWWobQx09ihjMKUeNOMmE6EqKioblJYbqyIkihu6Zdwo6xBQGhHDuhWXN3kA3NHskaVbsygxNG3T3tgUu/HlixFqaQXpifa/kudbEPTHJkW6ik2FPOnNe3Zydat226bOBH+mOv1586exTBs2PDhyCH6e+/eFSseR4fl3ywAACTExZonUhB5N/LFYvFTTz6xc8eOnj17/nvkaObFi9OnmSpAExISjp+EKaY///jjr1++QjsHpw5/Ku3VU8cOf74JdpSagzLv/gEB23/bhcPMMigrKy0tKYqKjomNhX01BqNx0d1z6pCn78g0JoYBhROkEnt++Nn3qAipqDC/qqIsPDI6PgF+aglAvPjU0txsWH1F8eehc0aC6N3bQpifx+NlZ+fAiU2n0xctehAAsGvXnp49rbaB5ubmzp7dKjhw5UqO7WEiDQ0Nc+8xhQgO/QOHFUycMtnimezfA+PIOef++/a9Va1ceBoVVSYDGhlmGuxyMvNmTIhEpjQUEQ5/ojoNfUWe0WhM7RHiI+RHwiolGq2tMQE/S6bnmF9WHxMCM0uZCuhguYthwTAkKhG2IXrSPvhm/bOLqKsaPXxip2osdwnn51zt5Okh6+1eOskjHPn2lDEtXfrwgvnz8m/c4PP5I0aOHD5iBIZhZWWlCxcuoKznsmXwZbp586Y16wlXmPgYrnb58cZPLN5aWGha0JyynvYjb2iYfvu4A/v2GozG6OiYW4aNjI2NNxLGf/7eNWPyONvW0yLm1hNqwPy8ObcP373zJ71eH5+QOHT4mPiERJ1et+fPX2ZPvIVhPRE4hvWJDaA2ar/BYJg/H8rV4cNHBAdbWDWXLBTVlpeX7dmz+847pzCspxMxGAzPpD0HAOg3dJzIh9lQO3f5A2hbsfopiwpOo7UQd/YMWj+RSrMAhXutJ9VDpdQaAU9o2ixBt54qreFULoxNjwy1XLPMbkf+R+CVCrQz10RyaFiys/qOzBWoRmFqBfGVtL97MiS2R21JqyZXBjklzpwMYk5sYnf7D+4vgfXk3eMjqT1zlz/AOOb3Ld+ZLhEmBVpRr64UWJ5YyAb0FXmDE6FktqBAKQh6eBcefIp8au41oNoyKBXHpERB+S8z/ZzbHJEF0RuMP+3PHNUn3IYIHdDN1Kvaaax3YP1OrOdoZgMIq2DXpJyOUFld57pFJUXSAGRDm5Stalkcsqe2rSeKn3amPbUNLeVulbnLHzDZ0OaXXcDDhgXKztS3PbDVLfj5UA3EVp9c6/AuW8BxnHLmxqX2ICzNxzI3pnweThDgVG7VyN4siqukedEayHzvWavDBTXzbUK3p5QxraiCIQKeg2djJL/SEWEhFu2pu4wps5LJw2lUmSIMbcZ26eHd2FCJu1YhpIDpUKPx+OXyMSlR1/JLeibG0vtQLbeiRoUCAswY13f3MTjDbGRoozURymbyTvzIZhHKXgPqFufdHhHKpHnxb7oxhfXtzYudAOtRYHNuFld2j7PQ95IQ6lNYw6yldSmXlKHIi3eIyKB2dii4V1kzohObXv84MjyICk3Ehpp6k9yLv7+fXG51/IK1sa2om9maCE0MwFksQjH221D2GlA3Ws8tn3yoJEeB2EVroUm1Hun1eo3OKPURguZSKh+x1XJCgiCUKrVCpbNR3d35NrRNCd0SAwVg1KDuhcWVGNZplUxOwzy2u2L1U4zQhHvRll3lNVf7Hb9cftugmEIrfaiw9bm5FVXf3Io6LsX0kzwmvOl4lYUBtWy1oQRb3gA3GlAB2Y7pKHnHHagC23c4HTgZIwjw6bWIOd2j6kbbQ56u5V1nTCNmLIkRHRlCWklSBjVroXxyPYwbZQ3xYdLpd8AeUDrFzU7ZtmNWvzaupqJKtun1j1uS71ZANaGAraC6YB+hw4vRuzeDhON4WKBUqTX6+/v1DBfKmyw2m2GU0bH2K8x4Fkh+NskqfINaUoWsAaNfYa0IZaMCddR6MhZKch3hSYNt21DKelrzrRKjA8vIEe7ktDTmd5KHY3qe+JNv9zzx4DRz6+lGqMhgi29rBQwDMcHiIofrxDjaoFGlo1bARcLMymeevb9eDopQwiNsKBsNqEMQoJOsp0MwFsMgTxOMHQKHMR87f8MU7CctaVRECBoLdKOsgUculJ4Y6WfNej48NrqTRaiq9WJHNqwneg/UWoPUxz1hNXsw/6AcPFdgV7uYu/13rd7ow+PBGGg4rQvALJpLtP4/Ne1Fo/GwuIqngHu6/CR9xs4jPKlVAyuda3nXBXxcwMctLYZRXlBs2hjrYRSVlKPFMAiCCAw0pd3zCivyCivYoD2x1olpG4cBAFA3p2dhsVmTHtt1O/QBuKk9WnstmK1WVMp6wsVLAJhyq9WsbJPMyT3BLuvvZF1pPe7R1tNGm1Pnk9yzR5vLYFgbmKZUaTEMEwlMPto/RzMwch1wwCYsyk9WhzxboyrL05NDsCjQb8Om12FHnDXrGRsqEfIdjpk6HR9RyxI4zBYASzaU6qRCTwGwmDRPtqF8D7We9kyn78xIqCG2v3ryc92aPaiGA9vq930BCKZ954fEhC78n6jbANXVU7U/v2VoMtUPEACIBKQDD4AOFyWRMX3VjP+pfQL8967FmoccU0jDY4Y9sTZvz/aS04fo+3lCUb8Fy+PHmGZ6GrSa9I9elOXDLns6t67dRhiNR9ZYzYHeuhaOjPHBof8OIyRGA66o0eUeM2QdJNS2FjIhYxFyIPQH7IPXeon6lnb+8CBkNP3EvPkjwl6eYwq/VNZrX/zyvEhgtaJ3wdCARSMDp2009Rxb46dlcWcLVe/vt+BP/LQMzvq7+4tixv5RSZJ37oo0Tc03dtu6P//XU5Wyejny39ExM1JDYoNFWw6UtUogAXAwowDJ6gCJ4Nk5yQqVFg2Q9vPFb+vZ45+s+p0X5QU10KPPbzBSZUzsSyVh1m5gVTCURQbUIVyQeW8/igc+AyL4I29UyAmDju8b5Ddpid+kJenP3qGqKLztlp7IruLjHsKHwfZQoqFSmjJOmjJO9tNa1UU4Llqt0Qb5impl8pCgFtND+IYZpUHkYhvMMCjO50vDY/iSVr3qSbfP6zN3KYoGKCpL+D5ScUDw6Oc/1GvUB9LuNmhbUrfS8BjC5nok0nBTOyZORdXCEgTdUgVTnlblnqz4vGWBFgocw9DEDTYztNkFpltPdOHWfoEjk+GLTxBAqTH4CHkRgcKtK4efy6tbudNC/W+oL/+piSENSlsvIyI2WHCTNFgWb2LsCZLw/liRgExntVyvUqnjwiRL70hadkfSS99clDW2jBrwE/OCfFt/fzEY0qWCEpFhgUG+fH8fHo7Bnw5keOem+s9N9Tcawe0f3kRzRthAmsf2JvE913l3i/yEi3SXtipU1ox7BIgkgszdwrM/w0qRrCs+Ql5k6gR84boxH+03fjDD9GPqH44Pm0fUlRi/epS8H8Zb8VPgwv8hA2pP36RtEifdhaznv68sVta2hLT63PVw0uT5Uz/+Y/fyqY6uD7X7sSn0WnphZGLM8z/79B4V+fD7VV/CgSMWgd9XlkMu3UxHIuKNTIZm5d2/iqgXiYdjq6bHDksOTonWXC5rVTmUmuCz4W7Ylu5cRHxs15NwdZP39tX8cRHOXsJrbwhFPnEhoq9XDl23eNCGP68pgdVWhYoqGc16wh8Gg4HYc7Zsz5my28YMEQrg+/LJ/oI1M8NHJkn2P9N9/Pp89ojQtFY2tI2PEHtEKCtioJ7ivLf8dXFLolyfDCfbI+uJUGkNFRlHiILzsKgnzLTaDz4TjkAmvn2iOetFlL8Ba5WCHvw/Kv1CGA21Mvi10eGiv49kOHA+fEHfeXCNgN3Lp9KtJ+wB3bEtby98eSe9057ESJYylNoy8uV/Lb/TCDBRv/G5eFKmIsTiVsZn6TARYTRz8hAlP/vHQS3/1eFy+k+MwUgseAeOXn3m9lapm5+WxW24O8oV4fc/VsBPyxPby5D1JEfTwz9zo0Ix9vkDAICVs5KtmRaLTaiIkkZ854l8NCT0icnd0n6rOHldycPBmhksapBvDeEpwVBWGFCHQIWf7vrrDPlpzsD+fdEFYv8GYsdqUF9uyrNEkNWgBpjEwKBlxQiDHhBAlDSU+rwE+cJuemRDk6Ic6BjpPQsunXJ28xqLGvPqX98SBCEOtDJ8yCEIIv0jOEA65W7T3ELPJcCHp2qUo02vVQMCYHq1slFO3zQ6w9cHb67ZZVqtBI7OHOQfGyzYd7lp3Pp8556PgIf5iXGtnrhYrKZPYFIoVTiOqbWGI1nwNAYm+FobMIjaHOCvAvmRO3elCDkDD00fUiFTLp3RMpX8+d/hr+ykvvCh8huMbEjHAwezSSwB9zj5SS7+434nkRKh/KtH4af8oa+MYYmtROj1XKIgA+hR8Is8YRlzdSPCoMeE4oiwwFEDu6GnZJ55N9/z8Fjm/LHE22EtY1X2WWtnW5t7EQAQmND+JRtbHuoqnHMalToOeCYEACq1RqXWVMiU1PZPRhlBEA/d3q1HtLS6QVVVb9piQiTpV2uL61oij9nl6jHr8t/Y02JSncXQBOib/3C6ZZ0+HMd9faE05vN4Q3uE7jxZguHYnUNaxsUKBIK2KyUIcPwyTDRduGFafAxNqtfo4G9toMT9BQaWsOsLzgYRyvcs67nvcDqqmnS7/Pz2KKyHh5x++671yURglGrW6/CqRhl6dm/9gS+BkpZ1FcB+VkJWRmVLTTPTjHoA+CgAWlqrjA6RCHnwiFqZPDJAqFBpJFKgfGCLeXXleHJhHwY28kKlZw+H9hkcP25a/XdwsamOQJiVFngQ9FfNVPRAolRDpbl4UvcHb+v24G2wmCK3WH4kqyozn7nsaF5leyrSxyZLj7/QxgIbC2+B4UtKfpqfO6OpIapbLNA1WB0w2ExyUuyHBy+unDPwkz8vDe7RMoPqzE3l2GRpmC+vXmlgayS0bdweDGVLEoltXZsOsSNtsSQ0ctBDzwcn9RWIJIFj5vmNvAsYdOD7p4CsBB6Bkb/z+uZxTfQlgjGMmmKKARDkJ6qsV4uEfB1uWr7cKajqoGKShLhhfXNWkaW0GsfIzABfXSxYeEvAo+OhxEuOC0iOgxZt3+WmTBfoTXO6hcBf2XpLaf3mBiQH1OLJizcHdIfjFyrqlAQBCisbDUaitkEdEgAX7HxheqKcLEcTkD/YrCIN2lDLi/6yEL5H5Y7caT1tRz+VNRUn1z+LLgcl9R2b9j7gCbDFW4gvFoOmWkCQ3wqegD74h1G/PWpg98KSSjhtJCK4rLKuViZHuRjJd8vFZguK/HJDMP61z+w/eUko1BTKGrgmBIc1tAbiu/T679Kh6uTj2JeLYxLDhHek+EpF+Is7OhQcPHZN8dJO+OYyoMvS0npdqB/PV2T61dRX5CH/HefxB3cLBICgzzA9eK5g1X1D2vy7fD4vv7xBytPvO1co5en/OJlP6FRLZ8JgaFQA/O6rSEeeAftqQtkrQnEPct7ZUrRG99+Rhmxdqim7ceWvx6ZWfA4j+vh95KKk5GCxkOQU+mErVj+FoxllzT8KpbVKDIDqGll0hCnOpddDf7nNliSUVcN5Vn8OY4bdBtMFB3eCDsMTkmuOu33CsLNhaDG9kXjwy5Kx62CmaExPSSf8bm89BrsqhnW3XEtLACAWwI8ZQbQqV2LA8N8NRvg+LZzUX6U1LLpzcJNat3iaqRd5CBlyrWgwfbToqSS3k/bm5w4d78ZgqPuTSA4474B18jO096DpW/6OHDzK/CZVLlxSFEiRKYTmRo4z10/niyVAp2Z0RCKRrdHqDQbCYrGW+TyR7F+2wIGhE2DNqUVCevaHyqKiCHSYuNFwLc+ru78HXsTxFxL3P93NfD9BisfOSbZklsBPwv0jTB8Ssn4JpuB1GrjfR8ibPAAmiD7925T9L7h206L1xEj/HXWgGgyEEDP+cTJfytMfulAs5en/PlO49a8MDAMiMkCkQn/GDPem44HjNrRrGdB2FH6yJ/RJl5/1hXlwseWHXzQ/DBeICQIYmkxZiCAeWelJ+w1AlyNFLSmiUYPgom/BZDETEqFGM51ncRpT4ZHdcB3N+Y9aFKGDHkoDGFZ9xYHCUmtgOJ6yEBYw3dj3C/AmCCDgY+F+Fl69W7pBSdigarvjqIMYCXCxSM3DwZLRQaiASSaHtntoDxi3DZAKUuKlBAFyi02ttCKhQKfT/b7lO2pjzCUIjoRBoMG9ouoaNUumD7laUr90RurNCvnSGam3J8Mv/hf/tRoTwyoR6ijuEqG4ZzjvbnUYbUQ/9SqFvCQf5/GnfNTKO8YwPOatIzCf/tlytCdVeQwAcMS3RSQe8IO1RyMUB+l3RBX11TXwk42e9YETFykv3tosO4IwnnwPtgbduWm3fxwcmkcx7tXNscMnwmKBja+CjhE3avK0TXvgqvR/fm3Ue+zyxZZY8g2sMNvxePzoHq086C33RYuFWHapxvyXzBU89RN8f5eMCdqadiu9c793nN8bDw4gCPDOr1cJslaJUa5kcaRLek4FwIjsAhgZOJ0LFWVeWb2/hH9Hb8JfDPRGEBlia7Srx4nQPHfYUL5HWE92yk/Ef28+Pun/fhAHhkz/9B/GTbK9mxqL8/RCXmRk2P5NG+NWDa7kx+zyv19EqDUYjCT21lzcseVba3+LRw4BSowwFU7bngRad/3y0TeWj39187hXNjFu0jTWH0i7m7ET4/HMTxi1b1KXLR5wbff31//+CXgX1yo1Hx2sfXpSyLq5zORJvdLw6PfMAl4XYSTAmHX5e55MGBArPvPhHajDF7XGGo3Eut+uVcjUDNNJjT6hMBiI/zXIVBqjTA0EPN68YSH3jgrT6o0AiwQAWzU5AE66q9ccK2TWkDLGi7CBtDc/dygj3/kJJQ8oY3Kv9Wyz9QimRF+8T+QfNOC+lX7RCXwfqV6lqL1+OfOb9we0XpTt7AtzFrzxTrY4VYOJAw21Q1QnpEb5pdYPhVYWig720QGgyjkh8JHiwPj9aWYZjU6pKDt3VFHVyqQ2lt7cvXxq3Kg7uo2fLg4KNep18uL8zO8/1DYyqwXLzsHifxswDiAMBpWsuvz88YYiGLLwSn7LaPgto+HxCcHDuktCpDwjAYrqdB8erMmvtlr4eSinSaltW5oeymnKKddYu8l857SNhWF+/OcniLtH+vqK+bVy7dHsmov58E2kW8/CGo1Ka8Hprqpr0uqMFyvxSF/tsasNKo0hKlhSXqdMivY/f702PCTotR8v33drr5AAaEBfmJ64brfVlirPSse7Bcw/uPP6YT1RflounrebAZIasZAXGRFGX3CcWlnI4uDewuJKHyFPg8FIqBjT5ZfLK4VwDVuOroa27CoKgIrJ5QgDfOxKZFU3wJUHc2XCh6f03fbPlQERrUx8mcpHpWgSSaQapQLVM5kbULoIZYMBXe9gWWhnilA+mxNH7Fyuw1FgW2dlNX0CkO2B56W1ypgQSW2jLCw0CFZIsX+4EYcrIQChUkMBq7LWo9S6twp9Y6Q8/Z7TBfLqMhABp0ZJRTyFBubBon1UABpiNZDwDp+E4fVhweBMHRvnt7abznTkWRTvsDDx0921hh2Un2h1dYb9Y0zKYYABMJrMxceGSsQYTNRw5rMrA9fSooFjmI3NdBcCys9Z4/pWyFoKPIxGQuIjlviIpSIeYyNtqJy+hfLqqc3tqSTA7pKmTnLh2+G8G42E28VXxw0o5cVTCrTNddlgiV8x7FqR+ghvlDVw/juHQ4h1jVKenuCLlRq9vLpsTIppbqnER9zySTO2XUSBFCsDtX9vd70d61npyPNZ6rwT7LKeHUStNYjFppda1Wga9dimF19U1ehuCc7hkUxITdpzphD67zROXC4bnWKa40XggpioVmMBykqZbb5InNJR64zSpqsKX+ZY1a7syOMsdd5Z5ri2W34iLx5lANBmz10GJ8EJviF+Ipa9DBwegFrgty+jSMrT81CjcDMLbh9w/HLZCXK0HfyRLq8ppS37Gh0TZXFDtxqMxMFMNw9SSGOlI+9yF74dzrvBaOS5deC8ufzsiAFFXrw1LUkfqmZOdYOatQPeOViLWNcory7j8fkGvZ5y4Xsmmmo5fjlwiZKicE5Caylqka/3nMcAGNvPNM3LjSJ0PcsceT7bnHdyLRp26eIOWk9KhHppnS4H65Dy9I3Wf5gX3D7g5/2ZGIYhM4p0qG0zOn9i/18PZgEPJM/Fjjy7TJXbuzadHv3k4Oh8avGgNoY3Tx644PYBlDtPmVEmhGmDaymzI5yUxjJH3oUG1BPL5l0hPzk4Ohk0i8RHDHsxbLDg9gG40MdyVBSZztaBpuNXLIw07eI98jirnHc2/Mpx8pPD04H9FzyeWmvw9/c9ftlW8icqRGouRS2qURZ8NduP62wozhLr6faJn+2Tnxe+eGTK4LjOOh0ODgdgxMJKiq0Oo1kwaUBISGArM1phyaNnDWmOO/IusqFsSVKwpGvTTvl54YuWVOCyWcOWzRoGZwwXVr/81X+uPDsOjrbRlZvWDZQ2lx7bY2XFAv6CSQN+OQiH25iSS6QNjYk0JZcImgj1oGpQV4OzRH6633baJz//ff/+f9+/XyY3XyiTg4NN0GYv2MgOMVgwacCcW1PMpKjpUBbkd1knQnE2WE+DwciGGEub8vPf9++nX/3jUMYfhzJ+2J0O55KdvV5SxZwax8HhlvQRzmP2a/BwXGsA+QVlpq3Qqjsv4OELJsGoaEtyqaK2tKL2nskD2WQ/2WJDWVHGxOOx4jTa5NjFllVoPv0FDpwHAPTvEYls6PyJA4Z0My0Gx8HhFizGweobVbUNitoGuEAIRX5hmflG3gL15oJJ/edO7E+XojU1Mow1iXjv7ERqn/w0EoTtbhz2tB69/sBodKG8ogZgICLYNCs++2bNioXjHnnrF75QlJJoej1/PcEVk3K4JwAKFSjORzHQ2ED4LzVYojmU2eobFxJgeSlQCpEIDiSFntaFojF9YT8Se2Kg6x1fRN6JpfVOM6Dtz7yzwHraaUB3rp138WoZjmPIgF4vbSipbsAAtvnFue99f2T6qGSl2Srbqz7718UnzsFhQlt2FcMwPz9fhVqHrKdc3tQ3Hi7zeaWgbkBiqJFgTLCnvnr0FJFlq0q/mQAEewwocKsNdXcWnhXG097qpTmv/YbM6ICeUd2iobc+byVcgGj3scvTRiUDDBxMz5k0og/9LsMS/M8Utj1+iYOj45hrkX7xgegnfUxKpF9AoIEg1FqDVmfQ6AwXrsA1rgckhVr7HsoaVZRdNdIW1QsNkGAA4xLxzlSgHu28OzQ6ZFB8EMCw3rGBSrVu1vi+wX4+aH9FHVxstk8iHPyRkVtCHX/w581/17tthCJHV5SfShXy3+XyJmRAg32F/oHmzZ1kUp0ARoLQGwi1Vq/WGdRaQ/a1kmaragXyblqfSDas9uF2Ecp3V9MRwRrrycCG9bxjUNyQXrBEDhXYUdYTgawnXMG4dyxlRictfPzvru3FDwt2QIDjOJZe4+fK0/FarIXCrH/H0AIgBI9cNkYoEPqTKjMsoMfYVOie6w3GE+fzzl0t7xsLF/w4l1sODSurSplYMGeE77aJn6zBzuL5lxYOpy4P6pMAALiYUzigB5wV1i02vFtseENTqzVrKDM6vJv/6YIu7cXzMCASw3HoUoHVrx9BgDqF1fUvbVtkakkfxk1ettRPm/j5mbKaKADaj4x+EgBYkp8UcOlkcvlkeDGvtKFWrr5amrF0ZiqfhysVCmQ9AQBDe0edv16VQj6m1ieSbWt2pjm4ALKzwN3jvBvZkjtqx+iQEf0TKDOKTCe6GuDbsmQCRWTeN0YLS89a5psND32z4SHAAlx0GgodFhgSRm6hjC0gJNSeFwSZyDB/EX2b1k2DNsb+EaEwruIU2PO+WDwTVP5JL6GXy00LJgf7mhLo1sGQTs3Iq6mVQxHQIDe9blMnDKEfh6wna0lzR1ko7gbnHbh/uY7OGR1SuustIwHmTOg3LMFeKbRo5VfAG6HGTTZ3tnQIhQ6Dtjg0rGUz2eUW03w4y/2roTkdGx8PnRF+p2AAlATJT9CG/DShNxjTc6p0eoNMrjTwpdPGDdz6Vwb9gPPXq85fr0KXkfxENLFgybkO0kEbiney9WTDWptOkZ8WoYvQ0l1vEQQojlsAr2Aeo3FcdSYE7Ko+n1dJdbYwbnfIIUHmuGVukOkTZak50Xmw/A2iv4BQfpJXg9qWn0Cu1J69Vi2TKxQKlZCHSXn6QxeL0ZLxSIT6SGA9U/8EkyHOb7DbpfKQaaEdsaGd2gKEMu/scd4dkp/v/HzatvWk21BkPYuQ9QRg1rh+oKsDrduMsX1jIuFyT8iMtrqZaK+kNc1eY+vPcmfBfAHtez1KaxXZhTKZXCHkwW+lT0BohBQm5atkqpbV6C4VUJdztcyinSaWidBOnriMd+q4T1pHhBfDsJ7kJ5mw34v3emIiQ8ylKGMBdPseh5K0NeaStouC8w1adQqpFoMsVy+1cKVIdulGpUKhRNYTfVZj4uOhCL1QTPfi+ycEEQRhbj29hnaLULwznXdyuQ62WNB2LBt3+HTO1Z3rrv3xrvlN8r/XyPeuaSA3zaCHW1tPyKzxfT0oAOrqM2FIUahOHbKfzc66DUmLcFYeySPeGioA2qb+JAjiv0vF5dX1gtaKRsrTn8guXzoztapBtXiaKYP0ytIpjLszvPgmrxCh7bOhnVTGxKquzXbz0a6sp2fMhN/V0znRZX9hGBYYJ0Qf1fzui4FZIK7V/wnPiK91ZhFoTGQIsnplFbVhYW3nOlrT8oLGRIbsOnZlSM8I9GiUSfVivtnwkDVLmpIQhOS8Nfl5PKu4e7hUq9XTd0qlEgNfsujO/pt3ZZXVwrEjZ69WZl8vpYKhniU/0zqrqsnhTqT2y09YbsYWA9ruVYtXzuhPfw4DeprWjDW3j+W1jQzr+cp3LKp+7UwDysNAYjfm4szUC7abtH1wQXONBgBgu5AePWDP7paXeqZEaHRkiN5AFJXA1Sy6QmW+tuyqnsBFAtyg1yMDGiS14L8fzypOjvKTq3QFlU000+kDACYS8JSEqHdcUHFZtUgi1SgVKiP/ydkDqcPW7c5nPFpiQCv/lT01oYj2GVBHS+vxTnPe2WM9O8KGXVkEAf1N9P2/lFdunvtlalDyn4wc2HrcBeHhGMAw88lpN5u3fvGBGhL7Px/5BaW2o6tlFbVV1XWgy2L2c348q7iqpj45yq+sTklZT6nUh7SeQCwS6nk+Up7+arHs4RmpdY2aRXcONhJwXA6jnsmDSOsUR74zsvBsc97bLT9Ba7OILCm1q/WQbyIyhGwL6drWU6nSNik05ptKrbW8qdpoRkJLpBkJQmsw6o2EzgBTxgbyAvmvceroPo5HA7wEJD8BIOjpI8p0QutQLq+Ra+imE6HRarV647zbUggA8svhXPDTuZVSnv5wZgnlwr8wPdGzIqGdA78zCj+9+Scey8wrb3HkzbVos/Xccc7qDHDvhgAgMT7SJNnJJGLLrw556WxuebcwWGmo0WjtlbQAKylp++vKoh9t10P670Zzhx2ZTji1tqjeQA5VoptOUn6KwkKDqmtku08XIKMp5enPX696anbqhp2ZTSqdr48AeCZp7YqEOtQjj7vaehq9TX6CDbsv067RpSfNepLXIkP8Mq50aet5WQWrNUvKqjAEbvqHdOvh6JDq2gZkPUOCA+z8mDQq1G0r2eZN2Zak9SZQ9RIBwKXiJkp1IsF+qVBmMBIM4YmaONUabUFl49IZqbVy9UPThijU+nsnDyQIcL0MStGD51vVM3mcCE1zsSPv2iw8OXIJeB8bdl8em+Q/uE88enKXkAil2c9zVwpNl7zx6bcHegEbOQUIA6CiSoZ2RIQF0idOtgXWPaE5X0Ff66w5MF1VU492aLVdyHoa9Kasuo9IlBwlovZr9YbcErmZ3Wy+F18yrn/0f1llWQUw/3bxRjUA4O+zhVKe/ggpRQurGp+YZapnemF6onkqqYuDu955Z68JaYf8pDh2Q34hp+g8uaG5iueuFFIb/cguKz8B4+2nyfT07FLKekaGB7U/wQglFAk5zu1MThllPUODA0BXAslPBg0KbbFM12w90dAQC4Wf5L9lUp4+PadCytOX1jQtmQ6l6H13wCz8qSvl9otQFuJSEYq70Hk3GlnlvDt9dMixG/JjN2BVzYWcoq93nWbcumX/tS37r3Vx6wkx+whUVMmQ2x4RFhgZTn7tkf3r2F+pqK6nHjYiLHD/6TwAQKbC+2tC6QRKW+KVGfl118ob6xoUdXA5OQsvr4EviQv1MRiJu28foNEZ771joN5IzBzblwDgagn8HTp4oVjK01+8UfPwDKupJAYs9OJdakP5LmrZtDHh1ctANtT8ModFKqqbhWfrXDn6sLT5kUntEWJeo5OeXYpMJ9KzXbYzniAIWZMpatGk1tOjInUNLYWfiOAAXylPX9EklPL0/16E3vrB89Bc7suA/vvxy1CQ5pfLn5w1ZOOfl4qqGhMivL+cth0JJZeUMXmE/OyI/24RznraJv1KaSvrSXqTlFeJPi2Ctha4Nv9IWbCerI4buQRhdC96sqFcpi6uUfKAkbHR71LX0FRWVVev0C6ZPqSgUr50RurNCjkqAl08bYhSo79nMvTfM/KqYGr+YgnlxXtoPVOaa4aM4C4KfeI4q5Z675KChE1UVLdy22HMsrWVQ5ftGhSLWYgGRIYHUdbT9MhdzIbmasPz9BFoa/JL4Ef2ND+GbkwDQsKHDUyGlRIFtQQBblZA5ymnCHYfZOTB0Z/7M4qkPP3Zq5VLZ6Q2KLUoHurRpLnAkee7wnlnWeU8TAAj+ZmbdYEqQrzFerU1n4ed6gL9f50PmS+yRHMWSaMz2PlQFoWnuUXuyli0ofqKPJNOrK3Iw2AS6RSZONp/HprL42RC6TypOouqGpH/XtUAZ5Qcv1xWWFpN1dUzyG8w0js7WbXah0sdeSfrxH2H0w0sct6p2swWBSqViKUScYivwNrG52E3yhrCNMVuPG8vQ6s3KlUapUqTX1hxw+JWUHG9wAG/jyE8LWSXOazAj+xJbUYCzBzXV6013HfHQLXWcP+UQSqN/v47BqKuJHIQfTUcbUfGRnPJLk87U0msxemOPN+58pMgCB5bnPcWo1lTesMkP5vXJ1IbeaaJvK3HqBkJ0FBUxsOx2BBpNawj5ugoWUqba+R2gPAAkVGjdNGDdwWkPP3eM3BY8qELUC6czIZtsgdIc7n3DOxKOnu1cvlM2I/0+IzBm3dl5RTVncy8yYlQOrgTrSdrujZtLe3QapK52RDKgqKWqiNOhLITggBIzxZUyBlbcVUjtfWNY/UKaGygFg9qUGifmDngZoVcytNnF9ZKeXqUSqpuUC++c7BWb6wjl5lDSfmjl1qm23VBEZpnKRiKe9fETwumk5Kf1B7bI3hvlDWEhwXpeOJu4aZFYjnYwyVl6GWV1S1TEULfLjZ1rSLQ9pFbLDMSYNro3kYCTBnZCxaBFsNiiXNkJPQAWQqaVVC7dEaqVm+slNmS/PmekI53riOPO7XwE7iVtqZwG43JPeKQ6jSfuQvn0BRC+Um2aZueCSdCObwbKU9/8gpMHO07B5NIKPN+nOxKOp8HU0YVdcqHpg0hCJBbAq3qvnNFNuqZvB5zEYo7y3k3unO5Dqs+O1N+0o5CNtS0nkQr+RmMLus5Ecrh7dTiQUqN/sGpgxuU2kV3Dm5U6RbdOVih1t8/ZZDOYETLxJ/KgRb2JJmglzVpbNcz5Xu7CGXYUCe48Mh64m7Tn3bVeEL5mRTH2IlsaBnpyOsNpny9qSUGHYFxIpTD+zmcWULOAIXG7tw1WASKmuL3k7L0wvXqpTNSG1W6eyYPgA0jl8u8Q4SmOcORx53jvAPAQutpHv1k3JMA4AJZM1xWUVtMLv/Aw+HANeoYPS7uFuHLOfIcXoxa4HetRCbl6S/l11BFoJcLapuXloOj6Qur4Po0hy7Ajs+rxbLpY/oAAFQa/Rd7s8VNpfcN9advI3v6juzpy34R2m7oIhTvuPNuMLil8NNWqp0BH8cEfB61hgTaCsitX3yAVqPRkqvxwPLPUGZ5vR6H67xzcHgxsCB0bF+dgZg/sb/eSMwe389gJEpr4NJyxy9Dz30POWv5ZoVcaeAbCfDz0bytf2X4iPg+Qh7wZDruyMNF5Tpct9T50c+2TSeSn3BU0lnmnCSL4BhWXqcUi4WoUAb9IoQEwcXc+UZ1QWVTtYgZAeDg8A5CjDJfP7+mxsaAwICG+obg4MC6unpc6GPUqhQGvq9Y0KTWUQdLeXqFAdaPr5wzsEqm+vHINaCRz50ANSmDU3ktE0zY3JjU7vU7e46+p6Mx0E5f6t0B4Wm6AwFHVESEh0aEh0RGhMZEhUVGhJJD0VttcJIF3/RqUHq6ViavlckrG7TdIriSJg6vpRYPqqxXPTwjtaxWsWT6kOLqpofJoOf9UwaRg51M1lPKgzObkfVEhAf5iAWeLUI7CFZ55WBHujY7t+/IXtNJyU/E+TOnhXw8Npq+gHPLGPPK5hG8Go22Rq6msvAXckviI6ACReh0+nK+5dV0OTg8HbGuEY2mv3Vg7OHMklsHxhzOLKX0JqU66Uh5+qUzU3OLZPtOwkVuuqYI7ZD5I5f3YqnwpHNZFarVG0vKYL6oGXKGOU1shocGYhgI9RejhbcAAIN7x4YE+YcE+ceGSDQaHWc9ObwYaqoIvWiJ0psM60m/qVdcEC7uWsP/nWBAO7Hws0Omk8LKiWIV1Sb5iWEgLiYcw0BtrWlmJR27xqxxcHgstXiQTm+85/YBaLaIRm8US3zNjSbDi9/6VwaGgYGJsBzw9yM55g9LT8d7ZTYJb3fXZqcUfrbHdLby3wnT4OQsVaiOKUJbgPKT1KSXi+pD/cVV1XAqIgVnO9nA4Hd2Dn5nJ7rg7nPxWo6T80TSc2DhUW2j2txoUqaTvnNk3yhM1BLssgbL65naZ0PbqUBdbzydITxbS2RzG0oNSKd+DO4c2Yvx1Hxw/fWyBs5/dzVBg8ahC93ufY5xU++nN8CQ9Etz6DtTXt7m8nPqYkh5elNB6M1aO/13QIpQAR+PCZVaE6HeDc5K5739j2+Sn83ml7luh6VHjQxrHttDDpWMiwkPC2gRoRjnv3cK3RY+g9Rlwfb3GDflfrRy8Ds7cSEsyKVE6OW3H+6cE+s61OJBaKqINaPJMJ1S2tWJg+OAFRHqQUX17RChjmXhO2XkUoeEZ4sBJfn2P+bCR/0lNQIezMgz1+chQf8vLKnk83A9LsYwIMH1J7jp9BxdgxCjTGngS2iZd4tZeMbOlXNgd/ynu7M0TfUenYtvR0aeJcOPnWQ9S27QXX9z64nm++oMLY58RFggw3oCABJiI/QGY1VNnYT03ztyShwdwXjPd8Z7vuNew06jFg8iWnvuFkOfCAXNi0eRUHtSSSwXoY6CO7jWpkvlpxuWfkNPx+KCEAlh8F3n8u/ugjKdnA1lA22mkvp3C8HFbaeS2I9DjrxDCpTAXChYO2o9GcXzNkAiNMxfGOYvNKgVBrVCb7bFhPqqtPqblXIufeQWGEaTs6GdBj3caTH0ST9MQROhOI71jgvqaiIUdyT6SS2eyErt2foxLPrv9INLqptsbwQBwvy5SSJuwKK55Gxo51CLB1nz3xE2UkljUqKtpZK8VYTalURCzrvLnFknWE8Y/QSOGFAOtmLbUOI/PtCJ59JFEevg8DpEm6kkQLu6dGbqT4evVVZWenpnp/3ZJHsVqGucd+d0GZnDWU8PpU2ZyenQTsBa/bxF1UmBrt5mvZ7JK8Htk59GFzjvTjOd5vKTwxOx0zhyNrQzvXgGNuKhiPAAH4nI8mLpHhcJtceRx+2Z+Ik52Xl3bbadk58cHO5KJQEMjO0f7TVdSW3a0LYVKKycd6b8RF1GnPzkaL+u5EQom1NJyTGBfEmAd4jQNsE7t+/ItHAbcBmc/OTgcBFtNnSC5nqmwUlhXUSE4jadd9MSlU7C+R30XPSTg8NFWPPfrZlOKe3q0OQIO4vqPUKE2rChthQoOVkY8xTtyclPDg6XevEUNrLwgBShQgHePdLf04eE2oPlfNm+w+l6vYHfvEYQB4eLaHc0k7ojVxnaOdBLQc3tqYK2B10ePyAmv1xOaOTAK0h783OLZaGWDSgBCNJ6Old+OhnOf/dcnJgF4ixpZxpNi6ZTQfPiqct+EmGQn6hOA0WoeVH9yJ6+VFF9k6yC/UX11sCtdW06yXq6qlTeHC595Cm4KIfOpeY7pyDUzlQSAGDCgFjgRViMhOJWlutwyl90oenk5CcHB8tTSXFhvkJp87Ryr6hnMreh1qKcmGfNpuPkJweHe1NJUktF9ak9w72mnskiuPlS784o/HSt9eTkJweHW7DRlcQA3TogMRSzXs/kBSIUZ3RtduJS706Dk58cHK7Dhv9uQ4oiESoW8rpFWK1n8lDoNrSVAiWcs1ocJz85OLyHdqeSEOMHxNhY9NgTRSgdvNXIJdY77+Zw8pODozNxNJUUIBUG+oq8VYTipsw76Pi85M6oWOKin14A/uMDTq9+d8VjclDYP5BJaqmeafyAGDuHhHqQCEU21KRACWNHMu+dV+zJgJOfnosT7R1nOt3oxSusNHTSb40P9xOQbY3e19mJmwo/O9S02Ul5J05+ehkdl42c8GSbFKWgi1AMA2g+k/eJUPJJmlb2ZcuMJQ4ODrbRvoZO0HzwyL6RBRXyqqrKxial+YOnROGXy6EX7HHgzliuozP8d27ZOA4Ot3vxDmXhpbR6JgDAPbclTx6aYPHBGdbTg0Qo3oHlOtwW+uTg4Oh82tfQmRTVMp2+T49484f9K7eV7+9ZYN9uerNdd+xU08nJT++mfXNAPDd3NCy4sX3foDN1/uxZ8diiw85AytOrjPxHp6UIBTxqZ871InPrmRjAzMN4xIim9iWPOOHJwdFBHPb8cBwbEcq0X2xOJQVIhZjAx0iAc3lVyIv3Ju3ZbgPa2daTk58c3oS27Orxy2WwbRoDEh+xxEcc5i9qc7tcWO/qExsW3PbwY7p9VLRVENqg0M4dmyTl6S9er3l4Rqpt65nfwEwieUQk1NY4AA4ODtdhIEwiVKHDYqJCbSuWmeND/zqaPaB7q0yODYYHNxKOa51hwXIejp2q8bN2QC0eJDY0MhLuUrMsvMrIF0t8FY3qo5dKAQA6g7GwUn7wTN7SmdCM9ukRT/fiPRrPW7SDK57n8GhwHOPxTbaGspul5TWl5TUWjiaYDr89Xry27Oqxy2UYwJDItUfhos3+dkSLUlTaLEKNBBiYFCrl6QurGh+YOggAcDy7HFlPG867uQj1MgPqhrQ7VzzfFWhHOshDM0jasquMPTFRoefzKtFlpg2lfdvwds2pMJIiV6HDFDosMDTM9uYXHHrppqzNx2wzCx/iL5by9KdzK5bOSCUIcD6vWsrT18nVjUodFQl9YXqiPefPfi8e96zEESc/OTwdAg6eMNmd/JslAIAZ4/rRbajJjBLt/A5qy67SpwJZtc5mUOpzpE2Ra7EgVEGLh5JD3YBCra+qV8EA6I1qaEkBQJFf23icCMXZbD05+cnhZVCmjWFNZozrRw+DWjN29ihEPh/HeTwqStAvLqDtQEHLuREdycJLRDAeWteouX8KdNuPXII/D1q9saxOIeXpr5fVP0i6894kQvmeoj05vBsPdckdAmlDPz9fjc6gaZ1HQsREhe76L3tIzwho7CpqYiJbZZZmjU/582g2CoOmW8nzoD+B86DIPXa5bGxKNPLiWz1yeY21nNXMcf3++u/KwLZSVYxUEkLK0ys14L6J/b4/dPXs1UopT19Rp1x258DP92YfyypbOjN1w87MG+XyExfz6cFQTwdnrfXkqpc4vFV++gh5/v6WRxDNGNuPspulFTWlFTX2h0EZzjvzkS0GCugQgNc8Vci2F08xIIIYEEEgKaoFQgKAK4V1AyIIfWNVUiivfwSRnnF5QARRKVMq1DoAQHpOq1SSRTyrnsnzsvAcHJ6bPvLzg3bTqpVrlit07Um3ofYIGvQncAwzt9G2AgVtPbC27Orw4MbhwY3DguXDguXIdAKSpFDegAiiT6gBmU7z+w6KAiezy6U8vVypq5QpHU0lsRnbBpSTnxwczkEY3UtrNBlOpcZA7Ud5JPNvW0wkLTvfLEUJwmoYlCE/fUSwdfIYGWlt+ROW8v7QjNL+9IxxfTPJP2EuQtGjYQCzv/4/rLkFQKSTLZ42BABw9FKpN4lQawaUGxTCweEq9AYjM49EtOHOIzM6e0KK7WpQJD+hweK39J6jMKgtKUoTuXxLs4EtBgcUdpRGBYa2jAG9ViJD7Ul1jRqvEaE4C7NGXPSTwyuhTBD9CwbzSDa/cHQpWl5Za+0wHYFT1hNKNjLmaJtWUrR1vNU8+IAy+0iEatRqe9L6iCmjeiNJW1NevHRm6tKp/YJ9RfPH92poaEIb8GQRatGAcgOSOTicD1kf2YK1PJI5M8a25H+G9bZrRpFODw2Qv7+vRS+++YQsiFwAwJ1j+jC8eD65IAeFkWjVe1pq04b6CFuS9WU1im27MgAGAgJ8AwJ8Pz1W9ekxCzFTD8K8jImTnxwcLkGAGWsbFGIBDpoL6RH5hbBh3Db94oM0Gi26jAHMvM8dPXhIgBRdZdxs7sXTj4iJDN11rKV8ytx5NxgIvCUk0HyvqNDd/2UPbi6NonemtgL+ZSLzpmxg96DwIAkAwJrqZJDfYGTMuGuSVbBtxh2XhefgcCe8dn0FMWB7tB0hbK0ZzW5n7mBIUYbO9fMz2WVK0l7Ph0Z/OhlLvdBWsxMVuj159tLSmaneoT0ROKsSR1z0k8OLEUb3Iv9v+pbJ5U1KlbpJobG2qdRa2qZhbIxa+uYHb+XC0wMF1/OLDUZCbzDCzQg3g9GoIy8bCEJngFcjI0KoWAHy4scPiG7zeU0f149uQ83NKD0BZSPu6YmRUG6cnWVK8m+64uWOTewO2EQXeZosfrLQGU+Mj2q9k2ixsqSvfianrFsYdH4RGg3qYwIWvfgGhd5gMBCYhsqnXyuqC/EXYxh2OKvannPae8Y0a+5QZiUARL3CUN0ojwgUCoRCa3eZPq4fAIDu0TPc+Rlj++46BnucMnNu2BiX57kK1P39mlznO0cXQSJsCSiaWU8UNcQwgJ2+UnY6p6yqpp6ynufzqs5fh55vpsL6/FBL2OhQavOuLact4pt78XZKUX77QhWWYJUIZbUC5WYvdSmGDR5q55FnLpwDnokAMwLAC/ETFZbXpfYIYeTlKdKvQNtE2c3IsCAACCMBwPWqlPhANO7ITvR62Gep15tK99tvSAEwkOWrtpluUYo2y2mUSnLoj5qnktBDsaReCGdD6JODo0uhh7YQYp7qSb9SWlEt6xYmQdYzMiyItJ7Q8v1z6ho6xpr8pIdB6fXwgVLoekslYqkEdhDRNpHER+RDbmododYReDNypamMtFHdYjSVWpMVbqmOKrBUHWUuRcnM/qzxKQ412nsEbFGg7EwfVZuNv2VQVyfrlTKCsfOP374bNDClW8/BwENIHTLwn92/2D5myvQFGecz4bdUILiafdrX15SWffjRlX/t+gd4FG2+rU1Niu7JQ0yN3ondTh37B7nAGo0msddQrdZUTtRuSitlQ3uEEADERoczJCdlNymBhVSW0UgADKTEB15sCrHxyALMqNUZkLuckBB/5uwx22fyxIqVf+z4UyLi+UtF/6WfCA01meaKispbR4+tkCmFtI4mQ7PdpzA2N6e2LUWbh0s5RYQ2ySp9gyJI5edmEcoWA8pOZPUN9KtBgQGMnfUNzHW45s+dOXrUMIVCCTwHg8FAf1IioVAi8dFoNEqVmuEJikSikpuXCII4l3HxYmbWXXNmbPtsw4XlWZPvnAe86G1VKk1v3/y5MzdvXK/V6fbs3R8bG31L6uDSgqzEXqmNjfbmkRkIo3sZqq6jywLSzqVfKaXsJjSd4dC4mFuFv09dc9RW6HQ6maweBlNJ6y8QCCQSiU6nQ8+OIGBpKE4YJCKeVCq9kH0JAHAzP//QoaMTbh3Xo0dSzo1rvXoNrpfVUQ/osPtPgOlj++0+1mJDh/WOPJMLI5gjQxudlEpysy+PfbvpDeBuWCg/LWZskXIJa10vQueD9W88cN8C2CasUFpUoGxLT1t8mg/ev/D9d9d+vOmLN956j3FTaeFloUCQ1HuoXG7ywv47tKtPn+RBQyeUlpV38Gm6OgZqLQtv7W3FMKyqNNdgMETG9UV7Avz9r+eeraysThk8pt1PVlGaN7JnEAFAZaOBbjdt24C9J6/2a0t+orr3SrkeEASPx9Pr9ZTzDgC4/Y473v/4o507/nhyxdMSctQIXOddBA/4L/1kYFDQiMGp+cXQ7/aXCKJ79Pvv3906nT42JgkdGREoADyBlMwjIeTyprEp0Tyc6N4t1sKptFarlBmlqggcNaCWFo43PaAbbSinQJ3DLUMH/7XjBz6fl30lt1/f3sBLEQoESqWKsp4AgFdWv7Xjl2/mz5350cbPgHfx4P0LAQCLH36C2tMgl5/NuHBL6mAfHx+VStWBPBIEWc/wABH0hTXOcVmE0b2M9ZdxDDCsJ3R7VTDywMMxZD2R6UQEBgURBIGUqb9E0KAXNuTmyeobggIDdHqjoDlQazF3ZTBiX+22sOa7OfszCienJlBevPNEqDtxvwFlofxsB6tfTePzeUsfe/rPv/5uM8TmuZjLtB5JUH/dLHTCKrXXbxT1SIoHrOGehXfBGW4n0uk7X39j/e4/tt93z7ytX37Xvocd2iPESJoiFCEpoMVJbMxD6hcXaOfjY6ZFiSBSibhOrkIWUySAD8vj4XTTiVjywIPBwSEVta1yO3odzCPxeDzbSWYcx8anmFdiWQZ1ow7sHkxeI5wUCUW4Jx7qfgPqHaxeuw7lWLoUoaEhb7/xP+hg/n2g449WWFQWFxspMvt6u4vBg/qjaAx9Z1k5DOENTR3YbgN6SelYCWf7QPJTrdFSelPQvEqSOefOnEXWE8lPqGSFwrAweJ56vV4oMGuDb+bY5bLUpDaiCgx4PNeZOcoiY13FgHqH/AQAdDXruf3bz0aPGo5yTf0GjdbpLCzz7Sj/fL7SziPnd0odKG5JCSJ7GhlBj76xDmQ9JWIBtYcwGgBBhg4IAl42IyJIotK0TMA7c3I/AODRx1fhGKHVGcxtqFwO02hDkkK6J0Sat1BRbVTonzM5sOaJCvjqtFojATIVjlleO0Qo/Tw6yYZyCpSjPdw6YezFzKyhqYNEItHpE/v79B9BlWp7NwbyaQpFLbaJbeBiXwC0GAboBhG6z+SPnN5gZOyng+Tn0UN/xURHnT6TseOP3fRbfcV8hvUEDLDm/0grmt5sN1s6qcg2qn5xge2wnuzMzrvTgHK9m55LVLwpMX3v3XM3fPB2cX4Wtce74ZG1Rxp1R0tBXYrcYCEMojTCL7uO4CEraY3ruWcD/P2v5FydPvteaqfBYABAQLeeqI0qkS4/m0nPblXTSpa1Bqp1hn/P3XCF9bQiQjvJjLJrnJ2H+u9dme0//d4gl/P5PKH1SRPehJTsICivMLXZeBM4jleXXQ3w9z989Pj4iTNNO8W+OKknGdYTmFnP9OzS9OzSiipaG1V4ENoUGj2ynv3jO2o9zecz2QHhhQaUk58eB4ZhgQH+UmnLWCBEYWExWaftbeGgCxezUOUWfWdkOOwdOu91IW+pVFJZkgMAWPfexwvuedjGkUN7QAtIhT7TrdtNOBOFHKx39Hw+sp4X2qpjbR9NMrf9nrHoQ8/JT5Yj4PPzcs6WlpUPGjqBvr9bAqw96niDI9v46Zcdgwf1Hz489djxlkqml154GgDw4y87gBchEokK8i4AAKbOWHgu46K1w+TyJtSB2j0+0oKfHm6hF8BIEAfO5GEANqE6y3paGS9iG1elldyjQDn56YlodTqCIGKio+iLl901e7q/v59c3uiURDyr+OqbHwEAv/74JZWO9/f3GztmRH2D3LNaddskO/M4AGDiHXdZtJ642JcgCJQ9A2QDFV1vRoUHoc2iffr71DXnWk+2iVC2KFBOfnoE4yfO+O/f3flXM7765se86/kL5s0aNBCO2Ok3qKW10WsgCGLtm+tfezWtrPDy/727ISgo8PHHlgAAxky4E3gRQ1MHBfj7w/HJ+yzIanrj/9AeIWKxqBvZ2YQaqAAABps9VC6ynu0Sod5iQDn56bnk5OalDB5z6J+dDy26B+3ZtXvfI48/4601TBs3b82/Wbjt8w2vvPQsGjhy66RZlZV2zXX3FJY/8pDdDVRAqTINwy9ovkDBt1QhnxJvbwOVi9PxrvLi3TBMxCOK51m5/IOXP81f311h55Hzn9/k6U/WdXSRpwnaHi9ijvMNKCtkMAcHB4frITzegHqE/OTg4GA/+RbW7LSdSuIUKAcHBwdrFGinJpE8SH6yMOLjCrrI0+xST7aLPE0Hx4u4Ci4GysHB0XUgPNWActVLHBwcnUBnFtW7U4Gy1n/n4ODwFPLbM17E0wwoJz85ODi8T4S6TYFy8pODg8MdIpTwMAPKyU8ODg6vFKHuUaCc/OTg4PACEepyA8rJTw4ODm8VoW5QoJz85ODg8A4R6loDyslPDg4OVopQzCPngXaO/HTRRC8Ol7JyzRY7j+TeX08k1vU9po4MWnbOeFC+l8lP7qvFwcFOSkhZ0/mt+i7tjud64Tk4OLomBHsNqEX5yaWPODg4Oj+V5Lp0PKdAOTg4uiyExxhQTn5ycHB42XgRlxhQrnqJg4ODVbjIi+8kBcrJTw4ODu8ToXyvkZ/hYWFjR4+Ji46tra3544+d165dMxgsL1YeGxvr5+cnlzeWlpbYeECxWBwdHW37j5aWlmo0pgWy+Xx+fHyCUCiorq6pra1p84Q//nhjXt61jRs3WjsgMTHRxt3z8/Pph1FXu3fvjmHMAjeDwaBSqaqqqiw+FJ/P79ev3+zZcyQSyfnz548cOVxZWWn/mQAAGhoaamtrLd4UFhZ2220Thw4d2tAg37t3T1bWJZ1OZ/HIkLBwX18/tUpVWVFmz6snk8lWr37N9mE8Hq937z533XWXn59fZmbmoUMHKyoqbB+fnNwrJ+eKjWMSEhJ4PJ7tv0u9HXCtXV/fqKgoWMdTUqJSqWzfsX///suWPfLUU09aO8DPzy8sLIyx02g0ajSa6upqvV5P39+tWzccx+08T8bbXVxcbPGdYnwYCILQanX19TKFQkHfHx0dLRaLdTpdcXGxxT8dHhYmEokNBkNZedtvtyvrmTpUEOr8deE7f+EjqUS689ff0GeaMLZEhTdt2sgwT3FxcQcOHKLvmTTptpISy2Z0yJDU7dt/tP2n77przpUr2QCA5557funSpdR+vV4/fPgwhaLJxn1zc6+dOXPmwQfvt3GAjbv37p2MvvDZ2TnUVQBAVla2QCCwdq9du/5KS3uOvmfVqueWLXuEcZherx8xYlhTUxP9T9hg27at69e/y9iJYVhWVjafz/yR/v3331555WX6npCQ0GPHjvP5LVbp1VUrCm5a/dicuXwjN/daZWXl+PFjbZzVsmWPrFr1nPlvydixo+vq6ize5bffdqSkpFAvpkVOnTodFBRk4wCj0di3b290+eeffx04cCB109GjRx59lPlq05k4cdKmTZttnMCCBQvXrrX6nS0qKpoyZbLRaNJf586d9/X1tfHnLP4h9MHLzMxcuHC+tVttfx0AAKGhocePnwQApKYOoX8RUB0on8/ft3svAODOWTMoCeK+heMxtijQzpefGIb9tWMnAODQv/9+vPmT3EtZAIBZs2avW/fuihVPqtWaL774HB2J4ziynjNnTr927dro0WO2bfvy4MF/U1L6Mn63EQUFN597bhV1deTIkXPnzjt48OA///xN7UQa9sEHFy1durSoqGjatKk6nW7atOnvv/9BRsb5Pn16EURH03z0c7CfefPmNjU1Uld9ff2mT5++ePFDM2bMrKioeP/999D+e++9b9myR4xGw+OPP37kyGEkGNete3fUqNHnzp3v27e3kYR+Dt27d1ux4slr1659/vln1M4bN66bn0NOzlUAwLFjx1566YWaGijJJ06c9MEHH86dO08oFFJ2HMOw48dPYBj2zuoXs7MuJnRPeuv9TW++v+mhu2fqtFrQXqZMmbpq1XNGo+GZZ57et28fACA4OPjNN9+67baJJ0+m9+vXx9xB+fTTz1JSUtp85NWr/ycUiqir7733vrW3adOmzQMHDty9e3da2ioMw37/fcf48RNWr17z+utrQMfYsmXzn3/+QV0Vi8UjRoxcteq5+Pj4v/7aPX36nfSDX399TWNjy4fBNm+//Q4AQKPR0O2+OdTzFQgEMTExt912W9++/Xbs2Llq1bN79uyGpqCm5ttvv3nwwUWnTqUPGMB8VT/f/CkA4JMtm11nPTtHhDpZgXa+/OzVM3nzxk8O/fvv2+/+H70TSSAQZGVlEwTRp08vtOebb74bPnz4yJHDZTIZ2pOS0v+3337/+eef2vQEAQB3333PmjWvb9myecOGjxg35eZeo4sOAMBLL728aNHiefPmXr4MDXpHFKhtNWRNgQ4bNlQulzMODgwMTE8/Qz9VdLC5Nfn8863jxo174YXn6d9SujA/efLEkiUP2TgxkUiUmZlVUFAwZcpkxk05OVcxDKNO+Jlnnn300ccWLVpMKExxAx8fyRc/7LiRd3X1CystPnheSd2p9NO2FeiFC5k+Pj5DhgxSKpX0/f/3f+tmz57zwQfv038ApFLpgQMHg4ND0FXbr7mdb5NUKs3IuFBUVDR58qQ2D3ZUgb711hvfffedtQ8D9ayRAh0zZhT6AbOH7OwcHo93zz0Lf/zx57fffuvbb7+x8/n265fy++87AADodxftPHs2w8/Pb+3a17dv/wHtiU3sPn7suNdeeVVWJ5t370LgYlwtQnFPj36mDhkCPaNj/zH263Q6giDo0cBhw4YBACjrCQBA1m3hwrupPdEk5l6nbbZt25qV1cpQ/vsvlLpjx9pyMDuf+vp6pMSpPcjZN9diaWlQXzz7rAPiF7101IOPHTuO9NZ/NT+yurqafhUFEI4fP0HtUanglz+pp+mXr334+PgAABjWEwDwxhtQMSxevJjaExUVlZFxITg45O2332KcGwLHcfTsHDqBqVOhDPy//4OCjuLZZ58BAIwZ45IPhsFguHYNWrfY2Nj2PUJUVBSPxztw4EBmZiYA4Omn4dnaSXb25R07fgcAbN7cMtNg0qTbAACvvbaa2sPj8V575VUAwP0PLQKen473+EL6M2fPwjkUT1gIuvfp04v+O4lhGCPOjWJGdJvy779H/v33SExMjEPnsH79u4xo0QMPwA8H8mXYg3k+AeU0evTowdjf0NDQu3ey7QgjA/TSBQQEoKsoIPDEE0+ZHzlu3Bj6+4LjuLkfdyMPuv88B3/J6CCndcSIEYz9CkVT797Jo0aNbC3cLg8ZMshcbSF8fX3Rs3PoBJYsWUK9DhQo/vPoo48B14AyPA0NDe27+9at2wAATz65wmg0FhYWSiSS6GgHvgurSU8O/XYiGhoavv/+e6SF0Z7PPoHmde1bb6o1auD54J7eu3k9/4bRYAwJCTn09/5vt309cuQoi4cFBwcDAE6caJE59OBdaGios84Hx/Efftg+adKkiooKZJ3Zw9Gjx0jdfZna8/DD8Eu+e/fejIwLb7zxpo3sk6Po9XqDwSASiXJzrx06dHjyZKYjj5BIJAAApJvoXMmCCighoY3svw0eeOA+AMDXX397/vzFd99dLxQKrR1ZUlIyd+5d5lq1gyQmJqGEEn0niomHhTnt80bntddWI+eJUURhP0lJLT+ljz0GPYP33jOFy+1BR2btGSUKb765VqfT+fr6jhw5qk+fPt27d7uWl2fuMrq7s5PwjHF2rmDy9KmbN2xMTk6OiYn+6quv0af2+vXrM2dOp45BlR9lZaWM+6LfavQ1djT4Zc6xYyfQH7p5M3/69GnAGVhMerZ5nqdOnabnr3Acw3H4sVYqlfPm3UXtP38+Y8KEcUeO/CeVSufPXzB//gIAgFarfeKJx//7z7GPuKWgWJ8tWz699dbbYmJiPv74E/S+lJeXT548iQoaoFdeJmPmxOvIOjD/Zj3bDnJzc0ePHnXixEmJRDJz5qyZM2ehp/bCC8///TfM/9qPXC7v4AeDgUjUkoNqHy+99PILL7xEXcUwjDJbgwczkz8oG27OmjWrf/qppc5kyZKH6dmhmzdhOmHIkNQOnioAYODA/leu5G7b9iUKqT3+1BPAW8C9YHQIQRDLn3pi4pTJDzy0uKCgwGAw4DienJycm3tt8WJTooPH41sM9iGBYF442T7mzp3Tu3fyokUPJCR0y87OsZ3HtBOlUmG+tXkvynriOM7n83Gcd/HihQEDUoYMGcQ4sqKionfv5N69kzdu/BipMKFQ+PnnW3Nzr9kuIbSH5csfI/3lEVlZWTqdDsfxmJiY7OycN954izo9c5lGnj+5p2PvS21tDXpq69e/Sz21Dz/8KDf3WpuFnC7GOZ839NHl8/k8Hq+6unru3Lt69042LzVVKBRNlmAUnzzxBLRr9F+Xr7/+igxSL+vgSRqNxi+/3IbjOIZhK1Y+1fHSFPaIUG9QoBRl5WVUzvfNN9+eN2/eiy++9McfO+shMHcUEmJKs1KgKjm12jnhGFSpfvr06X79+uTkXP355187rlyGDBncjnuNGjWCysL37Nlz1649gwYNlkgkWuuFQZs2fbJpE9SJ4eHhe/b87efnd+bMuaFDYY6ug9TV1c2fPxddfuqplY8/vmL+/PmbNm2sqKhA33ZfXz/GXfz8ofZU2iyktZ9t27Zu27YV1SH888/+wMDAs2czzH9LOg0b74KdvPPO21QWnsfjZWVlh4WF9eiRlJ3dEp+huOOO29vMwvv6+kokUjKes4fayefDkM6qVWlffPFFB094/fp3lyx5WKfT5V7NBV4E7unyc0D/ARPGjTff/+qrL6NU8iuv/I903mG3w6BBzO9MfHw8KVUsd9G0G4IgUH2+owl9V5CXl4cCgunpZ+jnEx8fP378eKkUfm3oVFVV3XJLqtFo8PX1bbcITU0dOmsW9JoZfPzxhrvumgMA+PXX36lUT0JCAuMwlIIvLips31+PjIwcN25cSAgz1FhfXz9ixDC9Xi+RSJwY8LVIXZ2FDxXydVA5hLMwGAy33AId7XXr1rfb40bln2SdbyK1xcXFoZ19+vS150EE5EtqrdPMjbhOhHp8Fv7D9e/97+VXLN6Unp4OAKDywmRnG3Madu/efTqoCEJCQj7+eOP99z/A2K/Xs+hjdPbsWdQicuLEKWrnrFmzP/vsixkzZlq8S2kp/Mlpt6v77bffrVu33uJN6Ezopty8N3HQEFhzprIjWGGRMWPGfv751gceYL4piKukCEJ1Tq7jxx9heDE5uZUL0r07TIuZV9d2EIVCsWrVswCA7dt/bN9v9uTJd6BANmNDD7thw8f2PMiaNa9TNXxdBNzTBydXVkKvedmSljZKiiefhDU0Bw/uR1cLCgoY39vAwEAAwKVLlzpyArW1tZMn32HeMpiQ0M1i1NVdIN0XEBCAcgUAANSjtWbN6+ZWEsdxpD7arSZQrv+zz0xtYHQefngpPdaGvm+oWxwBzwcz5ZHaB7JQjz223NxK4jjerx9sjDFvNHAuv//+m3kh16+/wsJYqqrciezZsxu1+X/7LbPAvk3Gj4c+3KlTFnJNqBQvPj5eLBbbfpCIiIi5c+fBmsKVFmrXPESEYl1OgS5dDost7l6wYOuWz3s0F2GEhIRu2/blggULNRrNL7/8gnYuWQJrp8+ezUBX+Xz+oUOwRm/FiuXUo23f/uP27T9GRkY6dA5KpcLHx+e5555HVzEMO3DgEIZhW7Zs7uR4uW3mzIE+9fPPv4AsplqtRqn27OyclSufprz12bPnZGZmUVXfdoJeOj8/UzQTFcaOHz/h8OEjQ4fegnb6+fl9+OFHaWnwhVq7FqoVAMDLL8Ns8n9H/6V86k3boHZ79w1Ybm2RnrGwKM0GOp0OFXVfuJCZlvY89dSmTr3zwgVYIPX226Yslj1IpVL07Oy/C4oayeXyyZMn33bbRLRn8eKHJBKpteEaHQd1cA4ZksrwuAcNGnyLFVAVxNq1b5ItBmstPuyJE8fRrxF9J/UII0eOuvfe+3788SdUJIcUq8fi8LeV7+mT65RK5cL77v35h+3du3f7bNNm+jARlUo1fPgt9A/0li2bly9/PDf3mkajQaUkr7++ht58gkJIjlaZDBkyODs7ZymJVqtFJYe7dv1l3vTpXnJycsrLy6Kiovfs2TtlCnTZHnlk6bvvrp85c9by5Y8vX/44/eB16/5v796WfEKboJeOLvAHDRpw8eKlqKjo779nCi56qU19ff1LL724bt26r37epdNqBeSr98+unSVF0GNoNy+//JJSqbz//gcefngp0rwUW7ZstlYzbxEej9e+2OL48WMvXMjcvHkL+h3FMIwgiNtvN9lTp9PU1LRx48dPPvnUjh07qQ5m2HL+ySZrd5kzZ3Z+/o2IiAhrk5nIdMIrhw8ffeyx5R999CG187vvLIjo+fPnMlryWIUja3Z2Si+82/13OuHh4ZMn3t6vdx+RSJSefmr79h8sDi4LDg5+/PEnhgwZfPLkyc8//4zhx/XqBT92+fn55q5rYGBgREREbW2ttYRmjx49nn762fj4+MOH//3qqy/bTBS02QuPTubqVdiTYw0Mw1CUjTosOTkZw7Dr169bjB7w+fykJFjgTT9AIBAMGDDwnnvu7d69W01NzTfffHPmzGmLA1ZQ6DA+Pl6hUDCmWKGzNf+74eHh06ZNnzBhglTqe/58xrfffmNx/NX4YQNmzJk/YPDQG9dyd/zyQwNZNWGDb3/7u81pTOj5pqSk3HvvfUlJSXV1sh9++O7kyZM2Qt5JST34fB7jNefxeKhZy+J7YfttwjBs7NixjzzymFqt/vjjj9qMF7XZC48+h1VVVfSmZPPzKSkpUSgUPXv2tJ0GLCgoQNNAzN9Qa28uukxBEIRGo6mpqbU9eww9CEEQSrfmBiwaULPueKwrGlCPW9a4TQPapRiWAs26/dhpQD2ONg2opxPb6csau9SA4t5kPTk4ODg6nEoiulASiYODg8OpuF6BcvKz4xQXF1tbY4PDnlfPfLKBF6BSqVyXpucAdolQBxSo+/tkuiyuy8Z2Bbz11Tt58oS3PjWvpD0KlJOfHBwcnosTRSgXA+Xg4OBoJw678DWlrE6+u7dIgqPdbFjTqtGlTeY/b7U4nIOjfUX1rZecs2ulOdytwww5ODg4PBjcm+QnBwcHR2dGQtulQFk0H4ODg4PDRTjVhW+Rn5wLz8HB4aXQRKiLFCgNzn/n4ODwJi/eIfD2RD85F56Dg8N7sbTahwuy8Jz85ODg8GoR2oZa5ArpOTg43E91kUdONrDLgHLVSxwcHC4lLD6GrfVMhAsUKJeI5+DgcCq4li0rMNpv7PB2ys+jXPE8BweHM2lUqAEbRagrFCgHBwdHx4gR1scI69++37SaizRICjyNNgwoJz85ODhcx4oFcLlTZYMKXR3Qu5+7Xu32idD/B+fN/pYLvEiHAAAAAElFTkSuQmCC"}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The plan is complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-31eff80e800e", "created_at_unix_s": 1788842396.012143, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 7, "requested_group_seed": 7, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [7], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "assistant": "Use the two inner, visibly mounted targets; S0’s central lane needs the teammate ducked, so S1 fires first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "assistant": "Assign S1 the other inner target.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T3", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "assistant": "S1 takes phase 1 while S0 remains standing; S0 then receives the clear ducked phase.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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+e0UGLcigMGK0wvMWl1SwHa1aBRzHsKwkOMTSMf/55W/d0L/FNcbto/RqFvgOKFAE8RZyj5EFBG+6nSygbbZTkquvRE6ndca0bCM8pv+MeSTR/QfjBVztY/1ssrjO1B//MvtqUGS0SCrltFr5vsaHpGxh8HOvkT8M166CT4ECRRBvYdsysgbiHQvfb9F3sHA/w4oeWEPSJE+s0zfh931G4r77P9/Ih6siqbTVwOGa2lqnNOEBIO/MsarCvKDI6Kk/7aNZTTxxbTpO++0IAKybNdHxG4mk0oFPvtR5zEPAcd9OHgo+BQ4iIYi3oCgr2f/p6wOfWnj3W2TeUXVRfkXBtbi2ncQysoJ3Zf61Ayvf1B/Kcee2/K/DXROf/PP8lldmhkQ3GzqXvPTTjLud+Dxf3T9w0tfbo9Naz9x2rqak6MrB3cGRsem3DKXW3vHmnIJzJ4xOGfHqR3x9E5666spVo7s3WlLkq/t8KQOUggJFEC/ixLq1F3duHPX6qoQO3ULjEkPjEnXpRMqDa5afWLdWeOTOd1+sLLzeZ+rTo99YTY9Z99QEs8PrjvD91DtSe9wybP47YfFJHe+6j+9A2LxwhlOK2qlqqsuuXTm//deT9cF1gBZURhAECTSwDxRBEMROsAlPmDBm5KPD0sC7SWlPMquRJtHtjmn4iSGuAwVKWLdxK8BI00/nxcfH8dsSMZnm4W7MZAQiCOItYBPeGm99/qsNn2HD+gfOh3Pt5REEcQQUqE1YDT9dHyWiRhHEK0GB+g4YiiKIl4F9oAbcKCP1u8Sy4ChSDtFbHerGjtEjBZUMw5395Tt+z8OznnT6XSY9Omdkj1bde5IFLQDg5NHMyS64C4I4HRSoAc2igqlDvYRJjxGzmO53hcWM0Gq1D0+bR73Gq+3E0Uyn30ijcUI+NoJ4BBRo43hm/B1ApdavpsD7q8Firg9Fq2tIeXM3INKVyRDaGVMPEF8BBWpMs6hgL4lBtRy3dPHcvzeut3iEKzUqEum9JkvsHCSTajRaiUT0cH/jBWmdAAOLX50jFolq61Qsy0ql4pYpzZx/FwRxAShQY26UKeiCMB5HxLIaDceHZgPHTuQ4TqPhHpp5uxs0KhKxr7z8gq7wOKusU0nEIrqEgyto0yKBvgOOfMUAFPEZUKB61m3cOmGMmVx6D8KyjFQium38fcpaVZBUoqxViUQsy1rwi7M1KpNI2rVM5ABYxuVeo4tDYOMd8TlQoI3w1ue/vvLUBPAELMOmJsZwBBIP6tfwYhmWsZx85kSNMsRr9EqBExOOvXOI8Nv3Xra2Zm+bAQ+6/okQrwYFakwzN2YwnbxMat92S4+iG7bQLT3KC7Od/JU5y1bZblshaN4AAQXqKmx3ou1HNg1dwxvxJ/Ni2OttoED9GgxF/cu8luRrrF1zfzjb3IIdDs4HBeoVrP3h90cfutsJjXezYCgKga5di+Z9pZGAl4LytQQK1IVYUp5pm92sPe03pr+EorFtutGN4qyTnn4W/2TOa42b16x80bwUFKjn7ek+fC0U9QZvvv7mooULlnj6KfzcvIy5c1v7Qp+Dfwo088APth/cdsBDEDjYHIqyYolWrQL38tFbH9QolNaPeXHJAnAjaE+PmBdM5Lvctt4GN5vXPwU6dxlZpxAA3n15epNsu+DtbyAQsBqKxrbpVpx1MrplRzcEgG8tql+nV0e31kl/n82+pRNZXoXj4O+z2QM6Niy1cjm/dObcZ136PDFKM9VS9mQU0I2BHcmMqYpqJcR1celjIKa84Hrz2iFfv12V0+hDtMWkQoQm9VQivTtgLApUuOejtz6obiwwNGKBDXEip+UYhvn03fcBoFVizCe/HwCAWzsnAUCwtGGygLq+WhMxF31qhgGOW/zGW05cEyk10viz6NU2VvgtP62AlLdu1tnGyyI+xPwZ9/Db/cc9G7gRqJWY1EaZvjl/Cr+94O1v/MyhH77xrvBbRZ2+7JObYcgsUeP59fsy8iz6q37GJwDYYs+mIpHJVIpq/Tcs+dWQ51UYHdMpPU5ZW+f0WyM+SqAI1O4GvpFMa6pqwL2EJ6drlIqakvyP3jYfBi5Y3OQ+QRuNGRJEKtrtySjo3z5Oow5OjYsUvtosIkT4rTy/hN9+at7ztj6KwIkAIJXJ6qjCWDHHmfEXAHROj7uUVwouQKtRR0Tph/4qKqqCpaLb+9zE8SsBcFBQVIbLqwRC+Gk7gShQu01KnBIW4hSZvrnYoO/PFibPfu67jz8g5UFbJ/HCemquzapqgBghIlQGAFsOn6O7bu2cqKt8ShrOFdX6SqBK3RhS1xaR1Yo6hlFd0umsdVLzpt/R+rM0oKlXWEVFFQAM6yvwV/3BxGJuITEhjpYE0D+DTyUwIO4hoAXqiEmdItOIEGKxVkkx/B7SoqVl3YyDM2jXuRNVZ/2hdv9KMwsWL6A9jzz7MvJ7tY1VGNZQFjaZBc/nVBp7Czp/1buTgYJCDySBUYfSW1/KK02JCXX/MyCux55kaRSoE0wqlKktJtVy2gWLX/z0HRJOGmGl/NH6d14x3uWwzURikaZOb01dk7nc6HH4rU7pJByrrXVTmXrhvXV/UOr/qriY2jq1TGrml0JQzQ8DUf+G040gPWfj0ShQV5nUikxZhjVomdpFdV1ZqNQJU5X4Xj9dk7md8YPVh39uazgralXBMonRTp20mPxC0vWZEB/loj7Q0JDg6hqFWYEKbl2G66MiPChQV5nUukwZYPZkZA/u3JDkeOLyJXKKtPH1l9RaYjURyxSWFy5a+KI+p+d1e0alpSImSMwo1Q3SbOjyq/9eP3hiqVHvVEJDgsVsQ96SkD+PXu7S3KY/GNNmvzR9TM++w+915EmEOQDjZ06mGxtWkNVJOUwCRfxeoL/9sdtK0TA3m5SXKdVPtbnIdH9GPt3o2SbWuqYa6rc7rDNS3p4x31xt0Gj9AYlxUdnXCsG5mATi5VWK0BDjkqzjZ04eX7+9fsW31q6n5QYPH913+C3Cnb3b6ZOdp4/pOePdP5r0gLw96faBJ15r0umITzB/xhh+++1VG20/0W8F6jocMSnVRahOpls2kJkV77y6zOiYo1nFRimQggElc8Z0YGCnVq1VltdQYRldmg9F8105bsNnI5y4pE//FD6BqcKE3ZEn5WYerKi4bM+fm/b8uUmhFU8Z1p7uPHxB7/3DhvakYp0+picALF21FQB2Luw6/K2zfB9Ch0Rxh9w/zqXeSb+2zdvt+FtGvBPem/NnjPntD1t/0ChQD5hUONo3b+nLdOdnm//Wzz3Xp0AajecYM6R328IbxCC1DuR1a7UcyWavJy+/KCmRDBaZNucT451XGqqe2lrV8y/NEYtZVrey8ej7mjDlzhJHT2f26H/7kw/p192bt2KXURCaktjsrj4taBxKxUqtSmcilVZrtBo1gF6g5/JJwizvUAD4eXb757br73Xk7z23diF5EaEyax0vf54w/ttwa6eEqtCbHH+ziMdBgXrMpEZJE7s2kf41ygtPP92pRUxiosnqvg053VBQpB9IYRjGwfUypSKo05h/yaXhp0wqMRtVx4RJKsxk0NtEUXH5yLv70J5Q8memV+sEmSIugqSLkUkBp/MvXarcq214U1SsKkZ6I+8aAExdJTe64Owvsp5cNJvINPXODSu+O3Dy8rR7B5EGRAZZ/HrfaRM56pR67FIxuXhb8hO8tVNC/YKvNQ0bZSdEuj9dkiR9yT7Egwib7bYPwaNAPW9Ss7lnyz/6iN/+5L3XG17gU0TBOWi1XERYcK1Ky7Cs0UWNGs7Wex7txNzbCA0Jzik00OdnSz6mCuOHcQAgp1LvxC3fLr/r4Re6tSIBcuaV/Lio4DNZ1156pb6nMr8osUvSjcra8zkkohfrFrtnGGbybSmVuvkCYhH5QNcfyjWdC88PvvM31d9Rp06KSCxS19YyooZAhCq1c4sooTH1B5usqOqOzCzElWAE6hVT760kxc+as9BApkzDLzZJrImL/ufk5ZRYg1mVTSUyVFKhsBCCWhqddxd/HrncJS3qwYkL+OAuJFjWpXXCrytfBWCSE2PbtkrNPPDzxf3cb3vO0M/wNMCgrkn7zpdoFFXCILFH6xg67KZQkk6PoxdJjaVbu6Ypa2t7t4svKLzB35TTmssDqCc3+9KUYe13XVTRjunIaOLK8tKGqJYRibUcGaATscxJecNlhfRun6xLO8Vp9b4NCtRbwlJeTlaiS16mL8+b0+jBtpNXXCUY+CYPMn7mw6aHGd3rgGGtORfROU3f9yoSi0zbyz3bNMSqrRNCRSL9MypqVYxWHRkdLTyYYZi4uFixiBGLWJZh7k2OL6xPzzp8oTAtmhVmgwIEU3cnxgsuort8alrrXRdJbzXVcZ1aIxWLqEYpFRVVDEDzFNI5kGTSD0MraueWa6eP6WUUzyI+BwrUS01q3YzL3nmP3/57sq0V22xnw4pvzTmUcXP1+z+PXO6sV9h1MromlYUF6Yd3yktLGZGE47hjmfoR9rsGdtRoObGIqSivOppFdlbXGGdEhQfT03V9xroPOq5Z5LW8IqMIFABW9Kp7/rThuFn927mec2ny7Q0RqKquTio2sxS2vr3OMPy9tBx35GJRjULZqTkpy7JszdbOLaKkuj7Qa/Iz/InWxXox4wgATBisTzCgnLoRZtsniphh/owx9g3Bo0C916S2++eH79bw22YzTM2jS7+/q3eHrUfPMywbG2Yw+SfPwbEjziGHhoYE02IifPg5cWin/9t3TqNtGB+PjI4mxzAwcVg3fd+C7j80qOzSIupcnlIXSJqFqC9fNwpHh99oBFpSUR0T0TDP/f0ulbr/kq9LLkVxHDktNTZE20wfgWq1HGtp+J0x7LjWfVtUppRIJJESycUSdniP5kfr1U9yA1p14reF6jQUa8/CMsXFDLK9bs95s7dFsboZf45AnZ5L72aT2lHbgGaY2iJTRspySg29emhIcJVSHRsqFR7w6ZKPk/SpSw2PkFtc3UpQz8QNi9kl1beg1RqScaXSaCW6gSD9u9BdWp/5z3D/nrmWHhcaHxeVe13vJtMglEI7kRPjozf/faFLi6je7eLz8ovE9VWgRGJjLy5qXXbXH6qoEGmwTFSUfWnS7e32ZKr59n61UhVaHxqbhTiUg6sFlRWV1cmxIakxbHVlmaklwyMixg5oteuCyoJYlfxHyb+p6WN6XbtR9b8dx+m3KFY7aFLmfAAJ1IewYlK7FWRdprvP5Q5OT+J7XksqalrESpUqMpSUVz9CZe+dTbD5PZRUVLP1VfUoNVX6Xk6OgzCdrSTmhEg1mh4XmhDX0O6u783kD9Dzz5nc9LhQYefm4QuFwlH4gxPNDMqVVNW16NiPSA2A2rMBQWhsiQqF6kZZlVTEVHJhI7u1uZJDOiVMLcnbk7eqkpMM6ZaaXWXwSPz7+vCnfcL9QrFmF1Vu2HnCklhH9GpBNy4rGzJ/kf5NyWFCgfqMSR2M5HiZNpi0sdRRXehn5oZ5xdeTYskIcpOx4T2IdQdp1PqUAKWyVqkrIS2cs6pUaYIkIktBpW5gx+CtNQSh9RdJjzNTj04/g0AcNOn2dlD3r+kBN/e5jUpNI5I9OPimA5etjdSbklNI7AkMkxJSm31NPxpWpBCD7pp1IBnVN/1skcSsVbNJWGysyGqN7kOorWqSWKeN6Zl5rWzLPl1HACJYNKmwgMylblIHKArUx0zq+OruvEm3fPeZ6SxSw/DT/H0UlnLubcTye9j8P/1bHjVR95Z1fY780h10npI+qJQEW2qSG12dHs+RTkjGzJGCxzh8oXBYv44AcL85e5JhpRZHZ0JPfQaCwJ62tOLVGq6yii4WwvXv2TH3er5WVxEmLlhNB5GCKs7v/PvktRKFRqNNrR+4V3KS4T3S5OVBwkuZ7di1blXhWf/9aT+/c/9Z2tFBvk6/RzdydToQUwIKdeq0D2zC+55JnbW6O51FOk+3ffuYKWZ7Hl2F1T8FvEktYdSzadwkN7wsFRw93uBIQY1PSnmp+ZxNHhqB1oF04m1t9pwutL0Vv/90rkoXVt+UFpd7nfy67jh8KSG+WZECQH4mNCx83MAu+7/6EwAGd0k6c7WsSqke0CGuvLp2/fZDHECPvvrglyALsxJ4Gr3xhgPMiXVgx3i+CR+Y6gTDMiJ2gAL1SZM6HooasXNjwyqkuiRTxrQX0vlY/VOwdPU2ZTUdB4eTB7bxpaeEPZuMaZPcwgWryPoksoYjDe35+dw7394kj4wUfds918rzbhyifPhEqm5KqIE9Gw1Cm4VL80pI54lw0m1CiEac2PX4oX2hNYp31l6kO7OK1LKQMFkIaIJiR/VJOZa5WRoULGzR0zQm/r62NOeNoGftP1uoZqV39m4JTVtuFTEABerzManTK3TySabvLn6xQPdrz/dCpsQ4NOXJlKWrtynqLclj9I7Mdtbq7cDo+xz0IbNAixUV+suqODZYKtJFiIT4KKm2jrSmr+RXiFiWZkq9vUluSwRKj1FoxaP6pn//+9+WqgnSeZw8BTfKb1QQS4WGhsRFUuUxCfHNCmpI+Nm9c9s7e7f86Gd9y7q6RlFRUTVhaEfgNJ+s/wfEMlVdHQ0/9ZY0/RDsas7TQSS+15WPcKff06u8vOpAduN1aREUqP+Y1BWFjucubijSPPeZp+mGfYVLFvx3nd2BKd2u00JJeXVMZKjp8DqANq+wOC6ioa8wOTZs3C1hu0/n89fpmk4MW1mjzCnUz548XUO6GmO1xL+jukZuzijfOKTxYCwyulkkKTloriS+uVb8vlM5LeJCVWptaCj521NeXaeLUrmCwhvBMnGHmwcc/3fviYzM0JBgXnxhkdEpSYlHMojTpWJWVWcwoEQjUCuWbMyq4vTEiNF9W16UG8Ta/C10bfkAtWf/Jg7Bk/85I2L0Zb78FS9PBXUu79lV7LmprHybhKgXcm8Ip3LK80typY7O7Dx/+bKll+quX+C31RwrpmvN8RPkg/S1RawE5P+e0/u0Wyv9Kn5/FxlM4DkwyKQmqQljdgfx1ZsYBsLDwwzMxYpDgyRp0aRh1yKV/GYV3iiXF1RqGb1Ye7RppuXgfK6+V6Rty8SU2LDN+04Le3UrKqomDu30V0ZeQXFFUly0MPa8qW17YRO+qbGnkBG9WtQPIhFapSUM6pKy/Zz71rzywj5QOwSKTXi/Yo6giInrZPrEfP1MfAB4+vX6zlNpmFNECQB9Yohf4iKC9pzOp5HjPzfCpcntGo64ru8xNCooR2XaJj2F33P5qj7dsrK6tnUCiV5FIqZGV0yE03LvP3qzWlUqEWkf/+Dqwv+kQ2njAi1mrQ2vJUQFVSkbshQKb5RzHFelUIeE6AWadb2iTq1RqbXFJWUiERsZ3WzO7Ac27zsd30zv9ObJccN7NL8gz9FNsWeVdXVlpeU9+w/mr9mk2NO6Ugd2jOeTQGsBqD0HpGmw/W47GIH6P+4JSw1k2kRRmr4U1UyfyrNh92lhob2u6dH/3AjXBaQX6Xyh0JDg8lJdBqjIoPncs01DVX++yMihc2QE/NauaUezCjs1jzxZTY4pyrnGqZTtOnbd1DfLxgi0WiOqKMo3E4Hqklg7t4iKDpOezK5slxxRXFlbqhAsOcXAVx8vbfQuy9//78VrlUL33dRWP/ndNAJtUuApPF7FSMf0b3UqP0Ab7GAyBI9N+EBvwnuJSe+YtsT6AXX1ISSNGWmxTiE92lrsWaIl4Cqra0Usc1ROSoqEyBoaUrxMe7SOoXVGRgzowOjmrUvEIrGIOXohLz0uVChQSk1p5fGxjaiHBGh79VNZ665fNBIodSgvUKWa9DJcKjToV22eLoij61n2wqRG7ztv2Up+jMsWUTZqVTqIxEegwkGkwElpmu+wQLEJH4gNfKebtLCA1Nbk+e71JyYvXNn4kvRqjWkDnG+DC1vfpjWJRay+Dj+nUQn/N6Yl7OiU9ntv76rPH6gvZfrvWTJZ3uzzhESHAzQuUCEqLVNSTgb06dCWEIWaPKfK7PqiJry8/PtGlfrOy08Y7Zk8c6F9zXlpcOg9t7TKyyuwPIiE2AoKNBCx26RGorSd+tZ6/VBPyxZ7ThcASLqmR+85nk1jRl6mNQqDMsO8NA+d188YGdg17ZguhKTD8WIRo9aY5AYwwNbnGXGMrhwSp88YjY+LAl29u6aOIPGH6eJQjh/IoholSe4S1qDK3NVSOuOIp7kt9zBRqtkQ9bsVgqUKmqLU6hpFXh75HHSDSORzqAXJoC4pndNj/yD1SgKU/k0PP1GggY5Zk9ptSSGjWhqN5xJ1RjVrqFsxdkhcnUqzef+5mCjSp0kHi/Yczzaq8nnnwI4aDbGQTMKOjo8Vs8yNEuPE/vJK/cKiQgwToUgUml9fPtkUG+0pPH7A3nZ8XwRvUo1aTe+cGEXeb5AIah1brsr2ENUGpYrJ3xCBUoWDSDnVkBPA9rQbjEARwpxlq+dNd2hOmxE3KutMezYBioz6N/u2T+S3a2vrhtxMOuYqa2pH39ZJqqvJRKNIXkL8Uno8NAitUqr4WstgLqHpH12lOzJQHq+r7OmAPfmzBuxNkibfZNirq7/wlfyKOrW21rYmvPuVShOYVIx0RK+Io1llYdH6Ze+QpoICRVwC3x43TS0i/ZtX6vs3a8z3b5K5Q0Z59RwcPHutZRxJR49vFgVZxMU8TGMF5VrGhXL1E5Y0HPEaHUGyz56m8CYlA+X19TzFbvSS3Uo9umyl0TSkbSdvBIJS5zs2Cz5QBOr9ZZX9Ek6jonlF1KT7TucJ84rMLlR56Hz+wK7NTVe6p0dwDFB7JsRFG82GMg1Ce7WNbVi1w7TAvuDsq7HdWxSfWDTm7yUbbzF9PLqf/0pK02+85cNh657dMWHA3iRxaIS62mWFAhzGlo5Us8NT206urCo17sYJBKXagf8LFPEI/IJudPEiOgeUFgQRsQw/HDRyQAdaxJNlmNTyhkrspvALkTIMGA7MNBKE/nPmWktd472+wL5B4/6hDQXDlhtY0qxJF485QPf/0+r+0pBkmsxE7dli4tNX/9ewDLXXYmOIalap85aZUSqgVVGgiKsR5hUBcNExMSKWufu2WJZlRCzD11jiOI4GmMeyiroYFuOolyB5lbbBaScmK4heTYPQVmn6iEl3ImdQH1rg35vf/HXJxn7bOz7ZpoiUAe2W+wcJgcUhMjWpotKuQF/jQ9hrGq406D2I6THEJwTqdKUCgN8EqvYNwaNAETfCkNIY0WFSA4PVZ2gW3rC2jB21J1/x01JBE9MglNZq0p1Yb0DGIAI9vmDcAEg6AJ/Rb8cfe40IVBJKBdojexPdL2zCb+38zM1v/np8wbjkkVOiuw08vmAc+Auo1KaCTXjEJYhY0BgOQQsrcOq7Nev38s3zhLhoo9EhYZF8XoE0bj10sTg8PNS4cLJMVF2rMYpb9beu/9Jo5SqxRt8J2z7vLypN/iuphfzCvXTj+tZvrm81nr3qZ6BSrYMCRVxCRZX55TCFMIaaS4iLNlWbUeOdnmSpECeJZEur+PvWn6ir09GgUKvn6wipM03AQpo2NgXe3fB3yhA8ChRp4J3VG52bCmouN9MMVHOWtGZqTxJP0uRQTltRUanmWNqej40MpUEo6W9NkOkb77pycPQ8rVI/X4jR1Tw+blzHGXFtiApmlMrMW7bCS5RqHwERgVaVFvjKz8OfaDQ3U9g8N7sKk5kFlgWB5KDOiXszCsSMvqegWDefMshwMiVXV0P1ermggp4lYg0OQDyqVO6dl2ca7fItpQaEQHccPDusn/f+DPwSyxOErDTPLb2qr5XZ0Imp21CqNMKJQBLGYEnkGiVJ0b+s+ypmWbHOmyeq6i+FeG+UOtPNSrV7CD5QBMqDoagHg1CjBrqw61N4RqP2NLeepsFEIL5Wnphl0Zg+0pE62crBtivV/UFSYAkUHeqBD9xCEMp3fdZnczIW85aYhu5LyuWCik66xeDMIk2+6ZTB4Yi38/Ly74z22KdUOwLV61kZ4AABJ1B0qJtb8VGhUn2tOQbq1JyFrk9BkqYArrahB5NGlDT5Sb/hhDpHiN8qFay2/R30ZkALFB3qBtb8d+m0Z141W2uOb57XJ8YLM4z0/61R6HowdV/FIpf3YA7YmySsKtI/eYf+Ua8Pc+Jd2idajJoRpyvVSqD68NBW4CQCRaC6caSOwj3YH+q2IFRrGCs25C0ZfhEGoGKRx7oveXsabTtXpojTldqoT80q9fK5E448Q6AIFB3qQRQKfaVexrjxbqpQPV44+NM/ecc/14fxayIhfhCiAkB6h+7HNn0s3NNj9GzbbxpAAkWHuoh3Vm8Ufstn4xeXV/PJ7UanGI2tC8eIRvZMzb3RtLWJnIgw5DT7KgsPu/FxEA8o1cin1pUaWAJFhzpdl1aQMFrj5HadLuMjZdraBmNezteX1BSL9GNEjc5VRxAvUWrACRQd6iJdmoOpT27XrxREsj4jZbwx9QNEOm96Q7PdevhJ0T74LfsjBqEB3ZEKAqUGokCpQwFAOKyEY0rO86Yek/WCyOLs1J5Umh43puP07dHbdOe/xw574lkQD4SoASpQs0PzNF8Mp3s6HWpSL09utyX8pGgf/Fa9asKtg26zxaooU/9WakALFJvziN3s20tKhQJA65vaJyc1rDNqRabl1zPxA/czpQa6QNGhCA0qm/Q5iGesU6+aQLcvXTx/6eJ5ul1L1l5vICoyomuXdlJp4zX9EB8FBUrANHvEKchEaqFJy8or/trf0B/au30cfs5+BtZGNBhWEmK21guCUMQz1ln5KGQiNf8PPzE/BgXaADo0MGlq+51HGt7ImiUU3qSHzxsv94T4OihQA9ChiO1oH/xWGh5M/9lyvEykPpWZR/+hTP0DFKgx6NCAwu7w0wjepE2VKZrUp0GBmgEdijjiXwxLAwcUqHkC3KFldVIIABwPP61P62ySSY3CUoxMfQIUaNMcGjgaLauTBohG3YAdDXxs4/sEKNCmOTQAQ1Gf1mhNqdet/o5hqT+BAm2EgHKopRoivqvRkOhwfzIphqXeBs5EsmeeUmBWb6IOjZLWgQ8SEh1elHMNANq0acOy7LXrBdVV5bHgFQgdWldpXHzaLNSk/JQnnOPkKTACtYmAikOt02g06pRSeK5DJpMlpzavripvd1M78D4wLPUtMAK1FYxDfT0ajWueAgBnzpyBM2cAIPd6fhB4L3xYamNMimGpR8AItAkEVBwqFrEdWiUlxUUxjMUlNnyxb7R7j179BtwKANVV5eCPI/jYW+pOMAJtGgESh65eOtV0J6fl8orKD56SHzlz5UZpFVe/WLFvRaPXivKKcq736N33/Hl9nXwfAsNSbwMF2mQCxKGmMCyTnBA1fniP8cN78Ds1Gm1OfunfJ7JOns8pKa/2FY0eO3wIAGyN6BpLxXf/Kkn2mZSsC1A/9ITjTk6BiYixWEwbsYKpQ/1jORC6KHFcgp1vpKigoUNj8sKV4JUU5VwHgNh5O514TW9Yac52mQpBmToCRqBOjkP9Q6N2S9M34LjY+buce0lPhaIOhqU49OQgKFAnO9Q/mvNFBQVWglDfM6aAouzc2Pm7XXRxb9CoIybFNn5TQYE6hB871J+kKcR19vRC7EjRp2Civo2gQB3Fjx3qN9J0evVPK3g8/LQEhqWuAAXqBPzSoWhPf7Kn4ybFsNQsmEjvwhx7P06z9zncEHv6HHak6FOwrAkPRqBOwy/jUP/APfb0ifDTEhiW2gdGoM4E41DE17EvJoVADUtRoE4GHeqFuCE29Onw07kNfAiktUlQoM4HHeqFuFRw/mdPIzAstQQK1CWgQ70Qv9ecN5sU/DQsRYF6wKE4NO8p0KFe0sCX+UtvKQrUAw7F9CYPgg51OtIADksxjckzuU2Y3oT4H1J7U/RNZ+L7ykJPGIG6HIxDkUBD6kADX9jG9/6wFCNQd4BxKBKwSP06LMUI1E1gHIoEOFIHYlKvDUsxAnUfGIciCDhQZM8Lw1KMQN2K98ehXr6quxeCw/reEJZ6KiMKI1B3g3Eogji3q9SDRaAxAvUA3h+HIoiPjuCDe8NSjEC9MQ4NwJXpEMQXw1KMQL0xDsVQFEGEOBiTui4sxQjUS+NQnKqEIE4fwTcJSyW6sLSZfddBgXoedCiCeK6BrxJkRNkjU2zCex5syyOIZxv4VKYykUrXwL9x+PwNG89CgXoFfunQ715/wtOPgAQWUodH8IUmtUWmKFBvwS8d6vdgFn2Ah6VMREw8BAyqujrwDiRSqdn9VsaU7Mtt2n/wWFNPuSk9CZzEkTNyupHaKh3cS678sqWXuOa9nXUXJuew9QPc9satvF83k+r2n7WN2N1VaoSwtxQjUO8C41AE8eYGvlFYigL1OtChrqbRsBHxe6ROMikK1Ccdil2iCOIUcnOz6T/7TkeB+qRDcVgJQRxHfu6CqUmbJFMUqPeCDkUQFyE/d0FoTyNsNykK1KtBhyKI07GiTiMaDUtRoN4OOhRBPGJPI8yaFAXqA6BDEcTVzXb7TIoC9Q3QoV4IpkP5EE5RpxG5udlYzs5P6jYBcFWl+WHRie58pMAEvelbyF2gTh4UqO/FoYYa5YQHoENdCqrT55C70p4oUN8NRTtYehUdarsKbZwUj970ReQuVicF+0B9kh0Hz1l5tao0343P4sM0akYm5zDa0xeRu8WeGIEacDbjn0Y/r46d+5vuzDx/RKVWmX3Jdew4eM56HKqr3tRIl+gPP3yRnGx8jEQsEn67+ffN635ZR7dbt2n92PTHOnfpnJ+Xv3bN2oP/HATf5MknHn3skUn8t5xYJnyVUdcCQF5+weixD9E9Uolk9qzp0x99WCqVfv/jurfe/W91dQ34FM1Tk39b922jh/XsdzvdkEjEj0x5aPpjk1OSk86cvfDO8o937NwLXo/cXeqkYB9oAy1bpNnxCU59+IGUlKQrV+2cS+s6h9oSiiYlxjdPTbF+THh4ON1YtHRRv/79AKCsrCwlNWXR0kUajWb0yNHgg0RFRqSlpVo/hmH17bPoqMiLZw8BgEKh4Dh4YsYjT8x4pM8td1y+chV8B5FY3Ohb5mmemnzs0G5SAVKlrqysvLl7lx+/XVVdXdOmQy+1WgPeity99sR6oNaoqbwGACHh1vzy1JPT3n17CQBcuZrdpAjUUj1QO7Du0CMZcrEkyMZLff/d6n79ej81a478whmjl/oP6P/q4ldLSkom3a8P3B6d9ujE+ydWV1dPGDvBt+qBmjJh/N0rPnlv95799z30mOmrV7NOhIQE33vfI3/tJ22Ubl077di6QavVJqRa++S9vB5oWFjo5YvHVCp1cotOpq/m55wViUTjJk7Zf+Bfuufn79cMHXLr0WMnR46+zwvrgcrdrk4K9oHaSUR4eF7uuXffXlJdU+PN/aEAoFYpHb/Lq4tfBQDengCwds1aAAgNDQW/pn+/3iEhwX9s20ntCQAnT5154+0PWZad+vAD4I8MuvUWkUi0act23p4AcP+kaVqttmePbuB9yD1kTxSo/eRfPx8ZGXH8+Km4hLbgadzg0EcmP/Lr+l8h8Hhm1gwA+OC/K4Q7v/r6BwCY+fh/wB+ZPGliWXnF+x+uMI7Es3NI6BCh79Lxp8lFdoN9oHaSn1/Ypl1PrVYL3kGj/aFqldL2trwpBQUFq1auEu4JDiHFaDnOIBHV/+jVqzsAHD9xWriztKycDKm1agn+yPQnnjO7P71lCwCoqKgE70DuUXVSsAlvJ63a3uw99nRnW55hmPDw8LHjx274vw0cx1npAPUPIiMiPP0IXsGsJ6eRntZr18ELkHs68OTBCNSvcHUcCgBpLdJWrl5Jtz/9+NMaT3cBI24gLS110ctzAeDWIZ5PupB7hzopGIH6G66OQ0uKS+4cfueoEaMKCgpmPT1ry/YtjlwN8X7SW7Y4enAnANx19wNVVdWefRi5N9kTBeqfuNShlZWkC0yr1T4y+ZGzZ84yDLP8w+V2Xw3xcsbdM+rQ39sBYMjwsYePHvfgk8i9ptkuBCPQwHWo412iC+YtILOzOpmpEaWsLldWk5EWX6e2lsxKCky+XPPxqhXvA0D7Lv0zzjTyf5RL8UJ1UlCggetQ20NRhmGGDB3yzHPPGO03HUaj3vQPdVJOnMwAgJTkJOFOqURC53qC/7L7z99G33UHACS36FRcXOKpx5B7ZeDJgwL1Z5zlUI7j5i2YN/KukUb74+Pj+W0/8ybPjz9v0E3YvV+4c8SIoQCwbfsu8FM2/PJ1504damtrE1I7qFRqTz2G3IvVSUGB+jlFJZVOcei5s8TFr73+Gr9HLBZ/8fUXADBq2DC/VCfl+x9JIZXnnpkZGhpC94hEojUrPwSAeQvILF7/Iy0t9daBpOhBWuvunsrVk3t34MmDaUz+T1FJZVxMI7NHGk1vmvv83E1bN/Xq02v9/62/LL8cGxuTmERatd98+aXfZzLdP2naz9+vuXzx2OUr2Wq1uk3rdJZlX5j3qr9OIti26Re6UZBrpgXTtcdtru67kPuCOiko0IDARodaeVWj0dx1x11Tpk56YNKkTp1J+YlapfKRSZPy8/LA39m1e1/Pfrfv27WpVTqZilOnUg0fPvas7/ySN5VmzWI9dWu5r32qTERMQzeW36OqqwPvwInVmGx/y406lPxF1cWhN6UbjJnY3ULPuFLsE9WYXIfXVmPywrcs9zV7YgQaWNjRlvfjzk3Ee5D7oDop2IQPLGx0KHoTcRtyn7UnCjQQscWhCOIG5L6sTgqmMQUituQ2IYhLkfu+PVGggQs6FPEgcr+wJzbhAxpXt+X5IXgE8T91UnAQKaBxhUPRm0iA2BMFijjToahOJHDUScEIFHHUoehNJDDtiQJFzDtUfq2kVUpMo58OqhMJWHVSMAJFmhyHojcRW5D7uz1RoEgTHIreRGxEHgDqDMRiIogt0HU9n7i3P79n3toD+NEhNiIPGHtiBIpYWxt5wty1rTq0w88IsRF5IKmTghEo0gjoUMQW5IFnTxQoYhPoUMQK8oBUJwUjUMRWUKOIKfIAticKFGka6FCERx7Y6qRgOTukCeDvDIL/JwhBgSJNAx2K4P8DPNgHitgDtuUDE1SnERiBIvaAv0gBCP7QTcEIFLEfjEMDBFSnJTACRewHf68CAfwpWwEjUMRRMA71V1CdjYIRKOIo+Gvml+CP1RYwAkWcA8ahfgOq03YwAkWcA/7W+Qf4c2wSGIEizgTjUN8F1WkHGIEizgR/CX0U/MHZB0agiPPBONSHQHU6AkagiPPB30lfAX9SDoIRKOIqMA71ZlCdTgEjUMRV4K+o14I/GmeBAkVcCP6ieiH4Q3Ei2IRHXA625b0EVCc4m/8HXuRAtd2Iax0AAAAASUVORK5CYII="}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The complete distinct-target plan and firing order are set; execute once.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-e09a99957ef2", "created_at_unix_s": 1788842393.380991, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 8, "requested_group_seed": 8, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [8], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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qErEgPjYiLrZb95hu3WPC0RIb3fw3Gv6NjQ6XSMQSibi0xu6iN4fQ0KTaezyd9QXOvXpyYO/oospGL0yXVVRb16SZMSqxok7lg+uzimo7LkIdTqpHi1DnKlCRSBQZFQUAeHW1mWTLqNGjz5xuqdGhyUhPBwBMmWL8GXenMk53LFoEABg2INH6K+ZkZfRKTErqN/DLZufd4bJocL+eer1eLBb7+PjU11us6fP2hrWWBMGu4omNhickIjJyUGLs0keenDBlekRUdGVFO2vXWdUO7SxjwnD/2CGSgEiBRGrQKkVC6hIlxAuKYLzFlNgwH6rmzOjSFhaXoY4bsaHs1B/XJr+jr76RVBTI2NqDbGn+gWa3ph4iK6qMH25MqE/nZ+P1BmLLgTSlmh03SB57S8blM5hePbxf9wu5lV6Y7lphbZNKe8vQHocvl6SMaeMH4pLyz1TPzcjbp0CVVLK4T58+ljaYOzdl0uTJ9N0XV61CN1LmzWMuaOXvf/xpaT+7du4UU8BxR3v3wcC5XG7LER7+F9Zw9O4LOyxY4uGVT3l5ma8kbxMRCes666k6/5EjraXyJ06aBN38JvNhTRzHr+QUf71x/aULZ719pJs2m3e9LUEfP8uAthOSkBdd1DZVYQJRaP+Zcvvjg2gBbgqzIVXzPdp6uorTl29U1ph85zFwI/NCfZNmyvBeWUV1QKcelhRbVNV4z9Q+GQU1EkyXXWwcgcIpEerB2KdA//nnnyVLljz73POPPvKw2Q3eWLMmJCTkheef++cfODL3gQcehJJt4ACVypj0oMnJvYHjeGhYGGu4DuKjDz+YM3fuV19/vXzpUj8/v5MnT9o45r2utgb+I0mhSKRvziYzie0et2DRnfMX3TFn2gTQLuspItVvvPDIVz9v++TTT0pyzl3IaqnOY4IKCW6b3xJMZGLQw/EkGTdKVzz86LkLaSKRePW76+9a3BIDVWl0JEkWltVkMKr/aG6/406SJNVqNf0o1baSvJYnMy3dp3n547/QjS++msm8i8Cwvz//Enpbz/33h49eeqAdGSEdLmHtEyKOZd4roT4fW4nol6lvdYyMh7odM7srcau6sZc/htm5tjE2RDXeq6hqGQiXL6uPCfWxY1eOQKvT5d4oMBjY7guOY4ESjTeuv1ZQU9Oonjuq94lrZQKDJrOwtrJeNWdU7wMXi2sb1cF+LWOFOTL6KNVDRah9BvS9d99ZsmTJjBkzAgMDTb3XyMiokBCYVdi5cycAICkpSSgUyuXy2lozqfAbubm9ExM3bfpmgTkTc/36dRgnHT9hwMCBAIB77lpsY8cQgiAK8nPje/T68JMvn15pLKRn8u7762G16ZFDoAOUl5Wq1SovL+9Fdz8Ifm1VjJlVAhOmK1aswHFcp9PJysukfr5M/zEqJqqhoUVZEASxcMH8Ldu2905M7NO3b465AlsWXl5eL78CB7YvvG0BcBwkSb791huvvf5mfOLAqLhedWU3IyLCWKFG6l6rVUip5cvqo0OglXEy9Es7J10FmyCDA2kFA7obOxt0Cwsy1nJ1IiQgS0rKTa0nukxKMF1Sz6ir+dXIea9rVM8e2etYeqkXpsssrFVp9DdKG0b2hQY0Icmip4jIz8oBnCTXfWyofS68Wq3+9ttvAADnzl8YOnQo86Hk5OQTlE58//21WqqJ8ltvvwMHI34MDZYpM2+dAZ81fLhQaN6Ib/nnHy8vr59+/kWn002bYEfL2McfWUoCsnfvPmvefR9rnXF6f/3G0NBwvV6/1v6aPiQ/aVbcA+3+/cufWPnMy8z1STEBH7//XuoLL+IY9sSDMG7LxDQkCgAoKio6fBga9J9+hpVP1pk2ffqxE/A8FxTcrCh3cNcPWWkpCkC/sO4Xtk/bcq9lFe3notGQNXXWyhK4MM8cRjUEsDI4imk9I8KC6LeuCRsOOgmyvLyqSWE+bRXpoxjRPy5P1iAitWMGxRdXNXnD3FFdo0o3fkhCaTUsC70ha0CppFVzE6y/UpsW1oXdQnPdJKFkdxLpnbffjo+Ln3rLLX//s0Wv1184fx7DsJGjRqF0857du79szp4nJ0Ort+lr82dNq9Wq1WovL6+UefO2UP4+iw8/WHfbwoVBQUHbtm61/fBQ5v3uRXM3/7Vj5Kixu/Yfk8lKS0uKIqOiY2Jg0xoDQTx4D9uutQN5Q93TK+5e/9XPM1MWzUxZVFSYX1kuC4+I6h4Hv7UkIB++746r6RmhkhbzA2cABUCkrguVAGBoFd56/blHjl+8LsDx/331+RPL763VCA0EQZIkGjhrSlZW1oIFLOVOkiR54pSZpByioaFh0d3GEAFBQjueXVBgutkzq17Yv2uPUCh68MWP9v74H6Y0o6HutVqVL6uPCvEhCb1//eVz9S3dg9wOpvVkrs8uMBafdQ5iX0avEwY1QJxdXCdXaOePTTybXaHRGUYN7H4+p8IH1+eU1OEGTV5ZQ12TevbI3tzsHpLqcVMntaeMafnyZXfecXt+Xp5QKBw9Zsyo0aMxDJPJShcvvvPxxx9D26xYAU/TzZs3zWouxKefwNkuP/n0M7OPFhYaJzTf/sd39h6hvKFh7vSJ/+7bbSCIqKjoESPHxMR0J0hi754dKTMm1qI4aQfkJ6IgP/e26aN2bvlNr9d3j0sYPmp897gEnV63a9sfowcmXL2cBjM8OG5crEIYDMvvhWVMySPGSHz8zW6j1WrLymS7du2cPXumifV0GAaD4dnUF6CUHjZB7MXwyk2kqIU0i1v2cFq08j60PP7GU9B6hge5avrPNqmRqxZP7p1ZBIOed03qjezmsKTYslrlvIn9corrJACWhXJThNpF7slfAefB/INhCTRn50Syq1myo8YdmfYD1SiMQ0F8fWwaPVleCW00CYeT+1TXN0m9WwSFt5fYYu29EqbaFGo9SZJavJ11AlaISehh45YDfaq9xQIYA7VgIcsZaZZuYUGnr9yMDvEhAfAS4efq22426CrImsJBsVJvsTAioqXx0qKV97E2+/uLn+jbRSVw+MblJuOQAZcTQtT16RGZVVyL6zUJ3btlFdXOH5NwLL1U2dTUK65b+s2aaUNjH3rx3b83vhkdCoPva3fmW9/hoHhYV9eZrLNDhGK9x7EHgHAKT+hIj6ioqnVesZ5EavTsmpQa5mLlKc3zXoLQIF9vL7FCpVVQYwFVaq1KrW0Ras3RPPrgMQCkEmFSjPEVOUdrKcoMFHIcfXmuTsP2JEytJ5NTlzvVc7eFGjzohqwB18Ox8PmyBgmAqaRauWZScs8bsgZvXH//c2uW33vP4qeN7Z/bFKGdT6oHTZ3ExVZj7ZyrwxVDCZk2lBanSH6yCA0y5uKr66AgVWmMwtxbYhSkSqrSS6HS0YOXmDYUJfc7E5XW4jir8kp2u+uxg3sUUkrNTbDvSkvNP8IJtLKc8QMiYaOvEJFeXj4Qeo8iAOpHxIDasqKB0GEQffby8oFRog+fbbkwTI4AGj18y6er2+iXyEFyT/7KZRHKXQPqEufdChJpAO3It6J5ZDXTmCL56edrUtlDgtBAaEmr62GBPdOStryQRASoJA+TuFDvwmp2La2zKS+vYnrxLLG28c1PYKywtS0qb3BBxw3bGU4NhLeXNHkw4BqC5kCQoe0TfuhqBY6DMX3NBOsSAlzjg6balE3CuG9DPcGFd7j1/OKz9T9/v8nWrVurE4VCgRlbhOAKpZq9qIyLt0ToLRESDADAFCpU+Y8RmCCvkF2iFBfq+Kiobe/JvKuL8i1Mj54rOq0t6KtCeWXdxjdhJtONOJEBpx1D7QhgjapATC/RMVFmF/q5Y0I7rc7MIY68GyQkna5ARdRwTHvJPWFHFdi+w2eAgyFAgHefB9jdPSrzbGrydDUddn1mtvxhYamLCgmABoh9JPBDERGa2TMmovXFzd2PvjlufnC6w7mqDB3ow2xb1xYYKK1RRof4wBl7zPcp5Ar0qT9woaXk01IGiWugSQHgNSDIKzEhhrTUJav13CT3zRr6055LluRnU125b1AE4BwY8w5nRSgXXXh7rSdroiTnEd5zqC02FH2toTKz56jQs1RKpY+kVRkTbT15OoiOERdhWk9LFpNrgloryxFJvJABZcoza/0GkSWl3snp7MoxfcPHhDZyJxKaas2RJ93ChnLRgNoFCTrJetqLFyoDYg+FNPFKSFBRXc+oRW9J3Xz6464F01v1K1k2IarTRCgXz6kzCubDg6y8dzQKniMIBMaA24mMsvEDInPzS3ontJr4mmVJmUwdGnvocnFnHWkXAnd3+WnsSNZZhPdsNYDVLIMH9rNcf46xDhhZT3o0ZJCvpKYODlLS4ZKeEVxRCihlZOOWISpO/1C9xAK6isC69eQcGO7rJfT3N34rxEK8sEhmduke5oMWvU5LLxP7dxPgmADHTCOhTXUOHhPstPGdnCutx93aeloZ5uRyVFpDeXmVjWaU3gpRXWtT775Oq2RCFodlQ816viIBlsjZCtbWmFrP1p8SOEkVgaKQtMvRymDjD73B6MGo1Fp5k9qGRdOoYC/cceE9wIZy4svRDutpS3f6zoyEGmIGqme8QA/ZTLq0LftHkynnMAACIrAZz2BRSWRBGtj/yYGzuQMijB9Bt7DA01cKoBdvMGACOMlHyMqvGkIj/Xe/hWnZrSWk4dEjn3grd9fmkrOtZm8XiCX971zZffzM5hIXzZmPX6rLz2I9fcpb35AEcWSNxTqSKW99441D62nAgEgoDNNqDfIq9bVjf3+3BajbmLipsKIRCM2PRnUtAjRFPQ5PeIC3QNXIvkr5+4jumdw9OsRY7ZA7Mujbf/MPl1k0N3cOD3hgTOCcT41jji3x24rY84WqD/ebiWX/tgL2+rvra7ZmH9vT5z8LI5pddvDNibpvtwhgoQbjC//g9B49I3xf+wk2fzEbxQqUip5b2Je+S5KkSmu4Wa5oOK8rqIbFT/kNBF3GxL1UEmbpAU4FQzlkQO3CCZn39qO47ysgoYJlWhVm0JPefrqh8+MHpVR/cAcALUNfsAkPYSOoPiYNFVjPkdjKn0cEvaW6DNtFt2wDQJCfpKJeXVMnjwqO1viEUJNtsMOguFAoDY8W+rTqAN9z+u1Ji5ajn4qiokToLfUKCB734nq9Rv1v6l0GbcuRSMOjSavzkUjDo3EqXIuatAsBEIbHSXoNh1Mw3Uwjt7xhJWhYWAkng+IsKjUs11WZ9Da4bWzMrGRYo24gyEaVztdL2CvS9737B+29pnhzp5lpjUN9hU/dEtKgtHYaETHBopuUwTL7EGtNkI9g6+NxyHRWyfVqPRkbJFo2PmjZ2Gmv/ZxxOhca/eRecFxpsJ8kNECSVwbXTBnRm27ugpIvJAkCpcJuQbCvXcsIPZKMD5dOHIQRBJi+/iaqrucCqW7bZETovs67S+Qn9K9LW0XoNRMfBhIf0ZWd4vO/0ysNEX0Us14Jff738pfHGqsO/cOh9awtIb5/lNoEwx/7NXDxa8iAot4/YwbHF5W0+q3a/hYTpi1E1vPQqw8qa1pCWkkLl/WcccesT7buXDnLrvmhCIAVPTfcy6tl7H9E0hDsgY1Yj2Qw/zVyG5zqimk9xw3pUVBcQUUnuAvr/dNNo/28hbOSIw0E+eQXF4nmsyQUYJ+tTJ7e3/fvi/IMWSuLmxznveEuaG0di0SI7XgSzm7ywb7qrZdbBHLvcPF3D0S9fd/Al368duGGMWjuJRELBPgto6g5PIzDhlveJgwSUe9u1/nyfRcr6J6tN3Rha+aFj+nps//ZHpPW5XNHhKa2sqFtfIm4I0I5EQN1F+e95dW9Wtw6fSLsbM+0ntBbLM8Rl12Dk6xH9DQ+JQW2QCZ+eqJ5E7LsbThLc9D9cIo9BAqMYgCIBZhGqyfMtdQ1fzxCUb/b4RwBO1fOYlpPOAb0n29yd8PTO+0/P3U0YlhTSHyUAo+w5yjgE2gaNIQDUjV6mTAacBJxVJ90ZShzuaIIQUtMj+4GgD3wvexSUzC9Mk0ePOUj2PHv2emtOon8tiJ2w12Rzgi/b30cWs8nNsuY1hMAcO1y+uSXYcfYtff3b6TmgmW3NDXplYVgXjFRRcETM+JT/yo/dUMpwMGaFOc2EuoApLsEQzlhQO0CFX666tVZ8tMKkmNfl3/9dFNZoTGV1I0qMTHo6c5pj61+TCQWS3oOZ7aeNE4AiYGobsbhgzsPX2jztfrOh1OnnP98jVmNmbP9R5IkvQJbFVfbCqGnI4bU0qD+8VmSBMSEZapGubL1AtwWJEUDvGHomYlaR76zq2rNjha3YMEQ/5hg0b6Mponr2uhyZC8iAebnhWv15OVidnwBx3GNntx1AY5BeiqlJazJhmFDD6YVVNW0jDzOl9WTJJg5xTg8+sW/4VV2Wj84qji/geBCOh64Z5MR3O3kJzX5j+vdRFqECnOOwjDoQ98RYa3a3mDKOnXWKVKHBshTB1zHnt0IB4TY198oH6hNxg6GHeeCfKHjTJIkQYKEbq0m+UFhUNaahOkwtFp57bylo63JvgyzCnF2T9mo0hpUak15nZJeSi+dJEkST5pMr6msV6EFuC270qGs++TuyJTBfqzWIXszGotrWybXulamHr82/+1dZqKiHWR4HExe/XLW4gwiG3bkwUkQbomnRqNauBxi7L76flJvelonZn8mjQ5eawN92NcMboDZshEXRKjQvaznvsNn0Ay6LpefPx6F32bI2fcWrkskAyNV86kGYhqlMP+M+PI2TFE7cGA/NKwTiOB4VrJOxhpRjsG8ML5o5X3GkqDWg8oFQvjl7vbGvlqTeMWk5nIWJlbyQqXnD4cmDe0+cU79T3CyKRthvgZjjjnj6nbMOsdZGlSGd3ZVrZ4TtmomXAAJLhSq/r4oP57LTojlVrSnW8qEROmJVW20lVs8Apo8U/mpleV4iYU6AhAkBn0vDLMyJQlq1UpbTysf0bmbygmJ0jBfQb3SwNVIaNu4PBjKlSQS10Zt2sU/qQ/6hEYMeejF4J79RBIffdJUfdJUYNB7/f0KBjJVWkNFZR3MOOjZ/UPNvhM0qLyqug7pTIIEhJ4Qorna24uqFiomn5Budj0rXWle5sRSB35FwZUeww5hb0bjoaymxSMCHpkUDDAwPN57eDyUhPsympyhN02JD4FX2XqTtD6O42Ivbx3VbLtNmJ0Gw0NaFeTm1ItHNKhDAmBeftXcBLkaim6RgHO/plRoQ81P+stBhG6VO3Kl9bQe/VRWl59a9xy6HdSz37jUjwAQ6BatBbn3gsocQFK/CgG7ZsVKKDciCP56MQzLeH5yqFA1cGCrme7/yBNNev0r2w/eJxRqCmU1DKLxWEJrIH86U//TGehEC3Hs2wejE8LEtw7wlUrwl/7pUHDw+HXFy1vM9EtlytLSel2on8BXYv5KiVPT4aHwgqn/zvRs0HgHelaSwrLamFAfkgRSgX7rqXxSp1o+D05WFhkAd6iiHHkW3KsJ5a4Ixd3IeedK0RrTf0dGrnWpZl1e5s5HZ579dDVUASs/hUV5VCQUC+jGGr1DTc3GZuwQGAalAxUanaHNJpsoq4Z+Y2aJHjkVpgsObAEdRiCm5hx3XR7PSbC0mJ4g7/+2ZMJamCka39unE67bm47Drowje/iYDkBCkDooQk3POysu9PgbT5kOssqpFz8we2iTWvfgHONY5GFUyLW8wdiahJlKcjmp7/zPru1dGAx1fRLJDucdcE5+hvYdMveLPRFDx5o+VHUNZs+9AmAyXaWB0xyB0HjWgEgCCIRAbzomsrRGqdERBiqmGRHGnuTStJ/ItT/gTKhxk2GNkVlCeg+EyqK8CHSY2HFwPuqcnT8DD+LEqoT9z8BPhwVJicfOSbZcKYHRzyWjW33cOI77+krRZFkvLEwCAHy1+4btU5IYmmPlUoH+4KViqUC/51zhpu1pGAYkVFBIxWxRxcC16Xhgvw3tWga0HYWf3Al9MuVnfWEuAGDospdMNxOIYSZd2yS/qgzFANBXwIHV5dWwsmTjm59sfPOTv7/8Wa/TqfKvWnohITUexQvTx4T47DlszLCb7cZUeGQnAKD/HY+YFaFDHkoFGFaVCadp7CAYjg9YDCdezdv3B/AkSCASYuF+Zs7eiHgoCRtUbY846iAECS4XqQU4WDrOTIuTUH/J3JGwwLZe1zK0QSQy33uVaVUDpDC0unTusJyS+uUpyTfL5ctTkqcnwq/W18daTc3CKRFqL64Sobh7OO8udRitRD/1KoW8JB8XCGd+3Mo7xjD81o/+hk0YP0pFa+p+eRUAEPbsL+huRFgQ/ggUnuRfsMDerBdPkydrmDoOel6WetmRJHHqAzgX8eyNO/1jjaX7iImrP48ZdQssFqBCCh0hduyMORt3wUnSt31P6FsqezyApT/ACrN/Hus+rlcrD/qLe6O8xNi1Ug3RKV/Ap36Dn+/S8UGf39uqUm1E7+Btr4wGAPznj8x2HEh2nfhsNlSUubJ6fx/hrX1Jfy+gJ0BEiLX5U91OhOa6woYK3cJ6clN+Io6989i0//7iFRgy98u9rIeyt37XKDN2mmgqzvXLOunVd2zkf04YmupwP+jakyd/NA0mordaWqOMISEYBluQtdmOvvZGxtG3V05a/fnEVzeyHtI01v+behf7VQQC0wOGdfuPGhuRAADMbnB958839vwGPIvrFZqPD9Q8My1k7SJ28qReaXjkZ3YBr5MgSDB+bf6uJ+MGxXjB/BJBYBi8lJKAJAjwzi8ZtU3aiOYmhxqtTqvVDag5Zbqfj3fCb92Fa0X49MiUkVG3jYnV6gmARQCAPT8jAACsol5zvNCMemXWM3GB1Hf+Z1dGvvMTSm5QxuRa62nL0KMDL90r8Q8adO/TflFxQm+pXqWouZFx5YcP6Q2uKkMH+VTXffecpM9Yv9lPiANCQXkusecD09J65ltVXTumFUpJA9R6P59tVUmjUypkF44qKluZ1MbSmztXzoode2v8pLleQaGEXicvzr/y83ptI3suPNkFWPxvBdYGpMGgqqsqu3iioQiGLDySv9Ia/kpreGxy8MgePiFSAUGColrd+gPV+VUWM3gHs5qU2rYV4cGspqwy8zNgH8wy09pqzqeFYX7Cl2aGxQWSEpGgvF7zy5GiWjhOgYzvbrTv5ZV1uTJRk9qQNAQGRpkUZOfp9AQG04/EhdzaBq0gMtinrFbZM8r/4o2a8JCg13/NuHdKn5AAaEBXzU2wMnG8e6XjXQLmH9x542HdUX6aL563n0HUFEOoN4fpkEGzoLE95XWqCnGrxuM8XQStLMcA8CB/qUqjGxzrSxtQVkNoOuJJZyPRD+bk5ZsxoT5icSulKVN5qxRNEh+pRqlA9UymBpQpQrlgQNfZWRbamSJUyOXEEWen63B4LzVTPGmcD09HUChV1DxIvl5iAXMqJ7pWiVnFQX9pkPU8d70azSMvlQgUGpgHi/JWAXj9VgMfweFTcIDvyGBwrpaL/VvbTWc68kJOd/y0MrOlW8lPs/Nc8iaSx0aG94KV8xklCitTOWEAKDT6/DJ5WIB3kK+EbF2qSxCkjzcs4MUIdvZPoTGMDLbYCKa6zvUiNNXOSKgHuvDtcN4JgnThsHeHG1AennagleUIhMKh8YEAkHRvVpb1RD+S66UNRVXGiKoAx2prG8RCXK3VIwUKR6NRBtT4FBMzagpSrCzU/pZ7QXVJR17IUeed5Jb15OFxFWgeY9p6mp2MJC2/rqKeERgiCRzD1FrjKCPEyQzZuAHG6igSF7HmjpeVsof5SiWmzf0IaVOOwrcPcAdyO8WR51DJQivnnWMBQF5+8rjWfw/wFqDFdIPaJm0r60kNeKC6ebWSR3dOH3QiQ3Yyw1i5UVpWTU8fD2Oj0ZFmF/SogSAPXHFxI4VUTo5NwjnovBsIwoWjNhG8/OThDiQgmS1ZmZ1Y0wvrzl6vxgDJWtA4YBaLpw+6c/og2oYiM2r9pZEZPXi1nI6oSptahud3MhwsrRdyzXmnIjjc0sW8/ORxFeKoPlfZU7K2RgiEJk0KQ4i68ormKbVNuHP6oN/3X8EwDHn0yIayPHoWd9wy8M8DcPpPtyPXyY48t0yVy0dtInj5yePW1ODWxmhCKTpjsE1SlDQusIUzN6JqqRxz5J1oQN2xbN4UXn7yuB2oCZ43Y0ZVs9w5fRAu9jYfFUWms3XJ3YlMMy1Nu7gjj3PKeefCVY6XnzzuDuxQKxCotQZ/f98TGdaSP5EhUlMpalaNcuCn2X6cZ0NxjlhPl3f8bJ/8vPT1wzOHxnbW4fDw2AErFlZSbLEZzZ3TBoWEBLYyo+VtJJfczpHPdY4N5cpIJI6M2rRRfl76uqWmd8X8kSvmj4Q9hgurXvnumDOPjoenbXRlxnkDpV5C262sl0h457RBfxyA3WmNySXKhkY3Tx/CHBXoRtWgzgbniPx0ve20TX4e+nDJoQ+X1MmtZ0Z5eFwNbtV6NmeHWNw5bdBtUwaYSFHjphzI73JOhOJcsJ4GA8GFGEub8vPQh0uYd7ceTNt6MO2XnWdgX7LzN0oq2V3jeHhckj7CBex6ewGOaw0gv0BmXAotuvMiAX7nNBgVbUkuldeUltfcPWMwl+wnV2woJ8qYBNTcFdzn+GU4LQfiyz+OoBsDe0UgG3rHLYOGxcM2yTw8rsJsHKy+UVXToKhpaDXHfX6hzHShHoF6885pAxfdMpApRaur6zDOJOI9s5lI++QnQZJc6EvEkp9m/fc37xuHbpSVVwMMdAv2RXev3ax+fPHEh9/9QyiWDEgwns8/T/JD6XlcEwCFChQXohhoTCD8SzeWaA5ltvrFhQS0msjEFIkETqwEPa1LReP7wcJ97sRA19nfqMmBpfUOM6Dtz7xzwHraaEC3vHX75RwZjmPIgN4obSipasAA9vlLiz74+cjcsYlKk1m2n//qkJMPnIfHiFaWg2GYn5+vQq1D1lMub+rXHU7zmVlQOyghlCBZ08bRPz0zjSNZVpX5MAlI7hhQ4FIb6uosPCeMp63VS7e9/hcyo4N6R8ZHQW/99qfhBEQ7j2fMGZsIMHDgTNa00a2mWBgZ53+u0GKzRR4eB2KqRfp3D0SX9PEDIvwCAg0kqdYatDqDRme4lAnnuB7UM9TS77CuUUXbVYIxqV5ogA8GMD4R70gF6tbOu43yEzGkexDAsL4xgUq1bv6kfsF+3mh9eW0jACApAU48m5ZdQm9/4PfP99S7rIUiT1eUn0oV8t/l8iZkQIN9xf6BpoM7qaQ6CQiS1BtItVav1hnUWsO16yXNVtUC1NO03hEub7TMBREqdNWgI5Iz1pOFFet565DYYX1giRwqsKOtJwJZTwBAct8Y2oxOW/zYHt6LdxxWeqebguPYmWrjHJYej6VQmOXfGPUEjBRgGI5jYpHYn1KZYQG9JiRD91xvIE5ezL2QU9YvBk74cSG7DBpWTpUycaDPiNBlHT85g43F8y8vHkXfHpIUBwC4nFU4qBdsmBgfEx4fE97Q1KonI21GR8X7ny3oul68WZNHT8LDetTGyXlQc3WpyNqP+XB6+aAeXasows/PmNVEAdD+VPSTBMCc/KTBAGx/R8lKDOSWNtTI1TmlacvnJQsFuFKhQNYTtiXtG3nxRuUAap9a7wiuzdmZ6qJpP3DXOO8EV3JH7WgdMnpgHG1GkelEdwN8W6ZMoInI/YFgBe4t88OGh37Y8BDgAI49jDB/CWuZE69BC3MljmOjQxtZh2HlSBQ6LDA0DC4haAllLlw+Ie3G0glB5Z/MEnq53Di9R7CvMYFuGQzp1LTc6ho5FAENcuOnMGvyMOZ2yHpyllRXlIXiLnDegeun6+ic1iGlO94lSHDb5P4j42yd9fCBp78DHkdto0ahw6zbO/9gO0xedGToxdyK1p0vPMe1bN/XQwengodTeKK7SH6CNuSnEb2BOJNVqdMb6uRKg1A6Z+LgTdvTmBtcvFF58UYluo3kJ6Kprhy4OR20oXgnW0/jXJucxC75aRamCC3d8S5JguLYO+EdzG00jlOPBBo7lLkw+RLY65CkTOxPD9PunM4XHP+AmCcQyk/qblDb8hPIldrz16vq5AqFQiUWYFKB/uDlYjRlPBKh3j6wnmlgnNEQ5zfY7FK5SbfQjtjQTh0ChDLv3HHe7ZKf//n9rHXrybShyHoWIesJwPyJ/UHXpkUzlleXlteYbmB3coLaPjoipHm3cLgh6MKwT6Bt57O0RnGtsK5OrhAL4K/SOyC0mxQm5SvrVC2z0V0toG9na9lFO00cE6Gd3HEZ79R2n4wRER4My3pS32TSdi/eMyFByoR+0REh6J6psSPtckzIln/Wd9vlwIUGrXoApRaDzFcvtZBZVHc1r0KhUCLriU5odPfuUIReKmZ68QPjgkiSNLWeHkO7RSjemc47NV0HVyyo7bWfNIfPZuVsWXt96/umD8n3rJHvXtNALZohy1pbT8j8Sf3cKADqhCMxnglLmtFOBcoOArB2Sz/Gykq1G7f4aOgAaJv6kyTJY1eLy6rqRa0VjVSgP3mtbPm85MoG1YNzjBmkV5fPZD2d5cU3eYQIbZ8N7aQyJk6N2mw3H+9IfyZlHvyVns2Kkm3HMCwwVoy+qvk9HjT9abf6T7pHfK0TaphSJvTbcTxzWG84pLq0vCY6IgSesw4XGKZMgJcoZJRlni5Ff9jwkCVLOiAuCMl5S/LzRHpxj3CptvWs8VKpj0Ho88DsgZ/vSJfVwLYj53Mqrt0opYOh7iU/UzurqsnukUjtl5+w3MyN5Sfi6ZSBzPcwqLdx4mxT+1hW08iynq/+xKHq1840oAIMJMQbRxkwoRVoVESI3kAUlUAhY7303foO0Yews9k6qzWaNnfoGWhlOXoSl4hwg16PDGiQ1Iz/fiK9ODHST67SFVQ0MUynNwCYRCRQkpK+sUHFsiqJj1SjVKgI4ZMLBtObrd2Zz9pbQkAr/5U7NaGI9hlQe0vr8U5z3rljPTvChh3pJAn9TfRbvZpbZppSZmtQ6k9aFhx63DUxa+yQ3UR+t6y8prKqzsa9CXAMYJjZbmw3qaV/90ANhSd84dqHyeX8RHpxZXV9YqSfrFZJW0+p1JuynsBLItYLvKUCfU5x3bKU5NpGzQOzhxIkbJfDqmdyI1I7xZHvjCw815z3dstP0NosIktKr2rd5JuMCKGGhXR56wnLDAlSZyANzX+1BkJPkFo9YSDImWP7hofZMV5IqdI2KTSmi0qtNb+otKDLgOQnACQzfUSbTmgdyuTVcg3TdCI0Wq1WT9w+dQAJQH4Z7At+NrtCKtAfvlJCu/Cr5ia4VyS0cxB2RuGnJ1/isSu5ZS2OvKkWbdae/1yw2APcg4FqEYCiYmsTQ9IYBxS2BQlAQvcIOu3EjJ2iQOr57LL4MFi6qNF0IetJ+e+EqcOOTCfsWltUb6CaKjFNJyU/JWGhQVXVdTvPFiCjKRXoL96ofGpB8oYtV5pUOl9vEXBPUtsVCbVrjDzubOtJeJr8BBt2ZjDuMaUn86cMl4gQv7TMrms9AQCNNupEalGqbBaMcPChERzHcAyj3HrYPaSqpgFZz6AgfwwDVxTGCqeuAKpeIgG4WtxEq07YjI4krxbWGQiSJTzReVRrtAUVjctTkmvk6ofmDFOo9ffMGEyS4IYMStEDF1vVM7mdCE11siPv3Cw81XIJeB4bdmZM6Ok/NKk7enNXkQhl2M8LmYXGW5749u2iWTDSCrNZMjbHkSur69EKWwQjhpkreKLaCp29VopMJwCgW1jgnlPXB8Rxeuy2YzHojVl1b4kkMVJCr9fqDdklchO72fwsoc/EgVHH0mXpBTChdzmvCgCw53yhVKA/QknRwsrGJ+Yb65lWzU0wTSV1cXDnO+/cNSHtkJ80x/Pkl7KKLlILGp14IbOQXphbdln5ycZo+CjdSHVfO5clo61nSDAUjO2mvLIOWc+IsKCIsCCYseTu986J8pNFg0JbXKdrtp6oaYiZwk/qr0wq0J/JKpcK9KXVTUvnQil6760wC386s8x2EcpBnCpCcSc67wTBKefd4a1DjufJj+fBqppLWUXf7zjLevSL/de/2H+9i1vPDBUcrl4iM/ahYFrSM5kypmC0vQyU9ZU6c620vNKYwY8IDzJnJboQgdKWeGVafu31ssbaBkUtnE7OzBkxCH1iQ70NBHnX9EEaHXHPrYP1BDlvQj8SgJwSeGE7cKlYKtBfzqtelmIxlcSCg168U22o0ElDNq10ePUwkA01vc2DMPslKK9qEYzItbfRfhq7V5oIz26hgTAOytisC0KSZF2TMQzSpNYz5+GobWgp/EQEB/hKBfryJrFUoD90GXrrBy5Cc7kvDfrvJzKgIM0vkz85f9in264WVTbGdfP8ctp2JJScUsbkFvKzI/67WXjraZ7W34IzmaXlzSWf0Ho2T2gmQP3R7fnKMIUnsp5M9cmtL5+TEUf1YSYbyurUxdVKASBYC/MptQ1NssraeoV26dxhBRXy5SnJN8vlqAj0wTnDlBr93TOg/56WWwlT85dLaC/eTeuZUp3TZAR3UugTxzk11TtXO+h1Mc5ktuR5jNqTWlBSneo1Y5PdY7ntXdtrN5KtDc/Vd0NLk1+cMKK36TZMYxoQEj5ycCIMsxTUkCS4WQ6dp6yiWmQ0AQD704qkAv35nIrlKckNSi2Kh7o1qU5w5IXOcN45VjkPFY6z5SdPm7Dd9tYmD11whc1tgaxj3A9lOs3Q5RJI5jFrQ/XluehGU015LgaTSKepxNH+i9BcnqASShcp1VlU2Yj898oG2KPkRIassLQK1dWPaG5xUNWgSS+E0dIr9DyKGBjcrw+nZvtwqiPvYJ247/AZA4ecd7o2k1egLqbFbafyPKaC0ahAbah6w+B4JI1SpckvLM8zuxSU3ygo7xMd4KT34tYII3rTC0GCeRP7qbWGe28drNYabhnZW6XRL7l1MBqVRDWir4Kt7ajYaDY1yhPtZOrYISG+4hBfsRCHBfz0IuKU29kpjrz5ZiLtlp97D53mkvU0Ul3aSm/y8rMzGehTLRLgQhHMDocHtNQnmlJSBduvXG7qQqXvriWEqNNhYm3r8UsJEf4VVbU+vr7KpiYdJl6ZMnDDliuPpcAuTdOHxZ66chOJ0BJqFoDrJfXncmDpKEKjgMpUR0IRysH2Ioh2d2kyK0JxB1pPzozaNDNjBI8L0eoJpBkLyuWspbiykV48aMZc96AGD6Ktp1QA6/ClAj1KJVU1qB+cPVSrJ2qpaeZQUv7o1ZbudjERoaVl1b2iApiuhETqBl3D2y1CzQZDhZ7V8dPMT5CXn64lXengCTJ5nIGKgKZAYYB/c4phvOUCFQn9lyoFTS+oeWp+8oatVyrqlN2CfFrS/RU1k/oEHs02joaguZKZw+VIqAO7heIOLfwELoUXMDw89kELT4JsuX2CGpV0MRemjMprlQ/NGUaSILsEWtV9F4roeqZRQ/teyq3AccxX1BIEkEj9RRyJ4TkBUxGKO8p5J1w5XYdFn52Xnzw8VqjBg2jhSf9VqPVLZg7RGQg0TfzpLJiaP0Ul6OuaNMx6prkT+0dHhuKkXqVSqlRK5p6vZOZwtibUgY68A9JmyHraWMHnBHjhycPjYNCg+P0XYG3TpRtVy1OSG1W6u2cMggNGMmS0CI2hZpaeMKyXtzf065EZdRcR6pCMPO4Y5x0ADlpPXn7y8LSJWgTHaNL+O/qbUVDTPLUcbE1fWAkLJA5egiM+c4rr5o5PguZSo/9697W/j2QF+UmW3NJ7xdzBaFlyS+9lVK6J4yK03TBFKN5x591gcEnhJ59q5+FxGMh/F3pBIakwCA0EWVoNp5Y7kQE9911Ur+Wb5XKlQUiQ4PejuZu2p3lLhN5igeVduoEK7bgjj3e8bklg2+iRTnbbWfKTh4fHErTwbFBo6dv7LsCuIlfyqzGRt85AIAtLNm+M7s5IjgMS/7+PZJnu89H5w7yFRsvAZRHaQRva0Rhop0/13k7hyRfP8/DYnkpSEsJGlW7JzCFUYycd2oxpOhHhQd5eIoEbS9AOg1VkHujIqE1BpzYNsdV0mspP3oDy8FjBS9eIY4BugDdlcPThK6XIaCoMQvSX9RSpQL98XnJ2Ud2+U3CSm0WTYWCUxencljZ63KwJRbS7LBTv+JRh3I948taTh8c6UoEewzDqL6CLlliylLkxvbJPbBDu1XXbDuCcL/zkk0U8PE6nBg8yEKSKEJIkuPfWwRo94eVDzcvdDDNNT9vTTdvTMAwMToDtC8xGQsf0brUTz4uE4u0etdkphZ/tUZ2t/HeSl5/uxNaHLA6m/vcR/38fgY+ivzzOwEsMLeOZLJjzqWlUmxpN2nQyV47pF4lJ2v5QuJxKarcNbacCdb7xdITwdOHYKB47mdoLtmta8F3LnCjv3Zvw3r2w+fnXd0AVM/2rVtOl/HZfV5xhwqlIBXq1Vi8V6K/erLHRfweUCBUJ8ehQqSUR6tngnHTe279/o/xsNr989NNdePkW5ry7EFhmKIG/0hV/Nv37iL8XVRNDi9C7foKl3TwOpAYPIkgwZ1xfS0aTZTqljLu3DI0FFkQo04v3PBGKc89551sgd0VYAhP+2FS6JhUsoAkPCpv+lXzVwrgTdcHTv5KjxUWH6eFIBfo/j91gjUqiH2WZTiaBvhIJ99spO8GGcu09d8htry7JY7r+Px7ja+ndmNd+vTlVdurkpDJXH0gXogYPIk16i1gynQqGF48iobakkjguQu0Ft3OuTad2/OTbgvC0QJvOLYOu8ufF5bSZShoYH4J7+Xc1EWqXAiUxJwrWjlpPfuymJ8ESnicnlQkFmI1TzvF0BFP/3XQAktlUEo5jfWODupoIxe2JfjpvukPS4fvg/Xf3xazbPlV2yhXH0uUw2yGUuYGVVNL4AVGWUkmeKkJxDjjvDrCeMPrJ4xFYCXryNrTzsSJFpSYi1Fsi7Bbo7Rki1EYbaqsCdY7z7qxRRrz8dFPaTBnxOaVOwFL9vCXTiUB3p1quZ/JIcNvkJ+EE591hppOXn56BjcaRt6Gd6cWzsBIPRYQHePtQ1btm8TwRitvS8RNzcNMQ52bbefnJw+OqVBLAwISBUR4zKqlNG9q2AoWTFTtSfqJRRrz85Gm/ruRFKJdTSYnRgUKfAM8QoW2Cd+5U704fZcTLTx4eJ2FjKgnHsaE9w7qICMWtOu+kQxPvjh9Bz0c/eXichCX/3ZLplDLuDk/sZmNRvVuIUCs21JoCpdpTY240wp2Xnzw8zvPiaaxk4QElQsUivEeEv7s3CbUF8wZ03+Ezer2h8/rN83RVTk4qQ0unPZGnfbSZSqJBj04aFG1Lk1B3wZIINW9ASUAKhbij5aeD4f1398WB5o+3pM7Div/O3EBhrtWIn484yE/iGUX1VsAtjdp0kPXsvAk5eP/dXXCSbOTVaOcUhNqYSgIATB4UAzwIsyIUt9Dx0yGv6ETTyctPHh6Op5Jiw3zF0kBLe3ZHEWpqQy0lkTD36k3Hy08eHtemkqTmiuqTe4d7TD2TWXDTqd4dUfjpXOvJy08eHq6lkligRwclhGKW65k8QITirFGbnTjVu8Pg5ScPDwdTSZu2p3mJBfHdLNYzuSlMG9pKgZKOmS2Ol588PJ5Du1NJCOv1TO4oQpngrVoucd55N4WXnzw8nYm9qaQAqTjQ12I9k7uLUNyYeQck3lHnvTMqlvjopwcw7mjkuKOR3N8nD43tDZmk5uqZJg2KtrFJqBuJUGRDjQqUJDqSee+8Yk8WvPx0Xxxo73jT6UIvXmFhQCfz0e7hfiI4MMcDR3bixsLPDjWc76S8Ey8/PYyOy0ZeeHJ8QCegRCiGAdSfyfNEqLDZAHZEfrpf4p6Hh8cupAK9wiC0lIVXND9E36UfRbfH9IsoKJdXVlY0NilNdz4gEs8og16w24E7YroO0iXyk/ffeXg62Yu3KwsvZdQzAQDunpo4Y3ic2Z2zrKcbiVC8A9N1uCz0ycPD0/m0b0Bnz8iW7vRJvbqb7nZ7divf373Aftz4True2Kmms0vJz5HB8nY8C8ewMzV+wD1pXx8QPnfU+XjpGllrpK0ddtNHVYTwkTkDxCIBvTLrRpGp9UwIYOdhfIMiAOdpX/KIF56cs55OOBYeng6lkgKkYkzkTZDgQm4l8uI9SXu224B2tvV0d/nZDpvo4+2FljB/SZtLRmEdAKCoQh6mKXbOO+DhMWMfFW0VhDYotIsm9JQK9JdvVC9LSbZuPfMb2Ekkt4iEWmsHwOMoeTg6pNF2/1rAUJMKHRYdEWr96jUoXkvNv8K7BTxOpwYP8jI0shLuUpMsvIoQevn4KhrVR6+WAgB0BqKwQn7gXO7yedCMJvXqzvTi3ZoO1X+6BJfLT9u1ob3ykDbQ0ZFMo0lDWlkVER4SH+7GBck8HiBFpc0ilCDB4J6hUoG+sLLxvllDAAAnrpUh62nFeTcVoR5mQF2Qduda8TwtDxU6LDAkrPUSaroMig9uz8uQgBaepeXVVs48S3iGuqcX3450EJ9BchVtZuFD/L2kAv3Z7PLlKckkCS7mVkkF+lq5ulGpoyOhq+Ym2PJa3PfibTegZBeXn6b+O2XaEOYNHL2KBCA+3NfeGCXDhtaY3YAEYPvRa8Y7lGXnc0k8LikIVTDioVRTN6BQ6yvrVTAAmlcFLSkAJzJkbe7c7UQozmXryTX5iWAGJZvloXm2HslAN8LD4HfOFhebGQAFgLyUW2nJhjJfNb+sHgCgF3jxXjyPC7PwPhIYD61t1CyZCd32I1dLAABaPSGrVUgF+huy+vspd96TRKjQXbQnF6CtW3REKEN+WgT570UV8vCwYJ3AS2RQh2mKqySx1hVuQnwMfdrnTkjaeTxrKDUvQml5TXRECOsjQS8hYPQysPISXIZ3yd03lYSQCvRKDbj3lv4/H8w5n1MhFejLa5UrZg/+3+5rx9Nly+clb9hyJa9MfvJyPjMY6u7gnLWeDq9eGhksN3XD0UrmYum5ra0bsKINWS49M0xpsyPf8pS5E5Lo26XlNWaDBd3CgmnlyotQHpdIUS0QkwBkFtZKBfqMgppllNuellspFegr6pQKtQ4AcCarVSrJLO5Vz+R+WXgbsWQZrZtLHMPCtcVt2ThoxFh2jfUwAMBAGA1dWGgQoNoN6AVeth0720LSwhMAIGO8lk7P/qpdLmqskGv5glCezk8lDU6AafeMgprl85K1eqKgXC4V6NNv1qAA6KlrZVKBXq7UVdQp7U0lcRncI+UnsowCHNhebxTmL0HjeYQWWvs1+++kWbuGbChTHu44BtM7+WX1TP/aughtHQBtRZQ5G7rrRKZxFfXEjJtVkYGw9bdQ4LHXRR4OppJ8vUVSgf7qzerlKdB0VjWoAADH0mHKSKsnSmtgADSnpP7BOcMAAEevlnqSCMU9uFGIROJlrDcKbbveyD841FLGnOG/R7NewtSG0pgtYLIkQq28BG2zzdpQOoNEW08AQJnQzE54eJyBVKBXqnUzRidq9UR5HWxVdyIDis16hWbxtEEAgOPpsJbeQJDXS+rQ8KTaRo3HiFCzBtTFptNR8pOmtKyNeiMAwLajMGOeX1YfHmq8tNroCJv1r5vdd2N6h1aWNXXyCrnW20scpYffKltgHi7LhiIbTZLgGsN6xoVJbdwzD09HkAr0GACEwIsggVYHNeOx9FKpQF9S1fjgnKFUxLNcKoDFTHdNh5b04o2q5fOSl8/qH+wruWNSn4aGJrS4tQg1a0A9pJqQOZ7Her0RLRih/44BHaMeyGiOLZ8SsrVdQ2ynzDGV3oEBUKQQa+qMMjM+XCqzQSSaNfamryUU4Mh6hgb7x4VJ+b4iPJ1DDR5EAjA8EZaInMqEqrO8VnnfzCEECa7m10gF+uuldSiVdJp6tL5JI6tWfLMjDWAgIMA3IMD3y+OVXx43JmPdFKGHyU+EUX6SttYb0Qkfqr2+0VhCG1pZDECAJeeaeaYu5VaiYiNZeU1URAgyx/ll9VHdQvUGQi5vYirEG6UNQMiuCRVQjVnzC9suNmYhElHDP4L80ZXP7M55eJyBVKC/cB3m2euawKNzh3y5M+N6ab1UoD9/veLhlORPtl4pqICppJyS+sdShukNBE45ZJZUJ4v8BoLV466prpxrPe48LdvQEkyMM9Yb0VgZz1NQBPtR5pfVh4YEopVtZsxZ15k5jKQ87chD57qgSi5v+bqEBgeYPndksBxZT3uhxWZIkD89JbXbh6553IcaPEil1S+c3B9WgNyAQzbPZVcAANRaQ2U9iodCQUAQZG5p/Y97LgsF2PJ5yZ6hPU0VqOt/eo6u/YTvKDoihDadzFr0li2aEeA4nNu5WYLqBV49ImEXWAEGY5p6SqViGCAI+BfHMIIg4V2SxCAgPCy4sqqWuXNvidi72Q6HBhsVIoaxkzznav0HeluUyQIr4QOqyNSgb+nLYLpzHh5nc4IqUbqcVy0AoEmtW3rrwG/3ZZ2kVtY1ggdmDMSoQNPylGQbtacbiVCPamfXOn3Uqt7I1Ia2DFRnGFHafz92FV45cUC0hBTT4aXVVkigUOuQwwIAEItFWDH86swa1i27uA6I2S42SR2OWCiIiWJPXtgSXKD+nMuSxYf5oHVarZYlRe2lJP8mcAIxCT0A9+gib7aT36ZUoC+skD8+b9in267eNTnptyO5yHSWVjc9ljJs4/arR6+WVlbVotKlgADftTvzgQeBc1l+tpuEOGv1Rqx3e5MKOzL9d5r2J2QwgHxqsVgErSfjEbN1phmqMKRPS2RVJnuiFrhDUFldT1vPbqGB3cJhlizEX+IxeT8et6MGDyJIkF8OtcuxdBk15r3hoTnDCBJkFtX6CPSTBkXfRw2NdxScSsdzWoG2P31klVIqz8NaiWMYLDlizFAqV2j8vATtM6YkAGIRjmo7mM/DMItXKoyqOhYLzb+BM5ktwhN2/wyDtl4swmEkgSQrKmu6hYdIBXqz8tZdGDl0uI1bnrt0wcnHwmMfp41ZeAWSotdLYW3yqcyyR1KS6R9A+7SnqRdPgX5GrtcNuGfUzLdOH0XbXrBJN0yCAy4ZaLU6LxGuMxjPDI5hPt4Ss4s3WrwkOAP6gzUQ7HI2SzHKdFUostGmIpRpPSPCApH1RF+fjMJ6ekIkFJm1epJ4eByPFMY6NffPGkqQ4CYlRVEF6OJJvTU6g2efca4oUEdWL5mDHsxDm05WvVFEOGVeSVBT3zJAvkhWYP0V6+vqJo4eQ6UddY88tvL5F1+gH1qYMv/KlatanV4gESFdK8DINhWiTk+IGCIUmU6m9WT1/pw9NrGotDIswKuissY3KqBcxK49sIXkYYP37vzD+jYz596ZdvEKJXtFOdfO+voay/WXPfL09h17gVtRJcuxvkFTk6JHIhx3CGflTYg/fXwvCshoNJqEPsPp0DPHiYuLvXD6gPVtHn38hb+37GCuwTCssjT7nfc+3PDZ/+xqznQ5r4ouCJUrwbKZg3y9RWiDDsY9zaWSKnyDuhnLDl0KVwyoU2crYg3mYZYZ0TdwDKutlxMESctHsVjUUN9Ab4UBEBAI7VdDQwO9N7lcXitX+0gEpy6cDQ0Nra+vP3rocGBQ0LgJ4//Zse2n73985eXXNBqdl0Rs5lBMSFeGDvSB6fgSWVVMVBhLeDJSXIxvDYZlFNUP6M6O3tqFwWCog+/UiEQs9vHx1mg0SpWaXqmncv0SiaTk5lWSJC+kXb58JX3hbSnffLXh0sr0GbNvB+4D880CAIICA1grlUpYggMAuGPRvM8/XafV6Xbt3h8TEzUieWhpQXpCn+TGRvuyyS6BIAjmmxKLRFKpj1anUyiULZ6WrtXFAMOwgtxL7XgtqUB/iSpjqmsEy2YNIghSiOObtqeh3NGquQlOyx252JfHftz4NnB/+Unn3039d1OTRRvQc9llg+KD88vq61QkXeiOyKSS5gIM9dbWqTEvf4E2rxjmNwf16UtvptbAJl333rfkjbffPHHs2GMrHqYfOn/lssTLK3ngkMrqOrFYBGOsGNmzm7f1MUjIgIoEuFBkvHpT8x0FWfmOEARZLKv0EgnEYuGxCt+OZ2zvX7L4w/ff+mTj12+/+wHrodLCDLFI1LPvcLncOD/4sYM7kpIShwyfXCor62Bi2tkxUEvpaSRIw6L6sNYjLWYwGCJi+6E1Af7+N7LPV1RUDRg63u2y8Clzb/32f5/89c+OlU+0+ElMJBJJcf4VpLVNFWibb9NL13j35MTfjlxPiAwor4Q9mdB6R5lOcxPHd2Pcc40N9QQFSlvPoxkVRzNsKjaakRzH7PfBtJ7GonTKgEJnQaXV4N5md4KsJwBg6YrlAICnH3uc+Sm+sfq1/36wbuq0W7b8/Y9Gq5N6QYPY5ghOWoQiwgPggRGaFslgCbXOUFytAALnZpDEIpFSqaKtJwDg1Tfe/eePH+5YNO/jT78CnsX9SxYDAB5c9gS9pkEuP592aUTyUG9vb5UK9hzyGFa/8vzTT8DL/xdffbfykYfat5NLeVU+Av3M4d1xHP6+ugJCT4l+Qss1aYCtFbYajcaYEydbHHlqNCQ7206P8JEbjG64SqVV6QgfSUuC/rknn0ro1VOnazXRoE4LzWsg5R56SwQKtY4kSfOW2ASt3qDVw+h7AcOJZrxZM7Hezskemcq0Xj2hMLlZ6IBZam/kFfXq2R1whrsXL4TNhE6eYa588+11O7duvvfu2zd9+xPwIJ5+4mFZWfmYCTPj4mLbZ0ClAn1eWcOK2UOFzUF8x7rtliOhCNfEQ11vQDuOSmVfUJ85tocgSbEQF0u8pV7CZjNkfBT572ahraeXRAQAlpOdnZOdzdrmxVdeguUdJ08hL9v2w0tXmp3T2AZaTHonERoa8t7brwEAdu/5t+N7KyySxcZESOh4sasZOmQgDOAwIobQhyiDRYjDkwd7mAEdNmpqcbGtHcLMUoMHrZqTQNo81N0J0L8yrKsYUIfIz3RV6ADvquaRPOGsyCdrJA+MoGWVxjf3fFNTUlTZ1IhhflJvY7qcxqz/TpCkD9W/A+7QpEoJMWvunG4REdVVVYU3870lIhXl7FsKBbgjm3/8atzYUSjX1H/IOJb6bh97//e0jVve0Sl1oPRAMibInkZ0Y0bfPIEOWk8j1O/HecON2hKhoPPVqIc0E2GM5Klk5Y2YI3lwrJX1DAsJwDEsxF+CYdCGKlQ6G0tiVRqdlaX/oMHvr18PAJg4ZjxaAyP0Ig851YgpkydkZsHci0QiOXtyv1DY6erXRRio0IoYeh48bKy3+OxcOqm83ZUK1LGzFmeoQgd6V4uEOKoBMrtNeVU9bT3DQwNxDMRGhxeXVob4S2rkGmVTo1AQ4C0R0v47HQBlQgdDzbL4jgWfbVgL6/bjBlBRUK44pI4lsrsxMX3PXYs2fPRecX46vcazEVDFxhq1e5SCdjJfuqLBkgUR2klFTtySRR2cdzNdFaozEGYH8yDrSd/uRllPdGq7R4fjGBYWAPsmyRsadJTEQP67GrNxGjgju7f/9tmGtXq9ISyqj1ZnzNF7Npt/+7tBLhcKBWKxZ14qWEipEQRl5fZ0luFxHKad6m2A9EAD6lj5SZOuNG9DmdYzIiyQHv2IfPvuMfAKhmyoRkfQ/ZXt4trlkyOGD62qqvZULYZhWGCAv1TaMiQfUVhYTPd19iQuXU5HlVvMlRHhMMh+kRqUxcMRmupcdj3DPUZ+WrKhZzJLWdaTtpvwT3OQNI6yoRFB3opGuYDQWvLfLXHu1P7w8NBLl9P7DR4HPBSRUJibdf7k0d2s9fFxsPbIXQY42s5vf/wDABg1qtUUki+vegYA8Cv1EI9L4JQIxT1Jfpra0DOZLSmj8NBAOKSHYTdZxMV0MxBkbKi0orbJLv/9+Wcf6xEfdz03z71GNNqLVgdLWaOjIv38Wsr1Fy6Y6+/vJ5c3OiQRzym+++FXAMCfv35Lp+P9/f0mjB9d3yBn1TbxdFkRKvQw+cka0hMTKG4Z0qNTtXnlig71La22O4f4Uiqsv0ns3dO0S8WH6z//77oNwFOYdEvKsUM783PSvvvh19wb+XfePn/I4AEAgP5DWoY2egwkSb71zrrXV6fKCjP++/6GoKDAxx5dCgAYP3m2qw+tq5Nvvsdd1zCgzpafHRzS0xwMbdVvwt5qQU8lKzt3wNDxB/dueeiBu9GaHTv3PfzYs/rmzJuH8ennm/JvFn7zvw2vvvwcajgyZdr8igozKUoel2M1He+s4lAXNBNx9MRHToGf/qEjtK+/xp/vP27jlne8uLEd++c/047AtZ4poO32IqY43oB2IfXEw8PTtSHd3oC6hfzk4eFxx3R8UxupJF6B8vDw8HBGgXZqEsmN5CcHIz7OoIu8zS71ZrvI27SzvYiz4GOgPDw8XQfSXQ1oZ1Yv8fDwdFmaOrGo3pUKlLP+Ow8Pj0eP7HQ3A8rLTx4eHs8ToS5ToLz85OHhcYUIJd3MgPLyk4eHxyNFqGsUKC8/eXh4PECEOt2A8vKTh4fHU0WoCxQoLz95eHg8Q4Q614Dy8pOHh4eTIhRzy36gnSM/ndS4jMepPL3mCxu35D9fdyTG+WNM7Wm07Jj2oEIPk5/8T4uHh5uUULKm84fqO3V0PD8WnoeHp2tCcteAmpWffPqIh4en81NJzkvH8wqUh4eny0K6jQHl5ScPD4+HtRdxigHlq5d4eHg4hZO8+E5SoLz85OHh8TwRKvRs+RkTEzNr1uykpCSZrGzbti15eXkEYebMYhgWGxsrlUrr6+vLysqs7NDX1zc8PNz6i+bn5zMP4LbbFsbExJw7d+7ff/fL5XIrT8Rx/OOPNxw8eGDbtm2WtklISGjzpTEM69GjB/NIevTogWHsqjeDwaBQKKqrq83uSiKRDB06dPbsOSKR6OzZs8ePH6+padlSKBR2797dypEAACoqKhWKJtP1UqnvtGnTxo4dW1ZWtmXLP4WFhdb383Tqaq1W88WGddY3w3E8MTFxwYLbQkJCMzOv/fvv/pKSEivbYxjWt29SVlamlW2ioqK9vCTWX5c+yRiG9evXf8GCBUKhcPfuXZcuXdLr9VaeGBwcvGbNm0899aSlDcyeZJIkNRpNdXW1Vqtlro+MjPT29rblOJnExcUJBAKZTKZWq9v8spEkqdXqGhrqm5pafazh4eG+vr4Gg8HSRxlEYWUDl9Yzdagg1PHzwrt24iO6DrRbt26HDx/FcbbEfv311/7443fmmgEDBv7119/MNaNGjWhoaDC7//nz569d28YvuW/fRPTtP3PmnK+vL/OhI0cOP/roI5aeKBAIrl3L+uabTevWvW9pm+zs67a8dEZGJn0XAHDtWpZAILD0rC+//OLjj9cz17z//rp58+azNtNoNEOHDkZXoPj4+L1791s5EgDAM888vXfvHtbK1atfX7JkCWtlv359CYIYOaCn2f38/M9etUq1/N7b6DXnMvJs+VwIgpg+fVppqXkz+tlnn0+bNo0+RWb544+/Bg0aZGUD+iR369bt6NHjrIdWrnz08OFDlp4YFRV16NARKwfQvXv3/fsPWHpULpePGTPKYDCgu9988+24ceOtHGdSUh+SJM1+nVQq1dChg+36si1d+tCpUyfRbR8fn4sXLwMApk+/pbi42Oy3GgAwc+aMgoKCzqwDtXnieIwrCpQj8hPHcfRt/vnnnz/44H21Wo1h2JIlS1599bW33nq7trb2wIF/0ZYCgeCvv/7WarWzZ88sKSlJSUlZt+7Ds2fPo5+06Z4vXEh74YXn6bvz5y+YMGHCzz//dPky/AIxOXjwsK+v7/bt2158MRWtOX36zOTJU159dfW7777TwTfIPAbbWbBgHlNoBAYGLlp0+x133Pnooyvz8m7s2LEDrX/11dXz5s1vamp67LFHz507BwCIjo7ZsOGTAQMGZGZmox98VVU18xiGDBmyZMl9Fy5c+O23X+mVV69eYR3AmDFjlyxZIpfLx48fiwTUiy+uWrp0WXr6tf79k0B76dev/9q16wiCWL361S1b/iFJUiqVvvba6wsW3Hbw4KGBA/vrdDrWU954Y820adPa3PMnn2wIDAyk737wwYeWTj76vtEWs3v37nv37vviiy+Tk4cqFIp2vzUAwIkTJ9555y36rlgsHjp02CuvvOrv73/06PHx48cyN37vvXdra2vN7sfUet5///0AgMrKSutOFf1+hUJhRETExIkThw1L/vbb7z74YN2mTV8DAJRK5Wuvvfr22+/u2bNvwIB+rKd//fUmAMCvv24uKCgArsbhItTBCtTl824iBXr//fe/8srqtWv/+9133zIfjYyMPHz4KEEQ/fr1RWsuXLjo6+s7aNAA2iGaNWv2+vUfv/XWm5s3/9Lmy7366ur77rv/2Wef2bNnN3O9VOqblnaxoaFh1KgRzPXZ2deZr95uBWpdN1lSoEOHDlapVKyNhwwZ8ttvfxgMBtqEZWdfJ0kyKakPa8tDhw5HRUXffvuijIx01kPopG3duuWll1ZZOTB08MOGDVEqlfTKU6dOBweHDBo0ZEhipI0KNLektq6+jr577twFf3//CRPGVVVVMZ+4YsWK559P3b9/H9NNxjDs0KHDkZFR6K71M2n2+E2fsmjR7e+++96OHTtSU1tsa2Ji4vbtO+vqaseMGd0RBco6fvpdZGXlAADGjh2NLCZSoNOmTbUeuGCCvvxTp04+dOjIn3/+8dprq218v7T/0Y+hMw4fPhIZGfXTTz8y9UHv3r137NjV1NQ0fPgwtKaTRyI5W4Tinic/AQADB0J/5MQJtktVVlZGkiTTr/f19VWr1cxwEjKFr7/+Br0misKKC2xKt27ddDrdX3/9afqQaVTBtSDtzHp3pnYWALB48Z0AgLfealFDbYJOHQq/YhhWXV2tpGBu88UXnwMAhg8f3u634O/vT4niVtYTAPDdd98BAJiOrVAozMrKiYyM+t//vrp5M9/KMdt1APfeey+lpl9grrx+HVqfoKBg4ARIkjx9+hQVbjJvndskMDDQ19f34sU0mUxGkuSiRbfb/tyCggIU8zl5Eh4DYvbsWQCA++67PyQkFK3BMGzHjl3ItQcemo7n1o/ZUVy4cB4AsH79BtOHkpL6sK6o5eXlrG1YqZ5Dh44cOnQkKCjI9gPIz88bOLC/WSFJB604gpeXl+lKHx8fU7euqqqqb9/EhQtblGCboFMnEonQb378+LHDhg1hbfPww49SP0VjQK0dINN5zz3QijHR6/V9+yYmJw+l12AYVlZWNmJE8kcfQWfcFBzH0THbdQALF97Wt28iy0dG+R/n+a3DhiUDAExjjjby2muvU39fQ8EWHMf79GH7HFb48kvY+YV5eVCpVG+99SaSomjNDz/8RMWaP62ra3EXPAzcI8du/v77bwRB9OrVKysrZ/fuPZbyAEhoHD3K/rUgn8guyWkLp0+fAQA899yzgEug4B0zsfbkk48DAI4dO3H27PkXX1wlFDqx48yTTz4ZFham1WqtJ6ytc/vtC5HTcOVK+meffc5K3DHR6XRTpkxqbGwETkYoFO7duw8AMHfubGfs/6GHlkoksDzANJxiI3PmzAUA5OXdAADcffddAIBvv/3erj3oTCLLmzf/UlYmE4vFt99+B47jI0eOrKmp+eyzT4EbjOwk3aOdXacxYEC/gwcPR0ZGJiT0/OOPv6icrOHcuXMPPvgASyOYXsNRpoWu+7ErTGaJS5eueHt7HzhwYN++vR3fm2l6VKlUDBvWIrXMcv58GvMuToE0+OuvQyWC+Pfff+fPT9m2bUdAQMDSpcuWLl2GUvCLF9+RnZ1t13FaP3V0rn/QoAGgA1RUVIwYkXz+fJpEIpk2bdqFCxfRz3vduvd//PEHu3ZFEETHP+6AgIAzZ85hGDZt2tSOXBgQ06fPQBFtBIZh9KV95Eh23OPAAfNJf1ZgHYU1vv32G3QXxTFDQkLsOjCdTot8Cya33DI1MzP7nXfeffttGAll5bg8D9zz5CeCIIgpUyb17Zt4220LqqurDQYDjgtGjx6TnX198uQpaBuhUNQ5PvXp02e8vb03bvz0iScec8gOlUqFyWImamkJHMeFQiGO4ydPnhgwoN/kyRNZG+Tk5PTtm5iU1Of7779D8VCJRLJ163brRVR2sXz5innz5mu1WodcnxobG/v2TezbN/H111crFAqSJEUi0SuvvJqdfd30R+5UBALB2bPnMQybNGmC7fkcK9CRAQzDhEKhQCBQKhXLli3t2zfRtKxYoVA0mUOj0TA3e/99WPK1ceNn9JpXXnmZcsy/6uDREgSxZg3MH2AY9uijj5im/j1MhHqsAqXJysqkL4Oo9O/LL79CieDyclgzHxpqjHnTiMXGmJ1DDmDEiBFBQcEHDhz49FOH+TJtik0LR5JMZ4eSk4f/8svmcePGSyReer2Zcnd0Bv773//897//AQD07Nlry5atYrH4wIFD06ZNBR3mhRdSO649TfmDAjnRhw4dCQ8PT0u75PBXsQKKvN955+0VFY5JVhw48C8zC3/1aoaPj3Tw4MEnT54w3Xj+/JQ2rbZYLEZik1n+jPwtWlvYglhsfojBb7/9umYNDIYeOXIYeDq458lPFHyZPn266UNPPPHYN9/AqrQFC2Am5MaNG2bzv/7+AbRf03HQa61ZA2P23CEt7QLKGl+4kMaMciYlJU2ZYsY+5uXdQGYoJiam468ulcIw5Z9/QkvXQfz8/MaPn5CQwC7C1+v1EyeO12q1YrEYvVznMGPGDMrMXXXS/tGn8NRTT8+a1c7o6oMPPoRu9OiRQC/x8cbqItt3K3RmcNxdRKgHZuEJgvjxx58//XSj2UcPH4ZXxcjIlpLDfv36s7ZBmtRRCpQkYZCuExIX9rJ9+/aSkhIMw7ZsaRk5+sILqV988eXo0WPMPsVR4Y6AAP+Kioq0tFYx2fYRGRm5adM3qanG0Qosjh49Sg2atKOCooNUUDj1JebMgTZu/fqPxWJxO57+3HOwWBVFPJjLjBlwZMHrr9t0pX/4YTigzvq4564A7mHyE1FXB9PoZkvbHn10JVVeY4y1EwTh5+fH3CAqKpoaR2RxCJ29rF79yqRJE1hBKI4we/ZMVO08efJktAZ5i5s2GdMLTAQUprnXdiCTySZNmrB165aO7yovD37ZpkyZSpcf0mAYhhyRdtf6tINJkyZMmjTBqS+Rl3cDZc9377Y7IYkqT/LzzfxCi4qKUGVSmxV7QUFByApPmTIJeJQItbuc3gMVKGU6YV3Lu+++9+uvv6G2Gsj3/OOPv8aPHy+Xyy9dgolaAEBKCizmOHTIGKzx8vLaswd+KZ9//jl6b5s3/7p5868BAdCvbwdr177/yy+bHeL5OhytVovGDn355f9QRl6hUJSVyYRC4bVrWUuXLqNLEe6///709GsAgLvuWmz7/tGpM/X1hg4d+ssvm9ev/5heM25oL9t32zumpfzQYDB88AFMiZw8eerNN9+iG2pMnDgxLQ1+yn/9BWswbATDMHTMoL388svmX37ZDJzM/Pnz0Fd67twU5vrBg4eMsAAabvDNN3BsHoprm4KKFt55513mSnoPo0ePufPOO7/77vvTp88CAD78sI2mEG6I3U6n46MYLpefSODMnTt7587dQ4cO27MHluPRVFVVMS+beXk3tm7dsmDBbdnZ13U6HcrYLlu2lDlmHFUstzuZm5TULzEx0XqnHBeydeuWVateCgoK+u67Hx544D5KVkz+4YcfR40a/eKLq158sdXQzKeeeuLatQzbd45Onengq4CAgOTk4Q5JUlN6GY7IfuGF1MWL71q8GJY00mzfvm316lds3xWGYeiY201ycvuHVNmOXq9fufLRL774ct26D3bt2kmHmz788CNLT1m2bOnp06d69IANlo4dO2Z2m08+2XD//Q/cckurLgE//WRmTPPDDy+3tBPOYs+cnZ0yFp6D/jtrVs7Y2NhFi24fMWKEUCg8cgQO+DXbvS08PPypp57p06fPoUMHf/jhe9ZYQzRCIy8vz7SmLzw8PCgoSCaTWQlxxsfHSySSgoKCNr14W8bCo4PJyYHjoC2BYVhiYiJzs8TERAzDcnNzzWbGvL29UUns9etwFDy9cvjw4bfffkdsbGxJSem33266evWqpcSan59fVFSUXC5nBcXQ0TJ3i5BKfWNionU6Hd1jzVIrJrNj4c02ZBIIBH369L377nv69u2rVCp+//33Q4cOmu3ShujRo4dYLDY9k1bOsC0n35ZtbBwLLxKJEhISGhsbZTKZldeqrq6uqamJiYmRSqVWXq6kpESj0fTs2ZN52i3ts6ioSKVSscYmUe3stDU1NW0G9PtYPgmdPysnE7MG1GR0vB2OvIcbUPfCFgPqqXTcgLodbRpQjyTGswwo7knWk4eHh6fDqSSyqyeReHh4eNqL8xUoLz+dAUmSxcXF9fX1Ttm721JZXl5Vye6Y5QHo9frOrK/isVmE2qFA3WYsQVeAmoKCQ50TOcJzjz0IPJHKykr+43Z32qNAefnJw8PjvjhQhPIxUB4eHp52YrcLX13K6eS7a4skeNrNhjVwiK3t3PGi+V4HPDztLqpvPeWcTTPNtUuBtn8SUB4eHh7PAfck+cnDw8PTmZHQdilQ1zeZ5uHh4XE2DnXhW+Qn78Lz8PB4KAwR6iQFyoD333l4eDzJi7cLvD3RT96F5+Hh8VzMzfbhhCy8y+Xn0AFJrj0AHh4ejxahpCcX0l/KyHL1IfDw8HRdbDKgfPUSDw9PV61nIp2gQDmQiA/ysdZ/m4eHh8cRYB0yoObl51HXF8/XKRWuPgQeHh6PF6HOUKA8PDw8XR7cTeUn77/z8PAAx9E+Efp/htmNj8MfSlsAAAAASUVORK5CYII="}], 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"}]], 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3beN3OFbmXSV/vUKjFmRACDFa8cVGl1SwHa1aBSzLCATiwKDGjnn05790Q/+NrjFuH+X/ZoP3gAJFEE8h7zhZQPCmO8kC2hY7JdmGSuR0WmdUu1T+Mf1nzCeJ7t+bLuBqHxvmkMV1pvzwp8VnA8IjhRIJq9XK9jc9JGULdzz3OvliuP4veBUoUATxFH5fRtZAHL7o/ba33MHfzwiED6wlaZIn1+ub8Ps/IXHf/Z9u4sJVoUTSfuAwjVLplCY8ABScO15TXBAQHjnlx/00q4kjJjVt2v+OAsD6pyY6fiGhRDJw9kvdMh8Clv1m8hDwKnAQCUE8BUVF2YGPlw98ctHdb5F5R/KSwqqi6zEdu4qkZAXv6sLrB1e/qT+UZS9s/aXLXRNn/3Fx6yuzgiJbDZlHnvpxxt1OvJ8v7x846asdkW06zPr9Qm1ZydVDewLDo1NuHUKtvfPNuUUXTpq8ZMSrH3L1TTjq5dWfjenZZEmRL+/zpgxQCgoUQTyIk+vXXd61afTyz+K69AiOiQ+OidelE9UdWrvi5Pp1/CN3vftidXF+3ylPj3ljDT1m/ZMTLA6vO8J3U4YnZ9w6dME7IbEJaXfdx3UgbFk0wylF7VS18orrVy/u+PVUQ3DtpwWVEQRB/A3sA0UQBLETbMITJmSOfGxoG/BskjqTzGqkWfQYPg0/McR1oEAJ6zdtBxhp/um8+MQ4blssItM83I2FjEAEQTwFbMJb461Pf7XhMzSsf+B8WNeeHkEQR0CB2oTV8NP1USJqFEE8EhSo94ChKIJ4GNgHasSNClK/SyQNjCDlED3VoW7sGD1aVM0w7Pmfv+X2PPzUbKdfZdJjc0dmtO/ZiyxoAQCnjmVNdsFVEMTpoECNaBURSB3qIUx6nJjFfL8rLGaCVqt9eNp86jVObSePZTn9QhqNE/KxEaRFQIE2TcuMvwOo1PrVFDh/GSzm+lBUXkvKm7sBoa5MBt/OmHqAeAsoUFNaRQR6SAyqZdnXlsz7a9OGRo9wpUaFQr3XpPHdAqQSjUYrFgsf7m+6IK0TYGDJq3NFQqGyXiUQCCQSUbukVs6/CoK4ABSoKTcqFHRBmBZHKBBoNCwXmg0cO5FlWY2GfWjWnW7QqFAoeOXlF3SFxwV19SqxSEiXcHAFqW3j6DtgyX8xAEW8BhSonvWbtk/ItJBL34IIBIxELLx9/H11SlWARFynVAmFAoGgEb84W6NSsbhTu3gWQMC43Gt0cQhsvCNeBwq0Cd769NdXnpwALYGAESTHR7EEEg/q1/ASMAKm8eQzJ2qUIV6jZ/KfmHDsqMH8h++9bG3N3tQBD7r+jhCPBgVqSis3ZjCdyiG1b3ukRNANW+iREuGB2U6+ytxln9luWz5oXj8BBeoqbHei7Uc2D13DG/El82LY62mgQH0aDEV9y7yNyddUu5a+OFNvxQ4H54MC9QjWff/bYw/d7YTGu0UwFAV/126j5n2liYCXgvJtDBSoC2lMeeZtdov2tN+Y5mAoai8JCfEFBYX3Pzjhpx/Wg88x9/WmzWtRvmheCgq05e3pPjAUbT4ZvXtu+W27T9rTc8zLWHptB2/oc/BNgWYd/N72gzsOeAj8B88ORT98a2Wtos76MS8uXQhuZMtv2915OZ9nrm3mNZfvCtt6G9xsXt8U6LxlZJ1CAHj35enNsu3Ct78Gf8BjQtG3Fjes06ujR4eEv85fu7UrWV6FZeGv89cGpBmWWskpLJ8171n33Ji44mJogFENhL1ni7jt/p1j5CGd3HMnCOUF15vXDvn67KqcJh+iLSblwzdpSyXSuwPbNPrhWyvlTQWGJiy0IU5ktSzDMB+/+z4AbD1yge68rVsCAIQEByqVpJpJldzoujS1f8lyI+06cU2k5HCjT6RXanTDJCk9gobHpMh1q27NOjni4SyYcQ+33X/cs/4bgVqJSW2U6ZsLHuG2F779tY859IM33uU/VNTryz65GYbMEjWdX7//bEGv1GiFst5wmInDXDYln0700qpVui2RyQ1cya/k9nRNiVGE3uS620C8BX8RqN0NfBOZ1tbUgtuRhISHxrddPP1Ri2HgwiXN7hO0z5jJMeH8h63CgvgPZYVl3PaT85+39aQNchSKhJp6QwG9K/lV5scO6duxuKSiTheZughGIAiPCGcbInMGIKU1KQgAwLZN1rXVWCgqqbiSX56ILXjfDT9txx8FardJASAoJMgpMn1zia2NUHN6dkjghPXkPJtVZYAIISxYSh/QtvNt3eIBIFCi7/UTSqTKOiLrKrnBVtn5lfpgkGU7JDppFWheNBkbE1mn0j/u2i4yIS6G5T+tMxe4GImQqamtCw4yms6rKxNFa0XBzmNXu7dxXnoZ4uX4tUAdMalTZBoWRCzWPiHKqHmq/1PlgjO4d8EyANjw9stOGghiFi5ZSHseOfafLQSA3h2jG3bUNdqCdi4N55aKGIlIUKfS8DtmGWAMDnXLqJfpe2VMHYr29F1YO37PUKBOMClfpraYVMtqFy558eN3Vpo/ZbH8kak6OVgn9/qxLLkBaszs6xXmv0xdU2Lq6+td0QdZr9YWlNaYhH58hxYVk/AzLibi2vVicBl1Km1jXxgMwB9HSfgZFxtxpaDcdfeAtCjkl63/uOdsPBoF6iqTWpGpgBEYNU5t4/L1q3Rj79nC/p1Jf1ywxDltydCwMKZhXY0AiTA+jhSE13f5NWiaBde2oLValowpUYwNxgDzx9Ecfejn6jhU9x0iEoDa0kJNGH4iJqBAXWVS6zJlgNl79tod3fQ9iR/9dnCgrhcyqKEXkkMoliqVRt2R6W0j5AoySF1bV7Lk5YVLlr3poLkCJEKl2iB0o4Yzr/lKySuVJ0UZDR+5AWquuFibvjCmzXlpemavW4bda/fldKVXTVU9ftZkurFh1bfJ0cG4Eh7iywL937Y9VoqGudmknEzpH6XcUmR6QNcLaZ546IbuyLKqWrNhE3Ihfv9joa4F7XyMA3GVWi0WiSxqCwA2rvr24ClZcnSwtfNp2TuGjbll2K38nX066aPp6Zm9Zry7zfodBQcFVskVJh8I/zY8YwoC4kwWzMjktt/+bJPtL/RZgboOR0xKdRGsk+nWjWRmxTuvkgEi0Kr5iYccFoWp653Uk5polFrkINSajYaiAPExETKndv/xsxGCgwLltQoTgfIZP2vywZmvcQ9PySw4vaS0Yu8fm/f+sVmhFT0ytDPdeeSSvtv0iLE9qVinZ/YCACrW3a/0zFxpWLq5d0rw1EFBK3bVdsnbdiF5VMeCPVkJg8fPmrxy0QrH3jficXDeXDAj83/b9tj4KhRoC5iUP9o3/zUyQDRh1uzMB56oqqg4ll3aKzVaVmDI2eYY3KcjjUDbJJE/+6ISIjKlsn7JoheXLH8LXAN1KA0/421rQduOUql6/qW5IpFAIBCMe2imU8557ExWRv87Zz+kX3dv/qrdJkFoUnyru/q2pbqkYuVbtbRaN57WwNEc+dRB8MKdQReSR3XJI4d1ydu2/LdS/bN/7b2tO0kpC5ZaW/j6j5MFJntu6xpXE4x5+L4ACrTFTGqSNLHpx0+5p16e+1x8vNnSvg2xoG48x5lhIKvVBksFcqXlbr2dx652a+2SzEepREz+59R1l0pKK0fe3Zf2hHZoJZozKoX/7N4zhVeuVO/TGkJXKlYVI5k0pNO8T3ZN/OBcSAjpIggNFFYrSE7Vil2kv6Xwh//OXjyn4UXfci/ff8ZMjjqlHr9CJNunI/kh3tY1rmHB11rDRsVJoW7QTJzQwznvHHEAfrPd9iF4FGjLm9Ri7tmy9wwZTh+9t1y/RQ9iYdfxq91a61vuDMM4uNrwXX26bD92UUVHnRlQ1BNrcIyfNXl8w/aGVd+Ac+G9bbqAMwBEh+isquOPozmFSw3m2rjKYK7cav1EAEqP9kTxWVcLYyICX3rldbqzoNBgtIu5JKgXCQVd25Ij7+lF1CYSkgGyDYdLyNrRF/PGjhp8/NA+2plQyMupKiwuN7k6h1AkVCuVjFBkotRuuqtwxtQfbLaiqgsnpSJuASNQj5h6byUp/qm5i4xkygBnz7iYyFwnJUWWV+uHTZhGRk74XaKuS60PDgqsqK2PDg6gD7vpBt+puX7Zfe627gkdEiNahUrXriRfKhGtDEF61gH2173n6G1Fhgakp0SeuFpdWV7ODxJ7pZJpAoo6ksBw7DIprXRbeptj2cU0CA0IDhneRXr8kOFmqqpqIE6v6fjYSPO7pR9DeCS5ycpyQ1TLCEValiwHLRQwp2Q3LL7TPp0TSSdGvWHKP+KNoEA9JSzlghErcuJk+vL8ufExFv6k7UPAMPTqR7NKexPLWJ6SMX7WZH4UdtC41pyzqKqpgxgi0D+O5nRrE8E3l1l7+ept3RNqdRldpBBZHGl6C0lQCQqlqlOc9BIYXltVVdNa13fM6IqY3JsYy7JQcqOCN8RUHB3X1vx+aPjJUVpdFxoorq1XBzR8kdSrNRKRkGqUuxYDQC+XYNYVQytq51Vqp2f2JmOJZxUOfV5Ii4IC9VCTWo/xlr3znuG0zzzt4I0JhYxaC2GBwqoq57UvHS45SsNPPkKRkGtWG6JLraEwyl0D0zRa9lxOcUpMsHnNEbFQP1mAxtHFDfMCuPElcT1py1MOTQzq80M13eYkrtJoz14tCwuS5BfJ0xL1Eaiqvl4isrAUtr69Tr+cdJ+llmWPXi6pVdR11bUhlq3d3q1thETXB3pddo57oXWxXj57lIw63qFPMKCcvhHS6OeINMWCGZn2DcGjQD3XpLb7593/fMhtW8wwtQxjKCWn1miDg4ObbJhz4adEpDdRo7AOO5QhX+0JZg1nuVIVLCWdpOGR5Kmqqpp770zX33hDylVKjD5RVF5rms6pPzcwNK6Mi4lgsou5JKek9l3pCBLlyIOhAPW6f9XvFJFY+2JuRUlZVUkZCHSX0mpZQWPD77w3TvpZdQ9LKurEYnG4WHy5TDAso/WxLEP3S1L7rtw2X53GYu1VXKG4fJZsr9970eJlUaxuxpcjUKfn0rvZpHbUNqAZprbIlJEI2DoNPTsVaWVtPf96BcUVHy/9Lyn4pBvDMRpCYdy9gkhtTRWXKApgGGXqlRptqHDMEE3RKfPHsotplGeRv8/lpcQEx8dGclmuNAjNyrpE+j11oS6A0Wyr+XHXZh2VXMmtYIwDc3pL8jpVcIDhrswhDmXh36Lqqmp5YnRQcpRAXl1hbsnQsLCxA9rvvqRqRKx13EfJfTFMz+x9/UbNLztP0IcoVjtoVua8HwnUi7BiUrsVZF2mey7k3ZGSwA8S5bUmbV7yXEJsxEYy+G50/XpVM+cx2vAetFq2qqpGKBLSfIDwIBLa1dZUFZWZ3rlGqxU2zNy3HFfGRkB2sUDXhLYYhHIhKp1wxXWDjh01+GgJ6WY9NNHCXFWlSqvR6u158y23G9+9mq91i1QpVDcqaiRCppoNGdkj9WpuvkVLcvbkrFrHigf3SL5WY3RLui8Swgc/7ufv54v1Wkn1xl0nGxPriN76Dt+cuhjrd+5X9G9ODhMK1GtM6mAkx8nUYFIu+clSV2eBfuTEqUvQ2XAyjVpD1ch506RfgUZ8nCbMT0Za5bwwTV6rqKlThfDCQypZfbcm7/V9OsVel51LSG496c5OUP+P+b1JE3vE1RGpaYTSASmCgznN+xbJLSb2BIZJClJeu64fDStRiEAnynoQj74l5XyJ2KJVr9UYfQJ0Q67RdR8oa5ol1mmZvbKuV2zdr+sIQHiLJhUXkay3ZnWAokC9zKSO+4wz6dZvP9HPIm0cXRekszOWGnkPW34xJH5ZZPREw+dgcCjvPNSM5j25DC885Brv/Neunjty5nvbj1wqHtovDQDut2RPAFjV9tgs6KXPQODZ05ZWvFrDVtfI6fvv3ystL79QqwtmYwLVdBAJAM43jGDxY89hGW1klfqkLu69m5/fulX5r/rPjwe4nQfO69MPSMR6j27k6ow/pgQU69RpH9iE9z6TOmtJTTqLlGwAZD7wBCmioesBbAg/XVk3w4GvgoaeUCOM4kodFwrquIO5uQZc451emtFlvM98T79wcWW55ZxNDqq2epBMvD1175li21vxB87kqdRkhsJNbWLy8smf684jV+JiW5UoAGTngkNCxw3ssOeyvvHeoRX5q4wOEf9xqvDnHUUaDZF1MpcOJQ2xEniafFCGAyyJdWBaLNeE9091gnEZETtAgXqlSZ2+urvRRNL5c3Xhp6FOvslgjtOw+lXw2prf6+T6RCJz+A15Glcait3pzpneOuh0bi1fuAbJco18Hffd2e3OjNZvb5aFhwu/6Zln5X43Da57+GSybkqokT2bDEJbhUoKdJ0S/GljcUEaUXw63ebsyYWfguBWL0xOe33N9jt0M6lEAqZartSybDaRruG6tjTnTaCvOnC+WC2QjOrTjlftC2k2KFCvj0mdHiVySabvLnmRv5/2Szq3GOhra35XmFmSaUqzJkFoO11cSazYEFdmpLYSCpiqKnJmgUjEvTw2QqKtl18tJF8D3EjU0XzV0XyZLREoPUahFY2+JeW73/5qLO+LzuPkKLpReaNKFw4HB8WEU+UxcbGtimpJ+JkYHz2qT7vt5/QaKygsoemuJy/k7PubjK3vP1OQEE+CRLmG2rneKc15OojE9bpy4p5+T+/KypqD16yVR0E4UKA+YlJXNLbnLTEUeZr3zNOcL+yYfb/wP+ttP9jEmNx2aaU8OtyQpymvVQRIQwqKS2PCSC+huo52MhJiwgJCo6Ijcsq516anxAKwucXEJmdqDbODorXl0VDeL73dlrOVmwY3HYyFR7YKJ2lSloq5WGrF7z+d2zYmWKXWBgeTL55KeT2NUouKb6S3i6QRKLUnVVhCfAwjDph4e8cvfjscHqEXcUFhycQhXbUsu/vUdU1TlmzKqqKU+LAxt7S7LDOKtbkxK11b3k/t2b+ZQ/DklzMsqmHlBh/Fw1NBnct7dhV7bi6r3yYh6qW8G/ypnLLCsjyJozM7L+bkNPZUff4lblvFCsT62adGtUIAIOMma7/Pyobl5mvr6k9Wm/ZRHBxkWlrJnMw9RNYqRnKj4DrDQGhoiJG5BKLgAHGbSBG3JkrxjUpZUbWW0Ys1I7VVgER0/Ip+CeiO7eLvSE/SJXjqOfrX3rCwEK6Dgv5Xb0kAoUah0fWlWqTJ2JPPiN5tGwaRCO3bxA3qnrTjgguXjPb8PlA7BIoRqE8xl1fExHUynbmAdJJSnl7+tX5LEuIUUXL0jaINbRJE/n0jVMJfiD3/MllHXqcS/gR53dR4ZWpKEhkb+ZckWlbzlmXmZsrTwvVJkooXJvdRq8rFQu0TK/9d9GgKlDct0FKBtRIEcREBNXUGwRXfqGRZtkahDgrSC7Sqtj5AIsroELXt78vt40OKK9W9B436ef9qrvegb0bazR1arf31IHUoDSerqmomDum693R+SaU8oaEGgi2xp3WlDkyL5ZJAlQDUngPaaLD9bjsYgfo+7glLjWTafFHSZrgJEa1abdxzRsBr0KenRP59I5QkYxZf4QRB58UzQqMZSibSPHxBn6pyWzoJk+uUylNy/TEluddZVV2ntPTNt2TbGIHKNcKqkkILEagulbVb24jIEMmpa9WdEsNKq5XlCn2wzDBwS+e4WU8+2eRVJs9aZOK+4d2jdp28LpcrAqQSRwJP/vEqRpLZv/3pQj9tsIPZEDw24f29Ce8hJh0+bWmTx9TnX+Ya4EEBRsU9rXP4IlFhj/b6BvjxHKPEgMryckYozugQdTyreNTANK2W1WhZqZgEsiKh4MjFfJrJxBcopba8+sTYJtRDArR9Cdz9mwiUOpQTaJ2a9DJcKTbqV22dwoujG1j2wqQmr/vWOytziuS2i7JJq9JBJC4C5Q8i+U9K0wIUaJOgQN1g0uIiUl6Tz+RFq20RKB8qU7LQk64NTqEtcZPGOFeZmGXZS0Wm3XZVVTUZqVEpreMNo+QNk95pBSY6U95EoDb2gfIFqqY1QgCiGoa2OIEG6L4SVGrttTLDoLmAYZLa2bSShy1KNY9SbVSqJDD4nlvbFxSQH5mfz+Nc4LBAsQ/Ur7tKm2tSc1Ha3EI3ijH3nikKDiVt4fSUyL0nrtEGONebuf9MAb8Nbl7O/fDFwoHpbY7rPCgRMfW8NZkpDKn5adAnq0twonVGYmPITHnz+7TFntxhOo2yooaBrLJKfWwoFRtN0j/9bzmdccRh+GawyssrvmvSp9+ualiqwDalcj2k8lpFQQExvm4QiXwUShAP6p7ULSV6G284y9/o33x7okD9HYsmtcOS5oxuxwWG0ohWpmHO2MFkT71Ks+XAhagI0qeZnkLGRjiZsizLMAytGw8Aowam0dwprZaViIUDJYZ+wHKzNZnNICqlyfb6x/bak3/8gH2duDiaM6lGraanj48gXxgBQlA6tuCKRZ/aq1QRCcV5SuUPIuXKIdeP7Wk3GIEihLnL1syf7tCcNhNuVNdzqUUAhkLFJoiFgq68wsm9O8ULBWSVJ42WTUyIiY9rJRGRUJI/w51bUK972wga3zVW95ODsyc3W4nWarLPntyrBuxLkCTeZNwjoT/t1cKqerVWSVeacgEOKpUmMKkYyYjeYceyK0Ii9cveIc0FBYq4BK49Tns29alFVxs6NHml86prlVzznNtgGAhoaA4bQjgWDp2/3i6GZKRHRYYXFTc9cYjSLiaYtVSe2W57msOZlBSNb6jnKXKjl+xW6rFlq02mIf1+6oY/KHWBY7Pg/UWgnl9W2SdhNSqaV0RNKq9VmtRJ4lxJR9VJizK9NS/X3XBwQ715skXtGRcTufWvS+ntIi8VKfXz0C0EoXrx/n3uOp3raZF/o3u2LT25OPOvpZtuNX+W7uf+CwBLN936wdD1z+6cMGBfgig4TC13anEAp2JLRyoAvPPyTJM9v59aXVNu2o3jD0q1A98XKNIi0CU3uLwius31aQLA0H5kVR+GgTG3RzMMnLis16hAwJgMvFAYXpko0qg3O8Ck7qeJPRNI490wR/REwzSkhzYWDV1hZEmLJl2SeZDu/7v9/eVBiXQsntqz7cSn//3FsKqKx2JjiGpRqfOXWVAqoFVRoIirCY+MJFXyyApM7F23pZGMTIYxXyGdhpbHs0u6G1fiMFYhOSaBLMVhBA1C+XU/jU/LGhU2Nb7yzW/+unRTvx1ps1NLSBnQHnnbSBQsCpKqSeWUTkX66pks72WhdUZdulEZg71CoE5XKgD4TKBq3xA8ChRxE2RRIIBgqci8W5PljQtRjmSVmqcxUXvSip/m4afeoYaF8sBCYVPDfwycWDhuACQchE/ow/HHXycCFQdTgWZc20z385vw27s9c/Obv55YOC5x5CORPQaeWDgOfAVUanPBJjziHhhoyPUx6dYsatBcXEwkZFserzc03o1OYEqNXN8TyhhFrFHm9rw5tIxrxZsj0uiz3zsX/Emlyf2X1EJ+4V66kb/96/ztprNXfQxUqnVQoIhLMO+RNHceX3PxDTUyrDfeGzmTUVVj+tCKPZskqJ5LwELsHJsCz274O2UIHgWKGHhnzSYnpoJa7JE0h2pO9wLjl1u3Z8PTVVXVKlaf7aQvFUqua1pPVMsrFQoA/MxTxG0hKlhQKjN/2SoPUap9+EUEWlNe5C0/D58OQpnGGuYWMzQpXN6S4QxGp2F0hUFJZ0B5VW2QVKjWrSBEiQuXapXynCJ9ppGItxiyM+YHIY4rlX3n5Vkmu7xLqX4h0J2Hzg/t57k/A58nLCyEDiLxMQstjWGMDKsbtLcwEMSfCCQErVJJ7Nm7Y7SWZWvrlDkKkq4vEhJvnqxptMcT8bAodZablWr3ELy/CJQDQ1F3YtSKNzOocWhpIT41HQUytSfT2EQggFKRQIDG9KqO1MlWDrZdqe4PkvxLoOhQt0GHdCwmt5uFlpYHeIwGl3TP87syR/VOzrthuXrbabM6dYiH8/KKb0322KdUOwLV/Oyz4AB+J1B0qDuxOJRkGHk3GVW3NEzO1tdykSvtzeR3ZVrPRkL8Walgte3voDf9WqDoUDew9j+vTXvmVXmtolWY9EaV0tyecTGRjWa366jV9WDSfkzalUnV6Yq2+YB9CfyqIv0Td+rvNn+oE6/SOR5H/92nVCuB6sND2oOT8BeB6saR0vh7sD/UPfxbWMElt/PzlhobFwKAwenxlXKSyi4StkBXJmdPk23nyhRxulKb9KlFpeZcOOnIPfiLQNGhLdsTCiA1zluyOC6kp1apPqswLN3uIfRP3Pl3/lBuSQ/EB0JUAEjp0vP45v/y92SMmWP7Rf1IoOhQF/HOmk38h1w2fmmlnCa363pCDZps6PoErdIovx0AurWJYFldBfkWgh9yWnxWAA+78XaQFlCqiU+tK9W/BIoOdbourSBmtOVV8iCpiJ/cTsaFlPpxoZzChhR3XaomB44LId6iVL8TKDrURbq0BCMEVqlUWRwXEgoYzpsekrNpPfykaB/8RvADBqF+3ZEKPKX6o0CpQ3U1fQ3DSjim5Dxv6jFZL4iWSW6RcSHXcUtGH/Od/xw/0hL3grRAiOqnArU4NE/zxXC6p9ORJN502rS30+OwJfykaB/8Rv3ZhNsG3W6LVVGmvq1UvxYoNucRu9m/j5QKBYAON3VOTIi1RaaV+Vn4gfuYUv1doOhQhAaVzfocRDPWqz+bQLevXL545fJFuq0ka68biAgPS+/eSSJpuqwf4qWgQAmYZo84BalQzTdpRWXVnwcM/aF9Osfg5+xjGKWP+DN0WImPxVovCEIRzVhv5aOQCtXcP/zEfBgUqAF0qH/S3PY7hyTUZCV6y3AmPXLR8opPiPeCAjUCHYrYjvbBbyShgfSfLcdLherTWQX0H8rUN0CBmoIO9SvsDj9N4EzaXJmiSb0aFKgF0KGII/7FsNR/QIFaxs8dWlEvAT/A8fDT+rTOZpnUJCzFyNQrQIE2z6H+o9GKeomfaNQN2NHAxza+V4ACbZ5D/TAU9WqN1pZXg4eBYakvgQJtAr9yaGM1RLxXo0GRob5kUgxLPQ2ciWTPPCX/rN5EHRohIYtteB1BkaEludcBIDU1VSAQXM8vktdUesjqnXyH1lcrbHkJNSk35QnnOLUUGIHahF/FodZpMhp1Sik81yGVShOTW8trKjvd1Ak8DwxLvQuMQG0F41Bvj0ZjWicBwLlz5+DcOQDIyy8MAM+FC0ttjEkxLG0RMAJtBn4Vh4qEgi7tExJiIpjGlyjyxr7Rnhm9+w24DQDkNZXgiyP42FvqTjACbR5+EoeueW2K+U5WyxaUVB46LTt67uqN8hqWZb0xGr1eUlCSm5/R55aLF0mdfO8Cw1JPAwXabPzEoeYwAiYxLmL8sIzxwzK4nRqNNrew/K+T2acu5pZVyr1Fo8ePHAYAWyO6plLx3b9Kkn0mBQBu6AnHnZwCExbVaDFtxArmDvWN5UDoosQxcXa+kZIiQ4fG5EWrwSMpyc0HgOj5u5x4Tk9Yac52mfJBmToCRqBOjkN9Q6N2S9M7YNnoBbude8qWCkUdDEtx6MlBUKBOdqhvNOdLioqsBKHeZ0weJdfyohfscdHJPUGjjpgU2/jNBQXqED7sUF+SJh/X2dMDsSNFn4KJ+jaCAnUUH3aoz0jT6dU/rdDi4WdjYFjqClCgTsAnHYr29CV7Om5SDEstgon0Lsyx9+E0e6/DDbGn12FHij4Fy5pwYATqNHwyDvUN3GNPrwg/GwPDUvvACNSZYByKeDv2xaTgr2EpCtTJoEM9EDfEhl4dfjq3gQ/+tDYJCtT5oEM9EJcKzvfsaQKGpY2BAnUJ6FAPxOc158kmBR8NS1GgLeBQHJpvKdChHtLAl/pKbykKtAUciulNLQg61OlI/DgsxTSmlsltwvQmxPeQ2Juibz4T31sWesII1OVgHIr4GxIHGvj8Nr7nh6UYgboDjEMRv0Xi02EpRqBuAuNQxM+ROBCTemxYihGo+8A4FEHAgSJ7HhiWYgTqVjw/DvXwVd09EBzW94SwtKUyojACdTcYhyKIc7tKW7AINEagLYDnx6EI4qUj+ODesBQjUE+MQ/1wZToE8cawFCNQT4xDMRRFED4OxqSuC0sxAvXQOBSnKiGI00fwzcJSsS4sbWXfeVCgLQ86FEFaroGv4mVE2SNTbMK3PNiWR5CWbeBTmUqFKl0D/8aRizdsfBUK1CPwSYd+u3xmS98C4l9IHB7B55vUFpmiQD0Fn3Soz4NZ9H4eljJhUbHgN6jq68EzEEskFvdbGVOyL7fpwKHjzX3JTSkJ4CSOnpPRjeT2KeBe8mQ5jT3Ftu7jrKswuUesH+C2N27l/bqZZLf/rG3E7q5SE/i9pRiBehYYhyKIJzfwTcJSFKjHgQ51NU2GjYjPI3GSSVGgXulQ7BJFEKeQl3eN/rPv5ShQr3QoDishiOPILlwyN2mzZIoC9VzQoQjiImQXLvHtaYLtJkWBejToUARxOlbUaUKTYSkK1NNBhyJIi9jTBIsmRYF6AehQBHF1s90+k6JAvQN0qAeC6VBehFPUaUJe3jUsZ+cjdZsA2JrywpDIeHfekn+C3vQuZC5QJwcK1PviUGONsvwD0KEuBdXpdchcaU8UqPeGol0aexYdarsKbZwUj970RmQuVicF+0C9kp2HLlh5tqa80I334sU0aUYm9wja0xuRucWeGIEacf7s301+Xmnd+nPbw4YNfnXR3O7du17Pz//o47Vr1n6tVqvBXew8dMF6HKqr3tREl+j333+emGh6jFgk5D/c8tuW9T+vp9sdUjs8Pv3xbt27FRYUrlu77tDfh8A7mT3zscenTuIesiIp/1lGrQSAgsKiMWMfonskYvGcp6ZPf+xhiUTy3Q/r33r3P3J5LXgVrZMT/7f+myYP69XvTvOdhbnnZ8x+ftNv28HjkblLnRTsAzXQrm0bGz81gUBQU5lLt2/cKG3bpvWKd19f8e7rHTv3vn6dLA/gCQ61JRRNiI9tnZxk/ZjQ0FC6sfi1xf369wOAioqKpOSkxa8t1mg0Y0aOAS8kIjysTZtk68cwAn37LDIi/PL5wwCgUChYFmbOmDpzxtS+tw7PufoveA9CkajJt2yRHVvXC4VG36kei8y99sR6oNaorb4OAEGhFvxSeP1iWFjoho2/PTxFX3T94Un3fbp6JQAEhyWzrNHATrPqgdqBdYcePSsTiQNsPNV3367p16/Pk0/NlV06Z/JU/wH9X13yallZ2aT79YHbY9Mem3j/RLlcPmHsBO+qB2rOhPF3r/rovT17D9z30OPmz/6bfTIoKPDe+6b+eYC0UXqkd925faNWq41LtvbJe3g90JCQ4JzLx1UqdWLbrlYO273j1+7dyKDl4088Y2MEmtwS9UDdr04K9oE2G4lEHBYWyrIsZ08A+Oa7ny9eygKA9HRrv47u7w8FALWqzvGrvLrkVQDg7AkA69auI18YwcHg0/Tv1ycoKHDb77uoPQHg1Olzb7z9gUAgmPLwA+C7tE9pW5h7vnu3NIXCOXWIfc+eKFB7CAgIuHGj9HLWFZP9W7f9AQAdU9uD23GDQ6dOnvrrhl/B/3jmqRkAsPI/q/g7v/zqewCY9cSj4KNIxOJ/Du4QCoWLXl3+6dqvwdcnF9kNRqDNpqqquk1K+s29Bpnsz7x7FPmJ5rRMv5irHVpUVPTZ6s/4ewKDSDFaW/orvJrevXsCwImTZ/g7yysqyZBa+3bgu/xz+FhMYqfPPNue0NKgQJ1DQIA0tQPp+jl+/BS0EO5pyzMMExoaOnb82I3/t5FlWSsdoL5BeFgY+B/1KhWXgeCByFo68OTAUXgnIBAISgpJB+jYex+GFqXJcXm1qs72MSWLtGnbZvWa1XT74/9+XFvrZdk8iLcj8wx1UjACdfgTFAgqy64KhcKvvvlxx47d0NK4Og4tKy0bNWzU6BGji4qKnnr6qa07tjpyNgTxXnuiQB3++HQJoUKh8ONP1s6a/QJ4Bi51aHV1NQBotdqpk6eeP3eeYZgVH6yw+2wI4nXNdj4YgdpPl8430XT6ifc/Om/BYvAkbHGo412iC+cvJLOzulqoEVUnr6yTk5EWb0epJLOSkJZF5nnqpKBA7WT8uDHHjuwhQ7E3ZWzZugM8jyYdansoyjDM4CGDn3nuGZP9Wq3Wojd9Q52Uk6fOAkBSYgJ/p0QspnM9W+6+/AWZRwaeHChQe0hL6/zt158CQExcx4ICz/0rcpZDWZadv3D+yLtGmuyPjY3ltn3Mmxw//LQRAKY8fD9/54gRQwDgdw/o8vZtZB6sTgoKtNkIhcKj/+wi+oi/Se7xY9AlZaTL0nGHXjhPXPz68te5PSKR6POvPgeA0UOH+qQ6Kd/9QAqpPPfMrODgILpHKBSuXf0BAMxfuLSl785nkXl24MmBaUzN5sUFz9KN4sLL5s8++/xLn635CjyJkrLqmCh9QRC705vmPT9v8/bNvfv23vB/G3JkOdHRUfEJpFX79Rdf+Hwm0/2Tpv303dqcy8dzrl5Tq9WpHVIEAsEL81/1+UkELYXMG9RJQYE2m4UNAvUibHSolWc1Gs1dw+96ZMqkByZN6tqNzPdX1tVNnTSpsMB91adait179vfqd+f+3Zvbp7SlSebDho097z1/5F6EzNs+VSYsytCN5fOo6uvBM3BiNSbb33KTDiXfqLo49KYUozETu1voZ6+WekU1JtfhsdWYPPAty7zNnhiB+hd2tOV9uHMT8RxkXqhOCjbh/QsbHYreRNyGzGvtiQL1R2xxKIK4AZk3q5OCaUz+iC25TQjiUmTeb08UqP+CDkVaEJlP2BOb8H6Nq9vy3BA8gvieOik4iOTXuMKh6E3ET+yJAkWc6VBUJ+I/6qRgBIo46lD0JuKf9kSBIpYdKrte1j4pqslPB9WJ+K06KRiBIs2OQ9GbiC3IfN2eKFCkGQ5FbyI2IvMDdfpjMRHEFui6njPv7c/tmb/uIH50iI3I/MaeGIEi1tZGnjBvXfsunfAzQmxE5k/qpGAEijQBOhSxBZn/2RMFitgEOhSxgswv1UnBCBSxFdQoYo7Mj+2JAkWaBzoU4ZD5tzopWM4OaQb4N4PgbwIfFCjSPNChCP4OcGAfKGIP2Jb3T1CdJmAEitgD/iH5IfhDNwcjUMR+MA71E1CdjYERKGI/+HflD+BP2QoYgSKOgnGor4LqbBKMQBFHwT8znwR/rLaAESjiHDAO9RlQnbaDESjiHPCvzjfAn2OzwAgUcSYYh3ovqE47wAgUcSb4R+il4A/OPjACRZwPxqFeBKrTETACRZwP/k16C/iTchCMQBFXgXGoJ4PqdAoYgSKuAv9EPRb80TgLFCjiQvAP1QPBH4oTwSY84nKwLe8hoDrB2fw/STgbAWm/yM4AAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant": "CAM A gives S0 a clear right-side lane to T1 while S1 stands; CAM B gives S1 a left-side lane to T2. Fire S0 first so S1 remains clear of the first shot, then S0 ducks for S1.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "S1’s leftmost T2 lane avoids the standing teammate and the central cover.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "Assignments and phase order are complete; execute the single volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-451c8e2a581b", "created_at_unix_s": 1788842463.9212, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 9, "requested_group_seed": 9, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [9], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant": "CAM C shows the leftmost lane from S0 to T1 as clear and the outer-right lane from S1 to T2 as clear. These paths do not cross the standing teammate, so S0 can fire first.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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C86+ul5KQaHHxjhfyKQHnY7n0NSBHHKBEQPOWQxufi+Vu736IHG03/PohkCnyowYwrncau3pGqAUCsqyk0OGoAUSTE2sqi90eH4NcIoM0DYrMOj+kOI6nKIrnhcqyQvnudYPv41hWal9FyiNjaYVYoPmsjNnJnEa0CI1xhT6m8OjKjf6+Al+7U1+XjsLtEaCAjmW+tepkQXRrdvsGxrk33322cNi8HELI8wLHC0VWaX1NQx8FBLFCqTolT17q/FIsUioAOQUR0FySxSSVEY9TBhQlRrEhhQaJUKOVW4llFBMupvKeqpraaqNGxCkv9bffMq9ucF2ZY4dg1YVWvsMa26cAALtxYrpfA01TtCjT0FTOe02s4J7iWd9RtFzp69Tzw9Xi6+kuXJz8M1JQKHYfsHiPikVKy0FOQQQ0K2QxujLiTB2KAhAtstA6S/avBe8TCIZilaSRSoqx1MDDKMVse1E5qZxYVXd3BiNOSs2Vt+c0ZkUEKSXwlH7QPL3Eta/CvkUMpxg52qznbRREE2p52jRonpbJ19Ne/LVa28cs7xg3+BZPGzy6KlrwmvzSF9JfMNcbFtEqd2wHYLtqJ6TY1pKz5T8jpf23F58Ocg0ioKOmjKi5qxheEPNwRHswIHAqaFraLd8cfrfgY9X/r9gT+4smjBJ240SXvq7CsVXPDTGChxE8OJQkViKl3fwMp6PoNKO/r8y5ixHcBd5jeKdbV9lrPTElJxSkWD9d4DTUpyobP5MRJCKgicviiKtpQYChcoiWy8odonWIdjBIH5M9F4k45g08begqXBLXQzTjTuG0F38tgYN4dOXRH4gZMM8c0GoWlcDrzCHGhAWaYmWEEKsVWkdjjZTXyFpmY/bz8G/WZDIQH45ybT4WfJ2EvIEdO7I4ojIi3yGEHC/gtbS0KA5XxvAVcaJZfoRktJVAGHXYPJDF6MqIw81YHIOZIFreRrxfx47++IR0QNE0zIsB9ATCWBTQ1CujKHmoqQSEFC3GmuXyD4U+SnZkBNEcI5ywaHFlbe27r7wEcpaC8jrGaLJ3tDA6PedF5ZIEwuhGkFIgoInJYuwpjRBCAQIkj+KfIbk4+L/iWlvaIrGUCOzYvHHlJZeDLOb3d94b/Q5mk/GW394FBZ7zusumzO0/tDNTL41AiFNAk5HFOJRRQJaj0pOIs7pDQtUUxWB5TOYFEQDYv2tHNn8Myy+8dNu7r+PtCdUlFAC7mruUd3C5Pc8//dzB3bsAAD/4f7f/456Igts16Hzwwd+k/yUTCIBNVbK3DBQ7jCG3I651waoYJo5IGbE0hkKEMk0cOXQQZDGL5kza9q60ja+qcyeh6m8IwMa9RxdPb0BLEEf3nIk1AMA///pXeA+upIIA7A5VWwIhoxboiMooBWFwcQs+Z/HaWVRJ5T0pAMROBEoxJInco8CGd14SBMFiZO0eftk5l4HcBNdShZYYKHIk8J8AFJqlibgrv/HtCfXBRu7x8sDv/qT9Mhj2xlt/kvBhCVnOM4/+6Ts/vDmBB7JYOgNlgKglNUpglKsAZVdjSLGLRpULYRR579X/4CT9QoukIx4fBwDosfksBWZrAdj+4VrNB3q5LB0ytLOlSQjkDFQUsV+1h8wlNxpQaaMmE8cl1czcLX5uWnAjemnRoDc9G6/aUjQDoBDIECGMArtabMlaoLKJKHX2JqqYmww7vMo/i03MwCAa9CjTNaSOXzN0CnuKJ0vPUI/8XxlOVCV0supQx3LA+6xGRimp8mWgqcP2oxt++reH7wejREB/Y1JbjNlkVKotiY+NComZn9mYB0pIgDMu+JZshEZfEhh0jNcvmW9iCWl2rSIggKH1rqqrQvDyIL/mzkF0ScA5bCxD+7mkcl3X3HrTU399RLVzYk0pMoqbOkEacLk9stouXP61syoaNcX3tjt/kY5nJyQJEdD8oalTeyUyuVY9T4KhqaNdwek9S2bWgEMOkB1AkEj/dhYFJFNwHSgwiUauFjiopWTjvqOLp4c0bMfEK7VzT168c9NGvcG45aMPzr7k63E9ljC6EAHNH2aML62uFuMngSW5+P+o/qi3H1WUe72on7nRoAeMfkpjAcvQDE2JI92za4zX4Y6RfVLyVUF5MThhSkW3aJBmBk31BAA4vb7jpUCWStNR7PVgW6/8QCy1OzdtBAD4vKjx0t3Xfz/8gF2DaFhQhrNux0jQbFcSDlAioHlKoEsyziALn/hbYjGIQcJAfyghuwT0aycphguFXAykze7e4PVAp9NNaawx6BiDnjGCdE68SASld1nDRlZZtc2dA+GHuPKGH1kCOQYpgeOFRedcsPmtV2N0LIyI2WQEAOS04F61Ag0m+deHe+N9ILFA8xStTvMUBUot+kEXP2izl5UEh9vkzsUAgdVT7jCNrgdWg5QrksW1/m390rreZBDkCJjdw8+fNDn6AwtMqVRPnE0zY3LdlOt+8NrTT8k7G6tLAYXSAXD6DW7GiLK5BfjZ/mMWoz662oIYrNqstW3fevrvCT+WCGj+gu1PALp6B0FOE6ahYbdnTRQsjB6blFGgZ2k5t6zHhizlibXF1gKgytAK56H77sRS+6s7U1NexdBUZal1yB4yKEUqhw7EFYMBRtEhPWdiDY7voV6OWFhRU0cK7xT/K301I1Y0YLXNKnAS8bArJIMlRoiA5jVakjNo95RYjap494kTzFuPaIweyrY30q11MegfHM68QR0pgqTC5vCo9L3YJLX7UmaYyQm8KFWLVzsivBzUs/QffntHU4ftH397IBUvHzhi1ovFM9DbxO+CCc47QBuf7Tu2ePp4Kf1D/IpmN1Yr3+6RrpDva/ppK48bXwWyz/w0m4xmk/GqFTPiXcUTAc0btK0w2fysqijZtKulrsxsNJsBEAYGQ1fx2WLDRR5yF3pLVUVJazsazuP1+Q364Gjyxx59EOQg0VO1Bhxer19QGobJw/Op93dQChtWk5PnTq6bsWCg+UuDpYTRGXQFhYzeMIp9YWS3Q8J2MRHQsYJ8XpsMLODUNs6o59LH4kGTLwab93Wdv6wE5AiJJWaFk0L1BAC88e9n5O32fjvOdU0r19/yU7wBed5j60Nbfe2oEGv0kCuAMf/8YF+8RyACmido/ra6eoLmp7wT+lw+Dhj0rHIVP+q59JddfdXQsOu1//wj0h3k9wIAuGBZRodTJo9Bx+w9qhFhj56uqwRna6GM3TRw6szxSXoqEkNfUEjRjNeeFT76BNSTCGh+cvF1V8jbj9yFhh3hXlinzGk8Ji57a6tK+weHlav4UbdAJ41DY9Bf11rAX3zdFWv//qz8p/JiIL6Xsv7BYFHA7++8NzuLdsJzs/A7xVXwH209pMx+ndGIKvr3tvRKqkoxpgLjrOOLAIhUqp8zcG5nPhWtEgs0Tzj9gm9ueOtFlXqiddMda674+m0XVJYEfYjif70+zhCoMc8GC1QT+b3gDeXFIBJLz7sY5E6CBI7GQABWLAzKqxwom9FYgXNdKYqymvVoBS+kRkDFSdppp1krrdXWdkh8R7RnqK9sylwUA8xlGQ3LsSbkLJE6CT374u+lLdyKUHFTNjcBUl0J8MWgulI0P0Pl3uvzy0boKSdMAaPKxn1HY7qfsj1fDHdP7YxXjzc1pROLE13dQyg4e9v6D+0cRfV85A8paDpDLNAxirSKHwqu4p+541L51u/clfXTkyIISpKmdEouKIE6zpFQmNKKMoFIDkFKJ6Qsg9Ln51IrkTtbmvAGL8ARSwPyCSKg+QTV1dUX2x3R71VexftpIwAhXe9kMY1dSeecde2udx8DmSUNnge1giYdoaZMeiZGDVWVPDR3DNWVmUP2dIV8TQmTkpXHxkB8SVZPQRCsBeavutrDU1kBAA/eiyoCcFEAACBVdQGjGEEiAppXRBITZQRGAgaNUDx5JdIxR8MsjVsUVXGkZEiTR0Nq8hJVQ1XqWVVR0twhFa2mnKHhmIomYgnBb95/QK4CAIB2OF24zgozsbZY2lJIqtXIKPU0EiksvkofxALNN9b+/dlw76EKlMCEKsnRNl7FT6q2HjrajaVrcoRakSxc47M0zUeNKaU1xSdlhL0FVaZByssEWJZJlYkNAQxv4y1v42orhcKGNMaOfuQCPfOH396Bt1NYgpVaiIDmIThajQwfHHIJB4/3o0Ji8bLhd/goSnUCoy2m4VeCcFNaNp1xHKm6KFrPi1jgIvdjfvOIdNO5E1IfelWan5/v7zq/ogQCUF9uhhCVIVF0in+n/3v26UxeIYZDFVZZbSWjFNnxDbXtbR2f7us+eSpKbgMApFw9UxJBIgKaz1RHsGLqysyegFziVXwU6Tl0tEtOtsdi2tqpdrPe8/3l/++fH6XqZcs63tUziK8EES8DaXCCRjne0ire6fWb0fUmVpk+3N4ay92UNQIAgPOXojKBrMwri2OOQDIiu2c/arAyZ3yxy51t/QnVEAs03wj+GiOf0gKENL55pNNe+cMIl870odKUSKR83KsQObpiNemspmDRffL1OcrkVhl0tVC9BJiWkllVFWMKpyFMjlpSlQADagM2WyJIREDzls37OlHyfCiBAauoySMI+KlUGfXZRiTzM5CFnuI4UmY64yldE9ffsUaW0WCWKwSf7WqpLw8JwSePnwtOM5W7t2kOIDGbjMfVlox4kdi0fz/emDG+VK5VjT5QIAF55fjszVbO3l8OIRkuOG268s8QVaCAsg6ltrq0f2C4y+ZTVBpq4/XxhrCMnD17D9A0dfliVKP9+7tvv+32uzNgfgaHaofvSo4MVGRphviuv2PNy39TeHgV88N9PDSkqOGGn+NXXHSZz89xHI+LkSgK0DQd3p3e5fZEn+xkNhkXTx8vC6i6VlVEtuY/3PJV7PNawhV2XHkByEoHKBHQbKGYdS6ZM0H+s6RIMj2a2/pRNslXjmTMT5UmoILA5Cr5sGjibZqmDAa0GCzQoWM+/sA9ynv2DiMzJ2FVDTc/qaiC16VIoEmMxx5Vj+RMLVESJDKg3Waj/qTZE/E2FE8DQRC8Pm7O8T8edrg9Xn//kIPjBZqmGIb2+3leQHGzz99+LfxQAYWlorgYpHcEwdcUhapq/4SYkkqJ3QBwB4DwHNjb7vwtyFaIBTr6nDrVOqEWrZUGbVJ23tCwNBnt5vueAwBceOF5cR3wrPn1bjtaz5qshapfZVufq77czEP66iVFT2yQbAGvj3v+huP0Ol+fi3p5b9DNp+TQoUNK0UQpfhNq8Z/tnUjlnX6qrqZM+ZChvr6KQqStcjLK0e7hR/56f8rUM3SPqjFoooxa8EbVM0UGeydSWIaEocQOTwzN6FBKk6G0qCBc13x+HkI4c8r1HM/3Dzl4XnC4vFgyPV6/Uc+uW/tkTM+kUE2V4lKBJGSlemaSZBygRECzgvUH7D+79zlFNnvIRTou9fT4uQevqF/zzJE7Lxr36Ef9WEYx02uN+zqkH2G5hfnPxr6TJ5h2HHVTAEyqsUIIzvvj7nNOW6A64K4vpQbd8lyzieNrVS+zrrqsvas/3LoYcCB7sElcsoljh+MrlHTbh03WwujClnKrzTXkMWWrR7gpRWVIsYB1zSh+FCYDuizVVhQrQ20QonrQdWtHKrWKoKGqkyW8gBWncGUggpQkWXqujDUuuGB1V2tTWXFB94BzcNjl8XFGPdveM+SN0B8kEg98e8KaZ44AAO58RZ1A0zXoOn1WabG58lCngxJQMGHtpi5Fp94aTTGCUBIpeQLEbjHFRKWqeBXf3tXv9Xpl0cS6idsAt3TYZk0qH7T77n5s3e3Xnql6lnvvvh0btkYjNiGh2+MrMpmVFwAZs0JVj/U6KyoMRgq1xphYk7mpHunokqlpfirJnsYvqD80JQlrrISt9WMo/0fUlKQ4kqYigTEeyQroxndQ4jRNU6UWPXZyAQCWnB3MrE6SDe+8lMKj5QqXfm0W3ujqDxoaDzy/IfYj3PO9BW8MHnfGBYs/eA0t/MP5cA86UykK3H1h9TcfOfj8DVOv+NvBZ390PBro+nHXazfPfvMrodWmzhLXsfTmfZLULpxarbzJ7fErNzoGggWCtGjDtHTYZk8dp2PpivISVvAO2n1up/22B5DdAgAoGt6FTyTsE8AqLLlTxbW/DD7N3B5fdYnZFaqqJlE9sWTLToa8AZVaBbaf+Ef2TiupjlKrGlVDNSceNncMpTwDQSb2cc0pEFBZK8PHQKN6hi2tqxfW4+2Dn72emJiqnsJgMFQUGhI+Wo5SRNkA0DhjJtbFUWP30eBxM+ldG9vNcxYsVO4/sdr9+Btfyn9CCL71yEGWpq7428EVi+c9iUQMUCWTHt0a8ciLpldXVZZDCLx+HqK8aWS0vr1xv1JVIYQ6hvZxvEGvb2rrn3ncOB1LlZWViFNykZ7KEylk3ZR7tGF36v8+3T9vCqo8KS4v1/SlyqqKnQM9Ng9NUVg6UzvuIn2tmB656yGctxSj+ZmdfVoxt//ytrgfE6qhmuqZ1iYA4bywSeqEkgIBDZdLeXvihDpkYoip1B/sQMvDK8454Yqa4Lky1NdbUWjAZ/amdS8f7bYPOv3X/fD7queL9BSy9fHhjtYrzjlB3OzFew5+9rpsg+Srnp4wrWFcdfGRNuktAwCmTUSa4vIJACALq90ecbnU135Y3v4YaCR8bO0yKSVV8DgkWzcedCyyTPU6WtRP5AK74LSZEGXGCMPDyGTGrX/1ej1NU/NmjKcpZFdK0xoBMNEcAMzkuiLg2B3UzVB36upTp/1vPdLQ9s7+uupgPKq4TDrNmo92YGMTe1T9HDToxFGR4gEfeThluSkJs3HfUZWAKv2DOD0Lp3zKMhpJOrEJ1jnorig3gXyC0m6eEqkJQOwh+B37j3nbv8Dbi06/KE2KqYLdtO7lCHIZvFJ0dPbL0nnFOXOvkIKtsPkIOqEVgYLg6g9CWGCgoypySAw3IJ344FBphuBnOdKFFm4sQ73w7L8uv+IqkC+cNj2Y9TahHgUiS4qs8p75U+u3HWhDgRpriIwqRTOtoDxEv4B0M7gLUGLlHsMgY7ZfVE8ZrGV2u7O0JPgu0B6nN9yYwoKoFNPVp07D8aj2rqCGyropO1UhBEc6bfWVhX5OKDnuVLQu+8nFWb68VSW3jujxzGco7VRfrJ6bdgWd7AMO764WDWt0TmMwooXpH3K899mepelv/KKCdbi8cihAoWuSenYE1O2DHa2Lp1cunl7ZfKRDDhEERBOyDI3icqKXu8xqoKhgeCH0sCFoSackl6pABP4vRJYP4Hjh2SefuOJ7V4M8Us9x1eoTIlxDsYzuOhBbw3MtnvlEuvZ++6SRZ3P7eaGpPYJ3H2komknR0yfdYdPejnmTkUyYC8wuJ/KEDgzaVRoKITpbcGph+GhyOTYlnzlK3ZRPgJYO28zjx+lZur7cfKTbidUT2SkBv6oSU4E1PGA1Kqj0Ilp1vwi+1hh0ozmxMkZT0jRiCD4MTfXErRuw7sgO0EgRJJWqCgJc+9YGt9cHxmfa5cEqz2OL2SDrF0Y+fauLDfK20t/U0hH8LGYd38AwFGqUxrmVvw35sPKSTcp6Qes+dLfF0yvf+yxY0oCfAukmBOjXBkFLp/R5LZlZe6THqXQIwJxd18einuEa2rvrzYo55yYgmkr+/Xl3FBmFAO4/OtA96GRoqqK42jskRZBCoEC34mdw/rLp7R3I/8AL8EjH0ASxC6Q4tC5EQykKlJWXsgz91vp9mlEpZWAKQtg56BZllzrSaZs2uU6vYxaUlaAIMOrFN3IbO2XAKntUNVKTFxkKgI07UR2n/DPz+vyxdzDJcroiq2fCWbifbdvn9vo6WnaBuWjIUhTmnHUtSClsZUXZ2xv34/MYn7v4xFXdD4vmkYCQobzCKSi6OrekiBGX5VAc2wIhWqTzfrD9UI/823B7/Nju2L2/BdsXShtW9RT4Q5QVc/bU8TQF5pehz7eAQTk9yI8mulkNBoNJDGcV6GDuBp1GVE/M/Kn19931SwGCgeKFFQmJZiQZVSopLwg2p293c68gfl+8ANxeTlwARKOyvFhengsCnD9j3La9rVhDVVAUKDTpIQAXLJ8FIPRxggAhL6DoEy8I7352AN9t4dRqvJpBC3aGLSgomF9WDMRTCyfQIBLN6cmYqsqtmJSSEd32DK+zqqhI+7j2DIXgY1DPxDjS1t18DF3jb7xwBPVMB+zQ4NBFK2Z5/YLXz+tZuq8PdQTQMTQUrcuJ9eU0BQ639k2bXM/Q9JziIpyegrO+cYBV2g798k+eXlNaVuL28jQN3t98UCmmvTaPdP6LvwHRvpDkcurkej2LngH9YAAlKiYqLAMA53IEI/WqiBMOYeVW0OnAf/9AUaCoIcSyKDwHtelu/1+wIFISCgj6ixYCCkhdlJLQzShKWqv34idkaNqoZ5wev8/Pm6L+EirKi2lxriQGnwvzZozDnXT7A0YoMhnxnUQBFN0yFPL8BOZLQMict2zGQKAhCA5J4YxulkG6iU6DsLd+4EjQX4aZOqExKVW1SJWOaCHpOwaSh45phZvFwfYUl64+Et59KvLl8Jwrb45+fJfHt2FL4lmcqVnC0zRlMjBGPQMh1Ot18rk7sb4cW4VTGio4n9dabOUFiPYEkqujf+0mA2sysBCC1Utn+HmBoij7sB0fnGVomhF/QxRlMJnmFBcxNGVhsUpCGkUsAr+2AJGCTqqIE856GXD48Ctv6rC1PfsvPwezymeKQ0BC7Wr0CiegNhyNLU/ht2p78842Gw8gONZwmepRVMu+FIqmJh0+dBGaUsi7vJxTXI74ecEUWT3Ly9AXh15b4Afgdjis5lIKUqUlVoWGFkbzpQYW5L0R2ikNh4WkohAuqQmrapu+Af1XOt0kTAXWE43Rumwk3FkqHI+PowK2QrZxx0g5TBdHqPoP6T4l254Bf8xniggSAKC0vHqc1RPJ9QkBePfjL6J0IMwAqsngOKNIDwCqeG3ptE2uCy7EBocCYYGoukkBoDMVAM7T3tFbV1tBUcgXrtfREAbnlollfaC4gAlEDJAJoimXIwadMGH5ANKhsCMV/W+0iR43b2n8rvJPZS59eNvag0f7Pj8yQkubWNi4rHPxJ0i+VRwaZoDY7a5W7+F4QW58p1LPTXs7Llyu+AEAUF5o6BtGZ474p1JDh4tMdPR4VHcgHoWs2rLito4e5b0mVpibe6Usfe2K10ypqttp/9RpAVUzPu2PT1U11ZPKyjrO5Ll4pKEyGh9I2GfRb/cO9HUN9AVj7qrg+6sfbDfpgKkYXeJ7htRexww4QNV5oPJbwL+ExiqLxVrgsAeDNvj3oDQHYjoDpMQXdMIbDPryQtA37G2W1DkYv1KrZ+jvQ447IfVcOTc8WI/lUllzLVbCDNVXFmWmyWO8ohlyxYhyJ419iV8PNi6Lw3rq8CEXc6nZrqmeqo55UoVfALy8kANKrMnCuSL0lJJODYlNezvOX4bcAtiyONIxOKG25IuvulXxqOSXvalXVZSJM2NfYOK6HiAfaFFgQNCIZanaQGCks70rewJcf8ea6AmwEILa0mjFSM1tvQePBMOb9rZgBClKEmhaBBSbipqn5M4D7fOm1bM0NTAU/BUNDNohAGUl1tC7h/zl43i9YnCVGLQNMb8bqyyFhRboc6lKrZXmJA7cY+nE8XqcD/DeJnXIXhajFrGGYebxDToG/ZxrS81HFVH7dBNveqbsCNa6QWMnVCpNKnQzkhE6YgTg/GXTw2d5hi+m5s9owH2O+wciG6GKnGpRPacrjztvRoPKnZpWUuVUxUAAuwY15l9WB7JzVGWp4apqov3o1M6eMvhQTFo5TLGYn0ni8frXrvtC+TO5atUoRJC0Szk37e1YMCUY6eV4QcewpSXW7ftaJ9RIJjQlJanE3b5BThiUGvQCoGeZkrKSt9bvUwaa8AYvCM1HQ9JOMRDVCCMtoRXx+lnHN7AMvaBU6umNnCri59tYZWnpjq+fZlwkntMO47xLotHnuKzOcMLt98ryYmVxhLwVXopOUaC8tLBvIFqv+DCrNpifhCKUgCottuLrt0JDM7ekSNhQjbJQiFFV+4a9DUZ9U4eNpqnHs3ImZQYIz5mHED753/VJ9rRNFazSVNRMMuB5lMBOAWr+9AYIIDYHMNi40JRR1mgBnDto22ot9PY2dc6bhLI0BvoGzj9tpl8ciChA+E4grYqhaZfbp2NoPy8ZN8qQ/YzjxlEUdUIJyqFBC0axQAVnBQT6VgIOGPUCMl1Tm3ufwkKg2A3QNDlyYzRC5XOjqgJlYmpqmHIJr6SstLA/goZqWLVio/TgPdCXS+GFvPR3FhCLqvorT5S3Db1SiWEUlF48uZdwU4cte8r8k8xhGpHPdrbEEoL/8PP9w44Ud0dNGFbzjBxXXynAbuwGFXP1ACUuvyhAlZUUxi6j4b8TOeNaQm8G0EtRlI6ldSwKNAkCPG/ZDI6HAoQOu0OO2jMMUnmKomZPbUAxfBr5yHACjRQITvNpltrqyWc+abpx1cy45OD8ZTNe+2Rvhs1PDMtKJaRVFdLlSpNIP3UKuTKH5ogXy0jqGbRqwxxDkMKuACkelcC6Z9RV1VuhbrQao6rij7Rw8hI5d3XUCwEyQ7/dG257oqSIroEv9qCGjfGSjggS+mmcccE3P3njBaWpiIMD2JqAABxs6Z43rV7+bYiRYCSj2/Ydk1f0wTM7qiCgNdqyYlmdRfNW8PK8XLJGUeiMoWmaZUBPX9BnKiVNiS/J7UIX6tJiK5PmCWAprzfXSjmK26pMyei0xIxQHHaP8JFTkbrJ4fKHZXNqh0KtBm31jHRoKiSmX1aapRoal1M1uqpCiBq1yLWqWVVeFT2H6ZEIHaeURI8goQwQrQiSx+t/4e0tIJtQ+0BxDFRlzXG8oBeb8SgHyc6f0QChlikaOLnVcST8OPF3gtVZNG8hS0UM2YfsDn1NA3JOlRZUVoumxINv7Fk6qWj2FI2ErVDQR7Ft3zH5E24azqj5KQ7AQVe7809pFLxB5x1jwKd48DLm9fEq3aQocKAVXQhZhiqzGjTVU7RqNb4xXIwkQ1PKhXwOE335r1TVkpiHXGSDqqo6TkXR0IQbqbzw1uc8H+ylILNzx+ZlE0YviISVTj6nlWczpejgrzrJcZ5KeWkhasmjMIuwt6u8tFD+YSnX76qn5/ngfF3NkL3sVjvcPrRgev2QLRgOwtpdUmyN5JLLQtEM5+nXP58xSV2T3j2QxqhXvEaoHFvHDbFkJqir2CUsrNRFH0unmA0BOpi6MtCnoZ6oDDTkq4sSkpo/czw+u/oHhquK9FniD00JKcypGkVV7VJ8s/F2PlWi7Osms2H7oe5+bdshliLOiy+5dO3LqDlc6gVUtRLG53NDfSUE3b025AZFUaRAFnc4qENEaSEIldG+geHy0kLgR3EkhmUDMVa1+XCgpWv+ZOQa0ww3qZJS/bxQWmzdvj+YDCDKKFrQRf4lUZqB+NEVTZlPm2xLJ8U6JnvXVyF9XjJmfu5xh0QJ5lr6saC29trlj53jUT27UjexdHayqI0shtJaueMVifIOUTyqlOhqx6dZd9IDOLOfLFdVY2gOU3gI+pG7HqquLJGzmqJLpzKCRFHqNqAdPUMbdxyK5VVlMgl0hI70smJSFBCEaEW9uPhEPrkxfQPDFdaQNsBKU6OiyCgVxetQHEnzsDgrsLVdKkrhBUhRQJUMQFEU3h4xwSVLRDMWqkotmkZo7Iv3px/8Hnjld+nwhO50hIzenGvpD6TiokusX1xh9eilOQWhUHIwSiqix9G/CE+kGdOnKCm1HoxVUpupmipV7dLsEiLWGiWwYO8cCCkr8nP8i29/nkwWyt2PrQPpE9DTxTiScpUtn7m1paaOAfeeQ53zptaDkdry4KQ/GFjFq9ygF5w2Q7qbaN62Bmr1BAH6+BBvqfLLUK7vUEIVTlQSywSVealRaq79tBGPhH173Scrz1wGskw0P22yfdpku+7M4ODscLbv/gqtoH1irUt00QwwOU71TBiVngJ9TN2DcNQo+vmk6RgV41F1n+xqXzan7kBr/huho2WoRlJVLKzRe0WDGBqeynT1DEglM7XKpViIRfXkK+t9/qB7PXscoGoLFK+yKa2TGCWDxgYFwPgKM6DA0R4XBSh5/a4K4LJiy5DGKgtqUKJ1nE17O85bOh2P08F79jd3LZgu5QNIeakQKkukYkmoyjZjE/P3dUgiJ+iVRVNQoZgjSCfmyhuDQ7o3JnSliKswKWEqiwzA76akGFTElU+4D7SA4XBUqr7S0tRp06njj4S0qyqSgpaPlH92JaqesnRiTHokCPjHPqWu6KH77nJ6g6JJ1y4CWYkkoH5OYMRN5UnbUF/V3S05/nG7+SgnbEEgdICz2g+0Dopl6UFtVD0Wp81Lv5MIVxexYVMIApReMc62pqjICVWaz5pNohnOEZ/GRKMESJX3M7UIEOJo/hG3d0J1Ia8I6Isx/QLVlyWfilg3sXcVtTsQ+4F1KLyrhNFS1a5w9YyhuadKOmX1pBjUKxZDCf4Cg8Ky6kcjDxOT1K1farzr1FugLrf3/FMa5dMan9DYCmisQhYQhGLjsVDFlP2kcugAt/PAg2vQ9YrjGJY9/xTVl6QKvFJyHEn+Ss5bqg7Z4+JOsYBT8XgxoUoVwlIlVGWzaGYb6TNC97jLscOU46EqoK8Z08cnG47pywF9FE8g0pklTlWaScD21DQ8MRv3oDDp4pkosQ/SkrucEgLdWQBQSSrW09lV4KPPdi4/RXsV39o18NGW/YnNb48F6plHpGjXuv/+R947oVqSnvZeR48Y6ikpLS5gOOWQlgOtwYuP3AMJC6xy6Bs+rHxAWZqPtXbjI7d0O06cMY7l0bYsoDh8JC/iWtt68J0LCq0FRnESYxgQApUpijUUte8VD97S7dD0geaZaCZvfmZgFY/Beoo7qigdnjimj79xHJVSBfQJmUdWVX2PlOpvNOrjUs8o0ompq6t56X1p/jaWURmljEZBaaLedvvdHh/39+c/eOPDLwrMpsrKsq2bN4BUExTmMy/8Fqozff05CEFbr53joVL1So2oZlIpmrJFQFEhiql+AoZSWhzi2g15+uoqLPgXgmfehvuINUtTkAEawZVAUWAByu3XSEqtRjmD+SyaaTVC77/quPXb0SS7V3YGXfuC00UXROs2FguaAX3UKYYRe4SK6kmkM9sM1dlmjRym6qjqOaJ01tdLcnnZGbMBAC++t1tpjSoN0rqa8o72iCaC0kR96L67xOIl/vRJOCo1hLwA6bNAVWAlpSjQa/PKhgBug0QBcPl34h4s/OHrz8lNMLE6y+atnxeqik16lsYRJ1VtHwXAsYAFarJYLEZdlLI/zBd71aZodZEep4L2GjQzbPKHlDhA7+iorauwTBpfdv9rkhN8wsRJO/bsEsSJm5rSiXMAlIGsJKnjO9qZEcu0CJlmtrlfElBaUivc89Sk1ds0krtTSX299ornhXW7cUxJZY2KMhq89Ha0aw09jMFEfWFjZxoFFPPh68/12rx+XmAZKlXT2LGSYtdnjw0NnBO7fJp0AQFFIfvTZijz+4+19dA01TXobul2LJg+Tm44HwVlNhXG7UFZL0RAY2HV1in142rbWjsghL29vUuWLHn3rTeLa2uip0+lUD0J2ayeuFlfdYSZwzIt3XasgGVF6rkwejGDIpJ0KnnxvS/xrz26jCamp8lL6ggCmiZwTvvubVs6Btxnn4H8kju3bgkR0OUzlBp5rK2HYajOASSg86ahwXMxPpFyRT9GBDQlRuiagbMAxbhdyN78xonoc+N5geXd25ts8lpemXZKpHNMCWikVqfVoZIqC6iKcD2VB54jtKyjF9d9KR9JpaSaMhqvpHr86hL7/6yPqfAvXcGpWKqAZs9fiBweoYi5qCHqiZH7j6h6TIyYVmWpNB8U0190LOXnsqIJa/bj83HfPklKSv18t3Qmcbzw+pf+NC3YCblCXI2iBbHnpGpnvy2k0KisyNR8tCOinop8/YxZyEJ870uaCgnWy6PS6qojymhtbSDDg9LQU46HXFhv5l6tjteZtkDjLZ3cu+MLhmVRlnWw008Aiurp7jvWi37ShcVFmoMEwnJRUZ6AtNgXv3Ocf9/B5H8wN3kLdPPEpRt3BIf6+jgBj7CWBZQwlplTEJyoJ0Q2aNyB7lwjFZ3FY58CcLTH8fluKaKlsa6PrKThuLxcV1fI+EJxpKOToal1e0JGEGXIAk2m3tzr54GfV2VZIyUVP31P4MtQpfSHpqOiBH5VLipqBUQyYOIEqycqdhA/2ezuiU7INLucZVEkNUlisU/Hnz7L4eHe2rBfZY3K0ydjkdHk1fOClctTIKCpatIxb+FJu7dtCct50rClzTSnaNUjiSZG3j8WLM304ecEcTiKtFoLTDklOkpIRFIFCPcdQeH42ZPKY5llpLSQoujpgillFABbDvUnIKPDLn9fr2I0hkhXv3PEDB8ViQhoytsaycyevxBv7N62BQIpHVWVh19ZIC3PlQ9ETSfHxvI8M2CpxCnuqPaLol7/knTuIMTBLmfZZLZb/nPJzJoNezp3N0lZcVhMVQ+RtTWKhoXr6cIpkkr6xMG9eoNhRBl9+q0dp86o0rQ9QZywoy6aEZLb0Xuea+nHi/G23mDHEFk6IURLdaKb6eDW0mP39jdQFPXF3tYOqD7VCIQRmcJ2y6ZmqQWJ2oLjK3Umy7DLt/erNmQkKcQU62kU6y+S3arS0/IiE5ZRGb3BoJRRKKrnUi31lJ+AQ+U6IAVBpIyLZkRwjQrOSMUuTmJspruNyGWf0u1EOgmJMkU0P6FCQAEA1qIijoe8IPh56ON4Pyd4/TzWUxXh9qk2EDWKi6S75WHxqPX7updM11ZP1KQGgFKrocfmPgJRgmrz/oNRnvmClcvVApoBxcSQSsp0kPImTBkrik8VJKU/mwW0sFg9ZVOAUBCgl+M5Dump1y/sPhDM/YhbTDGhpd5K01WppzCCegIAtgxpTAOVkVUVCyhLRDMPSFP/usy0B00JRDqzEyrq4FmaomiGYhkaGiCEOghB5aLjBAH6ecHrF1we/54Ii/0RnlIGihlUgT0D9pC5sKVWY6eonvh2rJ4jMnHa8ZlLpCeWZrpJd+vP7NdQIp15AIVTPNBUcwYCaACM2QCKC/RVJx3H8QIXWO/v3H8sPj2lotmnA8Nu/BdDU7U15R4HCrR0D7kBiGaByjgGuy0lVakX0PwTzZf+cL28fektj4BsYvEnNURDSUFUPkGJCkfTSNwYhtKzaAsAHS/A5QunYD31+pGefnmwVaWnIy/2sXoGkv9l9QzeHt1mDjCxSKrlof7yy++CpMk/0VTpZjjZo6SZ6T+f5XYoITt9oACAkkAEKdwHGkfNqHJTHBK8cPZkQYAfbz3gF/WU44XwYJSGnirqpng0rh3I6okt0OgOUJWAJmWB5qVojqib4XcbdSXNgBGaE2t5QhaqZ9KmqMJWlGWUAr1DyJtJ09SKk6bJ968sNvk5AZmo3hBf5xcHpF/H7InqhFCVeiZAfAKar6IZu25mp5ISDSVkOYVxmp8BUDVc0GgU19cDw55jPeqh31u/bDLoGPuwxjDwBVPRhX/7YXXVJqsLGbqeAI7B7pEFlIhmLHywYdsPz1v06OubQV5D7FBCxqGUGurycq19Tj48rx6CvgHUazESs8aXjNjIbX+bDViKY3eARrRA81g0kzE2w/lgwzaQHWTGCCWreMJoQImmJ/T5hSPddrmvkJLo6rn9cM+s8SFDR3gBsqE93QQI7Zbx8b4ydoyIZlp1kxfEpof53mqDqCdhFOE42Nw1POxKfUPFQAKTJ4GfMEt0MzHdxKIJIaRpKoEeBDlnhBL1JMQBJQV85BB8kggQtvU5hhxSR5sbLj05tV/HkNPX1u8CsVU8Ndt4eRWfoY70uWtsqnQTQlQcRtMUav8kpf8GyVc3KFHP1EL6+cdLr83TGxjiGy/h63dVBMnr55u6HX5OSEAN80pA06qb2AON7M0YRtoRCJFQzpLKS6boelIUgpcYcHiP9djlwJFTHA0bCxarBbVpD0PlAD3c5fD5BbZ6ChibApo+3RTEhvYQQApQ8XZazZtVPDE/U8UYGcNHxVTLEyseH9fa48B9geNSz6Iiq46lP9nRHOl3W1FWhNpjNncl41fNYQFNn27iAWoMlkwK0HkfHiKkH1KznwB+Xmjusru8XHTpfPvj7co/zQUFBj3D0hQvROx0J9Ml5uSPIQFNq24iF6cYF2KZWCcnEwixk8eGZ8oRIGztcdicPpV63vrdr0V5VGGhxaCT1ufKdFFlEujBtuEZjagq6ViPA++PcdZveByJHYOiGe7fxJ8gLc4FYcjkH0IaGIPSWZJcCL5nyN077Il9zW62FJgNrLIbCJqKKKbQK++mVNWeQEP7xByg2S6gGdBNTHg8nUAgjCIDdlSvabejSeYjIjD6YovBoAusGgMaumGXNP1YBTY/O/qdmqWfOS+g6dVNsUk1HlRNNJNASC3HpSIE7/FxXx3rdbiR6zMWqkpMuI4l2MCJAiNONuociNW2zQ0BTatu4pA6AJCi0pv3nq+poARCZvBx/MGjvQ6PtnqWl5VEaiGKN2QN5UdS0JSkCrB5JpqR/JsURUkx9XwnM0XxBEI66O4b6h/29g17I8mbg6NjbIIXyQEqR5BkmKrJCbxUHEdi81I3lepJ/JsEQvbDC7B/wOb0+Ltt7igx8RsvnBvDwZCGrt+p7QDFaHYkSQA2z3RTKhkS7U3i4iQQciIE39M3hEsqOwbcfCBnPhlGzEo61uv0+WP1sY6ygGZAN6V+SCI5UTJEIIyFCNKIQAB7+1AnOk6Abf2uKIZhQYFZc/+2Pc3hOzUahipAjeuj3iErBDQz9iYGt/YgEAhZRWHkELwsnfiH3DPkcYoVR1GkU2e0xvi82AGqAjtAAQBHe4PZUfGm0CtptvFs9oumRjxdQG8at+Akskkg5BwChH39AfUEoM/u6bd7NANHSsPTw2kobFWZtbtfPdHo0x3ILA1vwoTxKeQ14RR66eEgd3RTXqpTNEUKLQmEbKv0v+fm+2L3eMo4PVyPzRNuCEZasyt58IUNFy87vqosxDLt6kdmZmZgs1w38cfKCwKNCi3REGeQ9ZBUUMKY4umYG/RxvDAwGKJuHh/f1u8MDxxpqqccgvf6eI+ft5p0Fy87Pvxu1WWFV547X/5zu5aTdDQFNLO6ibybpLVHXJBUUMLoUlJgGNHwxAvKjkGXzy/Ea3h6fNynX3a0dA1ftKh2xDvPmzlR3l44Bzz75vaUOEDjE9A0iabmOh15OCGgSEskAiF3zM97Iq/fIYS9AY+njABh16BbVa8ZRT151iRve/2805NgE8+TplUBALYc6gepgM0i3RQTC5B/cwwMaCMQxkgIvifM8MT02739Dm9chicGQmhz+Ty+BPNFy6x6AMDKeTWCALccHmhrP4TNUGt96jrSZ1g3xelCJH+TQMgrOEEYGNCO59jd/h6bFHaPXToxEAKXh/NxfDLl7BCCPrvX6eH0rBSQ9nU3eRXOhBj1NERAiW4SCITE+pweF5zShvBwwBNBPcXAkQsHjmJXTwsrqZsAodPjR0M+vIk0oyurrMThrBZbn4/yABD0IQQb4oXqaRQxZdMnmsTeJBDGJp7IRZJi4Mjt54R4Dc+rV8/DG4IAXV5OgCO1q4tAe2cfAKCyogSvfXXGAuWtfk8wx96gowVe8ENgbz+EZLRuSoYqkSKu00UXJ3FvEgh5XMRZXKCPJKAChO0DLofbH696KiNIHC+4kYAm+FJ3HOo5YUplT+9gqVHjEEo99TrV+fnhpDIh/YMN2/C/kJIh1N1D1E2xu8cYUc8fnrdodF8AGaVJyEIGHT4/ZONVTxU+TvD4OACBZhLoiKxaOm3HoZ7th3ogBHqKc7ulqR6JwaalH1Kg7ybK4RwjkkkgEKJic/lswah7fJjkOe4Q+Py85rT3WIFg1anTeAG++slev+jlxBpqMgWNXIyhwDqiEcqmTDdF6ZR0k0zLIBAIChx+esA9cpe5SLhRzB0BAXD7eF/SzZQgABMbavYdGzQKyOnp4alIMppiAdXsW4xbcBLdzCrwKp50pydkEgjhoMPbWF0ojlxHBmd1scHl4/d/1e/wcKVFlsQOKxdxQgjdXs7PJxhBAgC0HJXmNRSzsLGUbu5H/gSjzxVJRnUU8MOkBTRsvhBq6iE2RUIdkUgLzqxFdoYSJSWkGxiwMFu6pASmxupCAYKvOux4xtGATUo8iktJlREkCKHL6xdToBK0Qe0uD94QIHC5fV63RynHBXomkjVqbz8UHohnE+u/iSER9fxWUhKJIsTCcbqeSEXlLV3Dg05/95BHNY5MVtK4xVSADo8fmW4JJYGiCUhiQyJegDYnSubXUVDHBl+bU9HR2eGwMwxLC9Ea17MxrtPRU/JQrLNE22MmnD76LDxhgfLPLTu+yMDSnkgnIXa+8qPU9Kn6nvA+8G4f3zfsgRAy8rDMsMyfuMxSjocuD5dME5AtB3tOPK7S7ua6htwcrz6QiQ0Je7u4gPM1FgGNWJ8u5h8xDBHN0ZRO5c5UyWi4khLpJCTGAR+SUayk2MTzcULHIBIp5d0YIMSvpEHl8XO8y8slE0FauXhae5/zq7Zg4irNhhRRKQ9uUhinVCQBjRQXEpOQSNf3UegKqimdmndIrZIS9SSkUElNtiOo9zsWnjDNk5U0XEwjKilEJi2am5RoEig6uN3TOeAqKSuVj4kSVAcGNMV0RFhV3jv6PymeTlI4R4ET586P63NPlUFKbE9CynEXTWCL0AbXfSjEfosspuFKWlRWeeOFc4LH9HL+EVbVI3Csx8EJQugIZFBSGtBTEacLBeV9Hg/W0yg/SGSBYjcANjmJd3O0cA7zJ580N7GrVgoNUgIh5bBVUvAaKWm4IMFoSmqUU+hxGxGvP/rEzRHxhCfhixqKGRx26gPLdr3RiF5JQK+ZqsnhR2OlKUOozpK4OEcH+xBqDTtn9lSjMY5R2uEcbWmuKi7tHpIWIwRCtispRss4lZXUo7A3eQF6vDyP0ydTi6ihTpdLVk+M3+en6Wj17mxOTBnKb+kEADQ01JaXaU8QjIWjLWmc+kIgZEhJQTTjFBVxcryX4yVPY0pxOtGaPbvmwhNikU4AQFGRdcqk8UlKp+SBIRByU0lBuJiG6SnPQ5vT6+V44B25T1KMDNqCa3YVSvNTc/1OBHSUpRMViul1c2dPTd7qJOpJyD+z1FJarbyDj+OdHtSHKVU4neo1u0yB2TzkU89xGusW6BMvvpvJp9MB0NIZ9Ej6FSW15VXj0P9R1Amzp7Esm5h6xmJ1tjW3gCygfmJjhp9xrL3xLHm/IBVvOWiW+u1yCB4C4PJyXl+0uqCUGJ5St73YZHpsCehooZROJcdPabRaQxpix2p4ih0DidVJGDtAcZIHDsEnnAQa3fBUNiodGhrZ/CQCOmrSCQCoqiyvr0MTVhNZsxNvJ2EMYAnMQcKN6FEXkaRD8JHiReFtnkd0gBIBHTXMZtP0aZNSHmQnOUyEfMLBBVOIBAH6OYFLopFdJJLpkE+W8KMARVFzZ0+Lnl+GIdJJIGCgOM4zmU6gsavn0GBM63cioKNDodViMhlizk9Cs6SiQKxOQl5iVESQAAR+Thh2+VKYQx+j4Rll/U4EdBQwm40Ggy721E6ingQCwCF4VIUJU5IEOqJ6xrJAJAKaaXQ61hL5mwsu2GMIshPDkzB2ECCUIkhJW6BJzgRN41hjQjjHNZQFP2uaLiq0Jh9k7x4aIOpJGFNACHlB8CXbSCQmYneAEgs0c1AUKCq0aA6PIu5OAiE6aBinl0fj4JNOAo2L6A5QIqCZw1Jg1unY6NJJ3J0EAsboD3F08jwcdvkyYX/G4wAlApohjAa9yYR6C8oQ6SQQomDRCVevOkH+08fxXMbkMx5IHmja0elYqzU4mcDldCpvJVYngRCOw6+wASEaQZymLPpkHKBEQDNBSXGhpnRGh4SJCAQZP8fb3T7UCdRrtzvR0HaMtSBkdHuGHaBEQEN49fW1I35eF5x3cfjO1998VeB5zZuG3DCbpfO1tc/W19VGv8+Tzzz/8N8ex9ulpSW/ufO2c1ee4XA4/3T/I0898zzITX70w6uu/u63ot+ns6t71QXfxNsVFeW/uu2m1eee5XZ7Hnn0iceeeMbvT01boIwxrr721ZefHfFu8xd9DW/odOyVV3zj+9dcUVdbs3ffwT/+5eH33v8YjAYQAJ8fmZ/hEdh0iGnsDlAioCHUjiQlmpx3/qrq6qqO9o7kpTPzhmdtTXVDQ330+xQXi4PBAFh26ikvv/gkAMA2PFxWVvrHe++8955fV4+bDnKQ4qLCEd84zUg/pMsvu/CvD9yLvlPbcEGB+a7bb73r9lvnLVze2qb+0rMZhmVHfMsy9XW1O7Z+JHbD4ex2+wlzZz33zD+cTtfkaQtGmpSeeigK1JYVnDy95oMdrQtmRhzckDHLNOS1PXrfz8GYIa5+oFu2bULz2uafHOU+X7/8sp/9/KcAgI72DtkCxdIZXT1nzpqVGemMq03kf196Zsnik7537Zo33lR/UEajsbV5F8fxDZNmY+PrFz+/8Wc//ZHT6ZowJejsz9F+oJdctPrvD//po082XPaNq1U3Wa2W5oPbeJ6fNHUBbuQzd87M995ey/N8LBePrO0HWlBgPnJoB8fxNQ0a76KrdR/DMBde+p0NGz/He178z+Mrlp+6bfuus1ddlu63bBRD8MphnBIQ7G86FtehwpX01Y2HoztAY2nCJEMS6ROkoKDgg4/f+9nPf+p2B697Q24oG56x2545kRh/569vAQBc84Mb5aXrvX980Ov1prauIwt54E/3AACuX3Or3AZt564927bvYhgmdoMut1i65GSGYd54a52snshW+NY1giDMnxcmaulBQz3FApNpkxviOo7d6Zb/xfXAWNSTCGjifPTp+1ar5cCBg8uWrBBLzUC+Sifm6u8hj+Gbb7+n3HmpaK/devMakL+suem2u377x1defUO5s7nlKABgQoM4ViDv+OY3Lh4csj3w0KOq/UePtaJWOJGr6VJFQXqSg7CMDgw5BoYcKXGAEh9o4vT19a1aeb4gBPMqstzdmTw+n0+1Z+euPQCAm2+6/r4/PQTyFKfTJcfQMDRNX3rxeQCATzcgJ0/+8cPrb9bc3zgBjT4cHk7ZQLdIONMWn3vp44N4Q6mhpcXBLMN4IXmgCXLOWauV7s64+mR7/b7cUk+LBc0dGRgcUu3nuRyLRCdJUWHh1KlT3nj1OQDAXx74GxhLXH8dWm20hQVLcwuGgnIonwtsymKagEOTCGiCJBZk9/rVRlxOYDYjT7zX41XtF2DaE5uzisMHtuKNl1/535/vH0MC2tBQj53gpy5fleEiThX7D8cXRApHtnQYStqkFGIalwOU+EATAUeK4vJ1YunMUfVEZwmFrs0paCWWy+j1+ora4ytqj3/y6ecvuWh1+9E9JYEEr/ymccL4bZs/QKuu1Zc7HPFZDAlgYQXlKKTUwmopIxTFFBmncTpAiYDGxxiUTozb7cEj7FX7x9RYUNkFfMttd379W9cAAD7/LCSklpdccN45Wz5bBwBYceaFW7ftyMAzOjj66tUjJ8YlwOsHkMeJrZos/0v+mGQJHxOxpHZqkuvSibEND4uTSNThV4YZo+fPhx+tF4t089wCffKxh1adexYAYOqsk/v7c8lrHyNKDeW6DwdamcfBGP0BxM4Yl04lypYomPKyUgDA2v/+D+QvS5ec/I3LL77uBu3AdB7zwbuvzJ41A5WrjZ+Rc3WrCZCYQUoS6UdmDK7Zw9m0GcVPSkuKlTv/9+p/cJI5yF/+9fhDl1y0GmfwyBgMI8wEzHVefvHJ2bNmeL3eqvppY0E9E4YIaCoNz7yUTsz1NyKV3LE12E6ioqK8vq6WFwH5y29/9xcAwHvvvKzcuVOsE8/dXirRaWioX3bqKWhj0lxlpnMGMEYNwSfvAE05ZAmfmvykvFyzK2ltbX/uhbXfvPzizmP7Dn51qLCwcFw96r0y8fj5IK956pnnv37pBQvmz20/uuerrw7rdLpJExtZlvnqUNPPf3EnyEfefv1FvNHdtj/81tnzlnZ2dWe6iDNbIRZoskF2iTGQ4nPjTb+8/NvXsiwzY/rUcfW1/f0Dk6YucLniKzHORVau/vqVV12v1+lmzph2/HGTaZr69pXXLV52DshTKivLR/sl5AykG9PoWJ1Z26QnTWR5N6b0Mda+aJDcWy4Hw1evmpuOLHqyhE8LJMhOIBASZuz6QIl0EghZhdFvd8SXhTnK5ucYFdCEpTPvI0UEAiEuxpaAEukkELKZG3MqBD+GBHR7q2TDE6uTQMhabDaps1xRUeI9OjMJO3bU82hLMynHJBDGmpK+njYHaP4LqCydCTyWuDsJhAzz1IdNqO8XQzfWFJ5yXJlSSbMTdgxJZ8yp7kQ6CYQMY1QUcbIsVWTWU6zeatZTlKShHb3Z2A6KzWN3p9rwjCFBgkgngTBasDTFCag1PEPTNEUxjNRvtqjIknwj+jTB5rd0QghjbPpLpJNAGF1WL2o82mPvGXLxAiyy6HVMCgrN0+oAzR8B1bA6xc6osagnkU4CIRtoqLKOq7RyvOBw+/0cnxPzDti8tTpj+/CJehII2QNFAR1Ll1hzpt0qO6aszpmzZsnbuTVYOIe6eGQJY+2N5/T7NaatDWi6YfMmTBS7u3OMSCeBkEN8d8UkHG1PYQp9uh2gOdkPVM5PUq7ZY5wQ2T00QNSTQMg2LKyAM0Bx4if+B3IBNndTO0XZRFYnMTwJhJzGwSFL7qkPmxiaqisrWDGrKlfKOtncdXeKFidZsxMI+QNDU1az3geZskIjTVNYQ7M2CTQHBFQzyB7beh1BFuwEQvZjYQWB0bl9PEUBmqIMOoYWf+HY9kysBikDDtCsFlAt6URWJ1mwEwj5hNFvv/K8OTaH72iPvbXXbtRL6onJZvMzSwU0Ui1m7Hm1xPAkEHIIlqHLioxlhcZZjWVOj59hciCFPhsFNEJ+EpFOAmEMQKEs+mJLzmTRZ5GARuwAErPhSaxOAoEwFgU0vPUcsToJhLGDLdUZS5mJII2+gGpJJ07tjOnhxOokEHIao1jE+dSHTd9dMSlXcj+zQkAjNQGJvQULUU8CIW945qOmskLjomnVZWYm+xvRj6aARg6yE+kkEMYWFlZw8bQAUfqn1aTXs4zFUoCj8DabIzu70I+agGqmdpL8JAJhDANPm1Pf1GHrHnKhFb2eoWnJkCoqsmRzCn1GBTTygj3WI5AFO4GQl8yaUDZ1XEnngLO9z+HnBJBTsKOzYEepnXHkyhL1JBDykqtXz0NjOFm6odJaV2bhBUHuS5nlNUhpF9BoXTuJ4UkgjHmMoX2UGYZiGBRByiHYjGfFk0gRgUCQsNkcqc1YyqQDNC0CmmT/JLJgJxDGFLZcy/1Ml4CS1E4CgRAXuBF9jmbRp0xASaSIQCDECyXNgQSvbD42f0rltIZSl9OVQ1n0KRBQEikiEAgJYPTbZzWWdQ+6hhzeAqPObNApbc/ENDTDDtCkBDTiQHYSKSIQCGBkls6u8/i47kFX96DbZGBYRRvQ7K9Bwvx/s5aGe18bIroAAAAASUVORK5CYII="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "Assign S1 the distinct outer-right target T2.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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C86+ul5KQaHHxjhfyKQHnY7n0NSBHHKBEQPOWQxufi+Vu736IHG03/PohkCnyowYwrncau3pGqAUCsqyk0OGoAUSTE2sqi90eH4NcIoM0DYrMOj+kOI6nKIrnhcqyQvnudYPv41hWal9FyiNjaYVYoPmsjNnJnEa0CI1xhT6m8OjKjf6+Al+7U1+XjsLtEaCAjmW+tepkQXRrdvsGxrk33322cNi8HELI8wLHC0VWaX1NQx8FBLFCqTolT17q/FIsUioAOQUR0FySxSSVEY9TBhQlRrEhhQaJUKOVW4llFBMupvKeqpraaqNGxCkv9bffMq9ucF2ZY4dg1YVWvsMa26cAALtxYrpfA01TtCjT0FTOe02s4J7iWd9RtFzp69Tzw9Xi6+kuXJz8M1JQKHYfsHiPikVKy0FOQQQ0K2QxujLiTB2KAhAtstA6S/avBe8TCIZilaSRSoqx1MDDKMVse1E5qZxYVXd3BiNOSs2Vt+c0ZkUEKSXwlH7QPL3Eta/CvkUMpxg52qznbRREE2p52jRonpbJ19Ne/LVa28cs7xg3+BZPGzy6KlrwmvzSF9JfMNcbFtEqd2wHYLtqJ6TY1pKz5T8jpf23F58Ocg0ioKOmjKi5qxheEPNwRHswIHAqaFraLd8cfrfgY9X/r9gT+4smjBJ240SXvq7CsVXPDTGChxE8OJQkViKl3fwMp6PoNKO/r8y5ixHcBd5jeKdbV9lrPTElJxSkWD9d4DTUpyobP5MRJCKgicviiKtpQYChcoiWy8odonWIdjBIH5M9F4k45g08begqXBLXQzTjTuG0F38tgYN4dOXRH4gZMM8c0GoWlcDrzCHGhAWaYmWEEKsVWkdjjZTXyFpmY/bz8G/WZDIQH45ybT4WfJ2EvIEdO7I4ojIi3yGEHC/gtbS0KA5XxvAVcaJZfoRktJVAGHXYPJDF6MqIw81YHIOZIFreRrxfx47++IR0QNE0zIsB9ATCWBTQ1CujKHmoqQSEFC3GmuXyD4U+SnZkBNEcI5ywaHFlbe27r7wEcpaC8jrGaLJ3tDA6PedF5ZIEwuhGkFIgoInJYuwpjRBCAQIkj+KfIbk4+L/iWlvaIrGUCOzYvHHlJZeDLOb3d94b/Q5mk/GW394FBZ7zusumzO0/tDNTL41AiFNAk5HFOJRRQJaj0pOIs7pDQtUUxWB5TOYFEQDYv2tHNn8Myy+8dNu7r+PtCdUlFAC7mruUd3C5Pc8//dzB3bsAAD/4f7f/456Igts16Hzwwd+k/yUTCIBNVbK3DBQ7jCG3I651waoYJo5IGbE0hkKEMk0cOXQQZDGL5kza9q60ja+qcyeh6m8IwMa9RxdPb0BLEEf3nIk1AMA///pXeA+upIIA7A5VWwIhoxboiMooBWFwcQs+Z/HaWVRJ5T0pAMROBEoxJInco8CGd14SBMFiZO0eftk5l4HcBNdShZYYKHIk8J8AFJqlibgrv/HtCfXBRu7x8sDv/qT9Mhj2xlt/kvBhCVnOM4/+6Ts/vDmBB7JYOgNlgKglNUpglKsAZVdjSLGLRpULYRR579X/4CT9QoukIx4fBwDosfksBWZrAdj+4VrNB3q5LB0ytLOlSQjkDFQUsV+1h8wlNxpQaaMmE8cl1czcLX5uWnAjemnRoDc9G6/aUjQDoBDIECGMArtabMlaoLKJKHX2JqqYmww7vMo/i03MwCAa9CjTNaSOXzN0CnuKJ0vPUI/8XxlOVCV0supQx3LA+6xGRimp8mWgqcP2oxt++reH7wejREB/Y1JbjNlkVKotiY+NComZn9mYB0pIgDMu+JZshEZfEhh0jNcvmW9iCWl2rSIggKH1rqqrQvDyIL/mzkF0ScA5bCxD+7mkcl3X3HrTU399RLVzYk0pMoqbOkEacLk9stouXP61syoaNcX3tjt/kY5nJyQJEdD8oalTeyUyuVY9T4KhqaNdwek9S2bWgEMOkB1AkEj/dhYFJFNwHSgwiUauFjiopWTjvqOLp4c0bMfEK7VzT168c9NGvcG45aMPzr7k63E9ljC6EAHNH2aML62uFuMngSW5+P+o/qi3H1WUe72on7nRoAeMfkpjAcvQDE2JI92za4zX4Y6RfVLyVUF5MThhSkW3aJBmBk31BAA4vb7jpUCWStNR7PVgW6/8QCy1OzdtBAD4vKjx0t3Xfz/8gF2DaFhQhrNux0jQbFcSDlAioHlKoEsyziALn/hbYjGIQcJAfyghuwT0aycphguFXAykze7e4PVAp9NNaawx6BiDnjGCdE68SASld1nDRlZZtc2dA+GHuPKGH1kCOQYpgeOFRedcsPmtV2N0LIyI2WQEAOS04F61Ag0m+deHe+N9ILFA8xStTvMUBUot+kEXP2izl5UEh9vkzsUAgdVT7jCNrgdWg5QrksW1/m390rreZBDkCJjdw8+fNDn6AwtMqVRPnE0zY3LdlOt+8NrTT8k7G6tLAYXSAXD6DW7GiLK5BfjZ/mMWoz662oIYrNqstW3fevrvCT+WCGj+gu1PALp6B0FOE6ahYbdnTRQsjB6blFGgZ2k5t6zHhizlibXF1gKgytAK56H77sRS+6s7U1NexdBUZal1yB4yKEUqhw7EFYMBRtEhPWdiDY7voV6OWFhRU0cK7xT/K301I1Y0YLXNKnAS8bArJIMlRoiA5jVakjNo95RYjap494kTzFuPaIweyrY30q11MegfHM68QR0pgqTC5vCo9L3YJLX7UmaYyQm8KFWLVzsivBzUs/QffntHU4ftH397IBUvHzhi1ovFM9DbxO+CCc47QBuf7Tu2ePp4Kf1D/IpmN1Yr3+6RrpDva/ppK48bXwWyz/w0m4xmk/GqFTPiXcUTAc0btK0w2fysqijZtKulrsxsNJsBEAYGQ1fx2WLDRR5yF3pLVUVJazsazuP1+Q364Gjyxx59EOQg0VO1Bhxer19QGobJw/Op93dQChtWk5PnTq6bsWCg+UuDpYTRGXQFhYzeMIp9YWS3Q8J2MRHQsYJ8XpsMLODUNs6o59LH4kGTLwab93Wdv6wE5AiJJWaFk0L1BAC88e9n5O32fjvOdU0r19/yU7wBed5j60Nbfe2oEGv0kCuAMf/8YF+8RyACmido/ra6eoLmp7wT+lw+Dhj0rHIVP+q59JddfdXQsOu1//wj0h3k9wIAuGBZRodTJo9Bx+w9qhFhj56uqwRna6GM3TRw6szxSXoqEkNfUEjRjNeeFT76BNSTCGh+cvF1V8jbj9yFhh3hXlinzGk8Ji57a6tK+weHlav4UbdAJ41DY9Bf11rAX3zdFWv//qz8p/JiIL6Xsv7BYFHA7++8NzuLdsJzs/A7xVXwH209pMx+ndGIKvr3tvRKqkoxpgLjrOOLAIhUqp8zcG5nPhWtEgs0Tzj9gm9ueOtFlXqiddMda674+m0XVJYEfYjif70+zhCoMc8GC1QT+b3gDeXFIBJLz7sY5E6CBI7GQABWLAzKqxwom9FYgXNdKYqymvVoBS+kRkDFSdppp1krrdXWdkh8R7RnqK9sylwUA8xlGQ3LsSbkLJE6CT374u+lLdyKUHFTNjcBUl0J8MWgulI0P0Pl3uvzy0boKSdMAaPKxn1HY7qfsj1fDHdP7YxXjzc1pROLE13dQyg4e9v6D+0cRfV85A8paDpDLNAxirSKHwqu4p+541L51u/clfXTkyIISpKmdEouKIE6zpFQmNKKMoFIDkFKJ6Qsg9Ln51IrkTtbmvAGL8ARSwPyCSKg+QTV1dUX2x3R71VexftpIwAhXe9kMY1dSeecde2udx8DmSUNnge1giYdoaZMeiZGDVWVPDR3DNWVmUP2dIV8TQmTkpXHxkB8SVZPQRCsBeavutrDU1kBAA/eiyoCcFEAACBVdQGjGEEiAppXRBITZQRGAgaNUDx5JdIxR8MsjVsUVXGkZEiTR0Nq8hJVQ1XqWVVR0twhFa2mnKHhmIomYgnBb95/QK4CAIB2OF24zgozsbZY2lJIqtXIKPU0EiksvkofxALNN9b+/dlw76EKlMCEKsnRNl7FT6q2HjrajaVrcoRakSxc47M0zUeNKaU1xSdlhL0FVaZByssEWJZJlYkNAQxv4y1v42orhcKGNMaOfuQCPfOH396Bt1NYgpVaiIDmIThajQwfHHIJB4/3o0Ji8bLhd/goSnUCoy2m4VeCcFNaNp1xHKm6KFrPi1jgIvdjfvOIdNO5E1IfelWan5/v7zq/ogQCUF9uhhCVIVF0in+n/3v26UxeIYZDFVZZbSWjFNnxDbXtbR2f7us+eSpKbgMApFw9UxJBIgKaz1RHsGLqysyegFziVXwU6Tl0tEtOtsdi2tqpdrPe8/3l/++fH6XqZcs63tUziK8EES8DaXCCRjne0ire6fWb0fUmVpk+3N4ay92UNQIAgPOXojKBrMwri2OOQDIiu2c/arAyZ3yxy51t/QnVEAs03wj+GiOf0gKENL55pNNe+cMIl870odKUSKR83KsQObpiNemspmDRffL1OcrkVhl0tVC9BJiWkllVFWMKpyFMjlpSlQADagM2WyJIREDzls37OlHyfCiBAauoySMI+KlUGfXZRiTzM5CFnuI4UmY64yldE9ffsUaW0WCWKwSf7WqpLw8JwSePnwtOM5W7t2kOIDGbjMfVlox4kdi0fz/emDG+VK5VjT5QIAF55fjszVbO3l8OIRkuOG268s8QVaCAsg6ltrq0f2C4y+ZTVBpq4/XxhrCMnD17D9A0dfliVKP9+7tvv+32uzNgfgaHaofvSo4MVGRphviuv2PNy39TeHgV88N9PDSkqOGGn+NXXHSZz89xHI+LkSgK0DQd3p3e5fZEn+xkNhkXTx8vC6i6VlVEtuY/3PJV7PNawhV2XHkByEoHKBHQbKGYdS6ZM0H+s6RIMj2a2/pRNslXjmTMT5UmoILA5Cr5sGjibZqmDAa0GCzQoWM+/sA9ynv2DiMzJ2FVDTc/qaiC16VIoEmMxx5Vj+RMLVESJDKg3Waj/qTZE/E2FE8DQRC8Pm7O8T8edrg9Xn//kIPjBZqmGIb2+3leQHGzz99+LfxQAYWlorgYpHcEwdcUhapq/4SYkkqJ3QBwB4DwHNjb7vwtyFaIBTr6nDrVOqEWrZUGbVJ23tCwNBnt5vueAwBceOF5cR3wrPn1bjtaz5qshapfZVufq77czEP66iVFT2yQbAGvj3v+huP0Ol+fi3p5b9DNp+TQoUNK0UQpfhNq8Z/tnUjlnX6qrqZM+ZChvr6KQqStcjLK0e7hR/56f8rUM3SPqjFoooxa8EbVM0UGeydSWIaEocQOTwzN6FBKk6G0qCBc13x+HkI4c8r1HM/3Dzl4XnC4vFgyPV6/Uc+uW/tkTM+kUE2V4lKBJGSlemaSZBygRECzgvUH7D+79zlFNnvIRTou9fT4uQevqF/zzJE7Lxr36Ef9WEYx02uN+zqkH2G5hfnPxr6TJ5h2HHVTAEyqsUIIzvvj7nNOW6A64K4vpQbd8lyzieNrVS+zrrqsvas/3LoYcCB7sElcsoljh+MrlHTbh03WwujClnKrzTXkMWWrR7gpRWVIsYB1zSh+FCYDuizVVhQrQ20QonrQdWtHKrWKoKGqkyW8gBWncGUggpQkWXqujDUuuGB1V2tTWXFB94BzcNjl8XFGPdveM+SN0B8kEg98e8KaZ44AAO58RZ1A0zXoOn1WabG58lCngxJQMGHtpi5Fp94aTTGCUBIpeQLEbjHFRKWqeBXf3tXv9Xpl0cS6idsAt3TYZk0qH7T77n5s3e3Xnql6lnvvvh0btkYjNiGh2+MrMpmVFwAZs0JVj/U6KyoMRgq1xphYk7mpHunokqlpfirJnsYvqD80JQlrrISt9WMo/0fUlKQ4kqYigTEeyQroxndQ4jRNU6UWPXZyAQCWnB3MrE6SDe+8lMKj5QqXfm0W3ujqDxoaDzy/IfYj3PO9BW8MHnfGBYs/eA0t/MP5cA86UykK3H1h9TcfOfj8DVOv+NvBZ390PBro+nHXazfPfvMrodWmzhLXsfTmfZLULpxarbzJ7fErNzoGggWCtGjDtHTYZk8dp2PpivISVvAO2n1up/22B5DdAgAoGt6FTyTsE8AqLLlTxbW/DD7N3B5fdYnZFaqqJlE9sWTLToa8AZVaBbaf+Ef2TiupjlKrGlVDNSceNncMpTwDQSb2cc0pEFBZK8PHQKN6hi2tqxfW4+2Dn72emJiqnsJgMFQUGhI+Wo5SRNkA0DhjJtbFUWP30eBxM+ldG9vNcxYsVO4/sdr9+Btfyn9CCL71yEGWpq7428EVi+c9iUQMUCWTHt0a8ciLpldXVZZDCLx+HqK8aWS0vr1xv1JVIYQ6hvZxvEGvb2rrn3ncOB1LlZWViFNykZ7KEylk3ZR7tGF36v8+3T9vCqo8KS4v1/SlyqqKnQM9Ng9NUVg6UzvuIn2tmB656yGctxSj+ZmdfVoxt//ytrgfE6qhmuqZ1iYA4bywSeqEkgIBDZdLeXvihDpkYoip1B/sQMvDK8454Yqa4Lky1NdbUWjAZ/amdS8f7bYPOv3X/fD7queL9BSy9fHhjtYrzjlB3OzFew5+9rpsg+Srnp4wrWFcdfGRNuktAwCmTUSa4vIJACALq90ecbnU135Y3v4YaCR8bO0yKSVV8DgkWzcedCyyTPU6WtRP5AK74LSZEGXGCMPDyGTGrX/1ej1NU/NmjKcpZFdK0xoBMNEcAMzkuiLg2B3UzVB36upTp/1vPdLQ9s7+uupgPKq4TDrNmo92YGMTe1T9HDToxFGR4gEfeThluSkJs3HfUZWAKv2DOD0Lp3zKMhpJOrEJ1jnorig3gXyC0m6eEqkJQOwh+B37j3nbv8Dbi06/KE2KqYLdtO7lCHIZvFJ0dPbL0nnFOXOvkIKtsPkIOqEVgYLg6g9CWGCgoypySAw3IJ344FBphuBnOdKFFm4sQ73w7L8uv+IqkC+cNj2Y9TahHgUiS4qs8p75U+u3HWhDgRpriIwqRTOtoDxEv4B0M7gLUGLlHsMgY7ZfVE8ZrGV2u7O0JPgu0B6nN9yYwoKoFNPVp07D8aj2rqCGyropO1UhBEc6bfWVhX5OKDnuVLQu+8nFWb68VSW3jujxzGco7VRfrJ6bdgWd7AMO764WDWt0TmMwooXpH3K899mepelv/KKCdbi8cihAoWuSenYE1O2DHa2Lp1cunl7ZfKRDDhEERBOyDI3icqKXu8xqoKhgeCH0sCFoSackl6pABP4vRJYP4Hjh2SefuOJ7V4M8Us9x1eoTIlxDsYzuOhBbw3MtnvlEuvZ++6SRZ3P7eaGpPYJ3H2komknR0yfdYdPejnmTkUyYC8wuJ/KEDgzaVRoKITpbcGph+GhyOTYlnzlK3ZRPgJYO28zjx+lZur7cfKTbidUT2SkBv6oSU4E1PGA1Kqj0Ilp1vwi+1hh0ozmxMkZT0jRiCD4MTfXErRuw7sgO0EgRJJWqCgJc+9YGt9cHxmfa5cEqz2OL2SDrF0Y+fauLDfK20t/U0hH8LGYd38AwFGqUxrmVvw35sPKSTcp6Qes+dLfF0yvf+yxY0oCfAukmBOjXBkFLp/R5LZlZe6THqXQIwJxd18einuEa2rvrzYo55yYgmkr+/Xl3FBmFAO4/OtA96GRoqqK42jskRZBCoEC34mdw/rLp7R3I/8AL8EjH0ASxC6Q4tC5EQykKlJWXsgz91vp9mlEpZWAKQtg56BZllzrSaZs2uU6vYxaUlaAIMOrFN3IbO2XAKntUNVKTFxkKgI07UR2n/DPz+vyxdzDJcroiq2fCWbifbdvn9vo6WnaBuWjIUhTmnHUtSClsZUXZ2xv34/MYn7v4xFXdD4vmkYCQobzCKSi6OrekiBGX5VAc2wIhWqTzfrD9UI/823B7/Nju2L2/BdsXShtW9RT4Q5QVc/bU8TQF5pehz7eAQTk9yI8mulkNBoNJDGcV6GDuBp1GVE/M/Kn19931SwGCgeKFFQmJZiQZVSopLwg2p293c68gfl+8ANxeTlwARKOyvFhengsCnD9j3La9rVhDVVAUKDTpIQAXLJ8FIPRxggAhL6DoEy8I7352AN9t4dRqvJpBC3aGLSgomF9WDMRTCyfQIBLN6cmYqsqtmJSSEd32DK+zqqhI+7j2DIXgY1DPxDjS1t18DF3jb7xwBPVMB+zQ4NBFK2Z5/YLXz+tZuq8PdQTQMTQUrcuJ9eU0BQ639k2bXM/Q9JziIpyegrO+cYBV2g798k+eXlNaVuL28jQN3t98UCmmvTaPdP6LvwHRvpDkcurkej2LngH9YAAlKiYqLAMA53IEI/WqiBMOYeVW0OnAf/9AUaCoIcSyKDwHtelu/1+wIFISCgj6ixYCCkhdlJLQzShKWqv34idkaNqoZ5wev8/Pm6L+EirKi2lxriQGnwvzZozDnXT7A0YoMhnxnUQBFN0yFPL8BOZLQMict2zGQKAhCA5J4YxulkG6iU6DsLd+4EjQX4aZOqExKVW1SJWOaCHpOwaSh45phZvFwfYUl64+Et59KvLl8Jwrb45+fJfHt2FL4lmcqVnC0zRlMjBGPQMh1Ot18rk7sb4cW4VTGio4n9dabOUFiPYEkqujf+0mA2sysBCC1Utn+HmBoij7sB0fnGVomhF/QxRlMJnmFBcxNGVhsUpCGkUsAr+2AJGCTqqIE856GXD48Ctv6rC1PfsvPwezymeKQ0BC7Wr0CiegNhyNLU/ht2p78842Gw8gONZwmepRVMu+FIqmJh0+dBGaUsi7vJxTXI74ecEUWT3Ly9AXh15b4Afgdjis5lIKUqUlVoWGFkbzpQYW5L0R2ikNh4WkohAuqQmrapu+Af1XOt0kTAXWE43Rumwk3FkqHI+PowK2QrZxx0g5TBdHqPoP6T4l254Bf8xniggSAKC0vHqc1RPJ9QkBePfjL6J0IMwAqsngOKNIDwCqeG3ptE2uCy7EBocCYYGoukkBoDMVAM7T3tFbV1tBUcgXrtfREAbnlollfaC4gAlEDJAJoimXIwadMGH5ANKhsCMV/W+0iR43b2n8rvJPZS59eNvag0f7Pj8yQkubWNi4rHPxJ0i+VRwaZoDY7a5W7+F4QW58p1LPTXs7Llyu+AEAUF5o6BtGZ474p1JDh4tMdPR4VHcgHoWs2rLito4e5b0mVpibe6Usfe2K10ypqttp/9RpAVUzPu2PT1U11ZPKyjrO5Ll4pKEyGh9I2GfRb/cO9HUN9AVj7qrg+6sfbDfpgKkYXeJ7htRexww4QNV5oPJbwL+ExiqLxVrgsAeDNvj3oDQHYjoDpMQXdMIbDPryQtA37G2W1DkYv1KrZ+jvQ447IfVcOTc8WI/lUllzLVbCDNVXFmWmyWO8ohlyxYhyJ419iV8PNi6Lw3rq8CEXc6nZrqmeqo55UoVfALy8kANKrMnCuSL0lJJODYlNezvOX4bcAtiyONIxOKG25IuvulXxqOSXvalXVZSJM2NfYOK6HiAfaFFgQNCIZanaQGCks70rewJcf8ea6AmwEILa0mjFSM1tvQePBMOb9rZgBClKEmhaBBSbipqn5M4D7fOm1bM0NTAU/BUNDNohAGUl1tC7h/zl43i9YnCVGLQNMb8bqyyFhRboc6lKrZXmJA7cY+nE8XqcD/DeJnXIXhajFrGGYebxDToG/ZxrS81HFVH7dBNveqbsCNa6QWMnVCpNKnQzkhE6YgTg/GXTw2d5hi+m5s9owH2O+wciG6GKnGpRPacrjztvRoPKnZpWUuVUxUAAuwY15l9WB7JzVGWp4apqov3o1M6eMvhQTFo5TLGYn0ni8frXrvtC+TO5atUoRJC0Szk37e1YMCUY6eV4QcewpSXW7ftaJ9RIJjQlJanE3b5BThiUGvQCoGeZkrKSt9bvUwaa8AYvCM1HQ9JOMRDVCCMtoRXx+lnHN7AMvaBU6umNnCri59tYZWnpjq+fZlwkntMO47xLotHnuKzOcMLt98ryYmVxhLwVXopOUaC8tLBvIFqv+DCrNpifhCKUgCottuLrt0JDM7ekSNhQjbJQiFFV+4a9DUZ9U4eNpqnHs3ImZQYIz5mHED753/VJ9rRNFazSVNRMMuB5lMBOAWr+9AYIIDYHMNi40JRR1mgBnDto22ot9PY2dc6bhLI0BvoGzj9tpl8ciChA+E4grYqhaZfbp2NoPy8ZN8qQ/YzjxlEUdUIJyqFBC0axQAVnBQT6VgIOGPUCMl1Tm3ufwkKg2A3QNDlyYzRC5XOjqgJlYmpqmHIJr6SstLA/goZqWLVio/TgPdCXS+GFvPR3FhCLqvorT5S3Db1SiWEUlF48uZdwU4cte8r8k8xhGpHPdrbEEoL/8PP9w44Ud0dNGFbzjBxXXynAbuwGFXP1ACUuvyhAlZUUxi6j4b8TOeNaQm8G0EtRlI6ldSwKNAkCPG/ZDI6HAoQOu0OO2jMMUnmKomZPbUAxfBr5yHACjRQITvNpltrqyWc+abpx1cy45OD8ZTNe+2Rvhs1PDMtKJaRVFdLlSpNIP3UKuTKH5ogXy0jqGbRqwxxDkMKuACkelcC6Z9RV1VuhbrQao6rij7Rw8hI5d3XUCwEyQ7/dG257oqSIroEv9qCGjfGSjggS+mmcccE3P3njBaWpiIMD2JqAABxs6Z43rV7+bYiRYCSj2/Ydk1f0wTM7qiCgNdqyYlmdRfNW8PK8XLJGUeiMoWmaZUBPX9BnKiVNiS/J7UIX6tJiK5PmCWAprzfXSjmK26pMyei0xIxQHHaP8JFTkbrJ4fKHZXNqh0KtBm31jHRoKiSmX1aapRoal1M1uqpCiBq1yLWqWVVeFT2H6ZEIHaeURI8goQwQrQiSx+t/4e0tIJtQ+0BxDFRlzXG8oBeb8SgHyc6f0QChlikaOLnVcST8OPF3gtVZNG8hS0UM2YfsDn1NA3JOlRZUVoumxINv7Fk6qWj2FI2ErVDQR7Ft3zH5E24azqj5KQ7AQVe7809pFLxB5x1jwKd48DLm9fEq3aQocKAVXQhZhiqzGjTVU7RqNb4xXIwkQ1PKhXwOE335r1TVkpiHXGSDqqo6TkXR0IQbqbzw1uc8H+ylILNzx+ZlE0YviISVTj6nlWczpejgrzrJcZ5KeWkhasmjMIuwt6u8tFD+YSnX76qn5/ngfF3NkL3sVjvcPrRgev2QLRgOwtpdUmyN5JLLQtEM5+nXP58xSV2T3j2QxqhXvEaoHFvHDbFkJqir2CUsrNRFH0unmA0BOpi6MtCnoZ6oDDTkq4sSkpo/czw+u/oHhquK9FniD00JKcypGkVV7VJ8s/F2PlWi7Osms2H7oe5+bdshliLOiy+5dO3LqDlc6gVUtRLG53NDfSUE3b025AZFUaRAFnc4qENEaSEIldG+geHy0kLgR3EkhmUDMVa1+XCgpWv+ZOQa0ww3qZJS/bxQWmzdvj+YDCDKKFrQRf4lUZqB+NEVTZlPm2xLJ8U6JnvXVyF9XjJmfu5xh0QJ5lr6saC29trlj53jUT27UjexdHayqI0shtJaueMVifIOUTyqlOhqx6dZd9IDOLOfLFdVY2gOU3gI+pG7HqquLJGzmqJLpzKCRFHqNqAdPUMbdxyK5VVlMgl0hI70smJSFBCEaEW9uPhEPrkxfQPDFdaQNsBKU6OiyCgVxetQHEnzsDgrsLVdKkrhBUhRQJUMQFEU3h4xwSVLRDMWqkotmkZo7Iv3px/8Hnjld+nwhO50hIzenGvpD6TiokusX1xh9eilOQWhUHIwSiqix9G/CE+kGdOnKCm1HoxVUpupmipV7dLsEiLWGiWwYO8cCCkr8nP8i29/nkwWyt2PrQPpE9DTxTiScpUtn7m1paaOAfeeQ53zptaDkdry4KQ/GFjFq9ygF5w2Q7qbaN62Bmr1BAH6+BBvqfLLUK7vUEIVTlQSywSVealRaq79tBGPhH173Scrz1wGskw0P22yfdpku+7M4ODscLbv/gqtoH1irUt00QwwOU71TBiVngJ9TN2DcNQo+vmk6RgV41F1n+xqXzan7kBr/huho2WoRlJVLKzRe0WDGBqeynT1DEglM7XKpViIRfXkK+t9/qB7PXscoGoLFK+yKa2TGCWDxgYFwPgKM6DA0R4XBSh5/a4K4LJiy5DGKgtqUKJ1nE17O85bOh2P08F79jd3LZgu5QNIeakQKkukYkmoyjZjE/P3dUgiJ+iVRVNQoZgjSCfmyhuDQ7o3JnSliKswKWEqiwzA76akGFTElU+4D7SA4XBUqr7S0tRp06njj4S0qyqSgpaPlH92JaqesnRiTHokCPjHPqWu6KH77nJ6g6JJ1y4CWYkkoH5OYMRN5UnbUF/V3S05/nG7+SgnbEEgdICz2g+0Dopl6UFtVD0Wp81Lv5MIVxexYVMIApReMc62pqjICVWaz5pNohnOEZ/GRKMESJX3M7UIEOJo/hG3d0J1Ia8I6Isx/QLVlyWfilg3sXcVtTsQ+4F1KLyrhNFS1a5w9YyhuadKOmX1pBjUKxZDCf4Cg8Ky6kcjDxOT1K1farzr1FugLrf3/FMa5dMan9DYCmisQhYQhGLjsVDFlP2kcugAt/PAg2vQ9YrjGJY9/xTVl6QKvFJyHEn+Ss5bqg7Z4+JOsYBT8XgxoUoVwlIlVGWzaGYb6TNC97jLscOU46EqoK8Z08cnG47pywF9FE8g0pklTlWaScD21DQ8MRv3oDDp4pkosQ/SkrucEgLdWQBQSSrW09lV4KPPdi4/RXsV39o18NGW/YnNb48F6plHpGjXuv/+R947oVqSnvZeR48Y6ikpLS5gOOWQlgOtwYuP3AMJC6xy6Bs+rHxAWZqPtXbjI7d0O06cMY7l0bYsoDh8JC/iWtt68J0LCq0FRnESYxgQApUpijUUte8VD97S7dD0geaZaCZvfmZgFY/Beoo7qigdnjimj79xHJVSBfQJmUdWVX2PlOpvNOrjUs8o0ompq6t56X1p/jaWURmljEZBaaLedvvdHh/39+c/eOPDLwrMpsrKsq2bN4BUExTmMy/8Fqozff05CEFbr53joVL1So2oZlIpmrJFQFEhiql+AoZSWhzi2g15+uoqLPgXgmfehvuINUtTkAEawZVAUWAByu3XSEqtRjmD+SyaaTVC77/quPXb0SS7V3YGXfuC00UXROs2FguaAX3UKYYRe4SK6kmkM9sM1dlmjRym6qjqOaJ01tdLcnnZGbMBAC++t1tpjSoN0rqa8o72iCaC0kR96L67xOIl/vRJOCo1hLwA6bNAVWAlpSjQa/PKhgBug0QBcPl34h4s/OHrz8lNMLE6y+atnxeqik16lsYRJ1VtHwXAsYAFarJYLEZdlLI/zBd71aZodZEep4L2GjQzbPKHlDhA7+iorauwTBpfdv9rkhN8wsRJO/bsEsSJm5rSiXMAlIGsJKnjO9qZEcu0CJlmtrlfElBaUivc89Sk1ds0krtTSX299ornhXW7cUxJZY2KMhq89Ha0aw09jMFEfWFjZxoFFPPh68/12rx+XmAZKlXT2LGSYtdnjw0NnBO7fJp0AQFFIfvTZijz+4+19dA01TXobul2LJg+Tm44HwVlNhXG7UFZL0RAY2HV1in142rbWjsghL29vUuWLHn3rTeLa2uip0+lUD0J2ayeuFlfdYSZwzIt3XasgGVF6rkwejGDIpJ0KnnxvS/xrz26jCamp8lL6ggCmiZwTvvubVs6Btxnn4H8kju3bgkR0OUzlBp5rK2HYajOASSg86ahwXMxPpFyRT9GBDQlRuiagbMAxbhdyN78xonoc+N5geXd25ts8lpemXZKpHNMCWikVqfVoZIqC6iKcD2VB54jtKyjF9d9KR9JpaSaMhqvpHr86hL7/6yPqfAvXcGpWKqAZs9fiBweoYi5qCHqiZH7j6h6TIyYVmWpNB8U0190LOXnsqIJa/bj83HfPklKSv18t3Qmcbzw+pf+NC3YCblCXI2iBbHnpGpnvy2k0KisyNR8tCOinop8/YxZyEJ870uaCgnWy6PS6qojymhtbSDDg9LQU46HXFhv5l6tjteZtkDjLZ3cu+MLhmVRlnWw008Aiurp7jvWi37ShcVFmoMEwnJRUZ6AtNgXv3Ocf9/B5H8wN3kLdPPEpRt3BIf6+jgBj7CWBZQwlplTEJyoJ0Q2aNyB7lwjFZ3FY58CcLTH8fluKaKlsa6PrKThuLxcV1fI+EJxpKOToal1e0JGEGXIAk2m3tzr54GfV2VZIyUVP31P4MtQpfSHpqOiBH5VLipqBUQyYOIEqycqdhA/2ezuiU7INLucZVEkNUlisU/Hnz7L4eHe2rBfZY3K0ydjkdHk1fOClctTIKCpatIxb+FJu7dtCct50rClzTSnaNUjiSZG3j8WLM304ecEcTiKtFoLTDklOkpIRFIFCPcdQeH42ZPKY5llpLSQoujpgillFABbDvUnIKPDLn9fr2I0hkhXv3PEDB8ViQhoytsaycyevxBv7N62BQIpHVWVh19ZIC3PlQ9ETSfHxvI8M2CpxCnuqPaLol7/knTuIMTBLmfZZLZb/nPJzJoNezp3N0lZcVhMVQ+RtTWKhoXr6cIpkkr6xMG9eoNhRBl9+q0dp86o0rQ9QZywoy6aEZLb0Xuea+nHi/G23mDHEFk6IURLdaKb6eDW0mP39jdQFPXF3tYOqD7VCIQRmcJ2y6ZmqQWJ2oLjK3Umy7DLt/erNmQkKcQU62kU6y+S3arS0/IiE5ZRGb3BoJRRKKrnUi31lJ+AQ+U6IAVBpIyLZkRwjQrOSMUuTmJspruNyGWf0u1EOgmJMkU0P6FCQAEA1qIijoe8IPh56ON4Pyd4/TzWUxXh9qk2EDWKi6S75WHxqPX7updM11ZP1KQGgFKrocfmPgJRgmrz/oNRnvmClcvVApoBxcSQSsp0kPImTBkrik8VJKU/mwW0sFg9ZVOAUBCgl+M5Dump1y/sPhDM/YhbTDGhpd5K01WppzCCegIAtgxpTAOVkVUVCyhLRDMPSFP/usy0B00JRDqzEyrq4FmaomiGYhkaGiCEOghB5aLjBAH6ecHrF1we/54Ii/0RnlIGihlUgT0D9pC5sKVWY6eonvh2rJ4jMnHa8ZlLpCeWZrpJd+vP7NdQIp15AIVTPNBUcwYCaACM2QCKC/RVJx3H8QIXWO/v3H8sPj2lotmnA8Nu/BdDU7U15R4HCrR0D7kBiGaByjgGuy0lVakX0PwTzZf+cL28fektj4BsYvEnNURDSUFUPkGJCkfTSNwYhtKzaAsAHS/A5QunYD31+pGefnmwVaWnIy/2sXoGkv9l9QzeHt1mDjCxSKrlof7yy++CpMk/0VTpZjjZo6SZ6T+f5XYoITt9oACAkkAEKdwHGkfNqHJTHBK8cPZkQYAfbz3gF/WU44XwYJSGnirqpng0rh3I6okt0OgOUJWAJmWB5qVojqib4XcbdSXNgBGaE2t5QhaqZ9KmqMJWlGWUAr1DyJtJ09SKk6bJ968sNvk5AZmo3hBf5xcHpF/H7InqhFCVeiZAfAKar6IZu25mp5ISDSVkOYVxmp8BUDVc0GgU19cDw55jPeqh31u/bDLoGPuwxjDwBVPRhX/7YXXVJqsLGbqeAI7B7pEFlIhmLHywYdsPz1v06OubQV5D7FBCxqGUGurycq19Tj48rx6CvgHUazESs8aXjNjIbX+bDViKY3eARrRA81g0kzE2w/lgwzaQHWTGCCWreMJoQImmJ/T5hSPddrmvkJLo6rn9cM+s8SFDR3gBsqE93QQI7Zbx8b4ydoyIZlp1kxfEpof53mqDqCdhFOE42Nw1POxKfUPFQAKTJ4GfMEt0MzHdxKIJIaRpKoEeBDlnhBL1JMQBJQV85BB8kggQtvU5hhxSR5sbLj05tV/HkNPX1u8CsVU8Ndt4eRWfoY70uWtsqnQTQlQcRtMUav8kpf8GyVc3KFHP1EL6+cdLr83TGxjiGy/h63dVBMnr55u6HX5OSEAN80pA06qb2AON7M0YRtoRCJFQzpLKS6boelIUgpcYcHiP9djlwJFTHA0bCxarBbVpD0PlAD3c5fD5BbZ6ChibApo+3RTEhvYQQApQ8XZazZtVPDE/U8UYGcNHxVTLEyseH9fa48B9geNSz6Iiq46lP9nRHOl3W1FWhNpjNncl41fNYQFNn27iAWoMlkwK0HkfHiKkH1KznwB+Xmjusru8XHTpfPvj7co/zQUFBj3D0hQvROx0J9Ml5uSPIQFNq24iF6cYF2KZWCcnEwixk8eGZ8oRIGztcdicPpV63vrdr0V5VGGhxaCT1ufKdFFlEujBtuEZjagq6ViPA++PcdZveByJHYOiGe7fxJ8gLc4FYcjkH0IaGIPSWZJcCL5nyN077Il9zW62FJgNrLIbCJqKKKbQK++mVNWeQEP7xByg2S6gGdBNTHg8nUAgjCIDdlSvabejSeYjIjD6YovBoAusGgMaumGXNP1YBTY/O/qdmqWfOS+g6dVNsUk1HlRNNJNASC3HpSIE7/FxXx3rdbiR6zMWqkpMuI4l2MCJAiNONuociNW2zQ0BTatu4pA6AJCi0pv3nq+poARCZvBx/MGjvQ6PtnqWl5VEaiGKN2QN5UdS0JSkCrB5JpqR/JsURUkx9XwnM0XxBEI66O4b6h/29g17I8mbg6NjbIIXyQEqR5BkmKrJCbxUHEdi81I3lepJ/JsEQvbDC7B/wOb0+Ltt7igx8RsvnBvDwZCGrt+p7QDFaHYkSQA2z3RTKhkS7U3i4iQQciIE39M3hEsqOwbcfCBnPhlGzEo61uv0+WP1sY6ygGZAN6V+SCI5UTJEIIyFCNKIQAB7+1AnOk6Abf2uKIZhQYFZc/+2Pc3hOzUahipAjeuj3iErBDQz9iYGt/YgEAhZRWHkELwsnfiH3DPkcYoVR1GkU2e0xvi82AGqAjtAAQBHe4PZUfGm0CtptvFs9oumRjxdQG8at+Akskkg5BwChH39AfUEoM/u6bd7NANHSsPTw2kobFWZtbtfPdHo0x3ILA1vwoTxKeQ14RR66eEgd3RTXqpTNEUKLQmEbKv0v+fm+2L3eMo4PVyPzRNuCEZasyt58IUNFy87vqosxDLt6kdmZmZgs1w38cfKCwKNCi3REGeQ9ZBUUMKY4umYG/RxvDAwGKJuHh/f1u8MDxxpqqccgvf6eI+ft5p0Fy87Pvxu1WWFV547X/5zu5aTdDQFNLO6ibybpLVHXJBUUMLoUlJgGNHwxAvKjkGXzy/Ea3h6fNynX3a0dA1ftKh2xDvPmzlR3l44Bzz75vaUOEDjE9A0iabmOh15OCGgSEskAiF3zM97Iq/fIYS9AY+njABh16BbVa8ZRT151iRve/2805NgE8+TplUBALYc6gepgM0i3RQTC5B/cwwMaCMQxkgIvifM8MT02739Dm9chicGQmhz+Ty+BPNFy6x6AMDKeTWCALccHmhrP4TNUGt96jrSZ1g3xelCJH+TQMgrOEEYGNCO59jd/h6bFHaPXToxEAKXh/NxfDLl7BCCPrvX6eH0rBSQ9nU3eRXOhBj1NERAiW4SCITE+pweF5zShvBwwBNBPcXAkQsHjmJXTwsrqZsAodPjR0M+vIk0oyurrMThrBZbn4/yABD0IQQb4oXqaRQxZdMnmsTeJBDGJp7IRZJi4Mjt54R4Dc+rV8/DG4IAXV5OgCO1q4tAe2cfAKCyogSvfXXGAuWtfk8wx96gowVe8ENgbz+EZLRuSoYqkSKu00UXJ3FvEgh5XMRZXKCPJKAChO0DLofbH696KiNIHC+4kYAm+FJ3HOo5YUplT+9gqVHjEEo99TrV+fnhpDIh/YMN2/C/kJIh1N1D1E2xu8cYUc8fnrdodF8AGaVJyEIGHT4/ZONVTxU+TvD4OACBZhLoiKxaOm3HoZ7th3ogBHqKc7ulqR6JwaalH1Kg7ybK4RwjkkkgEKJic/lswah7fJjkOe4Q+Py85rT3WIFg1anTeAG++slev+jlxBpqMgWNXIyhwDqiEcqmTDdF6ZR0k0zLIBAIChx+esA9cpe5SLhRzB0BAXD7eF/SzZQgABMbavYdGzQKyOnp4alIMppiAdXsW4xbcBLdzCrwKp50pydkEgjhoMPbWF0ojlxHBmd1scHl4/d/1e/wcKVFlsQOKxdxQgjdXs7PJxhBAgC0HJXmNRSzsLGUbu5H/gSjzxVJRnUU8MOkBTRsvhBq6iE2RUIdkUgLzqxFdoYSJSWkGxiwMFu6pASmxupCAYKvOux4xtGATUo8iktJlREkCKHL6xdToBK0Qe0uD94QIHC5fV63RynHBXomkjVqbz8UHohnE+u/iSER9fxWUhKJIsTCcbqeSEXlLV3Dg05/95BHNY5MVtK4xVSADo8fmW4JJYGiCUhiQyJegDYnSubXUVDHBl+bU9HR2eGwMwxLC9Ea17MxrtPRU/JQrLNE22MmnD76LDxhgfLPLTu+yMDSnkgnIXa+8qPU9Kn6nvA+8G4f3zfsgRAy8rDMsMyfuMxSjocuD5dME5AtB3tOPK7S7ua6htwcrz6QiQ0Je7u4gPM1FgGNWJ8u5h8xDBHN0ZRO5c5UyWi4khLpJCTGAR+SUayk2MTzcULHIBIp5d0YIMSvpEHl8XO8y8slE0FauXhae5/zq7Zg4irNhhRRKQ9uUhinVCQBjRQXEpOQSNf3UegKqimdmndIrZIS9SSkUElNtiOo9zsWnjDNk5U0XEwjKilEJi2am5RoEig6uN3TOeAqKSuVj4kSVAcGNMV0RFhV3jv6PymeTlI4R4ET586P63NPlUFKbE9CynEXTWCL0AbXfSjEfosspuFKWlRWeeOFc4LH9HL+EVbVI3Csx8EJQugIZFBSGtBTEacLBeV9Hg/W0yg/SGSBYjcANjmJd3O0cA7zJ580N7GrVgoNUgIh5bBVUvAaKWm4IMFoSmqUU+hxGxGvP/rEzRHxhCfhixqKGRx26gPLdr3RiF5JQK+ZqsnhR2OlKUOozpK4OEcH+xBqDTtn9lSjMY5R2uEcbWmuKi7tHpIWIwRCtispRss4lZXUo7A3eQF6vDyP0ydTi6ihTpdLVk+M3+en6Wj17mxOTBnKb+kEADQ01JaXaU8QjIWjLWmc+kIgZEhJQTTjFBVxcryX4yVPY0pxOtGaPbvmwhNikU4AQFGRdcqk8UlKp+SBIRByU0lBuJiG6SnPQ5vT6+V44B25T1KMDNqCa3YVSvNTc/1OBHSUpRMViul1c2dPTd7qJOpJyD+z1FJarbyDj+OdHtSHKVU4neo1u0yB2TzkU89xGusW6BMvvpvJp9MB0NIZ9Ej6FSW15VXj0P9R1Amzp7Esm5h6xmJ1tjW3gCygfmJjhp9xrL3xLHm/IBVvOWiW+u1yCB4C4PJyXl+0uqCUGJ5St73YZHpsCehooZROJcdPabRaQxpix2p4ih0DidVJGDtAcZIHDsEnnAQa3fBUNiodGhrZ/CQCOmrSCQCoqiyvr0MTVhNZsxNvJ2EMYAnMQcKN6FEXkaRD8JHiReFtnkd0gBIBHTXMZtP0aZNSHmQnOUyEfMLBBVOIBAH6OYFLopFdJJLpkE+W8KMARVFzZ0+Lnl+GIdJJIGCgOM4zmU6gsavn0GBM63cioKNDodViMhlizk9Cs6SiQKxOQl5iVESQAAR+Thh2+VKYQx+j4Rll/U4EdBQwm40Ggy721E6ingQCwCF4VIUJU5IEOqJ6xrJAJAKaaXQ61hL5mwsu2GMIshPDkzB2ECCUIkhJW6BJzgRN41hjQjjHNZQFP2uaLiq0Jh9k7x4aIOpJGFNACHlB8CXbSCQmYneAEgs0c1AUKCq0aA6PIu5OAiE6aBinl0fj4JNOAo2L6A5QIqCZw1Jg1unY6NJJ3J0EAsboD3F08jwcdvkyYX/G4wAlApohjAa9yYR6C8oQ6SQQomDRCVevOkH+08fxXMbkMx5IHmja0elYqzU4mcDldCpvJVYngRCOw6+wASEaQZymLPpkHKBEQDNBSXGhpnRGh4SJCAQZP8fb3T7UCdRrtzvR0HaMtSBkdHuGHaBEQEN49fW1I35eF5x3cfjO1998VeB5zZuG3DCbpfO1tc/W19VGv8+Tzzz/8N8ex9ulpSW/ufO2c1ee4XA4/3T/I0898zzITX70w6uu/u63ot+ns6t71QXfxNsVFeW/uu2m1eee5XZ7Hnn0iceeeMbvT01boIwxrr721ZefHfFu8xd9DW/odOyVV3zj+9dcUVdbs3ffwT/+5eH33v8YjAYQAJ8fmZ/hEdh0iGnsDlAioCHUjiQlmpx3/qrq6qqO9o7kpTPzhmdtTXVDQ330+xQXi4PBAFh26ikvv/gkAMA2PFxWVvrHe++8955fV4+bDnKQ4qLCEd84zUg/pMsvu/CvD9yLvlPbcEGB+a7bb73r9lvnLVze2qb+0rMZhmVHfMsy9XW1O7Z+JHbD4ex2+wlzZz33zD+cTtfkaQtGmpSeeigK1JYVnDy95oMdrQtmRhzckDHLNOS1PXrfz8GYIa5+oFu2bULz2uafHOU+X7/8sp/9/KcAgI72DtkCxdIZXT1nzpqVGemMq03kf196Zsnik7537Zo33lR/UEajsbV5F8fxDZNmY+PrFz+/8Wc//ZHT6ZowJejsz9F+oJdctPrvD//po082XPaNq1U3Wa2W5oPbeJ6fNHUBbuQzd87M995ey/N8LBePrO0HWlBgPnJoB8fxNQ0a76KrdR/DMBde+p0NGz/He178z+Mrlp+6bfuus1ddlu63bBRD8MphnBIQ7G86FtehwpX01Y2HoztAY2nCJEMS6ROkoKDgg4/f+9nPf+p2B697Q24oG56x2545kRh/569vAQBc84Mb5aXrvX980Ov1prauIwt54E/3AACuX3Or3AZt564927bvYhgmdoMut1i65GSGYd54a52snshW+NY1giDMnxcmaulBQz3FApNpkxviOo7d6Zb/xfXAWNSTCGjifPTp+1ar5cCBg8uWrBBLzUC+Sifm6u8hj+Gbb7+n3HmpaK/devMakL+suem2u377x1defUO5s7nlKABgQoM4ViDv+OY3Lh4csj3w0KOq/UePtaJWOJGr6VJFQXqSg7CMDgw5BoYcKXGAEh9o4vT19a1aeb4gBPMqstzdmTw+n0+1Z+euPQCAm2+6/r4/PQTyFKfTJcfQMDRNX3rxeQCATzcgJ0/+8cPrb9bc3zgBjT4cHk7ZQLdIONMWn3vp44N4Q6mhpcXBLMN4IXmgCXLOWauV7s64+mR7/b7cUk+LBc0dGRgcUu3nuRyLRCdJUWHh1KlT3nj1OQDAXx74GxhLXH8dWm20hQVLcwuGgnIonwtsymKagEOTCGiCJBZk9/rVRlxOYDYjT7zX41XtF2DaE5uzisMHtuKNl1/535/vH0MC2tBQj53gpy5fleEiThX7D8cXRApHtnQYStqkFGIalwOU+EATAUeK4vJ1YunMUfVEZwmFrs0paCWWy+j1+ora4ytqj3/y6ecvuWh1+9E9JYEEr/ymccL4bZs/QKuu1Zc7HPFZDAlgYQXlKKTUwmopIxTFFBmncTpAiYDGxxiUTozb7cEj7FX7x9RYUNkFfMttd379W9cAAD7/LCSklpdccN45Wz5bBwBYceaFW7ftyMAzOjj66tUjJ8YlwOsHkMeJrZos/0v+mGQJHxOxpHZqkuvSibEND4uTSNThV4YZo+fPhx+tF4t089wCffKxh1adexYAYOqsk/v7c8lrHyNKDeW6DwdamcfBGP0BxM4Yl04lypYomPKyUgDA2v/+D+QvS5ec/I3LL77uBu3AdB7zwbuvzJ41A5WrjZ+Rc3WrCZCYQUoS6UdmDK7Zw9m0GcVPSkuKlTv/9+p/cJI5yF/+9fhDl1y0GmfwyBgMI8wEzHVefvHJ2bNmeL3eqvppY0E9E4YIaCoNz7yUTsz1NyKV3LE12E6ioqK8vq6WFwH5y29/9xcAwHvvvKzcuVOsE8/dXirRaWioX3bqKWhj0lxlpnMGMEYNwSfvAE05ZAmfmvykvFyzK2ltbX/uhbXfvPzizmP7Dn51qLCwcFw96r0y8fj5IK956pnnv37pBQvmz20/uuerrw7rdLpJExtZlvnqUNPPf3EnyEfefv1FvNHdtj/81tnzlnZ2dWe6iDNbIRZoskF2iTGQ4nPjTb+8/NvXsiwzY/rUcfW1/f0Dk6YucLniKzHORVau/vqVV12v1+lmzph2/HGTaZr69pXXLV52DshTKivLR/sl5AykG9PoWJ1Z26QnTWR5N6b0Mda+aJDcWy4Hw1evmpuOLHqyhE8LJMhOIBASZuz6QIl0EghZhdFvd8SXhTnK5ucYFdCEpTPvI0UEAiEuxpaAEukkELKZG3MqBD+GBHR7q2TDE6uTQMhabDaps1xRUeI9OjMJO3bU82hLMynHJBDGmpK+njYHaP4LqCydCTyWuDsJhAzz1IdNqO8XQzfWFJ5yXJlSSbMTdgxJZ8yp7kQ6CYQMY1QUcbIsVWTWU6zeatZTlKShHb3Z2A6KzWN3p9rwjCFBgkgngTBasDTFCag1PEPTNEUxjNRvtqjIknwj+jTB5rd0QghjbPpLpJNAGF1WL2o82mPvGXLxAiyy6HVMCgrN0+oAzR8B1bA6xc6osagnkU4CIRtoqLKOq7RyvOBw+/0cnxPzDti8tTpj+/CJehII2QNFAR1Ll1hzpt0qO6aszpmzZsnbuTVYOIe6eGQJY+2N5/T7NaatDWi6YfMmTBS7u3OMSCeBkEN8d8UkHG1PYQp9uh2gOdkPVM5PUq7ZY5wQ2T00QNSTQMg2LKyAM0Bx4if+B3IBNndTO0XZRFYnMTwJhJzGwSFL7qkPmxiaqisrWDGrKlfKOtncdXeKFidZsxMI+QNDU1az3geZskIjTVNYQ7M2CTQHBFQzyB7beh1BFuwEQvZjYQWB0bl9PEUBmqIMOoYWf+HY9kysBikDDtCsFlAt6URWJ1mwEwj5hNFvv/K8OTaH72iPvbXXbtRL6onJZvMzSwU0Ui1m7Hm1xPAkEHIIlqHLioxlhcZZjWVOj59hciCFPhsFNEJ+EpFOAmEMQKEs+mJLzmTRZ5GARuwAErPhSaxOAoEwFgU0vPUcsToJhLGDLdUZS5mJII2+gGpJJ07tjOnhxOokEHIao1jE+dSHTd9dMSlXcj+zQkAjNQGJvQULUU8CIW945qOmskLjomnVZWYm+xvRj6aARg6yE+kkEMYWFlZw8bQAUfqn1aTXs4zFUoCj8DabIzu70I+agGqmdpL8JAJhDANPm1Pf1GHrHnKhFb2eoWnJkCoqsmRzCn1GBTTygj3WI5AFO4GQl8yaUDZ1XEnngLO9z+HnBJBTsKOzYEepnXHkyhL1JBDykqtXz0NjOFm6odJaV2bhBUHuS5nlNUhpF9BoXTuJ4UkgjHmMoX2UGYZiGBRByiHYjGfFk0gRgUCQsNkcqc1YyqQDNC0CmmT/JLJgJxDGFLZcy/1Ml4CS1E4CgRAXuBF9jmbRp0xASaSIQCDECyXNgQSvbD42f0rltIZSl9OVQ1n0KRBQEikiEAgJYPTbZzWWdQ+6hhzeAqPObNApbc/ENDTDDtCkBDTiQHYSKSIQCGBkls6u8/i47kFX96DbZGBYRRvQ7K9Bwvx/s5aGe18bIroAAAAASUVORK5CYII="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "The chosen outer lanes avoid the shared central crossing; set S0 to phase 1.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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A2ddO9e4anV9eL8I0N/KrpQ2qCYMSy6QKMa69kV/ddhHqcFK8WoQ6V4Hy+fzIKBhCev0NM8mWQYMHnz3TVKNDkZ6WBgC45x5jsXecIeM0e9YsAED/XonW3zHrRnqXxO7de/T+vNF5d7h+6tOjs1arFQgEYrG4psZiTZ+PD6y11OuZVTyx0fCERERGJiXGzn/62RH3jI+Iii4vK23dwTDyxa0sY8Jw/9i+woBIQijRqY3pLIVaS3UeMbYQok4m6jpk+L+sopVVje6a/E4gEvENGaHcfHN1VwDEhonRA43GGJEY0dOmOIxj0er0uw9fkiuZUZHkoWPTr57FtMoBPeIuZpeLME1GXnWDQj22X6ejVwunDmnhAnFL+WeK92bk7VOgckOy+K67LA7mmzJl6qjRo6k/X165Ej2YOm0afUErf/7lV0v72b9vn8AAHHf0NxREdXV1thzh0UOwhqNrN9hhwRJPLV4uEpmvJG8RPgnrOmsMdf4DB1pL5Y8cNcqQrDAf1sRxPDWr4MtNn1y5eM5HLNm207zrbQnq+M0W3NgNqa/Lv6xuqMAIfmjPiWY2MDr3TX+hVQ6xni4m1F9YZ394Fy2uPM4zV2+VV5n85jFw6/rFmgbVPQO63MiXAo2yf/fY/Ir6h8fclZ5bJcQ0mQXGESisEqFejH0KdNeuXY8++ugLL6545umnzG6wavXqkJCQl1a8uGsXHJn7xBPzoGTr3UuhMCY9KLKyb+E4HhoWxhiug/j4ow8nT5my9csvF86f7+fnd+rUKRvHvEurYVUNIEken681lNAziI2LnzFrzvRZsyePGwFaZT35pHLVS09v/f6PDRs3FGadv3ijqTqPDiokmDndeLdgoNPC8STpt4oWPfXM+YuX+HzBG+9+8uDcphioQqUhSTKvpCqdVv1H8cDsOSRJKpVK6llD20oy43axaek+xauf/oYebNk6kf4nAsN+3/w59LakIqOKKS2tiKDn62CLy6aHdDqEBeYXGovPGbsFglj6X4WG78dWInpch+eJfgTUUx3+M7srQbO6sVc/hdk5OtIGdaS/odLAft/EdG9OQq3RZN/K1emY7guOY4FClQ+uzcitqqpXThnU9WRGCaFTXc+rLq9RTB7U9fDlgup6ZbBf01hhlow+SvFSEWqfAn3v3XegIZswITCwaXwuRWRkVEhICABg3759AIDu3bvzeLy6urrq6mqFCbeys2FCadt2s2908+ZNGCcdPqJX794AgIcfnGtjxxC9Xp+bA/f80YbPzW7w7gefwGrTY/+CNlBaUqRUKgiCN+uheQO6R9MXiZ+vxM/3+RdfwHFco9EUl5agNRI/44VNf4wO+P4ZsHKga2LiXd262fLuIpHo1dfgwPb7Z84AjoMkybfXwEqdTp0SwvrBQ7IuRUsrjGKnQ5iZHwNrZ0fSGsbsGyK8HeAS02wxRHuNAd+4mPDsMoXrI7wkIAsLS02tJ7pNCjFN985RRVUy5LxL65X3DOhyq7hGhGmu51UrVNoZi2EfA5gQ636X9QWwlWzPCYbaZ0CVSuVXX0GTd/7CxX79+tGfSk5OPmnQiR98sFZtaKK85m1obdd/Cg2WKRPvnQBfNWAAj2deBe/etUskEn33/Q8ajWbcCDtaxi59ej4JyK5d71r97gdY84zTB59sCg0N12q1a+2v6UPyk2LRw1BaPr5w2eLnX6Wv7x4T8OkH76W89DKOYcvmwbhti+Tn5x89Cg36d9/DyifrjBs//r+T8Dzn5t4pK21l5NQSxUVFKAD96mvmBwgYwZqsZ2OTYKyoCoZ3SmvVHjbPXHMZSmszjR25lNcrztgjxoWQpaUVDTLzAysixbK7e8bfLq7lk+ohSR0LKhp8YO5IWq/QDO+bMGz85MfGdXtm/kKUSlo5JcH6O7nYhqbY0y3UU2wo5k8btGcj27ZtHzN2LLyZa7UXL1zAMGzgoEHIIfrrwIGlS5egzXLu5AIA4mNjTBMpiOzbOSKRaPmzy3bv2tW1a9d/jx1PvXp1ymRjBWh8fPzJ0zDF9MeePX/+8jVa2S950PKUN86cOPrFJjii1BSUefcPCNj5214cZpZBcXFRUWF+ZFR0TAwcV6PT6594cGY18vTt6cbEMKCwg1RC10+2fo+KkPLzcspLi8MjouLi4a+WBOQryxdmZsDqK4o/jlzUk2S3bmbC/ARBZGTcgB2bzp194onHAQB79+7v2tXiMNDMzMwZM5oFB65fv2G9mUhtbe2sh4whgiN/w2YFYydOMHsk/+yHceQbF//b8eGKZi48jdJyowGNCDM2djmdeic6RCyV6/JJu39RLkNbmq3X65O7hPgIeBGwSolGc2tMwt+S8TPmFNdEh8DMUqoMOljuYmAwDImKBS2InpSPv1334hPUnyot/GBnKs2PEs65keXi7iHrbJ46ySMc+daUMS1cuGDO7Adybt/m8XiDhwwZNHgwhmHFxUVz586hrOeiRfA03blzx5L1hDNMbICzXW7Y+JnZZ/PyjBOaU9bTdupqa6eMH3no4AGdXh8VFX33wCExMXF6Uv/3X3unThhp3XqaxdR6Qg2Ykz1z/KB9u3/SarVx8QkDBg2Pi0/QaDX7//hlxti7GdYTgWNY95gAaqHW63S62bOhXB00aHBwsJlZcw2FouqSkuL9+/fdd99EhvV0IDqd7oWUlwAAPQeMFPowB9TOWvwYWpauWm5WwanUZuLOnkHzD1JmEqBwr/WkxlDJ1XpACIyLOejWU6HWncmEsekhoeZrltntyP8IvFKBunJOJLuaJTtq3JGpAlXJjENBfMWtHz0ZEtOlqrDZIFcGNwod2RnElJiETrZv3FsM68k7xUVQa2Ytfoyxze9bvjM+Io0KtLRGWcY337GQDWhLs/slQMlsRoFSkPTwLtz4jOGjudeAqouhVBzeKxLKf6nxdm61RRZEq9P/9E/q0O7hVkRoUkfjWFWXsc6O+TuxrsOYA0BYBbs65bSFsopq500qKZQEIBvaIG9Wy2KXPbVuPVH81JX21Dq0lLtFZi1+zGhDG087n8AGBkrP17TcsNUt+PlQA4gtfrjm4V22gOM45cyNTO5CmuuPZWpMeQROkuBMZvmQbiyKq6R40RzIPO+Zq8MJNfMtQrenlDEtLYchAsLOo9EbLukOYSFm7am7jCmzksnDqVcYIwwtxnbp4d2YULG7ZiGkgOlQvf5kesnwXpE3cwq7JsTQx6GaH4oaGQpIMHVkj30nYA+zIaH1lkQom8k+9SObRSh7DahbnHdbRCiTxsm/6cYU1rc3TnYCLEeBTblTUNYp1sy4l/hQn7xKZi2tU7kmD0VevF1EBLVyhIJ7lTUjOrHprQ0R4UFUaCIm1Dg2yb34+/vV1Vlsv2CpbSsazWxJhCYE4CwWoRj7bSh7DagbreeWzz6RG1qB2ERzoUkNPdJqtSqNXuIjAI2lVD4ii+WEJEnKFUqZQmOlutv1NrRFCd0UAwVgaN9OeQVlGOaySiaHYRrbXbpqOSM04V7UxVlEY7XfyfSSMX2j8yyMQ4VDnxuHomobh6KO7GW8JQ8PbzhZbqZBLVttKMmWL8CNBpRvGI5pL9kn7agCO3j0LHAwehDgc9cTzO4e5bdbbvJ0M/sWoxsxY0qMqIgQg5U0yKBGLZRjmA/jdnFtXJhkyr1wDCidgkanbPsJi5eNsyktl256a0NT8t0CqCYUsBVUF+wjsHsyevdmkHAcDwuUyNV6f3+/ruGCugazg80wyuhYugszPgWSnw3SUt+gplQha8Dof7BWhLJRgdprPRkTJTmP8M79rNtQynpa8q0SogKLDS3cDd3SmNckgWNaQvTZjv3LHp9saj3dCBUZbPJtLYBhIDpYlG93nRhHC9QrNNQMuEiYWfjNs/fuZacIJT3ChrLRgNoFCVxkPe2CMRmG4TDBiP6wGfOJy7eNwX6DJY3sEILaAt0uriUME6UnRPhZsp4LRkS5WIQqmk92ZMV6ou9AqdZJfNwTVrMF0x/K4Yu5Ng0Xc7f/rtbqfQgCxkDDaaMATKK5ZPP/qW4vKpWHxVU8BdzT5afBZ3Qd4Z2bDWClczP7Fp+H83m4uckwSnILjAtjPoz8whI0GQZJkoGBxrR7dl5pdl4pG7Qn1jwxbWUzAAAazelZmB2sSY/tuh16A9zkLs29FszaUFTKesLJSwCYeI/FrGyD1MFjgp02vpN1pfW4R1tPK8OcXE9i1y4tToNhqWGaXKHGMEzIN/pofx+/hBnmAQdswqz8ZHXIszmK4mytoQkWBbo3bHoLjoizZD1jQsUCnt0xU4fjI2yaAoc5BMCcDaVGUqGPAFhMiifbUJ6HWk9butO7MhKqi+mtnPBSx0YPqvbQ9pqDXwKSad95IdGhc/8n7JikyDpT9fO7ugZj/QAJgJBvcOAB0ODCzoaYvmLq/5Q+Af4H1mCNTY4pJOHRA5etyd6/s/DcEfp6QiDsOWdx3HBjT0+dWnX201ekOXCUPZ171mwn9fpjqy3mQO9ZA1vG+ODQf4cREr0Ol1VqMk/o0g6TSmsTmRhiEXVA4A/YB9F8ivqm4fzhQcho+omI2YPDXptpDL+U1ahf+eqykG+xonfOgIAnhgRO3mgcc2yJnxbFXshTfPSPGX/ip0Ww19+DXxYw1g/tLH7//ghj13x9x23/5Px6pkxaU4f8d7TN1OSQmGDhlkPFzRJIABy+lItkdYCY/+LMRJlCjRpI+/niY7p2+TutZvfVutxK6NHn1OqpMib2pZIwS0+wKhjKIgNqF07IvLce2WNbgRDe5PWyOlKn4fkG+Y2b7zdu/tkX71WU5o25uyuyq/jIJ/GBcHgoWVsm6TVS0muk9Kc1iquwXbRSpQ7yFVZJ60KCmkwP6RumlwQZJttghkFxHk8SHs0TNxur3nn8A91nLUTRAFlZIc9HIgoIHvbyJ1qV8lDKgzp1U+pWEh5NWp2PRBJuHI6JU1G1sHh+x2T+xOcVmadLv2iaoIUCxzDUcYPNDGh0genWEz24p2fgkER48kkSyFU6HwHRIVCw7blBF7Orn9ttpv431Je3fGxIrdzaaUTEBPPvGAyW2acYa4LExJ6l8ch0VtRpFQplbJh44b2dF93b+dVvr0rrm1oN+ImIIN/m1y8GQ7pUUCIiLDDIl+fvQ+AYvHUgwzsr2X9Wsr9eD8Z/cgf1GWEDKR47Nonnuc67W+QnnKS7qFmhsmrkU0Ao5qfuE1z4GVaKpF33ERARyaPxuWuHf/qP/uOpxpupfzg+8AGyulD/9dOG12HE0p8C5/4PGVBbxk1aJ2Hc/ch6/vv6PHlVU0ir+/0LOk+YPWnDnn2LJ9k7P9S+ZybSa+kFEQnRL//s021oxIKPyr+CDUfMAq9XlmOYupmOWEgMSYRm5YM/86mTRODYiikxAxODe0Wp0oubVQ4lx/usfxAOS3csQh6291k4u8mHByv3XIW9l/Cq2wKhT2yI8JvnBqyd13f9HzflwOJQhdJyKc16whuDTkfuv1C8/3zxmOH9BXz4vXz2T+7qaeFDOov/eaHTqHU57BGhKc1saAs/IfaIUFbEQD3FeW96d1FTolybCDvbI+uJUKh1pZeOkbmXYVFPmHG2H3wabIFM7ljWmPUiS96GtUpBj/8flX4h9boqKbxsNLjwr2OX7DgeHr/HA3COgH2LJ9GtJxwDumt79gF4ese935rESJo8lFou5dT9ufg+PcCEPUdl4p1TZSFml2IeS5uJCKKYnYco+dk7Fmr5r4+W0G8xOj05533YevWF8c1SNz8til3/YKQzwu97lsJfy7Kdxch6GlrTw7e5XSob8fIhAMBz0xMtmRazg1ARhfX47lM5qEnosgkdU34rPX1LTuBg9VQWDZBvDukpwVBWGFC7QIWf7np3hvw0pU/vHugB+c96ctcqUFNizLN0MFSD6mASA4OWFSN1WkACYecB1O8lyBeOpkc2tHOkHSNGuk2HU6dc2LzarMbM+nMHSZKiQAvNh+yCJM9+ChtI93rQ2LfQcwnwIRT1dWjRqpWABJhWKa+voy8qje6bw3dW7zXOVgJbZ/b1jwnmH0xvGLkux7HHwycwPxGu1pJXC5T0DkwyuQLHMaVadywNHkafeF9LDQbRMAd4VzD85C5ez0fOwJNT+pdK5QunNnUlf/l3eJcd1wPuKqdWz4Z0PLAzm8QScI+Tn4bJf9zvJFIilJd1HP7Kn/xaH5bQTITeyiRzLwEtCn4ZDljKnN2I1GkxgahDWODQPh3RRzLNvJuuWTCC2X8sYTysZSzPuGDpaKsyrwIAAuNbP2Vj066yYJ/TyOSRwDMhAVAoVQqlqlQqp5a/LxWTJPnk+I5doiQVtYryGuMSHSI+m1VVUN0UecwoUQ5fm/P2/iaT6igGxEPf/IdzTfP04Tju6wulMY8gBnQJ3X26EMOx+/o3tYvl8/ktV0qQ4GQ6TDRduW2cfAx1qldp4L02UOz+AgNz2HSBs0GE8jzLeh48ehZVTbpdfu44DuvhIefeu39dIhkYqZj+FvxTJQ+9cKDm0FdATsu68uF4VlJaTGVLjT3T9FoAeCgAWlQljwoRCwi4RZW0LiJAIFOoxBIgf2yLaXXlKMPEPgys5IWKLhwN7d4vbuTkmu/gZFNtgTQpLfAg6GfNWPRgQK6ESnPeuE6Pj+n4+BhYTJFZUHcsrTw1hzntaHZZayrSRyRKTq5sYYKNuXfD8CUlP02PnTGoIbJjDNDUWmww2Ehi55hPDl99bmafz/641q9LUw+q83fkIxIlYb5EjVzH1khoy7g9GMqWJBLbRm3axa6UeeLQiL5PvhzcuQdfKA4c/oDfkPuBTgO+Xw6khXALzHCf1za2a6JPEYxhVBdTDIAgP2FZjVIo4Glw4/TlDkFRDRWTOMQN85uzijS5xThG6iXw9dXcuXcHPD0KSrzE2IDEWGjRDqY3pDpBb5rSMQTeZWvMpfUbByDZoRZPX72T1Am2XyitlpMkyCur1+nJqlplSACcsHPllIQ6Qzka33DDZhUp0Iaan/SXhfA8KnfkTutpPfopryw9ve5F9Dioc48RKR8Bgo/N20J+OQ80VAHScFUQfHrjH0b99tA+nfIKy2C3kQ7BxWXVVdI6lIsRf7dYZDKhyC+3+aPe3Gr7wYtDoaaQV8I5ITgsodaR352t+e4sVJ08HPtqXnRCmODeXr4SIf7KrjYFB0/clL26G365DOiytKhGE+pH+AqNd01taTby33GC169jIAAkvYfp4Yu5Kx7p3+L78nhETkmthNAevJgnIbR7TueQGsXCaTAYGhkAr32FwZFnwL6aUPaKUNyDnHe2FK3R/XekIZuXakpvX//zmUmlX8CIPv6IYVJSQ2OxkMRe9M2WrlqOox5ljTeFoio5BkBFpTSqgzHOpdVCf7nFIUkoq4YTFm+H0QPHwHTB4d2gzRACw5zjbu8w7GgYWkyrJx//qnDEWpgpGt5V7IL79rYTcFTFwE7ma2lJAER8+DMjyWblSgwY/rtOD7+nueN6K9S6J+7r16DUzJtsHIvc3xByLa01/rToqSS3k/LOF3Zt78ZgqPuTSHY474B18jO0W98pW/6K6DfU9ClFJpxSFEiQKYTmpg5nzp/OE4mBRskYEYlEtkqt1elIs8Vapv1EMn7ZAhuGjoY1p2YJ6dobKovSfNBmYofBuTyz9n0PvIiTKxP+eb6j6XrSIB5dk2xJLYS/hEcHG38khvolmILXqOB6HwExIQkmiD7/y5j9z715x6z1xAz+OxqBqtORAky/53SOhNAeuVIgIbR/nc/b9uclDANCQ4BIgd7GBPem44H9NrR9GdBWFH6yJ/RJl581edlwsuUFr5huhvNFJAl0DcYsRBBhqPSk3QPQ4whhU4poaF846VuwoZgJiVC9ic4z240p79g+OI/m7KfNitC+T6YADKu4bkdhqSUwHO81FxYw3T74C/AmSMDnYeF+Zs7e3R2hJKxVtDziqI3oSXA1X0ngYP6wIFTAJK2DtntAFxi3DZDwe8VJSBJkFhiH0goFfI1G8/uW76iF0ZcgOAIGgfrdFVldr5o/pX9WYc3Cqcl3SusWTk0enwgv/C//a9YmhlUi1F7cJUJxz3De3eowWol+ahWyusIcnOBN/LSZd4xhePS7x2A+fetitCZZfgIAcMy3SSQe8oO1R4Nlh+kvRBX1FZXwl40+9aFTVykv3lIvO5LUn/4QDg26b9M+/1jYNI9i5BubYwaNhcUCG98AbSN26ITJm/bDWen/+Eav9djpi80x/1tYYbZrSdywLs086C2PRIkEWEaRyvRO5gyW/wS/3/nDg7al3EMfud8t1u/tx5NIErz/axZpqFVilCuZbely9kYpwMiMXBgZOJcJFWV2cY2/mHdvN9JfBLR6EBFirbWrx4nQbHfYUJ5HWE92yk/Ef+8sGfd/P4gCQ6Z8/jfjKemBTfUF2VoBERER9s+mjbEr+pXxovf6PyoklSoMRhK7qa7u2rLD0nsRhiZACR2MhdPWO4FW30o//vbiUW9sHvn6JsZTqvqaQykPMlZiBGF6wGj4JvXY7AY3931/66+fgHdxs0z16eGq58eFrJ3FTJ7UyHVPf88s4HUSehIMX5uz/9n4pBjR+U/uRSN80dBYvZ5c+9vNUqmSYTqp1icUOh35v1qpQqWXKgGfIB4YGPLw0DC1Vg+wCACwFRMCYKe7GtWJPGYNKaO9CBtIeecLuzLyrk8oeUAZk3utZ4tDj2BK9JVHhP5BSY885xcVz/ORaBWyqlvpqd9+lNR8UrYLK2fOefv9DFGyChMF6qr6K05J9HXXmu8KzSwUFeyjAUBx4xTfR4ID/ffnmGU0Grms+OJxWXkzk1pfdGff4kmxQ+/tOGqKKChUr9XUFeSkfv+Jup5ZLVh8ERb/W4GxAanTKaQVJZdP1ubDkIVX8tul2t8u1S4ZHTywkzhEQuhJkF+t+eRwZU6FxcLPIzca5OqWpemRGw03SlSWnjJdOXljXpgf7+XRok4Rvr4iXlWd+nhG5dUc+CXSrWdepUqhNuN0l1c3qDX6q2V4hK/6RFatQqWLDBaXVMs7R/lfvlUVHhL05o/pj9xzV0gANKArpySs3WdxSJVnpePdAuYf7LrxsJ4oP80Xz9tMkrhSJCAiOoTRJxynZhYy27g3r6DMR0CoMBgJFWGanJK6MgGcw5ajvaEuzkIBUJFhOsIAH5sSWRW1cObBTKlgwcQe2/++ntShmYkvVvgoZA1CsUQll6F6JlMDShehbDCg6+wsC3WlCOWxOXHEzuk67AUO6yyroHcAst7wvKhKHh0irqqXhoUGwQop9jc34nAmJCAVSihgFZbGKDUfW4WuGAmh3X8ut66iGHSAXaMkQkKmgnmwKB8FgIZYCcTE0dMwvD4wGJyvZmP/1lbjSkeeRfEOMx0/3V1r2Eb5iWZXZ9g/RqccBhgAwwy5+JhQsQiDiRrOfLZn4FxaNHAMs7IYX0JC+Tl9ZI9SaVOBh15Pin1EYh+RREgwFoMNraMvoUQNtbg9lQTYXdLkIhe+Fc67Xk+6XXy13YBSXjylQFuclw2W+BXAUSsSH8Ht4lrOf+ewC5GmXkJoSZ5IrtLWVRQP72XsWyr2ETX90vQtF1EgxcpA6d/NXV/HOlY68jyWOu8ku6xnG1GqdSKR8VQr6o2tHlv04vPL690twTk8ktHJnfefz4P+O41T6cXDehn7eJE4PzqyWVuA4iLmMF8kTukoNXpJQ5bMl9lWtT078jhLnXeWOa6tlp/Ii0cZALTY8pJ+nWEH3xA/IctOA4cHoOT7HbyULyG0BBoo3Mic8Ukn04tPGVrbwZt0SWURbdrXqOhIswt6VqcnD6e6uZFCCisdeae78K1w3nV6PeHWhvOm8rMtBhR58Za0JL2pmikVtUrWNnjnYC0iTX1dRTHB4+m0WsqF75pgrOX45dA1SorCPgnNpahZvtl/GQNgRE9jNy83itB1LHPkeWxz3g1z0bBLF7fRelIi1EvrdDlYh4TQ1lu+Mc8Zn/TzP6kYhiEzinSodTM6e2zvXw+nAQ8k28mOPLtMldtHbTo8+snB4Xqq8KAWmjdP6DNnfBLlzlNmlAlpXOBcyuwIJ6WwzJF3ogH1xLJ5Z8hPDg4Xg3qR+IjgWAwrzBmfhAt8zEdFkelsHmg6ed1MS9N2PkYeZ5Xzzoa7HCc/OTwdOP6CIJRqnb+/78l0a8mfyBCJqRQ1q0ZZcGm2HufZUJwl1tPtHT9bJz+vfPnUxH6xrjocDg47YMTCCgssNqOZMy4pJCSwmRktNefRs4YU+x15J9lQtiQpWDJq00b5eeXLplTgoukDF00fCHsM51W89vV/zjw6Do6W0ZQY5w2UNJYe22JlRXzenHFJvxyGzW2MySWDDY2OMCaXSJoI9aBqUGeDs0R+ut922iY///3o0X8/elRaZzpRJgcHm6D1XrCSHWIwZ1zSzHt6mUhR46YsyO+yToTibLCeOp2eDTGWFuXnvx89Sv9zz5FLe45c+mHfWdiX7MKtwnJm1zgODrekj3CCOV6DwHG1DuTkFhuXPIvuPJ/A54yDUdGm5FJpVVFp1UMT+rDJfrLFhrKijIkgWHEYLXLiatMsNJ//AhvOAwB6d4lANnT22KT+HY2TwXFwuAWzcbCaekVVrayqFk4QQpGTV2y6GJ6BenPOuN6zxvamS9HKSinGmkS8d45Eap381JOk9dE47Bl69NZjw9CDktJKgIEOwcZe8Rl3KpfOHfnUu7/wBMJeCcbz+esprpiUwz0BUKhAcR6KgcYEwn+pxhKNocxmV1xIgPmpQCmEQtiQFHpaV/KH94DjkdgTA11n/yTyDiytd5gBbX3mnQXW00YDunvNA1ezinEcQwb0VlFtYUUtBrDNr8z68PtjU4Ymyk1m2V6x9V8nHzgHhxF1cRaGYX5+vjKlBlnPurqGHnFwms/rudVJCaF6ktHBnrr06Cki81aV/jQJSPYYUOBWG+ruLDwrjKet1Usz3/wNmdGkrpEdo6C3/sBzcAKifSfSJw9NBBg4fPbGuMHd6S8ZGO9/Pq/l9kscHG3HVIv0jAtEt/ThvSL8AgJ1JKlU69QanUqju3IdznGd1DnU0nUorVdQdlVPm1QvNECMAYxLxDtSgXq0825X65C+cUEAw7rFBMqVmumjegT7+aD1pdVwstnuCbDxx6XMQmr7wz9v/qvGbS0UOdqj/JQrkP9eV9eADGiwr8A/0HRwpyGpTgI9SWp1pFKtVWp0SrUu42Zho1W1gOFlap8INsz24XYRynPXoCOSNdaTgRXreW/f2P53wRI5VGBHWU8Esp5wBuNuMZQZHTd3yV/t24sfGGyHAMdx7GylnzMPx2uxFAqzfI2hCUBIwjBtjIAv8DeozLCALiOSoXuu1elPXc6+mFXSIwZO+HExswQaVlaVMrGgzwjPbR0/WYONxfOvzh1EPe7bPR4AcPVGXlIX2CusY0x4x5jw2oZmc9ZQZnRQR/9zue3aiycwIBTBdugSvsXLjyRBtczi/JfWLTI1pQ/jKS+b6qdF/PyMWU0UAO1piH6SAJiTnxRw6mTD9MnwYXZRbVWdMqvo0sJpyTwCl8tkyHoCAAZ0i7x8q7yXYZ9qnwi2zdmZYucEyI4Cd4/zrmdL7qgVrUMG946nzCgynejPAN+mKRMoIrK/1ZuZetY8365/8tv1TwIW4KTDkGmwwJAwwxLKWAJCQm05IchEhvkL6cvkjiq0MNYPDoVxFYfAnu/F7JGg8k96CX1dnXHC5GBfYwLdMhjSqZeyK6vqoAiorTOet0mj+9O3Q9aTtaS4oywUd4PzDtw/XYdrWocU7X1XT4KZo3sOjLdVCj3x3NfAG6HaTTaObGkTMg0GbXFoWNNitMtNpvlomvtnQ3M4Vn4eGj28pmAA1ACSn6AF+WlEq9OfvVGu0eqkdXIdTzJ5ZJ9tf16ib3D5VvnlW+XoMZKfiAYWTDnXRtpoQ3EXW082zLXpEPlpFroILdr7LkmCgtg58A/MYzSOs46EhKOqL2eXUSNbGM/b5ZAgc9zUN8j4izI3ONFxsPwLop9AKD8Nfwa1LD9BnVx94WaFtE4mkykEBCYhtEeuFqAp45EI9RHDeqbe8UZDnFNrs0vlId1C22JDXToECGXe2eO82yU/3//5nHXrSbehyHrmI+sJwPSRPUF7B1q3qSN6REfA6Z6QGW32NNlaSWvsvcbW27KrYJ5A285HUZUsI08qrZMJCHhV+gSEdpDApHy5VNE0G921XOpxpppZtNPAMhHq4o7LuEvbfdJGRHgxDOtp+CWTtnvxXk90RIipFGVMgG7bfihJW2kqadspOE+nVvYyqMUg89VLTVzPl167XSaTyZH1RL/V6Lg4KEKvFNC9+N7xQSRJmlpPr6HVIhR3pfNumK6DLRa0FdPGHT13I2v32pt7PjB9qu6v1XUHVtcaFlXfBc2tJ2T6qB4eFAB19pEwpChUp3bZz0Zn3YqkRTgqj+QRXw0VAG1Rf5Ik+d+1gpKKGn5zRSMhtKcyShZOSy6vVcybbMwgvb5wIuPlDC++wStEaOtsqIvKmFg1arPVfLo37fmp0+C1eu5GVPGfGIYFxgrQTzWn0zxgEohr9j/pGfE1VxaBRkeEIKtXXFoVFtZyrqM5TSc0OiJk74nr/bt2QHujTKoX8+36Jy1Z0l7xQUjOW5KfJ9MKOoVL1GotfaVEItbxxE/c13vz3rTiKth25EJWWcatIioY6lnyM8VVVU12j0RqvfyE5WZsMaCtnrX4uam96Z8hqatxzlhT+1hSVc+wnq9/x6LqV1caUAIDCR2ZkzNTJ2yfwfbBCc1VKgCA9UJ6tMOuncxP9UyJ0KiIEK2OzC+Es1m0h8p8dXGWlsSFfFyn1SIDGiQx47+fTCtIjPSrU2hyyxpoptMHAEzIJ+SksFtsUEFxhVAsUcllCj3v2Rl9qM3W7sth7C0hoJn/yp6aUETrDKi9pfW4y5x39ljPtrB+bxpJQn8TXf/XsktMc79MDWr459INOPS4HULgGMAw085pdxqXnnGBKgO2/z5ycousR1eLS6vKK6pBu8Xkdn4yraC8siYx0q+4Wk5ZT4nEx2A9gUgo0BI+EkKbVSBdMDW5ul71xH399CRsl8OoZ/IgUlziyLsiC882573V8hM0N4vIklKrmjf5JiNCDMNC2rf1lCvUDTKV6aJQqs0vihYGI6Ep0vQkqdbptXpSo4MpY53hgeFf/aRh3e2PBngJSH4CQNLTR5TphNahpK6yTkU3nQiVWq3W6h8Y04sEIKcE9gU/l1kmIbRHUwspF37llATPioS6Bp4rCj+9+RaPpWaXNDnyplq00XruumixB7h3QwKQEBdhlOyGJGLTXcfw6EJmSccwWGmoUqltlbQAKyxs+XJl0U3b+Rj8d72pw45MJ+xam1+jMzRVoptOg/wUhoUGVVRK953LRUZTQmgv3ypfPiN5/e7UBoXG14ePtrwbRbRJ8G96Gfo+Uxvb5vTpyaLudm2MhNo1Rh53tvXUe5v8BOv3pdP+oktPmvU0/BUR4nfperu2nukKWK1ZWFyOIXDjPwa3HrYOqaiqRdYzJDjAxp9JvUzZspJtXOQtSVpvAlUvkQBcK2igVCcS7NfypDo9yRCeaBCnUqXOLatfODW5qk755OT+MqX24Ql9SBLcKoZS9PDlpnqmEF9BiK8g2Jcv4sF4q7BxARhIvZ7FThHqbEfeuVl4Q8sl4H2s35c+orN/v+5x6MNdQyKUZj8vXs8zPvLGj98a6AVshi5AGACl5VK0okNYIL3jZEtgneIb8xX0uc4aA9PllTVohVrdjqynTmvMqvsIhYmRQmq9WqvLLKwzsZuNr+KJ0R9puTD/dvV2BQDgrwt5EkJ7zCBF88rrl0031jP1SepRaJiqk+dTR/+162V1Jp3E2wu485139pqQVshPihO3667cyL9sWFBfxYvX86iFvmW7lZ+A8fXTZPrZjCLKekaEB7U+wQgllAFDO7fzN4op6xkaHADaE0h+MqiVqQukmkbriZqGMAs/SWP5Z7GE0J69USohtEWVDfOnQCn6yL0wC3/megklQtG4r8RoM+c21SBCWYhTRSjuROddr2eV8+7w1iEnbteduA2jQldu5H+z9xzj2S3/3Nzyz812bj0hJj+B0nIpcts7hAVGhBsue2T/2vYupRU11G47hAX+cy4bXtUy768JpRMoMcYrYfA9p/pmSX11rawaTidn5vTqeGKlIe4s0/FUGv3D9/bR6slpI3qQAGQVwvvQ4SsFEkJ79XblgqnGVNKgft2KSiqj/ZvZDaHEn9+4exZ68U61oTwnDdm00uHVy0A21PQxh1lKKxqFZ/NcOfqxtPiTSe4SYlqjczajCJlOpGfhf+3SoyRJUtpgjFo0KLX0qEh1bVPhJyI4wFdCaGVADP/V8SSE9vBlaC4PXoL++8l0KEhzSuqend5/4x/X8svr4zsYI6rRkaH7/ssYnhh2KLXEx6eF2eg8nRYTSk4pY/II+dkW/90snPW0ztnrRc2sp8GbpLxK9GvhtzTBtelPyoz1ZHXcyCkIou6iJxtKpMqCSjkB9IyF/pLq2obiclgqK9NBFSXT8e6U1qEi0HmT+8tV2ocmQP/9UnY5TM1fLaS8+JiI0CkjewYHB/qJ+AqFXKGQG0SoH5/FqSTnNRnBnRT6xHFWTfXeLgUJmyitaOa2w5hlcyuHHtvUKBYzEw2ICA+irKdxz+3Mhmaqw7O1HdDS4BfPi+hqug3dmAaEhAeEMEch3siHJvVSNmz9+c+lfAmhvZBVtnBqcq1cjeKhFP4SASEUiQh4ZRnMaFP3JtbiDEced4bzjur7WJUAdrb85LCFiPAgWMhk+oShqgmWgmp0Np5JRhqqcTfNU/3tG15EV9OFerahCupECaGl/3syowQWgRpUZ355/fwp/TU6srwWGseT6cWMUUkjekVgAomPjxiZUT3OY7kIbTVWbKiDy5gOHj2r0+sJtshPyo6zyqC3O9RavVoLB8Dk5Fm7rniNTdVahC48OVtpF82UqaYe+e+4wAcoNPCxTvv0fX22Hsh4aHyvbw9lXr5VAVvbGWKjmQXSZdONqaSYiNAT564BAIZ2lhy+VkrFWnWsv205vMkIz7HykyRJ9llPUFnUTG9y8tOVpMmtzpHbBsIDhHoVDMBxtA6UPoJjExQaauUhg7k8cB6OSrqQVbZ4GhyPtGRqv817027kV59OvYMGd9bL4OxJVfVqIWF+56yacs552STcgdaTNaM2nTu1A4d7IUkgV6jkClVuaR1jKSivp5YesayeAY0NVOFNhRASQotj8F+USqqoVc67r59aq682TDOHkvLHrzV1t7tvzMCcKl2tmgjw9wvw9+MRBI+AHgTbJWgbsklmHXmed3X8NGM3OfnpTVxzmp5t5+gJoV6nmzjkrt9P3s4qgMHli4ZI6CFDKWhabtXy6cnr96SWSeUdgpqVLkkbpwPRaDQ4jndNTGS/CHWgI487tPATuBVOdXJw2IeE0PIJXEJoVRqdhNCizPtJw6iky9kVC6cll1bLn5zcnyRBZiG0qgcv5lOppGWzh6DpQCQSMbKeRIcu3v0FmIpQ3FHOu96d03VY9Nk5+cnBYYUqPEij08t1PD0JHrm3T71C88R9/WRK7aMT+2p0ejRN/JkbMDV/2pCglzaoGPVMEonYE3vcOcqRd0DCB1lP3G36kxOeHBxtQmJoWIfKPy/ehP+iQfH/XISC9MqtioVTk+sVmocmJMEBI7R6ppXzxgIAampqPVF+OqS0nucY5x0AFlpPTn5ycLSIku+HK+slhPZaTiVVBJqeW7VsOsy/L53Wb9OfaXnlcH6aI1dgf6asAunskd1h/bxK+/2RLJ1OO2/qANPdnsluYHkk1CEZebztzrtO55ZRm1yqnYPDMehJMG1ED42OnD22t1ZPzhjVU6cniyrh1HIn06Hnvt/Qa/lOaR1y9n8+nr3tz0s+Qp6PwEIRk4fQdkceb3vdkqF6gXVuO0N+cnBwWEJCaP+9WkAlkf5LK4JdRS7CriKpOZUY30ej06OKUdT7jhpBPyE5Hgj9fz92w3SfQ7oaprRhdyS07Ta0rTFQl0/13krhyRXPc3BYogoPKqtRLJiaXFwlmz+lf0FFwwJD0PPRiX0NjZ2MZfZ004kID/IR8T1bhLYRvE3Ou6tbLtlqOjn5ycFhL6gdfXou7CeSdgc2Tt66P50+WJ5uOgEAKJU0KglOMd1uRWibFKhhei8PiHhy8pODwzoSQnvGkHmnFy3Rm90xNqZW3hUbhIvaV/N/OjjrCz+5ZBEHh0sKQrX6h8YnKdW6R+7to9LqReIm/cjo20TZ021/XsIw0CchxBYRymZaLULxVo/adEnhZ2tUZzP/neTkpyex50l/S08detr/0NPwWfQvhzM4mVGCikANjUKUpkaTMp30lUN6RGLClr8UNnvxrbahrVSgzjeejhCebhwbxWEnY7rAWu4ZXzfNifLeIwnvPZIAAPhyNlQx47c2my7lp8eMM0xwOAoJob1ZKIUFoXeqbPTfgUGE8nl4dKjEkgj1bnBWOu+t379RfjaaXy766Sm8OpY+7y4ElhkK4VW66NeGQ0/7i3jwJ0GJ0Ae/g6XdHA6kCg/Sk2DysG6WjCbDdEpof47tFwssiFBPSSW1ToTi7HPekfDkBmi2LxgCE15sCk2DoU9leFDY+K11K++PPykNHr+1Di1uOkwvR0Jof/3vFqNNPfUsw3TSCfQVCvksaQTsUhvKts/cJrtZWXib7vrv+I+rpfdg/vfjnTHFp0+NglE5DtdQhQeRzT13s6FPhIzmxaNIqKfXM7UC3M65Np3a8ZNTnRxNUKZzdxKcPYLDvbSYSurdMQQX+bc3EWqXAiUxJwrWtlpPrnjem2AIz1OjSngEZvukSRytxtR/N1tFb5pKwnGsW2xQexOhuD3RTzSPN1u1Z/N9cP6752LWbR9TfNodx9JO5/kw678jrKSShveKspRK8lYRirPAeXeA9YTRTw6vwErQk7OhrseKFJWYiFAfIa9DoI93iFAbbaitCtQ5zruzRhlx8tNDaTFlxOWUXICl+nlLphOB/hxjuZ7JK8Ftk596JzjvDjOdnPz0Dmw0jpwNdaUXz8BKPBQRHuAjNlTvmsX7RChuS8dPzMFNQ5ybbefkJweHu1JJAAMjekd5zaikFm1oywoUTlbsSPmJRhlx8pOj9bqSE6FsTiUlRgfyxAHeIUJbBHftVO9OH2XEyU8ODidhYyoJx7F+ncPaiQjFrTrvpEMT744fQc9FPzk4nIQl/92S6ZTQ/hyQ2MHGonqPEKFWbKg1BaqHShHzoBHunPzk4HCeF09hJQsPDCJUwMc7Rfh7epNQWzBvQA8ePavV6lzXb56jvXJqVAlaXPZCjtbRYiqJAj07KinaliahnoIlEWregJKA5PFwR8tPB8P5756LA80fZ0mdhxX/nb6BzFyrET+xIMhP6B1F9VbALY3adJD1dN2EHJz/7ik4STZyatQ1BaE2ppIAAKOTYoAXYVaE4hY6fjrkHZ1oOjn5ycHB8lRSbJivQBJoac+eKEJNbailJBLmWb3pOPnJweHeVJLEXFF9ctdwr6lnMgvunKnenWs9OfnJwcG2VBID9GxSQihmuZ7JC0Qozhi16cKp3h0GJz85OFiYStr25yWRgOjYwWI9k4dCt6HNFCjpmNniOPnJweE9tDqVhLBez+SJIpQO3qzlEuudd1M4+cnB4UrsTSUFSASBvhbrmTxdhOLGzDsg8bY6766oWOKin17AsOORw45Hsn+fHBS2N2SSmKtnGpUUbWOTUA8SociGGhUoqW9L5t11xZ4MOPnpuTjQ3nGm041evMzCgE76s3Hhfnw4MMcLR3bixsLPNjWcd1HeiZOfXkbbZSMnPFk+oBMYRCiGAdSfyftEKK/RALZFfnpe4p6Dg8MuJIRWpuNZysLLGp+i/qSeRY+H9IjILa0rLy+rb5Cb7rxXJJ5eAr1gjwN3xHQdpFvkJ+e/c3C42Iu3KwsvodUzAQAeGpM4YUC82Z0zrKcHiVC8DdN1uC30ycHB4XpaN6Czc2RTd/ruXeJMd/tnZjPf37PAdmx6p1UvdKnpZI/8HBhc17oX4hh2tsrP0YfjJbSuDwiXO3I9Ik09Y42kucNu+qxCz3t6ci8Bn6BW3riVb2o9EwKYeRjfoAjAelqXPOKEp51nGcPyy1ppeTk4PDSVFCARYHwfPQkuZpcjL96btCfC2mhWllhP9shPBIEBoUgEfyt8m05FVb0aPQhTFVQIYx1+PAOD685Xe0/nWg42Q9ebspZSSbUy8MT4br/9m371VuXiqcnWrWdOrZ4hQhukpewXoa0woO0Xhv8u02DREaG0FWbsqZ4EVfXFAIDoEHFueYO9b2Ejg0PqbxXVVIocb505OOhU4UEiXT0j4S4xMZ0KPU8k9pXVK49fKwIAaHT6vLK6w+ezF06DZrR7lzi6F+/RtKn+0y2wKvleVFppPZ/2x/F0eHctqdHgoo7hvmGqghatJ4EBsY9I7CMK8xfasuCGAbgOnf6Pg6MF6K66zCR3pCdBn86hEkKbV17/2KS+AICTGSXIelpx3nNq9d5tQN2Qdmdb8TxhsFN04dloQ82T1DEY6nx7Biqg+AAAIDA0LDCEsYQyFr/gELRxx3APHs7RinQQl0FyFy1m4UP8RRJCey6zdOHUZJIEl7MrJIS2uk5ZL9dQkdCVUxJseS/21zPhnpU4cqP8pJzrhI4xJja0yvopQw+0RMsiFACgUiqNuy2pbPEr+PN4BvW4xT1zcDipIFRGi4camroBmVJbXqOQENqrtyugJQXgZDqMZVnH40QozmbryTb52RwyOiLE4nOGZc8x6L8DAMLDgqjBCtalIkPh0uStta+AS/FzsCELLxbCeGh1verRidBtP3atEACg1uqLq2USQnuruOZxgzvvTSKU5ynakw0g60Y/IdERIUh+FpVWUfaUbO6/55TURHUwGkQtIeLplGbT8QyFeyW7vJ9hOoSi0korlhq9BQmABspbUFELPBTOJffcVBJCQmjlKvDI2J7fH8m6kFUmIbSl1fJF9/X54kDGibTihdOS1+9OvV1Sd+pqDj0Y6ungrLWerKpeolm3aMZTV26VowdFpVVmg8QoyUPHFkd+yoge1kME9PfqEBaM3oPz4jncJUXVQEACcD2vWkJo03OrFhjc9kvZ5RJCWyaVy5QaAMDZG81SSTZ68WwWoZ6XhWcbU4Z3px4X0yydTm+0b2Gh0H+njOjV/PqyOmNlqO0hAoYNRbvWaJk/NY9OJXF4dCqpTwJMu6fnVi2clqzW6nNL6ySENu1OFQqAns4okRDaOrmmTCq3N5XEZqwbUE5+mvrvZoiiWTrKhu79D6Z3ckpqCFoKvkpaFxkIu3PzCdzqWxjPvKkNpQvP/Sev01+uJWD6nhOhHC5OJfn68CWE9tqdyoVToemsqFUAAP5LgykjtVZfVAUDoFmFNfMm9wcAHL9W5E0ilOeVcU/Hjli34r9TZyoqIoQyncWlVVERISg6SZF+pwKZTvqrzL1FDONJKsyKbCjdWCNgjLVZPT8Hh+uQEFq5Ekwfmrj7VE6pFLaqO5kOxWaNDMy/N+mrg9dPpMFaep2evFkoNQxPUlfXq3YdTUdmdOWUhLX7crxMgbrZerYx+tkW65lfVme7gmNEPM3qUALHM3IrKquMwhMAEBocEB8mKeFFW94rE7oOpUcJTG10WZ26W2yzKhMODichIbQYAHpCpCeBWgM1439pRRJCW1hRP29yP0PEs1RCwGKmB8cnAQAu36pYOC154aSewb7C2aPuqq1tQItHi1CzBtQbBrWgwTz2judBmNpQhv9uaUSBqTzk83mRgULKiQ8NDoA/OztPMGnOOjeGWKGNxmgKlxuSxOEaqvAgEoABibBW5PR1qDpLq+WPTeyrJ8G1nCoJob1ZJEWppDOGZ2saVMWVsu17LwEMBAT4BgT4fn6i/PMTxhysh8LzMvmJoAcYA0PDaJ/J/KfT6cmqethRjTTkYeiD1o161mCWcvJargQ2vo3hfXJKakRCAVoTEuRPTXp6q6gW8HxtjLFSR8yIEoSHGeVnh7BgenzA7M45OJyBhNBevAnz7NIG8MyUvp/vS79ZVCMhtBdulj01NXnDntTcMphKyiqsWTK1v1anxw1SwpLqZOAR7UW8LQtv6r/TxvNYvDfk5kPrmVNSEx4axCgzInCMsL/nNDKVyIwGB/kbrCewIQDK9OsZW9J1aHlFNXqQkVsRESCkggOeHb3m8Ciq8CCFWnv/6J6wvOQWHLJ5PrMMAKBU68prUDwUag69nswuqtnx11UegS2cluwd2tNUgbr/0nNg7Wd0ZChlOq3XolPwcBzDMA0u4uvgYEpU7l4vU1qZ8oSwnJvXk6SAh/uIxZjRnhq3xDBgKQAKK58wjNSTGAZfjhkkq54EOPwTvhDDQHhYMDKd5zNLkjoG55TUIO0ZGmzsaGc5usrB4RROGkqUrt6uJABoUGrm39v7q4M3ThlWSuvBExN6YwDwCHzh1GQbtacHiVCvamdn7GaETjgJB0RSQyHpI4UYUMFE6gaiJUS55RUylVaN3ykEwJ9Qowipj4/RH6cgm79SWq8UC42dt3V6GAVX59YJhXzKBE/q3yGzQAoEJv67QeTmFdjRmL1Pp2CkcFF8wDg1oJ1auTDnDnACMQmdAPtoJx/WxR9TQmjzyuqWTuu/8Y9rD47u/tOxbGQ6iyoblkztv+nPa8evFZVXVKOce0CAr0fn3E3B2Sw/W01CfEyLw9URJAB3DJHNnJKa0JBA+lMSIU+ghxVtdTqB3mCrFApmATxUlwazhWFApdZQ1lMo4PuIjEFJlUrTtL05A9eKEAG1NwLHGfEBDg5XUoUH6UmQUwq1y39pxYYx77VPTu6vJ8H1/GoxoR2VFP2YYWi8o2BVOp7VCrRt6SPmLYEhQhlP4xgGK94btaIeI3BSJxHygEqhxn2QS246KBNRXdckPOG8MUI+eoABIODjao2e/lp6XT2iXqYk4a0Msx4TMMWwW+qtoBYwK289hYH9Bti45fkrF518LBz2ccaYhZchKXqzqAal5p+emkwFwFqnPU29eNrl637hgHvN5JpNqZj4aEa/D+oxVZFOPU01TIIDLpujx4w2UaBX1OmMzrupCKVbT6GAT7eeYh8R+obVag31jqYxynRFGAYAn4fzeXhcdLjpEouWqPDiWq2AhqH3opDKJrVFzHJwtBoJjHWqHp/UT0+COwYpiipA547qqtLovPvEskWBOrx6ycbBPFTDpIhwM87+zbwW7pnSaumg/kbdRJnO7d/t6Nmr1+B+xvFqQj6u0uh1elJIAEsKEQOYWqsT8JpkbPNnIaWVNR3DxGhNh1AYbSgoLkexBVp6CsUu7CO5f5+/9/1ifZuJU+ZcupyKHu/57bthQweix+9/sP7jTzcDj6KiOMv6Bg0Nsk6JcNwhAGDRgsffe/t19Li8vLLv3aM0Gs+YCi0+PvbimcPWt3lm6Uu/796LCpZTLx4PCzMOaSstK08eOEataYo+tdic6ertCqogtE4OFkxM8vUxXhFtjHuaSyWV+QZ1MAghNysGthjQNtLi6CO6DaWgjA+OYdDFpqXb1Wr406mpqWlUrPCZoEA4w3VtTQ1VplRbW0uznnDllGnT7h44UC6XU/X8coVSJCCUao2Q4FvS+mmK0N4+MN9VWFwRExXGePbs9WLKdAIAIsKMsdrYqPCC4vIQf6FGq+fzWl+RptPppDVNjfCEAoFY7KNSqeQKY2tnmFjTaunW53ZO7qnT56ZMvvfVl5+bPnXiqLHTgOdA/7DU10pfSX19h//e1SepZ3W1dNeeff379enfL6k4L6Nj134ymXEDNqPX6+kfSsDnSyRitUZDP3i1BnpUvr6SOzcvAwCyb+Uc/+/U6FHDu3TuVJSX3qPPsIoKaxMuUEgI7RVDGZO0HiyYlKTXkzwc3/bnJeeP13SzL88KA+rA6iWD/24GuuFCY9XRhEWoEigspMl/pxelJ/XoKxbyZEqNEhNRtqN/736w3InmLFPV8qveXjNrzmzGWyMbKhYSMqWmXNjF4qFjwKwdLK1oEp6U6UQvwAzb4hhWUS3tYE5B28jV1PTEHkZFCQB4/NG5H32wZuu2HW+/+yFjy3feeg0A8L/V73/+xTcAgBUvv7l39w+DBw3o2iUh+5bHZFfpH5b6WhkrAQB3J/frk9QzPePGPeNnoDUhIcGZaWeOH/5zwJBxgPUUFBTRP9TUKfd+9cWGP/f+vXjZS4wtL509AsufaTeG7t26/vfvvtSLx6PiYY2nTSJUU//Q6B4/Hbt59GpRaTnsyYSsp0NMp2URinCbFMW9rXrJHFS/D2oNGtJDjSXn8WAKJyOnguqWhBALeQ0KNbKeZhHyCSGfIPW6Pn37XLmePmvO7KzMTOOb6nXUQu9uZ4k0eSj6DRQWV1DCs7TCKIFp1tOQ8jfUiGIAREfBgXQdAn1EmAbGB5zMogWP6fV6ZD0RU2c+AgA4sPdn4HWsfHk5AODBRxdRa6qqYLg5Pt7bZj8NDg4iSZKuTG9kZktravl8HjV8zhau3K4QE9qJA+LmTzEGQLwentfLT0a/D/poSOMGJLieWxkZKOxgGM+DEApQ+AZWs5vuE0YedXDHcp2x5cFzL71EEMTyJcv2791/u+AOIIGCVr1kOxqdHnW6MxGexvgC42h4BiHMI7C8sla2ULELHMcbGmSMlWqNJjDAARPT37qd36VzHGAND8x9ErQPps96LDS0WWMaGLQxBEAJAtdqbUoESQjt7ZLaRff1g3LEgGPddnaKUPcbUIdAyU8qItJiYYFSqULpIx+RwMcgMVF9UWiQP5R4BQ0EZn4fVEaezhtvfdCYY4HPkgDUak02a+n7TZOH9hZXanT6ZsIzPMj669Lza3rFBdo792crQHn/4ydOM9aXFJfGx8fyeISNV5ol8vKLY2MihI3xEBbyxZaP4b/bdgDv4vSZ84w1AoEAJZRs/06r8KCVkxPgL9/O4UaOg7pgsfZiQNsuPxvTR/Y1+zCLYTxPs2L3BoVaZSgCbREqQ+0QwmlaWK9qIV8xMTm2sKI+LEB0taH1YVBbSOoNZxm5ejWNsb6uvt4gVXhtNKB/f/GcjVvOdnkd6LXL/0VGQLHz4ceb1n64AXg750//AwB4eskK+16GwevQecONWhKhwPVq1BsUaOuKHzEMOu/NR0MyRwrZFQByCGny0F4+FQXl0CSxDX9/2Gq6to4ZK0BlPa4/Vy4jODiooUFWWFQcEx214oUldfX1W7Z+DbyX40f+jI6KPHf+0q49++x6Ya3btKfbsvM8Tx+7aVrZbhazI3xQvw+tVhtqyMLTW35Y8t9dQLqCWcbEElD001ciYaznGcpX0WzgXkl1tXToyEkoBFxWeGPNqlcOHznuQVUHdnEr80KAv//1G1lTZjxs72s/d0eDJQsi1EVmlF0KtBXpozQFlGwwasMjTCsoGf0+SADKK5vCi0qVKsRfWFWnUqi0EhGfoaFs99/bCdfS4BRMPXt2Y6z394PKVKfzjPLytqDX6+8ZP+PooT1rVr3y0GNPAe8C3R4AAEePn5zz0ALASnLMj+y0jhM9etwLWocYJBtGLwCy1O+Dbj3DQwPjoo03LnlDfb1C48UayiEolbCufvzY0Yz1kZHwNLYxAMpCfHx8IjrAKjE6xcWwY1bHjiyqFnAIEokYWc+1H25grfW0RIMUNiFt73WgbZn2PV0Riub4NWtDEfTUdoewQByDfTZxDAsLgDl4haxertLSp2z14qBeq9Hr9RJJ05godJaEQiGyrV7GrcwLaVdOoNoDCmQ6M7OygRchFApzs6/AdotT53748SbAbnJMpkuyAdKrDKgD5SdFmiJUozNvQ89eL2JUpFOmMS4aSoywANj1o6GuTqk22FAMWK+fb7fsP3CIIIiH5s6i1vzw7eeGkfJzgdfxf2s/hZ0NvlhPrSEIAjUNeHP1+8CLyEg9CQAYe+/9Fy9dBZ5Jg5tEKO4F8pMiTW7Ghp69XtQxTGJmPE/jkJ74GOiBIh1aW1OLlCyHWeY/BQfnbPjkvV92bl+04PGzJw+OHzc6Mys747px/JU3sXHztqLikokTxlw+9+9TCx9/842U0oLrGIZt2rK9oLBNBXOsYkBy3wB/WIJy5OCuiuIsxuLn5+stItRbDKgz5KclG0q3nuGhgRFhQcZoqPEfY2wZ2dCoYOicVldLDTFTzn83T4eY7idOnr1n9PD33n69c0LHz7/4ZsQ9U4CX0nfA6A2bvoyNjX53zevPLlmoVCqnz3ps9dsfAC9i8VNeMuCqoQUR6hQvHtux6W3gsWM3LdFbXMkncB6fTw96oimGrJBXWCYSEAUVMo1Of7nQKTnldj79g3V+/WCpjVvOfnmTp39Y59FOPiYAwDQdb7meCYF5eRmTo0BjIgHQIBsaHiAk1YoWb0AxYX6FFWysYOfg4HAEjq9n4nmf/KQP6VEbamtyFUrTiTQsEeovKpF6QLdHDo52To6tIzspOAXqmiE9ghAAnOIHcXBwuA8PV6Auk59eGfFxBu3kY7arD9tOPmZrRaiXljFxcHBwOB/SUw2oU6uXODg4OFxfVO9OBcpa/52Dg8NTyHFrUb2LDCgnPzk4OLxPhLpNgXLyk4ODwx0ilPQwA8rJTw4ODq8Uoe5RoJz85ODg8AIR6nQDyslPDg4ObxWhblCgnPzk4ODwDhHqXAPKyU8ODg5WilDMI5uJuEZ+OqmjF4dTeW71Fhu35L5fTyTG+WNM7ZlyzjHj4nleJj+5S4uDg50UGmSN64fqO3V0PDcWnoODo31CsteAmpWfXPqIg4PD9akk56XjOQXKwcHRbiE9xoBy8pODg8PL2os4xYBy1UscHByswklevIsUKCc/OTg4vE+E8rxGfoaHhY0YNjw2KqaqqnLPnt03b97U6eCMcqbExMT4+fnV1dUXFRVa2aFIJIqKirL+pkVFRSqVirGye/fuhYWF9fUtTPC5YcPG7OybGzdutLRBQkKClZfn5OTQN6P+7NSpk+n8zTqdTqFQlJeXm90Vj8fr2bPnjBkzxWLx5cuXjx07WlZWZvuRAABqa2urqqrMPhUWFjZmzNgBAwbU1tYdOLA/Le2aRqMxu2VIWLivr59SoSgrLbb+dujsSaXSVavetL4ZQRDdunW///77/fz8UlNTjxw5XFpaan37xMS7bty4bmWb+Ph4giCsvy/1dQAAAgMDQ0NDMQyrqqqqrq62/sLevXsvWvTU8uXPWtrAz88vLIw52Zder1epVBUVFVpts+m4O3bsiFudTpF+nIyvu6CgwOw3xfgxkCSpVmtqaqQymYy+PioqSiQSaTSagoICs28dHhYmFIp0Ol1xSctftzPrmdpUEOr4eeFdP/GRRCzZ/etv6DdN6puiwps2bWSYp9jY2EOHjtDXjBs3prDQvBnt3z95584frb/1/ffPvH49g75m+PAR27ZtX7Jk8b//NnsjUzIzb54/f/7xxx+1soGVl3frlogu+IyMG9SfcDrStAy+YTJns+zd+2dKykv0NStWvLRo0VOMzbRa7eDBAxsaGuhvYYXt27etW/cBYyWGYWlpGTwe8yb9+++/vf76a/Q1ISGhJ06c5PGarNIbK5bm3rH4szmffjsz82ZZWdmoUSOsHNWiRU+tWPGS6b1kxIhhlgzZb7/t6tWrF3UyzXLmzLmgoCArG+j1+h49ugEA/P39T506Q/86SJIcMCBZJoMn1ixjx47btGmzlQOYM2fumjUWr9n8/PyJEyfo9Ub9dfHiZV9fXyuHavaN0A8vNTV17tzZlp5t8XIIDQ09efI0ACA5uT/986I6UB6Pd3DfAQDAfdOnmkoQl08cj7FFgbpefmIY9ueu3QCAI//+u2HzZ5nX0gAA06fPWLv2g6VLn1UqVV9++QXaEsdxZD2nTZty8+bNYcOGb9/+1eHD//bq1YNx30bk5t556aUV1J9DhgyZNeuBw4cP//33X9RKhoYdMODubdu2O/YD0o/Bdh54YFZDQ5ME9vX1mzJlyrx5T06dOq20tPSjjz5E6x9++JFFi57S63VLliw5duwoEoxr134wdOiwixcv9+jRTW+AfgydOnVcuvTZmzdvfvHFVmrl7du3TI/hxo0sAMCJEydefXVlZWUlsg4ff/zJrFkPCAQCyo5jGHby5CkMw95f9UpG2tX4Tp3f/WjTOx9tevLBaRq1GrSWiRMnrVjxkl6ve+GF5w8ePAgACA4Ofuedd8eMGXv69NmePbubOiiff761V69eLe551ar/CQRC6s8PP/zI7NeEYdj58xcNG6zbtu1LAMDTTz/zwgsvXrhwoUeP7qBtbNmy+Y8/9lB/ikSiwYOHrFjxUlxc3J9/7psy5T76xm+9tbpFf4jivffeBwCoVKo+ffpY2Yz6vHw+Pzo6esyYMT169Ny1a/eKFS/u378PmoLKyh07vn388SfOnDmblMQ8q19s/hwA8NmWzc6znq4RoQ5WoK6Xn3d1Tdy88bMj//773gf/Rx+JxOfz09IySJLs3v0utObbb78bNGjQkCGDpFIpWtOrV+/ffvv9559/atETBAA8+OBDq1e/tWXL5vXrPzW7wdtvvzN79hz02IEK1LoasqRABw4cUFdXx9g4MDDw7NnzlD6iNja1Jl98sW3kyJErV75Mv0rpwvz06VPz5z9p5cCEQmFqalpubu7EiRMYT924kYVhGHXAL7zw4tNPP/PEE/NImTFu4OMj/vKHXbezs1atfM7szrMLq8+cPWddgV65kurj49O/f1+5XE5f/3//t3bGjJkff/wR/QYgkUgOHTocHByC/rR+zm38mu67b/LHH3/yyScfb90KjQXi+edfeOaZxf/3f+9/883XbVGg77779nfffWfpx0B9aqRAhw8fim5gtpCRcYMgiIcemvvjjz+/9967O3Z8a+Pn7dmz1++/7wIAoPsuWnnhwiU/P781a97aufMHtCYmodOoESPffP0NabX0gYfnAifjbBGKe3r0M7l/fwDA8RP/MdZrNBqSJOnRwIEDBwIAKOsJJ45Ph3J17twHqTVRBky9zhbJzLw5e/acO3dynn/e/DXPBmpqapASp9Yg79JUi6WkQH3x4ot2iF906qidjxgx0uCt/2q6ZUVFBf1PFEA4efIUtUahgBd/567GO1/r8PHxAQAwrKfhPgcVw7x586g1kZGRly5dCQ4Oee+9dxnHhsBxHH06uw7ghRdeBABQDhBiw4b1AIDJk6cAJ6DT6W7ehNYtJiamdXuIjIwkCOLQoUOpqanI3Nv+2oyM9F27fgcAbN7c1NNg3LgxAIA331xFrSEI4s3X3wAAPPrkE8Dz0/EeX0h//sIF2IdimZmge/fud9HvkxiGMeLcKGZEtyn//nvs33+PRUdH23sYer1u+vSpkyZNJEkHz5vqQEzzCQqFAgDQpUsXxvra2tpu3RKtRxgZoFMXEBCA/kQBgWXLlptuOXLkcPr3guO4qR93Oxu6/4T9dzIK5LQOHjyYsV4ma+jWLXHo0CHNhVt6//59TdUWwtfXF306uw5g/Pix3bolUloMgX5ajBydA0EZntra2ta9HAWgnn12qV6vz8vLE4vFUVF2XAurDJ4cunciamtrv//+e6SF0Zqtn0Hzuubdd5QqJfB8cE8fu3kr57Zepw8JCTny1z87tn8zZMhQs5sFBwcDAE6dapI59OBdaGhoGw+jR4/uWVnwmmczx4+fMOjudGrNggXzAQD79h24dOnK22+/YyX7ZC9arVan0wmFwszMm0eOHJ0wgenII8RiMQAA6SY619OgAoqPbyH7b4XHHnsEAPDNNzsuX776wQfrBAKBpS0LCwtnzbrfVKs6g3/+OWxI3Nmh7GznzTdXIeep1Qa6c+emW+kzz0DP4MMPjeFyW9AYsvaMEoV33lmj0Wh8fX2HDBnavXv3Tp063szONnUZ3T2yk/SMdnbOYMKUSZvXb0xMTIyOjvr6629QGvTWrVvTpjU5Sqjyo7i4iPFadK9Gl7G9wS/XYDbp2eJxnjlzjq6FcRzDcfizlsvlDzxwP7X+8uVLo0ePPHbsP4lEMnv2HBTDVavVy5Yt+e8/+37i5oJi3bds+fyee8ZER0dv2PAZ+l5KSkomTBhHBQ3QmZdKmTnx6ioYs/Nv1LOtIDMzc9iwoadOnRaLxdOmTZ82bTr6aCtXvvzXXzD/azt1dXUO+WHs2rUbw7Dc3Dttz5y8+uprK1e+Sv2JYRhltvr1YyZ/UDbclNWrV/30U1Odyfz5C+jZoTt3YDqhf//kNh4qAKBPn97Xr2du3/4VCqktWb4MeAu4F7QOIUly8fJlYydOeOzJebm5uTqdDsfxxMTEzMyb8+YZEx0EwTMb7EMelmnhJHuQy2WmS4uvoqwnjuM8Hg/HiatXryQl9erfvy9jy9LS0m7dErt1S9y4cQNSYQKB4IsvtmVm3rReQmgLixc/Y/CXB6elpWk0GhzHo6OjMzJuvP32u9ThUd9C8+M3rGnb91JVVYk+2rp1H1Af7ZNPPs3MvNliIafD2bnzxx49eqrV6okT73XgbjEM4/F4BEFUVFTMmnV/t26JKCxDRyaTNZiDUXyybBm0a/S7C8p0LVq0qI0Hqdfrv/pqO47jGIYtfW65i8NcThWh3qBAKYpLiqmc7zvvvPfAAw+88sqre/bsroHA3FFIiDHNSoGq5JRK9oZj+vfv14pXDR06mMrCd+3ade/e/X379hOLxWrLhUGbNn22aRPUieHh4fv3/+Xn53f+/MUBA2COro1UV1fPnj0LPV6+/LklS5bOnj1706aNpaWl6Gr39fVjvMTPH2pPueV6SbvYvn3b9u3bUB3C33//ExgYeOHCJdN7iZPAMOzgwUNxcXFVVVXDhjXFXtvC+++/R2XhCYJIS8sICwvr0qVzRkZTfIbi3nvHt5iF9/X1FYslhnjOfmoljwdDOitWpHz5JSzDagvr1n0wf/4CjUaTmZUJvAjc0+VnUu+k0SNHma5/443XUCr59df/Z3De4WiHvn2Z10xcXJxBqpgfReMdZGdno4Dg2bPn6QUGcXFxo0aNkkjgZUOnvLz87ruT9Xqdr69vq0VocvKA6dOh18xgw4b1998/EwDw66+/U6me+Ph4xmYoBV+Qn9e6d4+IiBg5cmRICDO0XVNTM3jwQK1WKxaLHRjwtYJQKLxyJTUuLu7cubOOsp4MdDrd3XdDR3vt2nWt9rhR+aehzjeBWmJjY9HK7t172LITvuGUWhpp5kacJ0I9Pgv/yboP//fa62afOnv2LACAygsbRrYxu2F36wZLmq3oMu/gwoULaIjIqVNnqJXTp8/YuvXLqVOnmX1JURG85bTa1d2x47u1a9eZfQodCd2Um45N7Nsf1pwpbAhWmGX48BFffLHtscceM/tslkEEoTonpyKRSK5evSYSiTZu3PDEE487741kMtmKFbBqaufOH1tRhAcTCRNgYAFFPOgL2u369Rts2cnq1W8Z6jFaqID2JnBPb5xcVgbHdy+av9D0qWefhTU0hw//g/7Mzc1lXLeBgYEAgGvXroF2ANJ9AQEBKFdAlSiuXv2WqZXEcRypj1arCZTr37q1WRUkYsGChfRYG7reIiMjqQ3g8WDGPFLrQPX/zzyz2NRK4jjesyccGGM60MCxoJFIGIYtX74MxUacyv79+9Aw/x07mAX2LTJqFPThzpwxk2tCw4ri4uJEIpH1nXTo0GHWrAdgTeFzZmrXPESEYu1OgS5cDIstHpwzZ9uWL7o0FmGEhIRu3/7VnDlzVSrVL7/8glbOnw9rpy9cuIT+5PF4R47AWsWlSxdTe9u588edO3+MiIgA3sjMmdCnfvnllchiKpVKlGrPyLjx3HPPU976jBkzU1PhEIMXX7Sj2gadOj8/YzQTDaMeNWr00aPHBgy4G6308/P75JNPU1JehpWAa6BaAQC89hrMJv93/F/Kp960HaaGP3gbllubpWsMLEqzgkajQUXdV66kpqS8TH20SZPuu3IFFki9954xi2ULEokEfTrbXwIAePfd9wiCOH36lFQqvbs5yII7HDSCs3//ZIbH3bdvv7stgKog1qx5xzDEYI3Z3Z46dRLdjegrqT0MGTL04Ycf+fHHn1CRHFKsHgvp/iSSizvXyeXyuY88/PMPOzt16rh102Z6MxGFQjFokPHSRWHQLVs2L168JDPzpkqlEgrhcOa33lpNH3yCQkjoKe/jxo0bJSXFkZFR+/cfQLngp55a+MEH66ZNm7548ZLFi5fQN1679v8OHGjKJ7QIOnV0gd+3b9LVq9ciI6O+/944jI+CXmpTU1Pz6quvrF279uuf92rUar6hYPPvvbsL86HH0Gpee+1VuVz+6KOPLViwEGleii1bNluqmTcLQRCtiC3efz/Mmw0dOmzo0GGMp7KysqZPnwocTUNDw8aNG559dvmuXbupEcxwyPlnmyy9ZObMGTk5tzt06GCpM5MhnfD60aPHn3lm8aeffkKt/O475ncKAJg9e1ZaGrz1shN75ux0yVh4t/vvdMLDwyeMHd+zW3ehUHj27JmdO38w27gsODh4yZJl/fv3O3369BdfbGX4cXfdBX92OTk5pq5rYGBghw4dqqqqrCc0fX19o6Oji4qKUCujtoyFRwdjvT4fw7DExET6ZomJiRiG3bp1y2w3Px6P17lzZzgAgbYBn89PSurz0EMPd+rUsbKy8ttvvz1//pzZBisodBgXFyeTyRhdrNDRmr5veHj45MlTRo8eLZH4Xr58aceOb822vxo1MGnqzNlJ/Qbcvpm565cfag1VE1bY8dtfLXZjQp+3V69eDz/8SOfOnaurpT/88N3p06ethLw7d+7C4xGMc04QBBqsZfa7sPQ1ofVmUalUKKDUirHw6HdYXl5OH5Rs+r6FhYUymaxr167W04C5ubmoG4jpF2rpy2V8LpIkVSpVZWWVlRZT1E5IkpRr3ZliMmtATUbHY+3RgHrctMYtGtB2xcBe0Kzbjo0G1ONo0YB6OjEun9bYqQYU9ybrycHBwdHmVBLZjpJIHBwcHA7F+QqUk59tp6CgwNIcGxy2nD3TzgZegEKhsDQHBoerRCjZTodyehbjx4919yF4MN569k6fPuWtH80raY0C5eQnBweH5+JAEcrFQDk4ODhaid0ufGURq5Pv7i2S4Gg161c3G+jSIrNftlgczsHRuqL65lPO2TTTXKsUKHubZ3JwcHC4Dtyb5CcHBweHKyOhrVKg7J02jYODg8NRONSFb5KfnAvPwcHhpdBEqJMUKA3Of+fg4PAmL94u8NZEPzkXnoODw3sxN9uHE7Lwbpef/XrBCTk4ODg4nCNCSW8upL+SfsPdh8DBwdF+scmActVLHBwc7bWeiXSCAmVBIj7IMI01BwcHhzPB2mRAzcvP4+4vnpe2ds5bDg4ODptFqDMUKAcHB0e7B/dQ+cn57xwcHMBxtE6E/j/QFPK4Mz5w9wAAAABJRU5ErkJggg=="}], 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"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "Both targets are assigned and phase order is set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-cd97641a7291", "created_at_unix_s": 1788842461.298273, "source": {"kind": "ark_eval", "case_id": "CrossfireDeconfliction3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "CrossfireDeconfliction3D", "requested_task_name": "CrossfireDeconfliction3D", "group_seed": 10, "requested_group_seed": 10, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [10], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\nCoordinate two labeled shooters in a neutral training range. Use all three role-watermarked camera views: CAM A shows the S0 sector, CAM B shows the S1 sector, and CAM C shows their shared crossing/cover geometry. Image order may change with the seed, so trust each watermark. Assign one different target to each shooter, choose who fires first, then execute exactly once.\n\nCall AssignTarget(shooterId,targetId) for S0 and S1, SetFireOrder(firstShooterId), then ExecuteVolley(). Assignments and fire order may be overwritten before execution. Target IDs are T0..T3 and shooter IDs are 0..1.\n\nOn execution, phase 1 fires while the teammate stands HOLD. The first shooter then DUCKS, and phase 2 fires while that teammate remains below the protected lane. Every shot uses the first physical hit: target is safe; teammate protected volume, cover, wall, box, stairs, planks, clutter, or equipment is unsafe. No aiming rays are shown before execution. Afterwards, wide phase-numbered trails, impacts, and the first violation remain visible. A safe complete volley gives +1; any complete unsafe plan gives -1. Invalid or incomplete calls give zero reward without changing the layout. The deterministic seed changes target IDs, arena notches, shooter relationship, and camera delivery order; there is no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- AssignTarget\n kind: method\n docstring: Assign one target to a shooter. shooterId is 0..1 and targetId is 0..3. The two final targets must differ. Valid assignments can be overwritten before ExecuteVolley; invalid values leave the plan unchanged.\n signature: AssignTarget(int shooterId, int targetId) : bool\n return_type: bool\n access: call\n arguments:\r\n - shooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 0\n - targetId (int) [required]: Target ID 0 through 3.\n constraints: range=[0, 3]\n example: 2\n example: {\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":2}}\n- ExecuteVolley\n kind: method\n docstring: Execute the complete two-shot plan once. Both shooters need different assigned targets and a fire order. Safe completion returns +1; any complete unsafe plan returns -1 and preserves shot/violation evidence.\n signature: ExecuteVolley() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExecuteVolley\",\"arguments\":{}}\n- SetFireOrder\n kind: method\n docstring: Choose the shooter for phase 1. The teammate stays standing during phase 1; phase 1 then ducks before phase 2. Valid order can be overwritten before ExecuteVolley.\n signature: SetFireOrder(int firstShooterId) : bool\n return_type: bool\n access: call\n arguments:\r\n - firstShooterId (int) [required]: Shooter ID 0 or 1.\n constraints: range=[0, 1]\n example: 1\n example: {\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Three current decision frames are available. Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "assistant": "S1 is aligned behind S0 in CAM B, so S0 must fire first. CAM A shows S0’s clear available lane to T1; reserve the distinct left target T2 for S1 after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":0,\"targetId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS0 ASSIGNED T1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "assistant": "Assign S1 the distinct T2 lane; it will be taken after S0 ducks.\n{\"name\":\"AssignTarget\",\"arguments\":{\"shooterId\":1,\"targetId\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nS1 ASSIGNED T2", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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q0Fu/3rHttScqDtqWqBi0GgNn+5bIVLjSlxeodrTCAuSEAYN98Ddith0zG41oxkCUrxfMXPjTwWHXL/KJQOH3R1ODMiomfcT48v1B0ya8u5JIgTa0pytrmx1uORJkEOz+H6Kzip+//4FdHN3lGqVIUiqhaDNNkqSEIikK/mjq2sqYgF0Iv3reyIUbDoYlpsx943O4Gbimva648PdXn+CnhsQmHE1MSzN5WITzp7jpM/FOu1wkciXs0tvgbMMYvRruNKEIjwQ+onTHL4Pn3pQ2HAvUL/DmYH37iSdk0sD/LnEXqYSqVPQRvemmmWNSE6NFbuhCjcZGhd5/82U0TRMEQRKoERUROBUC3YtBq/5g6oDojOx5b36pjIqRqUJThoy+5as/zCbj+vuuqz5x2Ml9zWbYo4eU+OCdTDDfwUad1uXGCqQEbuLgEzqYGQxZqJcboXcLHrzWW3/7hTsN2t34eKo0Mky1aWsZqq0JotG6KNERIUaTyWA0K2RS2CoPECqlzGA0KRWy0BB5t0+ZCDfIw/BoPFvy8ZWwBYxEocwaf9llT71JUpKr31+39q55taeOOn5hmS6RrjYpcF4HikCl8pTU2buFYL76TAbPulk6ISoNfvraaipB8ODxl9UTb3//xiNXgR43zI+PCc8fAyfmnDD3kuDID940awxBEjRNk7aBHhGkFaO9HGOHtnjrj8Vbf+w3ccb0596b8+onq+cOd3QwJYe+62hr7vzzovnWsARnbW3lzFyBttn7rRl49JsM+y6e3gl3UQwWes64tZOf/uT4yMMnmcEkMzFH07SEmaeTSimaBhKKJEhCo/XZl20XhHjCreQ9AC9ecoMrZkwGALy+zOEeMFz6jbOVHDni3m2lgCB4a4EQpX/+zJ1wbK44E5mWSVISbpJHEQqNVlt0DHQas9EAaJogSalSxU1ecbl9Lcxx7VrpcI9x72g6VwqChx4mUO81mpESe+PsMVJoSkIhl5nMZrlUYqZpRqYwcQyV2qvqb7BD3eORFz9y57DS3V+7PGYzpSvWKvpfMqd464+ik5K0tRP52T3bhqTdEZ3Rr/50EXvMmEWPwUL3r/gbuHrHuiXXXvXet7d+vWP1FSOEtyoioiiZjDaby3a6Tkm5w6QHXwAAtFw4B4KKHilQb8b1KoVcpXA6P9gLwcP5rvUspQrr99h/5r20cvrye9Jo29CYBsR6angjocw3VzxoFfHbenDdhz+9f+kAFK5SMlnW+EtNOp1PhvAAgKrjB1Gl563f7Pzilku4JVNx/QZdt2oDAOC7e6+x3cEy4vF4wQ0lk41Z8EjenBsBTX8+fwrowQINsDySNxo43y5vqLNUI7tPQabPajWCDxyKdhUmTVvV5s+iL7nhRzAA/tjWZGxpkCX2ISRSYALGlqpvP3jiW+bI15ctGqZoPkUmLd1z/lLTcS2Q7qDgXa6RFz4sCHXdmT0Q5dPrxt/02a9R6X3v3nJS01h3ds92ZURM5tgpyJVbVzxSc5JfFTDtmXfY/iYsenXbR7OGuGwp8um140Ev2VTuyJZVfjgZB891xvt1aQWZEZ18BM5D9WKHgp6WWfJJHSiaA/U5VEh4/NX3y5NtzVZoo6Hpz+9a/+EPliPHz42ccAV7TNWaF/U15dwDePOzX1Oj2gnFNaZ9kbTItObbepj3F87Apg4bO/XxV0Pjk9hrzu7ZvumpRbymdhMfWD547k2ivxErUKE6DRp184WzRb9+f+S7T0AQEqAC9YnyEAWZEVigPqOnODSQBdr1uMiDCf7o/cZ6GdL2PHrwHCjGD+BZ0R5nT/fzYK8/bfFs6V+uE2Kgd3gWCxTjOVijvZJHXvCLZ0EwqxYL1G62lAMvpdhTxq4+BGvUAQQBVzGA3sojHno2qFXr5RxoAE6DCgxoR0WborG+xsO7Y2l6QuderdD4NHl4dEdLPUGQxg51R6vP1rf4aQ7U5RD+pRXPPrXUm4btGEeedZ8u82zgRqDOhSj0rCvhepGVwuGnh69WJxzaXlshD49W110AALzzyltqrfg2auwzPPE87FaJ6fE84kY8K5SsaEgr+vbs2znVBm4E2sX5ejd8jQNSN3D8Is25/t4xA1Kc3PWZ9z9avngR9wLLkL5JNABnqi1h6Z0P3S+RwMYZgRyBAgAeW/rQqyve7OQTYXzFG14Fs85VG/QC9V2Zp1sBrxUsU49fIZ5A87MSYQ87AoauNAB/nTg3Lhe24ENt7tB/eY9RZhXo4sceAp2jawSK6fGeDdwhfGDTw0f37/yfw0H00ueWevcKrXjhCYIgtq//H/rR1imKsShsDIp+Qv1BO9MJJTCQtRSFyqk/CmvG5sS3h/Tv7tPBuMvD7iXBkGe9F2jBtIWBE4R2SdQZWKlouNmsJ13cnTixa+ZGc7NTa+odjhUUcn3xhbO8K/8ohHm/MQPjAACD+4hsg9yNKNssu6IrpQ5azMiZ7pw0vet4DQA1F+cloqtb1fCvAKPs2LyuO12MfzzbQyJQX6gwODQqD4vUtTXHZMPNbJ++fT73Jq3e4fY13Yz15fnX3Y8AACZAlRBHzjQrZSRc5c0cEBYC95BAGKw9evP7wD+rmukiuKeoCIWlcRHQp93OzmPiu++OyLbbGmZIhmUFcKu6wy647sV1Tj2JniDQ7ranXzT69suvCa/U6o2Ln13+/vMrUKbFf8Yc0jeJO+fIcvcjD3j/oFZj7CysdqQbHiID+SD0juhvQcf5YOs3TLfTEwQaYPhmrxFHcoyJt4wEvSA1TvybJjZc5eYjkISPO6LSNH97NGezn4FkTwruhycxaNWWn0kJ9zdCv0VppW3KgsmWAZqUDk6P7GB2f8P0doH2gGlQN3nn/952OYG49LknfJhr+vvUBdGiH7a+h72AokUAwO4T5eMGpYs+2u4T5QNSY0EAQEkok17HlQ5NA5IkSi84KaWgczNhrYherw+oFT5mkzE80jpCb25mf6PTla3oyksuyuauaKMBqK3zTbNOTBALNDHS3YAlYHnn/95mJtd8llR5ZNmjvh3df/PJGzQNPn33feFNWUli22p2Aofm9c/ULisdltILzZeMEuSprdIBANQEvHfQL9Xa2g4AmMr8Lkx1ll38ie3Z8/BGoCopEbxxovuoO/Rjc/swnwMY95AkmvuH/yMAwRZ1w83mbr2Ns32bEzwQkkIudae398HTpaEKyx/xeEV1eIhl4O/LnLWvGyoTzDCXe83UUQP4xrFKhwCgOiDtKZcQOqOzvxABCL5DGU5XNaVEB30IgunUEF4u9dlm0L5ix/5T7tgzP8s2h/jXiXJUvG0Ndmz/R5LEX8fPWXcs5+xabveDhdSEKE/O1C2Nvvt/yx3dVHyh1baE3GpPpmIG7D5RSxJgfG7CiYoz6MpBaZmg8/g0FFXJKbXO7No4zFVb95/NSw/EVtZ6oxkAQm8wyqS2P8GIfjG0g9+opjYQvwYwXS3QxEiVxkBrDPpAi0MjwlQjp80BAJyy3xa1D8duKIq0yZBNWDD/ZzGj9WaLW/2FyPTozGsXAgDGWwsGq5tEvg/S4yzBS6uan4hAFTMEAdo01pt8O2Hoh+lHlFpx4lDWnolxkeUXakFgkBpBmM00QRIms0jUDBz/RgnxkaermrrsPDH+xuOkaoiUVEkJ1p7z5neirqVzxGQPQf/CU/oCACbPnD0qP+vam2/kHUaRBPuPsI+iHKVcuhaYm0Ufu01rYUZuV2E1+hepooT/WtU69M92b0aa7L9AZt3n79Nmc22TNXMtBveX2LqfX1rfGRYseRL92/vbuo8eneGTx+TuKoxQMfXzXAhA4PCzp+JxBHq6rj0nOSCGVA0lcEOrmOwhrRfgRiuNpYfDQhQEAWbNv2XjF2t88hRO8tp/FJZPyrPc9P6rby1+7EGfB3i2mUKBFm2OcWLMAMpXW6Dc3ReaYOZJOeFnvO0t16rWrXh2xVL3uzHR9Htf/T4iL3P1uy+z1xVctmDkAEsXiIVzhgMAFr0GN173Kwmc3wLTe+dAT1Y2B4hDeSgiYhtKDrv9KXWhSOf869IhZVWN/qrysRYVctM4XFU6KfrJzbQs1NHpbPvQBgis0rU6g1IudTrJSmzdfwYJlGtPAMDsm27Oyxbv6lTRJm8UbLmq0+nPVrUc3vPxB0B6xbi+V15/C7p+3ynLhMA+qzqRUt33aYhKqdZo2R9hCj5BZGfs6lo8Zu+xBH0hvbqmAl0gJbKOlnqYV1EF9/buUmWIUi5FdYU0AGVVDl055SKRFhU1deIf11Pn67u3FPRCbeOaDz5El5OilM0aIztHKDYvTK/8+On3n3+Xve2vI2dSY0LgrGj/VN4jHylzlp+pqKqjafrRB5cMyLIUzC5Y8uTIAfFqE3XP7Px737LNmCOlivrUiVJ5XwaJCXYrTa+827bcdv3KL/hZMkyQE/QC7WhtQBfMRkvAxc2KwvWIVY0+L5zsAthiydw+0YmJYuKz/yTSAntyN5YYNyh99wm7DW+7nuZWzcVzrvqr6HUAQFWT1j4CBaLSWfzsEsY7n3fGOidL4VfsmSPbB2RZ5sdXv/syamd371tVrCjVJuquWYPve/tX5z5FSl3+0S/uPDXXnujH9Su/gH8svI6zp+ADgV5/+6PffCKycLu7gBWUQQttNhNu/k3sN20SpH8Djtx+KeikrSNf8T/TlXffLLxm/crP0WVYN+FJXWqHTiclTWnxYZfMugTFnvDLKSL8nQcuY13JjuXve9vOpwtm5IYqpGzgyR3yp0YQ9nkkqUt7InYzcbQHTbQwgU3QR6BCHNW0HzhdSgAglVhmSAOtPRqCIgmT0Qhkbn8HcDTKDT8T4qIqAqboxwrhvxqwgsxI5juERt21C7Is8fvmP48AGugMxvqm9qT4SDaPxE0ipSXHTh/RRyjKh9618+mtl+ZEhcrZw7qsY6mi9RSn7NeS4v/tcNWE3AS4FQruNBqMAg20PNKLzz0dZn1vCSlmUi5hCmr7sRqCAGMGwo/EsXNlg/tkOc8gdX2dk8FoprzYrMJScG7XK+R8vTolplMrXriVBj6HV4LuJhIKauvIGf6kp/AaGBuazcVnYecnqYR6+oVXAQBRURGzRmei2NMWUZ6qXb/9BCvKq8b3S4sLFfr08VKbT6uq66TMmfDySG5gGycoW0/R9loUR3Drr4cqaQD+LKxmYgWYNMMy7S6CIAJ1LkeEywMQNG15qyH2nCwanTMQBB6CJLVruPZMjPNoZVSXEqaUImskRSkb2g1u389W0CXqSlEqa5t1BpiqMhpNjz98X3aGZYXCyJ8Ps0Hl4yu38UR5giNKtYm674qCu163zXiiw1IjCL3RLLNeGRMqFf4uHzz/7j3MHK6Qy/Ng89PlH9k6ik7IsyS4VCoF7+CURH7Hv1uTLb/IZ5sOohfzz8JqpgMUlmlXE6ACnXntInZBDrfVrnOM1ka8iNZ2kbU6bZwFPGgQ5rySyc1SUF+xae1Hc268G10+UNowvJ+zdpkswvDTo2Gmo+y8P0q12rSG0FBontqWDopivvY8GQo/+fzL7gv08MlyFPAVH9n95qlD107MnjzrOkdBpYGQ3TN7sDDwvOt12zFas+S+KwoWvfYzSRKhobAkAFHVpOVF07/tPzNYsAIVZZDgxEKh9kLZce5NOwvtZUrAv77lhSmEO5WKculw/mI5nbVRXqi6GF2ob9YoUoa4frEw3ShQn+eRNq39aMXyZ9DldrWmtkXPXcUoKkdH+G+qiusXH9XSQ+BGHZx4Wqs3eRZ+xjPhZ2BnlNDIN0TF/2qsqm16//l3Fj97n6h3nADnQFmYv7jJaELjd5i/GjFxyU1TJRIKJZHy+6dMHZb25jd7eK5cVHSeDTznjsnKTAwX+nTRa5UjB8TX1MKCORaDwcATKLLnB8+/a/lzCEjJyq2q/oM2GQmK/xlkZTphsCUszc5MKTtXKXyQCzXiJWsUanwjuIzpLREoWtE4whp/RSop96UZ4MsZ3YFN7LqTW+fWads+rtYXYW9R0aiBA/23oMAfvP/8O0k27xDOXCkK85JdqG02GC3fPUajCVV0oSRSwWULjhbDsA7pMj0lbtrwdORK0YE8G3iim/adqk2PcrZYgw0/HdkzQd62afsBggCUXA5IiZHpyixiUuuuIU2trqdZHYkysL9Je7FAuyCPhBrH+VaabJPwfskwattbVASAD3qLtWp0K557xUFbZT9yFVP08/7z74AgRKszAGBb9VBl/SZwYk/3OXDcto4+OiJEKpEsWPKkxiyZkJv84aMz7uToct+p2nXbjiNXakyS++fZuZIbeKLgdHJB6oZtB9gHF+aRhIN3HjW6MNsPZmNElOX4libL7ARPpsK+/c5DyyNllgA5Pysgumj3YAI3AnXuUEfG5G6iICQ3M05spaPrL+niyiZN125pycjFQnV1vbCWHqkTgca866zFkj0Fkb/xq8uXPfbMiy7vaTCaTlfYqrgamtUarQ6Fn3q9ceSsu5ANM1Pjpw5L48WeXFfePSt/yduWpUrc4FQYgWp1BrS/k6Pws0WtD1FKjCbaYDLzMp5Gk0nCTAe7NClFEqwcRcnrn6aQUiMHwllmmunh7/K1wgSEQH0+Dcq0nmNK+xiHImM69yOXySOzeVkUR2scxw3q48SPIXLp4dPi+y9eNH0uSRIqhcxgNBlNJrOZHjnYF803rYSHhzq6iWtP7pVChwbyBuvcVkaC8LNTp11Z22Qy2crVi4/suu/ILlVo6NUT+u3ed+z6cXBu8fi5Zqlesui1QqRLiVx1+7QcnkyXvF3FyjQvI2bqUIttnSAafhaea6xp0kolZH2rrrHdkGAo495qMNK80jXWpKjFPSzACpXtLKzK6Zc6JKcPRRJyKSWVkCRBSCj4FyYJuMsp/KpoMyycU2BJVf1hC5MxvTQCRQ5VyKjj5xodLQYnvJr3YVc6OvIjo8g5cplU06GPDFOyzxMZppJIyKjwEIoklAoZQRCUryfquXkkjx5Z1KE+5P1X31z82EM+eSjuyPc3fhWByC89riDr3Pkad1JqsEDtCGzQZa6EaaK4cEVMFsqY08WnSuLCLXVCZkAfPdM0OFXZoW6Do914yaLXfka61APp4jn5wlnRtVsttuUlkYR97XjhZ3WThunBbIKNC2k6KTXrQrltZa3ZZHD5SQyLiLx8fKTdnDjnXU7DNVd6goCFKD//eRBWAbeg7w/Jwjkj0DGbCz2qV8X4X6B+nQbd8M2Hzzy5lP04OVwSjhC0teVd5yj8BABMuuIaGtAoYJFQFEGA2KgwiiIjw5QURSnlUqTILtr33dUCQUfhpwiBG3ryYe3Jmf30kpnXLmST1yFK6EqNdS4kOcnu/XPlZNuP67cds5NpZhQrU96saPJLW+HbaeEQWXxa5kOrTz0xDWbJH/yg5K17YM27TKQe+WhJZXOLJZDkYTaZSGbwru4whCicVf6yAwnbe5tjUzQyG5wZo2m39FpNjSAPlNTm9Yn84bc9sqQCtmoK+7QXRaBsIaRWb1TKnJ6qUJni11koq4TjHbRQZ0Rehj+iSK8xm2mlXKKHwUowLHH3Pfy/g6bdtoWJO7DJa7YSqF9mysYdJw6WOFzbKrMOoXP7WAKCucNjKRK+9Ov3VXJnRaNUkq035AJAV5cWVd8zHp3uwIEjLafONDJQjr6iZtI94e/MMEckkS1VtaGZBICl+4rEjOz73y1cOru4xLY60yXwlATvAcv7wvr2qG/pUMhlh8q1feIj5o3r+/OfBw+U1KBXUpYER/QpWbncOJRbhYqV2m0C9ff2nCpbCb17cnOg0WpB+DmmIPPvI2dQ216pF0sn/YwRhsMEL0nNsm7l5y6D0AD5MhCiN4PGVkuUJLf2JUBEqCSadhgACqlp1CgUUhnzJTr/jiX5mTBQfWwZf9uo5athwoeSUEZdB0FJWZmi/7IyRQefsVZWctdWtKl16JsU/Zem6awcuOFdAwBlJ6H1Tlw+CHUSkQDbHGt7RQm6EJaYYkoe1LDrB4kkqrpRAxrhTELLmcOWzibVZ48tnV3wxlZq7fL9dfCUwsJCLfMYZocDDkd/TsubnQBlVfALpqquSUYR8ijTqp/2M82b49QmSTupmJ+nFA7eWZ8KleqmT/NjxWPqo/UOJ+57JJ0SaKiM1BlsyeJ58x/4/gu4XbCvoI3uL/VzL/JkSLBf6fjcU0ufe2kFCBg2rf1o9vV3Ep0IiImAfsNZ/jBm++xWhErCdSUzd2n78kiLt30saQCOnmnimrQpLJ//LHIFqtJvaWpCJuXKVLi+izv8+KcIlt+Pz087WFqXmxYRY4bPFR0d+cSjM17ZWDZ0xfew+u3W3Is+OQIPvh3Gd+VrXjQxNae1Z8vAWZggosZeb/r9P0RqbtnBf9hHfuzWqTIJuXntctG5YJejeFHgYvgmbVOrGtpTBjNKqRFEbEzElj3Q6RmhkcinTPgsiQ1XzL9koKgZHYWorE95Sv3ujyLeI1w9aaCoWH2l1FB1sQ87pyjbLIOAuHD59mPVBZmw46UXj98pgZbWtmfF2Ra0+Rydwa2MgXONCsPPoCBEpdTqDPtLGtjVBCxb95+tFizXQaxb+bkje44blF7fClMZAQJJ00g6+0saJgxOatUaY8NkPFdGxto1J0bMmxzHnbhkLv4pNCma9omIsnxZtjTBI5FMaZqGkzYkgVwJALhsbA563QgCXD4+urXVFgg3kPARGprBKxvLYsxNJx+5JD8z9plF09e+ej0AYGBOTtHJk+1lx7j7U8FA+4VJbZJ4UF0Xm5zJTrX+uL+2ua4S0LTa5LOps+omKDUZ09nk0rH5eRkx+wth2AtdlhFlpmlusAkA4Po0QiW7fdogj3yKlHr0TH2xPAzo7IYL/lAqV3MgVHbk2B7OegqPlWf3aJyv5zkTc3/84zjzh7M9vptPQYRHW5p6+YQjW+C2aD7EmkcCSmZ63lkeyb1F4glxUQTb4TwWTkHQNAioCBTuWnH9naGhIWqN1mQ0wepCGmSkJ4guPWI1ipLvXHvuPgJ3Nk6NCblowAB2uZGjZe+nzteLrkQSroX3SRYe7T8KJ6CzY/aXWFpis6PsogqRYrVh2a7fqNxdTHYUNYSKBXStre3D+kZHRsO3gVIORSaVMBVzloiYrrFuQ48i0Ny8nPS40Fc22hUeobBUbZKMGpgwJidx7c5zvGdpaYJperVJkpEQdsXYLOEBRSdPEgQcwsPD0CielAgj0NbWdjQtm5Eq/uv/daJGrbZ+LxJETJTl3a42SRZenhsilzofibNmVJskIQrJwhm5mwtd1zvb+XTuCK3OuGpzoblDfPqF51MhXJ/aOY5DZIzd+3bdtmNwS137YMGR7w7u3WE2mS+2phZjmUeOjHVqEhqs21YId2m0/1AJnyKgk0geNdQQRdhiI5CHt+7AW7jJLVoKol9t09pVS//9HbxUs9+yExQTjbL5H958pZAzZ21rw9ltnNmROIwxncwqEiAmzPL5pDn/RwOw90RlBtNy4WBp3eA+kWYz/dXe2hjzqRhGMUvm5r+2+QwblgIS/HlaU1gCyy3VJsnonMTRAxOQKyOi4OczAgC9Caz6yVqPKVUsnJErlKkFs1HdAYQOdZJH1OiMaPCOfrx0RAY60miit/5T+vtOXY3GMr/fQUvvnZtPAILnU0F8ajlVtUkSGSK77bJBopOh3JVXb3+9Ew2YADNnsnDuCECDNVtPNtbzK71chqj/tUbHAADkOyVTRwGfsdr2LQsAGD0omdekat22Y4fLGnlRJPKdmSmw2cFJLTKcZZV6UvCFPbx/wphBlluFKIzwL7i3qKogMzrQBSqVW97oWr0JBaHuI9zMi9fn7Xy9BgWhPQChPccVZO0+Yhc3dQvLV2/RMrVBougTRoBSW+aHHWWz85XIpLC4jMG25b0VtGE19xp2IxMJRTCVl3zs9q5Dd2H+b+/xC8iecJa8tI49htXla5vPoNgTrY5HYSl76x+l6mPFFpnOvCgjOyWCK1PE2p3nUHDKxWlTUWdFGH8crmDtCT/2uX0PHIfj963/lKJr4pRGlIWHfQA4cowJk988Ncd5cok33mev7J9tGdBwYc+f9SnbKWbh3BEnzzVuOVDOG/LzlMouGUDwfCeU3cHiGtE6CpZh/RNQb6pJBckoPUiSxL5i24svUKrtiZKTYhz9KdiuLgWZ0e1qnY8FWjBtoW9H8Sb7DnWdhX2zBWdtkHjfEGdJ2i5l+aotWo2LcZwTJMpQFH9x5yuRSXmjEGHOZ+aEQQRBNDe3cgfyLW0aYcMn0RcH/Yzsyb35fBsZA5pQKShPl2hKFJ62QnX7ZTm8W9cdaog5AC2mNknmjMnMSgxnA0/k0+oam6AdwXMKD5OZTo5S1LVYRtyhoSrWd/HJKQsvz9uw9R/Wns7lODw7bkJeikufopE7/K/cupZfJ/7n5oWo0KcUYENUvcH04abCvHj4ISRJ4tAZW5ka202craNwJDtWqcJhCnKcTqdD9RWoCbeEIvafbqIkVIhKid5gCO6zsE/EfTr0RMzCSAvtzMNSFEFR7u6/E3yIhJ/cIm3OJyhg1zqiPJKT5fA+tKcnG3bCPYk66Ur+I5rNBMlUXTJwMz9s2kdCkWzOZ/q4HJOZJgmCJInZF8egjFBdg2XukiQJs5lGYZ1aZwhxrzU1u5aU+z4R6jIpIWbWqAw7Xeott6pNkqF9Yyfmp/BkunZ/fYy5RFSmfBxMO0QzHVR5/FV4vkNnWf4UEqLKSY+KDLEc9vPfJat/0gFAgbLjapNk+oj0gWnRTgbvbfY+nX/JgNhwZ8VPxYX7ucN2NRufuvJpQZ+w7X/Boq7J/Swjy+1Hq5Eyo8LgaD0/Cw66dx2voZShaAqblZ2o6bgXLh6chDTH1qUhdcL4VCbrsH6tqjVa9g3G4lKpCJTRpZiH3XuyOjUpJtAFuuGbD599cqn78aKwQBJ1KuKVLgEAxg7J/OswnM8KZCwLBAmH4ac79iw8W5qX0a8zp3FebssvLf33us48VNEZkdecoggzDQizUSaR6422+sqIqCg2CgsJj5h1cQwBPxVwJwteBoFdaYbmLm03OF7QxeVv6+A9MR4ml0Tfb5Z0fJ256qcDaEr0X9MGhaukNl2S4O9yXfFZy63XTcxOilaJyrSquo73te28mIndopVl59GK5ChlWbVNWKw9LS9IbX1+RpQkEZYllLWAnYcsftTR0sWuJkP/KQcXyviDd+HIXW0/7SDq04I+YaJJITYvNM+6HkxnNG/aeeJoGUxahivRK2CEk5iZ4Qc5Iapz0w3rB98hSJ17T1bPnJBrpun2Nn4g395h4CUY3VHq8H4xFEVEx0TTNE2SRGqSITM+JNAFCstcSmEpD+0qEHNUW+5Op6JAKwV1EzdjT43O3WqwfR1J/LhSHupbV4oCA8YQlVqjZVYQiEGAmHA5v8DXlvmx6I8HspJWZ0TZdieDYt7gHbWxcQRrww+2VcSYm2KYFM2Sufk8V372VzW6VW2S3DsnX0IRnANcj98ReX1E1raazHT/5DBrCRcMPwkCHDxtKQyIi1AuWzD9xdW/OPIjdzJ0aL+4iYNdDN7Zkbvz6iu1vU/RS73zWDuaxa5r1amsGSHL8dUN3ESQXEJeOTmP/ZEGYP32wiNnoE/Z2U3kU/g1KVAqchwBwIHimsvHDzLR9BWT40iC+Ofkhcz4kIMltYM4TV6cJRgFSm1ta2ffc4kJcbB7C3N6mfGwgtP3An349XVvPHIVCDCcd9kItFG82Uy3t6u5m0YAkuKFn87t6XL54xlZX74r4Xu1zd+uFMVsfYO2qUXa1POMZrcYHACCtumPl/xxs1WHaB9SNwc9DWSU+YbPya9vZkfxBVmxkwv4o/jXfz7DypQZ9DvGjc92fWOL0Wg5wZAQSynesL62k1/10/6E+NgaDdCUnFoM3U1yFcmVo5ozeNeaJYvn5FOk3cHiM6HujdyFK2u5bfaLKlp4iSCWYf1h0d7oHId58GGMRhHqRMW+knrWcVddkm8tSAN7TkB7outPVXdwk3WiGyI4J6tPImyvyTz03uMWLwdBBMpO3zpJxLvbXyN4MJgBrFCxX+zIwrWno+WPAIChfWPrmuE7ZkcjE0gmDjrB9gwydo8rxWHelej9rTOY5FLX5RbsK1BlHbwnxkWx1jtVo3M0ZBuRHWPN0tsN3nldnD36SjXfAL+bkbn3fn1z6TnLKP6O6YPClFKeTNFZo3WcXixJ2nm0on9y2KnKVtaeMHyzvWIEO36nAa2Tx36yCTZnQr67ZGjq4IxYJ37870bbwZ0ZuQMr3GW1zn2a7bhkjYW7r0mbWoemOE9V2wpXs9ItG6lB0Vn/hAdKatm/pjcOZd4tljQmAb9a/2bsiW70vUC3/vYL8EME2vmuGqJBaGBWMm1a+9HMaxeZAGH7qJtNKAiNUEm0jheMiwbUnWkJ6hddOsVo0Mulwre1+K/A6g/GnmKdt9EHxtGQze7u3GchPFMn7xpkUvLrm9//3TLG1zFlmLxqfC/YcaRiQEoYTQNut1N4viR18HQTW7z5899wRzmdPJFbRBUBa4Da9xyFjfrVJklUqPzWS3NcBpto/O6mT0OQj5j/tra2o+XIEjkcuaMaNeHEJetTbr8CgpNb50kTXZBQsBGqbezCdRx6FkD8ffw80hwBZ0IiT1aJLBDgTu+4i72XgyAC7bVQAHYvF10tLnq8I0+ydZGB5kpHCOf4RWH1B23F/O5Ofs/IEGmzmt9awf7uHtvTOWhobwk8mRp1t4bwjkfxNA0GpMCczJmadm74CV2p14/JsQRf7Pi9Q92CiufX7rTtbsJW+AMPB+/u1DCpHVazAjarjnCUW2d9inLfFw9OUmttrxabVd97snpCAUxssrl1IciesTERwL4LFzcIdTm9AydA7fm70OJlyymBgGfDNx/OvHaRcEl4b4CyOqFDb+pgvIl+tk0IOv606+JtbSAC1pUeBYyO9MeOvgnHj3mhrpU3XrMfvNvZk/A2/HSCcAjv6FQdjeJ3HYODd/gFozVw7Ym65bKX89Ijft9/emBqhDkkls0XacySe2bnSylSKFPkU5eDd49qmFoFyTre35SX9XZSq4TiSpRVnz4uhyQJk4lOTYLfhbzUEBcUfibGRW3aXUQQBC/87ORkKJovIph2q34RqJ/ySISrRLynBFol0/JVv7KJnfxx047uhkt0YD8RZrMdBNvzIthd2ckgVCzz4yIdqLFWTRL2dUs8a/owoYiCUJ88FJr6hA2fmvlDUYVcVloFhaWjpffMyScJ4vf9p0sq2/pmJ3EV+al1MlRjktw8dWB0mMKRTNFafm7a3c2Ru9qWpm+DMiQJlXuqEvqUrRw6UFxz2ZgckgRzJsVSTOXvXvvUkEiQaB28O3lHuOtQJmAZkR2TycyuovAzMT7KbM17BkEECs/Suj7BUR7JnRaZIvsFcS6veG7Z0udc71bmD1c6In8c7HYONcpp8+PElcGrS0+DUO7gnQPTwZO51NACW47GRIRwH5OG4zVh3ZLNnl4kjvyF/a9vNMG6JXS5uknLCz87dPqYcMWAVBiLfbwBhpwJ8bEdtPSa8X0IwjZ+5ypy3fZCdFltklw9oV9qbChXpryRPpIpb0Gn87RSq219rJ2qKBI4KlSzf3Zb/e+VUwbb+oYwSXAkx4NMAMgNLdkMkui6bZ/MGrFe/vnvYgIAU0yeXwTqTh7pyF+/uv+AZrPrV/19Bx3e3LGn2M9d6kon5I+bpu+hrnTzPc3Lm4sO3tm/H/o/KQHfMK2tbQaa5JqUR1I8M/IlgLnD0uMZcaamNdfV1sSdDEINZqKRsXy0/bmxumENEhkqa2iyLABXa408eyL6p1qa5gzvF71p9yka1q6QqzdYvjD0AEam3MlQrkz/Olnf0lTE+rFvUrjWYN/omnMwXMjvqoZJbdWlkS1Ps9La7tZ4mRtUUiTJTayj+iHRIFEs/HSWQeKaXfQLmxfbsuEn90qfCXTNs9d47Uf3cWQ5tErHkUNd29NH/uykKx3R410p+s6OCpUZrNWOXBxuH8/5E8qSB+gri1mTNrdplDLKVqXPHJkQITfrLN48U92KrpQwH1f0Xye5t86En8yJ0RKm0tRIk6Ia5XK0vM0WfraJrImAm3kwKXhm7VPIxt2WjnBs/ootnlebJAoZddfMwU7izWpmvzzWj8mxkR6M3HXib/7OzznyEuv7i2tIAq5eE4IOgOEnAdBSV1F7enZitF3uCFUl5/WJNDkSKM+G3c6L9121fuN2R7f+fuBsbloEd+Emr0umc1d6V8kk7kq4TBx0ht7mSic0t9pagbAvqv3cJedGwcsuS7Y0bdRXFpO0WaezG8RotLozWmsTPFgTYwlzDrfbsjFdMBMqYfzOapRn0jxmTSprT9HOUmgIPyhZGRUqrao4u3GXNikhTmOS3DNnsJSiXtl4mpfC6qAtyXdrA9A854P3lqZ6d2QKrGkl68FthDVyZG9lj3c9XnZaNpIQF0kU1zrPHaH3w+/7Trss4eOemFRC2H1ncyZAqwvPCzdbhX/BAHFl6TnxNQm8BZ1ALI/E2pPF2aIj4c+0i+73HsSVHtoT69L521rK6dXGm/oU5n0cvfbIpCggRaAMAJKmF8b0Ivz87N+333r/J9zz4ZwSjTTKmlQuFV89cYhZJy5KKWkcyXw6hvdRbt5fAQDx6SYYfMUAoCdki2cP5pag2mRqssk0XCX717RBLmXqXQ0TDzerLIb3i+FOa/LG5sIMku0A9v3g4eexuVUTHR6i43RjsDyyffi5cfcpNAHq3ySSSyfygHMcMIJjNxwkfNB1yXXTzMzd1kS8nwbgCOxKJ+jNMO0joYiIULuhQHObxvHUp2eFR6y2jtrNdnYRN++Z8vm/bT8imQrNjkxqMBG8bw7YjMPaAZqFsmoXJlfJ8FNNkhsmDVjz407LYqS6BklCP14JakpMyDUXZ7NhqZ1MDTaZisabSKbqCxUiNUw6u0+No4YDnRzI28bmgtp4GCSmJVYftwWJbE8DRxOgjk6ssVWNTkyY3EeNZnhI/CdE5ERm7RNKycELTt7o0Jh2mQDxBZ0depPo7tsucfkZU1Tt1IYP6fxsKHalh9BS9B5hNuykabGcD8Gb+uTb02TNAk0fnnq+3lkbzU7i9ewnG4HysNco/H1I2tKEC9HRAZOIZeoOrjGtWF6H9saajNAIaEBCGRodduX4vulxYcLx++lWaEkk0zGDEkcNSBSVKdqzxM3Be4iwIBRYXv8wbjMH68HsfTU6o8rtVUDc8FNt7fEIoUUO4H6CaZpfkuGF3FHuiNeNASHxTotsqOg8TuTsKeJFQO3L8NPdp8Zj8C5HljzAlloBZkBY3vHwK018MMus4bPmf1jOVLeyU5kBiPOaUMG4nk9ZjdrJe1SS0K9IZ63SJwG7DYnWLLl3bgFJEEKZbi9RHz1lken1k7ITo0LckqlgLzm1IK3koo2NFaaqTESgTuo6Ufj5x/7TwtXJlgM4H3+UYjpe0YISieybCiH0KW+Wlr1eqYCxP/t8Zh0cFaEMki0CtTQZcW/g7DxUDEDcPEutnvO9LwYOLf0HNxBjAlJaLpcJu9uxqXOYN7cioWAzZr8mgrqi9pNBOK5HJcAm6AOCHZW7hDUgqgxVmCR94sPmjesraskNO0+iCVM2iuyQhgkPU7RX2meKgBOZOsLrgbylrpPZ/w8mRU430aSdf7mDdxOjOTRgRYkOpFHbL+XUp3CWllMdBSvnrQ8Iky6cEjfLGbg54diN3DZ3/NFjJ7wIP737xbAruwVuIKbjjtSYvDn0pi11bttjwX/G9C3uL0xCr0Mx5wWQJHi5ey6rv1NNtvH7tRP7JUeH8sNS5jDUeB8YbGbkypR1n/MaJsI+Bc/DRUaezX2nJYrmjuyTUXZwNXe2unVASjiZYNvpev9ff7CX0Zc0+2NTq0YqgcUYvC4tljNiHhZ9Z/M24AqOlUgoRuYl4rktMh0tPHDLngQ436BJjYEJCuzNAIEXiNlWznmbOg+K8NPfsDLdtKuIzdHfMwsu9RGO8dndm7kyda8gtN39U3KZkefWdfI6wyJGZMcoFfJ4ODSBmjtr1RxFElVNHUb18YVzR7S0tO8up0aMncS9I9enFDCbjUAPt4alDTTJnTtKiJBz1WmOzQsygdI0rdUbL9S32+WRFDKXsnRuT02b5W0xNCv2UBl/o0RM4GjUbK4v1Pqm+wGqKOKmdDRNbaooW4TVS7CrsbeO8VFXKo1EZPBukamDeFPNkSmbghdmkLwYyNuFn5yelnl9IveeqmM/4vERcuRNtHcWq7kE5l2z+ZiW09XehgOfElKCRptsoxzUmepWiiTMsXmiqyEDWqA07NNlrKhrq23W6OBchkOEg3dCzJWOGJoVW9ci3iMO0+340J5InZ/9+3ZWo6w9e6dJhZZUMn31RWdCFe2VHi1Acg7XoTFhsoY2WGwgWtfJhp/29dq2j/jZ6hbUu2T65IsYXXoD16dIpiOyY+B6JwfqtJzE6w9dDQISo8lc3aSuqG3TwvVY8Ivn9NmKUf0sYzdeBMoKVOvKlUw7Bosr7WczLN06Drf3xr55vRDkUOc1Rr4dwvuqM1MXgAbv3O4hHdIwJFAuIdbIkS0IRREoQRJ6k4vKIfEMvnUDohHZMdWtRu6yIvRh3bgLtqfL6xPZQsYVnYS5r4v6x7ZScbDI34rGJLl+cv+EKJXXMnWfgBMoTdMmmm5p152taW1V69hmwGjr2kiiTSjQCKXEpStZeNLkQRBYoL0O3qAeESKRSyiyRacd1G/An9u3x6Ul9yqBOpOp4wXvAICq6jo2OWNiBs5m5iPsSKY8hyL/otw3qh/i7QCGVgEhgTo5Z9an7AQo6PECNZnN7VpDY1tHc3tHq1pvsnZgIghCJZfIZRJNhyHUbGlLA2vpSYqnTqEu3fQmyxE1Dj8xkLqKC+ycW//+/S9U1iju+b53ChRhqWFicCLTVmhA/lJ2mqC4nZl4MmUdSpMSs17H5oUcVSb9sr8iPDJKY5JcfXG/lNjQtTvslp+KEhoFd6nzBwExB6ozGBvbOmqbNC1qnclMs1EnSZIKGUUwRREt7TqT2RxqPV8tnBI1aZlFGt5JE7sS4wzasitZSGhEanqf4uJiuz15ex8docmAM4RHMlULlsaHh4cKE0oEbZJaP4wGmuTWYHJlKixLElYmUSSRmx7ZSsVGALB5dxEr9ClDUvOzYh3JtL2pxk8y7U6B0oDWG0y1zZrK+naNzsjZugf2nZZJyDCVrF1r0OoN7E3cSiaPpDli2OCPd/JncDAYR6jCownapG5vVbe3bNv6G36hLA5lULRXCldzqgVhqahMuQWYNEmptR0SihSumEC5dWFlEgAgPSW+sJrmNSrde+zs3mOW/fJy0qOmjegDABD1qW9lKulGdZ6va69v0ag7LDkiBvgahapktJnuMBjrW7S8DdEyMtIBUNNOvTk4bxD3Jrl14fwdE5KxQzFuEhJpaVyibndzaWJvNCmwl6mTBL14ZGo2m8xmJq3Or75kKpNaeD2VS5qlN0/NiQyTF1bzzciVafmFulUX6tgJ0C1HHBYpcmXqnU+7WqA0oNs0+rpmTV2LVmOnTiCVUFIJKaVInd7ILMBysZckkmadISQyVKFhihuG90+QWTf/wGA6jxruIA1nQmMe24ZfT4/DUiAyxufKlNO3iVd9SZ+tbqFpy7qJImbn92kj+vRJBd9uO4bCTIokbp82KFQpFYaZrEzdmR7tZHDadQI107Raa6hqbK9p0hhgxz3b6nuKIkKVMpIkWtp1GjgP4lSdIXENLVo4MU0QKD41GM1SitToDHqDCQsU4wd8s9AZ5fGDOpXUBWHpCLiDC02RZFGTVJikGpgavruSQktOv/71CCvTBTNylXKJp8bsvEy7QqAmM63u0JfXtjW1dRisXzIMhIQiwkPkmg5Du1bPNNx2EXUSBNEBW37Aw9jRvcFkkstkJi3doTeFerNZAKZL6y7dKboMKGIe+x0E5FadQRqWAqcyLa6BifgQlTKEMqIjLx2WlpsRc7KkHJZpl9UDRYJw/f4XvxxGMpVQMDINUYhEpl7jZKTv9zImdYehrKq5uV1nZPLr7PVyKfxVaRquPzWazJs+uK/sfIPw7r/vKwUA/PDbXvSjSi7VGU28Bf8kScRFqGqa1H2To/okhAsfhJ0GxXOg3U5wabSuvCLmcduKad/S4zXKg1eHbxnju7GciS2fYivkX9lg6ynFw7J+340JUJ/gX4GaaXrvySqtziChSDNNsy0hCIJQyiXfvnk392AnAgUAXHMJ7Kpy70ufN7XpBIEqkRClqmvWxEepcvrECodbWKCBRrBotAsaiPQ2jQpl6k55Kcqq/32y2sRss6o2SeRS8l/TcuUyyolMEVkRfsyL+HcI39jWwSzEhM1NWXt+sWJBVITIJm5ZqTE8h/LsOX/p6jClTGyYTxvNZooitTqj2Uzz+k1hAhDkTe5iygCkd9rzu9cWs5evfvT9bk49AfuuJYxM1IoEQIIQc9Oanw+hNihRofJbLh0Ie0qJybSsxeQ/mfpXoNUNauQ7dmP3L1YsgN33WjSiDhVl4bxRza3am55YJZdKNPY9IllgHklC6gxwNoCyb3OHCVgCOfzsAc3rvPam8MouMCkQTT0xMu2XHJHTLx0QYOvh89CeDNw5UL22Ce3brDRJCvrGTspPBQToGpn6cQhvNJv/KrzALZFF9mThOfTHrQePllahy0/fNRNduPp++JfTMlVK0eHKxlbx7gAquVQqIdu1hmHZCWEqfqc7PITHBKA9AyH8FPWmE/xnUpezpWjkPndsVmZiuDtzoNdOGrhyh7v7FXktUz8KtE2r31dUbR1xEzIpecWlo9FNcybkoAs79sHFWAWDMtCPGcnRXG8iklNTTp8pJwkiOlxZ76DpnFRCRYTI61u0eZmx8dYSaJ5AcQYJ4w69wZ6eerO7TAqsMp02os+PRQZuZxP350A9xSOZEm8+eZu5wy+7GBaVN1Q2WB5ZIZOYTLTBBANJOQXeXDofAPDwK1/xz0awV1RictKwAcnZaTGvf7qFu0xeeMf4KFVNoyYzKSIzkb9NPHIoFigmcEbu3SjQgYlwS58XH77JVw94dVfJVDTSVJsk/VMiLx8Fg7Cul6m/BErT9B4m/45+DFPJ2zR6bv5HIRHfNQU59PIpI1RyaUZSpNFkJuAGOMQLH25y/ozxUSH1LZrYcNWgDNgGlXfrF3s93nwU0zvp8bkjJFCWYDcp4Mg0LjbqkqFpYSqZz03qSKb+Eqi6w/BPUZWtrxJBoLaACGVImISiJBQ5aXhfowmuh9V0GErON/RLiR4xKC0uUhWmgs2sENUNlsXIzh1aqe/l7XIwQePQgBJoTzIpsMr02kkDAQDuz4F6LVN/CbS8trX0An/XTBaFCpYpyKVUWKiKnSTNyYjLSo7Kz07iHcwK9O53fbkgBIPpFod2e+7IkUB7mEm7BihQ+HbxqUNpQO8rqm7XWpp1Mu2q7JZpKlRhFEWYzPSEIVkSipRQhEohiwyFIaRQoMihOVkp6PKkB9f48FR7cE91TGA6NDDDT3+bFPRQmfqlDlRvMGk6bAWb5Vp+XVGWCvarlVJkZnKkO1vSs/YEAPzx1i3oAjYpj8/+fTt2KMaHLHvjSx+a9Dv/V5UGXwTqXXImO0ZuMpsVMsl1UwuEt7JBaEaqbduTljbY0koI1qh3W6RhuiUIDfzxu3NwTOqZQP2UvM5Njpw0LHP7viJRgc6ZDBduCnHkUAQ2aRCtMQ8WsEAdgU1qN4Tvsiqfsf0Txw5OkzJtj+dOKhiSk+7+fSPCFE40+uWUipu2pYHeDdr0vLvPAtPzwaP7buhI/8S1o7il8qMHe2BPlogwhahDXXQS7TXg2BPTA0wKgmSqtOsEuvQ6yzrOziN06IUNL2GDYjA9xqQgSJJOku6yp5Pws7y6Od3VVDfXoRc2vETToDz92pdvBU9+9rcvzheDwXjJst5kUklwxZ7CKdETXz2N7AmvwsN4DMZvKXhPWdYLTAoFuuZP368bdW5P72Y/ubRufg7+Hw3rSbE9MUFBt9cwdRfL/GbSbpepfyNQn9sTepO2izWxPTGYXmtS0N1hqR8FOoQ8DoBvBu/Im9wBelkmLF+FNLRxb3j5ljFPrsHToBhMQLOsp5jUvxHoz99+DACYcc0dnoafKI/kzJtWEmPCquttu6bgWVAMJohYFuQmJYaMm+C/R8+KlIS3HkGXkUZ5Ai354VVH1huZJqOBZZ280JtcWIGix3kKR6CYgFyJ1O1zoF2cRAqENU7+Nqnfs/Ct4XCxZnjrkZ+//fimYSHFYvkqB23mwRmn3rTemff/GAwmiFnmz5jU5zLtojpQpNEvDx65cWgIe2Vl8mwndynLFOlrZ8OhL12IFK8Wx2B6rUmBrwf4fh/CowsxRCPTGBSOyEeNGevOfUUbg4rqsbqhjXM1vPjUmj1OHhk7FNMLh/BBMX7v+tF9J03qrwh0ZJSljXxMGGoGKmvRiG/p7oijJVU2hzoNKxNiwqob0DQoHsdjApRunwDtASwLvJjUZxGowJh2bDlYOXqgpbmnmxEoDEL7ORvFc2XJCNQ2G/rU584iUByEYro+Au12gfaACLTLYlL3ZSrppC65MabwsJg4izRpcAH4CAdBJs4lYTC9jmX+iUmtYSmccrz60fd8IFCBMUV0CW+KjxP1HDfPvnvnjrETLnbzeW2P4PaRB06We/rgGAwmqFnmR5Peiy6ImlTSaWPGM//Py+L4DM8ejIb2XL+/0uWBuGkmBtMjWeZLk7KV6A5NKi7Qi6It9qTN5pAQVYdJ5JiUpFjrc3CezgEzx+c0NTSgy+frNfyTdMARbh7JKYkx4dX1rTj2xGAwPjWpnUN5JnVrCH+opHZoNgoz7bhQVe/kXimJMdwfSZKwG877MkrFmXcMBuM/k4o41AOB9k8ORcHmxh0nhLeKuhXqtdoSb7IQgEDr2mlHC488g/8gOPzEBCw4Bd9T50ndEGg/y+r1WRcPcjRg37DzpFDRuekR3B/bNDqKiUMTo5R/7doxdrzHeST+szLsP37Oq8fBYDC9lGUem9RhEOp+GZOzuc7ZE3LQhTPnbAmcNo0OXUDeRP/1Nvp015vuZJAwGAwG+CImdSZQ2mxG/8+5in+MqDERyJhOHt6d8ztaUmmXR6LB/hMOQ05sT0yg0e2Dd4xfTep2BEqLGNN9af5zqhYm9wckeJ3zcT5Ur2ls9+5hMRh/gL3ZS0wqspSTW8MEAJBKKCfGdOJNJE0AwPjBqejCwdLa3DS4nqy+tePqudOcnNahkxXs5dy+CS5/jZrGdhx+Yrp9QWfAerNHruPsGjgmJTwQqMTqRFFjinqTNeaciweZzTRJEjQATU22gnwAgE6nRxdkUlIZk+Hy7GlA5/VNdHnYyl+LXR6DwfhPo75S56ihI7k/7j20zycPiwXaeV58eL5bAh0b2+ZEmlxvssZEpfI0cxNJEOwBtfXNvPvq9QZUxiSTkgqLQJ0N6glAOI9AsTox3ahRH4acPHX6VqNYoH4yqYhA85SwQp4QhKtIi6w0Z463ZN4pkmS8aR/hMlassRfowdLavPQogUDtsH8MmmCuEDr05z8OndXbejNjMF2G+YbPu8abvtIotqefePHh+eLt7LgOPVBSx14/a8IgmqahLplIkxBalhNR1tTZ2TM+NvKXPcUAEHnpkTRNN2lMGVnZLk8ROZQrUKxOTM/AfXXaIMDegx5rFAvUfzgtY6JhnHj5uBySJGxjc0J8zM25Dlp1+8Fzg9LCuQcQBLjsouxf/ylFP5qMBm3DWefToNYdOWl7b+LAE9PLvMm9+zB4dy80ivEHziJQNNAGAKQmi6/XhDiQaU1dE/ea+NhIgoBG/mVPCYpA0YM3miNT4vj5wfJyW/PQs4ZQT38lDCZg0bdpAQATLvZuGR4DZ9TnpkZxBOo/Or2lh2DeEwjsyYSf8DjmP/AggiCQQ0+3EqdbWwQPiqWJ6cns3LHDe41yVhXiaLTbgQGmkEItalUH9Aa0GMk9UB5JMC+aGBcldjWoafFslyQMpiexc8cOZNJOMmrYSGRSTKAItDNU19qFnwlxUexlFIAWlsPkEkEQ56qFsScG07vwRqNi82ZYo92Cw03lBqvqUeMP19OgHLYd4OeOEuNtAgU0MJnpLXvhNCgAoK5Fmxyj2tfMOcDPnC87AwKD1KzMXvUrd9nvG+C/OJoDFcUA5LExkRnpKWFhIah6zwX2h2zb+ht7uanZFsSEKsT3kvAfo4flgl6DwznQY5pY1qHuw7cnJ/yEEHadlWPDFZ49OgbTc5ECXUtDzT+1jehHhUKWmpKYnBgnk8k87fMbFRnF0yime5JIBAGnQVEQ6ungnbm/yANyLhN4GhSD4SKnYGJAZ5J2dOhLT5eXnoY7JBIEiIwMz0hPjoqKIAnSvV7pFo0aOtT4FQ7gLLxj7AbvdtCF5c1oFH+uuoVIcGtyAIPpAcjClE5G8UKNoh9pGvaUYNtKSKWSxITYtNRElVLp5/PFdFqg6BvufGWt82lQXvjJH7xzsf/ajNNV1MnTXJ4GBtMzkIVB63mhURaDwVhxvrrifDX8gQBhoar01GSaph2sDMT4EWdj82MappjJjT/KtgOCZp0O7kUAMGNMf/bH1FhVdgputIXpdcjClMikLpFTBmRScWjQ1qY5frK0rr6ptq6xrr6ptbXdYDT68lwxnRzCu5wGdZE7spcq03fEQlK0Cn9pYnotbo7onUSjPGia7tDpO6xNIymKDFe5uAumM7iVHXI+MnAnd8S9jnsjtieml+N+KOo6GhVgMnmyEAbj10L685W27p9OEA0/BaKkj1vL6d0/AQymp+KpRt08MipM3omTwnRaoJZpUA/WHUWKlC4J7sVOg+qN5rM1di3rMZhei88mRjGBFoEajGZ3cke8iFJ0CTzvsLRY3J4Og7HhvxE9JrDqQHm5I+6yd+fzm3bToHgUj8F4W+rk0Yge083NRLjToMLckd0qI1cPZW2WDLR6U5LR1gAUg8F4MTFKETQFd3HEBJhAXU6DinddcsWMMQNQHgluMmPGf3gMRhys0R4yhDcYzVIJ6XDdkdWa7rSRMXa0250EFRC5+AN7fnd5zPDRl7CXp0ye8MSj9+fmDqyqrP7Pqs8+XfOV0WgCQcWP6z5PTUl2fswna75+74PVvCuvu+aKt994OSl9EAhO7rnrX3fcxu73LU5Vdc2sK25El+PiYpctfWj2zGlabcf7//l41cdrDAZjwA7qUShqoommNh2biE9JSf76q/+6vO/FE6ezl2fMuOzxx+5PSkrctevv55e/Ul5+vhO/Qc/Ex2vhnYjQKGhqcNmwlAv18Mq4COUFO6N2D+npqW4eSZJkzfmT6HJDQ2NaWsqKF5eteHFZwfCJlVXMArsgITkp0eVvHRkZwbsmKTHhvX//X9B9W3CJjAh3+YsTpCVcuP7aee++/QoAoLmlNSRE9fwzjz//zOPDLppccb4SBGrhPavRpja4RXlUmFxCUWlpKe4/18EDO6Kiokwmc3t7+5QpE6dMmfjK/7314YeuFdyrcNgPlMtglWWLJBSBSiRS0aYhhBvShFse1baRvGQ9QRxujwYB1iOyrvIUlHvyAOFNp4v2h4eH/fjTzwvuegBdc/11V7771gq4+1PKQLRbSTD2A/3+2zXjx426feF9GzdtcXRMTk7/Hb9vgH9co8n9CDTA+4FefeXsle+9vv2PXdfeeAfvprCw0LJTB0wmU9+BI9RqDQBgSEHebz+vM5lMiWmDuusXF2q0usqhzU00ISwLDQkJKTy2x2g0ZvcfKrzL11/9d/Tokf/737onlj7HrGiijhfulcvlY8ZeUl3toh58dG/qB+pxR/riKpEvQG65krFDzf5jDyivbUP/zte18+yJ1p+B4EEmlYaHh9E0zdoTAPDN/9YXl5yG+/Hl5oAeikRCvfv2Kzt+32A296L1LW+//hIAYPF9jyN7AgAOHyk8cPAIRVHuD1kCZGK0qU2HAlKXjB4N9whB9mRWNJmmXjoHAPDuu6934qx76xAeNVdG06DChvOEg0gTeRNdEEoTAHBEHQOCEIVS0dDQ2Nhkt+s9AGDLb9v7Z/ftm5VxrPAE6IlUlcPfq76+ISd/LArPewP3PbT00JFj63/YyL2y7My54cMKMtLTunda0OuJURSBOjpyzJiLAACffvol98rzzHzFiOEi4WpvplNzoHHhMpNAneU1jDQJcW8GqTS5tLa2DRw8Rnj9zBmXwn2Yz8EOuD2Strb2a2+8Y/+Bw6A3oVZreDk0kiSvuQqGYzt2/Q0CAC8mRk000dzuMBRdsOBWAMAPP27iXV9efj49PVUmk+n1lmYlGI8FajQaeHOgNmla4e7b0TOk6RK5XJ6V2QeN70APJWvAcNC7iQgPHzgwe+MPXwEA3nz7AxAwyMKUZB1pFlsu6HxQLzp51jcrA2bMBGOsCxcq09NTMzLSi4tLfXDSvTYCjQuHm7SU17SVa21fYjQAVO/zJoIkyXOlh2Aqaf7C7j4XjB8pLdqHLny3fsMbbwWQQBEkk+N1X6MINk3PXiOVwghJJwgzNRo4CxwRYTeJ18txN4nEltMbDGaoTkHIiex5RB3D/gO9A5IkK88VUhT11Tfrft/mg22+MYGJTCaLSx4Qlzzgk8++vvrK2RfOFUYJCrwCAVJCIpN6FI1y80vW1dV0UCd7AzQLz30JSZIgSeKYNra3SZNXEEpR1Eer19z/0JPdfToYP8JO/D229LnrbloAANj7l20n4Z6kUbRGAMWhXJTMFkwajbvzrb0BD4fwNEwOkSTRC10pZED/frv+gBPtN9929y+/buvu08F0Hdu272S2vQzECJQLKfFgYhRp1EQTldW16empoaGhvFvj4uEwtKICr0ey4cF31DFN7DEt/IftCQCYM3s6sufgoROwPXswF48fs/K9IC5+9CIURQVMObl5vKLR7H59URWKH06z1wzhMfC9NTD74w//DQDo029IdY1bjfoxQcp/V79z9ZWzMzNgiQWLXC7vwRr99rufAAArXnyKW3tPUSRBEA0NjX4+0yADC9RjKIrasQ2WVWdkD8XzQT2eF19+EwDw2y/fca88vG87LDVf8zXooRqtrKyWyaSXXTKBXcL05x8/AwDuf+Bx/59mL24m0ht4+IF70IWzJbB0icfjTz7/309hnSCmZ/Dpmq+vu+aKEcOHXDhXWFxcKpVK+2ZlSiRUccnpR5+wrHQMLtyZGB1+0ZSq8yfWfvNxVVVNdXVNTk5/hULx19/7du/e01WnGRzgCNRjHn7QIlBML2HG7Otu/ddimVSal5szoH8/kiTm33r3uImXg6DFZSiq1xtS++SfLCpOSkoYOjRfoVA8+PCy2XNvdH81fS/BrW5MPQaPOvT4lcDsxuQ/ArwbU2/+Q3taeM+2d4pysOUn7saEwWB6C56m6bmdRpt6fTSKh/AYDMbLwnvQ6zWKBYrBYGzRqKehKNW7NYqz8BgMplMdSSj7TqO9ChyBYjAY30yMUoxJ9xXV7yuybALU48ECxWAwvtSonDIgjfaGlxUP4TEYjC87kgAAuA4dOdDSCbNHggWKwWD80qpZ3gs0iofwGAzGXyN6wNFojxzUY4FiMBj/alROGXqqRrFAMRiM5+LAGmXAAsVgMF5CejWi70nRaO9qJoLBYPyB3u2N6bnoTJZtl4I3xYQjUAwG01lkYUpZGNxyrrdFo1igGAwmUDQKgg0sUAwG40tknjuU1WjQhaJYoBgMJiBCURCERaNYoBgMxi/IesHEKBYoBoPxI7IerVEsUAwGE9AToyCANYoFisFgAn1iVB6oGsUCxWAwXYesZ2kUCxSDwQSlRkEAgPuBYjCY7kHGONSLZaCB02kUR6AYDKY7kXkVigZI0SgWKAaD6WZkQTsxigWKwWACAlkQahQLFIPBBBCyoNIoFigGg+lRE6PyLtQoFigGg+lRoSjoQo1igWIwmMBFFtgaxQLFYDA9XKOAwR8OxQLFYDDBgSzw8ktYoBgMJpiQBdKIHgsUg8EEGbKAmRjFAsVgMEGJLAA0+v84jgwfwjruqgAAAABJRU5ErkJggg=="}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "assistant": "Set S0 as phase 1 so S1’s obstructed-by-S0 firing line is used only after S0 ducks.\n{\"name\":\"SetFireOrder\",\"arguments\":{\"firstShooterId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nPHASE 1 SET TO S0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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VXFrlmjxL255VJOTFRod3ie4U07lTTOcwtEVHNf0bBf+NjgoTiYQikbC4yjWl9bUNij9PpTI+wNk3zvTvGVVQXu+FaTIKqmUNqsnD48pkCjGuzSiobrsIdTjJHi1CnatABQJBRGQkAOD1N8wkW4aPGHH+XHONDkVaaioA4K67DF/jGH3Gaf68eQCAwf3irD9jVkZaj7j4+D79P2ty3h0uiwb06a7VaoVCoVgsrqmxWNPn7Q1rLQmCWcUTHQXfkPCIiIS46MVPPjP2rknhkVHlZa2sXWdUO7SyjAnD/aIHivwjeCKJTt0o4Ot/ovh4XgGMt5gSHSrW15wZXNr8whI0cSM6RMLyxe+oX98IfRTIMNqDbB7+gVa31t9EllUY/ridQ8Ttn43X6oj9x640Kplxg8RRd6ddP49plUP6xFzOLvfCNOn51Q0K9d2Dup64XpQ0soUviEvKP5M9NyNvnwJt1CeLe/XqZemAGTOSxk+YQF19efVqdCFp5kz6hnb++NP/LD3OoYMHhXpg39GfR2DgvK7OljM88Res4ejZG05YsMQTy5/18jJfSd4iAhLWddbo6/yHDbOWyh83fjx08xvMhzVxHE/JKvxi26Zrly94iyW79pp3vS1BnT/DgLYSkqgruKpuqMB4gpC+U+rsjw+iDbgp9IFUTdco6+kqzl3PKa8y+cxjIOfm5ZoG1V1DemQUyIBGOTg+uqCi/sGJvdLyqkSYJrPQ0IHCKhHqwdinQPft2/fwww+/8OKqp558wuwBa9auDQ4OfmnVi/v2wc7cxx5bBCVb/34KhSHpQZGVnYPjeEhoKKNdB/HxRx9OnzFj5xdfLF282NfX98yZMzb2vMuqq+B/JMkXCLRN2WQ60TFdZs9bMGve/On3jAWtsp4CUrnmpSd3fvfblq1birIuXs5ors6jgwoJ5sxqDibS0WlhP0laTvGyJ566ePmKQCB8491N9y9sjoEqVBqSJPNLqtJo1X8U981fQJKkUqmkbtWPrSTTb0tNS/cpXv3kZ3Rhx84p9KsIDPtl+2fQ23rxP998/MpjrcgIaXAR4zEhwmj6tSL938dWwvvc1BqdI+2mTifNPpTQqG7s1U9gdq5lDANRDdfKKpob4XKlNZ1DxHY8lCNQazTZOXk6HdN9wXEsQKTyxrXpeVVV9coZw3ueTi/h6VQ386vLaxTTh/c8drWwul4Z5NvcK8yS7qNkDxWh9hnQ995d//DDD0+ePDkgIMDUe42IiAwOhlmFgwcPAgDi4+P5fH5dXV11tZlUeE52ds+4uF27ds82Z2Ju3boF46Rjxvbr3x8A8OD9C22cGEIQRF5udmzXHh9t+ey55YZCejrvfrAJVpv+8zdoA6UlxUqlwsvLe94Di8APRsWYGUUwYbps2TIcxzUajbS0ROLrQ/cfIztH1tY2KwuCIObOnrX/t997xsX16t07y1yBLQMvL69XX4ON7XPnzAaOgyTJd9atefOtt2Pj+kd26SEruRMeHsoINeqvGe1CSi1XWhMVDK2Mk6Ge2jnpKjgEGRy7ktcvxjDZoFNooKGWqx0hAVlUVGpqPdHPpAjTxHePvJFbiZx3Wb1y2rAeJ1OLvTDNzfxqhUqbU1w7rDc0oN3iLXqKiNyMLMBKst3HhtrnwiuVyi+/3A0AuHjp8qBBg+g3JSYmntbrxA8+2KDWD1Fe98562Iz4CTRYpky5dzK815AhfL55I75/3z4vL69vv/teo9HcM9aOkbFPP7mYBGTPnr3WvvsBZpxx+mDTtpCQMK1Wu8H+mj4kPymWPQjt/qNLVy5//lX6/vjO/p988F7ySy/jGLZyEYzb0jENiQIACgoKTpyABv3b72Dlk3XumTTp5Gn4Pufl3SkrdfDUD2lxMQpAv7Txe6ZP23yteRfl56JuyCqZtbIENqwzh+kHAlhpjqJbz/DQQOqlq0KHgHaCLC2taJCbT1tFiOVD+3a5La0VkOqRCbGFFQ3eMHckq1doxgzsVlwJy0JzpLUolbR6Rjfrz9SihXXhtNBsN0ko2Z1EWv/OO7FdYifeffcv+/ZrtdrLly5hGDZs+HCUbv7j8OHPmrLniYnQ6u36wvy7plarlUqll5dX0syZ+/X+PoOPPtw4Z+7cwMDA33791fbTQ5n3B+bN2PvzgWHDRx06elIqLS4uKoiIjOrcGQ6t0RHEogeZdq0V1NXKnlv2wKad301JmjclaV5Bfm55qTQsPDKmC/zUkoB84pH5N1LTQkTN5geuAAqAQCkLEQGgMwpvvfXik6eu3uLh+Oc7t69c+lC1iq8jCJIkUeOsKRkZGbNnM5Q7SZLk6bNmknKI2traeQ8YQgQECe14Zl6e6WHPr37p6KE/+HzBopc//nPP+3RpRqG/ZrQrV1oTGSwmCa1fzfWLNc3Tg9wOuvWk78/MMxSftQ9CH9qsExpVQJhZKKuTq2eNiruQWabS6Ib3j7mUVSbGtVlFMlynul1SK2tQThvWk53TQ5I9bumk1pQxLV26ZMH8+3Jv3+bz+SNGjhw+YgSGYVJp8cKFC55+egU6Ztky+DbduXPHrOZCbN0CV7vcsvVTs7fm5xsWNP/9p6/sPcO62toZk8b9deSwjiAiI6OGDhvZuXMMQRJ//nEgafK4ahQnbYP8ROTlZs+ZNPzg/v9qtdqYLt2GDB8T06WbRqs59NtPI/p3u3H9Cszw4Lhhswqh0y19CJYxJQ4dKRL7mT1GrVaXlEgPHTo4bdoUE+vpMHQ63QvJL0EpPXis0IvmlZtIUQtpFrec4TRv+SNoe3rNs9B6hgW6avnPFqmqUyyc0PNmAQx63j++J7Kbg+OjS6obZ47rk1UoEwFYFspOEWoX2Wd+AKwH8wuCJdCsXRPJrmHJjuo7Mp0HqpIbWkF8xDZ1T5aWQxtNwnZycWVNg8S7WVB4ewkt1t43wlSbXKklSVKNt7JOwAqdu3W18cj+4kpvIQ/GQC1YyFJamqVTaOC5lDtRwWISAC8BfrGm5WGDroKsyk+IlngL+eHhzYOX5i1/hHHYLzu+pS4XFMH2jesNhpYBlxNMyHp1jcgorMa1qm4xnTIKqmeN7HYytbixoaFHl06pd6ruGRT9+Mvv/rLt7agQGHzfcDDX+gMmxMK6uvZkox0iFOs5mtkAwio8YSI9oqyi2nnFeiKJwbNraFTRNyt3aVr3EoQE+nh7CeUKtVzfC6hQqhVKdbNQa4rmUSePASAR8eM7G56RdRhLUXqgkOVoS7M1KqYnYWo96Zy93q6euy1U4YE50lpcC3vhc6W1IgBTSdV1qvGJ3XOktd649tEX1276z/tZTfVMLYrQ9ifZg5ZOYuOosVau1eGKVkK6DaXEKZKfDEICDbn4ShkUpAqVQZh7iwyCtFFf6SVXaKjmJboNRcn99kShtthnVVrOHHc9akDXfL1ScxPs+6XVrz/CCtTSrDH9IuCgr2CBtq60P/QeBQDUDO0MqksK+kOHQfDpq0ufWbfV28f3wxdhESEAYEI4UGnhSz5X2cK8RBaSfeYHNotQ9hpQlzjvVhBJ/ClH3oimzmq6MUXy09fHpLKHBCEB0JJW1sACe7olbX4ikQDokzx0uoR451cya2mdTWlpBd2LZ4i1bW9vgbFCY1tUWuuCiRu2M0TfCG8vV+qCANvgNQWCdGbe8G1vGc3b/ftGGY6Dkb3NBOu6+bvGB022KZuEsd+GeoIL73DruePTTd99vcvWo43ViVwuxwwjQnB5o5K5KQybt4jvLeITNADA5ApU+Y8RGO92PrNEqUuI46Oitr0m864uyrfQPXq26LSWoH4VSstl296GmUw34nQaXHYMjSOANao8IbVFdY40u1H3HRnSbnVmDnHk3SAh6XQFKtC3Y9pL9mk7qsCOnDgPHAwB/L17Pcac7lF+26YhTzdS4dRn+sgfBpamqJAAqIBQLIJ/FAGhmjZ5HNpf2DT9aPcp883pDudGY0h/MX1sXUtgoLiqMSpYDFfsMT+nkC1Qb/2xy80ln5YySGwDLQoAfwMCveK6dSYtTckyXpvkkamDvv3jmiX52SAr9QkMB6wDo19hrQhlowtvr/VkLJTkPMK6D7LFhqKPNVRm9pwVupeisVEsMipjoqwnRxvR0OIidOtpyWKyTVCrpVkCkRcyoHR5Zm3eILKk+ldyLrN8ZO+wkSH17ImEJltz5Em3sKFsNKB2QYJ2sp724oXKgJitkCZeCQnKKmtotejNqZutew7NnmQ0r2TJ2Mh2E6FsfE+dUTAfFmjltaMueJbA4xkCbqfTSsb0i8jOLerZzWjha4YlpTNxUPTf1wvb60w7ELi7y0/DRLL2Iqy7UQOrWQb072O5/hxjnDCynlQ3ZKCPqEoGm5Q0uKh7OFuUAkoZ2XhksILVX1QvIY+qIrBuPVkHhvt48f38DJ8KIR/PL5Ca3WJCxWjTatTUNq5vJx6O8XDMNBLaIHNwT7DT+jtZV1qPu7X1tNLm5HIUal1paYWNZpQ6ClFZbdPsvnarZEIWh2FDzXq+Ah4Wx9oKVmNMrafxXwmc0ReBopC0y1FL4eAPrc7gwSiU6roGpQ2bql7O3NjjwnuADWXFh6MV1tOW6fTtGQnVde6vnPwS1bIZf+23zD0mS85hAPiHY5OfxyLjybwr4OiWYxey+4Ub/gSdQgPOpeRBL16nw3hwkY/g5TtrQyL8Dq/D1MzREpKwqGEr12Uf2lt0wWj1dp5Q1HfB8pgxU5pKXFTnP3lFlpvBuPtd63aTBPHPWot1JHet2+2NQ+upw4CAzw9Vq3V1Fcr0k798tR8oW1i4Kb+sHvDNd6O6Fh5aoh6Hb7i/N09Rz/yV8hMLHpwQExVsqHbIHhb45V+5J0osmpsFQ/wfGxkwfauh59gS/10WfSlf8dFRM7Hs/y6Ds/7u/4Kp2Ud1F78/N7zJZQe7T8u+3M+DhRq0D/yiSV27h/u8+S0c/mI2ihUgEbw4tzd1lSRJhVp3p1Ree0mTVwmLn3JrCaqMiX2pJMzSDawKhrLIgNqFEzLvrUf+yE4g0gfL1ApMpyW9fTWDZsUmJFV+OB+A5tYXbOzj2FD9HJPaMqz7MGz5d0MD1ymuw3HRzccAEOgrKqtRVsnqIoOiVOJg/WIbzDAozudLwqL4YqMJ8N0n3Rc/byn6qsjLivjeEi//oNEvb9KqlH8l369TN5+JJCyKtLoeiSQsCteHa9GQdj4A/LAuoh5D4BJMd66Q+9dYCRrml8PFoFiLQgnLdRUmsw3mjOo8NRHWqOsIsl6h8fHi94jwee/RhD/T5W8fNLOscYgP/9m7g2sbrb2NiM5Bgjt6g2X2JsaeQDHv16e7INNZUadVasnoQMGSMYFLRt3z5ndp57Kh0U/sAftKg3xFIf6i2yVwz11De1LDXVDyhSRBgITfKRDOtWvu0CPJ2DDJuASMIMCkTXdQdT0bSHbbISN893XeXSI/oX9dbBShV417AojEgpSDwks/Ujt14b3kU18LWfVj6aujDFWHfmHQelYXEV8/pT8Ew1f8ELDwTWRA0eyfkQNiC4qMvqu2v8Ru98xF1vPv1xc1VjWHtOLnLuk+ef7ULb8eXD7VrvWhCIAVvDjEy6u59z88fiD22DasayKY9Sb5G1zqim49Rw/smldYpo9OsBfG66eGRvt686cmRugI8pkdV4mmd4nPwz5dnjipr88vV+vSpEYWN7GL9+b7obV1LCI+duAZuLrJh0cqf73eLJB7hgm/eizynUf6v7In/XKOIWjuJRLyePjdw/VreBjahptfJgwS6V/doUulR66WUTNbczSha2eGjewuPvpC1/Ebc9kjQpONbGgLHyL2iFBWxEDdxXlvfnavZrdOGwcn29OtJ/QWS7OEJelwkfXw7oa7JMERyMS3K5sOIUvegas0Bz4Kl9hDoMAoBoCQh6nUWsLcSF3z58MX9LkPrhFwcPlUuvWEPaD7dmcfhm/vPe9/29aIYVU+8XESPMPuw4E4wDRoCBtSVVopPwqwEmFkr9TGEPqWIg9GW+euMTqAPfa19FpDELXzSl3QXR/DiX8vTDKaJPLfZdGb749wRvj916eh9Vy5V0q3ngCA9OupE16FE2M3PNq3Xr8WLHOkqcmsLAT9FxNVFKycHJv8c+nZnEYeDtYmOXeQUBsg3SUYygoDaheo8NNVz86Qn1YQnfyi9IvnGkryDamkTvoSE52Wmpy24o0VAqFQ1H0IffSkYQFIDER2MrQPHjxxucXn6j0Ldj1f2r7WrMbM+n0PSZJeAUbF1bZCaKmIoX6rVe55gSQBMXaJor6u0XgDbguSov7eMPRMR6kh1x+qWHug2S2YPdCvc5DgSFrDuI0tTDmyFwEP8/XC1VryeiEzvoDjuEpLHroMe5CeTWoOazKh2dDjV/Iqqpo7j3OlNSQJptxlaI9++Rf4K3tPH9hVnFtLsCEdD9xzyAjudvJTv/iP691ESoTys/6FYdDHvyJCjcbeYI0yZcZZUoMa5PUnLGOuboQDQujjZ5AP+kNGDYAT5wJ9oONMkiRBgm6djBb5QWFQxp5uk2BotTz9kqWzrcq8DrMKXexeslGh1imUqlJZI7UVXztDkiQeP4HaU16jQBtwWw6lQlm35YGIpAG+jNEhf6bVF1Y3L66VXqIcsyH3nUNmoqJtZEgXmLz6/oLFFUQ2H7gNF0G4O1bfjWrh5xBjztX3lXhTyzrR5zOpNPC3NkDM/M1gB5gtB7FBhPLdy3oeOXEeraDrcvm551/4aYZceG/uxjgyIEIx6214VdXIzz0vvP4bJq/u378PausEAtjPSsqkjI5yDOaF8XnLHzGUBBk3lfP48MPdac2RapN4xfimchY6VvJCxZdOhMQPihk3veZbuNiUjdCfg7bGnGF3K1adYy21Ct36QxVvTA9dPQVugASX8xW/XK07lc1MiGWXtWZaytg4yenVLYyVWzgUmjxT+amWZnkJ+RoCECQGfS8Ms7IkCRrVSllPK3+ii3cax8ZJQn14NY06tkZCW8blwVC2JJHY1rVpF/uSF4lDwgc+/nJQ9z4CkVgbP1EbPxHotF6/vIaBmwq1rqxcBjMOWub8ULOvBDWVV1TKkM4kSEBoCT5aq721KKqhYhIHd7LrXqmN5mVOtP7EU+RsmTHsEP5Mq/87o2HhUP8nxwcBDAyJ9R4SCyXhkbQGZ+hNU2KD4a9sjUlaH8dxoZe3Rj9su0XokwbDgo0KcrNqhENrlcH+MC+/eka3OiUU3QIe675NydCGml/0l4Xw3Sp35ErraT362VhZenbji+hyYPc+o5M/BoCnmbcBZD8EyrMAqf9W8Jg1K1ZCueGB8NuLYVjaqgkhfEX//kYr3f90WzD+rZ22n7w4BGqKxkoYROOwhFpHfnu+5tvz0Inm49iXi6K6hQrv7ecjEeGv7GtTcPDULfmr+83MS6XL0uIaTYgvz0dk/pcS1y+Hh8ILpv473bNB/Q7UqiT5JdWdQ8QkCSQ87a9nc0mNYulMuFhZhD98QIXekWfAvppQ9opQ3I2cd7YUrdH9d2TkjEs1ZbdvHnxqyoWtb0AVsHwrLMrTR0Ix/06M7h390mxMRg2EYVAqUKHS6Focsomyaug7ZpaoYRNhuuDYftBmeEL9muOuy+M5CYYW0xLko18Wjd0AM0Vjeorb4Xd71yk4lXFYV7FpAxKC1EARavq+M+JCT6951rTJKqtG+Ni0QQ1KzaLphl7kwfqQa2mtYTQJPZXkcpLXf27X8S4Mhro+iWSH8w5YJz9Deg+cseOP8EGjTG+qSIfZcy9/mExXqOAyRyAkltEQSQAeH2hNeyKLqxpVGkKnj2mGhzIXuTSdJ5L+E1wJtcsEWGNkluCe/aGyKC0AbSZ6NFyPOuvgd8CDOL2629Hn4V+HAakXj+2TbEkpgtHPh0cY/blxHPfxkaDFsl6aGw8A2Hk4x/YlSXRNsXIJT3v8WqGEp/3jYv6u369gGBDpg0IK+ogqGq5NxwP7bWjHMqCtKPxkT+iTLj9r8rMBAIOWvGJ6GE8IM+nqhrobjSEYANoy2FhdWgkrS7a9vWXb21t++ew7rUajyL1h6Yn4+n4UL0zbOVj8xwlDht3sNKb8fw4CAPrOf9KsCB34eDLAsIqbcJnGNoLheL+FcOHV20d+Ap4ECQR8LMzXzLs3NBZKwlpFyx1HbYQgwfUCJQ8Hi0ebGXES4ieaMQwW2NZomlsbBALzs1fpVtVfAkOri2cMziqqWZqUeKe0bmlS4qQ4+NH64qTR0iysEqH24ioRiruH8+5Sh9FK9FOrkNcV5eI8/pRPjLxjDMPv/fgXOITx42S0R/b96wCA0Be+R1fDQwPxJ6HwJH+GBfZmvXiK29LaiaOh52Vplh1JEmc/hGsRT9t20C/aULqPGPfG9s7D74bFAvqQQluIHjV5+rZDcJH0374mtM2VPR7A4m9ghdm+FTGjexh50DseivQSYunFKqJdPoDP/hf+fRePCdz+kFGl2tCeQb+9NgIA8P5PN1txIpky4YVMqCizpTV+Yv69vUk/L6AlQHiwtfVT3U6EZrvChvLdwnqyU34iTq5fcc9/vvcKCJ7x2Z+MmzJ//apeapg00VCY7Ztxxqv3qIj3T+saZLgvdO3JM3tMg4nopRZXNXYmIRgGR5C1OI6+Oift33eWj39j+7jXtzFuUtXX/JV8P/NZeDzTE4Z1+08ZBpEAAMwecOvgdzl//Bd4FrfKVJ8cq3r+nuAN85jJk5pG3ZPfMQt4nQRBgjEbcg890yWhsxfMLxEEhsGfUhKQBAHWf59W3aAObxpyqFJr1GpNv6qzpo/zyUH4qbucXoBPikgaFjlnZLRaSwAsHABs1WR/ALCyGtWpfDPqlV7PxAaS139uV0a+/RNKblDG5FrraUvr0bFXHhL5BSY89JxvZBe+t0SrkFflpKV88xF1wI3GkARxpeyrF0W9RvlOWyn0DwGl2cQfH5qW1tNfqiL9pJovIXVQ6313waiSRtMol17+V15uZFLri+8cXD41etS9seNneAWGEFpNXWFuyneb1PXMtfCkl2HxvxUYB5A6nUJWUXL1dG0BDFl4JD9fqf35Su2KCUHDuoqDJTyCBAXVmk3HKnMrLGbwjmc0NKpbVoTHMxoySsyvgH08w8xoq+lb80N9+a9MCe0SQIoEvNIa1ff/FFTDPgUyNsZg30vLZdlSQYNSFz8QBkbp5GXe1mgJDKYficvZ1bVqXkSQuKS6sXuk39WcqrDgwLd+SHvorl7B/tCArp7RzcrC8e6VjncJmF9Q+/XDuqP8NF88bz8J+iWG0GwO05ZBs6DenlKZokxoNHico4OglmbpAB7oJ1GoNAOifSgDyhgITUU8qWwk+sKcuX6nc4hYKDRSmlKFt0LeIBJLVI1yVM9kakDpIpQNBnSjnWWh7SlC+WxOHLF2uQ6Hz1IzxZP6fDjagrxRoV8HycdLyKMv5UTVKtGrOKgPDbKeF29VonXkJSKeXAXzYJHeCgB/v5VAzDtxFjb4DgsCF6vZOL+11bSnI89n9cRPKytbupX8NLvOJWciOWxkSA9YOZ9WJLeylBMGgFylzS2pC/X3DvQRkcalugRBir1hAS9GMLN/cpVuWJDFQTCVMteL0GQ7I6Ee6MK3wnknCNKFbe8ON6AcHK1ALc3i8fmDYgMAIKnZrAzrib4kt4prCyoMEVUejlVX1wr5uFKtRQoUdqPpDajhLiZm1BSkWBko/SzPguqQjjyfpc47yS7rycHhKtA6xpT1NLsYyZVcWVkNLTBEEjiGKdWGLiPEmTTp6H6G6igSFzDWjpcWM9t8JSLT4X6EpCFL7tMLuAPZ7eLIs6hkwch5Z1kAkJOfHK713/29eWgzPaC6QW1kPfUND/ppXkbyaMGkhNNp0jNphsqN4pJKavl4GBuNijC7oVt1BHksxcWDFJJZ2ZuEs9B51xGEC7s2EZz85GAPJCDpI1npk1hT82UXblVigGRsqA+YwcJJCQsmJVA2FJlR60+NzOjxG6VURFXS0Nye386wsLSezzbnXR/BYZcu5uQnh6sQRva6wVyS1Rg+4JsMKQwmZKVlTUtqm7BgUsKPR1MwDEMePbKhDI+ewfy7+//vGFz+0+3IdrIjzy5T5fKuTQQnPzncmircWo8mlKKTB9gkRUnDBkc4syOqlswyR96JBtQdy+ZN4eQnh9uBhuB501ZUNcuCSQm40Nt8VBSZTuOSu9M3zYw07eCOPM4q550Nv3Kc/ORwd+CEWh5Pqdb5+fmcTrOW/IkIlphKUbNqlAVfzdbjPBuKs8R6unziZ+vk57UvnpgyKLq9ToeDww4YsbCiQovDaBbckxAcHGBkRktbSC65nSOf7RwbypZOJJZ0bdooP6990VzTu2zWsGWzhsEZw/kVr3110plnx8HRMpoSw7qBEi++7VbWS8BfcE/CT8fgdFpDcklvQ6Oalg+hdwW6UTWos8FZIj9dbzttk59/f/Tw3x89LKuznhnl4HA1uFXr2ZQdYrDgnoQ5d/UzkaKGQ1mQ32WdCMXZYD11OoINMZYW5effHz1Mv/rr8Su/Hr/y/cHzcC7ZpZyicubUOA4Ol6SPcB6z3p6H42odyM2TGrZ8i+68gIcvuAdGRZuTS6VVxaVVD0wewCb7yRYbyooyJp5+7Qr2c+o6XJYD8dlP/6AL/XuEIxs6/+6EwbFwTDIHh6swGwerqVdU1cqrao3WuM/Nl5pu+lug3lxwT/95d/enS9HKShnGmkS8Zw4TaZ38JEiSDXOJGPLTrP/+9iOj0YWS0kqAgU5BPuhq+p3KpxeOe+Ldn/hCUb9uhvfzf2e4VnoO1wRAoQLF+SgG2jkA/ksNlmgKZRp944L9jRYyMUUkggsrQU/rWsGYPrBwnz0x0I32D2pyYGm9wwxo6zPvLLCeNhrQ/evuu54lxXEMGdCc4tqiiloMYNtfmffhd//MGBXXaLLK9qqdfzv5xDk4DKilWRiG+fr6yJUaZD3r6hr6xMBlPm/mVSd0CyFIxrJx1FfPzOBIhlWl30wCkj0GFLjUhro6C88K42lr9dKct35GZjShZ0RsJPTW73sOLkB08FTa9FFxAAPHzmfcM8JoiYVhXfwu5lsctsjB4UBMtUjfmAD0kz6mX7ivf4COJJVqnVqjU2l0127CNa4TuodY+h7K6hWUXSVoi+qF+IsxgHGJeEcqULd23m2Un4iBMYEAw3p3DmhUamaN7xPk6432l1bXAwDiu8GFZ69kFlHHH/tx+x81LhuhyNER5WejAvnvdXUNyIAG+Qj9AkybO/VJdRIQJKnVkUq1VqnRKdW69FtFTVbVAvq7qb3DXT5omQ0ilO+qpiOSNdaTgRXree/A6MG9YIkcKrCjrCcCWU8AQGLvzpQZvWfhij84L95xWJmdbgqOY+crDWtYejyWQmGWv2P6O2AkD8NwHBMKhH56lRnq32NsInTPtTrizNXsy1klfTrDBT8uZ5ZAw8qqUiYWzBnhu2ziJ2uwsXj+1YXDqcsD47sAAK5n5Cf0gAMTYzuHxXYOq20wmslImdHhsX4X8jquF2/W5FGL8DButXFxHjRcXSKw9mU+kVqa0LVjFUX4+hqymigA2lcf/SQBMCc/KTAAx9/pZSUGsotrq+qUWcVXls5M5PPwRrkcWU84lrR3xNWc8n76x1R7h7Ntzc5kFy37gbvGeSfYkjtqxeiQEf27UGYUmU501d+neckEivDsbwhG4N4y32x+/JvNjwMW4NjTCPUTMbbpsSq00XfiODYipJ5xGlbORK7BAkJC4RaMthD6xuY3pNVYekNQ+Se9hL6uzrC8R5CPIYFuGQzp1CvZlVV1UATU1hn+ClMnDKYfh6wna0l2RVko7gLnHbh+uY72GR1SfOBdggRzJvQd1sXWVQ8fe+4r4HFU16vkGsy6vfMLssPkRUWEXM0uM5584TmuZes+Hhq4FDxcwhNdRfITtCA/DWh1xPmMco1WJ6tr1PEl08cN2PX7FfoBV3PKr+aUo8tIfiIaZKXAzWmjDcXb2Xoa1tpkJXbJT7PQRWjxgXdJEhRGL4BXMLfROE49E2jsUObC5ENgr0OSNK4v1abdPpMvWP4Hor+BUH7qrwa2LD9BXaP60q0KWZ1cLlcIeZiEpz1+vRAtGY9EqLcY1jP172IwxLm1NrtUbjIttC02tF1bgFDmnT3Ou13y8/0fL1i3nnQbiqxnAbKeAMwa1xd0bJo1Y2llcWmV6QF2Jyf0x0eFBzc9LGw3BB0Y5hto2/tZXCVPz5fJ6uRCHvxWevuHdJLApHy5TNG8Gt2NPOpypppZtNPAMhHazhOX8XYd90nriPBgGNZT/0kmbffiPRMSJI3tExUejK6ZGjvSLseEbP7P+sN2OHC+Tq3sp1eLgearl5q5WSC7cbtMLm9E1hO9oVExMVCEXiuke/H9uwSSJGlqPT2GVotQvD2dd/1yHWyxoLbXflKcuJCRtX/DrV8/ML2p7o+1dYfX1uo31cAlxtYTMmt8HzcKgDrhTAzvhCXNaKcCZQYBGA9L3cbISrUat/jTUAHQFvUnSZInbxSWVNQIjBWNhKc9k16ydGZiea1i0XRDBun1pVMYd2d48Q0eIUJbZ0PbqYyJVV2breaTA6nPJ82E39ILGZHS3zEMC4gWoo9qbtdFpl9to/9J94ivtUMNU9LYPgdO3RzcE7ZUF5dWRYUHw/eszQWGSWPhTxQyylJPl6LfbH7ckiXt1yUQyXlL8vN0amHXMInaeNV4iUSs44sfm9Z/+4FUaRUcO3Ipqyw9p5gKhrqX/Exur6omuzuRWi8/YbmZG8tPxHNJ/emvIaGnYeFsU/tYUlXPsJ6vf8ui6tf2NKA8DHSLNXQZ0KEUaGR4sFZHFBRBIWO99N36A6I/wsEm66xUqVp8QM9ALc3SkrhIgOu0WmRAAyVm/PfTqYVxEb51Ck1eWQPNdHoDgIkEvEZS1Ds6sFBaIRJLVI1yBcF/ZvYA6rANB3MZj9bN38h/ZU9NKKJ1BtTe0nq83Zx39ljPtrD5QCpJQn8TfVdvZJeYppSZGlT/z5UM2HrcMTFr7JDdRH63tLSqvEJm46PxcAxgmNlpbHf0W9+YAJUeT/jAtQ6Tn/PTqYXllTVxEb7S6kbKekok3nrrCbxEQi3PW8LTZhXKliQlVterHps2iCDhuBxGPZMbkdwujnx7ZOHZ5ry3Wn4CY7OILCm1y3jINxkerG8L6fDWE5YZEqRGR+qa/lXrCC1BqrWEjiCnjOodFmpHv1CjQt0gV5luCqXa/KZQgw4Dkp8AkPT0EWU6oXUoqausU9FNJ0KlVqu1xH0T+5EA5JbAueAXMsskPO2JlCLKhV89o5t7RULbB357FH6ykowb19CFoVYLjfNKaytE0ZZ/4rGU7JJmR95UizZpz32XLc4A92CgWgSgoNDawpAUhobCliAB6BYTTqWd6LFTFEi9lFkSGwpLF1WqDmQ99f47YeqwI9MJp9YW1Oj0Q5XoplMvP0WhIYEVlbKDF/KQ0ZTwtFdzyp+dnbh5f0qDQuPjLQDuSXKrIqF29cjjzraeBCvlZ2bqNQwDErFXsI/A0hbkI+DzML7JtPzNB9No1+jSk/5Vhlt4sO+Vmx3XegIA6m3UifqtUWGzYITNhwZwHMMxTO/Ww+khFVW1yHoGBvphGEiRGyqcOgKoeokE4EZhA6U64TA6kryRL9MRJEN4ovdRqVLnldUvTUqsqlM+Pn2wXKl9cPIAkgQ5UihFj101qmdyOxGa7GRH3rlZeP3IJcBOSH2PupLgRUWEmK1EzM2X3pbWdgmVaMsLGSJ088G0sd39BsXHoBd3A4lQ2t0v38w3XGLry283mgQjpTCbJGPT+11eWYN22CIYMcxcwZN+rNCF9GJkOgEAnUID/jh7q18XVvduOxad1pBV9xaJ4iJE1H61VpdZVGdiN5vuxReP6x95MlWamgcTetdvVwAA/riUL+Fp/9FL0fzy+pWzDPVMq2d0M00ldXBw5zvvrDMhmakG5x2ZTn1/IdN6ogGyOIapcS/z6yPdrruWUXBVv6HuxMs386mNfmSHlZ9MDO+wXjfqp69dzJBS1jM4CArGVlNaLkPWMzw0MDw0EGYsWfe5aw/5yaBWri6UaZqsJxoaYqbwU/+vVMLTns8olfC0xZUNi2dAKfrQvTALf+5mie0ilIU4VYTiTnTeCYJVzjs9fUQSRFyPGABI1MRi2sGSVwCtnv6LDmI7+YSqCk0f7dTtulO3YVXNtYyCrw9cYNy64+itHUdvdXDrmaaAP1FFUsMcCrolPX9TSheMtpeBMj5S59OLS8sNGfzwsEBzVqIDESBpjldeya2+VVJfXSuvhsvJmXlHdHxxdIi3jiDvn5Sg0hAP3jtAS5Azx/YhAcgqgj9sx64VSnja67crlyRZTCUxYKEX71QbyndSy6aVCa9soekrGxUejNpXqI5AdMNtaW1EJ5gg1kAR2lw3xwDZUNPLHAizH4LSimbBiFx7G+2nYXqlifDsFBIA46C0wzogJEnKGgxhkAallr4OR3Ut8wMc5O8j4WlLG4QSnvbv69BbP3YVmssjV6D/fjoNCtLckrpnZg3e+tuNgvL6Lp08v5y2FQklp5QxsVl+ZqZeg/Kzu1FMk9KhKBN0R7++K458Tf230ZIIpcNZT/MYfwrO3ywubSr5hNazaUEzHnqr7fnI0IUnsp509cmuD5+TEUb2oicbSmTKwspGHiAYG/0u1bUN0vLqGrl68YzBeWV1S5MS75TWoSLQRdMHN6q0D0yG/vuV7HKYmr9eRHnxblrPlOycISO4k0KfOM6qpd5b1jfXssvpLYC3pbWd0Ndbj9ZCJJTDLs7fbM7zGLSnfkNJdfSjZdPjGLvtHdtrN5CpDsvWdkJbg28XfnhP02PoxtQ/OGzYgDgYZsmrIklwpxQ6TxkF1choAgCOXimQ8LSXssqWJiXWNqpRPNStSXaCI487w3lve1+zQ4EKhy4/TW8mAZg+Np5hQyn5ibBFhHJYge62o2Al3fChH1x+01gg6xgeJywwQm89megj1xz88J6mG/W2NFSVZhfXSHjac/rE0dGr0Fye1ieUrupVZ0F5/eIZgzU6srwWzig5nSZ163qmtmDFhjpYJx45cV7HIuedqs00GHQ+jgn4PAGfh3r+0JbXtPWN8VerVGqV6ra01ksoqK6po/8SaHGv2E6GNWc47KXZbaeZTjoGBWpD1RsG+5FUjQpVbn7pbbNbXmlOXmmvKH/uz2QK3ZgSJJg5ro9SrXvo3gFKte7hKQMVKu3D9w5AXUn6QfQVcLSdPjaaqe/ytDGVxFoc7sjzHSs/SZLkscV5b7Z9lcWGZs36RtjHxsDUbYwI9K6WazEAZDXNw9CCA+FAz1AVsyaUw3bC/EWEqtHKAUq1zvoj3Gh08GJHHRkJT3v4IhyWfPwadK3OpsOGsb/05vLwRdiVdCmrbPlM2I+0ImnQ9gOpGQXVZ1PuUM2dDHJrCfp4EVYtOee8bBLuQOvJmq5NMytGGG7Qd68LeHjX6PCu0Z1i9VtMVFh00yYSCUUiuAoCodMWlBml1KtkdWW1ak6EtgK1lkCaMa+0jrEVltdTG7sCPx2AKjywVq5eOTPhTmmdhKdNz6+S8LQolVRRq1w0bZBaS1Trl5lDSfl/bzRPt+uAIjTbnCOPe9bETzOmk5KfAIDBw5qXJm6q8tS3AxouGW5IL6wJ8fPq2yMyONAPbZQxLatVc5FQu0htDElTWNxS5MH07XpDB+q8ZAmZhTKCBNNH9yZIMGVkL1gEWgjjLZf1kdC/9KWgqXlVS5MS1VqiTGbNgch1k0ioAx153KGFn8Cl2CRg0hQhai3BLO3W28+yCkNXzNQRcRgGamSGqwCAQb07I0saHSzmXHgOj0HC0569CRNHRy7DJBLKvJ/WdyVdza5YOjOxtLrx8emDSRJkFkGreuRygZV6Jo/HVITijnLeCVcu12HRZ6fLT2pynXVD3yk0AMNAVERoiJ9XeQWs6qDwxrW5+moPDg7PoAoPbFRpH506qLZR/di0QfUKzWPTBsmV2oenDNToCLRM/LkMaGHP6hP0sgaV9XqmXE8XoQwb6gAXHllPGyv4nIDdkbPUxhCNjilCqTQxShHzcDMviCW1BRwcjuVESpF+Big0dpdvwe8Faoo/qpel13IqliYl1is0D0xOgA0jtHqm1e4sQh3iyOOOcd4BYKH1NCs/m22oGUceyk/DJQzERIWF+jeLUG9cmyOtLeGbH67OweGmKAW+t4pkEp72Rm4lVQSallfVtLQcHE2fXw7LUY5fgx2fWYWyGWPiAQAKlfaLw+leDcUPDfGjbyN7+ozs6cN+Edpq6CIUb7vzrtO5pPDTottuK5gl+Uk1A8L/wgMNc8Aw26oUOTjcDlgQOraPRkfOv7u/liBnj++rI8jiSri03Ok06Lkf0s9avlNa16jjEyT48d/sXb9f8RbxvYU84M603ZHH2163xLOte8ShtGw6GfLTFke+ubmwqUOmS+dOOoIsM46EcnB4GPp5IoVUEulkarE+rQSniqTkVmICb42OkOtgzTipPxguoay/OjmxCxD5/fJPhuljuosIbaMNbWsMtN2Xem+l8LSy8BElP+mmk050iITz3zk8mCo8sKxGsSQpUVolXzxjcGFFwxJ90PPhKQP1g5006DC66USEBXp7CdxbhLYRvE3Oe3t3bdpqOluUn3QRii6H+gl1SrlOKdcab1EhPiqNLs+4qJ6Dw/NA4+jT8qC/lXqnEgCw81AaMpqmphMAgFJJ4xNgVqDDilB+25cMaxfaFO60Ij9TG0P6k/CzUqAw1+XZ9ALD/L24Gm8OD4aaKkIvWpLr+MhoMkwn/aZe0YF/efkTSriAUgcEZ33hZ5uTRS2RqgixtHEdMhwdhCo8UKMlHpiUgGaLqLSEl9hodI5ZKbrr9ysYBgZ0C7ZFhLKZVotQvNVdm+1S+Nka02nkv5OtWPadg40Men//oPf3owuuPheP5bR+nsj5DOhuV9UrTY0mZTrpO0f2icBEcNSOddjsxbfahrZSgTrfeDpCeLqwN4rDTgIHjkMXYh98iXFT7+c3w4nXr86h7+z32m7uPXYsEp7WUBB6p8qs0aQOY4hQAR+PCpFYEqGeDc5K5731j2+Qn03ml5Of7kLswheQuszb+yHjpsxPnhv0/n5cCBcFoERo2ntLXHSmHksVHoimilgymgzTKaFdvXtQNLAgQt0lldQ6EYqV3TzGspFLbRKezQZUz56TnP/OwWErwYSsUccX63NEEpN/qcMYO5+bA7vjPzuYqmqomTcBNikxOJfdvKQd+4eEbnzjCdsPZsnwYwdZz6LbdNefs57uDvHAt8QD37r6LDoQVXggaey5mw19IuQ0Lx5FQt29nqkV4HautelU+ckN1OVohjKdnA1lAy2mkvrHBuNeLaeS2I9djrxdCpTEnChY22o9bSye53ALGEaTs6HtBj3caaWK3jSVhONY7+jAjiZCcXuin02zNtipPY0fg/Pf3Rez5pKzoe1DFR5oyX9HWEkljekXaSmV5KkiFGeB8+4A6wmjnxwegRVDydnQ9seKFJWYiFBvEb9TgLdniFAbbaitCtQ5zruzuow4+emmtGgiORvaDliqn7dkOhHo6kTL9UweCW6b/CSc4Lw7zHRy8tMzsNE4cja0Pb14BlbioYgwf2+xyOKEDc8TobgtEz/hqpXuk23n5CcHh6tSSQADY/tHekxXUos2tGUFCivnHSk/UZcRJz85Wq8rORHK5lRSXFQAX+zvGSK0RfD27Tsina1AOfnJweEkbEwl4Tg2qHtoBxGhuFXnnXRo4t3xHfRc9JODw0lY8t8tmU4J7eqQuE42FtW7hQi1YkOtKVACKkXMXbQnJz85OJzqxVNYycIDvQgVCvCu4X7uPiTUFszny46cOK/V6vh8tnXKc3garY5mUnfEf3jEoWfEYR76ABGzA+pBE+jy+ISo3JI6UuUha+Ekr//c7JAR8waUBKTeejpWfjoYzn93XxyYBeIsaXsaTStrewBjS+orFgb6iqpVUISajmga2dOHGtHUICtl/4gmS+CWujYdZD2dviAHBZc+checlEPnUvPtUxBqYyoJADAhoTPwIMxGQnELy3U45BmdaDo5+cnBwfJUUnSoj1AS4En1TKY21FKUE3Ov2XSc/OTgcG0qSWKuqD6xZ5jH1DOZBXfOUu/OtZ6c/OTgcAlWupIYoFsTuoVgluuZPECE4oyuzXZc6t1hcPKTg8N5WPHfrUhRJEK9hLzYThbrmdwUug01UqCkY1aL4+QnB4fn0OpUEmJ8QpSVRY/dUYTSwY1GLrHeeTeFk58cHO2Jvakkf4kwwEfkqSIUN2TeAYm31Xlvj4olLvrpAeA/POLw6ndnPCYHhe0DmSTm6pnGJ0TZOCTUjUQosqEGBUoSbcm8t1+xJwNOfrovDrR3nOl0oRcvt9DQSb81JsxXoG9r9LzOTtxQ+Nmmps12yjtx8tPDaLts5IQn26QoBV2EYhhA85k8T4TymwxgW+Sn+yXuOTg47KJ1DZ2g6eCRfcLzSuvKy8vqGxpNH7xfBJ5WAr1gtwN3xHIdpEvkJ+e/c3C0sxdvVxZeQqtnAgA8MDFu8pAuZh+cYT3dSITibViuw2WhTw4OjvandQ2d3SOap9PH94gxfdjfM418f/cC27Ntfavu2K6mk5Ofnk3r5oBwuSNLaMtyRsXDHkp74eHYuUpfKwd4aeoZeyTGDrvprQqC/+T0fkIBj9qZkVNgaj27+TPzMG4xoql1ySNOeHJwsBdCp2tFXM72QkYbU0n+EiEm8CZIcDm7HHnxnqQ9W21A29t6cvKTgwMAMCzIpuHEamkWuoABTOztJfb2CvUTtbgF+whv3JG1+N2m20d5SwWhtXL1vLHdJTzt9ZzKJUmJ1q1nbi0zieQWkVBr4wA4ODicgVqaNaZfZCu0yIiQ+vNWXWwEj29cm6mB0jIqIsTafeC5lMN0eUi9FS++Cg/00tUzEu4Skyy8guB7iX3k9cp/bxQDADQ6Ir+s7tjF7KUzoRmN7xFD9+LdGvdbtINLvnN4CnYoxFA/UVp+TYuPqJZm8XnwS30qTQoAUCmVV7PL0E3FJZVWz8W+UzcrRSVNIpQgwYDuIRKeNr+8/pGpAwEAp9NLkPW04rybilAPM6AuSLtzxfMdgVakg9w6g8QYOiHXYAEhoS1swYZC9BEhzDQOHR4P1+nXk0AQJJY0ri+lPYtLKq2Y0Znj+964I2vx5FvMwgf7eUl42guZpUuTEkkSXM2ukPC01XXK+kYNFQldPaNbi0/kFl487l6JI05+crg7amkWzjOkpFVKpS2mrenL18J3UC3NIgjS10dielNUREgLUpQEVA5ppFUbbbYgVE6Lh+qHugG5Ulteo4AB0NsV0JICcFoviq3jdiIUZ7P15OQnh6dCWRN6aNKSaaOwrhDpwtbPz9BgnnunCF1IGteXbkONnkv/FPrEPdmWLLxYBOOh1fWqh6dAt/2fG/Cp1VpCWi2X8LQ50ppH9e68J4lQvrtoTw7Pxq1dcrugmzkdaUjvHDiZPrhnJ2TajLI9tC/frPH9fvs33VIqSS3NwjDM19eHus+pNOnYfpGE/ikQSeP6AgCYz0V7iqRxfQ+cvDmgq5HGbDGVhJDwtI0q8NDdfb87nnUpq0zC05ZWNy6bNuDzw+mnUqVLZyZu3p9yu6TuzPVcejDU3cFZaz256iUOz4OqMRJ6edP3m49UGn/5cMvjepH1tPEcmFK0tFmK8pqmCln34s1KUTUQkgDczK+W8LRpeVVL9G77lexyCU9bJmuUKzUAgPMZRqkks7hXPZP7ZeE5ONwXysz5igWmt7bszlsVNHr5CUy9eFPo9ho+V5MNtT0Vb5pKGtANpt3T8qqWzkxUa4m80joJT5t6pwoFQM+ml0h42rpGTZms0d5UEpuxbkA5+cnB4WD5icwcr8mSUjFKM9meUiN5qM/PWAyDqgnDA8oblSIRnABPFTMxnqLpsUBUuJnnShrXJ0X/FLankny8BRKe9sadyqVJ0HRW1CoAACdT4VOrtURxFQyAZhXVLJo+GADw741iTxKhlgwoNyiEg8OJKDU6S1nppLF9o8LNyEMAwOwJ/WwpZlJrjQwQPQza4nNRXrxZhgfVDwuqQ1tCJxJt3fzU3UN4vYN1J85dT+hEpqVnJXQiY3yU3UN4CZ3IW7eyu4fw+oYSt4pkqD2pul7lMSIUZ2HWiIt+cngkGhJneNlUHsn0y8eQh9bDoMLIXvSrBK0U1DzGt9OfS1paOay3+SkeamnWqTRp66Zf8nCssqRw6czEpVP7BvmI5o/vVVvbgDa3FqFmDSg3IJmDw+mYiVEa27WksX2tuPNWkIgM+XE/Px+mF2/Bt2RIUVMvnkp/2dtlH0rroZJWyncfuAIw4O/v4+/v89mp8s9Owf5R98W0jImTnxwcTkGAETX1jQG+Ysb+3DtF3WI7m71L0lhYeESZzuLSSpIkb9yRJZhUGgkworpOHuQHq+jlKq2Pl9FXmyCx3LyW69gpBnQNMjUFPD5fp9Xa3WUPKU25IxvQNTAsEL52S6qTQW4twZhx1yArZduMOy4Lz8HRfuiI1ggUuos9PD7CUhi0yXE38xStWnMXo5xRtTSLp2+x5/H5SNIqFCrquBa67Gmh27OXbiydmegZ2hPBZ1XBPBf95PBghJG9NMXZ9O/a6TSpfiwT0JEkQZA4BuucCJJE1U4kCeOjhL7JcuroeABAWXk1nGvXO1ypUll+cCZyhdJK4JLHs3iT3iL7IOcdnozxgVERIQdPpg9qqsm3IkXpNao2yk93EaHcODvzFOXeccbb3blbV8AmOsjLZPuLxbD8Aiv+tRkDhwGMNFE8AoyoqpVX16kApubpJadWqw2QCM9mVkvEXsbHwvvSBausHnbli0WGJv2aBpWfWFCrFQJQRyjVAICIQJHpeczQtzZRCpTZRkUjaVwf1OOUknHb+tB79wJns/zk4PB49J5xa7AiKtEi7NqmeKWJ9TS45xjAZHVKWb1SpdaIRTxkPWsaVDTrCQilQTD6+koYiamcXDjrEwnPa5a67Jvgt23ldNam41mtQLnZSx2KYYOG2HjkxWuXgXuCRKKXoNn21csN/rUVV9oUNPEIAPO9RgIeptIHWwMk0AiapbpOQRlNuNiRSNj0qNB6op2U9eQ3TWjGeGYaqIAtUlQfk0CpJNtfplkvXg/JknohPhu0JwdHB6SurmFw92AAQNcY06AezcUG4MJNaWxoc+7+ak5532j/FDnTWRZG9uJV5KIJ8JbkZ3UdbBMyMZ0IrKy6HlnPWq3Q1HrqC6R4JH3UU55R/YD1qCg1DMX60Hv3gi0KlJ3powpa7ZtZqqtlvfqNQJeff+bJ1199kbpp8rT7rl1PBe5A4uABfx78yfoxU2YsuHI1hb4Hx/GyooylTz3/2+9/ALeixT9rQ4O8axzsOwQALFvy6HvvvI4ul5dXDhw6XqNx2FJolufHQW114Sb0kWNDxch6dgoNIAjyyIXsfjEBTQqUiU4DZ3ZotPDW/vE9/jnzr/UTeOWl5MMHDqLLG7dsuffeSejyri+/Xf3SKwzrCa2tkKdQ65pHPRGYWSlKN6OUDUVdAA4RoQ2yMp/ATvo3z8UilC0GlJ3IamrpVwMD/Bk7a2oN63zdTDkTGhpSLas5evTvoKDAuyaMPXr4511ffffq6+8A1qPT6egvSiQUisXeKpWqUWEY90sPqCFwHM/PuQY89M/a2NiILhz7c9+AhL7V1bJ9vx4cPGjA4EEJ0vz02J6D5HLDAfYijOylk2ZjvGbPupsZ+QnO3yym7CYAIDwUWRzyyPlsZDFM5ScdktAFSIQkQdTW1iJTiwEgEAjFErFGo6FeHTS1ao3hGa9dEYvFRYWFR4+fSpo+eeniR+6+a9zgwaMZD1yv0PJtCDXMGNeXKUX1Vfqzxvd1tAh1sS/PCgPKTvkJAIjrM8xUuTB2AgAWL3owNDTk+N8n7394GbWzMDdl6eMPb/hgM2VkWcv1lDT6i3r04YUffbBu564977z7odnj/fx8czIu2T4/jW3Y+GcdmjhoQELftPSMuybNRnuCg4MyU8/9e+z3ISPvafWzi0V8DUnW1TUk9ghm6M/zxpKz2XTq32m1hgAYsCI/EdStt/MKhw4Y3JwHn5n00ZZPjv7556svvcy4y+NLl4jF4p07Ptv4/oZarXD1a29/9cWWGdPvnThx/OlTp2kHYiqNjs/jU6mksf0i7+QVdTXXBcCUoqWVUeEhVibytVaEGl60q2woV0jvAFY8uRjancdX0Hc+v+oNAMC9kycCz+L9d9+8nXkZw7Dv9v4PeDSrX34WAED/UayqgmWYXbpEt+VhVSpYFYSgop/nbxaXVsgo0xkeGhgeBjeYa9fnmDAAjl3MQQdbkZ+oKR7ljlCUUyTgoQ2Q+u5ykiQJHWNbvvJpAMDba99DmXdoUpfB1/7NNzvVGuiwU/EGU8utM/Hi6cwY1/casxsVppJsHznKclxvQFkrP23nsSUrn1yxSq0PP1GoNfB7IpEw+/bcnaWPP5xz+050twF/HvnbsY+cc5tdS93et/Dx0MheZWUVDn9kQq0c0iOYYTrR1fCwwIiwQMpoUsZJoyMwvfy83mC4oyWozLtOR+h0RKNSgzaVPnSr1REKlYaxeXnD6c4antGMZ4VC4ePDzPKL9AVSdkCCGfoue8qMDutt6KSyF3PjRcqMnqljGlAPIP1m5r5fDZF4ivVvvwaXhfn3DPAs+g8aO3LsFGXTamgOJL9ASldnLOTzHR/Df3ftccijeXuJKNMZobeb0HSaOxLDwNHz2X1jAmx52Dqd0OzWSEDXW0PyarVC+qbAYIHnufOXGI9zM+MWCtfQd6pog/L8TApCDUaMmldCs2kzaINRhvUORyLU0aAnJjtQDNQD5KdZ5syaHhkRXl5emXsnH3gWpWXOamH+8/PnbDxyfrvXgd64ejIiHEbcPvx424YPt7TloYSRvRLEMK8S5m8YewzVoqqFrNSUIdFFFU7xebv3gJ1UKTdgbodOeQU8ST9fSV1dvVBgqHmyRK4Nk0r6xgRSP5BoWIm9qaSWIqGg/UOirEgieRjDhyUiqTJo2F2uPhcOBxAUFNjQIC8qlnaOilz1woq6+vodO79qywMSJMnH8aJyuw3idXkL/nsr8JFABVpXzzwZlb7dnlHGZBa8NZNKnJ06b6fsvCsNqEf2bi6cP/vTzRtg3UaXfoyoKIebUl0tGzVuKlX6um7NK8eO/5udA0vWW0eaIhSwhsZGWFovETOD9UIhjKVqjD/DjCl5oKVJJXQstFpZXLjJRiyI0HYyo+xSoO7uvx/+/b9DhwzSanURMX1cfS4cjocgiLsmzT7x16/r1rzywCNPeMZbjH4J+sQbzbQHAISGQrUr15A4Rqo1OjmtehRRV4dalbCuXWilrE0TSpojkfqpJxczjJqpNGo1QYIU+wW1hc5O6zjRo3eZAfU8+Zl+/UxYWEhFRWWfAYzyYw63xNvb29/PlxHzlUpLYKlmbAzwFNRqGJe8a8IYxv74XnGoKcvsvZD1HNw9uBvdejarPVjweT7dUNZK/RsS7H/0Apy51zc6oBXWs1Ui1LmwSIG6tfy8ePZoWFjIteupk6fd5+pz4XAMOZmXhAJBVGx/ZGIQyHRmZpmfvOmmqFQqaiFPBIZhPj4S6oUz6t+R9UzsEWy2op+ym4x2gMNns0BOedutJ6tEqGvKmDxMfq56YUXX2C63sm9z1tOT+M+GTwAAuz/fTO3h8XhoaMBba98HHsTur74HACx/8nFqz0cfrIM1v08+D22Elw9Gqw2irCcgAV1+nk8vPp9eXFpOawcIa24HgNZTb8H6xzhSe1qoCe14CtSt5ecrybD+Jq5nd9MpFR9t2v6fjc3fQA43Yuv2XUsWPzxl8sSrF/7+7Iuvw8M7PbNiKQBg247dhUV2rC/Eftas27Dk8YfWrXll6r13/37gz4cfmt+3T++CgqI/jhxnHIms55AewSQAKPRpRm+GGWaFYPSFTJqs57WWGgGcJkI9xYB6mPzEHTcploNtDBwy4c3XX3r26WXvroMDmZRK5cKHlp09dxF4HJ27Jnz/zWeTJ901csRQAMB3e//3wkuwF5mOTqtrtp4x4efTjSeeWOgCAAAcOX8LA0AT1P2aHct5ODwS6hQvHtuzrb3HBblF8Tx7l3/w3Jf5vw9gR7YtzH95m7u/WOfhpJcZ5gXjoagD1curOWAa0aQ3TUGR04NnsjAAm1Adoj3pmIrQllJJmMe68BwcHCyHbj2pNipdSz1UTrKercLxIrS9DahbyE8ODg5T9KuEkI36BY0LVWpoijBY8Inp/6WgX8Uw0Ndp1tPmzs7mU3P4OXAKlIODo2WEkb3SYMuSW0O6twJ1I/nJtsCWk+ggL7NDvdgO8jJbK0IdDJdB5uDg6DiQ7mpAPax6iYODg520Z1G9KxUoa/13Dg4OdyHXZFJ9e9JOBpSTnxwcHJ4nQl2mQDn5ycHB4QoRSrqZAeXkJwcHh0eKUNcoUE5+cnBweIAIdboB5eQnBweHp4pQFyhQTn5ycHB4hgh1rgHl5CcHBwcrRSjmlr3w7SM/nTTRi8OpPLd2h41Hcn9fd6Sz83tM7Rm07Ji+eL6HyU/uq8XBwU6K9LKm/Vv1ndodz/XCc3BwdExI9hpQs/KTSx9xcHC0fyrJeel4ToFycHB0WEi3MaCc/OTg4PCw8SJOMaBc9RIHBwercJIX304KlJOfHBwcnidC+R4jP8NCQ8eOHhMd2bmqqvLXX/ffunVLp4PLWJvSuXNnX1/furr64uIiKw/o5eUVGRlp/UmLi4tVKrjGFp34+PiioqL6+nrr992yZWt29q2tW7daOqBbt25W7p6bm0s/jLratWtXDK0nS0On0ykUivLycrMPxefz+/btO3v2HLFYfPXq1X/+OVFWVmb7mQAAamtrq6qqzN4UGho6ceLdQ4YMqa2tO3z4UGrqDY1GY/bI4NAwHx9fpUJRViq1/nTo3ZPJZGvWvGX9MB6P17t3/Ny5c319fVNSUo4fP1ZaWmr9+Li4XhkZN60c06VLFx6PZ/15qT8HACAgICAkJATDsKqqqurqaut37N+//7JlTzz77DOWDvD19Q0NDWXsJAhCpVJVVFRotVr6/tjYWBzHbTxPxp+7sLDQ7F+K8WEgSVKt1tTUyORyOX1/ZGSkl5eXRqMpLCw0+9RhoaEikZdOp5OWtPzndmY9U5sKQh2/Lnz7L3wkEUv2/+9n9Jkm9SsHIrZt28owT9HR0X/9dZy+5557JhYVmTejgwcn7t37g/Wnnjt3zs2b6fQ9Y8aM3bVr94oVy//+2+iJTMnMvHXx4sVHH33YygFW7t67dxz6wqenZ1BXAQCpqekCgcDSvQ4c+D05+SX6nlWrXlq27AnGYVqtdsSIYQ0NDfSnsMLu3bs2bvyAsRPDsNTUdD6f+SP9yy8/v/76a/Q9wcEhp06d5vObrdIbq57Ou2PxY3Mx7XZm5q2ysrLx48daOatly55Yteol09+SsWNHWzJkP/+8r1+/ftSbaZZz5y4EBlpcDB2Zsz59egMA/Pz8zpw5R/9zkCQ5ZEiiXA7fWLPcffc927Ztt3ICCxYsXLfO4ne2oKBgypTJBGHQX5cvX/Xx8bFyqmafCH3wUlJSFi6cb+nWFr8OISEhp0+fBQAkJg6mv15UB8rn848cPAwAmDYryVSCtPvC8RhbFGj7y08Mw37ftx8AcPzvv7ds/zTzRioAYNas2Rs2fPD0088olaovvvgcHYnjOLKeM2fOuHXr1ujRY3bv/vLYsb/79evD+N1G5OXdeemlVdTVkSNHzpt337Fjx/788w9qJ0PDDhkydNeu3Y59gfRzsJ377pvX0NAsgX18fGfMmLFo0eNJSTNLS0s/+uhDtP/BBx9atuwJgtCtWLHin39OIMG4YcMHo0aNvnz5ap8+vQk99HPo2jX26aefuXXr1uef76R23r6dY3oOGRlZAIBTp069+urqyspKZB0+/njTvHn3CYVCyo5jGHb69BkMw95f80p66vUuXbu/+9G29R9te/z+mRq1GrSWKVOmrlr1EkHoXnjh+SNHjgAAgoKC1q9/d+LEu8+ePd+3b7ypg/LZZzv79evX4iOvWfOmUGhYFR0A8OGHH5n9M2EYdvHiZf0BG3ft+gIA8OSTT73wwouXLl3q0ycetI0dO7b/9tuv1FUvL68RI0auWvVSTEzM778fnDFjGv3gt99e26I/RPHee+8DAFQq1YABA6wcRr1egUAQFRU1ceLEPn367tu3f9WqFw8dOghNQWXlnj3fPProY+fOnU9IYL6rn2//DADw6Y7tzrOe7SNCHaxA219+9uoZt33rp8f//vu9D/5D70QSCASpqekkScbH90J7vvnm2+HDh48cOVwmk6E9/fr1//nnX3788b8teoIAgPvvf2Dt2rd37Ni+efMnZg9455318+cvQJcdqECtqyFLCnTYsCF1dXWMgwMCAs6fv0jpI+pgU2vy+ee7xo0bt3r1y/RvKV2Ynz17ZvHix62cmEgkSklJzcvLmzJlMuOmjIwsDMOoE37hhReffPKpxx5bRMoNcQNvb/EX3++7nZ21ZvVzZh88u6j63PkL1hXotWsp3t7egwcPbGxspO//z382zJ495+OPP6L/AEgkkr/+OhYUZFi+3Pp7buOfadq06R9/vGnTpo937oTGAvH88y889dTy//zn/a+//qotCvTdd9/59ttvLX0YqFeNFOiYMaPQD5gtpKdn8Hi8Bx5Y+MMPP7733rt79nxj4+vt27ffL7/sAwCg312089KlK76+vuvWvb137/doT+duXcePHffW62/IqmX3PbgQOBlni1Dc3aOfiYMHAwD+PXWSsV+j0ZAkSY8GDhs2DABAWU8AQFoalKsLF95P7YnUY+p1tkhm5q358xfcuZP7/PPmv/NsoKamBilxag/yLk21WHIy1BcvvmiH+EVvHfXgY8eO03vr/zM9sqKign4VBRBOnz5D7VEo4Je/e0/DL1/r8Pb2BgAwrKf+dw4qhkWLFlF7IiIirly5FhQU/N577zLODYHjOHp1dp3ACy+8CACgHCDEli2bAQDTp88ATkCn0926Ba1b586dW/cIERERPB7vr7/+SklJQebe9vump6ft2/cLAGD79uaZBvfcMxEA8NZba6g9PB7vrdffAAA8/PhjwP3T8W5fSH/x0iU4h2KlmaB7fHwv+u8khmGMODeKGdFtyt9///P33/9ERUXZexoEoZs1K2nq1Ckk6eB1Ux2IaT5BoVAAAHr06MHYX1tb27t3nPUIIwP01vn7+6OrKCCwcuWzpkeOGzeG/nfBcdzUj7udDd1/nv2/ZBTIaR0xYgRjv1ze0Lt33KhRI42FW9rgwQNN1RbCx8cHvTq7TmDSpLt7946jtBgCfbQYOToHgjI8tbW1rbs7CkA988zTBEHk5+eLxeLISDu+C2v0nhz67UTU1tZ+9913SAujPTs/heZ13bvrlSolcH9wd+/dzMm9TeiI4ODg438c3bP765EjR5k9LCgoCABw5kyzzKEH70JCQtp4Gn36xGdlwe88m/n331N63Z1G7VmyZDEA4ODBw1euXHvnnfVWsk/2otVqdTqdSCTKzLx1/PiJyZOZjjxCLBYDAJBuonMzFSqgLl1ayP5b4ZFHHgIAfP31nqtXr3/wwUahUGjpyKKionnz5ppqVWdw9OgxfeLODmVnO2+9tQY5T6020N27N/+UPvUU9Aw+/NAQLrcFjT5rzyhRWL9+nUaj8fHxGTlyVHx8fNeusbeys01dRld3dpLuMc7OGUyeMXX75q1xcXFRUZFfffU1SoPm5OTMnNnsKKHKD6m0mHFf9FuNvsb2Br/aB7NJzxbP89y5C3QtjOMYjsOPdWNj4333zaX2X716ZcKEcf/8c1IikcyfvwDFcNVq9cqVK06etO8jbi4oFr9jx2d33TUxKipqy5ZP0d+lpKRk8uR7qKABeudlMmZOvLoKxuz8mvRsK8jMzBw9etSZM2fFYvHMmbNmzpyFXtrq1S//8QfM/9pOXV2dQz4Y+/btxzAsL+9O2zMnr7762urVr1JXMQyjzNagQczkD8qGm7J27Zr//re5zmTx4iX07NCdOzCdMHhwYhtPFQAwYED/mzczd+/+EoXUVjy7EngKuAeMDiFJcvmzK++eMvmRxxfl5eXpdDocx+Pi4jIzby1aZEh08Hh8s8E+5GGZFk6yh8ZGuenW4r0o64njOJ/Px3He9evXEhL6DR48kHFkaWlp795xvXvHbd26BakwoVD4+ee7MjNvWS8htIXly5/S+8sjUlNTNRoNjuNRUVHp6RnvvPMudXrUX8H4/PV72vZ3qaqqRC9t48YPqJe2adMnmZm3WizkdDh79/7Qp09ftVo9Zcq9DnxYDMP4fD6Px6uoqJg3b27v3nEoLENHLpc3mINRfLJyJbRr9F8XlOlatmxZG0+SIIgvv9yN4ziGYU8/92w7h7mcKkI9QYFSSEukVM53/fr37rvvvldeefXXX/fXQGDuKDjYkGalQFVySiV7wzGDBw9qxb1GjRpBZeF79ux54MChgQMHicViteXCoG3bPt22DerEsLCwQ4f+8PX1vXjx8pAhMEfXRqqrq+fPn4cuP/vscytWPD1//vxt27aWlpaib7uPjy/jLr5+UHs2Wq6XtIvdu3ft3r0L1SH8+efRgICAS5eumP6WOAkMw44c+SsmJqaqqmr06ObYa1t4//33qCw8j8dLTU0PDQ3t0aN7enpzfIbi3nsntZiF9/HxEYsl+njOIWonnw9DOqtWJX/xBSzDagsbN36wePESjUaTmZUJPAjc3eVnQv+ECePGm+5/443XUCr59dff1DvvsNth4EDmdyYmJkYvVcx30XgG2dnZKCB4/vxFeoFBTEzM+PHjJRL4taFTXl4+dGgiQeh8fHxaLUITE4fMmgW9ZgZbtmyeO3cOAOB///uFSvV06dKFcRhKwRcW5Lfu2cPDw8eNGxcczAxt19TUjBgxTKvVisViBwZ8rSASia5dS4mJiblw4byjrCcDnU43dCh0tDds2NhqjxuVf+rrfLtRW3R0NNoZH9/HlgcR6N9SS51mLsR5ItTts/CbNn745muvm73p/PnzAAAqL6zvbGNOw+7dG5Y0W9FlnsGlS5dQi8iZM+eonbNmzd6584ukpJlm71JcDH9yWu3q7tnz7YYNG83ehM6EbspNexMHDoY1ZwobghVmGTNm7Oef73rkkUfM3pqlF0GozsmpSCSS69dveHl5bd265bHHHnXeE8nl8lWrYNXU3r0/tKIIDyYSJsPAAop40Df0sJs3b7HlQdaufVtfj9FCBbQngbv74OSyMtjfvWzxUtObnnkG1tAcO3YUXc3Ly2N8bwMCAgAAN27cAB0ApPv8/f1RroAqUVy79m1TK4njOFIfrVYTKNe/c6dRFSRiyZKl9Fgb+r5FRERQB8DzwQx5pNaB6v+femq5qZXEcbxvX9gYY9po4FhQJxKGYc8+uxLFRpzKoUMHUZv/nj3MAvsWGT8e+nDnzpnJNaG2opiYGC8vL+sP0qlTp3nz7oM1hc+ZqV1zExGKdTgFunQ5LLa4f8GCXTs+79FUhBEcHLJ795cLFixUqVQ//fQT2rl4MaydvnTpCrrK5/OPH4e1ik8/vZx6tL17f9i794fw8HDgicyZA33ql19ejSymUqlEqfb09Iznnnue8tZnz56TkgJbDF580Y5qG/TW+foaopmojXr8+AknTvwzZMhQtNPX13fTpk+Sk1+GlYDroFoBALz2Gswmn/z3b8qn3rYbpoY/eAeWW5ulZ2dYlGYFjUaDirqvXUtJTn6ZemlTp067dg0WSL33niGLZQsSiQS9OtvvAgB49933eDze2bNnZDLZUGOQBXc4qINz8OBEhsc9cOCgoRZAVRDr1q3XtxisM/uwZ86cRr9G9J3UI4wcOerBBx/64Yf/oiI5pFjdFtL1SaR2nlzX2Ni48KEHf/x+b9eusTu3bacPE1EoFMOHG766KAy6Y8f25ctXZGbeUqlUIhFsZ3777bX05hMUQkI3eR4ZGRklJdKIiMhDhw6jXPATTyz94IONM2fOWr58xfLlK+gHb9jwn8OHm/MJLYLeOrrAHzgw4fr1GxERkd99Z2jjo6CX2tTU1Lz66isbNmz46scDGrVaoC/Y/PPA/qIC6DG0mtdee7WxsfHhhx9ZsmQp0rwUO3Zst1QzbxYej9eK2OLcuTBvNmrU6FGjRjNuysrKmjUrCTiahoaGrVu3PPPMs/v27ac6mGHL+afbLN1lzpzZubm3O3XqZGkykz6d8PqJE/8+9dTyTz7ZRO389lvm3xQAMH/+vNRU+NPLTuxZs7NdeuFd7r/TCQsLm3z3pL6940Ui0fnz5/bu/d7s4LKgoKAVK1YOHjzo7Nmzn3++k+HH9eoFP3a5ubmmrmtAQECnTp2qqqqsJzR9fHyioqKKi4vRKKO29MKjk7Fen49hWFxcHP2wuLg4DMNycnLMTvPj8/ndu3eHDQi0AwQCQULCgAceeLBr19jKyspvvvnm4sULZgesoNBhTEyMXC5nTLFCZ2v6vGFhYdOnz5gwYYJE4nP16pU9e74xO/5q/LCEpDnzEwYNuX0rc99P39fqqyassOfnP1qcxoReb79+/R588KHu3btXV8u+//7bs2fPWgl5d+/eg8/nMd5zHo+HmrXM/i0s/ZnQfrOoVCoUUGpFLzz6HJaXl9Obkk2ft6ioSC6X9+zZ03oaMC8vD00DMf2DWvrjMl4XSZIqlaqyssrKiCnqQUiSbNS6MsVk1oCadMdjHdGAut2yxi0a0A7FsH7QrNuOjQbU7WjRgLo7ndt9WWOnGlDck6wnBwcHR5tTSWQHSiJxcHBwOBTnK1BOfradwsJCS2tscNjy7plONvAAFAqFpTUwONpLhJIdtJXTvZg06W5Xn4Ib46nv3tmzZzz1pXkkrVGgnPzk4OBwXxwoQrkYKAcHB0crsduFryxmdfLdtUUSHK1m81qjRpcWmf+yxeJwDo7WFdUbLzln00pzrVKg7B2eycHBwdF+4J4kPzk4ODjaMxLaKgXK3mXTODg4OByFQ134ZvnJufAcHBweCk2EOkmB0uD8dw4ODk/y4u0Cb030k3PhOTg4PBdzq304IQvPyU8ODg6PFqEtqEWukJ6Dg8P1VBS45WQDmwwoV73EwcHhVEJjothaz0Q6QYFyiXgODg6HgqvNrKHAArA2GVDz8vNfrnieg4PDkdTLlYCNItQZCpSDg4OjbUQJa6KENe89bFjNRRIoAe5GCwaUk58cHBzO4+kFcLnTxloFuprQu6+r3u3WidD/AyDGBNzh1M4FAAAAAElFTkSuQmCC"}]], 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Combine CAM A, CAM B, and CAM C; no pre-volley ray or hidden solution overlay is shown.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "assistant": "The complete assignments and phase order are set; execute the volley.\n{\"name\":\"ExecuteVolley\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} diff --git a/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_results.jsonl b/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_results.jsonl deleted file mode 100644 index 566d2a2427484f4317b899d991ac788e609ce3f1..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_results.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 1, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 1, "actual_rollout_group_seed": 1, "initial_attempt_group_seed": 1, "final_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 131.8389, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49122 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46030 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 3505, "reasoning_output_tokens": 3423, "total_tokens": 9604}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 3505, "reasoning_output_tokens": 3423, "total_tokens": 9604}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49122 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46030 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45758 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 12299, "output_tokens": 334, "reasoning_output_tokens": 263, "total_tokens": 12633}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 18398, "output_tokens": 3839, "reasoning_output_tokens": 3686, "total_tokens": 22237}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "Observer B’s RIGHT route exits the grid, so the inward two-hop route from K7 is its LEFT slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49122 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46030 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45758 chars]"}}]}, {"role": "assistant", "content": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45758 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B’s RIGHT route exits the grid, so the inward two-hop route from K7 is its LEFT slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 11136, "input_tokens": 15940, "output_tokens": 183, "reasoning_output_tokens": 113, "total_tokens": 16123}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 18944, "input_tokens": 34338, "output_tokens": 4022, "reasoning_output_tokens": 3799, "total_tokens": 38360}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s RIGHT route exits the grid, so the inward two-hop route from K7 is its LEFT slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both valid commands now use the observer-local inward two-hop route from K7 to the marked central socket.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s RIGHT route exits the grid, so the inward two-hop route from K7 is its LEFT slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49122 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46030 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is two straight hops from K7 along the center row. In Observer A’s rotated local view that route is LEFT; in Observer B’s it is RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45758 chars]"}}]}, {"role": "assistant", "content": "Observer B also sees K7; from its differently oriented local camera, the center socket lies two hops to the RIGHT.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49638 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45758 chars]"}}]}, {"role": "assistant", "content": "Observer B’s RIGHT route exits the grid, so the inward two-hop route from K7 is its LEFT slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49454 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49022 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45498 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both valid commands now use the observer-local inward two-hop route from K7 to the marked central socket.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15488, "input_tokens": 20039, "output_tokens": 61, "reasoning_output_tokens": 11, "total_tokens": 20100}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34432, "input_tokens": 54377, "output_tokens": 4083, "reasoning_output_tokens": 3810, "total_tokens": 58460}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both valid commands now use the observer-local inward two-hop route from K7 to the marked central socket.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-f2bb343d0452", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0001::codex-gpt-5.6-terra-medium", "job_index": 0, "case_index": 0, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 2, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 2, "actual_rollout_group_seed": 2, "initial_attempt_group_seed": 2, "final_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 200.7381, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50994 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 3852, "reasoning_output_tokens": 3780, "total_tokens": 9951}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 3852, "reasoning_output_tokens": 3780, "total_tokens": 9951}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50994 chars]"}}]}, {"role": "assistant", "content": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45870 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 12636, "output_tokens": 1898, "reasoning_output_tokens": 1824, "total_tokens": 14534}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 18735, "output_tokens": 5750, "reasoning_output_tokens": 5604, "total_tokens": 24485}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}", "assistant_preview": "The right branch is available from B’s wagon but only for one graph hop; reduce the hop count to stay on the path.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50994 chars]"}}]}, {"role": "assistant", "content": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45870 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}, {"role": "assistant", "content": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45870 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The right branch is available from B’s wagon but only for one graph hop; reduce the hop count to stay on the path.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12160, "input_tokens": 17834, "output_tokens": 1766, "reasoning_output_tokens": 1694, "total_tokens": 19600}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 36569, "output_tokens": 7516, "reasoning_output_tokens": 7298, "total_tokens": 44085}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The right branch is available from B’s wagon but only for one graph hop; reduce the hop count to stay on the path.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observer-local commands are now valid and set; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The right branch is available from B’s wagon but only for one graph hop; reduce the hop count to stay on the path.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50994 chars]"}}]}, {"role": "assistant", "content": "Observer A’s wagon is two local leftward hops from the marked zone socket; K7 is visible in A’s camera.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45870 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}, {"role": "assistant", "content": "Observer B’s wagon at the local left vertex reaches the socket by two local rightward hops. K7 is visible to Observer B.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45870 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49942 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}, {"role": "assistant", "content": "The right branch is available from B’s wagon but only for one graph hop; reduce the hop count to stay on the path.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"K7\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45714 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49922 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50862 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observer-local commands are now valid and set; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 17536, "input_tokens": 23511, "output_tokens": 106, "reasoning_output_tokens": 61, "total_tokens": 23617}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 37504, "input_tokens": 60080, "output_tokens": 7622, "reasoning_output_tokens": 7359, "total_tokens": 67702}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observer-local commands are now valid and set; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-7811d3a0c096", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0002::codex-gpt-5.6-terra-medium", "job_index": 1, "case_index": 1, "model_index": 0, "worker_index": 1, "parallel_slot_index": 1, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 3, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 3, "actual_rollout_group_seed": 3, "initial_attempt_group_seed": 3, "final_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 163.3332, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}", "assistant_preview": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46562 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51046 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2406, "reasoning_output_tokens": 2335, "total_tokens": 8505}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2406, "reasoning_output_tokens": 2335, "total_tokens": 8505}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}", "assistant_preview": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46562 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51046 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50910 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 11189, "output_tokens": 2242, "reasoning_output_tokens": 2168, "total_tokens": 13431}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 17288, "output_tokens": 4648, "reasoning_output_tokens": 4503, "total_tokens": 21936}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}", "assistant_preview": "For Observer B, the same marked socket is one hop from M2 on B’s local forward branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46562 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51046 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50910 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51162 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "For Observer B, the same marked socket is one hop from M2 on B’s local forward branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 16728, "output_tokens": 834, "reasoning_output_tokens": 766, "total_tokens": 17562}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 34016, "output_tokens": 5482, "reasoning_output_tokens": 5269, "total_tokens": 39498}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "For Observer B, the same marked socket is one hop from M2 on B’s local forward branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observer-local commands are set, so execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "For Observer B, the same marked socket is one hop from M2 on B’s local forward branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46562 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51046 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "The rendezvous socket is one hop from T3 in Observer A’s local rightward branch. Set A’s route first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50910 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "The shared M2 landmark aligns one hop from the marked socket in A’s view; replace the earlier A route with that correct local branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51162 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48874 chars]"}}]}, {"role": "assistant", "content": "For Observer B, the same marked socket is one hop from M2 on B’s local forward branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51162 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48782 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observer-local commands are set, so execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 16512, "input_tokens": 21466, "output_tokens": 80, "reasoning_output_tokens": 37, "total_tokens": 21546}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34432, "input_tokens": 55482, "output_tokens": 5562, "reasoning_output_tokens": 5306, "total_tokens": 61044}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observer-local commands are set, so execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-db574de58dcf", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0003::codex-gpt-5.6-terra-medium", "job_index": 2, "case_index": 2, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 4, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 4, "actual_rollout_group_seed": 4, "initial_attempt_group_seed": 4, "final_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 185.7202, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}", "assistant_preview": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 1909, "reasoning_output_tokens": 1833, "total_tokens": 8008}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 1909, "reasoning_output_tokens": 1833, "total_tokens": 8008}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}", "assistant_preview": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 10699, "output_tokens": 877, "reasoning_output_tokens": 799, "total_tokens": 11576}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 16798, "output_tokens": 2786, "reasoning_output_tokens": 2632, "total_tokens": 19584}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}", "assistant_preview": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 14884, "output_tokens": 1447, "reasoning_output_tokens": 1368, "total_tokens": 16331}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 9600, "input_tokens": 31682, "output_tokens": 4233, "reasoning_output_tokens": 4000, "total_tokens": 35915}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14464, "input_tokens": 20259, "output_tokens": 870, "reasoning_output_tokens": 788, "total_tokens": 21129}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 24064, "input_tokens": 51941, "output_tokens": 5103, "reasoning_output_tokens": 4788, "total_tokens": 57044}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "With right outward and forward unable to sustain two hops, Q5’s left branch is the interior diagonal. The rotated zone position puts the rendezvous two hops along that branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With right outward and forward unable to sustain two hops, Q5’s left branch is the interior diagonal. The rotated zone position puts the rendezvous two hops along that branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 25680, "output_tokens": 454, "reasoning_output_tokens": 372, "total_tokens": 26134}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34176, "input_tokens": 77621, "output_tokens": 5557, "reasoning_output_tokens": 5160, "total_tokens": 83178}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "With right outward and forward unable to sustain two hops, Q5’s left branch is the interior diagonal. The rotated zone position puts the rendezvous two hops along that branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observer commands are now valid and configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "With right outward and forward unable to sustain two hops, Q5’s left branch is the interior diagonal. The rotated zone position puts the rendezvous two hops along that branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "For Observer A, R8 is the visible anchor nearest the marked lower-left rendezvous direction, requiring one hop on the observer-local left branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "The rejected left branch shows R8 sits on that local boundary. Its one-hop forward branch remains on the graph and is the likely route toward the rendezvous.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"R8\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "Observer B’s Q5 anchor lies one graph hop from the lower-left socket; with B’s differently oriented local view, that branch is its local right turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "Q5’s right branch is the outward boundary, so the target-facing route is its remaining inward forward diagonal; the zone-to-B orientation places the socket two hops along it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47658 chars]"}}]}, {"role": "assistant", "content": "With right outward and forward unable to sustain two hops, Q5’s left branch is the interior diagonal. The rotated zone position puts the rendezvous two hops along that branch.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"Q5\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45954 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47854 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observer commands are now valid and configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 25344, "input_tokens": 31306, "output_tokens": 54, "reasoning_output_tokens": 10, "total_tokens": 31360}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 59520, "input_tokens": 108927, "output_tokens": 5611, "reasoning_output_tokens": 5170, "total_tokens": 114538}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observer commands are now valid and configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-b6cfb62c2f80", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0004::codex-gpt-5.6-terra-medium", "job_index": 3, "case_index": 3, "model_index": 0, "worker_index": 3, "parallel_slot_index": 3, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 5, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 5, "actual_rollout_group_seed": 5, "initial_attempt_group_seed": 5, "final_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 202.0128, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}", "assistant_preview": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49070 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 1300, "reasoning_output_tokens": 1230, "total_tokens": 7397}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 1300, "reasoning_output_tokens": 1230, "total_tokens": 7397}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49070 chars]"}}]}, {"role": "assistant", "content": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 10080, "output_tokens": 2865, "reasoning_output_tokens": 2783, "total_tokens": 12945}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 16177, "output_tokens": 4165, "reasoning_output_tokens": 4013, "total_tokens": 20342}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49070 chars]"}}]}, {"role": "assistant", "content": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}, {"role": "assistant", "content": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 16251, "output_tokens": 2441, "reasoning_output_tokens": 2362, "total_tokens": 18692}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16896, "input_tokens": 32428, "output_tokens": 6606, "reasoning_output_tokens": 6375, "total_tokens": 39034}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}", "assistant_preview": "V6 is the neighboring anchor to the marked socket in Observer B’s rotated view; its local left branch reaches the socket in one hop.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49070 chars]"}}]}, {"role": "assistant", "content": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}, {"role": "assistant", "content": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}, {"role": "assistant", "content": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46434 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49110 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "V6 is the neighboring anchor to the marked socket in Observer B’s rotated view; its local left branch reaches the socket in one hop.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15360, "input_tokens": 22616, "output_tokens": 482, "reasoning_output_tokens": 407, "total_tokens": 23098}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 32256, "input_tokens": 55044, "output_tokens": 7088, "reasoning_output_tokens": 6782, "total_tokens": 62132}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 is the neighboring anchor to the marked socket in Observer B’s rotated view; its local left branch reaches the socket in one hop.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both valid observer-local commands are configured with the two respective routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 is the neighboring anchor to the marked socket in Observer B’s rotated view; its local left branch reaches the socket in one hop.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46642 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49070 chars]"}}]}, {"role": "assistant", "content": "Probe Observer A’s visible K7 landmark with a valid local route so the updated camera can reveal the planned endpoint.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}, {"role": "assistant", "content": "The marked socket aligns with K7’s grid node; M2 is two straight graph hops from it in Observer A’s lower-left local branch. Replace the probe with that route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48730 chars]"}}]}, {"role": "assistant", "content": "Left from M2 is confirmed off-graph. The target lies along M2’s remaining two-hop branch toward K7, so use the forward local slot.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46434 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49110 chars]"}}]}, {"role": "assistant", "content": "V6 is the neighboring anchor to the marked socket in Observer B’s rotated view; its local left branch reaches the socket in one hop.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46422 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48630 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49110 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both valid observer-local commands are configured with the two respective routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21888, "input_tokens": 27636, "output_tokens": 50, "reasoning_output_tokens": 8, "total_tokens": 27686}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 54144, "input_tokens": 82680, "output_tokens": 7138, "reasoning_output_tokens": 6790, "total_tokens": 89818}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both valid observer-local commands are configured with the two respective routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-fdf5a07405e6", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0005::codex-gpt-5.6-terra-medium", "job_index": 4, "case_index": 4, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 6, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 6, "actual_rollout_group_seed": 6, "initial_attempt_group_seed": 6, "final_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 196.0726, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45222 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50170 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3908, "reasoning_output_tokens": 3839, "total_tokens": 10005}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3908, "reasoning_output_tokens": 3839, "total_tokens": 10005}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}", "assistant_preview": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45222 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50170 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44990 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 12687, "output_tokens": 1756, "reasoning_output_tokens": 1678, "total_tokens": 14443}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 18784, "output_tokens": 5664, "reasoning_output_tokens": 5517, "total_tokens": 24448}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45222 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50170 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44990 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45174 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12160, "input_tokens": 17742, "output_tokens": 541, "reasoning_output_tokens": 469, "total_tokens": 18283}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 36526, "output_tokens": 6205, "reasoning_output_tokens": 5986, "total_tokens": 42731}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "The rejected rightward route establishes that B’s local right exits at T3; use the opposite local turn for the two-hop in-board route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45222 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50170 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44990 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45174 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45174 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rejected rightward route establishes that B’s local right exits at T3; use the opposite local turn for the two-hop in-board route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 17536, "input_tokens": 22195, "output_tokens": 339, "reasoning_output_tokens": 264, "total_tokens": 22534}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 37504, "input_tokens": 58721, "output_tokens": 6544, "reasoning_output_tokens": 6250, "total_tokens": 65265}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected rightward route establishes that B’s local right exits at T3; use the opposite local turn for the two-hop in-board route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observer commands are now set with valid in-graph routes; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected rightward route establishes that B’s local right exits at T3; use the opposite local turn for the two-hop in-board route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45222 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50170 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Set Observer A’s route using a visible landmark and a two-hop forward path toward the upper rendezvous region.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44990 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "The rendezvous node aligns with M2’s upper-center position in Observer A’s board, so use adjacent Q5 and move one local-forward hop to it.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"Q5\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45174 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible T3 anchor; the matching rendezvous position is two hops to Observer B’s local right.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45174 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48506 chars]"}}]}, {"role": "assistant", "content": "The rejected rightward route establishes that B’s local right exits at T3; use the opposite local turn for the two-hop in-board route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"T3\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44698 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48414 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observer commands are now set with valid in-graph routes; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21888, "input_tokens": 27060, "output_tokens": 128, "reasoning_output_tokens": 80, "total_tokens": 27188}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 59392, "input_tokens": 85781, "output_tokens": 6672, "reasoning_output_tokens": 6330, "total_tokens": 92453}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observer commands are now set with valid in-graph routes; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-b2065fc528e0", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0006::codex-gpt-5.6-terra-medium", "job_index": 5, "case_index": 5, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 7, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 7, "actual_rollout_group_seed": 7, "initial_attempt_group_seed": 7, "final_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 161.4205, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}", "assistant_preview": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45462 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49682 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3280, "reasoning_output_tokens": 3198, "total_tokens": 9377}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3280, "reasoning_output_tokens": 3198, "total_tokens": 9377}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45462 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49682 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 12072, "output_tokens": 1090, "reasoning_output_tokens": 1025, "total_tokens": 13162}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 18169, "output_tokens": 4370, "reasoning_output_tokens": 4223, "total_tokens": 22539}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45462 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49682 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 11136, "input_tokens": 16463, "output_tokens": 788, "reasoning_output_tokens": 718, "total_tokens": 17251}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 14720, "input_tokens": 34632, "output_tokens": 5158, "reasoning_output_tokens": 4941, "total_tokens": 39790}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "M2 is a boundary anchor; its only two-hop in-graph direction is observer-right, which reaches the intended socket.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45462 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49682 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "M2 is a boundary anchor; its only two-hop in-graph direction is observer-right, which reaches the intended socket.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15360, "input_tokens": 21163, "output_tokens": 117, "reasoning_output_tokens": 46, "total_tokens": 21280}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 30080, "input_tokens": 55795, "output_tokens": 5275, "reasoning_output_tokens": 4987, "total_tokens": 61070}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "M2 is a boundary anchor; its only two-hop in-graph direction is observer-right, which reaches the intended socket.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both valid observer-local routes are configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "M2 is a boundary anchor; its only two-hop in-graph direction is observer-right, which reaches the intended socket.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45462 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49682 chars]"}}]}, {"role": "assistant", "content": "Rendezvous is the grid coordinate reached from A's K7 by one observer-forward hop, and from B's M2 by two observer-left hops. Set A first.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "Observer B’s visible M2 anchor reaches the same socket with two local-left graph hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "The left route from B’s M2 is off-graph, so replace it with the inward two-hop forward route.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45042 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47906 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}, {"role": "assistant", "content": "M2 is a boundary anchor; its only two-hop in-graph direction is observer-right, which reaches the intended socket.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45202 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48242 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49362 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both valid observer-local routes are configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 20864, "input_tokens": 25802, "output_tokens": 50, "reasoning_output_tokens": 7, "total_tokens": 25852}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 50944, "input_tokens": 81597, "output_tokens": 5325, "reasoning_output_tokens": 4994, "total_tokens": 86922}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both valid observer-local routes are configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-1367a1b29103", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0007::codex-gpt-5.6-terra-medium", "job_index": 6, "case_index": 6, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 8, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 8, "actual_rollout_group_seed": 8, "initial_attempt_group_seed": 8, "final_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 189.4479, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3930, "reasoning_output_tokens": 3856, "total_tokens": 10027}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 3930, "reasoning_output_tokens": 3856, "total_tokens": 10027}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 12716, "output_tokens": 481, "reasoning_output_tokens": 408, "total_tokens": 13197}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 18813, "output_tokens": 4411, "reasoning_output_tokens": 4264, "total_tokens": 23224}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12160, "input_tokens": 16498, "output_tokens": 428, "reasoning_output_tokens": 356, "total_tokens": 16926}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 35311, "output_tokens": 4839, "reasoning_output_tokens": 4620, "total_tokens": 40150}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15360, "input_tokens": 20840, "output_tokens": 930, "reasoning_output_tokens": 854, "total_tokens": 21770}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 35328, "input_tokens": 56151, "output_tokens": 5769, "reasoning_output_tokens": 5474, "total_tokens": 61920}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "assistant_preview": "V6 has no straight two-hop B route, so use B’s interior M2 anchor. The nearby socket is one local-left hop from M2.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "V6 has no straight two-hop B route, so use B’s interior M2 anchor. The nearby socket is one local-left hop from M2.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 19712, "input_tokens": 26301, "output_tokens": 739, "reasoning_output_tokens": 661, "total_tokens": 27040}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 55040, "input_tokens": 82452, "output_tokens": 6508, "reasoning_output_tokens": 6135, "total_tokens": 88960}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 has no straight two-hop B route, so use B’s interior M2 anchor. The nearby socket is one local-left hop from M2.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observers now have valid landmark-local route commands configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "V6 has no straight two-hop B route, so use B’s interior M2 anchor. The nearby socket is one local-left hop from M2.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 is visible to Observer A; from its local map the rendezvous lies two hops to the observer’s right from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45850 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "The rejected rightward route exits from V6’s boundary node. The two-hop interior route from that anchor is the opposite local turn.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "Observer B also sees V6 at its forward edge; the rendezvous is two interior hops forward in B’s local frame.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "B’s forward ray is the one that exits at V6. The corresponding interior diagonal toward the rendezvous is B’s left turn, two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"V6\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45854 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49178 chars]"}}]}, {"role": "assistant", "content": "V6 has no straight two-hop B route, so use B’s interior M2 anchor. The nearby socket is one local-left hop from M2.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45578 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49558 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48978 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observers now have valid landmark-local route commands configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 25216, "input_tokens": 32188, "output_tokens": 75, "reasoning_output_tokens": 29, "total_tokens": 32263}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 80256, "input_tokens": 114640, "output_tokens": 6583, "reasoning_output_tokens": 6164, "total_tokens": 121223}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observers now have valid landmark-local route commands configured; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-e87c91b7378e", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0008::codex-gpt-5.6-terra-medium", "job_index": 7, "case_index": 7, "model_index": 0, "worker_index": 7, "parallel_slot_index": 7, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 9, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 9, "actual_rollout_group_seed": 9, "initial_attempt_group_seed": 9, "final_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 173.2465, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2916, "reasoning_output_tokens": 2843, "total_tokens": 9015}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6099, "output_tokens": 2916, "reasoning_output_tokens": 2843, "total_tokens": 9015}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}", "assistant_preview": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}, {"role": "assistant", "content": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 11701, "output_tokens": 2074, "reasoning_output_tokens": 2005, "total_tokens": 13775}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 17800, "output_tokens": 4990, "reasoning_output_tokens": 4848, "total_tokens": 22790}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNKNOWN LANDMARK\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nUNKNOWN LANDMARK\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}, {"role": "assistant", "content": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 11136, "input_tokens": 17067, "output_tokens": 1454, "reasoning_output_tokens": 1380, "total_tokens": 18521}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 18944, "input_tokens": 34867, "output_tokens": 6444, "reasoning_output_tokens": 6228, "total_tokens": 41311}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "assistant_preview": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}, {"role": "assistant", "content": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 16512, "input_tokens": 22428, "output_tokens": 222, "reasoning_output_tokens": 152, "total_tokens": 22650}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 35456, "input_tokens": 57295, "output_tokens": 6666, "reasoning_output_tokens": 6380, "total_tokens": 63961}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "assistant_preview": "From B’s M2 anchor, only the rightward two-hop route remains within the graph and reaches the interior socket direction.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nCOMMAND LEAVES THE PATH GRAPH\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}, {"role": "assistant", "content": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "From B’s M2 anchor, only the rightward two-hop route remains within the graph and reaches the interior socket direction.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21888, "input_tokens": 27168, "output_tokens": 151, "reasoning_output_tokens": 79, "total_tokens": 27319}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 57344, "input_tokens": 84463, "output_tokens": 6817, "reasoning_output_tokens": 6459, "total_tokens": 91280}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "From B’s M2 anchor, only the rightward two-hop route remains within the graph and reaches the interior socket direction.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both commands are set and valid; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "From B’s M2 anchor, only the rightward two-hop route remains within the graph and reaches the interior socket direction.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46114 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51274 chars]"}}]}, {"role": "assistant", "content": "Use the landmarks nearest the marked socket’s inferred positions: M2 for A and F3 for B, with right-local routes.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "Observer B can use its visible F3 landmark; the marked socket is one right-local hop from that anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"F3\",\"turnSlot\":2,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nUNKNOWN LANDMARK\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "The prior B landmark was rejected. M2 is clearly visible to Observer B; its differing local orientation requires the left turn for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "B’s left route from M2 is off-grid. The corresponding valid orientation toward the socket is forward for two hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":1,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nCOMMAND LEAVES THE PATH GRAPH\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49346 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}, {"role": "assistant", "content": "From B’s M2 anchor, only the rightward two-hop route remains within the graph and reaches the interior socket direction.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":2,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=46434 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51650 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both commands are set and valid; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 26240, "input_tokens": 32448, "output_tokens": 49, "reasoning_output_tokens": 7, "total_tokens": 32497}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 83584, "input_tokens": 116911, "output_tokens": 6866, "reasoning_output_tokens": 6466, "total_tokens": 123777}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both commands are set and valid; execute the synchronized rendezvous.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-0a44b2f9f25b", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0009::codex-gpt-5.6-terra-medium", "job_index": 8, "case_index": 8, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "AsymmetricObserverCommand3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "AsymmetricObserverCommand3D", "requested_group_seed": 10, "requested_env_id": 0, "actual_task_name": "AsymmetricObserverCommand3D", "actual_group_seed": 10, "actual_rollout_group_seed": 10, "initial_attempt_group_seed": 10, "final_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 3, "max_attempts": 1, "max_steps_per_attempt": 7, "rollout_step_budget": 7, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 3, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 7, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}, "duration_s": 150.188, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "assistant_preview": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50998 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45994 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 4506, "reasoning_output_tokens": 4438, "total_tokens": 10603}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6097, "output_tokens": 4506, "reasoning_output_tokens": 4438, "total_tokens": 10603}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "assistant_preview": "Observer B’s target node is one leftward local hop from the visible M2 anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 7, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "AsymmetricObserverCommand3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER A COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50998 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45994 chars]"}}]}, {"role": "assistant", "content": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50998 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45670 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Observer B’s target node is one leftward local hop from the visible M2 anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "action_extracted": "{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 13284, "output_tokens": 1045, "reasoning_output_tokens": 980, "total_tokens": 14329}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 19381, "output_tokens": 5551, "reasoning_output_tokens": 5418, "total_tokens": 24932}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s target node is one leftward local hop from the visible M2 anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "assistant_preview": "Both observer-local commands are set, so execute the synchronized routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 7, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "AsymmetricObserverCommand3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 7, "max_rollout_steps": 7}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nOBSERVER B COMMAND SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Observer B’s target node is one leftward local hop from the visible M2 anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: A\n - landmarkId (string) [required]: A landmark ID visible in that observer's camera.\n example: K7\n - turnSlot (int) [required]: Observer-local turn: 0 LEFT, 1 FORWARD, 2 RIGHT.\n constraints: range=[0, 2]\n example: 1\n - hopCount (int) [required]: Straight graph hops after the landmark anchor.\n constraints: range=[1, 2]\n example: 2\n example: {\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":1,\"hopCount\":2}}\n- RunRendezvous\n kind: method\n docstring: Execute both planned graph routes and end the round. Missing or wrong plans visibly stop apart and earn -1; both wagons at the marked rendezvous earn +1.\n signature: RunRendezvous() : bool\n return_type: bool\n access: call\n example: {\"name\":\"RunRendezvous\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 7 operation step(s).\n- Across the whole task, you can take at most 7 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50998 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49066 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45994 chars]"}}]}, {"role": "assistant", "content": "The rendezvous node aligns with the local target position reached from K7 by two leftward observer-local hops.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"A\",\"landmarkId\":\"K7\",\"turnSlot\":0,\"hopCount\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER A COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50998 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45670 chars]"}}]}, {"role": "assistant", "content": "Observer B’s target node is one leftward local hop from the visible M2 anchor.\n{\"name\":\"IssueCommand\",\"arguments\":{\"observerId\":\"B\",\"landmarkId\":\"M2\",\"turnSlot\":0,\"hopCount\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nOBSERVER B COMMAND SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50814 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=45470 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both observer-local commands are set, so execute the synchronized routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}", "action_extracted": "{\"name\":\"RunRendezvous\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12160, "input_tokens": 17614, "output_tokens": 65, "reasoning_output_tokens": 23, "total_tokens": 17679}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 36995, "output_tokens": 5616, "reasoning_output_tokens": 5441, "total_tokens": 42611}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\\n', '\\r\\n[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer\\'s own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ClearCommand\\n kind: method\\n docstring: Clear one observer\\'s current command without ending the round.\\n signature: ClearCommand(string observerId) : bool\\n return_type: bool\\n access: call\\n arguments:\\r\\n - observerId (string) [required]: Observer ID A or B.\\n example: B\\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\\n- IssueCommand\\n kind: method\\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer\\'s feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\\n signature: IssueComm...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Coordinate two village wagons to meet at the rendezvous socket marked in the zone camera. Observer A and Observer B face different directions and can see different landmark IDs. For each wagon, issue a command that references a landmark visible in that observer's own camera, then uses the documented observer-local turn slot and hop count. The same turn slot may correspond to a different world direction for the two observers. You may replace or clear commands before running. Run only after both commands are set. A run executes both routes and ends the game round: both wagons stopping at the marked socket earns +1; any other final stops earn -1. Rejected malformed, out-of-range, or invisible-landmark commands do not change the plan and earn 0. Camera delivery order, observer poses, landmark IDs and visibility, colors, and rendezvous target vary deterministically with the seed. This is one game round; prior-round shared history is not used. Each synchronous edit returns a new decision immediately, while the configured decision interval is only a maximum fallback. The shared operation-step budget ends the round if exhausted. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- ClearCommand\n kind: method\n docstring: Clear one observer's current command without ending the round.\n signature: ClearCommand(string observerId) : bool\n return_type: bool\n access: call\n arguments:\r\n - observerId (string) [required]: Observer ID A or B.\n example: B\n example: {\"name\":\"ClearCommand\",\"arguments\":{\"observerId\":\"B\"}}\n- IssueCommand\n kind: method\n docstring: Set or replace one observer-local wagon command. observerId is A or B; landmarkId must be visible in that observer's feed; turnSlot is 0=LEFT, 1=FORWARD, 2=RIGHT; hopCount is 1 or 2.\n signature: IssueCommand(string observerId, string landmarkId, int turnSlot, int hopCount) : bool\n return_type: bool\n access: call\n arguments:...[truncated]"}, {"role": "assistant", "text_preview": "Both observer-local commands are set, so execute the synchronized routes.\n{\"name\":\"RunRendezvous\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nWAGONS STOPPED AT DIFFERENT / WRONG SOCKETS\n\n\r\n\r\nReset context:\nCombine all three role chips. CAM delivery letters identify fixed sensor slots; semantic roles OBSERVER A, OBSERVER B, and RENDEZVOUS ZONE may occupy different slots this seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-2a4910035ca4", "output_path": "AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "AsymmetricObserverCommand3D-seed-0010::codex-gpt-5.6-terra-medium", "job_index": 9, "case_index": 9, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} diff --git a/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl b/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl deleted file mode 100644 index 2845d15a20a6e0e6d05db994737829ab5cd646d3..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:202e2b0aec7b7dc471a87043060fef6f30fc57b950d4049789eeac8590008bef -size 14026578 diff --git a/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_results.jsonl b/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_results.jsonl deleted file mode 100644 index c123339c82e00a894807ef4764fa84d278f63fed..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_results.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 1, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 1, "actual_rollout_group_seed": 1, "initial_attempt_group_seed": 1, "final_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 399.5734, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44034 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29858 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 7664, "output_tokens": 144, "reasoning_output_tokens": 94, "total_tokens": 7808}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 7664, "output_tokens": 144, "reasoning_output_tokens": 94, "total_tokens": 7808}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant_preview": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44034 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29858 chars]"}}]}, {"role": "assistant", "content": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36966 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 7040, "input_tokens": 10482, "output_tokens": 390, "reasoning_output_tokens": 327, "total_tokens": 10872}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 8832, "input_tokens": 18146, "output_tokens": 534, "reasoning_output_tokens": 421, "total_tokens": 18680}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant_preview": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44034 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29858 chars]"}}]}, {"role": "assistant", "content": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36966 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 14148, "output_tokens": 330, "reasoning_output_tokens": 267, "total_tokens": 14478}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19072, "input_tokens": 32294, "output_tokens": 864, "reasoning_output_tokens": 688, "total_tokens": 33158}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "The final preview confirms full front glyphs for both ALLY A and ALLY B, while HOSTILE sees the neutral dark back. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44034 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29858 chars]"}}]}, {"role": "assistant", "content": "Start with a central socket that is clear in both ally views, then inspect the configured preview.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43114 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36966 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43206 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}, {"role": "assistant", "content": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43674 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37374 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30742 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The final preview confirms full front glyphs for both ALLY A and ALLY B, while HOSTILE sees the neutral dark back. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13312, "input_tokens": 18355, "output_tokens": 99, "reasoning_output_tokens": 42, "total_tokens": 18454}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 32384, "input_tokens": 50649, "output_tokens": 963, "reasoning_output_tokens": 730, "total_tokens": 51612}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The final preview confirms full front glyphs for both ALLY A and ALLY B, while HOSTILE sees the neutral dark back. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-c3cd98c07067", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0001::codex-gpt-5.6-terra-medium", "job_index": 0, "case_index": 0, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 2, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 2, "actual_rollout_group_seed": 2, "initial_attempt_group_seed": 2, "final_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 468.7695, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 7664, "output_tokens": 505, "reasoning_output_tokens": 447, "total_tokens": 8169}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 7664, "output_tokens": 505, "reasoning_output_tokens": 447, "total_tokens": 8169}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "assistant_preview": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}, {"role": "assistant", "content": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41902 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 7040, "input_tokens": 10851, "output_tokens": 997, "reasoning_output_tokens": 922, "total_tokens": 11848}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13056, "input_tokens": 18515, "output_tokens": 1502, "reasoning_output_tokens": 1369, "total_tokens": 20017}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}, {"role": "assistant", "content": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41902 chars]"}}]}, {"role": "assistant", "content": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37790 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42122 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 15144, "output_tokens": 733, "reasoning_output_tokens": 684, "total_tokens": 15877}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 23168, "input_tokens": 33659, "output_tokens": 2235, "reasoning_output_tokens": 2053, "total_tokens": 35894}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "assistant_preview": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}, {"role": "assistant", "content": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41902 chars]"}}]}, {"role": "assistant", "content": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37790 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42122 chars]"}}]}, {"role": "assistant", "content": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34446 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37826 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42106 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14464, "input_tokens": 19760, "output_tokens": 1276, "reasoning_output_tokens": 1224, "total_tokens": 21036}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 37632, "input_tokens": 53419, "output_tokens": 3511, "reasoning_output_tokens": 3277, "total_tokens": 56930}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant_preview": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}, {"role": "assistant", "content": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41902 chars]"}}]}, {"role": "assistant", "content": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37790 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42122 chars]"}}]}, {"role": "assistant", "content": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34446 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37826 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42106 chars]"}}]}, {"role": "assistant", "content": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34678 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38058 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42474 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 18688, "input_tokens": 25509, "output_tokens": 302, "reasoning_output_tokens": 243, "total_tokens": 25811}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 56320, "input_tokens": 78928, "output_tokens": 3813, "reasoning_output_tokens": 3520, "total_tokens": 82741}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "The configuration is complete at S4, yaw 1, height 2; commit it.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33722 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}]}, {"role": "assistant", "content": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34154 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37510 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41902 chars]"}}]}, {"role": "assistant", "content": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34386 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37790 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42122 chars]"}}]}, {"role": "assistant", "content": "The middle height is most likely the hostile-screened tier while preserving the two ally sight lines.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34446 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37826 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42106 chars]"}}]}, {"role": "assistant", "content": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34678 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38058 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42474 chars]"}}]}, {"role": "assistant", "content": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34498 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37958 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42830 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The configuration is complete at S4, yaw 1, height 2; commit it.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 25344, "input_tokens": 30881, "output_tokens": 103, "reasoning_output_tokens": 58, "total_tokens": 30984}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 81664, "input_tokens": 109809, "output_tokens": 3916, "reasoning_output_tokens": 3578, "total_tokens": 113725}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 001\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The configuration is complete at S4, yaw 1, height 2; commit it.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 001\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-ab1d22c1b1a9", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0002::codex-gpt-5.6-terra-medium", "job_index": 1, "case_index": 1, "model_index": 0, "worker_index": 1, "parallel_slot_index": 1, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 3, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 3, "actual_rollout_group_seed": 3, "initial_attempt_group_seed": 3, "final_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 120.9233, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44026 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36662 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29694 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 757, "reasoning_output_tokens": 699, "total_tokens": 6867}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 757, "reasoning_output_tokens": 699, "total_tokens": 6867}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "assistant_preview": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44026 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36662 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29694 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43234 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30090 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 9549, "output_tokens": 437, "reasoning_output_tokens": 377, "total_tokens": 9986}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 15659, "output_tokens": 1194, "reasoning_output_tokens": 1076, "total_tokens": 16853}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant_preview": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44026 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36662 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29694 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43234 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30090 chars]"}}]}, {"role": "assistant", "content": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43362 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30302 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13267, "output_tokens": 833, "reasoning_output_tokens": 763, "total_tokens": 14100}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 28926, "output_tokens": 2027, "reasoning_output_tokens": 1839, "total_tokens": 30953}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "With S4/Y0/H2, both ally feeds show the full front panel and glyph line, while the hostile feed sees only its narrow side behind the building. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44026 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36662 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29694 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43234 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30090 chars]"}}]}, {"role": "assistant", "content": "The provisional S4 display presents its broad face to both ally feeds while the hostile view is largely masked by the house. Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43362 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37306 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30302 chars]"}}]}, {"role": "assistant", "content": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37486 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30478 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With S4/Y0/H2, both ally feeds show the full front panel and glyph line, while the hostile feed sees only its narrow side behind the building. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 17989, "output_tokens": 324, "reasoning_output_tokens": 260, "total_tokens": 18313}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 23168, "input_tokens": 46915, "output_tokens": 2351, "reasoning_output_tokens": 2099, "total_tokens": 49266}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "With S4/Y0/H2, both ally feeds show the full front panel and glyph line, while the hostile feed sees only its narrow side behind the building. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-072300c0ac1f", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0003::codex-gpt-5.6-terra-medium", "job_index": 2, "case_index": 2, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 4, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 4, "actual_rollout_group_seed": 4, "initial_attempt_group_seed": 4, "final_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 425.2841, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}", "assistant_preview": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42082 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33350 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 775, "reasoning_output_tokens": 725, "total_tokens": 6885}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 775, "reasoning_output_tokens": 725, "total_tokens": 6885}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant_preview": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42082 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33350 chars]"}}]}, {"role": "assistant", "content": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33194 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33638 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 10240, "output_tokens": 47, "reasoning_output_tokens": 0, "total_tokens": 10287}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 16350, "output_tokens": 822, "reasoning_output_tokens": 725, "total_tokens": 17172}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant_preview": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42082 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33350 chars]"}}]}, {"role": "assistant", "content": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33194 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33638 chars]"}}]}, {"role": "assistant", "content": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33466 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42422 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33778 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13548, "output_tokens": 825, "reasoning_output_tokens": 773, "total_tokens": 14373}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 29898, "output_tokens": 1647, "reasoning_output_tokens": 1498, "total_tokens": 31545}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "assistant_preview": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42082 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33350 chars]"}}]}, {"role": "assistant", "content": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33194 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33638 chars]"}}]}, {"role": "assistant", "content": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33466 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42422 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33778 chars]"}}]}, {"role": "assistant", "content": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33698 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34166 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 18225, "output_tokens": 1118, "reasoning_output_tokens": 1059, "total_tokens": 19343}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 32256, "input_tokens": 48123, "output_tokens": 2765, "reasoning_output_tokens": 2557, "total_tokens": 50888}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y5 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y5 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "Yaw 5 shows a full front glyph to both allies while the hostile sees only the edge, so commit this configured pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y5 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y5 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42082 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33350 chars]"}}]}, {"role": "assistant", "content": "Select a central foreground socket that is in both ally views; orientation and height can be set next.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33194 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33638 chars]"}}]}, {"role": "assistant", "content": "Use the highest tier to clear the low socket screens while remaining comfortably within all camera frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33466 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42422 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33778 chars]"}}]}, {"role": "assistant", "content": "The allies share the foreground-side viewing sector, so reverse the default-facing board away from the hostile-side sector.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33698 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34166 chars]"}}]}, {"role": "assistant", "content": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y5 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33598 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34306 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Yaw 5 shows a full front glyph to both allies while the hostile sees only the edge, so commit this configured pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 17792, "input_tokens": 23793, "output_tokens": 87, "reasoning_output_tokens": 35, "total_tokens": 23880}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 50048, "input_tokens": 71916, "output_tokens": 2852, "reasoning_output_tokens": 2592, "total_tokens": 74768}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Yaw 5 shows a full front glyph to both allies while the hostile sees only the edge, so commit this configured pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-2f3ab5dc55b4", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0004::codex-gpt-5.6-terra-medium", "job_index": 3, "case_index": 3, "model_index": 0, "worker_index": 3, "parallel_slot_index": 3, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 5, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 5, "actual_rollout_group_seed": 5, "initial_attempt_group_seed": 5, "final_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 386.5158, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}", "assistant_preview": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37866 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29830 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6112, "output_tokens": 1001, "reasoning_output_tokens": 946, "total_tokens": 7113}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6112, "output_tokens": 1001, "reasoning_output_tokens": 946, "total_tokens": 7113}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant_preview": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37866 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29830 chars]"}}]}, {"role": "assistant", "content": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38294 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28594 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 9792, "output_tokens": 256, "reasoning_output_tokens": 197, "total_tokens": 10048}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 15904, "output_tokens": 1257, "reasoning_output_tokens": 1143, "total_tokens": 17161}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "Yaw 4 shows the purple front glyph fully to both allies and the neutral back to HOSTILE. The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37866 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29830 chars]"}}]}, {"role": "assistant", "content": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38294 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28594 chars]"}}]}, {"role": "assistant", "content": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29126 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38218 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28850 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Yaw 4 shows the purple front glyph fully to both allies and the neutral back to HOSTILE. The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 13325, "output_tokens": 566, "reasoning_output_tokens": 505, "total_tokens": 13891}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5376, "input_tokens": 29229, "output_tokens": 1823, "reasoning_output_tokens": 1648, "total_tokens": 31052}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Yaw 4 shows the purple front glyph fully to both allies and the neutral back to HOSTILE. The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "Configuration is complete: both ally feeds show the full front glyph, and HOSTILE shows no valid front glyph.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Yaw 4 shows the purple front glyph fully to both allies and the neutral back to HOSTILE. The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37866 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29830 chars]"}}]}, {"role": "assistant", "content": "S2 is visible beyond the private lower screens in both ALLY views, while it is screened from the HOSTILE view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38294 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28594 chars]"}}]}, {"role": "assistant", "content": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29126 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38218 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28850 chars]"}}]}, {"role": "assistant", "content": "Yaw 4 shows the purple front glyph fully to both allies and the neutral back to HOSTILE. The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=28954 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Configuration is complete: both ally feeds show the full front glyph, and HOSTILE shows no valid front glyph.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 17767, "output_tokens": 56, "reasoning_output_tokens": 7, "total_tokens": 17823}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17664, "input_tokens": 46996, "output_tokens": 1879, "reasoning_output_tokens": 1655, "total_tokens": 48875}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Configuration is complete: both ally feeds show the full front glyph, and HOSTILE shows no valid front glyph.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-af2e7ae14fbf", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0005::codex-gpt-5.6-terra-medium", "job_index": 4, "case_index": 4, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 6, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 6, "actual_rollout_group_seed": 6, "initial_attempt_group_seed": 6, "final_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 180.1994, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33930 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44058 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 890, "reasoning_output_tokens": 844, "total_tokens": 7000}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 890, "reasoning_output_tokens": 844, "total_tokens": 7000}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "assistant_preview": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33930 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44058 chars]"}}]}, {"role": "assistant", "content": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42418 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 9670, "output_tokens": 1516, "reasoning_output_tokens": 1459, "total_tokens": 11186}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 15780, "output_tokens": 2406, "reasoning_output_tokens": 2303, "total_tokens": 18186}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant_preview": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33930 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44058 chars]"}}]}, {"role": "assistant", "content": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42418 chars]"}}]}, {"role": "assistant", "content": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38242 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42562 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 14452, "output_tokens": 251, "reasoning_output_tokens": 202, "total_tokens": 14703}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 30232, "output_tokens": 2657, "reasoning_output_tokens": 2505, "total_tokens": 32889}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "assistant_preview": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H2 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33930 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44058 chars]"}}]}, {"role": "assistant", "content": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42418 chars]"}}]}, {"role": "assistant", "content": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38242 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42562 chars]"}}]}, {"role": "assistant", "content": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38446 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34878 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43222 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13312, "input_tokens": 18556, "output_tokens": 1150, "reasoning_output_tokens": 1086, "total_tokens": 19706}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 33280, "input_tokens": 48788, "output_tokens": 3807, "reasoning_output_tokens": 3591, "total_tokens": 52595}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "The low placement is configured with all three values and keeps the hostile’s line screened.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33930 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44058 chars]"}}]}, {"role": "assistant", "content": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42418 chars]"}}]}, {"role": "assistant", "content": "The default facing shows the front plane toward both ally-side views, while the hostile is on its reverse side. Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38242 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42562 chars]"}}]}, {"role": "assistant", "content": "Use the highest tier to keep the glyph above the foreground socket screens in both ally feeds.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H2 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38446 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34878 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43222 chars]"}}]}, {"role": "assistant", "content": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38538 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=35066 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42926 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The low placement is configured with all three values and keeps the hostile’s line screened.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 17792, "input_tokens": 24161, "output_tokens": 331, "reasoning_output_tokens": 288, "total_tokens": 24492}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 51072, "input_tokens": 72949, "output_tokens": 4138, "reasoning_output_tokens": 3879, "total_tokens": 77087}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 111\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The low placement is configured with all three values and keeps the hostile’s line screened.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 111\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-d66c82f1997e", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0006::codex-gpt-5.6-terra-medium", "job_index": 5, "case_index": 5, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 7, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 7, "actual_rollout_group_seed": 7, "initial_attempt_group_seed": 7, "final_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 124.8544, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31878 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41602 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29746 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 7150, "output_tokens": 149, "reasoning_output_tokens": 103, "total_tokens": 7299}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 7150, "output_tokens": 149, "reasoning_output_tokens": 103, "total_tokens": 7299}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant_preview": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31878 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41602 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29746 chars]"}}]}, {"role": "assistant", "content": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32338 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30102 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 7040, "input_tokens": 9969, "output_tokens": 113, "reasoning_output_tokens": 61, "total_tokens": 10082}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13056, "input_tokens": 17119, "output_tokens": 262, "reasoning_output_tokens": 164, "total_tokens": 17381}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31878 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41602 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29746 chars]"}}]}, {"role": "assistant", "content": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32338 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30102 chars]"}}]}, {"role": "assistant", "content": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32634 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40418 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13343, "output_tokens": 599, "reasoning_output_tokens": 538, "total_tokens": 13942}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 22144, "input_tokens": 30462, "output_tokens": 861, "reasoning_output_tokens": 702, "total_tokens": 31323}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "The configured pose visibly satisfies the required ally-facing and hostile-edge-on pattern.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31878 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41602 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29746 chars]"}}]}, {"role": "assistant", "content": "Select the central upper socket as the clearest shared sightline for both allies.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32338 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30102 chars]"}}]}, {"role": "assistant", "content": "The default face is broad toward HOSTILE and edge-on to both allies, so reverse it 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32634 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40418 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32746 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40454 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30334 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The configured pose visibly satisfies the required ally-facing and hostile-edge-on pattern.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 17802, "output_tokens": 55, "reasoning_output_tokens": 14, "total_tokens": 17857}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 23936, "input_tokens": 48264, "output_tokens": 916, "reasoning_output_tokens": 716, "total_tokens": 49180}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The configured pose visibly satisfies the required ally-facing and hostile-edge-on pattern.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-bbd868235614", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0007::codex-gpt-5.6-terra-medium", "job_index": 6, "case_index": 6, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 8, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 8, "actual_rollout_group_seed": 8, "initial_attempt_group_seed": 8, "final_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 396.3719, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36646 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6108, "output_tokens": 1305, "reasoning_output_tokens": 1261, "total_tokens": 7413}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6108, "output_tokens": 1305, "reasoning_output_tokens": 1261, "total_tokens": 7413}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "assistant_preview": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36646 chars]"}}]}, {"role": "assistant", "content": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30362 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43442 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 10081, "output_tokens": 249, "reasoning_output_tokens": 198, "total_tokens": 10330}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 16189, "output_tokens": 1554, "reasoning_output_tokens": 1459, "total_tokens": 17743}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "The displayed preview is centered at the middle tier and is unobstructed in both ally views, so retain that height.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36646 chars]"}}]}, {"role": "assistant", "content": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30362 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43442 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}]}, {"role": "assistant", "content": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37286 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The displayed preview is centered at the middle tier and is unobstructed in both ally views, so retain that height.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 13588, "output_tokens": 524, "reasoning_output_tokens": 470, "total_tokens": 14112}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 29777, "output_tokens": 2078, "reasoning_output_tokens": 1929, "total_tokens": 31855}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The displayed preview is centered at the middle tier and is unobstructed in both ally views, so retain that height.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "Both ally feeds show the full front glyph, while the hostile feed shows only its edge/back; commit this completed configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The displayed preview is centered at the middle tier and is unobstructed in both ally views, so retain that height.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44366 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=36646 chars]"}}]}, {"role": "assistant", "content": "Use the central rear socket for the clearest shared ally view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30362 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43442 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37094 chars]"}}]}, {"role": "assistant", "content": "The unrotated preview is front-facing in both ally feeds while nearly edge-on to the hostile feed.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37286 chars]"}}]}, {"role": "assistant", "content": "The displayed preview is centered at the middle tier and is unobstructed in both ally views, so retain that height.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43562 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37358 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Both ally feeds show the full front glyph, while the hostile feed shows only its edge/back; commit this completed configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 17962, "output_tokens": 90, "reasoning_output_tokens": 39, "total_tokens": 18052}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 21760, "input_tokens": 47739, "output_tokens": 2168, "reasoning_output_tokens": 1968, "total_tokens": 49907}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Both ally feeds show the full front glyph, while the hostile feed shows only its edge/back; commit this completed configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-761a043ad378", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0008::codex-gpt-5.6-terra-medium", "job_index": 7, "case_index": 7, "model_index": 0, "worker_index": 7, "parallel_slot_index": 7, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 9, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 9, "actual_rollout_group_seed": 9, "initial_attempt_group_seed": 9, "final_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 85.5038, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33962 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44102 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 511, "reasoning_output_tokens": 461, "total_tokens": 6621}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 511, "reasoning_output_tokens": 461, "total_tokens": 6621}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "assistant_preview": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33962 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44102 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34474 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42474 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9295, "output_tokens": 698, "reasoning_output_tokens": 629, "total_tokens": 9993}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 15405, "output_tokens": 1209, "reasoning_output_tokens": 1090, "total_tokens": 16614}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33962 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44102 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34474 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42474 chars]"}}]}, {"role": "assistant", "content": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34522 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38106 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43358 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 13275, "output_tokens": 845, "reasoning_output_tokens": 785, "total_tokens": 14120}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 28680, "output_tokens": 2054, "reasoning_output_tokens": 1875, "total_tokens": 30734}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "The configured glyph is fully framed and unobstructed for both allies, while the hostile view remains edge-on. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33962 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=44102 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has good clearance; configure it first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34474 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37974 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=42474 chars]"}}]}, {"role": "assistant", "content": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34522 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38106 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43358 chars]"}}]}, {"role": "assistant", "content": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38142 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43354 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The configured glyph is fully framed and unobstructed for both allies, while the hostile view remains edge-on. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12288, "input_tokens": 18000, "output_tokens": 140, "reasoning_output_tokens": 88, "total_tokens": 18140}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 20096, "input_tokens": 46680, "output_tokens": 2194, "reasoning_output_tokens": 1963, "total_tokens": 48874}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The configured glyph is fully framed and unobstructed for both allies, while the hostile view remains edge-on. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-0a895268b515", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0009::codex-gpt-5.6-terra-medium", "job_index": 8, "case_index": 8, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "MutualVisibilitySignal3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "MutualVisibilitySignal3D", "requested_group_seed": 10, "requested_env_id": 0, "actual_task_name": "MutualVisibilitySignal3D", "actual_group_seed": 10, "actual_rollout_group_seed": 10, "initial_attempt_group_seed": 10, "final_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}, "duration_s": 75.3904, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant_preview": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37254 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43774 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33898 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "action_extracted": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 576, "reasoning_output_tokens": 516, "total_tokens": 6686}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6110, "output_tokens": 576, "reasoning_output_tokens": 516, "total_tokens": 6686}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "assistant_preview": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSOCKET S4 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37254 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43774 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33898 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43002 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "action_extracted": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 6016, "input_tokens": 9370, "output_tokens": 283, "reasoning_output_tokens": 220, "total_tokens": 9653}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7808, "input_tokens": 15480, "output_tokens": 859, "reasoning_output_tokens": 736, "total_tokens": 16339}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant_preview": "The middle height keeps the broad glyph fully within each ally view and well above their lower socket screens, while retaining the hostile edge-on view.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "MutualVisibilitySignal3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nYAW Y0 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37254 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43774 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33898 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43002 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}]}, {"role": "assistant", "content": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34674 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The middle height keeps the broad glyph fully within each ally view and well above their lower socket screens, while retaining the hostile edge-on view.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "action_extracted": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9216, "input_tokens": 12939, "output_tokens": 769, "reasoning_output_tokens": 711, "total_tokens": 13708}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17024, "input_tokens": 28419, "output_tokens": 1628, "reasoning_output_tokens": 1447, "total_tokens": 30047}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The middle height keeps the broad glyph fully within each ally view and well above their lower socket screens, while retaining the hostile edge-on view.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant_preview": "S4/Y0/H1 shows the marked broad face unobstructed to both role-labeled ally feeds, while the hostile feed has only the unmarked edge. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 6, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "MutualVisibilitySignal3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 6, "max_rollout_steps": 6}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nHEIGHT H1 SET\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "The middle height keeps the broad glyph fully within each ally view and well above their lower socket screens, while retaining the hostile edge-on view.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37254 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43774 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33898 chars]"}}]}, {"role": "assistant", "content": "Socket S4 is centrally visible in both ally feeds and has a clear elevated placement, making it the strongest candidate before setting its orientation and height.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSOCKET S4 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43002 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}]}, {"role": "assistant", "content": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nYAW Y0 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43134 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34674 chars]"}}]}, {"role": "assistant", "content": "The middle height keeps the broad glyph fully within each ally view and well above their lower socket screens, while retaining the hostile edge-on view.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nHEIGHT H1 SET\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38082 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=43102 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34746 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "S4/Y0/H1 shows the marked broad face unobstructed to both role-labeled ally feeds, while the hostile feed has only the unmarked edge. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "action_extracted": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 12416, "input_tokens": 17590, "output_tokens": 196, "reasoning_output_tokens": 133, "total_tokens": 17786}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 29440, "input_tokens": 46009, "output_tokens": 1824, "reasoning_output_tokens": 1580, "total_tokens": 47833}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nUNSAFE VISIBILITY PATTERN | VIS 000\\n', '\\r\\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer\\'s socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- CommitSignal\\n kind: method\\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\\n signature: CommitSignal() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\\n- SetSignalHeight\\n kind: method\\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\\n signature: SetSignalHeight(int heightSlot) : bool\\n return_type: bool\\n access: call\\n argument...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: <...[truncated]"}, {"role": "assistant", "text_preview": "S4/Y0/H1 shows the marked broad face unobstructed to both role-labeled ally feeds, while the hostile feed has only the unmarked edge. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-57778649b690", "output_path": "MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "MutualVisibilitySignal3D-seed-0010::codex-gpt-5.6-terra-medium", "job_index": 9, "case_index": 9, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} diff --git a/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl b/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl deleted file mode 100644 index a907e8d252da2c2b4fd1c9bd97abc4d86c82fe22..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0156-MutualVisibilitySignal3D/codex_gpt-5.6-terra_medium/MutualVisibilitySignal3D_seeds1_10_trajectories.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-c3cd98c07067", "created_at_unix_s": 1788843216.000177, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 1, "requested_group_seed": 1, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [1], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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3OBKKwWC6xRBesp9QwoKFmxqKpg2UXmWCKUdB+wkniJxw5ifW8kTxk4yoZqW8Htrw8b69e3Uaqqa6uqa6mgBctcx+StTaxDXoG90cHshjMJhuJKBIPQHg5509CqUu+XxeqWuy2+PRW9Vi4abLbTYapg2CMzyKZeCQOCIXycuUNFJS91SQbRVfSi1O/Q/JNuNEUAwG012G8JL9lFKXhOgnLUy+Bw9iNVBGdTqjXiO2rRPYW+q9YxAjKdpWAPZVUrfn2VEPOgDA2Y+/1Vy28f13vpJfcezooUBuRYPJUw57C62KniMFAHCylJGCI3qbmyMAHshjML2W+5ctOxIjGIjqetZu3dIpF7rwvHOj7v/xt82J9gOdIK7IJOZ+AlaDop+0Whuyn0Lup9Pldnv96ahrcn1TdkYKUAV8u/fFsYRnPfZWS9lGyV0SAPz3c/h9uenK4ZJ//eCbEo5jS0orWlqajWrobSkY/QwbyKtoVWqysamxGbWsP1ZlT9GTKBiKc0IxmN7E/cuW1dZUNzW1RH1WXhipsR/s4LXY5DFR91O2AwkN4e+fPwxtIPWslUU/hQLNJsl+Cn1DNFA9BZB6orZ1rSJX2JuvHH6zTD0BQdTWFLkq9knqGQk62KRXD8gVxX5IttlkEL0qTmnCYHoNSy+/rLamussux/Mx22skJKChuSOBjOQMr88rFW5ajVox+qmDullnE1LZBfvp8XpDfUM6TIDhrPooc0cIVIOkUdMkSTQ5xM+fquYAXFdZAGsoBtMLWHr5ZaDbQCZmP3lx7kiwn16vV0uJwVOF/QQ8i+wnGrzrTMElj1oj3pxP0IhqDMaYh8BmTKKAEgQxOD8D7e+fpm/10hgMpqewtDupZ0KTSKhviLRaMLSf3gj7yYbsJ4p+osE7nGsyw+WMOoaooJV1zbRaWENJgJU9TRKAJAleGMJ7PD4AiKRkS5MNxkdUKlUgEFCpYCY+TgvFYHouU8eNLS0pke+xWJOLi09EHulD7dsFNN6GDl43LwmUlJRG7icdDWSC0c/5cvtJh9lPxkeK9hMAyX5Gdk3udOQJUiRBoPU89Fo12k8Q0QfyGAymh7Jp337FgDWqenY6paVlsZ4iE7KfQf+ZkZwB1VNa8B0AvVVN0yrRfkpdk4P2UzxLYpNI0VHkkUq7g74UbgggAdWqafQMQYBBA6IM5HEkFIPp6RpKnK7LK0KNfFwBvX/eMHnfEGQ/xec4DkY5c8yML3QGKXWpC+wnIACKehIEbERPkYTQCxSoaEmsCbkJRQN5tI01FIPpuWzat/9wQUFIyuQFOWG0q5iGj/YVOh+v2B8vBiq1f4eWU7Cf0ppxAIjz+v4AQ9MwvFjX2IzG7/LoJ1RPoWsy3bn2U1jDgyJJThi8kyRJ07CnMgAsTYW9ZN6scVu3F7bv4hgMpntiY9jCgoKRo+FaltCORpFKcVdxbTUAmiVpdpcsKtpWoirIcPuBeAJ6/7xhSEBh7ieA9tOit9CAozWUZD9h2zpdmP2srm/KykgJ2c9UcdGOUFZrx7C1+MxC8qnPDyxGdVa2JdmsM+s1ahXqRg/qbZ764Lp18GGdjQeAQuYUzyZhML0FG8MeLijIyBb6ukUYreLaGvlDNUWIvTnaglRKPrwFaqWf5eBciyAmEnQi9rO2vjmq/USFm3L7CdVTFYDHh+ynx2CGrUY6jsvhogBwtUAHqlZRfpevhfN4nVS18I6CS3RGeWGTw4dqk9BcPAaD6QXYGNZWFnN6R458AYt2D4DR6Faxk4wf/RTsp5i6BPuGyKKfgv2EfUPk9hO9KgPlfgpaZjCb0DEdvX0ZPA/8AdbtCzjcPq+fQR8U4tGyF4mRCp436UO6iSOhGAymHZCAICOEmIxjP6EwBQs3tbRQuCks2oEQCzcNenHyXbKfDTD7EtpPP7SfQp6TXzMOLoXUifA8LExyeQMtLqihHA8zVTmO5+Eyx3CtY/gf+K/oSBlWdM1yE4pnkzAYTEdMHRnHfqK2dWjw3gb7aUpusjuXLLtGHLwzjDcQs/6yfQwdmD10YPaQ/OxB/bP6ZaUZjHparUY59mhVObS4HEEQfkrDqbWEVkfqcEkSBoNpP1GHxHSsuaOg/WzJSIbt4IP2kwtrW2fQS23rJPuZYUpOStV9+NKnKPTZbHdmZqTynXGzBpOBVqsZhqlsFFeOQw2ZKJLQqGiaJj0+/uyxedUNLaiskyCAw8fRwmcESRKsigq4lKFkXJuEwWDaTcw80KD9TEf20+F0A46rqG2E9lPqGxLMnFfaz99D+wkfM4xPsJ+2htDMeHzaEezlODhw1wgZoBaTlqZI9EWRpNmo0WlVGg2tVlF6nbo5mBaKwWAwnSygYalLgv1EmfO0gTJpxJWEQ31DgoWbkv1EFUdy+wklVbCfydbOmYiPCg8bNbEBIcpJkSSKewpDeFjZSVOkKiipAwdlRb4cR0IxGEz7iFeJlJGcrqVphvE7g/YzJ88ax37ChCG7U5o7AgxTKxxja7BV1YWOiYolbxroABzHu7wwUbbJ7vH6oO1Fs0dw9VAxMooG9aHaJAwGg+k0AY1jP42C/YTqmYD9RKlLMvvJJ1tN2dlik+PORRrv81CuYbmUzmRxevwOt9/jC/A80GngnLtQ8SkcT4DzJg6KPA82oRhML8bm9nW1A0X2U+obUlHbiPZH2k+P14tSlyLtJw+As8Uex37e8szD0Z+Iup6c7OlYu8prmx1un8vtb3F6vX5Gr1Whsbw0L6VR0clJUeIJWEMxGEw7BTSW/ZSIYz91JgrNHUn282RVPbKfAPBGgx7aT1LIgjr1uDwBf4D1+hmnB2qoiqYUUkyhFCcMBoM5RQ60TfazTrCfkKD97J+eJNhPHjBMe+xnAlBUzBZ5PA9DnwGGc7r9Lo9fDIMGERLtQUoyzCVQgE0oBoNps4Aq7WeDzH5y3NHSatF+orG5rG0dsp/p4fbT4XSJ9pOBCUzIftY2KFdG4keMhQ3673oJnAJ4oSrJz7DNDq/bG5D6maKSpCG5qQCAFmcXmWIMBtNbCXegqG1dksx+AmDQiato6IXOS1LhJkQVQPYTqmfQfpq0GtF+AlBV1+R2Qy3OMCcrLnzr9RehjfVO7Sl6bxzHN9ndDqHWUxrI+wPiUiCTzug/euRAxUuwCcVgMIlDhretA7Hsp8vtWbLsUpn9tMG+nwAg+4kOR4Wbkv2sqmvKzk7TG0ngJ6PaTxltCErSwQVFEsHu9DndfrswoYQ01OsX60p/21e6Wei3fyqYrKqYrKo4RSfHYDDdBDJB+2nQ6z58/RvJfmZnpEL72ai0n1CCg/YT8MDt9qLBe1NLaGFOfsRYZD9PyfidDBNrtzcQYFi7y9fs8KDEpt0nxHguAGBQXio2oRgMpt2QkfZT6hsi2U8AgMJ+ohenG5X2s7axGdnPytqm7H7QfqLBe5IlfEXi9CT4deph/H7U+87u9DXZoYYOzgmWVAnU1DScOg3FJrRrwH4fc7ogI+2n1LYO2c+mFti5I0H7iTLnAQD9+sG5o7qo9nP5Ymg/Fz2O5sRbyfjstN53rMPls7V44JwSzytCBpEaisFgMAkJqGg/66PbzySLackfLgvZz7p49hOO26H9DB4j2c/wwCVST0Q7kzLFV/Ft6h/qcPma7G6Xxx9gQrlNNTVRlo3Gs0kYDCYxB6phK6ptKPczuv187auQ/cyMbz8h/fqlQ/spHBPVfiIYlovXar+TrKl80olhoYbW2lx2Fyztkl9g3uwpnXI5DKavMbkPT5mSyH6qaBLZT9S2Lpb9rGyT/TQlNbU4Rfspq0RC9pNledi1s7NrgirKq+IfwLB8daOzyQFFP8CwLAwiAIOGkueKIrAJxWAw8YET7hXVtpz0LLRmnClgARwH7SeraWpxJFlMH772ldQ1uV/QfqYbYddk4Kea7c4/PHL9h698kZGRorCf6aawaSJkP5F6QnMpdIwnAJhh9Ir9PkAXUVztJAhg0KqPFNdSJKGiybLqRo2KumTumT+s3dlVd4FJFCY5Sv8XObU0/E1jGGWucSS07QT+vmM6ETi8RerJMH5vwGdUmY6WVg8blg5YkGSBTTfqbM3paTp7o9fh9kEBDdrPpFRds91pTbZ++MoXtY3N6RkpyH5CAY1tPxEsxws16W2+XVuzVKHPDh3aX9rvZ5XnKi86HOskqAjU4/JQJEGRhJ8hAgzHaVV+v5hjL4H71WMwmDjQov1EbesE+ymop2g/oRSmwQIks9FgNpuAKlDfaE8zJiH7aU22Lll2aSL20+vhJfvJcTxJinNHE2lnnKWVFbgcTkUB/InCqEveJwbPsxxcdw7FRlmO/+rHbYsunfr92h3yo7CGnnZatY0ZQgCuLJDTVXeEwYjQkv2kSS2KfubmmvQaTSz7mWZMap/9lM+8ozWLYOyVgrNY2/7yu7MeewvEJqpVbZ96It8rJk/xPAcIIaDA+wBDeokAqzShGExfptX4CejbIRTS6xO7JgMA0OBdr7GiyXe5/UTRz/pGuzT5Du2nML+UkZGy9I4Fisl3eeY8sp9om+V4aehuaqmjSUkbuygEOiQ/a0h+FlrXc+jAbINBZzDqLUlWnUGv0mq/Wbs38iV4NgnTZ6FtJ6SvqAcMpBNd7qxXQmupUNs6WPvo9kj20+F0mSw0mnzvl5lab7OL9tNsFO3ny58h+7ni1c8TsZ+coJ5S6FNHU9Qpk02DSVRwYSHPgCTQlY1OFUGwLIP62knwMIOA9ZGEz8NYjWIBKwaDCYlFNA3NCCYw9c0QSqhyPNJ+QvUEoKK2EdlPwAOpbV3QfvIZwuA90n46XR6knl4P/+vOo1Jap+Q4m06WG9RUIhNJ6IgHbr/QYDIaTEZLSrLBYhK+zBaLyWDUS19Wq8WSZEWHxT8nRRJ0eNW8UBYFE5vUOli6qmDCgNaHJxgMpq9Byu0nQrKf8IGPIgDh9nqR/YR7hNQlmF3/8qe1jS3IxSlyPwEARuEkRSU18ovJ5ZIWZsBlzwUX0pR/dYAcvh59RX2WIkmtmlbTSgfM8XBCKcrqx65QCxIMBoNBCC5MmDtCk+/ibmQ/fRS0n1mpehOVZkiKsJ8gbO4omv3My8xA9hMAUFrVKElidUvXNzMW1zpGEATQqCmTXq1WhWsoz8M4gyrKEB6bUAwGo4BEhZsIafAu2k8AcrLSgCrQYAvNHcntp+Jccewnz4MB2bB/KACgwZ1ojWbnZNfHOAVJkgad2mLQqmhK0cqeYbmoC89hMBiMHFJuP9HgXW4/0cNUwX6i7Vj2sz5oP13R7Oe0CYOQ++uC3ksJw6so0mzQmA0h3w2XR+ZgdWdkZSdwNS6/5qyuvsdeR1+um8b0Psj22c8mu3hMv37pbre3vrElLWg/DRH2k+X4jXvE+bsaZ0Da72fDlntTkKD3HDAgL/SSiHmhWEjqqFZRVqNWo6al0T0v3BTL8SlJURaewxraKWANxfQOaJQ5L9hPjdx+ltc25malSfZTin4CJtBkd1mTLZL9rG+x6bUwDiBPXQqzn+MHbd5fjNKY4L81ZSAlK8CJfeshfNvU02Edgo6iAF+jSnIkhVZVoiiaFXS5FmSiPXZVEiukxxOAQNehhRioT007NbRaRQE18Ks5bXWB18eInfQBAW0o4FucfosspWndL1svmHl2h77fGAyma7kly9ko+r3OhzxZ0yRPXZLsZ24M+wkfJlvk9lOvVUn2EyG3nyWVjZv2CerJ87UucT0iWINEEhpaMIwyIY2vnpY0MYoalVidnaLu1ahV6KI5/pN5TNkoTb3VpIUTSigZABA5GVaYJJwHi6/krPtlKzahGAwGCFZLrENPspg8Xq9OqxXtZ01jbnaY/USZ8+2wn4svmrRlf0nkt5sigE5Ftm/kbmo+ho6lAZ9p6udtKkM5UjzP0yo1I+SlchSVxQg6ru3XLyujtKxCOADuUBGEQaemSd4MNEANvwNaDW02aFmWb3F6WY6naYJFBpkAChOKNLQLW0dhMKc5zLKzT2bItwpyYOScqWOQ/dRptSH7ma20n1A9I+wnIP3R7WfQUqYkmbfsLyEIGFKsl02+E0I5PKrjlNvPmB4yhl4x7RIySqOR5oiE5CbAspyKJi0mrdmo1appWqjQR0dEmlBc3InB9HH44Ab9/ndb061C3Q7PTx6TDXzWuPYTbYT6hiD1jGU/R/RP3dQEVZiAAVBZnw4CqChCnkcfTz2jPy2t6UFI9jPOG1aUPAltlEN7UNhUI0wo0RTJsCwlFEkNzU0pKm9MSTI3Cu9CDu7S1I6uE1LjiT7bewLTC5ALDW3Uq6tt9swk08RJI1IMHPCF7GeqIdS2Dh2dlGyVt61Dk+/yUxeV1ORlZKDtZKsZRT95HsijnwitiowsplRyygbKwhRRKKeKCeYDaNQUQar9flajokMXx+P1BEhQ7Pp46TSml0FqVNRlF5218MoZlL8FRT/RE4L9dITmjgT7KfTOZCqChZtR7GdGxq+7oP0sqWwclhea86FplcIP0sEhfEwSlq3+A0KdlSVgpzqBgM+PKpDkDjVoV8VdvkDIHatpSqdRaVQUIMRZewBAfbO47pMc3KUJg+lrKMa50ANSFLn/8MmxE8b+tPPg4ZIaaD+bYFSU1MCD5fYTbeQg+2kLVSItvftKxeT7hJEDthyAc0ccx9e0SG3kg1clCDp+I5FTafq8Xq9iuC85UARFESgMKjFocL8WZ9eXn2IwnQ+TPEj+hb/FiRMZJRRn4ZMtusPHKiafOZEGLAD+3YfKeVB+4eQzIqOfFbU2KKCos3LQfq549WM0eEf20x0AU3JTdghzUxEyCR9SJKEjY7eyI9omqGUn4Sx8m1AsRy/EQMMUkxTcsXRVhuHyB2bagmvqSeBIKKanE0tDE4xW1wYPA3VNoFcTdY5FFFBKCEcOyk2hafLtTzZrATh7TH+5/bRG2E+4sIfQNXnp3VeuePVj6YwllY3jRwzYfrCUIGBzo9oI+5k5dWFzyfYptz81ani+NJFEAGDJmwYAaCnfGHGTYVrWKSgcKExaisZXG4+OzIez8C6PX6fRNTt8VlOUZncYTJ+NVmdIh4HeHNSONUMdXI+IgIecLK8FgPj9kunvf7nNojFHsZ81tpycVuxnVmZaU4tD9J3KmfHgSh40pddrldPwApbcafJGdvIj1KbDoF7ZVq6yolIW00z824H+J8LKuvlFBXVfHjg42xbsDxDfhO4M5OBqRQymFxBfWaDx/G3zfvRg5FD4GVJYVDEgL6OB5bcVlALA/7itMEw9g9FPeds6iZJK28RhWUgAGZartSsbawplPoCmSYNe2/G77yjBs/tlk0hRQUFSaJNTw7IO4s8m1SadoYg3RX51wrvAYDCdDVwoLQH9gQ503PCBjkYYrzxcVDlqWM6hoxUmDeBIVVJ25vcb9vUfMkDxGsl+Cm3r/HL7OX5k/4NFFSQJa36iRj/RcnIkQWg0qsTfSedPKYXXj0bpvRSO9FbQ5FKdQSl8TMS6yqHYEAaDOX00uiL6o3eeXYMC6nK5DAYD7LvRaD90tGL0sJyDR8sJwM+aOurf/y25KFtpP1H00+XyGJLCAoIllbYbJw75dRfUYpbjGmSNlxBSm3maIilhEok/BRpK0zTDwKZ0YWeIciW4S0xrTdHFuhOUS59lDr1Ta1oSqFdGkdIpoBjI47V2MZhejxgDdbrcxTAAKgoNUoKKynpU5w7NaUmN3qBLTtFI9tMQtJ+NTb7icmg/55wzcv3OIpKEheSkYDWRKqGzCW06IP6WYr2Kcldv5Zvgyfng9JFIQkoZOqjREbDqyRZPUAA9oU+bHNhACmTm5nkZkNkPRieOHoWZVSRBGHVqlVqXZDHB0iPU00Sg4Ggp2tjc0AxF0KLeGq2QP7JAHoPB9FEBPVpSMzQ/Cz2eeuaooXnpRWV1KANJo6K/+GW3UVhnzeF1sDavOPkuK9zMywBo/H60tJogCI7nCQAcfmVtJUFA5aKEITBFEiqV2AeeAKClbGNS/+lwgfgnfxd5i2c98bZkFV0OFwBhraloAJwOaS5MZNCI4fA/5cqso2HD8k8UFtIUSbKsx8lU+F014fmeCRJVPXFK0ymi1UhxX16XHHN6oZEyZaVbkAOF+fDF1cMHZR85WSutHZQzoH9upvG3jftpDeD5yrlTRotHCvazxQmrdGZOGbZ2+1GYusShPHlSsp8oGEqQBEpQ99vtpFW949d958yckMgtbnviVrRRt7ca6MRsqvicKDwC5VIo8T9xuDDsucQDBzGQkgf41iKnmE6hVdXD0RJM1xH+Ry/6L5KAqaC+AFtdVe+wu3fuPe6wwVDmkP6ZBEHkpOiG98+8afHszJw8qJ6yws3xw8WG8EH15EmCOFbrra5tdLs8Xo/X7/NzTID0ewwkZ6J4A8HqSb/L7txXWCxNzXRlrXn/EWPl02sEGfqC2fXB7w6slQ//Cu2U/hsBLu7EYHoo8G88ka9waPkeSghcOp0uo9EAAOGwiU3qCAIcOVE5YnC/zBQrAJzMfnpbnMo8eThUZz0h4ylIs4qGQVFSiIF25E2qjKIDVfNRitMjcMMlRoJLHmXlDx01KGNQTlJhnuW3DVslCZXfedRthE6nAwQUWZ9PfMs8KX0zMJg+zc6+2hoGOtBh+VlVtTC1U0VTmUIjO6fTfeBI8YEjsJcSAGAUyg89XnmiwlbtATL7KXbxkAwaQRDFVc2orTslrPxOkSRJwnJ7Cq4wH0pKhw3lhBe06XbVvAd9Jf6SHEKsMCs/dmTpvAlXX37W9z/8SqpUPEWr1PLl5Hinn2MAaTDoLRajxWI0mw0mk8FsNqBttYrSqCiNGn7k8MIK8oAHzREF8tiEYjB9BzosVSiGnB0qqhg9NKegqCLTqh7aP9UfYFav2WzU6uT2k+U4Sph6V6spjudZBnoziiRpCn7B6SOCVOn0aKW2+ppa6Ea7uE8czz/34orQGpyhLCZhVM6DQICjSI5hObUKenFxkC6mUEnRhuCG8F+rWR1pQvFsEgbTRxBjoNkZFhTZy89OLiqtyckU5l+CDBuUXVAEK16tJm1VTUN1re28ccMl+4kKyVE1fWWjgyJhnzrYc54kcnPSMzNTklOSSJ1ObTLo9Go/42dYvzUt6fbfXxnMb2qDjLpZys1SvK89a0TJIxhw/ffwLAECGnCSJAmG5VAOKaFA2BGp+ZEmFIPB9EUHKgENI0m6vWIm/NETVUMHZhUVV6M6HBVN7j1cLNlPIbIZei0BX0uQRhgxrGt2qWhKraK0GmhDSWFQj45G7Y6kFyV4u3pKqLmkYNp/4nhYmDJF82zIcvI8w3Jw+SPZlVXCIJ0iYSYWD3hRLIV/Dh8NNXzKENabk4g0oZGl8RgMpnsy+tZHEyxHjAqNWru1OPzIkJWUwwzQ8hqHQuN2C73lTWaYm67iWEk9WRbmzKPtqkYn4AHHcSRNwrGuMGukokm1ilLRFC3MI0laGy66oBPxsJQO6WwQ9NDPhuqRPB4fSfONHld6uhUZS57gKx28XkvoNNSAFOvg/qkpFj26Y5IA55xzhpQB9e13m0O5UMKdy7s0YfXEYLqhRLZezdguFaKH9M8EHHC5xWmZ0UP6bdlThLZVtJjrXl3VIMzLA4fdYzLr9h45GbooGRqCkzATSBztojl9UvhSQQEVIqHxW9C3hawUU9SIbVWDXaGeMsLG7E7KBBPqOZ4ONiYdmWvSamD4gWC9J4or4iUfhn9WFXu1E0xwF1ZPDKaH0r4EcTiEP1ZeMzQvC6lLwTHYHS47w1JSWS/XO6fTZRA01G73SPYTZn0Gj6mxOWma5NBqbYKCQumkKYok0VQSSRJHj4iFko8/dnMH32210P2kPXleQXQUSxBsVlpana2l/dNZwRNi6cRgei7yovOoT7USA83OsBwvqwl/nfKFLqeruAIO8CUkD1jRYIcz7cIOHvAUSbb4CeDnAO+HaaGk12rUpicbp0w+IzPFaDYk1MjuVKDIfocz7+GLeYhzTRxPEKDgyMmlV07nhCklVNqPqlR/rVO2BMVgMD2IGJ2FYq0LHF1CeeUkEtpBiXt4AM6ZMLSmoRnwICs7tbqqQTry7X/c/cy/VhaV1FJB+ynYTILhOBj0FPKWjCqOIgm4QJtWpVXTGjWtpll7c4ujBSac7j54YtG1sxV30xUzLxHfCkZIt5I/iT4UCII4Y8SA/YdDwQr5YXiSCIPpobR9nB5PWKFcjh8R1qwh2EteypEEG7cf4ll+6IBMtOfdVWtR9BORnGyua3Ejeyf4TYKmScZuZwDwAYBG2sOH9kfZ9aeOrFQzEJaer7ZFT3KK2h41wIoBUziKF94y3Ai/zyNHQzLKayxp+tB5sJJiMD0LV82eTlJYqHhQQJ0uNwD60MsIgAqTJG5YPOd/H/yUnWE+WdXg8YdWeD93ysjNOw4L43Y4eYQuDSeOCEKXlJ5k0jJ+r1oFVwmGyUFCoyZBZIUc9baXISFcvgBJUpyglRRFs6x4P8crYe8lT8pwEMxhVanFWK2Dh9HbQICt0pgclMabpiEpKqCGHZUqVBqOZAhApLtPHCmza9W0VkP3z7TmZVqsRq1aTQcYdsTAfhoV5QuwPM+rVdS6TfvacdsYDOa04GxNMds7mwSTd6CAZmWlVlc3SONx5KvkIdASoTOTvKW8xLlTRhYer5CcGKo7IghA8wGfl9OqaLXwGNlPeMHgRrvVE214UkYIYk1ywnJGDG0leWX/ZgC8cT5e/ByhSP7KMFNmg5qmKMB4qmv81dJLUI9U0aGC/H6pJZWhgAYGg+m2PHffhdW1sLdvmxBH4QkcCAV01/6jk8YOQ+3sBuSmb9tVmCXMKW3adWTwgAx0oEpFVdfZBYkMCR8ykfBfcfoIJi2hhHlCCIDSQvonBZeAF0VZkiGFA20p22iVt1WOARNcJ0PVIk55UYQK8AEVCJ8BIzWA82XQ5ehRgIev8rjcdZWBOgC0PDAmp6CnUikdCVhB2YOxzYigMbrNqI4Z97PDYHoriVjR0CTSuDFDahpgxLJ/dip8ZbSX8oLpC11AUEO4PgcFVQcmfgpSSRKEx97igXoLHLweFndSpE6jshg1ZoNmxgXjs9ItbXWg0u1QPMMKuh98ItJ4AsBFWQVl2Nhx0yYMXDxv/Eff7Vrx+Xrx1fIpIakbv9S2ThJNUfpxzBOD6UnwHWz+i04S+4BQOzuDVpWdZi4tr4s4WtyRlW4uEdKYtu4szM+DTZt+23HovCmjNCpKp1YFGBZOHwl9Q2ASqFqnomH0M1sLR/Eq6QnAbNuw6+rFc9r/lliHOhgD5cXYq/zNonG9MDUU3qeeDLi3bNu3f//hxqZQDqmgkeLLUY8lqUA/bDYJKycG01dzQgnZfgWixiy768rXX/lcp4EPCQBuv+vaDdc9Ib1gcGbSluApzjl7dH5e5tatcCXkGy6bVlzVSNOkRk1RFFzDQ6g8EsqQhGAoamcHk+kFcyrdVLu7gtKM2JuuHRQcLZswNHNEjvWgz1vsDWiF5Z7k8+lGnZrneE8wzIqoqa6XtgcNGZhs0gHOK1s6CiurSIJLNEuLleIVODBdSbjZSuD4aDLKxxLQ11/5fNmdV7z+6hfQaWanPfbI6+NGDoSTNh6YEnT8ZA1sVkSJndlLUMo9D4rL6wbmplfVNBBC+ieaj0ETRqgKXiX0ZULTU3BMzAm5RDzPshxaHKlNb6aD0IAFLFNW1biz4CRN0wAIAio7tZel+mdatGo6xaJLMulU4mJzwdJU4du/dd8xk15cEKm9eQS9kwSXG0LLbwAAyvpqC15Md4aPK6ORVpQeMe3Gqk//CTX01S+W3bPw9Zc/hYv0ZqZs2wWXEhrUPz3q2aMomtB0Q8oelbouBZ+UXCf8HPjs458XLb0QdDmRty0XQC0ZcDnsXpKwtzSXJiqNOK0eg+nl8LGtaFiY8PWXPx2Sn3mspIYkiMGD+6tVlM8truoBCKKs2iH3a2izWOjeNDA3HW1IoqkSUj8DQnPi0P7IJPU2Is3CAxIEWIIS2p2QIJSaChtEEVogdkE5Fu1bEdzywkgolaRH63LA/nVo2ijOHUYKMDy8dQ2NM7zF60RiMN2EOCPdWFaU5nh+xlX3wenyY99DySmpETS0Otmss7vEPMoNuwo1Yoknv2nzgannjmn1VoSe7oIj7fDwO9YJYEhBJp0sT1GEkF3PR0n/jMSrgZlMNEXwbDT54+Gam8HcVfFdyF1pvWGwJJxMMtGRtXYxmJ4Yrab73hrRkTJKexmVqEf5CwIlXwc1NOtYSbXFqG3wwnXZbrhq5sovN+RlmY4J0c9lty147PG34l8JdbXruHpGRdJKOdIeClZ0RgmwypbwgOgoRpj4UilajYgqKUQkkHdH4YcGm5CWBQBaFCTNdVwS0ApblMvhtXYxfUGIa4NGoXvqaU1dMIu+taAc3y4ZpQv2bSOE1M5RY0eoBl4QKF4naCgsw0H95/VadWl5rTx8+vorME4KANi468i0ScOjXqO6SsxsZ/WpWammUYPS8zItOrWoVjlDcxXHE215P5HqKYcFpDhZT8c/J89zwOtnNJTQxBQQJxr8APgJgjAbtTkZ5hSrASVmGfVqFUXlDYQtmrRquuok7PgnzIbhSSRMX5/uyxAO6+lTgu1wesg/0XPnT6+UdVoSNPSXyPOOHJwX/aIacUpawfWL5qjVcL0MkiS27D7aUN/QUC8rf9x9BACgaMjUKkvGZ/3nOFRGqRY+HFkeaGIn5Hjg9gZoHYXKq0waXgjdkmYDSfF+t4OFZfs839wkhm6F5HpgVof8Jg+A1pwG6nGDO0zvpNesV0ycgnPCGGhRYVFzY43TCYfq48ZPhYWP0RKeXIqGIzKyczKqKlCxfIi9h8QlkU8pXjqLjlbwTnMJpovyXj9r1JLSCkioRF++YrzQ+kQ6PDRL1r7+1ZhTEbNLPNzcPYeZmNNLR/6Q4Sj33HOhE6wN9gk2D5lnP/ad4tyoJWio4Ui7rsnz4OChEzcumgXXPW7XEk7yWXjh7r2AoHieU9SvM3RSgin3ijb1sifEak4phQH9N7TYHKaraFX1escoEtMTgebrWNFRm5NX6ZPdARX6ogdcFnmoyWoWG3Cki504EkFYLFn8AgCcMWrQnkMl+w6XHiiM0qu4TVBwlU1AAFE9NXBSvhWEqK54mNsHv6TF30PN6oPeEsYfCGFZJ2FGDP3b4UQsDAbTe6CRHlSVl2Tn5hcd3o0mlAT652tFjRual1ZUVm/QqedeMLmiutGgUydeLxVr7qvdNTw8oSF42CuEJYRsTw4utkmQlC88G0nwqsEk1mjoaai/QjO84JnhQ46AnaJb12LBuWI3isH0eDoSjhOzOwEA63766tLLr62uka96NB7UvA0AKCqpGZqfWVRWb7c7NWrqpGxlpGH5/Y6WwFlpBfuKmzUqSqOmU636ZLNOr1XrtLRBC5XX4fb1H5KbmxUcjLcRpJ5CXTxKUBByj7T9eHfEbUTOwgfTUhmGAUAT7exosgiDwfShADQh/NueVTkNhrSAR+w/f6yocMjQEZKG8oQj75xFlV++bk5PrXExQ3LTjpXXjxmWe6w01HyTDfijnve6eWN1GhVBgO37jjM+t93ntsua3NfWNQxYNKvdzUTgom+yrnpCT7voy3goiMxLlTcTyUpN4lmxk4jb7XcDkGTVyw8+WHAchownDJHvpNTRhBiD6RXEV73JvauzQTusKO3z+mm1EXhEW0fS9n452orKevFUlb+ePWW4xwnFr9QJVBqytLrOnJJkTkkaNgrqSJ3DZ7aYzRYzKuWU2He4FLVlijpUR7FF0Bl46SyhK58XBOeXWEKrCUjt5OOtyhkOUVTRRBIw8dNq0uVmmPMyrclmHUybD97pueMHQxk9JP5Kof587oYKcUoLg8H0MQ2l62orrMkZUrJO4aHCdKJMF3z6ndW70cbiiydKr0F6qmDG1NFoQ2e0vLNyXXJSsCGxIDRovXgkmmjChmV5GuaJtg29w2ZPChtTyNOYeFJDcD6K9wYn651xHKibg/EETTAgAADINNMmvZokSZ2GpjhfZVVtZVWUezDrxKZ/InhOCYPpqxoqhgnTiENpo2gACt5ZBSuR2orOaEEb76wMvRzVkcP/C+mTweWCYWgAEAA2se/sak4i2IieIFU8F9GpnmWKSmvdXtjNc9DocbWVcMEPuCSdmKUk5ivFmt06cKh4zCjY4i/8klhBMZjeRuIhUXpoauM7K8XSzDg4XH6TQd2qaIbdhKCVcj06VFgS9mxsWr31+NWcUdQTgMFnjL10+sirLp74y8YCjzfw4n+ggNI0JV1Mo9NZrUZfgElPMcFW0BTJsJzD5TMatHkZlulnjvh1W2GoOUoooR6DwfTVNZFiyZ8Cu8ubngGX8UC0+qrSOk+SSaumqYH9ktKTDDoNbK181piBMuWMPghutTcczTRFTiKFreQRpF6flRZaWxPQ/pZfN+3+ZvV6nuc0epPsiuJ3yefxNBMsTZF19QEVTUk3avN6bI1NQkvTUF8+LJ2Y7k9+f21bX1JyMqFmZn0EojUNjcj0CeeWay5AG++sXLdhR2R7zZi4XI6B6dpbr78IJs8PyZb23/Oo2MZp675ji5bMRdsvvPH98jvmgc7GZnekyX5/DhZVjcxPmzIyt6y2uaLO4fUxWmEJk1CVkVAfBcvfw8+D2kqxHH/kaCkA4KzxQ7B6YnqEGrb7Kr1MRvkO/MXG11A6vmgm6E9j8fb7q0FngyaIWKDVMGFLGbvJdJoM+6krqjnVFJ9mUlXV2mgATpTV0DRNELT8e+NosjnC6z8JgmB1yXqtyqhTa9X0pElj+qWb62qUhf9xmKyq6DXtGDA9Swo7gpSfhAHxF5VbfPHEj4JT7YgOimZMhH6cqOO74PF4juPQIsl6NqEsTjk0xTPAGjkdT1AanvXFuAFld2eKFCe40EOvyqpWUSa9pn+mZXBuSmaqSVhLVJwP+3XbYY/LcbwktKInAgdB+zLdXwo76w32Mk/aDuItKtcpXDxj9Lb9Zajx8Bn5aWHWWaw4R/2ORMVCiaJo/7/+9cmjL0xL/FqxVFLaTzNNGp0J8GGZTAoFdQWgPEoVUXkpGpNBraIpwHiLT1YVJ1ysbzYOoEiiyR6aIsP0OHq9FHaQXjm077iGdkhAB6QbzpkyfMfB8uMnYRa9TqMyGTQNtvALyjZk6xpLreNEoNNLzMsFB+bCGvN0yISqObt8EkmjM/UfkAuEztASCgeqIxmKJDiOBBSJKtvjtNBHT3Fw/irK+2M5PsmcjzW0O4ClsAu+t31WSQnh3+iLyiXC4osnVjc4dhZUoBaiAACDXqxl3LztsCVVLImtc4EUi45lebNRY9RBW8dxvFoNc4Y4nlfT1ORzRsa4t1YYMHBQTU2D122XlcMLCNEAT8oIlVqbmgb7RR2pq0lOOgMGN3kD7HMaYMtUVrS4kzdNQ1IUo6ZJgnRTQKOGWanp7hNQhzmepAiOFxuFyNujEIRYC6D4JjIe2AmQAUBPGd1svA4mmFMihUK9A9bNrkT8bleBPm5FExLQ3OyUrFTTjgNwDlpY5EOlUVGSTpiCAgq3hfWCYHjRb6c4kiKA1+X3RotwlpRVoY70/3jje6nw6awn3t76xK3xb6a+siyzX16DZhwPyObmZsYvfhLSam1ScpIxuBKJrS5sigmhMJiw/RLPsRzgODLU6jQ8vEAQRM6Yi41WWDCqYPOXz6ONe++6Xdr5t5deiH//fRbFnxzWO0wv0NDoAnr1RRMOFFWrVdSBI3AyjiQIjTp0pFpFa7VwdaMLrrzXW/6jlAwECID6LYmnb+PdJE59ZVlaP8ADKlUHgKC+JBRND/B70AF1ZSdQnmcGAcf7AR7GOT3eQL+ULBMF7/BE/Qmaprmk8VD0/QVGnVqjolBIYeold1CkuCwyoryiQqWK8o06/+o/0bxDpZL0H/LIvcuf7u0airUPE/VXoqSPjevhmkjyx7QKyiITCFAkYTZokJVDAUqpByjPAxVNItfpY+FOeVsQmlbmt0t93aV1OqO00RRy2Z9e/odHXngtwVuvryxL7ZdfZoNj80jCeiiF3UzIgl5/199QDgAAi6CMasPcqfTehV+OvH379ztcfun14n+F/8yYdiY6bOPGjWhj9tnnrNm6BfQEsBRiTsWvU0mfUdLoDpQg4IyQsLAvekho1CFTRpGkQRdaDVi+TFBo5CuVOopLq4MJc+/a+/Mr8Vq6o+5Nrd2xWyd2LSmvdxLAwUZTS6d6hPBZAIAbCtm5F94wIC/boNMCwFdU1aak9RsqprqGaGqqTUvNBABs2iTqoByjyRJVPZFudoflObEUYroP+d1MSYlTKqAoFXTetFE/by2SnqAoAtaJC8BlLUgyJyu5otrG8TxJEjDXB4Da6tJ0KJqh+5TEdOY1f4682Pg5d3n9obU5Dwgz5LMXXA9l6/PXx8+8Ae2PXzvFwv4fCC3NOXTq8LJ3glCrVAaDvsFmu+qy+XtWQgEdNSx/y5aQJSwuKYs87a5d28+aPCGWDlZW1ZpMFjSzJHhoUTJ5ng8wDEVRwpolfNDSdqgjM5ZCTK8hv7cP7UMOVKEd0HUGA3/CckCi02QYTqMW88vzcrO9ZQck0Zx77f3VBV+K5w2Kr5ytmzYMHWQxpkSpzJmx6M7mBseO3a3P6ulpSUCBP3o3Z+Ge+cDRwgK0LVfPyGxQdP8XzpnR3NQiCmOwPB5NwfM87/F69QYTWhJJSG4V1RPwPEWhqnl525SwbyUWREwfJ7+bGdJTJKChP/t95UQ/A8EGFwwi4cgcrqoG455+BgqosN0/J/NoWUh51SpKOsmWTb9GuxxRXHySEXMzlWbPbDYeP1EK0uZOqv+pw++LnzTpTI+zMZpm8l6vj6bpoPCJKikpPsNyaP040VELnxzJSVbBZsrOgqqpwkLARNhvTF/N8MBg+o4hDU2VKBeDI4n6JleYAxWUwusLSAcXFECLJ8U0N2/aFCcUCAspee7o8bJAgAm3gLFWFm4/Op1e/nY4uISy6CkPHi4i0QeCGI0VtZKmxc8ScZfYe0k8g9GgD9NN2b8YDKZN5PfXoi/QuwQ0fBRKgPIaMU0dSSdynS4PFFBkuz796E3F2J8giNQUs0y5YCd69C/ao9XAlu+SoEpPmk1GwAMhOaotdx9dr2EsUv5ehEehKAQNS9yFe5BJJBGzYh6iUqm2bfheOkKuoVs3rUdf2zevzxkwNDd/WG7+sLa9DQymT5Lf82U0bAhv0Kl9foYJBNbtLLhp1sAmu5hWGVwYHW7bXULPjjCpDZa0C3Ms4kR8sA29KF1Bauud/fJg3aT0suB/hH/gNTpq64RzhwmoJ0Aig7l3689wvmhL5bTZC9RqjV6vkxoqcwCkZ+edKD5ZXlGblmwYNnyUWg3lnAvGMRZcfWNpaVlmhkZFq+Qnz7780kAAhmXrG/zFx7vvuoMYDACgWTPA6hMrYroJ+T05Qho2iZSZamqyezweb6baCzu5sfIYqOhDPb5AgOHk1k9W6RhyeVDFItaIl16haKZsNhuDZyBhpzoZH9gGXpdc3Ka35LGdPNhQfeQonZkhFpaqKGh0AQAXXbZEumme9VdXKdeeY5lAksXIBAKVFVHaeR0+fCAr+1wGxQOQARW/QagtXuwpLQym29CsGdDqMWySWB7i0GQkchgFRLPV15Q0zIHmZMBVOmrr4cgdSR+tUjGBgBAyFIfw/gDLcpxisC8/CXqYnGRsanIKCtNKl5Btv66RzjB91qWT63+SLOiHeypt1nQQvuK0rQVGZkMN6YVCUXQGs44kaIo2pVrD0wCce1bpxy8GAFRXh1p5ZmVEWcmaJAgVTfu8Pr/Pp9bAYgH0DpS2WK6ewXXyWKE7HwpM5KRO8lbBNZAxmG5FgvYztUn87aV0nvZJcB+Za5IJKABJZp00bFfEEFmWRwLq9TGBABsrZ0ceSEX6QghmdNfmn69YfCtJEAPyxW89w6BsJOLKJUOMengbJ44dYwKB/ZmzxtSslU6o15Dv2wZdnxwaGidZoV1lfE6tTplCL9XFo8u3FgsQU5F4Dq4dim5bpVKRJMUE4Hl8Xi+tUgvlU+JbGzUiTzq7UFIV6tO38be9+QMGqFQqGq4Fcrqz6jGYLlHh1KDONujgit+dS48wpCEB/WLN/rzslMw0U1QBbbK7GWEU7PUzbm+Aki1JBK2rkGOfkpFL+BuRzpqNhNlogjNEqHJp1jxbo7zPXRiMl29sjLJUsnh+QKxoGrw0qW2GLrp6ymqGqmoaxACtbH1QioLzSwajgWPh+2IYv1qtlT4ikszCNwdJp9isScirJ0ByklWtVqF9WD4xfYqG7M5Xz9NlSNu6+EdYKSdBBqeAws9CEkRpVbNHSGBiGNbtDZiDXZfQoehV1VXVyWmiQJVWwA6hEhpapyY1kQWP9fX1MJJi1Xp9PrVYfSlK34d7KpPHXeiuricFEnkzHq9LbzTHk9Boc18RhxBHCo8OHYpm0omAXxzLwxQCrVqRf4W0lOeByWRQJIpiMJgeZ0jb6n7CBJQkCCktXOFAaxvFpSxYjne6fRZjKPlA1mNefBBxFf5oUfGIkaMV5Un19YLI8sDpciMhgkLJy64rdPTQCJVP37ITLqH2oCu0PjoXL5vAYRF17NKDQ/s2SztnXHiFRq3mhKuXlpXXN9iyMtPMJrGVCcfzySnJJEnSNJWcZNXDonuwZ19C94jBYHpuhDRcQOUOlIBDb1SMRBCEz88Eg4bA4fIpJpHQBL08jQmGFlkO7hcUqqWlXt60Keg9Ue4SMJsMblcwVi07M8uyBAFMRoNQKURvoKYBsEqtVvn9fiK841yr7Ny8RnyPBLhqCWw5yjAMy3F2u0On1Wq10GN++eUXCy6ZATOTss7VaFSjRl7FcXxVrc1sSG6w2aW1kEpPHK2pqTJrRzttvNhGlCA8bo98ZB9gAqnB42sqoMtOkMyc0NrRGAymm0dIlQ5UnpOUZNY53HDOKCh9UCCYgN/tlfYIRwZz7AmZmMJ4gOAf0QnHjj9DkmZp5C6hUsHCSsWdjZi7pKq6nqZpk8koZKESFEUNmXWDranF1tQMgM7ZUOIhRA+o1xndXpamdHaH2FtEo9VoBSeIuOWOPwAAVq76fOTIM0pKQqsd8TzvdIi9oS+56ByjRVwhSX5Mi91htzuMRkPxiaPoraWlZ8L3RYre2+VG6i9KZ9t+AhgMpscqaYQDDQ3hQV6Wtbre4XJ5SJJgAgHULRTNI4WN04M59kgs0bYwd40msIPqy3kJCmaN/frTF/Kx9eiJ59GwLF3s0rFt07oxg2EAdPAcutbm5FhQdPwEraL9zjq92WrU69QatVbDuT1+XXK21MrY62WTLGIyKRpTG7TEwJw0ezASe+RwofAGqUCAiTVRLuXMy9UTAFB0vESn08O8esFNq1Q0yRIul8dkDEsDmDJ50iN/egBtr/hw5Zo3xTH89DNG6nS6p//5ttD9xH/2BbB/6IyRQ9Cz6w8fkz88f8qo19eGQgcSd8wLW3Hvje9DPffumHmOuPOXntGEFIPpNUP78EkkweiJ24BITzYyLFde3aiQG3+AZYI59qLxlBxp0MPu2QKbiVAkdc7My1U0hBIgCDDprEkpaWmAByzLoSJLjZZI15qcDn96Zr8BAwceKC4+Yx6U3JGD+3Ec72dYjuWKnc2pQpxRmCiH0zUerzdOkJNhwhLyJRxOp8kIJ3zEhP/gKXieZ7mwt4kq6GGBlgFKs8/n53iOJKixuTTP0wTB13jh8VlaJ9CBx55/02JNXvGfl7/fuHf4sGGP/flPS5dcc8O0gUJhPlTPe+5advhwweNP/HX3pr1/Ww4b99mEjDEknUadyinUyAIAls06V6GhCvWESQ4czK+6dtbEsMOCSgoA+GztngaiWwSJMJjeakhpu03pQOULqKnVlFEv9JwPF1CO5ZocnshZ+EGDcghXPdoeM3Gax+PLysp0OJwK/d2159iUKTqlrrm8x4uKJk8IUwSNWs3zvE4LI7Ajhw/zuus1umSSJNRqmhcipD5/9PEyAZsqKQQ0WDQkVFhJAooimEj5URd6ZD9nDQkN/7+uPOLTQynkOH5cLopLwP1ZWueOgtKs0TCz1WJNPu/sSeh82SbmwTuv//ur76OXP/3Pt/fv33v4cEFTY9O9d/9h196CLaX15wwIi3VK6onopzZX+u1RpXPFN1sJAiyZMxnE5tM1uwmCSAPaeoA1FNNHye+SKvuIWXhZcw2eByh/KLRXgAegug7+eQePFH1reWnpiPTgupUwZEnW2+xatTpsmjvuzLjUfxNZP7GwB/C00JnU7yFVNI0m+o16HRNg2BZHVKdJURQbUC4cj64spMqHxR9km3B7fDIMiTY2ioHRlT/vzclMtZ3YmjXi/KEpfqdTrNc8XAonwUYOSHU6nYdKYKTAbLGMGCiuPcdx3PLfL0Xb06ado1FRTY1NvFGMgdTW1f5YWXvzNXDCquBQlO7OUaUTqScA8dRz1c+7xI8Enm8glN+B08WyWedK25++H8rnXTZ/lrT9+ndr5Tt/3XpAemr9gUPxz59KtnmyDs/XYTpfQGGbT9lgnIcjVhDpQHkeNLaIaxoLR4YUVp7SBOeFCLiYh/zFYS01ZfuNBu2gIUP0eo3Hg/7shf5PJKynF1YoAgGGO36yYfx4OPYHBJx3MpuNDMPaHU5hrK2EE1RYvocgwJhRQ+1uVrKfcnbt2n7JReeUlJwcH1HhCQtAM8zVtj05RGima6TMQrrcXoZhvvvxFwDAW6++sGPrRr/Q6hldQ6dRNXjswKh69433xowe89XqrziOm332GSwLCo9EV0+o4A1KOfh+M4yWzpsMvXBUVv60E+Z4EcBuE2oWUqMvGHW6pFP5lEw9hw9KefnuRc+88eNtC8+vb1Yu4jpjzKg4SjpjzLD6gh1oQ/HU+gNo0g+D6Xxe+mjb8ouHKhPp5UN41FAJALB2yxFBEMngDDPv87ORs/BQyIIrp/M8PB522QiHIMlJk6aoCEeAV7auk3UxDlaBQh8qPosWEQlV1sMIg8piMQYYxi1OgstOFaVZJ3zZ8ardsb4d6bnE9gLo746HaxqV3roMkYA98/zJQwYN+e+//3vbnctvu3M5AOCXn1a///TdsE7DA9262Wz5+ZefkqxJl1182bP/+FtFVV1RcZR+JcdOwCK5iqpaMK6f4im73bFo1gTAs42NoWVRJFb+vDc5yQoI3tHUzHYD6Yyvngoef3m1VAScZjV8+gP8QUhkZcACsFXf7bLojS2Mc+k119135x+lZx++5zrF2QpPVA7IH/jaG+/Nn3tejcs5fYSyTmZDIXTBt8tCxog38Swcpi28sLoocggvWTM4lJZ7T51G5fNKo0JRng5WW0ZlNCPRRE2b0EtOHN40eNRU5WrrsSHgmvLaoGkL3YP8mDMnjvN6GiuawrshqIDeAr9AeLxPB0BxzT4pGTOOdCbOrvIoa81DzDCke+zEsfPmnod23LXs7huX3LR37ecHt29Ae+z2lo8//ejjTz/aum77tvU7Jk4dPzJVTJmKhIDrNbcZW1OzmgTdUz3HTxgEAHjuta8jj/zo2y2AIBzNzawVfqZGOtDqWpiDq1ZRLYzz7399Yeb5M9/593OFBXspmr7nwb8++/IHN18wlBVaK2zfL/ZMeO2N92C1bnXDjDPFJQglMjKTAMzIAJdeeuY332yXPyWX1E/X7LGR8Dfqu/88tPqr0C/P9xv2n3TCwcHfFl9YXgdvLGrWBKZH02pB50sfbUMbkZNI4jZBgG/WHZxz7gj00GoxGnTq5qgF62GyGzbej99WQ0xcku05cLxHJuJcueCq/3vgkYlT4SrziFde/9dZk89a/vy700f3o1IolUrl9Yr6/uCfH3jp7y/DKGoDXLNega1E/Il8ukP4Kwdg4RTxR0Cl6D7bW7hw3NBYt0GlG6JnHnQ5cwblnCg5OSi/v7Tn2Ve/AoCYO2Pyp8fCPsk+/Bb+xN32lljqqWDm+TPf/8+LhQV7AQD7Dxf/828Pv/Dvlcuf/99z9y2V1POX9Zs/eP9/111/Y6R67j9aDo6Ww9yyEYMl9Yz8Hf1kzR4pzqBQT7iSjTENaWhuuqm8ziF9VCSbVLZgGnJUYb09Wlz7TSEjTZ5BoeCLdfvqeFnEDHPqSbygM+YsvHiiYA06nJRXRS/+4WAbt5DuStuVNnFNt17P+o3r/u+BRxQ7hw8dcXDvTgDAt59+Zzabp84S/0KeeORJp1OcoUoESUnFh/tCK6e2NdTQxZyQpdMeK626fO5ZJ4pDowe03dTUZE1KYizw9/DhRRfv3g+DRbX1UWIULpeLNMLDxkw4+6NVn0IvmWSor28879zJAylx8Su3y71k6fUME2iujtLZGqonAPYWx4XnjfX6AvJf9E9+3nX1nEloe+VPO0mSspFeeZRWUM996C8Lqedz73z/0C3zkIaiA+TqGZmOFlU9V363I4nQXT1zfKyp1S9+2duVzb2kiTg0L9fq1FxqAtN33ba47uvN+8wcPeM85adsmyBjViIBMHpoP+lhU7MyGwkxeTDfZPegtNCDu9ehBYVAH8PWZGtobNi9ae+7b7w3a8asG5bcuHMjtC13XncZAGD+lRfptLrVn32fPyB/zbe/WC3W6ReKI/2+A8WHEocVOEjRXj27anWr5/nz8tvHTTzrx7Vbnn3+XwMHD4cZYC7x5W6XmzRr/7j8oftuvcrrhWFxl8spfUnqyZnUDY1N0mfYJz/vUqin4AlYhXoKrcRZAHiknrA+TUc+9w5c5SXNqp87a1zUu5XMaVT1/Hg1tMBXzwwNXCIJdk3sXswYOQR9gQSoqahXfIHTzdeb9329Gda52MnQEr+JI43fo9bCi9srv4ejmLwsK3roI3RR+xt5Xfa0JMOxk6JlYDnO7e21jdkzc9Ji/fjnLpg9euQZ7731/pjRz8Npt/VrH3r0AVQaxfP8xKnjH7j3oZeff+XDlSv+9+G7oLfz84mI+bEM8zd74YhEikojqBRZW6+4IPtZfrL4xqvn/P7eR6acM33ceKh6jQ0Nf7xiCjzCqN6xYdfcBbMHGOgoH3LNDpoAnEk9OS8LTTA2NMBf2gsmDFi3pxRtw7PZmlUkP+u8cXKzjDDq1cUtoYbckobec93cAFylIDp5uuQyj80m5FQkp4a82EffbgOwFGJCnLf82Zo9gAANfCc0e+8Uvlr5TW5OLto+VnjwzRefQtszRg7ZXVg+aMzIEdkZHMuwsOU6Z2uEb3lPYel5E4ZIa6khzj97jLT9zfr9le7of1PL5oc+dV7/LlR610GQdMofLjg3+udfItC/7Sk9b4LY5Hjj/torpg+UKWYoS1LF+3herJXUGS0eZzAaqtJrVBQqqknNyPzkBxic6psUHD4oD4MqeP6l555/6bl2nParVxdE7rzsziizMT2Ln4WpcAVrjsCdNdUxTUpBNVS6ZQ/eix4OGzr8o/9+/NbPBbfNGf36y29W1VQ1NDYMMAidCsIhzbCZFmwrU1Y9KTdT/tQFwd9/+OtM8lxqqCZYzrHiski5b9GRh4pKDhWVtMhLS2QMzdSNsw4XRVyWmtbc3GK1WmxB4Y6KuHB29xjR7d60l+O4V/720MnS4oysfg88+dILb32Cau0A4G+65XfX33iz/Pjlv1+642AxTbfSiPKh22bf/dJH8dVT/nD9VpjKdsTWKE36tSl3QqGe4jl/K0h8IC+3n1BAT9b7pPGksFZH6DkVH+q6FCBCvzoNzS6jStz/7le7NMFPyIbaGJPUmAS4aykU3yVXhdVixVfVXiCjbUKtVm9dt13+KXW06Mj1t133/lsfwF4EE6EP3b0p9BH+5kc/zL50lk34Y4vMpqjgo025IfVsy7D58wPHrxwTr6PwN3uPSDOBchxUK2O1+obGbhLaNpthmsvkaRPnjoUfBrXVlctvW/jCW59OPG/O7t/gQo0XX7rA5XI8/sCysJfxwOVwrd6+7+Izxxl0KrkJlWMlzM28PZZ0Sqz6fkd6kiEyZULaGDy03wOvw+D43XPP9QXHBDXe0Pxbplav0FwpUFPVHO+TLA50QFbJk2zWSQsYLZ437mhJvfTwqtlnHCwsQdsEQQSERHFYbdltxhddQ5si4qkk9OkN2WmtOkpMIqDyhPFjJ+zdL86SwzmlUXA8WGrzTTovNByeMHbCW6++PXnaRKlBTJv4WTDCIOFow+cHlMfPylem8baDbqKeQiMImEMyf9IZ8pzrd1555uh+GDI+dKIqOTnl2Sdg+rMcHgBGKKdcvX3fnAnRLV7hsTr5Urxx1BMKt82JPPv9N17i8ig/fo4XVaZTpqVzx8+cA38TvvkWvkQiUxtq/ZNOGus4592Xzg6w4oWzrakJaujfb7kA5tK8sw49pC+ZHvpsNBu1kgP9+udd118xtSE4H7p5b2lDswfZcTWHkyqUPHx1lPLKt1+Cf1dYMTuRDZs2vP3aOzZb470P38sy7F8fezp/QP6d118eUeQGt9unnp3C2pLKOKkUKCkN7t8fllOxcGzMHLXTi8/n83g9f3npfZZl/3TPzTVVMMZdt34tYYepeFnpcKYkf/CIh594QYhTlzyyfFl6Wli4mySI2mATy+NCtcjgQWLkZNFFU1Ahr6SeP22EA+2508bJ1VOIgUA5IlJNkeoJM3Z/O6imwD+/38gD4PYxsdRTIsBw8kruRAbyo1ItTa6AXEbpnzYXLZkr1sBdNHVYk82Wk5VSUd24+NKzq+sdBcfFXEVbfV1Cq2rI8FQ364JzUH1HNCXOmpb99kutnGHbxirp4KgHyAfpWIgBAH98+N5hQ4ateOcjNGz3+/2zL5npL4HJT70AhZ52K6bOOuePN9167U3L/v4qLFLgef4PN17hEvwVCvSNm3jm9Qsv1OsNb7z3+Xuf/HDD1Rc5XU7SIOY+2pqUOeRIRhF8o49I0RQXh1UBooc/bCpMTU1qgS2AoXQCAO6dNx0tLxRJhR+llIWFYDK1+s/X7lk0ezwTXO2CYQIw/yg8UL7kkvNbNaFIPWHKhzdwtKxhZJaVvuWKyV6XrDOISofWH3r/806b9upr0hlLJePQjmhmXwuAShw9dnTytLBIceSkz559e+JM6MVC8RHVZ7/Dsfjnu2//9N0XcMCbk/fE3//97/e+fOyWS04eO8Tz/PULL0THuN2u6xde+P6nP+b2zz8kW0W8rQzJCfWkaGlqZq2h1kony6to2UKQiFVr9uX1Cy1h3xicnVMB0Oh0y3eu2xvW7VdOqwP5hhb3gWM1R8vqXV5RSWm5ev531XrQMVDTZSYAz9677WerbNtYdeu913biCaW/569eXYD/tjvIXUvHtzpf1zdn6qLyxUdflpwsvf9P9x2qg31qDtXZ1sw6Z+u67X9559sbpg3cXx7FIuQMOwNsC01Y75C5SxsVFlqZPXwwETebTa6ecQjaT6GVRAS19eJcYkNjM5mmu3D08BMyCyyx7XCpNJBXZEAcLXGcrG5qqA1T2CgZc20lUiiVbUIwnUqv/5Nua+1K1Mk6BI57dAoqlXr61OmKneUVZYMGDi61+XYX7HW5XNPmTpU/W1CYaCEiyl2Doc/CkKIVF4rGjhSCxXK+PAjbkslZOG4omRaKcn66r+iq8Gjy+ePyFC/ho7Vwg+llTaEJHoXLLTpUyHFcMCbRYQHt4wYTc1rAk3WnhUsWzt+9ae/mtVsvvmpeUzOcF7l0/oJBAwffce18AADKJFOr1P4AnNu5fjFccGHbjrCWWqeUyPrmz2JHk8k0qMg/HYatDucMV+afkSnqqKn1X2/eF7WSXRTQxRdP/Gh16/2KsGhieuJkXZusvcK04oAJYuqsczat3bJ2tZi+I7TFWV54EE6XHzpccPzEsa3rt3u8Hp0WytPFV8E1ebo/UfPVYsEmweU5FCTkQLFunmoevnrys5/AlDqM4tvS1m9Iq1N2KOEhThjksju/lmtorw+YJIjH64mclzMGNxbdcDVFUWNGj/X7fYcKW1lBoPuTeH1nTAHFoonpWZN1iWQ7tFUQsXomDsuy8gKHVokVnu4m33O5hkYtAI0uoD1aNyM7fSTYleu0t+HC9rNb0U3+hrue0O98FZqXGxzWNSCcmoT/plCZskT8/IeelfzQCbPwXUl36IWF9K4do8s4Z+usU2EwpxrpN//tlz4GyRp5/kMvy3lIZCBPf7n7RC/wnnJuv/mOlJSU9RvXb9uxtYCzjBZyXBAkSf7vzffzcvP++co/vvnu605RvfYpKRZNTPcnwd/tr3qXbsqJM3jvkQ40Pjs37kYrITMMc+WCqwAAL//7pT0r/4WeHTN6zLtvvFddU7152+bH//TEA/c8cN6csMy19oGlEINREBmPjlqsrBin90Qh7j0Cunb1OpIk5Q14nn/6hXt+f+/Nn77GsrDj1LtvvPflN1/89e+wC+yfn/y/Xb/tmTJxyo7dYS1bMBjMw1dPRn1w4nPrvdfCUXxi03fbNla1ajUU+Q89grZ2COm+JFmTvvnua3kDngcegf215l59C3oWAIDUE7VC4Hn+H8++ePruF4Pp8dx677Wnol65p8wg9SoH6g/4L52/4MlnnpDvnDh1PIqBjhw+UnH8J1+sWnTlNV17j72NdszptSkv4t5/fj9vcr70cM55UdoStwkcb+n+XNZz1LNXOdDZl8xEDcl3btz9/Rc/Dh0cVgwrreUisWV7j1xCua/x/U6xjXfHweqJ6XR6jwN1Op0Tp46ffcGcZ//yXEZ6xsf/WwWXVq6u/L9F0ZfA9Hj6Vi/90wJBEBPGTbxw9oWHDhf8sOYHn88X9bB+2Tl2e4vDKXbTiaWhe6qdbZodxoqJOdX0HgFFrFn385p1cJEWAMCjDz9+2cWXPbtizcNLZx88rOwNk50VvYcxprP462NPXzRHrIm+4tIrH334cY7jbjo/on0DSX7zybcPPfrA2vWwLXkiYGXEdBN6yRCeoqj77vyjYudTzz55+MjhrLxBqN+B1DobccF02JQfc4pIT0u/aM68x59+bOLU8ehrxrzzSZJ84q2v5IcRBLFzI+xiQ5JRm91gMN2aXiKgLMsuvea63//uD4r9aPK91CaOHMePDZWUnXPmuQWHD3btbfYhrrpsIVxN7IdvpT12e8vGzRvyh4fWBP961be7ftuzU8gkIyMWIsZguj+9ZwhfVlF2yw23UjT1yuti5vxVl12VlZn19MN3o4ebt23+z6vvTJs71eVyPf6nJ2iavnnZTaf1lnsz+wv2w3Vek1Pkqwrf99C98sKwnH45sy+ZaWuywbWIsYCeGuJkO8Tq9vDsJzvDFoTDxIaYt2g26DnEz3358L8fDR8aluny3of/++8zj8ApJms6XA/6X29NmgDnHziOW7Dokqrqqm7STKSHEv/Hseu3PShmsvrH1R98/P7xE7CRuCSgBRxcahyxe9PeR5/68/c/fRf/cvgn0uk/o1igH1MBZ5GnkSXInIhss14cs+49DhR2ebl5MXQ9ScmBQECa0pWvOHb73bd1yoUi6yV6VvJa1zDpvAmLrrzm3jvvu/jCiy++8OL4SRF4CN99kI8SoqaRxVfVZ3uvXPZyAUXYmuC6V51FgrVlPasHV5ex6vOVqz5fiWb5/v3i65MmTH5vY/EN0wbCqHR47xoCLQaL6XK2/LJNowmt6bZ1+5Y3HlgqP2DOzLkP3POA1ZpUXlH24KMPHj9xLH5ybmZfGrr1QgGNT6yfbo8rwu3mWC1Wi9lyslxcQpZl2dvvvq1fds43n3xrMFmArU5xPElgAT0N7N60l+O4625dcvjIYQDAPb+/9/rFN5wtfMihA77/4seM9IzVP3y7a++uhZdfveq9T17/z7/ffu8/p+NmuyM9TEDb9OGG0q1Rv4Nb750XvzEMpnP55bv1zS3NM+fPkO+srKqA8WjKAoBSQAkSz8J3Nf3zBgAApkyfxPPiEpUv//ulXXt2/uuFVw0mi8vRYjAYMtIzlty8+EhRIQDg2++/eeUfry373e+xgPZUAY1Fq6UpWDS7mBdf/ed9d/5x8dVLPvrkQ2nnyvc+AQCcLFYuS6tI0cV0DZQQNtFoNF6vV9q5ZfuWH1e9g7ZHDBtZU1uD1BNxzwN37dy4W6fVeby4kK/nC2giupnIko3yflyJNC6MNeTHKzhKrFj5wdJrrrv/7uX3373c6XSSFKnXwZW7b7kyetYHHsJ3PcWlxTC9b+3WFSs/ePHVf6KdPM9//NrTaHvXnp3zr7xI/pLrr4VLFmP17PECeirWa0QkOBEUtXchnkSSc+Flc4xG4w2LbzznrHN4Hra/+ua7r43NysE7Wli8qqoykW87pnOZMn3Sjg27ll5z3dJrrgMAHDx08JEn/lRaWDdAWKtDgVarvWvZ3XZ7aI4e08PyQCU6cUmiDiZhSDLaB9WzHTmGkoCizNx2R8DjT/r1wZ9FR35GBEEsWbT0hiU3JiclAwDqa6uXLzxXkaur0+o2rd3CsuyU6ZPiny0Tz8JjEgf/rZ5qpDUd46/mKAfHUtoEz/MrVn6wYuUHAIBnnnx2zsy5E6bO3rNpjXTAgP4DPv/wS4Zhzjz/VBmXHkpPHcJjugPt8BqpwSRtaSlHOTiZrCvZvWlvS0vLBfPPl+/80+MPz5k5d8ndj33xzWqUq/u7m26745ZlVdVVlyyc36X31xPAAorp0hiLtNjOqdZKbEJbxev1WiyhQbqcspOlaONvTzw7d9bc5196buVnsCACowALaCdwcNfRzjhNj2dkHoygIZ5+9fuox5w40Yw2brrzo8667sZdML102qQcxf6Dwv4+jtcdvYk1AGD6hedt/3Xn7k17n3nh6c+++gwAkD8g/6N3oVDed8vVsNF1Vr+5s+a++Oo/sXrGAk8idUIlLxZQhXrGIjVdv/03ManwzPCWEw117lgvidTKqEQKaJyD+w5xBDR3YJZep9/w02+krI6W47j5Z49wu2Dz/zV7StRqdeQLJ04NtYWMBE8iYTCdr55IJQcN63/i6MlBw/rHUszIlxwua09zA6yeieD2uCdPC03NkSTJcZxRUE8AwNkXnCk/WKfVpaSkpKb0oVL3VqEXTgk1uE2cT3ccaMerMBgAwKBh/U/F90FSzGmTcrB6tg/5quCReLyeisqKispEff1XscPcvSZ3pZ0x0PbJbvdX4T7Viau3gtVTjlaviTOK79w8s8SzzXrNFN/pnETqiAqfKPWlpKZZjfberZt5KeFN3wBQyzqPHa+q7fI7wvQ8tHpN4sHKh6+ejPrvRE01w3lmvWcWvrGhvrGhQ2dISYW/H7kZFgC6qYBG4petDBwpr/HB4ouRx6xbLec7pXL5Va8woT1YQDtOYwOscmtsqG+3F0YS/JYNzofsLy4HPVZ8yxrF7CJMLybBub7E2baxKsE+Z0gre5+B7QECyvPdd8ExJMFNLY52+EG5KzRo1T1CgjG9Xj1jNTCL1Ytnm7BfLqO9wFf2KgGNr548AERs2e3O4it3hejf9klwxxG76WIwgiDeeu+1bWpgBtrVw6x36GwPEND4EDEkFelmW9UTSQkRW5fbTaefsFMUE71TmFqUlUEQ0Ai36oJ/t3TmI/dcTgoN5MsrG6df8STDsB25mU/e/5OKpi5f/FfF/g/+s9xo0EbuT5A9a57ValUjz7tfvvPJBxYuvPQsxU5MV3JZr9DN3iOgkRDtkjZRdsNP0omq1w3VU74NLTABfD5fblwX/Ol792dnJb/x/tr/rfxVr9f8vOqR4u3/GjV9+eEyW7vja4tuePaHL/7yzr/vveX3oaHjyOF5aamWy659qn1Z9AAAvU6j1ymraDRq2qjXtu+EGEwkeCUvUdqItqhezxrz8uFfHSE7K/nihc9++/XOqtqm4yU1A6fcxTDsoQ0vAAAOl9nQV5tvj+c//vTXflkp8p3/fOZ3ew6W7Cmq6dj9YjCnll7oQLuA7mMnQZdLvNvr9fkCklFd/uf3m5pdct/qcMWrZrGajej+WC5UyvnQs59eu/D8H798Km/iHwAAm755Eo71boS6jMF0Z7CA9jb4U/mZ4PUF1n37xPZdx5598cu6etjZc+cesT1dgjTbxTprObkp1nPnPLL556ffeOaWFas25vVLnbXgyVjBBCjBPDhYihuFYE4/9Cmqp+yUWk9M11nOxMK9My954qk/X3vBtNFffvgg2vN/f/low6ZDoDP41xvf3X3H/HlzJnz4yUaPxx9fguPHaoVV6uC/Zbtf65R7w2C61IGeujp3LM2nNw776F8/flRYRWf+3In333nJ3x5bXFbRcO3NL6pU9LAhoWTAgsNlo0fmyR+2euZVX2y5+475HMf9++2fpJ2Kk7T1nOfOeUT+8MF7L1swb3JuipUgiFEjctt95nJcd4DpoUP4xKW590ntaZ+8spj1LXYYu+R5fvWPu1b/uGvuzHGPPbQQNu5MNr350u3SkefOeUTxMMFL+ANhSVGdcs5ISJLoyJlb9b/x0Wg0uI8B4vaLZr75wy+gx9LzBPQUueBuq7anXTQl7rztomuvmqoQl59+2ffYQwsJgqiubVI81RGBi3WSTjknAIBluVN05kRoNV0sSmZyOBqNRq9VH2hL6VpHUs1OHY0N9VdNHtOO+mnEaRff3iygPd3Ydql0JhAAffWtH669auov3z4x85InpJ1ff/wQMqSgJ9DV9xknsiwvZoi6P+6rfF6fz+vLTW6bEXY4QwkSGq2myWnOg510gMNV3oM6mTUK9dOINolvSmrau59v8OuZTrwZLKCdILWxOnG0ozTztOlQYpNIl17z7DcrH97889PyndPnPXYKb6xnoTCMiYgjf3p+LXxeX2N9fWN966vGxyIlDTrBp2+6AgDwyLtfgG5PY0N956on/Am/+49Ey9poneXjDb917uV7Bx1fE6mDMbVOoC1FVxaz/porz1Wp6A9WbWhpSWhljt5MzzDfUbh10ezTewMpaWkdUfB28NnOTp7cboMDZTwtUUev558V3UX/4V8rOnBjfQtpVvf0K2kCtNjdb767BvR6YsQfQ09hOkYXq+epoBOG8L9uOxBVUl+7e2mbzoMFF3P6vXakMmKt7EVcFR4zRVEIxJvf/3Jqh/AdJJZRVcBxCf3C3vXqh6DbcEqXNe4iT9qtukV1OpG/U/I321cl8rQP4bs/rQ75aalvJmr+Jp+l7NxOmrGMqgKSJBLR0FfuXNKmq3crwe2GlNuae0oAIVESm87GYOJw1eQxKGsqKxX2cHjig6/b4EDlPYnFnm98mLbKZbdT1FYuqQla0Y6zccfBDpZR9QIH2pWlNa28I8kLRzXF8l8KqZupvP8gJmGwAz21MVB5T2Kx51v4L7T0ECmsvBWx4pdfktq2utTTQpsyPbUG64OdIaBRZYWi1SwTsyq8h9I1Yt3WHEkM5nTmgSpS3xRSGUs6e27kTfI6Hldz4iaRptUltXWR+2OdofepZxcHJboArNR9mdOcSN/T1bOtMIy/dbWNVaOC6b1KjVW4h4IrkTpBN3t2LCm+Xoc/q9ZZKIr4YN3GLro3TARtrRzHnFK6Lo2pJ8L3tffT2VMxKr2ZJskP1mPBPT1gtT3VYAHt7aLZo94SrTPTFLkCC26Xg6W2fWAB7aFS03feSTxonXnlxk2n+y76HFhtQZD/B4rHJq/i1NAyAAAAAElFTkSuQmCC"}], 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"}]], 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"}], 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Lve5xJBSDwfSKIbxsP6GEBSZuqima1lM6xghLjgL2EyaIHDDzE2t5ovhFRlRLuLwe+uWDfXv3atVUbU1NbU0NAfgahf2UqbNKa9A3uXg8kMdgML1IQJF6AiCcf+ZYVLrk9Xrkrskut1tnUUkTN50uk0E/ayjM8IQtA4fEEblIQaGkkZK6p5Jsr/hSKin1PzzbhAtBMRhMbxnCy/ZTLl0So5+0mHwPHMSpoYxqtQadWmpbJ7K33PPboaysaFsB2FdF3ZJnQz3oAABnPvRaS8XGt9/4QvmKE8aNAEorGiiesttaaSZ6jRQAwMFRBgqO6K0ungB4II/B9FvuXrXqcIxgIJrXs27rli55oUVnnxV1//e/bk60H+hkaUUmqfYTcGoU/aRVmqD9FGs/HU6Xy+NLR12TG5qzM1IA4/fu3hfHEp7x4GutFRtld0kA8N9P4c/l+ktHyf71na/KeJ4rK69sbW0xqKC3pWD0M2Qgz9BMarKhuakFtaw/Wm1L0ZEoGIprQjGY/sTdq1bV1dY0N7dGfVQ5MVJtO9jJ1+KSx0fdT1kPJDSEv3vxSLSB1LNOEf0UJ2g2y/ZT7BuihuopgtQTta1rE6XC3nDpqBsU6gkIoq72iLNyn6yekaCDjTpVfq4k9sOzTUa95FVxSRMG029YefFFdbU1PfZyghCzvUZCAhrMHYlkJGd4vB554qbFoJGin1qom/VWsZRdtJ9ujyfYN6TT+FneoouSO0KgOUhqFU2SRLNd+v6pbvHDdZVFsIZiMP2AlRdfBHoNZGL2U5ByR6L99Hg8GkoKnobZTyBwyH6iwbvWGFjyqC3i5XwCRlStN8Q8BDZjkgSUIIhhBRlo/+A0XZsvjcFg+gore5N6JpREQn1D5NWCof30RNhPLmg/UfQTDd5hrskElzPqHJKCVtW30CpxDSURTvEwSQCSJARxCO92ewEgkpLNzVYYH2EYxu/3MwysxMdloRhM32XmxAnlZWXKPWZLcmnp8cgjvah9u4ja09jJ181LAmVl5ZH7SXsjmWD0c7HSftIh9pP1kpL9BEC2n5Fdk7scZYEUSRBoPQ+dRoX2E0T0gTwGg+mjbNq3P2zAGlU9u5zy8opYD5EJ2c+A/8xIzoDqKS/4DoDOoqJpRrKfctfkgP2UzpJYEik6YXWk8u6AL4UbIkhANSoaPUIQYGh+lIE8joRiMH1dQ4lT9fJhoUYhroDeff5IZd8QZD+lx3geRjlzTKw3eAa5dKkH7CcgAIp6EgRsRE+RhNgLFDC0LNaE0oSigTzaxhqKwfRdNu3bX1RYGJQy5YScEDo0mUaIdgueTwjbHy8GKrd/h5ZTtJ/ymnEASHl9n5+laRherG9qQeN3ZfQTqqfYNZnuWvspruFBkSQvDt5JkqRp2FMZAI6mQp5y/ryJW7cXd+zFMRhM78TKcsWFhWPGwbUsoR2NIpXSrtK6GgDUK9JsTkVUtL1EVZBRtgPxBPTu80ciAYW1nwDaT7POTAOeVlOy/YRt67Qh9rOmoTkrIyVoP1OlRTuCVa2dw9rqNYnFp14fMBtUWdnmZJPWpFOrGNSNHjRY3Q2Bdevg3XqrAACFzCnOJmEw/QUryxUVFmZki33dIoxWaV2t8q6KIqTeHO1Bnko+qhVqpY/jYa5FFBMZOhH7WdfQEtV+oombSvsJ1ZPxw+OD9tOtN8FWI53HaXdSADhboQNVMZTP6W3l3R4HVSO+o8ASnVGe2Gz3orlJKBePwWD6AVaWs1bETO8oUS5g0eEBMBrdhu0k40c/RfsplS7BviGK6KdoP2HfEKX9RM/KQLWfopbpTUZ0TGcvX4EgAJ+fc3n9dpfX42PRF4V0tOJJUqRCEIy6oG7iSCgGg+kAJCDICCEm49hPKEyBiZsaWpy4KS7agZAmbup1UvJdtp+NsPoS2k8ftJ9inZNPPREuhdSFCAKcmOT0+FudUEN5AVaq8rwgwGWO4VrH8D/wX8mRspzkmpUmFGeTMBhMZ0wdGcd+orZ1aPDeDvtpTG62OVasukIavLOsxx9z/mXHGDEke8SQ7OEF2UMHZw3KStMbdLRKhWrs0apyaHE5giB8lJpXaQiNltTiKUkYDKbjRB0S07FyRwH72ZqRDNvBB+wnH9K2Tq+T29bJ9jPDmJyUqn3v2Y9R6LPF5sjMSBW64mL1Rj2tUrEsW9UkrRyHGjJRJKFmaJom3V7hzAl5NY2taFonQQC7l6fF7wiSJDiG8jvDQ8l4bhIGg+kwMetAA/YzHdlPu8MFeL6yrgnaT7lvSKByPtx+3grtJ7zPsl7Rflobg5nx+HQg2MvzcOCuFitAzUYNTZHoRpGkyaDWahi1mlYxlE6ragmUhWIwGEwXC2hI6ZJoP1HlPK2njGppJeFg35DAxE3ZfqIZR0r7CSVVtJ/Jlq5JxEdFgI2aOL8Y5aRIEsU9xSE8nNlJUyQTkNQhQ7Min44joRgMpmPEm4mUkZyuoWmW9TkC9jMnzxLHfsKCIZtDzh0Blq0Tj7E2Wqvrg8dExZw3C3QCnhecHlgo22xze7zQ9qLsEVw9VIqMokF9cG4SBoPBdJmAxrGfBtF+QvVMwH6i0iWF/RSSLcbsbKnJcdcij/cFKNdwupTWaHa4fXaXz+31CwLQqmHOXZzxKR5PgLOnDI08DzahGEw/xury9rQDRfZT7htSWdeE9kfaT7fHg0qXIu2nAICj1RbHft74+H3RH4i6npzi4Vi7Tta12F1ep8vX6vB4fKxOw6CxvJyXUjN0clKUeALWUAwG00EBjWU/ZeLYT62RQrkj2X6eqG5A9hMAwaDXQftJilVQ3Y/T7ff5OY+PdbihhjI0FSbFFCpxwmAwmG5yoO2yn/Wi/YQE7Ofg9CTRfgqAZTtiPxOAomK2yBMEGPr0s7zD5XO6fVIYNIBYaA9SkmEtQRjYhGIwmHYLaLj9bFTYT54vKa+R7Ccamyva1iH7mR5qP+0Op2Q/WVjAhOxnXWP4ykjC6AmwQf/tz4JuQBBnJflYrsXucXn8cj9TNCVpeG4qAKDV0UOmGIPB9FdCHShqW5eksJ8A6LXSKho6sfOSPHETwviR/YTqGbCfRo1asp8AVNc3u1xQizNMyWEvfNM156GNDQ5NN703nheabS67ONdTHsj7/NJSIFNPGzxuzJCwp2ATisFgEocMbVsHYtlPp8u9YtWFCvtphX0/AUD2Ex2OJm7K9rO6vjk7O01nIIGPjGo/FbQjKEkHFhRJBJvD63D5bGJCCWmoxyfNK/11X/lmsd9+dzCNqZzGVHbTyTEYTC+BTNB+6nXa917+Sraf2Rmp0H42hdtPKMEB+wkE4HJ50OC9uTW4MKcwegKyn90yfidDxNrl8ftZzub0ttjdqLBp93EpngsAGJqXik0oBoPpMGSk/ZT7hsj2EwAQZj/Rk9MN4fazrqkF2c+quubsQdB+osF7kjl0ReL0JHjrflifD/W+szm8zTaoocNyAlOqRGprG7tPQ7EJ7Rmw38ecKshI+ym3rUP2s7kVdu5I0H6iynkAwKBBMHdUH9V+3nMVtJ/LH0I58TYqPrus9x1nd3qtrW6YUxKEsJBBpIZiMBhMQgIq2c+G6PYzyWxc8buLgvazPp79hON2aD8Dx8j2MzRwidQT0cGiTOlZQrv6h9qd3maby+n2+dlgbVNtbZRlo3E2CYPBJOZA1VxljRXVfka3ny9+EbSfmfHtJ2TQoHRoP8VjotpPBMvx8Vrtd5E1VSadWA5qaJ3VaXPCqV3KFzh//vQueTkMZqAxbQCnTElkPxmaRPYTta2LZT+r2mU/jUnNrQ7JfipmIiH7yXEC7NrZ1XOCKk9Wxz+A5YSaJkezHYq+n+U4GEQAejWlrBVFYBOKwWDiAxPulTXWnPQstGac0W8GPA/tJ6dubrUnmY3vvfiF3DV5UMB+phtg12Tgo1psjt/df817z3+WkZESZj/TjSFpImQ/kXpCcyl2jCcAmGPwSP0+QA9RWuMgCKDXqA6X1lEkwdBkRU2TmqEuWHj6d+t29tRVYBKFTY7S/0VJHQ0/aSwbXmscCW09jn/umC4EDm+RerKsz+P3GhhjSXnNyJHpgANJZth0o97akp6mtTV57C4vFNCA/UxK1bbYHJZky3vPf1bX1JKekYLsJxTQ2PYTwfGCOCe93ZdrbZFn6HMjRgyW9/u48HOdPFIU6yRoEqjb6aZIgiIJH0v4WZ7XMD6fVGMvg/vVYzCYONCS/URt60T7KaqnZD+hFKbBCUgmg95kMgLG39BkSzMkIftpSbasWHVhIvbT4xZk+8nzAklKuaMptCPO0sphOO2OsAnwx4ujLnmfGILA8XDdORQb5Xjhi++3Lb9w5rfrdiiPwhp6ymnTNmaIAbgKf05PXREGI0HL9pMmNSj6mZtr1KnVsexnmiGpY/ZTmXlHaxbB2CsFs1jb/vqbMx58DcQmqlXtmHoi3ysVTwkCDwgxoCB4AUt6CD8XbkIxfYX/G7wRAPDYiU515sa0N34CBnYIhfR4pa7JAAA0eNepLSj5rrSfKPrZ0GSTk+/Qfor5pYyMlJW/XRqWfFdWziP7ibY5XpCH7sbWepqUtbGHQqDDC7KGF2ShdT1HDMnW67V6g86cZNHqdYxG89W6vZFPwdmkPiejmK6Cth6Xb1EPGEInutxZv4TWUMG2dXDuo8st20+7w2k00yj5PigztcFqk+ynySDZz+c+Qfbz3Rc+TcR+8qJ6yqFPLU1R3SabeqOk4OJCnn5ZoKuaHAxBcByL+trJCLCCgPOShNfNWgzSBFYMBhMUi2gamhEoYBqYIZTgzPFI+wnVE4DKuiZkP4EA5LZ1AfspZIiD90j76XC6kXp63MLPO0vksk7ZcTafOKlXUYkkktAR996ySG806I0Gc0qy3mwUbyaz2ag36OSbxWI2J1nQYfHPSZEEHTprXpwWBQubVFo4dTWMyfltD08wGMxAg1TaT4RsP+EdL0UAwuXxIPsJ94ilS7C6/rmP65pakYsLq/0EABjEkxwpq1W+mFIuaTEDrngssJCm8tYJcoQGdIv6KEWSGhWtosMdMC/AhFKU1Y+dwRYkmF4+ZsejeEyPIbowMXeEku/yA2jwDu1nVqrOSKXpkyLsJwjJHUWzn3mZGch+AgDKq5tkSaxp7flmxtJaxwiCAGoVZdSpVEyohgoCjDMwUYbw2ISeEjxWq/J2ai4Cg4kBiSZuIuTBu2Q/AcjJSgOMv9EazB0p7WfYueLYT0EA+dmwfygAoNGV6BzNrqmuj3EKkiT1WpVZr2FoKqyVPcvxUReew5xywsQUm03MqYVU2k80eFfaT3Q3VbSfaDuW/WwI2E9nNPs5a/JQ5P56oPdSwggMRZr0apM+6Lvh8sg8nN0ZObMTOJvuueKMnr7GfkcXzpv2WK0P7h334N5xkQ9hYcX0DGTH7GezTTpm0KB0l8vT0NSaFrCf+gj7yfHCxj1S/q7W4Zf3+7iQ5d7CSNB75ufnBZ8SkReKhayOKoayGDRqFS2P7gXxojheSEmKsvAc1tAuoWt7T/xap/u1ThdLTDGY7oNGlfOi/VQr7efJuqbcrDTZfsrRT8D6m21OS7JZtp8NrVadBsYBlKVLIfZz0tDN+0tRGRP8t7YCpGT5ealvPURon3raLcPRURQQapkke1JwVSWKojlRl+tAJtpjY5I4sTyeAAR6HVqMgXpVtENNqxgKqIBPxWtqCj1eVuqkDwhoQ4HQ6vCZFSVN63/aeu7cMzv188Z0M0oN1STj2gkMuDHL0ST5va6HPFHbrCxdku1nbgz7Ce8mm5X2U6dhZPuJUNrPsqqmTftE9RSEOqe0HhGcg0QSalo0jAohja+e5jQpihqVWJ2dou5Vqxj0ojm+E3lsxVh1g8WogQklVAwAiJwMCywSzoOTr5Ss/2krNqF9BZx3wnQrgjwPPclsdHs8Wo1Gsp+1TbnZIfYTVc53wH5edd7ULfvLIl+bIoCWITs2cje2HEXH0kDINA7yNFegGilBEGhGxYp1qTxFZbGijmsGDcrKKK+oFA+AOxiC0GtVNCmYgBqo4E9Ao6ZNeg3HCa0OD8cLNE1wyCATIMyEIg3twdZRGMwpDrPsHJAV8m2CHBi5YOZ4ZD+1Gk3QfmaH20+onhH2E5C+6PYzYClTkkxb9pcRBAwpNiiS74Q4HR7N41Taz5geMoZesR0SMkqtlnNEYnET4DieoUmzUWMyaDQqmhZn6KMjIk0ontyJwQxwhMAG/fY3W9Mt4rwdQZg2Pht4LXHtJ9oI9g1B6hnLfo4enLqpGaowAQOgij4dBGAoQllHH089oz8sr+lByPYzzhsOm/IktlEO7kFhU7WYUKIpkuU4SpwkNSI35cjJppQkU5P4LpTgLk0d6DohN54YsL0nMP0ApdDQBp2qxmrLTDJOmTo6Rc8Db9B+puqDbevQ0UnJFmXbOpR8V576SFltXkYG2k62mFD0UxCAMvqJ0DBk5GTKcLptoCymiII1VWygHkCtoghS5fNxaoYOvjgerydAgmI3wKdOY/oZpJqhLjrvjMsvnUP5WlH0Ez0g2k97MHck2k+xdyZbGZi4GcV+ZmT8vAvaz7KqppF5wZwPTTNhfpAODOFjkrBsDc4PdlaWgZ3qRPxeH5qBpHSoAbsq7fL6g+5YRVNaNaNmKEBIWXsAQEOLtO6TEtylCYMZaISNc6EHpChyf9GJCZMn/LDzYFFZLbSfzTAqSqrhwUr7iTZykP20Bmcirbzj0rDk++Qx+VsOwNwRzwu1rXIb+cCrEgQdv5FId5o+j8cTNtyXHSiCoggUBpUZOmxQq6Pnp59iMF0PmzxUecM/4sSJjBJKWfhks7boaOW006fQgAPAt/vQSQGcXDTttMjoZ2WdFQoo6qwcsJ/vvvABGrwj++nyg+m5KTvE3FSETMK7FEloydit7Ij2CWrFCZiFbxdhy9GLMdAQxSRFdyy/KsvyBUMyrYE19WRwJLT383+DN+Iuy3GIpaEJRqvrAoeB+mbQr4maY5EElBLDkUNzU2iafP2jzRoAzhw/WGk/LRH2Ey7sIXZNXnnHpe++8IF8xrKqpkmj87cfLCcI2NyoLsJ+Zs68vKVs+/Rb/jZ2VIGcSCIAMOfBXuKtJyMb4oZoWZcQ5kBh0VI0vthYMqYAZuGdbp9WrW2xey3GKM3uMJgBG63OkA8D/TmoHStDHViPiICHnDhZBwBx64rZb3++zaw2RbGftdacnDbsZ1ZmWnOrXfKd4ZnxwEoeNKXTacLT8CLm3FnKRnbKI1TGItAQ3lauqrJKEdNM/MeB/ifBKbr5RQV1Xx4yLNsa6A8Q34Tu9OcM2JWyMZj+RHxlgcbz18370Z0xI+B3SPGRyvy8jEZO2FZYDoDw/bbiEPUMRD+VbetkyqqsU0ZmIQFkOb7OFt5YU5zmA2ia1Os0nb/6zhI4u0+RRIoKCpJCm5waUnUQP5tUl3RaWLwp8tYF7wLTFri3CKa9wIXSEtAf6EAnjhpib4LxyqIjVWNH5hwqqTSqAU8ySdmZ3/6yb/Dw/LDnyPZTbFvnU9rPSWMGHzxSSZJwzk/U6CdaTo4kCLWaSfyddH1KKXT+aJTeS6HIbwUll+r14cLHRqyrHIwNYTCYU0eTM6I/etfZNSigTqdTr9fDvhtNtkMlleNG5hwsOUkAYd7MsS/9t+y87HD7iaKfTqdbnxQSECyrsl43ZfjPu6AWczzfqGi8hJDbzNMUSYlJJKEbNJSmaZaFTelCzhDlleAuqaw1RRvrSlAtfZYp+E4taUmgITyKlE6BsIE8Xmu3V4FTSZjuQIqBOpyuUhgAlYQGKUFlVQOa5w7NaVmtTq9NTlHL9lMfsJ9Nzd7Sk9B+LpgxZsPOIyQJJ5KTotVEqoTOJrbpgPhaS3UM5arZKjTDkwuB9JFEQkoZPKjJ7rfoyFZ3QADdwW+bHNhACmTm5nlYkDkIRidKSmBlFUkQBq2KUWmTzEY49Qj1NBEpLClHG5sbW6AImlVbo03kj5wgj8FgBqiAlpTVjijIQvdnnj52RF76kYp6VIGkZujPftptENdZs3vsnNUjJd8VEzfzMgAav5eU1xAEwQsCAYDdFz63kiCgclHiEJgiCYaR+sATALRWbEwaPBsuEP/IbyIv8YyHX5etotPuBCCkNRUNgMMu58Ikho4eBf9zMrzqaOTIguPFxTRFkhzndrCVPmdtaL1ngkRVT1zS1E0IWaPjH0DUFCdyHjZ5KJ4biulaaKRMWelm5EBhPXxpzaih2YdP1MlrB+XkD87NNPy6cT+tBoJQtXC61HIR2c9WB5ylM3f6yHXbS2DpEo/q5EnZfqJgKEESqEDdZ7ORFtWOn/fNmDs5kUvc9vBNaKN+bw3QStVU8TlefBjKpTjF/3hR6F9X4oGDGMjFA0JbkVNMl5CgPrbJg8Y3H7MqxjoYTAcI/aOX/BdJwFJQr5+rqW6w21w79x6zW2Eoc/jgTIIgclK0owZnXn/V/MycPKieiombk0ZJDeED6imQBHG0zlNT1+Ryuj1uj8/r41k/6XPrSd5ICXqC05E+p82xr7hUTs305FzzwaMnKNNrBBm8wer6wE8HzpUPvQV3yv+NAE/uxGD6KPBvPJFbKLRyDyUGLh0Op8GgB4CwW6UmdQQBDh+vGj1sUGaKBQBeYT89rY7wOnk4VOfcQeMpSjNDw6AoKcZAO/MmGYPkQFVClMnpEbjgEiOBJY+yCkaMHZoxNCepOM/86y9bZQlVXnnUbYRWqwUEFFmvV3rLAin/MDCYAc3OgdoaBjrQkQVZ1XWwtJOhqUyxkZ3D4TpwuPTAYdhLCQAwFtWHHqs6XmmtcQOF/ZS6eMgGjSCI0uoW1NadEld+p0iSJOF0ewquMB8sSocN5cQntOtyVYIb3RJ/Sg4hzTA7efTwyvMnL7v4jG+/+5lkGIGiGZVyOTnB4eNZQOr1OrPZYDYbTCa90ag3mfRoW8VQaoZSq+BXjiCuIA8E0BIxQR6b0N4MLgjFdC10SKlQDDk7dKRy3IicwiOVmRbViMGpPj+7Zu1mg0artJ8cz1Ni6l2lonhB4FjozSiSpCl4g+kjgmS0OrRSW0NtHXSjPdwnThCe/Ne7wTU4g1VM4qhcAH4/T5E8y/EqBnpxaZAulVDJ0YbAhvhfi0kVaUJxNgmDGSBIMdDsDDOK7BVkJx8pr83JFPMvAUYOzS48AicmWoya6trGmjrr2RNHyfYTTSRHs+mrmuwUCfvUwZ7zJJGbk56ZmZKckkRqtSqjXqtT+Vgfy/ksaUm33HppoL6pHTLq4igXRwnejqwRpYxgwPXfQ6sECGjASZIkWI5HNaREGOKOSM2PNKGY3gw2oZjucqAy0DCSpMsjVcKXHK8eMSTrSGkNmofD0OTeolLZfoqRzeBzCfhcgjTAiGF9i5OhKRVDadTQhpLioB4djdodyU9K8HJ1lDjnkoJl/4nj5mDJFC1wQcspCCzHw+WPFK/MiIN0ioSVWAIQJLEU/ykqCTZ8yhDXm5OJNKGRU+MxvQfcmQmjZNxNf0lwOmJUaNTardXuQ4as7CSsAD1Zaw/TuN1ib3mjCdamMzwnqyfHwZp5tF3d5AAC4HmepEk41hWzRgxNqhiKoSlazCPJWhsquqALcXOUFulsAHTXxwXnI7ndXpIWmtzO9HQLMpYCIVTZBZ2G0Kqp/BTLsMGpKWYdumKSADNmnCZXQH39zeZgLZR45couTVg9MZheKJFtz2bskArRwwdnAh44XVJaZtzwQVv2HEHbDC3VutdUN4p5eWC3uY0m7d7DJ4IvSgaH4CSsBJJGuyinT4o3BgqoGAmN34K+PWSlGKNGbKsbbWHqqSBkzO6gjLCgnhfoQGPSMblGjRqGHwjOc7y0Ml7Pr9DvqlKPZrIR7sLq2cvB9hMTi44ViMMh/NGTtSPyspC6FB6F3eGyM8xlVQ1KvXM4nHpRQ202t2w/YdVn4Jhaq4OmSR6t1iYqKJROmqJIEqWSSJIoOSxNlHzowRtA56gRu590pM4rgJbiCILLSkurt7Z2PJ0VOCGWTgym76KcdB71oTZioNkZ5mMVtaHPC3+i0+EsrYQDfBnZA1Y22mCmXdwhAIEiyVYfAXw8EHywLJT0WAya9GTD9GmnZaYYTPqEGtl1B2HV7zDzHrqYh5Rr4gWCAIWHT6y8dDYvppTQ1H40S/Xn+vCWoJg+AbafGESMzkKx1gWOLqFCeBIJ7aCkPQIAMyaPqG1sAQLIyk6tqW6Uj3z9n3c8/u/VR8rqqID9FG0mwfI8DHqKdUsGhqdIAi7QpmE0KlqtolU0Z2tptbfCgtPdB48vv3J+2NX0ROYl4kfBiuVWygfRlwJBEKeNzt9fFAxWKA/DSSIMpo/S/nF6PGGFcjlpdEh3y0AveblGEmzcfkjghBH5mWjPmx+uQ9FPRHKyqb7Vheyd6DcJmiZZm40FwAsAGmmPGjEYVdd3H1mpJiAuPV9jjV7kFLU9qp+TAqZwFC++ZbgRep2HS4IyKqjNabrgebCS9hWw/cQgnLV7QNcoLFQ8KKAOpwsAXfBpBEATk2SuvWrB/975ITvDdKK60e0LrvB+1vQxm3cUieN2mDxCLw0TRwShTUpPMmpYn0fFwFWCYXGQ2KhJFFmxRr3905AQTq+fJCle1EqKojlOup5jVbD3kjtlFAjUsDIqKVZrF2D01u/nqtVGO6X2pKlJivKrYEelSkbNkywBiHTX8cMVNo2K1qjpwZmWvEyzxaBRqWg/y40eMkjNUF4/JwiCiqHWb9rXgcvGYDCnBEdbitnRbBIs3oECmpWVWlPTKI/Hka9ShkDLxM5MypbyMmdNH1N8rFJ2YmjeEUEAWvB7PbyGoVXifWQ/4QsGNjqsnmjDnQJbnJEkyYvLGbG0hRTC+zcD4Inz9eLjibDirwwTZdKraIoCrLum1lcjPwX1SJUcKigYlFpWFQxoYLoVTTJcEtJjDe9M2C6w/RywPHnXopo62Nu3XUij8AQOhAK6a3/J1AkjUTu7/Nz0bbuKs8Sc0qZdh4flZ6ADGYaqqbeJEhkUPmQi4b9S+ggWLaGCeUIMgNJi+ScFl4CXRFmWoTAH2lqx0aJsqxwDNrBOBtMqpbwoggGCnwGhGTBSDXhvBn0S3fML8Flup6u+yl8PgEYAhuQU9FAqpSUBJyp7ILYZETRGlxnVMeN+dj0po51XUgyma61oMIk0cfzw2kYYsRycnQqfGe2pgmj6gi8gqiFcn4OCqgMLP0WpJAnCbWt1Q70FdkEHJ3dSpFbNmA1qk14959xJWenm9jpQ+XIogeVE3Q88EGk8AeCjrIIycsLEWZOHXHX+pPe/2fXupxukZytTQnI3frltnSyakvTjmGffU1JsPwcyQieb/6KTxD4g2M5Or2Gy00zlJ+sjjpZ2ZKWbysQypq07iwvyYNOmX3ccOnv6WDVDaVWMn+Vg+kjsGwKLQFVahobRz2wNHMUz8gOA3fbLrmVXLej4W+LsqkAMVJBir8o3i8b1YmootE896Xdt2bZv//6ipuZgDamokdLTUY8leYJ+SDYJK2cvA3tSTI/VhMaRUUljVt1+6cvPf6pVw7sEALfcfuUvVz8sP2FYZtKWwClmnDmuIC9z61a4EvK1F80qrW6iaVKtoigKruEhzjwSpyGJwVDUzg4W04vmVL6oDncFpVmpN10HKCypmDwic3SO5aDXU+rxa8TlnpT5dINWJfCCOxBmRdTWNMjbQ4cPSTZqAe9RLB2FlVUiwSWa5cVKWVbyknFIZAWO6clS19od1pAOOBiMklCz1UEZFWIJ6MvPf7rqtktefuEz6DSz0x68/+WJY4bApI0blgQdO1ELmxVRUmf2MlRyL4DSk/VDctOraxsJsfwT5WNQwgjNgmfEvkwoPQXHxLxYSyQIHMejxZHa9WY6CQ04wLEV1U07C0/QNA2AKKCKU3s4anCmWaOiU8zaJKOWkRabC0xNFX/8W/cdNeqkBZE6WkfQP0lwuSG0WCkAoKKrW/BOT3YsGQynuj24V1pyBoNpL0JcGY20ovToWddVf/wM1NAXPlv1+8tffu5juEhvZsq2XXAhmqGD06OePYqiiU035OpRuetS4EHZdcLvgU8++HH5ykWgx4m8bKUAaki/027zkISttaU8UWnEZfW9jr9OKkQbOPqJ6RKE2FY0JEz48nMfDy/IPFpWSxLEsGGDVQzldUnjI0AQFTV2pV9Dm6Vi96YhueloQxZNRiz99IvNiYP7I4vU24mchQck8HMEJbY7IUGwNBU2iCI0QOqCcjTajyKw5YGRUCpJh9blgP3rUNoozhVGCjA8vG0NjTO8xetEYjC9hDgj3VhWlOYFYc5ld8F0+dFvoeSU1YoaWpNs0tqcUh3lL7uK1dIUT2HT5gMzzxrf5qWIPd1FR9rp4XesE8CQgkI6OYGiCLG6XohS/hmJRw0rmWiKELho8ifANTcDtavSu1C60gb9MFk42WQiVrAvkUgfpjvA9rO7o9V0YkGb/kSkjNIelpH0qGCpv+zLgIZmHS2rMRs0jR64Ltu1l81d/fkveVnGo2L0c9XNSx986LX4r4S62nVePaMia6USeQ8FZ3RGCbAqlvCAaClWTHwxYa1GJJUUIxLIu6PwQ6NVLMsCAC0KkuY8JgtopZWMFezr8kgfJg6PnZiFG873sBDXBYxC79TT2vpAFX1bQTmhQzJKF+7bRoilnWMnjGaGnOsvXS9qKJyGg/rP6zSq8pN1yvDpy8/DOCkAYOOuw7Omjor6GjXVUmU7p0vNSjWOHZqel2nWqiS1yhmRG3Y80Z73E6meSjhASsl6Ov45BYEHHh+rpsQmpoA43ugDwEcQhMmgyckwpVj0qDDLoFMxFJU3BLZo0qjo6hOw45+YDcNJpF4HNp49nO7L6BdGoQNOD/kneuHi2VWKTkuihv4Ued4xw/Kiv6haSkmHcc3yBSoVXC+DJIktu0saGxobGxTTH3cfBgCENWRqkxWTsv5zDCqjPBc+FEUdaGIn5AXg8vhpLYWmVxnVghi6JU16khJ8LjsHp+0LQkuzFLoVi+uBSRX0mwIAGlMaaMAN7jD9k36zXjHRDeeEMdAjxUdammodDjhUnzhpJpz4GK3gyRnWcERBdk5GdSWaLB9k7yFpSeRuxUNn0dEmvNN8guWigsfHGTSkvAISmqKvXDFebH0iHx7MknWsfzWmy2N2jSC4RBULpBm6seidw0zMqaUzf8hwlHvWWdAJ1gX6BJuGn287+k3YuVFL0GDDkQ69piCAg4eOX7d8Hlz3uENLOCmz8OLVewBBCQIfNn+dpZMSLLkPa1OveECazSmXMKD/Bhebw/QU8VUvFQTX+6sGkeMkDKYbgebr6JESq0NgdMkuP4NudP5FkYcaLSbpI5vexve8EnGxZOkGADht7NA9h8r2FZUfKI7Sq7hdUHCVTUAAST3VMCnfBmJUVzrM5YU3efH3YLP6gLeE8QdCXNZJzIihfztdiIXBYPoPNNKD6pNl2bkFR4p2o4SSyOACjaRxI/LSjlQ06LWqhedOq6xp0mtVic+XipX76vAcHoFQEwLsFcIRYrUnDxfbJEjKG1qNJHrVQBFrNHQ01F+xGV7gzPAuT8BO0W1rsehcsRvFYPo8nQnHSdWdAID1P3xx4cVX1tQqVz2aBGpfBwAcKasdUZB5pKLBZnOoVdQJxcpIIwsGlZTBrHQY+0pb1AylVtGpFl2ySavTqLQaWq+Bymt3eQcPz83NCgzG2wlST3FePCpQEGuPNIMEV8RlRGbhA2WpLMsCoI52dpQswmAwCRWN1iVc79ybA9AJNwANh9br0/xuqf/80SPFw0eMljVUIOx5M5ZXff6yKT211skOz007erJh/Mjco+XB5puc3xf1vFefP0GrZggCbN93jPW6bF6XTdHkvq6+MX/5vA43E4GLvim66ok97aIv4xFGZF2qsplIVmqSwEmdRFwunwuAJItOefDBwmMwZDx5uHInpYomxBhMvyC+6k3rts4GfcWK0l6Pj1YZgFuydSRtG5SjqaxqkE5V9fOZ00e5HVD8yh2AUZPlNfWmlCRTStLIsVBH6u1ek9lkMpvQVE6ZfUXlqC1T1KE6ii2CrsBDZ4ld+TwgkF/iCI3aL7eTj7cqZyjEkcpmkoCFnxajNjfDlJdpSTZpYdl84ErPmjQMyugh6SOF+vO5GiullBYGgxlgGkrX11VakjPkYp3iQ8XpRIU28PAba3ajjauWTJGfg/Q0jDkzpRY4WoP5jdXrk5MCDYlFoUHrxSPRRAkbjhNoWCfaPnR2qy0pZEyhLGMSSDXBeynBE0jWO+I4UBcP4wnqQEAAAJBpoo06FUmSWjVN8d6q6rqq6ijXYNJKTf8kcE4JgxmoGiqFCdOIQ2ljaQAK3/gQzkRqL1qDGW28sTr4dDSPHP5fLJ8MLBcMQwOAALCJfVfP5iQCjegJkhH4iE71HHukvM7lgd08h46bWFcFF/yAS9JJVUpSvVKs7NaBQ6Xjx8IWf6EviRUUg+lvJB4SpUekNr2xWpqaGQe702fUq9oUzZCLELVSqUeHistCHo1Nm5cefzZnFPUEYNhpEy6cPeayJVN+2ljo9vj/9R8ooDRNyS+m1motFoPXz6anGGEraIpkOd7u9Br0mrwM8+zTR/+8rTjYHCVYUI/BYAbqmkix5C8Mm9OTngGX8UC0+azyeneSUaOiqSGDktKT9Fo1bK18xvghCuWMPghuszcczTZHJpFCVvII0KDLSguurQloX+vPm3Z/tWaDIPBqnVHxitJPyet2txAcTZH1DX6GpuQLtXrc1qZmsaVpsC8flk5M76dgsKa9Tyk7kVAzswEC0ZaGRlT6hHLjFeeijTdWr/9lR2R7zZg4nfYh6ZqbrjkPFs8Pz5b3//4vUhunrfuOLl+xEG0//cq39/z2fNDVWG32NMXn5+CR6jEFadPH5FbUtVTW2z1eViMuYRKcZSTOj4LT30PPg9pKcbxwuAQ2PD9j0nCsnpg+oYYdfpV+JqNCJ/5i42soHV80E/SnsXj97TWgq0EJIg5o1GzIUsYuMp0mQ37rYbM5VZSQZmSq66w0AMcrammaJgha+bOxN1vtofM/CYLgtMk6DWPQqjQqeurU8YPSTfW14RP/4zCNqew37RgwfUsKO4Ncn4QB8ReVu2rJlPcDqXZEJ0UzJmI/TtTxXfR4As/zaJFkHZdQFacSmhJYRRcJOR1PUGqB88a4gPDuzhQpJbjQXQ9jUTGUUacenGkelpuSmWoU1xKV8mE/bytyO+3HyoIreiJwEHQg0/ulsKveYD/zpB0g3qJyXcKSOeO27a9AjYdPK0gLsc7SjHPU70hSLFQoivb/+98f/eXpWYm/ViyVlPfTbLNaawRCSCVTmII6/VAe5RlReSlqo17F0BRgPaUnqksTnqxvMuRTJNFsC6bIMH2Ofi+FnaRfDu07r6GdEtD8dP2M6aN2HDx57ASsoteqGaNe3WgNfUHFhmJdY7l1nAR0eol5ucDAXFxjng6aUBVvUyaR1Frj4PxcIHaGlglzoFqSpUiC50lAkWhme5wW+ughHuavorw/jheSTAVYQ3sDWAp74Gc7YJWUEP+NvqhcIly1ZEpNo31nYSVqIQoA0OukuYybtxWZU6UpsfVOkGLWcpxgMqgNWmjreF5QqWDNEC8IKpqaNmNMjGtrg/whQ2trGz0um2I6vIgYDXCnjGZUmtQ02C/qcH1tctJpMLgp6GGfUz9XwVjQ4k6eNDVJUayKJgnSRQG1ClalpruOQx3mBZIieEFqFKJsj0IQ0lyAsB8i64adAFkAdJTBxcXrYILpeilUDBSwdPYY0o+6GgxwK5qQgOZmp2SlGnccgDlocZEPRs1Qsk4YAwIKt8X1gmB40WejeJIigMfp80SLcJZVVKOO9P985Vt54tMZD7++9eGb4l9MQ1VF5qC8RvVEAZAtLS2sT/ompFWapOQkQ2AlEmt9SIoJEWYwYfslged4wPNksNVpaHiBIIic8UsMFjhhNIzNnz+FNu68/RZ552PPPh3/+gcsYX9yWOww/UBDowvosvMmHzhSo2KoA4dhMo4kCLUqeKSKoTUauLrRuZfe6Tn5vVwMBAiA+i1Jp2/n1SROQ1VF2iAgACpVC4CoviQUTTfwudEB9RXHUZ1nBgHH+34BxjndHv+glCwjBa/weMNxmqb5pElQ9H2FBq1KzVAopDDzgt9SpLQsMuJkZSXDRPlBnbPsz7RgZxhZ/yH333nPo/1dQ7H2YaJ+JMoG2LgeromkvE8zUBZZv58iCZNejawcClDKPUAFATA0iVynl4M7lW1BaDq8vl3u6y6v0xmljaZYy/7oPb+7/+kXE7z0hqqK1EEFFVY4No8kpIdSyMUELeg1tz+GagAAWA5lVBPiTuX3Ln448vbt3293+uTnS/8V/zNn1unosI0bN6KN+WfOWLt1C+gLYCnEdMfHqWzAKGl0B0oQMCMkLuyL7hJqVdCUUSSp1wZXA1YuExQc+cpTHaWl1cHkhbfv/fH5eC3dUfemtq7YpZW6lpxscBDAzkVTS4dqtPhdAIALCtlZi67Nz8vWazUACJXVdSlpg0ZIpa5Bmpvr0lIzAQCbNkk6qMRgNEdVT6SbvWF5TiyFmN5DQS9TUqJbBRSVgp4/a+yPW4/ID1AUAeeJi8BlLUgyJyu5ssbKCwJJErDWB4C6mvJ0KJrB65TFdO4VD0S+2KQFt3t8wbU5D4gZ8vlLr4Gy9enLk+Zei/bHnzvFwf4fCA3N27Wq0GnvBKFiGL1e12i1XnbR4j2roYCOHVmwZUvQEpaWBVfRkdm1a/sZ0ybH0sGq6jqj0YwyS6KHliRTEAQ/y1IUJa5ZIgQsbac6MmMpxPQbCvr70D7oQMO0A7rOQOBPXA5Icposy6tVUn15Xm62p+KALJoLr7y7pvBz6bwB8VWyddMvI4aaDSlRZubMWX5bS6N9x+62s3o6WhZQ4IvezVm8ZsFfUlyItpXqGVkNiq5/0YI5Lc2tkjAGpsejFLwgCG6PR6c3oiWRxOJWST2BIFAUmjWvbJsS8qPEgogZ4BT0MkPaTQIa/LPfd5IYpCe4wIJBJByZw1XVYNzTx0IBFbcH52SWVASVV8VQ8km2bPo52ssRpaUnWKk2M9zsmUyGY8fLQdrCqQ0/dPp9CVOnnu52NEXTTMHj8dI0HRA+SSVlxWc5Hq0fJzlq8ZsjOcki2kzFWdBsqpAQMBHyiRmoFR4YzMAxpMFUSfhicCTR0OwMcaCiUni8fvngwkJo8eSY5uZNm+KEAuFESoEvOVbh97OhFjDWysIdR6vVKd8OD5dQljzlwaIjJPpCkKKxklbStPRdIu2Sei9JZzDodSG6qfgXg8G0i4LBGnQD/UtAQ0ehBDhZK5WpI+lErtPphgKKbNfH778aNvYnCCI1xaRQLtiJHv2L9mjUsOW7LKjygyajAQhALI5qz9VH12sYi1S+F/FeMApBwynu4jUoJJKIOWMewjDMtl++lY9QaujWTRvQbfvmDTn5I3ILRuYWjGzf28BgBiQFfV9GQ4bweq3K62NZv3/9zsLr5w1ptklllYGF0eG2zSn27AiR2sCUdjHHIiXiA23oJekKUNfgGJQH503KTwv8R/wHvkZnbZ147hABdftJZDD3bv0R5ou2VM2av1SlUut0WrmhMg9Aenbe8dITJyvr0pL1I0eNVamgnPOBOMbSZdeVl1dkZqgZmlGePPviC/1+GJZtaPSVHuu96w5iMACAFnW+xSvNiOklFPTlCGlIEikz1dhsc7vdnkyVB3Zy45QxUMmHur1+P8srrZ9ipmPQ5UEVi1gjXn5GWDNlk8kQOAMJO9UpeMc65Ork0na9Jbf1xMHGmsMldGaGNLGUoaDRBQCcd9EK+aIFzldTHb72HMf6k8wG1u+vqozSzquo6EBW9lksigcgAyr9gFBbvNgpLQym19Cizm/zGC5Jmh5iV2ckchgFJLM10JQ0xIHmZMBVOuoa4MgdSR/NMKzfL4YMpSG8z89xPB822FeeBN1NTjI0NztEhWmjS8i2n9fKZ5g978JpDT/IFvS9PVVWSzoIXXHa2gojs8GG9OJEUXQGk5YkaIo2plpCywAcez7UTboKAFBTE2zlmZURZSVrkiAYmvZ6vD6vV6WGkwXQOwi3xUr1DKyTx4nd+VBgIid1qqcaroGMwfQqErSfqc3Sp5fSujsmwQMk16QQUACSTFp52B4WQ+Q4AQmox8v6/Vysmh1lIBXpCyGa0V2bf7zkqptIgsgvkH70LIuqkYhLVww36OBlHD96lPX792fOG1+7Tj6hTk2+bR16TXJwaJxkgXaV9To02vASenlePHr5tmIBUimSwMO1Q9FlMwxDkhTrh+fxejw0oxKnT0lvbezoPPns4pSqYJ++jb/uLcjPZxiGhmuBnOqqegymR1Q4NaCzjVq44nfX0icMaVBAP1u7Py87JTPNGFVAm20uVhwFe3ysy+OnFEsSQesq1tinZOQSviaksyYDYTIYYYYIzVyad761SdnnLgTWIzQ1RVkqWTo/IN5tHrYyqX2GLrp6KuYMVdc2SgFaxfqgFAXzS3qDnufg+2JZn0qlkb8ikkziDwdJp9SsSayrJ0BykkWlYtA+LJ+YAUVjdter56kypO1d/CNkKidBBlJAoWchCaK8usUtFjCxLOfy+E2BrkvoUPSsmuqa5DRJoMorYYdQGTWtVZHqyAmPDQ0NMJJi0Xi8XpU0+1KSvvf2VCVPXOSqaSBFEnkzbo9TZzDFk9Boua+IQ4jDxSUjRqBMOuH3SWN5WEKgUYXVXyEtFQRgNOrDCkUxGEyfM6TtdT8hAkoShFwWHuZA65qkpSw4XnC4vGZDsPhA0WNeuhPxKkLJkdLRY8aFTU9qaBBFVgAOpwsJERRKQfG6YkcPtTjz6Wtu8gXUHvQKbY/OpZdN4LCIeezynUP7Nss75yy6RK1S8eKrl1ecbGi0ZmWmmYxSKxNeEJJTkkmSpGkqOcmig5PuwZ59CV0jBoPpuxHSUAFVOlACDr3RZCSCILw+NhA0BHanNyyJhBL0yjImGFrkeLhfVKjW1gZl06aA90S1S8Bk1LucgVi14swcxxEEMBr04kwh+hdqFgAfqlSMz+cjQjvOtcnOzWul90iAy1bAlqMsy3I8b7PZtRqNRgM95ueff7b0gjmwMinrLLWaGTvmMp4XquusJn1yo9Umr4VUfryktrbapBnnsApSG1GCcLvcypG9n/WnBo6vrYQuO0Eyc4JrR2MwmF4eIQ13oMqapCST1u6COaOA9EGBYP0+l0feIx4ZqLEnFGIK4wGif0QnnDDpNFma5ZG7DMPAiZVhVzZ64Yrqmgaapo1Gg1iFSlAUNXzetdbmVmtzCwBaR2OZm5A8oE5rcHk4mtLa7FJvEbVGrRGdIOLG3/4OALD6w0/HjDmtrCzYxFwQBIdd6g19wXkzDGZphSTlMa02u81mNxj0pcdL0FtLS8+E74uUvLfThdRfks72/QYwGEyfVdIIBxocwoO8LEtNg93pdJMkwfr9qFsoyiOFjNMDNfZILNG2mLtGCeyA+vIegoJVYz//8JlybD1uytk0nJYudenYtmn9+GEwADpsAV1ndfAcOHLsOM3QPke9zmQx6LQqtUqj5l1unzY5W25l7PFwSWapmBSNqfUaYkhOmi0QiT1cVCy+QcrvZ2MlyuWaeaV6AgCOHCvTanWwrl500wxDkxzhdLqNhpAygOnTpt7/53vR9rvvrV77qjSGn33aGK1W++gzr4vdT3xnngv7h84ZMxw9uqHoqPLuOdPHvrwuGDqQ+e35ISvuvfJtsOfeb+fOkHb+1DeakGIw/WZoH5pEEo2etA2I9GQDy/Ena5rC5Mbn59hAjb1kPGVHGvCwe7bAZiIUSc2YezFDQygRggBTz5iakpYGBMBxPJpkqdYQ6Rqjw+5LzxyUP2TIgdLS086Hkjtm2CCeF3wsx3N8qaMlVYwziolymK5xezxxgpwsG1KQL2N3OIwGmPCRCv4DpxAEgeND3iaaQQ8naOmhNHu9Pl7gSYKakEsLAk0QQq0HHp+lcQAtePCpV82W5Hf/89y3G/eOGjnywQf+vHLFFdfOGiJOzIfq+fvbVxUVFT708N93b9r72D2wcZ9VrBhD0mnQMg5xjiwAYNW8s8I0NEw9YZEDD+urrpw3JeSwgJICAD5Zt6eR6BVBIgymvxpS2mYNd6DKBdRUKsqgE3vOhwooz/HNdndkFn7o0BzC2YC2x0+Z5XZ7s7Iy7XZHmP7u2nN0+nRtuK45PceOHJk2OUQR1CqVIAhaDYzAjhk10uNqUGuTSZJQqWhBjJB6fdHHywRsqhQmoIFJQ+IMK1lAUQQTKT/qQo/s57zhweH/l1WHvToohTwvTMxFcQm4P0vj2FFYnjUOVraaLclnnzkVnS/byP7xtmv+8cLb6OmPPvP6/v17i4oKm5ua77zjd7v2Fm4pb5iRHxLrlNUTMUhlqvLZokrnu19tJQiwYsE0EJuP1+4mCCINaBoA1lDMAKWgR2bZR2ThFc01BAGg+qHgXhEBgJp6+OcdOFLyrSfLy0enB9athCFLssFq06hUIWnuuJlxuf8msn7SxB4g0GJnUp+bZGgaJfoNOi3rZ7lWe1SnSVEU5w9fOB69slgqHxJ/UGzC7UnJMCTa1CQFRlf/uDcnM9V6fGvW6HNGpPgcDmm+ZlE5TIKNyU91OByHymCkwGQ2jx4irT3H8/w9t65E27NmzVAzVHNTs2CQYiB19XXfV9XdcAVMWBUeitLdOap0IvUEIJ56fvjjLukrQRAaifCfwKli1byz5O2P3w7W865aPE/efvmbdcqdP289ID+04cChOCeX6pZFcL4O08OECChs86kYjAtwxAoiHagggKZWaU1j8cigwipLmmBeiICLeSifHNJSU7HfoNcMHT5cp1O73ejPXuz/RML59OIKRcDP8sdONE6aBMf+gIB5J5PJwLKcze4Qx9rh8KIKK/cQBBg/doTNxcn2U8muXdsvOG9GWdmJSREzPOEE0AxTjXVPDhHMdI1RWEiny8Oy7Dff/wQAeO2Fp3ds3egTWz2j19CqmUa3DRiYN195a/y48V+s+YLn+flnnsZxoPhwdPWECt4Ynrv/djOMlp4/DXrhqKz+YSes8SKAzSrOWUiNvmDUqZLO8IcU6jlqaMpzdyx//JXvb778nIaW8EVc54wfm4iSikeG9MHacAAl/TCYrufZ97fds2REeCG9cgiPGioBANZtOSwKIhnIMAteHxeZhYdCFlg5XRDg8bDLRigESU6dOp0h7H4hvHWdootxYBYo9KHSo2gRkeDMehhhYMxmg59lXVISXHGqKM064dOOVe+O9eNIzyW2F0J/dyxU06j0tmWIBNzp50wbPnT4f1/678233XPzbfcAAH76Yc3bj94B52m4oVs3mcw//vRDkiXpoiUXPfHPxyqr64+URulXcvQ4nCRXWV0HJg4Ke8hmsy+fNxkIXFNTcFkUmdU/7k1OsgBCsDe3cL1AOuOrZxgPPbdGngScZtF//B38RchkZUCX+eE3u8w6QyvrWHnF1Xfd9gf50atnj/T7Qrx28fGq/IIhL77y1uKFZ9c6HbNHh8+T+aUYuuBbFCFjxKs4C4dpD0+vORI5hJetGRxKK72nVs14PfInVZKngzXmsRktSDRR0yb0lONFm4aNnRm+2npsCLimvCZg2oLXoDzm9CkTPe6myubQbggM0JnhDYTG+7QAlNbuk4sx40hn4uw6GWWteYgJhnSPHj969sKz0Y7bV91x3Yrr96779OD2X9Aem631g4/f/+Dj97eu375tw44pMyeNSZVKpiIh4HrN7cba3KIiQe9Uz0mThwIAnnzxy8gj3/96CyAIe0sLZ4HfqZEOtKYO1uCqGKqVdfzj70/PPWfuGy89WVy4l6Lp3//x7+/8UrLi7GGc2Fph+36pZ8KLr7wFZ+vWNM45XVqCUCYjMwnAigxw4YWnf/XVduVDSkn9eO0eKwk/Ud/8509rvgh+eL79Zf8JBxwcPHbVopP18MKiVk1g+jRtTuh89v1taCMyiSRtEwT4av3BBWeNRnctZoNeq2qJOmE9RHZDxvvx22pIhUuKPQeO9clCnEuXXvZ/994/ZSZcZR7x/Mv/PmPaGfc89ebscYOoFIphGI9H0vc/PnDvs/94DkZRG+Ga9WFYy6TfyMc7xL9yAC6fLv0KqBTtJ3uLL584ItZlUOn66JUHPc6CoTnHy04MLRgs73nihS8AIBbOmfbx0ZBvsve+hr9xl601lnqGMfecuW//51/FhXsBAPuLSp957L6nX1r953+99ffbA40KAfhpw+Z33v7f1ddcF6me+0tOgpKTsLZs9DBZPSM/ox+t3SPHGcLUE65kY0hDGpqbbjxZb5e/KpKNjDVQhhxVWG+JFtd+VaxIU1ZQhPHZ+n31giJihul+Ep/QGTMLL50oMAcdJuWZ6JN/eNjGLai78naVVVrTrd+zYeP6/7v3/rCdo0aMPrh3JwDg64+/MZlMM+dJfyEP3/+IwyFlqBJBVlLp7r7gyqntDTX0MMcV5bRHy6svXnjG8dLg6AFtNzc3W5KSWDP8HN63fMnu/TBYVNcQJUbhdDpJAzxs/OQz3//wY+glk/QNDU1XnBlsreZyulasvIZl/S01UTpbQ/UEwNZqX3T2BI/Xr/ygf/TjrmULpqLt1T/sJEnKSnqUUVpRPfehvyyknk++8e2fbjwfaSg6QKmekeVoUdVz9Tc7kgjtsrmTYqVWP/tpb08295KzcKlkQnm5VLLtY3rt5LovN+8z8fScs8O/ZdsFGXMmEgDjRgyS7za3hFcjIaYNE5ptblQWenD3erSgEBhgWJutjU2NuzftffOVt+bNmXftiut2boS25barLwIALL70PK1Gu+aTbwvyC9Z+/ZPFbJm9SBrpDxwoIVg4HIadlOzVEx+uafM8D9xzy8QpZ3y/bssTT/17yLBRAIASt17NkGqGPOrWkybNH+750103XebxwLC40+mQb7J68kZVY1Oz/B320Y+7wtRT9ARcmHqKrcQ5AASknnB+mpZ88g24ykuaRbdw3sSoVyub06jq+cEaaIGXzQ0OXCIJdE3sXcwZMxzdQALUVjaE3cCp5svN+77cDOe52MjgEr+JI4/fo86Fl7ZXfwtHMXlZFnTXS2ij9jfyOG1pSfqjJyTLwPG8y9NvG7Nn5qTF+vUvXDp/3JjT3nrt7fHjnoJptw3r/vSXe9HUKEEQpsycdO+df3ruqeffW/3u/957E/R3fjwekR/LMH21F45I5Kg0gkpRtPWKC7KfJ0+UXrdswa133j99xuyJk6DqNTU2/uGS6fAIg2rHL7sWLp2fr6ejfMm12GkC8EbVtLwslGBsbIQf2nMn56/fU4624dmsLQwpzDt7otIsIww6VWlrsCG3rKG/v3qhH65SEJ08bXKF22oVayqSU4Ne7P2vtwE4FWJynLf8ydo9gACNQhc0e+8Svlj9VW5OLto+Wnzw1X/9DW3PGTN8d/HJoePHjM7O4DmWgy3XeWsTfMt7isvPnjxcXksNcc6Z4+Xtrzbsr3JF/5tatTj4rfPyN8Gpd50ESafy7tKzon//JQL9657ysydLg6CN++sumT1EoZjBKklG8AqCNFdSazC7HYFoKKNTMxSaVJOakfnRdzA4NTApLDqoDIOG8dSzTz717JMdOO0XLyyN3HnRbVGyMX2LH8VUeBhrD8OdtTUxTUphDVS6VX+8E90dOWLU+//94LUfC29eMO7l516trq1ubGrM14udCkIhTbCZFmwrU1EzNTdT+dC5gc8//DiTAp8anBOs5GhpRaTct2rJQ0fKDh0pa1VOLVEwIlM70TJKEnFFaVpLS6vFYrYGhDsq0sLZvWNEt3vTXp7nn3/sTyfKSzOyBt37yLNPv/YRmmsHgHD9jb+55roblMffc+vKHQdLabqNRpR/unn+Hc++H189lXc3bIWlbIetTXLSr121E2HqKZ3z18LEB/JK+wkF9ESDVx5Pimt1BB9jhGDXJT8R/Og0tjgNjLT/zS92qQPfkI11MZLUmAS4fSUU3xWXhczFiq+q/UBG24VKpdq6frvyW6rkyOFrbr767dfegb0IpkAfuntT8Cv81fe/m3/hPKv4xxZZTVEpREu5IfVsz7D50wPHLh0fr6PwV3sPy5lAJXaqjbFaQ2NTLwltm0ywzGXarCkLJ8Avg7qaqntuvvzp1z6ecvaC3b/ChRqXXLjU6bQ/dO+qkKcJwGl3rtm+b8npE/VaRmlClVgIU4tgiyWdMh9+uyM9SR9ZMiFvDBsx6N6XYXD8joVneQNjglpPMP+WqdGFaa4cqKluifdNFgfar5jJk2zSygsYXXX+xJKyBvnuZfNPO1hchrYJgvCLheJwtmWvGV/0DO2KiKeS0Kc3Zqe16SgxiYCmJ0yaMHnvfilLDnNKY+F4sNzqnXp2cDg8ecLk1154fdqsKXKDmHbxo2iEQcLRhk8PhB8/ryC8jLcD9BL1FBtBwBqSxVNPU9Zcv/H84yX7Ycj40PHq5OSUJx6G5c9KBABYcTrlmu37FkyObvGKj9Yrl+KNo55QuK0O5Nnvvu4Cpzv86+fYkap0yrhy4aS5C+An4auv4VNkMjXB1j/ppKGed9xx4Xw/J71wtiU1QQ39x43nwlqaN9aju/QFs4PfjSaDRnagX/6465pLZjYG8qGb95Y3triRHVfxuKginPuWRZle+fqz8O8KK2YX8sumX15/8Q2rtenO++7kWO7vDz5akF9w2zUXR0xyg9sdU88uYV1ZVZxSClSUBvfvD6mpuHxCzBq1U4vX63V73H999m2O4/78+xtqq2GMu37DOsIGS/Gy0mGmpGDY6PseflqMU5fdf8+q9LSQcDdJEHWBJpbHxNkiw4ZKkZPl501HE3ll9fxhIxxoL5w1UameYgwEyhGRaoxUT1ix++tBFQWe+XajAIDLy8ZSTxk/yytncicykB+bam52+pUySv+w+ciKhdIEuPNmjmy2WnOyUiprmq668MyaBnvhMalW0dpQn9CqGgrcNS3aQA5q4IimzBmzsl9/to0zbNtYLR8c9QDlIB0LMQDgD/fdOXL4yHffeB8N230+3/wL5vrKYPFTPyBMT3sVM+fN+MP1N115/ap/vAAnKQiC8LvrLnGK/goF+iZOOf2ayxfpdPpX3vr0rY++u3bZeQ6ng9RLtY/W5vAaciSjCKHJS6SoS0tDZgGiu99tKk5NTWqFLYChdAIA7jx/NlpeKJJKHyopCwnBZGp0n67bs3z+JDaw2gXL+mH9UWigfMUF57RpQpF6woI5j7+konFMloW+8ZJpHqeiMwijResPvf1pl6W9Bpp0xlLJOHQgmjnQAqAyJUdLps0KiRRHJn327NsTJ6EXi7CvqAH7E47FM2++/sM3n8EBb07ew/946aW3Pn/wxgtOHD0kCMI1ly9Cx7hczmsuX/T2x9/nDi44pFhFvL0Mzwn2pGhtbuEswdZKJ05W04qFIBEfrt2XNyi4hH1TIDvHANDkcCl3rt8b0u1XSZsD+cZW14GjtSUVDU6PpKS0Uj3/++EG0DlQ02XWD8/ev+1nm2zbWH3TnVd24Qnlv+cvXliK/7Y7ye0rJ7WZrxuYmbqofPb+52Unyu/+812H6mGfmkP11rXzZmxdv/2vb3x97awh+09GsQg5I08D24IJ6x0Kd2mlQkIr80cNI+JWsynVMw4B+ym2koigrkHKJTY2tZBp2kXjRh1XWGCZbUXl8kA+rAKipMx+oqa5sS5EYaNUzLWXSKEMbxOC6VL6/Z90e+euRE3WIXDco0tgGNXsmbPDdp6srBg6ZFi51bu7cK/T6Zy1cKby0cLiRCcioto1GPosDipaabFk7EgxWKzk84OwLZmSyyeOINOCUc6P9x25LDSafM7EvLCnCNFauMHysuZggifM5R45VMzzfCAm0WkBHeAGE3NKwMm6U8IFly/evWnv5nVbl1x2fnMLzItcuHjp0CHDfnvlYgAAqiRTMSqfH+Z2rrkKLriwbUdIS61uJXJ+8yexo8lkGlTkH4pgq8MFo8Lrz8gUVdTS+i8374s6k10S0KuWTHl/Tdv9irBoYvpisq5d1j7MtOKACWLmvBmb1m1Zt0Yq3xHb4txTfBCmyw8VFR47fnTrhu1uj1urgfK05DK4Jk/vJ2q9Wiy4JLg8RxgJOVCsm93NfcumPfERLKnDhP1Y2vsDaTNlhwoe4oRBLrrtS6WG9vuASYK4Pe7IvJwhsLH82mUURY0fN8Hn8x4qbqPvde8n8fmdMQUUiyambyXrEql2aK8gYvVMHI7jlBMc2iRWeLqX/MyVGhp1Amh0Ae3TuhnZ6SPBrlynvA0Xtp+9il7yN9zzBD/z1SgvNyyka0AotQn/TaFpyjLx6x/6VvFDF2The5Le0AsL6V0HRpdxztZVp8Jguhv5k//6sx+AZLWy/qGf1TwkMpCnP999vB94TyW33PDblJSUDRs3bNuxtZA3jxNrXBAkSf7v1bfzcvOeef6fX33zZZeoXseUFIsmpveT4Gf7i/6lm0riDN77pAONz86Nu9FKyCzLXrr0MgDAcy89u2f1v9Gj48eNf/OVt2pqazZv2/zQnx++9/f3nr0gpHKtY2ApxGDCiIxHR52sHDZO74tC3H8EdN2a9SRJKhvwPPXo07+/9c4bPn6R42DHqTdfeevzrz77+z9gF9gHHvm/Xb/umT5l+o7dIS1bMBjMfcumoT448bnpzivhKD6x9N22jdVtWo2w+oc+QXs7hPRekixJX33zpbIBz733w/5aC5fdiB4FACD1RK0QBEH45xP/OnXXi8H0eW6688rumK/cVzJI/cqB+vy+CxcvfeTxh5U7p8ychGKgY0aNCTv+o88+XH7pFT17jf2NDuT02lUXcecz354/rUC+u+DsKG2J2wWOt/R+Luo76tmvHOj8C+aihuQ7N+7+9rPvRwwLmQwrr+Uis2V7n1xCeaDx7U6pjXfnweqJ6XL6jwN1OBxTZk6af+6CJ/76ZEZ6xgf/+xAurVxT9X/Loy+B6XYPrF76pwSCICZPnLJo/qJDRYXfrf3O6/VGPWxQdo7N1mp3SN10YmnonhpHu7LDWDEx3U3/EVDE2vU/rl0PF2kBAPzlvocuWnLRE++uvW/l/INF4b1hsrOi9zDGdBV/f/DR8xZIc6IvufDSv9z3EM/z158T0b6BJL/66Os//eXedRtgW/JEwMqI6SX0kyE8RVF33faHsJ1/e+KRosNFWXlDUb8DuXU24tzZsCk/pptIT0s/b8H5Dz364JSZk9BtzvnnkCT58GtfKA8jCGLnRtjFhiSjNrvBYHo1/URAOY5becXVt/7md2H7UfK93CqNHCdNCE4pm3H6WYVFB3v2MgcQl110OVxN7Luv5T02W+vGzb8UjAquCf7lh1/v+nXPTrGSjIxYiBiD6f30nyF8RWXFjdfeRNHU8y9LlfOXXXRZVmbWo/fdge5u3rb5Py+8MWvhTKfT+dCfH6Zp+oZV15/SS+7P7C/cD9d5TU5Rrip815/uVE4MyxmUM/+CudZmK1yLGAto9xCn2iFWt4cnPtoZsiAcJjbE+cvng75D/NqX9/77/qgRIZUub733v/8+fj9MMVnS4XrQ/35t6mSYf+B5funyC6prqntJM5E+Svxfx65f96CYyZrv17zzwdvHjsNG4rKAFvJwqXHE7k17//K3B7794Zv4L4d/I13+O4oF+jUV8mZlGVmCLIioNuvHMev+40Bhl5cbroKuJynZ7/fLKV3limO33HFzl7xQ5HyJvlW81jNMPXvy8kuvuPO2u5YsWrJk0ZL4RRF4CN97UI4SopaRxVfVJ/qvXPZzAUVYm+G6V11FgnPL+lYPrh7jw09Xf/jpapTle+lfL0+dPO2tjaXXzhoCo9KhvWsItBgspsfZ8tM2tTq4ptvW7VteuXel8oAFcxfe+/t7LZakk5UVf/zLH48dPxq/ODdzIA3d+qGAxifWb7fPTcLt5VjMFrPJfOKktIQsx3G33HHzoOycrz76Wm80A2t92PEkgQX0FLB7016e56++aUXR4SIAwO9vvfOaq649U/ySQwd8+9n3GekZa777etfeXZdfvOzDtz56+T8vvf7Wf07FxfZG+piAtuvLDZVbo34HN915fvzGMJiu5advNrS0tsxdPEe5s6q6EsajKTMA4QJKkDgL39MMzssHAEyfPVUQpCUqn3vp2V17dv776Rf0RrPT3qrX6zPSM1bccNXhI8UAgK+//er5f7646je3YgHtqwIaizanpmDR7GH+9cIzd932h6uWrXj/o/fknavf+ggAcKI0fFnasBJdTM9AiWETtVrt8XjknVu2b/n+wzfQ9uiRY2rrapF6In5/7+07N+7WarRuD57I1/cFNBHdTGTJRmU/rkQaF8Ya8uMVHGXeXf3OyiuuvvuOe+6+4x6Hw0FSpE4LV+6+8dLoVR94CN/zlJaXwvK+dVvfXf3Ov154Bu0UBOGDFx9F27v27Fx86XnKp1xzJVyyGKtnnxfQ7livEZFgIihq70KcRFKy6KIFBoPh2quum3HGDEGA7a+++uZLQ0v44B0tLF5dXZXIjx3TtUyfPXXHL7tWXnH1yiuuBgAcPHTw/of/XF5cny+u1RGGRqO5fdUdNlswR4/pY3WgMl24JFEnizBkGR2A6tmBGkNZQFFlbocj4PGTfgPwd9GZ3xFBECuWr7x2xXXJSckAgIa6mnsuPyusVler0W5at4XjuOmzp8Y/WybOwmMSB/+tdjfymo7xV3NUgmMp7UIQhHdXv/Pu6ncAAI8/8sSCuQsnz5y/Z9Na+YD8wfmfvvc5y7Knn9NdxqWP0leH8JjeQAe8RmqgSFteylEJLibrSXZv2tva2nru4nOUO//80H0L5i5ccceDn321BtXq/ub6m39746rqmuoLLl/co9fXF8ACiunRGIu82E53ayU2oW3i8XjM5uAgXUnFiXK08djDTyyct/CpZ59c/QmcEIEJAwtoF3BwV0lXnKbPMyYPRtAQj77wbdRjjh9vQRvX3/Z+V73uxl2wvHTW1Jyw/QfF/QMcjyt6E2sAwOxFZ2//eefuTXsff/rRT774BABQkF/w/ptQKO+6cRlsdJ01aOG8hf964RmsnrHASaQumMmLBTRMPWORmq7b/qtUVHh6aMuJxnpXrKdEamVUIgU0zsEDhzgCmjskS6fV/fLDr6RiHi3P84vPHO1ywub/a/eUqVSqyCdOmRlsCxkJTiJhMF2vnkglh44cfLzkxNCRg2MpZuRTiio60twAq2ciuNyuabOCqTmSJHmeN4jqCQA489zTlQdrNdqUlJTUlAE01b1N6MunBxvcJs7HOw504FkYDABg6MjB3fFzkBVz1tQcrJ4dQ7kqeCRuj7uyqrKyKlFf/0XsMHe/qV3pYAy0Y7Lb+1V4QHXi6q9g9VSi0anjjOK7ts4s8WqzfpPiO5VJpM6o8PFyb0pqmsVg69+6mZcS2vQNAJWi89ix6roevyJM30OjUycerLxv2TTUfydqqRmuM+s/Wfimxoamxk6dISUVfj5yM8wA9FIBjcSnWBk4Ul7jg8UXo4xZtzmdr1vl8ot+YUL7sIB2nqZGOMutqbGhw14YSfBrVpgP2V96EvRZ8a1okqqLMP2YBHN9ibNtY3WCfc6QVvY/A9sHBFQQeu+CY0iCm1vtHfCDSleo16j6hARj+r16xmpgFqsXzzZxv1JG+4Gv7FcCGl89BQCI2LLbm8VX6QrRvx2T4M4jddPFYERBvOnOK9vVwAx0qIdZ/9DZPr+OAhFDDpButlc9hcDThX4tUkLEOx2alTEsO2PCkNxEnv7Ra3dW7H4R3Va/+vtYh33+/gNtnuq2my/4/P0H1GombP9586d+/v4DNEWBDnHtstlFv/4zcn/Rr/+kqT7/me/TXHTbl+gG+gV9wIG2l/Y6TuRh0b/yc4m49rYHrqr7EKJtQwtMAK/Xm9uWC978I+y2e83tLx45XjN+TN5rT99csfvFvCm/Cxshfv3RQwzT9qfrhde+XnLe9C9XP7jo4r8o9//+1qUbNh5gOa5jVfQMQxl0msj9UXdiMB0GfxtL0ka0R/V6lZ1M3G/KrrPDGAxQgBZf+vjPW4qq65q/37A/b8rvAADLLjyzqMKKbnD56BvOS0Q9EUg6/3jnZfKerz56CLrIu17tmHpiMD0GFtC+bSdj0VWKGcbgXFh10GJ35KZY5NujT396vKRavuvxkRdfMOOsBfcDAOxOPuxGUTp4I0NmuP/0a+G5sycUn2wuqrDmDc1VMfSQ0+/o0gvHYLqFfjiEH+AI3fmdcKgY1gls/vHRF1777vM1OzweH1z59sc9ymN+/uaR2++VViWLpMUmzbNW8uDfPpj746PHtj57zuKHXnv65pde/yHLbIx1BovJAARwsBw3CsGceuhumk/ZJXM9MYnTWaeZcLj3rAX3f/zW3bfdfN5tN8Plxtxu36VXP9VqkzqDPPvE9TV1zXv2w9XK2sXMhQ9s+uHvn7xzj9vte++jjXGORBIcP1Zr0WlhcevuFyMfykmxlNfjyACmdzvQ7pvnjqX5lMdhL78WJrh1WvV1K85ZsWzWt5/c/5vbXy4qqRyUnTxt8jA0eG8vgiCs+X7XkkVTlU8fNyZP3i4sqgi72+Y5I68EZcByUyydPPNJPO8A00eH8IlLc/+T2t6QvDKbda2t0G+63N6XXv/hpdd/+Pbj+//z/KqzFtz/0f/uLi2ve+ax6+SDn3nsupZW51+f/DiRM2/49dCSRSELlr367C3y9lkL7g+725l30ckzt1mrEB+1Wo37GCBuOW/uq9/9BPosfU9Au8kF91q17Q2iKbNhzSM0TZ69KKTk6KPPt/zmunloe0h+xpD8DPmh06cOr2/s+Cq4YVrWSdHsmTMnQiLlYqFVzeG71Wq1TqM60J6pa0UV1i6fytl5mhobLps2vgPzpxGnXHz7s4D2dWPbo9KZWAD0N3e88tYrt1120ZmffLE1uPO6eR6vP1KGNv/4aA8LU68jTmRZ/u0S7fmtBx71erxejzc3uX1G2O4ItvtUa9TNDlMe7KQD7M6TfaiTWZM4fxrRLvFNSU1789NffDq2Cy8GC2gXSG2sThwdmJp5yvxmYkmkY6U1BwpP3HXrkrtuXSLv9PvZuRc83L2X11cIM4yJiKNwaj4WXo+3qaGhqaHtVeNjkZIGneCj118CALj/zc9Ar6epsaFr1RP+ht/8590JHkprzR/88mvXvnz/oPNrInUyptYFtHPS1aTxBWfPGN3c4nz3w42C0KvCDD1On333Ny2ff2ovICUtrTMK3gE+2dnFye12OFDW3Rp19HrOGdFd9O/+/W4nLmxgIWd1T72SJsbeA2V7D5SB/k2M+GPwIUzn6GH17A66YAj/87YDUSX1xTtWtus8WHAxp95rRyoj1sp+xGWhMVMUhUC8+u1P3TuE7ySxjGoYPJ/QB/b2F94DvYZuXda4hzxpF/ZN6YVEfqaUb3agSuQpH8L3ftoc8tNy30zU/E0ZzuraTpqxjGoYJEkkoqHP37aiXa/eqwS3F3LS2tKHAggJkVg6G4OJw2XTxqOqqaxUAwDg4Xe+bIcDVfYklnq+CSHaqpTdLlFbpaQmaEU7z8YdBzs5jaofONCenFrTxjuSvXBUU6z8UKAuhPI2lsV2gh1o98ZAlT2JpZ5voR9o+S5SWPkjLf8rI0tte13qKaFdlZ4aveWPXSGgUWWFolUcCxt29Cd6RqzbWyOJwZzKOtCw0rcwqYwlnX038iZbH7ezJXGTSNOqsrr6yP2xztD/1LOHgxI9AFbqgcwpLqTv6+rZXljW17baxpqjgum/So1VuI+CZyJ1gW727VhSfL0OfVSlNVMU8c76eO3mMN1Ke2eOY7qVnitj6osIA+39dHUqhtGZaJJ8ZwMW3FMDVtvuBgtofxfNPvWWaK2Jpsh3seD2OFhqOwYW0D4qNQPnncSD1ppWb9x0qq9iwIHVFgT4f6bpstF8WtS4AAAAAElFTkSuQmCC"}], 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant": "The default preview faces the hostile broadside while both allies see an edge. Reverse it 180 degrees so its front faces the allies and its back faces the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y4 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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khrF5QmVyOe8JRdEkBAIhQq7gGsBBArSdOovcIqBzZg4XRZMQCARC0vzkJ++pOeUKGeyj6WN0xdbl+W5iKWTi/cVQFgGl9dSsCUP5UWSEIhAI1+w5k15SR4UEqaob7UsxtRO8spSULCuFhaZL2X/DQgJKK2ojwzq1TY5AINxZ9q4NVEWHBxgpHztAGV+q5/s/nnLLAuWLQfMgIxSBQHDsem+Z6FTsOZMeFhIYHR4wZmBMx1/g2XQQCTA0TTMAhGg1wmhSmx4bAoHwC779fodopEfXqLAgdWSIRk4SnVY9oYDSDAQwthYiYcEB/NMoHI9AdHIkzc+cCn1kqEYtCMd7rpA+Uk+mNQrp4bC7EgtQqHCVitRoSipqkRGKQCCcmZ9qbVBcuLpcz2QU13Va25MDDw4NCg7VhoYFa1QypUKmVJDdeyZIbopyQhGIzoaj+XkmtYAk8LBgTY847eCBXW4WtrPcR+/WccYJAicJDMcxpYJUykkZSSgVpDCvHoFAIDj2nEk34rKgAGVEiEYuIxobGocO6upj89OvbFYcdnliZ/AEjpNQTHEcx/r1T4S5TQ4aioxQBKLdMUKWP0LGtWJvHrveWyaav8dFBKkUstiIwJAgFRc1qdc1eCh57apoiDNwiqLt3sDah96vZB6BQPgekXruOZNeUd8YEaqJiQhSKsgW7bpt9MX774rr9CazGWqoxRC1dsubNX4gwDBkhCL8yiZCtGXDOAYQisDsnKKgAKWwAlGz6UDWGV7XYKhvNIrsULhEicDnTBjYotOEQCDaGs9+qHa894BoZM+Z9OiY8EHdQqeOGUQSzlrBu6GWLVJPv5NeslFvommGILBAuTili1PPssq6iNBA4fiwCOZ8GRJWBKLtuSWkiUSiBMKy1tJENbHlxSrbZf7d99tEzwYFBwWq5RGhmrAgBaAwQPidlrmi1eQKN1F0o95UqzMkxoU6Pt23Z5zky9p7NAlNIREdA6Hqtd7kfc+Z9AC1Ij5Kq9UooVlFmwDdfE1i/NCCbCkkTTMMxjQ0mipqGmiGgRXqHXA0QhEIRLvQUJmsxrKZyfNLODg8vKLOGBGikZHWyTtlAkDG9lTzNX41+YWhNIYBJjNdXF6HY0AFg2t2R0jRDIFjA/t3v5qSKRxHE3mEZ5NKfl7ZrEklwjc4mp9nUguiokIT4+PLagwapcDRRzWtobbn/MP2ZLyeSM/9j2aYypqGOp3BYIJ/2cL3mD/lFq++IwKB8FMkJ+8hodrQIHV4oDwxNoQC9qVDKI/m8h0IWzJXo95sNJrh0vgATC4jMkrrekTC338Cx+6ee+uP285MHjcEGaGIJnHHbOTmlS2ZVCJ8Q68esWqFLDJUIyMJQJkIQkZBEYVm1g9/nr5n3kjWDu282ASUomkzRdfq9AADQRql3FqpH5YbsUaTHCfyHXsKiWaRiE5ufnZJiOoSpQ3SKCzBEVZDf/7z9JLZw++ZN9Kiob6bv2Oe7Ks1TWS7fC6aZvQGc02dvrZebzJbsh/Ssku5Yzh47BKnoR0mHN+dqJQcv1gVyN98flCdgumxmdNjM1/oeqStDwTRBNFhAeEhGrvET8q0ZN7on3ecAwBwGurbk+ieHGKCW2siXo9FM4zeaGbq9RgGTGaKomjCeu44I7RLbJifG6HuTyHRLLJNQLrZXszPuNiI/PKG/t0ixVtzGvrnSc4OTT94DPgUJ6LoSiuxVoojSawoYBjGYDRX1+mr6vRwkRJbqV5ohIpo10Yoom3VE4mp3xKoDVIpZWOHdKExqZrzAju0CVpVHqCNiVludman8Anxc15EekkWw9qhheV1VXX6hkaTyUzBwvWWp8DsaSO8egyIzoKkXCIN9UPzMy23PEAtj40IVBBArZQplEpnGlpxytUUvrCxsVAPb14+Yl4hnehh68/dHQSUMtn1JmUYpqKmoUFvqqrXl1c3mMwwxDRt7JANmw5QFpPUBjJCERz6ykp9pbRnGQlle0EbEd5A4VFhAVGhATISN+j1UJIIqe4dVFuF4F0ppC/zqpzWpKqpN9cAM0k11tU3bKuokREEhmGZuWVqtdJgNM+aOmLn/rM+PE6EvyPUTWkNdV5494WuR97MmdBKB4ZolvmpjQjftP0UrtGOjAhSW9PmDXq9Qqk0YYA2+3XSEubzd8RDY7pwN11dva6mjr+RVCNJsYY3w1AUYzRTRrNZpzfqGo079p01O1RvQkYowgX/GnrV9flpuX2K6ht4Sz2Dw0N6dw3Pq9QLF3Yb9HoZSRAy/+3Ui7XFm5J5N1Ka3IhhGJoVTAowNANn+t9vPrh8ydQd+5ARivCCenI4m/sDAJShEpVuhKDqot4iJCI0MjQgMTrgyWW37Th8oaHGlivN2aEYkJtNRtCxpRNzd0NXZaUtdekZpnf3WIaxmZx5BeWBGoVjbA2tjkd4rJ7clq9cGNiktjYppoiWmJ+9eyd0iwsJ06o37zxx74IJWw8k11XWiDS0qo4KMxdxI4FKPW6tmCeCAYCQW54KljWtuRX18iYUjWk16fR0R64Ko2IY6N0tpnf3WBhNK6stLKstqWwoqqhXBWiUavW3G/d7+J6IToP76unZ9ghvoY2AYhqXEBUfrQ1nG8Zx4wumJAWFaYVbGvT68GBNpSzWGBBvDIj36VeA+Zd6OrVANdpA2N+Dposr6miKEuo+wwCzmdIbYWbT7GkjOvYsnp8YnjX59g8FgfB56tKm7aeUKnnEsG5w2TvL91uP3LtgwvzJSX8cTK6tsLNDw7Tq77eeunPmLXV6ZVWjdGSJgWlM0AKNVaoqDN5czOkb6XTnXXBNYIDtpg3kbsJdEAQu2hHNamhYROi3vxwQ7Q6FkhAtNCeREep7jDIVAGDa6L7TR/ffuidZ+NT3W2Fwb/7kJLVAFjgNvXfBqE27L7ZBVAdrxf00N9/eqQ+UYRguU1VGEDSOG0xmoR1KM4CiGIZhUK1lhNd1UOQMTYqyc5/JAupa0riCo5NXOZA0P5fcNlSlkN05PemnP04tnT+Kf+r7rUdWLJu15I5Jv/x2uL6qVqShhYcOg3aHB2tB3RFQgiAoSuwPJglcpoD5DHqj2S4uzzBDByReTMkRbb/mrlHvbDzV/CNBtG+UoaF8qMe7VmRyiV1sQSlVBA8VN2ih+Tl7fH+lHKoBidGOGvr1hp0rls1afPvETb8fEcWUQCdWzyaCSJzHE2PL1GsDlAqZTW1pmBxKUzTTp0dsWaXdb/6BAyc9OhJEB8Er6okm8r40Pw8dvxKoVvAteHkNFW7z9YadX2/YeeeiCaGRIcDnYF5Zye7k9dywJjrJ8eahgHKnkraeUBzHA1TyoAAF//YwlETRjQazZYW8PX9fMro5HwuBkABpqM/Uc3RSr3qznbRwGur48q837Fw4b1xQqF1cvn2AgYCoJMmbm1rZtIAmxMVI9NFj/yNJXKtRKOUkvzaBohldo5Gm6b494kRGKNLQTot3VQ9paGtjlKm6dY2OCFY/t2o+oYRzeR4So81S4fWvN+y8Y9EEXkMx4EcERA9jb0kSt6ikJtqFetQ0tIkpPLdHbrcKGRkerJFZy4MygGnQm2hWX0Uv2r//RLMPBNH+QXrXHs3PkCBVkEb59Y+77188xX0Nra3TBfrKDh308MvCh5hNKMU3L1TQa6aSQrcmQeCUw9p2fn+MVUQxDMRHBtXp9DX1elY34Syee7pvz7i09AJh6+P9+0/8fcmY//6M/KGdhVYqtuRieRLCfSQTmY0yVdcuUWFatUxGAIb5+oddK+657asf9+Fmg0hDSZVEKaa6Oh2hshPcljPgwZe8tKcW101mwBvrPmnRUk7brqx6HBKkCg5Umim6QW+EifYudZq1Q/3KwEe0Fq1aqg5paEs6gCUQVF4N4cz87NU9gaGw8ooGbuQ/H/3O3QnX2jIfSIwuL7G9S3iUzU5SK+VtJ5GtpaFXv/qX9W6YJwLKB+Nsh2H9H0ngSgUZEgirq5qtTZM4enePvZFZKDRC0ep4BMIfSNBSJqou2H6SeSWzZFqfII2q+mbq9YE94XLtatymsAUWRZWgIKte+FDlsMHAh2wz7oKPXvCtaArB3JDIliJtgVrLz/PeAFuwHccwtUrOAKBrNFIUjXPLlBjYvFNyV6jCSIfHB5WSkRHq8YoALkO23+Bu1fbj6rjwGnMjQyrie0dxT+07lTptVL/mfjXD/vZucXEZl+8oUAy3SoG0Hle/et03b9S8KTwnrASOaVRyAscMBnOfrhEZ+RXcCXL0hCI6PD6rM4801IvsOZM+Z2yfsiqMwG1hZF49xUrKt0QSzDlffnf163/7ULjPpuWxyfk046cq2QwBTewSn5NX4OxT8UpK4JhSIVPJSAKuUeKzQ6XfAxmhHRXUpaOdMm9cX85ZN3ZUf2HV5JyS2t7dYqZNvZUfeW7tt9PGis3Sl99dDQD4xzNL//PeT+50andUTsa97XTF52HFvJgkf9NNJwKKYYLJugWGG7KE4m3PEjgWplVBhykGeieE3cir4ExUtDq+k+Bj9USxeG+hYJdswnJrGNh54IJKoxR47MDNjDzLA3Z07AgotRy7zmaOH5JAmehXn34fYKDBSREmC4wzaWSEj+tZiWy/2AlodlZuYrcuEltZhVOkrlqNuFdfn36JN9LEq+OREdoheTNngs80FKmnv8AASqACtORKRAcLs77kvMNkFnQMoIA6TwLlsdigOfnlQOA0kZHiPPy+3SKvp2a3dyPUlhpiF29EtA1IPb1IeU1DXESQcOTazaLKKlt9EEd5mzLKZoSevpQ+fEB3fiMMwwqLy3TF50UZQ07kUTDdd2fm3x5oOohkzaSXekoUXmMdAH37JV5vayO0yeQ4E9B2Jyrdr36GaCsjFKmnd3E0ejAAXnnqDv4hwzBsy0hLNiPDMKeTLW3TMACefXrx/r3n+g7oAQA4n5zGuyklL+wTmzY7jAkEw+eh+TaLwlsNULc2xTCsR6+EjJt5bWiEulPtkUvvuChVG41nBLsNoq00FKmnd/kpuXxcV1XytWvsI6bBAAtU/vPp22vrLFMtbkYeBF1zAlPRymtPzweAIQMsl8ywpL6chlrKB4vyx11hL6P+qqG39QvZlVrVbAHlk0BtIw7eT44b+ZV94u06fHGbkeyvnONEHoXjEYg24afk8jCqGhDBKhmYOrJPYhzMma+sacjIL2PDSnClpsFo4tSsVueqyufgPnFjk3oDAI4n30gvqOzFL0BvNpifm6Lr3nkUrPnctYa6MYV345PxXlRuUwxgPXsnpN+whPMQHZtWMkKR+eldKohgYJWr7EKYM1NWBQ3P/KLK3/batfGAKUoPz8nKKxUNZuVBtZ05/paaiioMA2OTev+85ZjjGzVHCf3aFH39lf81qaEWAXVVTwTmMTFuLjPltowJhoZnTWg1MkI7CV7XUKSerQbj+Oh2tvRnr24xDMNwusmr53dbTky+tadoF4Ul5QCADFZPLbS00DFXr0jiAP1cQ5u2QFV8vQBWNGPC4O/Y1evZ3NiJcrgMrKLS0imFu1NRAn/fEAjPQOrZSvQNdqjJjoEf/3S3Ac9PW46HhQR3CdOw01JGKfNuTFgwo/cPDdVRlIYgXGuoRUAjYuLg/zBMbwbRcQn80wEqeff4sFPHzzCUmevOWW7VSneICA1ERmgnwVtGKFLP1iMhXJy4Da9xtWrF4omF5fVlVTo2ySlQISM1Kll0WKBGRQIGnDxnicIbKRjb6Ne/V2rKTb2BVsn5NZ7NMkFdFEnyM1OUlgECzstff+V/IWpCMqZERsfb5JInI4U7ZRhJYJmpsgCVu0WrbDUFGIAJTy+iE/DKhYGoprJ/I6FcLzw2BwAQHR5gpmiKYmQkbhdPF7jv3nlm/rvfHeLuqxREv/69ADjaCoXmBDLathpKUTpAaGQW32aIWkLRyIzUVNc7EX6Ewf27XU7LgoMUzFoQrACznA7L/6wyCo3Q8MCychSOR7gFMj9bm4hgtWikgg0lCTEYpddollRYZp9KhcPsnbFc800pHuN2sU5WPtu0nnBCuDqvvEGH0xrCIp1Lk8J/Sob+36ZbevQYMOjWsSMnTRlnSaVn4LacevI1Q+E6eHiDj9QalUqtVGtUAQFqTaA6Lsqy2sHZWi9Eh6QlCojUs03YduCis6dOXkg/eSGdb3Kx+zhseHXlclq9Tl+nM1y5fL2ZbyXSgiZbZ7RxOfa8mvqEcDUw4TpBs/eZ/eyakpIah/XslidIol+3yG6xwcGB6mOHj4l+MWiMCNQoSBzjUkSha9paGBTDYRqTZWP2qYiwwLIKZIR2FjybyCP1bCsKSmpEGhqgUUhv2irGEOPGdL6NoGWshgZAOxTAgBIAINTeGeo0Cp+Tdo2oLVz1yZrX/vOP+OhQtUZhMFo6pTA0Y6JohmZwkoCWKGueCt0mlgJ33Jlh/40IC2x7lzDCX0Hq2bakpQtSth0WFPFr4VMzCsLDI6R3gTU5f2dasX+RbzVUGFByKqBw3s6AZ1/8lDe14yO1+aVwaSPr/YQaqgAM1Errqln2dVLtQNh/ReF4n62LR/gMZWioBxF5pJ5tzlvPLrXck0r43nfUYp++9uTtH/8kHTXCPH5vi3hKqYefQMtya+q7ONFQ5wLKVwLlilbZn1kZCdv4UTSDsTYoR1o6rMQcHNclQmHAQLO7TSE6Er4sdodwE6MJLn4XsXLpRMc4kiT1ukbRyPIVc0sr3CnH05Rtykef/UlDu8cEZRbBuBnm3A6VElBjYyNNkjjOt3w3m2magfB5BZUVVUZVaEK0sntMaEiQKlCtUCllY8YOZj89dvxsqvhM29daRuYnguPNnAm83aqvhPWxEK2K0WSLhxiNEnrqmjWPzouJCHrsuU85IXjwUUEZJ1evc5KRJIzJWwxQaQ1tEwM1s6Sie0wYgeM3C6ptc/kqgw6YeA21CWgjBYdUBAXksNmz0WjmV3BW1NQr5Zg5SGE5ExiYOn1UTESQXEacOJtW5d6yI4pmSAKeAaSenYQmjdBtOYkA2Ap480oqYoCsmL8vC9AAIGHyJBAWXXCzPqE79bo6CS6i8ByxEQGWLQ9deuDumMee/fTzN5ZzI2fzbWe7qaxNqeelXyPWUKaNDNPScj0AFTCdHoDe8cE38qvzauoBhTNAxmuoTUChdDrJMsAVgbpGo9FE8R8kO6cwP7/YnQJW/EuKQDCggHD5LKIzayirnm5xzRRte1Dlqjhhk/UJEY5kFVQY9XDG6Ez+YiMsQaTrmYUAgJceX1BUWmU0UezSxGZ56qQWGEloqL/M4m/klGYWykcNjga07EZ+NaehQGbCTIDXUDI2QltYJq562ag34RhGWovPK3AzjZEmM+XaR3ElNXvJgnEYhtEMQxL4gdIKfzkTCD/DffVE+IBXHl9IMwxNw5uJojC2e7lKKSNw3GAyn7hwg9ts9aMLGYbRG02RsTFqBlYgKipqolymFA6S6ScLNx0wMRgwGQ8n548eEivHFZIaSjqqJw8bO7J8LIqi2TrVNtgnaT6jHgAwsG/Xazfy7bZprY+GaMdGKFJP3zRfcN9NUdtgen/9Lu7+W88sEl7pSrYDHUdkWCCGge494Y9fXnZeQqLEKnD3cDKd9zMIYDYxpAyjT14qjAxT9YqLvJFf3Ss++GZhGZDRnIbCs0PrdfxrDDJuBRFcywXDRtZxNmHevs8ezpWft31u4YReBWz+af8KrTXnrxB51rwCisi34V819zfMuYZjZGQgsBVLJuXw/vw53UFt9guPzOIG63R6mpawfHafyhw2rE94SMifO0+UVdWbzFTIhYwet/T39OicmZ2Yf2poaUVjWXVOl0jopu8VF3GzqIxb6AkFFFdq+BeoADzdjVBcGdFppLmEecGvx6X0SrVSFqhWBKjlYVqVRimnGUalkAWq5Xm5ef5sit4SUif8w0L4jDdzJgxGp9t7NGlmClvXaIPkZmC72MuMMHtpw2GYfQgA968E/XtafNDf/3786RVzkwZ0lcmIiJjoL3/c3+LDd69eCOYXGkpReE5pRX5ZLc0wfeI5DQ1wXg+UraMsNDqFv0sF5fqIEM3CSf0iQjQyEj+RfEOvq9frYE59FfsaDaw2YOkl77i2oc25WBXoTk8kFJrwFm/mTPDavhBepareYGh0TDuUEFBODQgCo2k6L9tb/SbavOZS0xDATGEKwLCeDdp07HJuQVldXERgXk09qTOYgtRwOTwlWDAvUEz42QhzY70J0MG2VfMhKgBM+puZBdnWJn84DoszcbVF2LQDfz8pCAQCwoB/PDzXTNEmijabaYqG0Q7+Vt9g1DdYijBFRkYCAKLiYr/+cY/JTGGk3EtuOszFPtrQ+DLRQGattkTTFE4QgKIpglGTWI3OkFNS0zVKS5qJkEoDCFVI/ATxFmg9FgBAPW3rXmyr+2ndO8MKKLs9+4sCxZdpR15Qac6a4kfI7MJiiJb3lE4AthgFLpXUyYOyNX3Asw9OhxeoDCcI3Faf0soRa99NAMCcsbCXHABgxd0zuDvJWeK+SS3A7yRChts0lABmGUkazbiSwBUqYnC3sEYTczqlgGQIOUYZRa9kzEYG9kUpzsorvvfu+Vt2Hm/kOpc6F1lWUrksewzggKvNJNzQD08QojVoUvVoQTLnZYDSNtsYGOtgL00znIO6ukjlMmJQ7/jyqk4UNpAJNFRJ0N1jwoqrGuJCAnQmw+DEGLWCJDFjDSBUopf1HXILNCX6xi2eOWT/yeuO1eatMBiG23s4MdH/LbN5JJ4IhF/CdehsEoLAs/JKRyb169K3C19oLdlqgbbS9e0nsmGiwZKZt7ALiUBwWS1FAxWO3ywq6xUTQZJADyg9AHZVQXFKz9DU5cupN6+n6+obuEGaYSxiiUENNRhMBra+XQnbQm7I4D4xYQEBKrlSTspkxMVr6XaHgDyiCERbcz27tCTAVuuzqg467m7mld0959bm7YiVgYbSqvYXD3Kbc1RIgBbU19QunjHklz2XUtPzY8LhbCnAopS0yYhTQC8dhb+aCptuJsYE94uLzaMtGZ3CiHxZrTk8WN2/W0RMWACszMRG2k9fshdNP3bD8flxCSAnk5Jegm3BrTo1CEQ74I7pwwLVtshPXQO0gMqqG37cfsbZSyaOsPU0/nHb6TMX0iOCNa+/verl5z8vq6qfuWBS6x1t25qfo/qE5ZfUgODQ1Jt5g7qFxoQHiH4dIqMCM3PrpAWUwKCbnzKZisqqAxSkrq4eJwlrhAgSHypTyOjCotLiEleL29vq58id4IObKUojrJshEO2dM1eywoM5fx28kMuq4QqanOKat59eKNqyss6Sb5981WYVLbxt9NUrcFnny89/DjoBXWNg9w5SqcnIkogk19bUKpXOeyJZMkEFGmh1drpSRYZHkDTaYax6RMvp1tXOWdStq1I0gmg94OJsByYN7+E4GBqo5G5TRtrWGiX1jZHcLQM6FCXaqBJtVEEZnMXW1OvNel3XmBCSIEhrazkhzhPpubWbgnNDwOor1sd8Q05LDN7iGLX8tFlbIiE6M+4ro2jLrBzbckOEd2GsgV2YLcN2i7x1UGJljVNHVag2gL/oJRbEMK31/finfmgDxfF2mAfK3pHMA7WdH0ITDC1QQSNjmmEItqEHfMxVFMFg5VAAQHm1bnDPKLtdtcanQfgN3rUiHfeGJNUrcJNI7h/4GANLZo1w/RKhtlY6FK5n/FjsPKc5WUNNWaBW1CTFMGywyH6b7GpKIScD1PLI0IBAjSKhO8E9vH7tpkfHjvB32mTGjUzUlnP/uK6pN3NF+vDnwUvOtp83eUhb2T+Yj9/P8e3d1lDXAmo7dTTNNBrMChmGE9BterPUEBaiHtQriiYqCDhCN9TVNtZbJwgAaKzOVdGaJUQ7wm+9kxIHBkv9IpyyNCnc2KjrER8mV9mKiUREhIs2y86znUdeW2MjuQpt4E+J2vVMkyd9zJ22th/+T2X/haDgpPvbkyTFJXNJXCrsIiPrnJ1mdHoTgcuhWmJAy66Fz8kp0qjkdhXt4H9Snf2Qhvo9fiuXHnwKNOV3hrHRVrvSkdhwrcQou/J29tgB3KP62tbNS8FAW1JSmBcbP7KmplZRl9piC9TuPmMwUrTSlsoE28xZ64Ry/+IYq50MrBLKr/rsGMo5QpZ/1hQPOgodQytdgLyoHF6s5MArr5wtIgwAiIuQUtuWcfHkxaGj4TLINiE1OAlOZgpgZT9N1FBojQcA2w8OrugFMprnA7UrZ8capHBFPIZRFI1jtqA+jLqztZWtPmq7WTsyQNuQDq+VbtLJvag/JZcvSQrjC0zCqIZGRTOMsZFdTegfFKbcADTz+68H1YGqmTNGAX+DNuSYu4vGcGsUXgKhejbqGiTcHVzekhVr+pK90dkxTND2AJdTKbq19UH5KZ30LLFXJ1tzEshIIkyrjokKOXIlPy4xOpa9HUkpCosICY0IDg4PPp5aHBGuDQ8PCgsLOnOzPCBQrQ5QqTSqI1fyg6PDXLyDx9Q2GIor6+sbjUXF1bv3nALtAddTeJsPVKVUGI2Wok18Pr3bwTlkg3qZTnTN+4ROOOUnCbxRb6IZRi4juEJqbK43QRAYw8B+PTiOETjGAFimksBxksAZBk5DMQzTqOTJlYYJSV7uDFhapauu11PsGpyi4ur1G3Y9sOw20TZXv3odtBr9qpO5WXzzBJQ1QqXzQJ1qpHVVEkrwbG2QVrYJHX7Kr1bKLLaRwnIH9t+VEziOc547jVIGS60xsEJlgFoOq4UyAIc9e0h5bV43VY28Ng/gNYp6p06ABC0sN6GszuJHZA1ldbFOC5f07R6Nk7LS6gbG2g/4l9+PLV+1GviQoVF1BqM5pUpiXi6Xy11F4RO7J2ZnwhoiPFb7U1xllQEgnMvIp0FDvaEBgPz8Qu5H7MGlM9hu0eDCJVsdVrvOcwjnIK30WzqwiUqyiYn8ffb6hderjCRwa1hDISP49pyc8sqtrSiahUkdIdRTIRUNipJKXVWdHq4CF+x7/96DU6dP9pkvkDMJlQ1ZZGWRNlApfN9a+cBmT+FFkXj+4c1inVJOJkRp+yVGRMGCTAPkbE0mAMDxZK5+KNLMJkBy2a7pMCaqswvVagWC/WczVCrbEkaSwI1BCVFBCUYA1JFRBlJ6eRJsfVyTDO3Q4G7uHEZlUUZJZb3RLG4slH/jEpg+2WdqQtOMQk7SDGOmYNQcYxe9cmcp9/KB4f3jmpdIzysobmyEZe2tJIYrlAqZDDcVFpUWFTdR1h9JKdLKDk/7NVGFNpI4e8aHFFXU6fSwG7Aj+/cenMYaob6BYQCBY6FBKqierM+X00HJUixO1sLTFMC5+nUWdDItZqwX7sBkpmQC4x8AcC01i3MAL1s8NTRI9cufx+0z7DsFSCsRkn8JfqWnu89kPrhAIpJuJxHOrtlWENbjhy/W1DWYKZiyT9M0LrDVeCMU+AQch4JJQFewtcCHtYS8ZDhc2gJVqZUN9boGnensFdiQLyAkFACzXtjKgwFFBcXDhw9UykmFjDSaKI1aMaR/t9Ag1YGT185fybRze3ZQ9URaiWjJX4ufSKo7V+eYgbGtegyNBrPeaHYhzft8ZYRmFlT1TAgV9SnibHMoqQ4ni6QwOcGw+UmYTUwTuveiARamVT/8l1H9h3Q5sO/y2o82c34Qa/k6JjYuurKyRiEjuBzQykqQm2/JqGf3wXSJCc4tqgYdBSSXiFb9i/ITPW2aVrCH9Eaz64QeXxqhzYK0qKc9uFkHzOZaY81bH/wEs8Os/mRtgNJghB0+SGBmXaIMl5QLYdWZYZi0G3mUIrh3lBzIFYLTjbVXoURVKhCdRk/bKiuRYifvrmmJEXrOpB3eOq0lpIuJXL2eRzNAKSOG94vtkxB+I68ij63PDLu9Q+vTasqyvtWUtGyjTBuglocEqUICVTMm3xobEahUkGfPp7QL2USmJaIzT/ndcXu2Kpt+PSwjbbmozshJO++ZEXrOpOX/pWkKB9hwhdc6M0v7QEmMBhjQKEgZRucVlatIoKurDwgKELbpyCzVR4VqekdoF8+fGKiRy0hYsf7Q6dS8guK8gmKYL2Y7J36ho8i0RHRmE/XnZEH7YkL7zjbY3Yjj8uYr/P3UrbAK0ZoF/c4XmqYM84kNy7i74b69B6Obue8z+gDcoRPHOYOlDZqkkqbnVbq/f1sUPi9bnODK1Q7hHzY2NGIhSl4NZ4/tHRmiuZKWfa0OBpqcYGt0OjSSuVCKdWyjsqesOt0U3CZvjehstKqJ+g4ro7zIXhCoLew0ZxXc1x+3azzhMZTZ0vrXNfnpV6KbNlVtbKl1zDQg4xU2p+UZvQaXWfY4nPAkYOMqD9RSqM4KgWPCTKi8/OKCQky4jMH6IktrOUBanaPC//mNVnLrzAAAwTJXJy7Qvc0QiDbHH7yozeXX3481a/szOVW3dnVa/6gp9YTkG+xXZFrlNB/A7UfY6k27LaAkVdVgVqtJ6Mc1AIUCGFRKWaPexFX55DdVq5R6o5m0rvQSAd2jrLkpKM4kNs1nT53br3e3dz/7CPilXSlpPIbXlHN3yjXItES0J+D1UthmdaZPbNrszmb5jaSEGWdysrUJxMcmAlDjsXq2ShCJlKsBANFduxdk3lQAg7N6oDCdvtGkUbLOTusITbOZ+tZ6oH0nPizcvov1TqDKtqMXnv6bcJs333+3TebgeTUW1whSRkQnobVNVIb994MfTw2IYTWlFchvJOnCJoxQn6knK6BydfcesCxVQeZN1z5QimYos1kuw+Xc8iYWi60JwKjZK2FPlewcbjwyxm4BrMHq4gi2t5CPHDkyffSY9MLzwOeUx/YML0z3/fsiEB1eT68VGUc0x1nZLApDegIgCIj5Sj2H94uTEFBn6mnpXSyYhnML7GmKhlYngY2Z/QhpLe7Pk5jYlfOKXrhwUdcAnQCC3vHwfxGhqspK5ExEIDpmVOqDH0+10sbueEJ9aXu629KDX/0Z0++2oUMGBgVZMgBImSX3VRRHOn7sqOXlGPt9CNQTMMBkMsG1rrDmoO19vPyZEAiEz01UpvmCGKVm19pIUdJgaK4R6nv1hOpnMtOlueJOSQZNP6C/zOtecP/ZK6d0FW2jN+H11fnR0fG8YoqwaKZVQLlT3NAAl9RzCfnc5B/VZEYg3Kc4v8z9jcPx5r0qOj5CcnzVnOkAgM+273Xx2jiV5j8PTv3H/+13//AUBExxMVCEM211IaPuh+M9IDLEXRcumV2mAipLoVACt6TsRISF3Dr98cQuMTis7Q9pMIGzp8XtkuNjgtNvHhWtu4eN4FmtpGiKK2kCtRODhaG4MiVmmPNlbTzHrwRFIBCeMnlwH8fBg5e5yrzSTOrX0+lTtw74fP8JqJuzJs1dOAIAsO2Pi7yMxgXartjM+gbuTqxKE6wmqhuo/zw4lRvZtOFb18f8858XuRmwgjB7IKMiI7RNzE8ooDRtEU0ZSUZFxihJU+/ePRITYpUKeOgnTliytLTBoc52ARUTg0WouLR52NMYDmMEISiIZ7VGZSSJ6tMjEM54/q5Z1fXOsnjAZ9v3OQ6O6GmZHQYGaV99+wt+/IlHH6hLOcLdn9iv5133r9I3NjQ26EgZOJx8xZB/nabpIUN6sP0jskRmzKNTx8AL1mjcsvkU7JuksBVULqhj1v/4BwDgxZULLAeQEAnHy111nHeBgZdRBhhoiUL3UWqYuVnSYHRmhLaVekIBVRGWb4vAaYOuIjIhvjg/u7KkQJTrSVEUm7EE7zsqoNlMs3X0BMWYuHtWBeXkU/K1bRKCRyB8yappYx0HP9t3/Im5UyiGs7zg1dG/V0RhUXVwgCWA7aikq+ZMu5GV88MeaB6KwHH81be/OHZo19ZN3zIMM3HqnI8/X//tuy8f2PoDt8HIcVP4jafNvuuvD99J4njKtWyKpkkcULT0TJCgoWVo0ENLU6G0TGwfuHs+AGDtF1uX3jONu/PiygUh7GGPGNkfegwq6jdtsOxh38Hz0yZLrAlVW5WHhzJjatzEliXCcNwyZ+XLv/UNV6eVWwxeoRHahuopEUTCMIwWlA21zseZ6ppas5mRyaTtR7Ymk/gZzhzls0m5k7Hjjy0AgBkL7larlDISvrvBDP+A8krPtubHRCDajNUzx3AyYLIXKU5VCYyiGIvZlXLT4qnkNJT7t7ZeTwsKDBcW10wePICdoV/jB40m48I77oe22C/ruZHD+7dHxcTf/7fXD2z9Iapf0tgps2maWrNyifAAzDQ4cOrKlFFwbwQOj1AkowxlxAho/ckBY2AwfaNOqbI5Bx+4ex5gQENFCWCYtV9shau9Gw0vAIymmZNXc/nNGuok6r1JQgA4G8bYRY9qlRzH4K4omjax7TVE6snRtuopJaDWO9xyTOt8HCjkcpPZ6cwCujgFrz178vCM+ffgOFZdngFweVCgGrcWxJu7aB5bzb7K0KDJKeUKQSE6V2Qj0KMXRsdHrJw9QTT4xY4j940aotZofv3OktK7kp172jZgfXlty4e7T6yeAY9KhjMiDaX00LtHKKG7jJdRzvb8bP3W55+4o7Zej7HFfcyCZzk4GWXVEypLQb6lI+S1m/lRYYHhodpP3nvzk+cehC0xrqWvfmlaXnYGjjE0g+nqbV2MkvonHjqXQZv1vIzW1NaXl9u+lLAQbUUVXPnz++GUqaP7i5fWALD/9A062KqqWtWbn/8OAOjdPV64jaMR+sf2s47rGTHGzMCqxJhCTkZo1QFqhclMmSla12g0UTSo9loJpVYRUHbhJmtMWufarClteVahUDQaKM7IPHvcEoyLv+NOWNrkSmZYaHC/vr2Cg4IA6wztP2xifn4+ACAkAFcpCQoQFCVKWyLMjDUlwvE7QXRQJg8eILSb7J7q34u7czBFIiUZALBk7C01NbVabZBwcOXsCQ2VNTqdU+/byqlj/EJD95yQVTauWjLVUUOHJfW8dC2vvsGoUtnlDK16YAEro9ueXD4Pw4Ach83Zr6RllpRJ/OSkZBRXVdXd/cAT73z6M03Tp08c/vyDN4UbxCcklJeXvfOlZXnlE8sWmEw2w5AJVO6/loHV6ZP6i5NtOPWE+h4sc+tKjYYrnm801IcLxgRGqNXQCpbIs2cAHGQA0AGQodcBva6NlvMzHlqgcoUCA0ANA+Uwx/PMyUP8U8PHzQoIDFCrNQq5LP6uB+CkOy8/r7AKAKJLXBQGgKmxLrNCnJlFUZSlzLL9scFzVGemKIsio7BSm7Nqjti4+2y7Jf4gicjQO3RGLIuHU6FJOHlwvyFDE3v2nTp6vCU4S9P0PeN7MjDkaKebYyfPXLT0wTWPLOZHRGJ6Ivn6mCSJWLM6VKurrGnyUNtcRk2hqs9+3s9paGqGbYabngUX7/Xq2zsnN99ocihKFKH5aNsBvFJ/56yRTb7FiiW3BWmDn/7Hv0aPmzx6HCyduWJKb77Q0bsfvbtn/+7hCdFLlz/28YatK5fO4sYpa3yICVSeyysZniAusLT5kuVb2JsmtXJP21R/4zClwAjFOkYfFEkBZft3YrDkkpFqvHw5ZeK0uQpVoKUyCGuMUiZ9Xl6J3Q7YaT6c6TOMyWCQKRQWZyfb0ZPrEiXYmPsHPqvTNVAUI5PJkHp6i1VzoEe/WQFcSemEgngaVjN76rZxepPdN9irb8KaTzbC+tskmDELJrj88ae083piv54YQdA0PeW2R+ISEr/48O2baVcVSuVzr7370/HMJaPh+jfWzWdJpV60FE42XZilyVdzxiT1qampxTEs0Lqag0MTCmvlusYfTFFOQ2eNs0y9hXy6/cCcQX2dvZAOVW7aedqVhqrxlOLygTHhtTXV/3pxNayJXlR+bN+Jrw/cuH9CdzhVHzeU2/BcXvG5f72SPGNe3OBRW7fDgISIc3kl+UzT9eGbS4PcprN/HIdJUX64nH9g70Gg7lqzVN4moJVVpZVVpXX15RqNSqO2/CqVltrNFyIjLBWJhPlJSoWCtp5xo15PyuSc4OIAUyrlDKnmA2nWGvbwAENCgiqrxB2lEe6EbiW18rPt+0ICoL+/ql7aZ//RM/c++d737qgnB1GtZwBQyHCDQENvpuVxBh0DwP49ySbYxdvpXxtDwZhAXELi808vNxmhA92g1//zucff+fT7+1a/9N2HbwAAth+G19KJc5cP7dw8adYdA/olsO1d8ySVNESlqW5swDDQUF/f2GjfR9ZKRbmlepZ/YgpV/ZGSOb2HnYuQY8+xq5NGutLQjacvQa9xkbTXOPnYhUm3TairtzkKx00bk3zsgrMdijwGPmCfk3B8S/CiiRoVqI2ICBOcPzuuZpZKr4WvqYf+I20gGawNtFwMrNL16SsxXeKaI/HxJc5ilctlNE1jXIUmDJhNRrnC8qnk9ovlLV3mWeNUqVASRAPDgPj4qJPnt4HWwXWMglun4c42ws2crdbwOnISM5rF7phV08aSbHc/1ssiGLdKqjMZTblRvGrONGd2qJBf91yoKCujgpUf7jq2+rZxMhzYm6EQgxl89tPeVUumygkJhxHGUAxmCxwPHnpr8mmo+xy8+Ql/cVXk+2vXFeTnnb6QMmkWyMoq6tYtpk8fqC/Xr0M3upAbGZY4iVyj1Kjsqzq6AVFtpoJdFsD1FXsz8iW0VUtKz5Hdo7qm+sCOQyMmJPEjPXvAX53sSgMIhvLKG6Ecf+78E/iWBjneGhrqLRO1pK4GXL4AQOCAwUNNMkJedc6dV5GhWhhBo2jh9cbgOE4QsN2m5bG1/BLNS6cgZQmmHRCwkTK3sIjBGJORncuzuWlsEyV2J+z/U9MKFAq5QiFTqYwBgZrQEK1arZw+4a69R+DEsIW4E9KdKFiAUXE9WTjCue38CkkNNTc2kioViYs19O3PfgUAPLfqL2xTFhjRdNwhp7OcjK6YNtJgMCjYb2rPUWjdzBg/hNuMCrb8FdKsE0ZSQ7kJ6aNLYHYhiYOaWtt8Ir2odkjPKJ0OGokfvL/uqaf/uvT+VccO79uy+ceS4kJZfF+8BFY4v1oXqI7QjJ84OWnc0Nvnw8PecejCsFJLQ4UGvfg3IDasmdVu7fET9Wwlps6ZnHzsQvKxC0ajMfnCudEj4UTh9Wef4Dc4eyR57LTRGrVm75/7a2pcRd5ajwY53pL5u29M1GuXL4SHhQJgWToUlwh9IADskNzY4U/KOjln8+JtNeuAw6AIPlHp+uVTNM1MmX07vDIZeBGnZ2ZTpsZB/XqRMnL4kN5sziiorq4BwFxXXVFXjRnYZA4vMqp3N4VSbKGcTMvatvGP4NBwgiB+2fDFqaN2ttjS5Y+/M2o8huHvf/Le9z9/B/yAD3Yde+q2cY4xwWFJvd79YsfYEf3lcrtQ5sr75n22fR8no0/cP1eOw07WbF1sSHqGrWXL9L5w/QlHZmau6H5paTkIsbgmP9574onpY/IKS+SkeKUdVmk0hao+2nPyjiG9amrrhHP5njG2WPn3G77d+OcvP3y5YdzEaeMmQvkuLMh9fukk7tmje46NnTaa/W2GCs0EKZPzS7Ba/TCHcLCQnVcz7hjRT/KpzZfsmk90KpLGDR3Yf9BjjzzeJaHL9t3bX3n9pYDqUv6pN15Ze/LAafidfvHRN999LbmHJ+8des8dNhuWY+ETMMezw9DNuYnK1TkSbV+QnQlNmbC+qdUSmQPYlFl3CixQjCQJjVoVFKRRKZURYVGV1XY/UzFRoUUlEh2XLpzaf/tdtzMMU1ungxnyMjIru9qyyp2BSfgmY+OwIf0x2K/eqp6WJ4HRYGQAc+nyhcjQwFGDErvEhMhkBGa1ktYumZFfbjNtgjVQ8at1dsHKz/adEFmgjgJ6590Pjx43tUFXf+XCGW1IaN8BtwAAOP96WJ+ktz/5XiaTr1l519Ws0t17D93MuLnk/sXcCwfilgjvVVrr4yk8F8aBC+ocjNADZ29MH38LxYAM66yWZ491Gvj0gmlFxWWVNU4dzXtOpE0f05Ow/iISchVlhDbj7hPXeQHlWDCgh6OAbhJIVTysmSD9y3r+eiGtxgfERnG/zLeOHv/kX1/U1dU8NmfovzedKCjMf+RJWIR74dyFLz/3qmiaKcLFmedLu5bHOl3i3QlTbjkBrQ+GSy2d8eS9dufcUUA909DwdviN6CosohERLpGfL7O3VzgkJjX2ZZTt+PP3XwAA46fPDwoM4DqRmvVFhEwZt2ie0WiSy2X1DYZ6duV/o96kUCi4KbxMRlJmDKqn1W3KXWt6geHZu2vkoF6xcZFavjhej+CYFbcNghdneACsRM3KqEg6OVZNGyOUUUlGj5v64TuvpF+9DH8kWM/suv9tuu+Zf3733qs4jsvlir8+DH9IBiRGzPnLrO2/7gT+wQe7js3v311vEE9mr2fm9+wSbnLMehHw/tZ9d420TMklwULt/iA49ZRk6zVxvS4R50S5GVb69el39vzOpHFDrxVaNkj5bTNQBT65CkaKo6Oio6OihYEOR1cdojUQiSaCQxNmbKhUCBNWuaCNLR/eASmvEPui5FNHb5tzR/LJffc9uLKhwYRhmMlsBmPgFKyhXtdYr+MMzOhwXBNkCc1nZ9smg9U1dSHBuFwuw3FcqVTKCFBZVRsRBresgpN3y7R9ch/5oeswPjt3gkRux4s/79HUwc1eeHgeJ6O8kjqyatqYQ2euHUyx/O4ZjUa53M4Izc68QaqV5ga92WQiZbItG9ef2AILxtTV1vI5cfAnocbL/oQW8kdK5gypoO32K2kz+jbx287FbV2wL83SQcAOe/OzJWSy0x+NRiP0uA0aCH8XsysNw8fb4glzZ8177YV/IvVskmZNgMLZ+VN5LHzJlo8ttT8QLggnKxgGxMqNhUbYQZlr8+Zie5uA1pex11JAF7kiKCws5vYly2Ukccfd9xM4DpWRq7nkHE49GYahaYazNQ0GA0kSOI7NHaIAQMEKM7t20zIVtpk/kwZKBAdmjB3w2fYiXSC8mN/8nyVi+PSyWaGByuEjof/LaKIOHYZFS4VM7g81RU9ROMzVt6UkMAyz7tOff9v4zaGdWxsaGgnCsHPLRn2BWD76DhjyxUtv/++bL4E/sccxaMsmJ/Ozdb/FYDDk5uce2X3sRvqNNS/8jcDxzz/8IioyekYSdMLaLbCwptYjPOC5xTAn15Gv3k9vlnSKpuqdWXNjZUXChzfyKgd2h79DBWa7ZCayvMqyRJ9QxmlUpFIh69MtijY3lhbaLlpoLeIyhUxmJ8gMw3Y5FqgqeznAskwAU6uUsC4JTS8aCqP8wnzs3w9c/MuUQXx9hIr8q8wAiQzhjIxsutKIh0IrkpNROC3dsPPpZbMvnIOFDq9dz66ut5t1FpfalkLJlHK5zOa2e3TZ3M83bLv9ruW337Xcsqs3X7hQYFcw8bMftptN0ByOi5VI+EJ4xqIlC6ZOmvqfN9754xf4K1hbWzN+xjjcII6EurmgV3RJd7D4hldEk2fUhNiv3rcbOXXE2qXTnrd/kV4N0QlPL9ZUEn0cWSB8SAYH2aw/tla8Wdj8naOyqjowKBh247SGqLh3gVLJ3u0hKw0JtZtZnyorMSm7WUP2jDC9ecJgOCEVjny5+9DMAX24vGvX6AIVa3/fL6tsmD9xsOst07JKBveOFY48umwudE1kFz3zwIqlyx97+oU3U2fNe/upe/gNVt0zB7rzUvOTL15Ju5H2w0Zx2jnCM/Yf2i+am1vcMQK2796+ffd21w47x/jGlo8XdMKLvLk4002RFjuT0c4G5vaWRRU6sQ8UdoyjWaepdTcMA+rr64OCQtgMUOn9V1aIl3/k5hdj4dD/8sEucMeQ3qJnfz1whZNR6KABTmMXzjCFqn+9kv6XQWIPYGq5oLyTDJzLsv3dKBQKg8Hi3Fz3zf+t++b/Jk+Y8s6bsKPyubxiu70EwHOy7O77kYC2ixAH0lBnnDpS+NDTS73zlSCcYCegsIgp1E+aYJeR8JbogD49ahvYwh/2U/Yrly9GT5FeV4CF20pYb750Q6ShvHry7L4m0X6Am78749cr7noAp02e9u/X/ysygg4eOcCFMoISgg7uPCx8lqZpg9G/Qkl+TnNTu4TBDY7O7G5D+BUMAEXldTFhjtMkMTgGq+/Zwa014uL2QnOTIGBkiLufW3KGu6ONAmeuudWHr23Tm/cdhCmlZw6fu3XicH7w1MEzJtbjWVtXy2V0j58xDgAwa8ZsHMcXLIZ1SxGt5K1rbnCDRzhhR5qL8Dq6QKibmrr6dDZ1sKfAhEvRiX2MYY5pTGwYnbZvVNwRum6MnzHu6J5jotoKEwdagkVJ44aeO3qef/bOe/+CipS2XnzDMbjhzE83agL0YjvzciLvJ6JVZRRKnzUDG4+SbgpnJ6AYAEHh+tyyq6DD0dCgSxo3VCaT9e3dV6fT5eTlUBQltNGFOYmucWb4oOvZndBws4IbTW6AQLQhFQoFGRDSifpqmEymK9eutFJkA0Uz3AQFNxAdho5cn8ZNAqpLAwYOcGFmPnT7u87WCCMQnYFmLcB3VgeSx5fVJFqbDiWgnsWCoUQuHsG521oIMkIRPuPtX842y2HS5N6Af8hxdLuS1w4loJ4tdOODFe573xzdnSgijPA9vOp5rKTe0s3Vq566/x7YKg3WmV21/NKVi6IaZiHBIUaTsU2KkLYqHVZAvbjQzZ0Y0cIntiLzE9FWtO0iomQ2fSUlLaW0rGTC2Ilff/aNwWB4ZLqlYKtGozmy+xhfbZNhGPcDtv5PxxRQd36Qebl86Oml7kR7n1s8wvWfKYrCIzohn33whbBpHaw9LJefPHB61l0P7dz4FQDgyO5jFZUVM+ZbWs4kH7sgyshu1zTVkrRD89DTS9FaNwSiJfRiOy8JMRqNDMPMvW8VPzL79tv4+3+5ZxFBiItzt186pgWKaNeRjT1HYVPlZoWAXcR8m6R9RS38jdVrnvjuqx/efeu9f7y0hrLWAxo+fhjfykG0hLp/X4nKv+2XTm2BIvzcH7fjbNaOs7ZWTgg/JCUt5detmyeNn3Tm8LnkYxeO7D7GtSl0xusvv5Gbb6u83t5BFiii1TXUfZuUe2GTViSO47cMvmXerPkVlRW//LaxtMzSOk0EhmEymcxoFDdEQXiXN/+79s3/ru3bu9/Xn32j0WhO7D9F0/TySRLtEs4egX1wFy3pOIVjkIAi2lmM+Lbps9a++iYX1cUwbPl9KwAAD0zs4Vi+4NzR83n5eQuXzPfiuyOckXYjdczUUbCJWVz81o1/frUv9aFpts6pOI5z6tnBuragKTyinbH21TcPH4PlB0dMSBo+ftiICXCF2DeHxLUNucsV0apgGJZ87IJKaatdCbuWFeQfOnpQJrdN5EeOGHX2SHJZeVkHU08koIh2Rv++/QEAf33uaX6EpunH//oYX2sRzihfezv52AU0c/cBDGv1b2WbtQgZPnQEwzDZlbCo7vw5Cz5977MPP/vgtoUzQIejY1qgrZFX7GKfxfllzm5eP4xOTtqNNJh/dj9sJc9z6szJ+yd05x/OnDbzkScfHjttdFscYKfj4SceDAsNSz52Ycgg2ENboVD89uPvAQEBj90LPSc4jr/6/Gvrf/jm2x/Wu7/PLR8v4G7A78Fm3zUddFAcYxei/Jgm4ULAXEIGvyitWaAUGQ9w/cPz3Vc/cHbopSuXvv3hm8PHDvPfkehrSj52wR0fKPqOWviNBAVpt278IygwiB9Z9vC9OScPAgB+2H06Pk6iKbfkXJ4reOZYtcefl6h05CCSo804LMZVmX7XGTMD8RrPNBThXe576J7ePXuve/v9IYOGrHsbrsalaXrlzIFGh06fCN9QW1szedZE0SB3pS24a54X+1/5IR1ZQB1pMqkwIjzi2y+/i4qM4h5W11RPmztFuEHXhK6//vg753GDfzezJ7Xm8SKkuZF+Y+4ds7kgxj+eeXbx7Xf9b2+KZCCe4XptI/yMJztK38DOJaCuGTl85Kfvf96ob/zfN1/m5OUMHjh48e13nTt6nvevYRj2209bTp899dgzq+Ji4v7YtO3A9kNT5iAN9R3DbhkWrA05cHg/95BhmH+ve/vf62DUaPT0BT/+vBEE+/BoOg3uezmKnU/227uxKQkSUBufvv+50WgcN20M93Dnnh3clbn6jc8+fAku7N38/W8AgMeegfcLigqmz5u690/LlYzwDW+8sjYqMlrSg2ZWIAeLX/UN/IktNzG7WZV2Fz6xddOhfO5+uwgiIQG145ffNopO0KUrFytLi7j7iV0T9x7Ywz9VWVUJAPjv2nf+/uIaH3xVCADAg4+t2LZ5x/ovNjywchl/Qt54ZS283n7+Fp0i3+D+0rJRzivtCp+SnKFzJSJREKk9ce+S+37747ec3Gx+ZMWq5XyEFwBw8bJdX08AwOiRFosV4QOKiot+3vzzkjuWJB+7YDabaZqWy2Hn2TdfeMpogFmHHiOyd/zW6dYupHOUG8V2+REXCYL+/y0gC9TGqMm3njxw+rcffwcAUBSVdj31sb+uqq+v557lsoKLS0pEZ1CpUPryC0P89/1/r/vonckTJi++/S6lUrV125bf/vhVUyX+XgAAL/7zhcbGBhdnzFnejJ8HLtoRp9yrtNvmNaE9BgmoDZPJNHz8sK5dEl9Y88LwYSMG9B94eNdRhmGWT+rJh3dJUlzKULgGBuEbKIrad3DfvoP7XG+2a+/OloQykIZ6zKkjhZ2k0i4SUDE5udkrVz/C3R87etyH//3om0PpD0zswY3ExyX4+BtCtJwOGf9F+ANIQC2Eh4X/bfWa5199Tnh2jp889sDKZeu/2KBUqQGAU/h5s+av//4b4TYpaSm+/MI6Ax4sDeIbrJbHRngcw+Xm7O0i+IvwE5CAWmjUN86YOnPbzj+PnzouPEFcke0bRbXQOM3L6ZLQRTR5f/tdWFoN4ScNVj3WTQTCAzryWvjmLuk9fegsSZKPPbPq9NlT3EhIcMi+bQcAABMHxgEAiK69Dmw/9MPG79d99C4A4Oje42qVOmnc0LxMS56TI0q1q+rcCHfo3yW0yW0yrudwd0aOt9WghAZpqUQQKTxSfeScJdmwMzNoeJ/mvsSLnehFoCBSu2fkpBFnDp/79L3PhINms3na0ETufk1NzYuvPb/2tbfuueteboSrIOsOE4aLSyqga9gr0skJYsb1JkRTSJMbIBBugqbwdtw6cThJkvNnLxgyaEhWTta3P6xnGCZAsMJ6175du/btcvPk9k4MSXQuAROGxyMN9QrlpQ09+nTNuJ7To09XN8Wxf5fQlFy4DgKBcJM3Hlh08VqGaBAJqBiz2fzbH7/+9sevoPn0Tgxp1vZIQ71Ijz5dvbk7RIdm5eypFWXNK9frqJ5IQL3Ak/cO/WqDdCE1F+YnAtFp21Z73QF6x4jBzX1Jc9XTGZ3LAvWsdC6fIvPQ07O5b1oU6t13SOCBa4oj5/Id/aGIzkzPWEv5RACAQW9bkJpXWQ38D17snvOSkorU0wM1bEM6l4C2MEVm1ITYLRMksmQ4v1t4pFry5Y6OTqShHY+eMQIRbOaqfKFotiPedjAbhVeQUhGLYdiV1Mwm99O+FFNEZxdQj39FHRf58uELd7JkuA2QKdoeSQiVqDnaXNFsR7x23wI3pTA9S3gSmihe3jHovALqWV0Zd4ojuJ8lg6LwiGbN8SVtVfdn+gMT4zEAGo0mbj9umn5X0zqFFHpG5xVQD8oiuKOeCITXTV3Xc3xJi1iSmlpLaTGEt+iYbY3bcEFFZ2DdP+/LTf6Eu+388Xlnm6UcfbdLXLjrXQ3ok5By9N0hA8QZSOGhgSlH3+3ft4vdrz1B/P7jS+4c4ZcfrZbc0s2XIxBugixQRPO4efJ9hVz2j9d/OHwyJSYyZMv6NbnJn3RJely02akdbwSolTjeRK2/a9fzAtTKPzf8Q7SH83vfzi+s2LznonAx0rbNr7l5kEqFXKWSWETLD4qy6Id0h0W2dHpj+w3pINqETmqBttOFt22OTEYq5LKuw5/4ecuJopLq81eyOOH77uMnhJslxIXFRrm7poDbw4HNL/Mjlw78By6TnfcKp3Tc7aN3VpnNlFsHSWq4O3X1tOjGDyaEBgtvldV1ldV1Br0BqSeiWXRSAUV4hjZQBYuq2NuVa/75/Sv/+UU4cvyPfznapC5Y9uQnPbtFJybALF2lQhai1fQf/zfuqcHdE3rGRvWKjezVI3bGwtclZVF0q6yuc+xvjEC0BmgK3yp01KXW5ZVQmzLPfPTNz4fe/3JHVY0OduL746Rwm5xzH2/cajfCM7hbfIPBEgIWkpFWWF+vP7LltbHTXzy+d21uXnmIUhmihL1SqqrroCLvXTtjwb9a85MhOimbz15uycuRgCKaR9fhT/z+zZrlSyYtXzIJAEDT9IrHPr2ZYSnod9/SiRiGffjxNi40HKMNonQm/rVVNU6jwDMXvX5879pNG6DhuXTFe8KnNm342/WbBboGg0Iha9ahHt8Lu3UiOhWbWyaIzQUJKALSMzbKffff6r99Bb0/GLZ08fjHHpq5/vMnftx09JMvd+EY9uiKGWOnv+jZOX3p9Z/eeHnp1Ll2kaLRt/aJjQn1bJ+Or+IldWB/W3z/akqu6KEH74XoeOLoDkhAW4W2nb+7nxjI4756Bms11ezMnWaYHzYe+WHjkVefX3z3neM/+XLX4V3/qqrWrXvrAX7jF9bcrjeY/vr8enf2fPkqLIqsN9gsVgDAO2uXXbmWy+0TZ1sArHvrgRvphZ//3x7QAr74YCV/f+z0F0UPW7JnRMdQxnYjoCtnTeXvV5SXdYCT7kX1HNQtvtr5tNf3fP+/p7olRook5vufD8+YMgQKHI6HBGtGDu/FPzVkkKUWdUsYNMAuG3Tk8F4E0dLgp+gjINFsJTb760XqdwLqsQ42a2OvZzJ5MaPehW4OSowHGPBACv1KPQEAj//tfzt+ffHNV+9+4Z8/8oPffvEkl2AkkqHje9fedf+6/MKKlryjcJ8KhezANhhoaskOEV6hwyujrwW0rXTQHVbOmvrFzv1NJoS6I6ZKRSzXTk5YW4FLMHQxca7uKEvoamob3v9029OPzRXGZ2iGmTgL5mwiOgZIHNugqZyf16SS/Jtwv8yMa77auBd0MronRs2aPtRkpr5av49GSZftBFHZEQ+ayiEAAB8+frcf+UB9g6S+ozIzHpOZXfLJ/9ztDYVobfyz9LJ/8tHjd7dwlcWRM1e9LKBrl99+4aql6jACgfAiSBxFfPykpSeuCxialh5nbx4IpTPEArp2+e0AgNzimma5NZF6IjqqWnmQE9ZKR9Ih54KTRrWK6w/DcZGGMi0TSmeQos+DpBDRjuioKuO33D1xvLHB0iLMKxw61aJovgv9FWoo02KhdEZn8YEi/IH2qHd5ldVCI1T0sH3RLSqSZhjabPI48Otd9Wxz/W05SEARHVP4Ot7H7x4dyTBAqIDNxWyCBU8R/iig+WW6+AhLHUZEO8JP1AEhwtHONRmR/HVcAUXq6W8gZfQuPaKjGAALjSI7rrkwADTRmaDdgn39jqVyLaK9gBaK+AY/XxuC8AeQD9SPQMroPyD1RLgDElBfgJTRf7hv8oTGeuT2RXgHJKAtAiljuzMMkXoigPf4f2S8Sylq0hlCAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant": "At the preview’s elevated position both ally glyphs are fully visible and above their screens, while the hostile sees only the back. Lock that highest height tier.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H2 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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4yX5CCfNP3NRQNG2g9CoTLDny20+YILLDzE+k5YmiFxlR9Up5PbThq3179+o0VEV5eUV5OQG4cpn9lKi0iGvQ1zo5PJDHYDAdSECRegLAzz57KCpd8njcUtdkp8ulj1eLEzcdTrPRMKkvzPAoloFD4ohcJC9T0lBJ3VNCNlV8KbWY+u+facaFoBgMpqMM4SX7KZUuCdFPWki++w9iNVBGdTqjXiO2rRPYW+S+pS8jKdpWAPaVUjdnW1EPOgDA2Y+/V1+88bMPf5RfccSwAUBuRf3FUzZrA60KXyMFALCzlJGCI3qLkyMAHshjMF2We5cuPRwhGIjm9azduqVVLjRr4rlh9//+9+ZY+4GOFldkEms/AatB0U9arQ3YT6H20+5wOt3eVNQ1ubouMy0JqHye3fuiWMKzHnuvoXij5C4JAD76Dn5frrtskORfP/+5kOPYwqKShoZ6oxp6WwpGP4MG8ipalZxorKutRy3rj5VZk/QkCobimlAMpitx79KllRXldXUNYZ+VT4zUWA+28Fps4vCw+ynLgZiG8PfOGYg2kHpWyqKfwgTNOsl+Cn1DNFA9BZB6orZ1jSJX2OsvG3S9TD0BQVRWHHWU7JPUMxR0sEmv7t1TFPv+mWaTQfSquKQJg+kyLLnk4sqK8na7HM9HbK8Rk4AGckcCaYlpbo9bmrgZb9SK0U8d1M0qi1DKLthPl9sd6BvSYnwMF68PkztCoDlIGjVNkkSdTfz8Kav3wXWVBbCGYjBdgCWXXAw6DGRs9pMXc0eC/XS73VpKDJ4q7CfgWWQ/0eBdZ/IvedQY0XI+fiOqMRgjHgKbMYkCShBEv5w0tL9Xir7RS2MwmM7Cko6knjElkVDfEGm1YGg/3SH2kw3YTxT9RIN3mGsyw+WMWoaooKVV9bRaWENJgJU9TRKAJAleGMK7XB4AiITEuDoLjI+oVCqfz6dSwUp8XBaKwXReJowcUVRYKN8TF59YUHAi9EgPat8uoHHXtPC62QmgsLAodD9pqyFjjH7OkdtPOsh+Mh5StJ8ASPYztGtyqyMvkCIJAq3nodeq0X6CCD+Qx2AwnZRN+/YrBqxh1bPVKSoqjvQUGZP99PvPtMQ0qJ7Sgu8A6OPVNK0S7afUNdlvP8WzxJZECo+ijlTa7felcEMACahWTaNnCAL07R1mII8joRhMZ9dQ4nRdXhFq5KMK6L2zB8r7hiD7KT7HcTDKmWVmPIEzSKVL7WA/AQFQ1JMgYCN6iiSEXqBARUtiTchNKBrIo22soRhM52XTvv15ubkBKZNPyAmiWZNp+HBfgfPxiv3RYqBS+3doOQX7Ka0ZB4CY1/f6GJqG4cWq2no0fpdHP6F6Cl2T6da1n8IaHhRJcsLgnSRJmoY9lQFgaSroJbOnjdy6Pb95F8d0fB7qBcuHAQDPnpx0uu8F035YGDY/N3fIMLiWJbSjYaRS3FVQWQ6AZnGK1SGLijaVsAoyyHogmoDeO3sgElBY+wmg/YzTx9GAozWUZD9h2zpdkP0sr67LSEsK2M9kcdGOQFVry7A0eMxC8anHC+KM6ozMuESzzqzXqFWoGz2otriq/evWwYdVFh4ACplTnE3qujzUayPW0G6FhWHzcnPTMoW+biFGq6CyQv5QTRFib46mIE0lH9QAtdLLcjDXIoiJBB2L/aysrg9rP9HETbn9hOqp8sHjA/bTZTDDViMtx2FzUAA4GqADVasor8PTwLncdqpceEf+JTrDvLDO5kFzk1AuHtPF7Ceme2JhWEtxxPSOHPkCFs0eAKPRrWInGT36KdhPsXQJ9g2RRT8F+wn7hsjtJ3pVGqr9FLTMYDahY1p6+zJ4Hnh9rNPjszk9bi+DPijEo2UvEiMVPG/SB3QTR0K7KlhPMW0KCQgyRIjJKPYTCpN/4qaWFiZuCot2IMSJmwa9mHyX7GcNrL6E9tML7adQ5+TVjIRLIbUiPA8nJjncvgYH1FCOh5WqHMfzcJljuNYx/A/8V3SkDCu6ZrkJxdmkzguWS0z7E2rqyCj2E7WtQ4P3JthPU2Kd1b546RXi4J1h3L6I8y+bx4A+mQP6ZPbPyezbK6NHRorBqKfValRjj1aVQ4vLEQThpTScWktodaQOT0nqCrgtFvR1um8E0+0IOySmI+WO/PazIS0RtoP3208uqG2dQS+1rZPsZ5opMSFZ9+Wr36DQZ73Vnp6WzLfGzRpMBlqtZhimtFZcOQ41ZKJIQqOiaZp0efizR2SX1zSgaZ0EAWwejhY+I0iSYFWUz6EMJeO5SZ2Rp0blht3/UK+Nj+0dpk1MbPc7wnRTItaB+u1nKrKfNrsTcFxJZS20n1LfEH/lvNJ+3grtJ3zMMB7BflpqYrUMzQj2chwcuGuECtA4k5amSPRFkaTZqNFpVRoNrVZRep263l8WiukOLhV7VUy7CmhQ6ZJgP1HlPG2gTBpxJeFA3xD/xE3JfqIZR3L7CSVVsJ+J8a2TiA8LDxs1sT4hykmRJIp7CkN4OLOTpkiVX1L79M0IfTmOhHZtc4rFFNN2RJuJlJaYqqVphvHa/fYzKzs+iv2EBUNWu5Q7AgxTKRxjqbGUVQWOCUtcdosKoTmOd7hhoWyd1eX2QNuLskdw9VAxMooG9YG5SZguNn7HYE6ngEaxn0bBfkL1jMF+otIlmf3kE+NNmZlik+PWRRrv81Cu4XQpnSnO7vLanF6Xx8fzQKeBOXdhxqdwPAEmjukbeh5sQjGYLozF6WlvB4rsp9Q3pKSyFu0PtZ8utxuVLoXaTx4Ae4M1iv284bkHwz8Rdj052dORdp2qrLc5PQ6nt8HudnsZvVaFxvJSXkqjohMTwsQTsIZ2GfuJLSqm3SCj20+JKPZTZ6JQ7kiynyfLqpH9BIA3GvTQfpJCFVTb43D5vD7W7WXsLqihKppSSDGFSpwwGAymjRxok+xnlWA/IX772Ss1QbCfPGCY5tjPGKCoiC3yeB6GPn0MZ3d6HS6vGAb1IxTag6REWEugAJvQDg4unsd0RAFV2s8amf3kuCNF5aL9RGNzWds6ZD9Tg+2nze4Q7ScDC5iQ/aysUa6MxA8eARv03/FqW7wrXpiV5GXYepvb6fZJ/UzRlKT+PZMBAA32djLFmPYHj+Ixp8OBorZ1CTL7CYBBJ66ioRc6L0kTNyEqH7KfUD399tOk1Yj2E4CyqjqnE2pxmllZ23zj1RegjfV2bRu9N47j66xOmzDXUxrIe33iUiBjz+g1bEgfxUuwCe2wYPuJ6YCQwW3rQCT76XC6Fi+9SGY/LbDvJwDIfqLD0cRNyX6WVdVlZqbojSTwkmHtp4wmBCVp/4IisWC1e+xOr1VIKCENdXvFeaV/7yvaLPTbbwvGqUrGqUra6OSYWMAmFNMOkDHaT4Ne9+XbP0v2MzMtGdrPWqX9hBLst5+AB06nGw3e6xoCC3Pyg0cg+9km43cySKydbp+PYa0OT73NhQqbdp8Q47kAgL7ZydiEdgqw/cR0TMhQ+yn1DZHsJwBAYT/Ri1ONSvtZWVuP7GdpZV1mD2g/0eA9IS54ReLUBPjV9jBeL+p9Z7V76qxQQ/tl+adUCVRU1LSdhmITisF0bchQ+ym1rUP2s64Bdu6I0X6iynkAQI8eMHdUFdZ+3rcI2s+Fj6OceCMVn63W+461OTyWBhfMKfG8ImQQqqGYLgAexWPaQ0BF+1kd3n4mxJkW33ZxwH5WRbOfcNwO7af/GMl+BgcukXoimlmUKb6Kb1L/UJvDU2d1OlxeHxOobaqoCLNsNM4mdRzw+B3TsR2ohi0pt6Daz/D2880fA/YzPbr9hPTokQrtp3BMWPuJYFguWqv9VrKm8qQTw0INrbQ4rA44tUt+gdnTx7fK5TAdCmxC24Fx3ThlSiL7qaJJZD9R27pI9rO0SfbTlFDXYBftp2wmErKfLMvDrp2tPSeo5FRZ9AMYli+vtdfZoOj7GJaFQQRg0FDyWlEENqEdAWw/MR0ZmHAvKbdkpWagNeNMvjjAcdB+spq6BltCnOnLN3+Uuib38NvPVCPsmgy8VL3VftvDV3/5+vdpaUkK+5lqCkoTIfuJ1BOaS6FjPAHAFKNb7PfRXu+5oNxOEMCgVR8uqKRIQkWTxeW1GhV14cwzf1u7s73uAhMr+zQnoh8w0hOmQQwG0w7A4S1ST4bxun0eo8p0pKh84MBUwIKEONh0o8pSn5qis9a6bU4PFFC//UxI1tVb7fGJ8V++/n1lbX1qWhKyn1BAI9tPBMvxwpz0Jt+upV6aoc8OGNBL2u9llec6dTQv0knQJFCXw0WRBEUSXobwMRynVXm9Yo29BO5Xf3rB9hPTwaFF+4na1gn2U1BP0X5CKUyBE5DMRoPZbAIqX3WtNcWYgOxnfGL84qUXxWI/3S5esp8cx5OkmDsaQ9ujLK2swGGzKybAn8gPu+R9bPA8y8F151BslOX4H3/ftvCiCavW7pAfhTX0NPLsyUkP9drYEoP52N5hrXpHGEwQtGQ/aVKLop89e5r0Gk0k+5liTGie/ZRn3tGaRTD2SsEs1ran/u+sx94DkQlrVZunnsj3isVTPM8BQggo8B7AkG7CxypNKAbTnWESG//0qqShW2KYxpeioi2NRGM6HaTbI3ZNBgCgwbteE4+S73L7iaKf1bVWKfkO7aeQX0pLS1pyyzxF8l1eOY/sJ9pmOV4aupsaqmhS0sZ2CoH2z8non5OB1vUc0CfTYNAZjPq4hHidQa/San9euzf0JTib1EnB9rPl0JYT0lfYA/rQ3XqFVFpLBdrWwbmPTpdkP212hymORsn3HunJ1RaraD/NRtF+vvYtsp9fvPFdLPaTE9RTCn3qaIpqM9k0mEQFFxby9EkCXVprVxEEyzKor50EDysIWA9JeFxMvFGcwIrpIKP4030XGEhYDU3zFzAV+7K64bcpMHM81H5C9QSgpLIW2U/AA6ltnd9+8mnC4D3UftodLqSebhf/184jUlmn5DjrTp4yqKlYEknoiPtvnmUwGQ0mY1xSoiHOJHyZ4+JMBqNe+oqPj4tLiEeHRT8nRRJ08Kx5YVoULGxS6+DUVQWje+OVcjsZ2H5i2gFSbj8Rkv2EDzwUAQin243sJ9wjlC7B6vrXvqmsbUAuTlH7CQAwCic5Wlghv5hcLmkhAy57zr+QpvyrBWTx1egr7LMUSWrVtJpWOmCOhwmlMKsfOwItSDAYDAYhuDAhd4SS7+JuZD89FLSfGcl6E5ViSAixnyAodxTOfmanpyH7CQAoKquVJLG8of2bGYtrHSMIAmjUlEmvVquCNZTnYZxBFWYIj03o6eLZk01esRXbT0z7QKKJmwhp8C7aTwCyMlKAyldjCeSO5PZTca4o9pPnQe9M2D8UAFDjjHWOZutU10c4BUmSBp06zqBV0ZSilT3DcmEXnsNgMBg5pNx+osG73H6ih8mC/UTbkexntd9+OsLZz0mj+yL31w69l2KGV1Gk2aAxGwK+Gy6PzMHZnaEzO4Gj9r4rzmrve+xytMO8aWw/Me0G2Tz7WWcVj+nRI9XpdFfXNqT47achxH6yHL9xj5i/q7D7pP1eNmi5NwUxes/evbMDLwnJC0VCUke1ioo3ajVqWhrd88JNsRyflBBm4Tmsoa1CUzW0GaN4DKYdoFHlvGA/NXL7eaqytmdGimQ/pegnYHx1Vkd8YpxkP6sbLHotjAPIS5eC7Oeovpv3F6AyJvhvRTFIyvBxYt96CN809bTF90dHUYCvUCXYEgKrKlEUzQq6XAnS0R6rKoEVyuMJQKDr0EIM1KOm7RparaKAGnjVnLY81+1hxE76gIA2FPANdm+crKRp3Z9bz596dou+35g2BttPjIIbMuy1ot9rfciTFXXy0iXJfvaMYD/hw8Q4uf3Ua1WS/UTI7Wdhae2mfYJ68nylQ1yPCM5BIgkNLRhGmZBGV8+4FDGKGpZInZ3C7tWoVeiiWd6T2UzxUE11vEkLE0qoGAAQWWnxsEg4G06+krPuz63YhJ4WsAnFdDR4aR56QpzJ5XbrtFrRflbU9swMsp+ocr4Z9nPRBWO37C8MvTZFAJ2KbN7I3VR/DB1LAz7d1MNdV4xqpHiep1VqRqhL5SgqgxF0XNujR0ZaUXGJcADcoSIIg05Nk7wZaIAafge0Gtps0LIs32B3sxxP0wSLDDIBFCYUaWg7to7CYE5zmGVnt6yQbxTkwMgZE4Yj+6nTagP2M1NpP6F6hthPQHrD20+/pUxKMG/ZX0gQMKRYLUu+E8J0eDSPU24/I3rICHrFNEvIKI1GyhEJxU2AZTkVTcaZtGajVqumaWGGPjoi1ITiyZ0dFjx+x7QPkmrRn/26NTVemLfD8+OGZwJPfFT7iTYCfUOQekayn4N7JW+qgypMwACorE8HAVQUIa+jj6ae4Z+W1vQgJPsZ5Q0rpjwJbZQDe1DYVCMklGiKZFiWEiZJDeiZdPRUbVKCuVZ4F3Jwl6ZmdJ2QGk80r/cEntaJ6QjIhYY26tXlFmt6gmnM2MFJBg54AvYz2RBoW4eOTkiMl7etQ8l3+amPFlZkp6Wh7cR4M4p+8jyQRz8RWhUZOplSSZsNlIUUUaCmivHXA2jUFEGqvV5Wo6IDF8fj9RiIsdFO202dxvYT0/6QGhV18QVnzb9sCuVtQNFP9IRgP22B3JFgP4XemUyJf+JmGPuZlvbXLmg/C0trB2YHcj40rVL4Qdo/hI9IzLLVq3egs7IE7FQn4PN40QwkuUP121Vxl8cXcMdqmtJpVBoVBQgxaw8AqK4X132Sg7s0dSjGJwbiSBhMG6EY50IPSFHk/ryTI0aP+GPnwbzCCmg/62BUlNTAg+X2E21kIftpCcxEWnLnZYrk++ghvbccgLkjjuMrGqQ28v6rEgQdvZFIW5o+t9utGO5LDhRBUQQKg0r07dejwd7+008xsebiV57sDYc1iYnSF/7eRYJJ7Cv/wt+o2AmNEopZ+MQ4Xd6xknFnjqEBC4B396FTPDg1a9wZodHPkkoLFFDUWdlvP7944ys0eEf20+kD43sm7RByUyEyCR9SJKEjI7eyI5omqMUnYRa+SSiWoxdioEGKSQruWLoqw3A5fdIt/jX1JHAktMMi11C3xYIlNRKRNDTGaHWl/zBQVQe6NGFzLKKAUkI4sm/PJJomP/h6sxaAs4f3ktvP+BD7CRf2ELomL7nzsi/e+Eo6Y2Fp7ajBvbcfLCII2NyoMsR+pk+YX1+4ffzNTw8dlCMlkggA4rKhuWg4Fdr8MUjLWgWFA4VFS+H4ceORITkwC+9weXUaXb3NE28K0+wO056EppKQ/YwCVs+2i1anSYeBrlztFClD7V+PiICHnDxVCQBx6+LJn/2wLU5jDmM/KyxZWY3Yz4z0lLoGm+g7lZlx/0oeNKXXa5VpeIG4npPkjezkR6hNeaBa2VautKRUFtOM/duB/ifCyrr5hQV1X+7TL9Pi7w8Q3YTu9GV125Wy25lG1RODaQnRlQUaz78370cPhgyAnyH5R0t6Z6fVsPy23CIA+N+35Qeppz/6KW9bJ1FYahkzMAMJIMNylVZlY01hmg+gadKg17b87luK/+xeWRIpLChICm1yclDVQfRsUmXCGYp4U+hXK7wLDAbT2sCF0mLQH+hARw7qY6uF8cq8o6VDB2YdOlJi0gCOVCVkpq/asK9Xf+UnvGQ/hbZ1Xrn9HDWk18GjJSQJ5/yEjX6i5eRIgtBoVLG/k9ZPKQXPHw3TeykY6a2g5FKVQSl8TMi6yoHYEKa1wQWhmNipdYT0R289uwYF1OFwGAwG2Hej1nroSMmwgVkHj5wiAD9twtC3Piq8IFNpP1H00+FwGRKCAoKFpZZrx/T/axfUYpbjamSNlxBSm3maIikhicS3gYbSNM0wsCld0BnCXAnuEstak3SR7gTV0meYA+80PiUBVCujSKkUUAzkUWyoey4U057p+OGn+04w3RkxBmp3OAtgAFQUGqQEJaXVaJ47NKeFFXqDLjFJI9lPg99+1tZ5Ck5B+znjnCHrdx4lSTiRnBSsJlIldDahTQfE21CgV1HO8q18HTw5708ficSklIGDam2+eD3Z4PILoCvwaZMFG0iB9J7Zbgak94BCduQIrKwiCcKoU6vUuoQ4E5x6hHqaCOQeKUIbm2vqoQjGqbeGm8gfOkEeg8F0UwE9UlgxICcDPZ5w5tAB2alHi6tQBZJGRX//526jsM6azW1jLW4x+S6buJmdBtD4/UhROUEQHM8TANi8yrmVBAGVixKGwBRJqFRiH3gCgIbijQm9JsMF4p/8v9BbPOuJDySr6LA5AAhqTUUDYLdJuTCRvoMHwf+cUlYdDRyYcyI/n6ZIkmVddqbE66gIrveMkbDqiUua2ojokeIaEC/rjZDU3dYlx5xeaKRMGalxyIHCeviC8kF9Mw+frJTWDsrq3atnuvHvjftpDeD50pnjh4lHCvazwQ5n6UwdP3Dt9iOwdIlDdfKkZD9RMJQgCVSg7rVayXj1jr/2nTN1dCy3uO2JG9FG1d5yoAv8tUThRP5hKJfCFP8TeflBz8UeOIiAVDzANxY5xbQK0VUvGQSqgMtAoLs2BtMmBP/Ri/6LJGApqMfHlpdV26zOnXuP2ywwlNm/VzpBEFlJukG90q9bND09Kxuqp2zi5qhB4q+sXz15kiCOVbrLK2udDpfb5fZ6vBzjI70uA8mZKN5AsHrS67Da9+UXSKmZ9pxr3mvwCHl6jSADX7C63v/dgXPlg78CO6X/hoAnd2IwnRT4Nx7LVzC0fA8lBC7tdofRaACAsFnEycUEAQ6fKB3cr0d6UjwAnMx+uhvsyjp5OFRnXQHjKUizioZBUVKIgbbkTaqMogNV82Emp4fghEuM+Jc8ysgZMLRvWt+shPzsuL83bJUkVH7nYbcROp0OEFBkPR7xLfOk9M3AYLo1O7trshQ60IE5GWWVsLRTRVPpQiM7u9154HDBgcOwlxIAYCiqDz1eeqLEUu4CMvspdvGQDBpBEAVl9aitOyWs/E6RJEnC6fYUXGE+UJQOG8oJL2jS7ap5F/qK/SVZhDjD7NSxw0tmj15wyVmrfvuLVKl4ilap5cvJ8XYvxwDSYNDHxRnj4oxms8FkMpjNBrStVlEaFaVRw48cXlhBHvCgPmSCPDahGEz3gQ4qFYogZ4eOlgwbkJV7tCQ9Xj2gV7LXx6xcs9mo1cntJ8txlJB6V6spjudZBnoziiRpCn7B9BFBqnR6tFJbdUUldKPt3CeO51945YvAGpyBKiZhVM4Dn4+jSI5hObUKenFxkC6WUEnRBv+G8N94szrUhOJsEgbTTRBjoJlpcSiyl5OZeLSoIitdyL/4Gdg3M/corGqMN2nLKmrKKy0TRw6S7CeaSI5m05fW2igS9qmDPedJomdWanp6UmJSAqnTqU0GnV7tZbwM641PSbj51sv89U1NkFEnSzlZivc0Z40oeQQDrv8eXCVAQANOkiTBsByqISUUCDtCNT/UhGIwmO7oQCWgYSRJp1ushD9yomxAn4yjBeVoHo6KJvfmFUj2U4hsBl5LwNcSpBFGDKvqHSqaUqsorQbaUFIY1KOjUbsj6UUx3q6eEuZcUrDsP3ZcLCyZonk2YDl5nmE5uPyR7MoqYZBOkbASiwe8KJbCP3lHAqneNGG9OYlQExo6NR6DwXRMht34aIzTEcNCo9ZuDTYvMmSFp2AF6KkKm0Ljdgu95U1mWJuu4lhJPVkW1syj7bJaO+ABx3EkTcKxrpA1UtGkWkWpaIoW8kiS1gaLLmhFXCylQzrrBz30soH5SC6Xh6T5WpcjNTUeGUue4EttvF5L6DRU76T4fr2Sk+L06I5JApxzzhlSBdQvv24O1EIJdy7v0oTVE4PpgBLZ+GzGZqkQ3b9XOuCAwymmZYb177Flz1G0raLFWvfyshohLw9sVpfJrNt7+GTgomRgCE7CSiBxtIty+qTwpYICKkRCo7egbwoZSaawEduyGqtCPWUEjdntlAkW1HM87W9MOqSnSauB4QeCdZ8oKIlWfBj8WVXg1o42wV1YPTGYTkrzCsThEP7YqYoB2RlIXXKPwe5wmWlxhaXVcr2z2x0GQUOtVpdkP2HVp/+YCoudpkkOrdYmKCiUTpqiSBKlkkiSOHJYnCj5+GPXt/DdlgvdT5pT5+VHR7EEwWakpFRZGpqfzvKfEEsnBtN5kU86D/tUIzHQzLS448UVwa9TvtBhdxSUwAG+hOQBS2qsMNMu7OABT5Fkg5cAXg7wXlgWSrrjjdrUROP4cWekJxnNhpga2bUFiup3mHkPXsxDzDVxPEGA3MMnl1w2mRNSSmhqP5ql+leVsiUoBoPpREToLBRpXeDwEsork0hoByXu4QE4Z/SAipp6wIOMzOTyshrpyA/+e+dz/1t+tLCS8ttPwWYSDMfBoKdQt2RUcRRJwAXatCqtmtaoaTXNWusbbA2w4HT3wRMLr5yuuJv2yLyEfCsYodxK/iT6UCAI4ozBvffnBYIV8sNwkgiD6aQ0fZweTVihXI4aHNSswd9LXqqRBBu3H+JZfkDvdLTn4xVrUfQTkZhormpwInsn+E2CpknGamUA8ACARtqDBvRC1fVtR0ayGQhLz5dbwhc5hW2P6mPFgCkcxQtvGW4E3+fhIwEZ5TVxKfrAebCSYjCdC0fFnlZSWKh4UEDtDicA+sDLCIAmJklcs2jGJ5//kZlmPllW4/IGVng/d/yQzTvyhHE7TB6hS8PEEUHoElITTFrG61ar4CrBsDhIaNQkiKxQo970aUgIh8dHkhQnaCVF0Swr3s/xUth7yZU0CPhrWFVqMVZr42H01udjyzQmG6Vxp2hIivKpYUelEpWGIxkCEKnOE4eLrVo1rdXQvdLjs9Pj4o1atZr2MezgPj00KsrjY3meV6uodZv2NeO2MRjMacHemGI2N5sEi3eggGZkJJeX10jjceSr5CHQQqEzk7ylvMS544fkHy+RnBiad0QQgOZ9HjenVdFq4TGyn/CC/o1mqyfacCUNFsSa5ITljBg6nuSV/ZsBcEf5ePFyhKL4K81MmQ1qmqIA4yqv8JZLL0E9UkWHCnJ6JBeWBgIaGAymw/LCPbPKK2Fv3yYhjsJjOBAK6K79R8aOGIja2fXumbptV36GkFPatOtwv95p6ECViiqvsgoSGRA+ZCLhv2L6CBYtoYJ5QgiA0kL5JwWXgBdFWZIhhQNtKN4YL2+rHAHGv06GqkFMeVGECvA+FQjOgJEawHnS6FPokY+Hr3I5nFWlvioAtDwwJoqNI5MpHQlYQdn9sc2QoDG6zbCOGfezw2C6KrFY0UASaeTw/hU1MGLZKzMZvjLcS3nB9AUuIKghXJ+DgqoDCz8FqSQJwmVtcEG9BTZeDyd3UqROo4ozaswGzZTzR2WkxjXVgUq3Q/EMK+i+/4lQ4wkAF2YVlIEjRk4a3WfR7FHLft31xXfrxVfLU0JSN36pbZ0kmqL045gnBtOZ4FvY/BedJPIBgXZ2Bq0qM8VcdKoq5GhxR0aquVAoY9q6Mz8nGzZt+nvHoYnjh2pUlE6t8jEsTB8JfUNgEahap6Jh9DNTC0fxKukJwGzbsGvBohnNf0usTe2PgfJi7FX+ZtG4XkgNBfepJ33OLdv27d+fV1sXqCEVNFJ8OeqxJE3QD8omYeXEYLprTSgh269A1Jild1z29uvf6TTwIQHAzXdcueGqJ6QX9EtP2OI/xTlnD8vJTt+6Fa6EfM3FkwrKamma1KgpioJreAgzj4RpSEIwFLWzg8X0gjmVbqrZXUFpRuxN1wxyjxSPHpA+OCv+oMdd4PZpheWe5Pl0o07Nc7zLH2ZFVJRXS9t9+/dJNOkA55YtHYWVVSTGJZqlxUoZJrHRg/EKHJjWIthsxXB8OBnlIwno269/t/T2S99+43voNDNTHnv47ZFD+sCkjQuWBB0/WQGbFVFiZ/ZCVHLPg4JTVX16ppZV1BBC+SfKx6CEEZoFrxL6MqH0FBwTc0ItEc+zLIcWR2rSm2khNGAByxSX1e7MPUnTNACCgMpO7WapXulxWjWdFKdLMOlU4mJz/qmpwrd/675jJr24IFJz6wi6JjGKHVqsFK9XiumY8FFlNNSK0oMnXVv2zctQQ9/4fuld899+7Ru4SG960rZdcCmhvr1Sw549jKIJTTek6lGp65L/Scl1ws+Bb79avXDJLNDuhN62XAC1pM9hs7pJwtpQXxSrNOKyegymi8NHtqJBYcK3X/umf076scIKkiD69eulVlEep7iqByCI4nKb3K+hzQKhe1OfnqloQxJNlVD66ROaEwf2hxapNxEpCw9I4GMJSmh3QoJAaSpsEEVogdgF5Vi4b4V/yw0joVSCHq3LAfvXobRRlDsMFWB4eOMaGmV4i0epGEwHIcpIN5IVpTmen3L5PTBdfmwVlJzCCkFDyxPNOqtDrKPcsCtfI07x5DdtPjDh3OGN3orQ011wpC0efkc6AQwpyKST5SmKEKrr+TDln6G4NbCSiaYIng0nfzxcc9Nfuyq+C7krrTb0k4STSSQiBftiifRhMJ0xWk13vzWiQ2WUdjMqUY9y5vkKf/JraMaxwvI4o7bGDddlu+byqct/2JCdYTomRD+X3jTvscffi34l1NWu5eoZFkkr5Uh7KDijM0yAVbaEB0RHMULiS6VoNSKqpBCRQN4dhR9qLEJZFgBoUZAUx3FJQEssZKRgX3F3XWwL002EuNJvFDqmnlZU+avoGwvK8c2SUTp33zZCKO0cOmKwqs/5voJ1gobCaTio/7xeqy46VSkPn779OoyTAgA27jo8aeygsNcoLxMr21l9ckayaWjf1Oz0OJ1aVKusAT0VxxNNeT+h6imHBaSYrKejn5PnOeD2MhpKaGIKiBM1XgC8BEGYjdqsNHNSvAEVZhn1ahVFZfeBLZq0arrsJOz4J2TDcBIJ093TfWldwig0w+kh/0TPnDO5VNZpSdDQP0PPO6RfdviLasSUtIKrF85Qq+F6GSRJbNl9pKa6pqZaNv1x92EAgKIhU6MsHpXx/nGojNJc+GBkdaCxnZDjgdPto3UUml5l0vBC6JY0G0iK9zptLJy2z/P1dWLoViiuB2Z1wG/yAGjNKaAaN7jDdE26zHrFRBucE8ZAj+Yfra+tsNvhUH3kqAlw4mO4gieHouGIjMystLISNFk+wN5D4pLIbYqbzqDDTXinuRjLRXm3lzVqSWkFJDRFX75ivND6RDo8kCVrXv9qTKvH7GpAYIkqBogzdCPRMYeZmNNLS/6Q4Sj33HOhE6z09wk2959tPfar4tyoJWig4Uizrsnz4OChE9cunAbXPW7WEk7yLLxw925AUDzPKeavM3RCjCX3ijb1sifE2ZxSCQP6b2CxOUx7EV31kkFgvb8yEDpOwmDaEGi+jh09YrHzKn2i06dCX3Tvi0MPNcWbxV/Z1EY+5+UIiyWLXwCAM4b23XOocF9e0YH8ML2KmwQFV9kEBBDVUwOT8o0gRHXFw5we+CUt/h5oVu/3ljD+QAjLOgkZMfRviwuxMBhM14FGelB2qjCzZ87RvN0ooSTQK0cratyA7JSjxdUGnXrm+eNKymsNOnXs86Ui5b6aPYeHJzQED3uFsIRQ7cnBxTYJkvIEVyMJXtVfxBoOPQ31V2iG5z8zfMgRsFN041osOFfsRjGYTk9LwnFidScAYN0fP150yZXlFfJVj0aBig8AAEcLKwbkpB8trrZa7Ro1dVK2MtLAnB5HCmFWWsG+gnqNitKo6eR4faJZp9eqdVraoIXKa3N6evXv2TPDPxhvIkg9hXnxqEBBqD3S9uCdIbcRmoX3l6UyDAOAJtzZUbIIg8HEVDRaGXO9c0cOQMfcAFQJbTCk+Fxi//ljR/P7DxgsaShP2LLPWVj6w9vm1OQKB9O/Z8qxU9XDB/Y8VhRovsn6vGHPe9XsETqNiiDA9n3HGY/T6nFaZU3uK6tqei+c1uxmInDRN1lXPaGnXfhlPBSE1qXKm4lkJCfwrNhJxOn0OgFIiNfLDz6YexyGjEf3l++k1OGEGIPpEkRXvXFdq7NBM6wo7XF7abURuERbR9LWHlnaktJq8VSlf509fpDLDsWvyA5UGrKovMqclGBOShg4FOpIlc1jjjOb48xoKqfEvrwi1JYp7FAdxRZBa+CmM4SufG7gzy+xhFbjk9rJR1uVMxjiaEkdScDCz3iTrmeaOTs9PtGsg2Xz/js9d1Q/KKOHxF8p1J/PWVMiprQwGEw301C6qrIkPjFNKtbJP5SfShTr/E9/uHI32lg0d4z0GqSnCqZMGIY2dMa4D5evS0zwNyQWhAatF49EEyVsWJanYZ1o09DbLNaEoDGFvIyJJzUE56F4tz9Zb4/iQJ0cjCdo/AEBAEC6mTbp1SRJ6jQ0xXlKyypLy8Lcg1knNv0TwTklDKa7aqgYJkwhDqUMpQHI/XAFnInUVHTGOLTx4fLAy9E8cvh/oXzSv1wwDA0AAsAm9q09m5PwN6InSBXPhXSqZ5mjRZVON+zm2XfYyMpSuOAHXJJOrFIS65UiZbcOHCoYPhS2+Au+JFZQDKarEXtIlB6QXPvhcnFqZhRsDq/JoG5UNINuQtBKuR4dyi8MejYyjd569NmcYdQTgH5njLho8pDL5475c2Ouy+175X0ooDRNSRfT6HTx8UaPj0lNMsFW0BTJsJzN4TEatNlpcZPPHPzXtvxAc5RAQT0Gg+muayJFkj8FVoc7NQ0u44Fo9FVFVa4Ek1ZNU316JKQmGHQa2Fr5rOF9ZMoZfhDcaG84mqkLTSIFreThp1qfkRJYWxPQ3oa/Nu3+eeV6nuc0epPsiuJ3yeNy1RMsTZFV1T4VTUk3anG7LLV1QkvTQF8+LJ2Yjk9OL21TX1J4MqZmZt0EojENDan0CeaGK85HGx8uX7dhR2h7zYg4HLY+qdobr74AFs/3z5T23/Wo2MZp675jCxfPRNsvvbPqvltmg9bGYrWlyH5/Dh4tG5KTMn5Iz+LK+pIqm9vDaIUlTAKzjIT5UXD6e/B5UFspluMPHykCAJw1qj9WT0ynUMNmX6WLySjfgr/Y6BpKRxfNGP1pJD74bCVobVCCiAVaDRO0lLGTTKXJoJ+6YjanmuJTTKqySgsNwIniCpqmCYKWf29sdRZb8PxPgiBYXaJeqzLq1Fo1PXbs8B6p5qoK5cT/KIxTlXSZdgyYziWFLUGqT8KA6IvKLZo7Zpk/1Y5ooWhGROjHiTq+Cx6P5zgOLZKsZ2Oq4pRDUzwj6yIhpeMJSsOzngg3oOzuTJFiggs9dKvi1SrKpNf0So/r1zMpPdkkrCUq5sP+2pbnctiOFwZW9ETgIGh3puNLYWu9wS7mSZtBtEXlWoW5U4Zt21+MGg+fkZMSZJ3FGeeo35GoWKhQFO3/3/++fvSlSbFfK5JKSvtppk6jMwE+qJJJoaAOH5RHaUZUdpLGZFCraAow7oKTZQUxT9Y3G3tTJFFnDaTIMJ2OLi+FLaRLDu1brqEtEtDeqYZzxg/acfDU8ZOwil6nUZkMmhpL8AVlG7J1jaXWcSLQ6cXm5fwDc2GNeTpgQtWcVZ5E0uhMvXr3BEJnaAmFA9WRDEUSHEcCikQz26O00EdPcTB/Feb9sRyfYM7BGtoRwFLYDt/bbqukhPBv+EXlYmHR3DHlNbaduSWohSgAwKAX5zJu3pYXlyxOia1ygKQ4HcvyZqPGqIO2juN4tRrWDHE8r6apcecMiXBvjdC7T9+Kihq30yqbDi8gRANcSYNVam1yCuwXdbiqIjHhDBjc5A2wz6mPLVbFo8Wd3CkakqIYNU0SpJMCGjWsSk11noA6zPEkRXC82ChE3h6FIMS5AIpvIuOCnQAZAPSU0clG62CCaX0plA0UsHS2G+K3ugx0cysak4D2zEzKSDbtOABz0MIiHyqNipJ0wuQXULgtrBcEw4teK8WRFAHcDq87XISzsLgMdaT/7zurpIlPZz3xwdYnbox+M9Wlxek9sms0I3lA1tfXM17xk5BWaxMSE4z+lUgsVUEpJoTCYML2SzzHcoDjyECr0+DwAkEQWcPnGuPhhFEFm394EW3cfcfN0s5nX30p+v13WxR/cljsMF1AQ8ML6IILRh84Wq5WUQcOw2QcSRAadeBItYrWauHqRudfdrf71O9SMRAgAOq3JJ6+iXcTO9WlxSk9AA+oZB0AgvqSUDRdwOtCB1QVn0B1nmkEHO/7eBjndLl9PZIyTBS8wxPVJ2ia5hJGQdH35hp1ao2KQiGFCRfeQpHissiIUyUlKlWYb9R5C/5F8zaVStJ/yMN33/dMV9fQDqV9tl4Dkstgk5eaTNipAHNayOmWEVK4JpL8Ma2Cssj4fBRJmA0aZOVQgFLqAcrzQEWTyHV6WLhT3haEppX17VJfd2mdzjBtNIVa9mfuu+3hl96M8darS4uTe+QUW+DYPJSgHkpBNxOwoFff8SyqAQBgIZRRbZA7ld678MuRvW//fpvDK71e/K/wnymTzkSHbdy4EW1MP/ucNVu3gM5Ah5JCTGcnp/tFSMM7UIKAGSFhYV/0kNCoA6aMIkmDLrAasHyZoMDIV5rqKC6tDkbPvGPv6tejtXRH3Zsau2OnTuxacqraTgAbG04t7erBwmcBAE4oZOfOuqZ3dqZBpwWALymrTErpMUAsdQ1QV1eZkpwOANi0SdRBOUZTXFj1RLrZEZbnxFKI6TjkdDAlJdpUQFEp6OxJQ1dvPSo9QVEEnCcuAJe1IMmsjMSScgvH8yRJwFofACrLi1KhaAbuUxLTqVc8EnqxUTPucHsDa3MeEDLk0+ddDWXru7dHTb0G7Y8+d4qF/T8QWpqz6dTB094JQq1SGQz6Govl8ovn7FkOBXTowJwtWwKWsKAwsIqOxK5d288aNzqSDpaWVZpMcSizJHhoUTJ5nvcxDEVRwpolvN/StqgjM5ZCTJchp6sP7QMOVKEd0HX6A3/CckCi02QYTqMW68uze2a6iw9IojnzynvLc38Qz+sXXzlbN20Y0DfOmBRmZs6UhbfX19h27G48q6enJQEF3vDdnIV75n1H8nPRtlw9Q6tB0f3PmjGlvq5BFEb/9HiUgud53uV26w0mtCSSUNwqqifgeYpCs+blbVOCvpVYEDHdnJwOZkjbSEADf/b7ThE9DATrXzCIhCNzuKoajHt6GSigwnavrPQjxQHlVaso6SRbNv0V7nJEQcFJRqzNVJo9s9l4/EQRSJk5tvqPFr8vfuzYM1322nCaybvdHpqm/cInqqSk+AzLofXjREctfHIkJsQLNlN2FjSbKigETAT9xnTXCg8MpvsY0kCqRLkYHElU1zmCHKigFG6PTzo4NxdaPCmmuXnTpiihQDiRkueOHC/2+ZhgCxhpZeHmo9Pp5W+Hg0soi57yYN5REn0giNFYUStpWvwsEXeJvZfEMxgN+iDdlP2LwWCaRE4vLfoCXUtAg0ehBDhVIZapI+lErtPhggKKbNc3y95VjP0JgkhOMsuUC3aiR/+iPVoNbPkuCar0pNlkBDwQiqOacvfh9RrGIuXvRXgUiELQcIq7cA8yiSQizpiHqFSqbRtWSUfINXTrpvXoa/vm9Vm9B/TMGdgzZ2DT3gYG0y3J6fwyGjSEN+jUHi/D+HzrduZeN61PnVUsq/QvjA63rQ6hZ0eQ1PqntAs5FjER729DL0qXn8pqe49sOG9Sepn/P8I/8BottXXCuYME1OUjkcHcu3U1zBdtKZ00fZ5ardHrdVJDZQ6A1MzsEwUnT5VUpiQaBg4aqlZDOef8cYx5C64tKipOT9OoaJX85JmXXOTzwbBsdY234HjHXXcQgwEA1Gt6x3vEGTEdhJzOHCENSiKlJ5vqrC6Xy52udsNObqw8Bir6UJfH52M4ufWTzXQMuDyoYiFrxEuvUDRTNpuN/jOQsFOdjM8tfa5KLGjSW3JZTh6sKT98hE5PEyeWqihodAEAF1y8WLppnvWWlynXnmMZX0KckfH5SkvCtPPKyzuQkXkug+IByICK3yDUFi9ySguD6TDUa3o3egybIE4PsWnSYjmMAqLZ6m5KGuRAs9LgKh2V1XDkjqSPVqkYn08IGYpDeK+PZTlOMdiXnwQ9TEww1tXZBYVppEvItr/WSGeYPO2icdV/SBb0yz2llvhUELzitKUBRmYDDemFiaLoDGYdSdAUbUqODy4DsO9ZoR+1CABQXh5o5ZmRFmYla5IgVDTtcXu8Ho9aAycLoHegtMVy9fSvk8cK3flQYCIreaxbmB6DwXQoYrSfyXXiby+lczVPgrtJrkkmoAAkmHXSsF0RQ2RZHgmo28P4fGykmh15IBXpCyGY0V2bV1+66EaSIHrniN96hkHVSMRli/sb9fA2Thw7xvh8+9OnDa9YK51QryE/s/S9OjEwNE6Ih3aV8di1OmUJvTQvHl2+sViAWIrEc3DtUHTbKpWKJCnGB8/jcbtplVqYPiW+taGDs6WzC1OqAn36Nv69N6d3b5VKRcO1QE53VT0G0y4qnOzX2Rpd68+j7RSGNCCg36/Zn52ZlJ5iCiugdVYnI4yC3V7G6fZRsiWJoHUVauyT0noS3lqks2YjYTaaYIYIzVyaNttSK+9zFwTj5mtrwyyVLJ4fEF/U9VuS0DRDF149ZXOGyipqxACtbH1QioL5JYPRwLHwfTGMV63WSh8RCWbhm4OkU2zWJNTVEyAxIV6tVqF9WD4x3YqaNu5C0J6GtKmLfwRN5SRIfwoo+CwkQRSV1buEAiaGYZ1un9nfdQkdil5VXlaemCIKVFEJ7BAqoaF1alITOuGxuroaRlLitW6PRy3OvhSl78s9pYkjZznLq0mBWN6My+3QG83RJDRc7ivkEOJw/pEBA1AmnfB5xbE8LCHQqhX1V0hLeR6YTAZFoSgGg+l0hrSp7idIQEmCkMrCFQ60slZcyoLleLvTE2cMFB/IesyLD0Kuwh85WjB4yDDF9KTqakFkeWB3OJEQQaHkZdcVOnpohJlPv7CjL6T2oCs0PjoXLxvDYSHz2KUHh/ZtlnZOmXWpRq3mhKsXFZ+qrrFkpKeYTWIrE47nE5MSSZKkaSoxIV4PJ92DPftiukcMBtN5I6TBAip3oAQceqPJSARBeLyMP2gIbA6PIomEEvTyMiYYWmQ5uF9QqIaGannTJr/3RLVLwGwyOB3+WLXszCzLEgQwGQ3CTCF6AzUJgBVqtcrr9RLBHecaZefmNeJ7JMDli2HLUYZhWI6zWm06rVarhR7zhx++n3fhFFiZlHGuRqMaOuRyjuPLKi1mQ2KNxSqthVR04khFRZlZO8xu4cU2ogThcrrkI3sf40v2H19RAl12jKRnBdaOxmAwHTxCqnSg8pqkBLPO5oQ5I7/0QYFgfF6nW9ojHOmvsSdkYgrjAYJ/RCccMeoMSZqlkbuESgUnVirubPDMxWXl1TRNm0xGoQqVoCiq/7RrLHUNlrp6AHT2mkIXIXpAvc7odLM0pbPaxN4iGq1GKzhBxA233AYAWL7iuyFDzigsDDQx53nebhN7Q194wTnGOHGFJPkxDVab1WozGg0FJ46gt5aSmg7fFyl6b4cTqb8onU37CWAwmE6rpCEONDCEB9kZ8eXVNofDRZIE4/OhbqEojxQ0TvfX2COxRNtC7holsP3qy7kJClaN/fXH9/Kx9bAxE2k4LV3s0rFt07rh/WAAtN8MutJi51hw9PgJWkV77VV6c7xRr1Nr1FoN53R5dYmZUitjt5tNiBOLSdGY2qAl+mSlWP2R2MN5+cIbpHw+JlKiXKqZl6snAODo8UKdTg/r6gU3rVLRJEs4HC6TMagMYPy4sQ//6360/cWXy9e8K47hJ58xRKfTPfPyB0L3E+/Z58P+oVOG9EfPrs87Jn943vihb68NhA4kbpkdtOLeO6sCPfdumXqOuPPPztGEFIPpMkP74CSSYPTEbUCkJhoZljtVXquQG6+PZfw19qLxlByp38Pu2QKbiVAkdc7US1Q0hBIgCDD2rLFJKSmAByzLoUmWGi2RqjXZbd7U9B69+/Q5UFBwxmwouUP69eA43suwHMsV2OuThTijkCiH6RqX2x0lyMkwQQX5Eja73WSECR+x4N9/Cp7nWS7obaIZ9HCClgFKs8fj5XiOJKgRPWmepwmCr3DD4zO0dqADj734blx84hfvv7Zq495BAwc+9si/liy+4ppJfYSJ+VA977pjaV5e7uNP/Hv3pr3P3gcb91mEijEknUadyi7MkQUALJ12rkJDFeoJixw4WF915bQxQYf5lRQA8O3aPTVEhwgSYTBd1ZDSVovSgcoXUFOrKaNe6DkfLKAcy9XZXKFZ+L59swhHNdoePmaSy+XJyEi32ewK/d2159j48Tqlrjncx48eHTc6SBE0ajXP8zotjMAOGTTQ7azW6BJJklCraV6IkHq84cfLBGyqpBBQ/6QhYYaVJKAogomUH3WhR/ZzWv/A8P+n0sMePZRCjuNH9kRxCbg/Q2vfkVuUMQxWtsbFJ048eyw6X6aJeeD2q//zxmfo5c+8/MH+/Xvz8nLrauvuvvO2XXtztxRVn9M7KNYpqSeih9pc6rWGlc4vft5KEGDxjHEgMt+s2U0QRArQVgOsoZhuSk67zLIPycLLmmvwPED1Q4G9AjwA5VXwz9t/pOhbTxUVDU71r1sJQ5ZktcWqVauD0txRM+NS/01k/cSJPYCnhc6kXhepommU6DfqdYyPYRtsYZ0mRVGsT7lwPLqyUCofFH+QbcLtUYkwJFpbKwZGl6/em5WebDmxNWPweQOSvHa7OF8zrwgmwYb0Trbb7YcKYaTAHBc3uI+49hzHcffdugRtT5p0jkZF1dXW8UYxBlJZVfl7aeX1V8CEVe6hMN2dw0onUk8AoqnnitW7xI8Enq8hlN+B08XSaedK2998FqjnXTpnmrT99q9r5Tv/2npAemr9gUPRz59MNjlZh/N1mNYXUNjmUzYY5+GIFYQ6UJ4HtQ3imsbCkQGFlZc0wbwQARfzkL84qKWmbL/RoO3bv79er3G50J+90P+JhPPphRWKgI/hjp+sGTUKjv0BAfNOZrORYVirzS6MtZVwggrL9xAEGD50gNXJSvZTzq5d2y+84JzCwpOjQmZ4wgmgaeZyy54sIpDpGiKzkA6nm2GYX3//EwDw3hsv7di60Su0ekbX0GlUNS4rMKo+fufT4cOG/7jyR47jpp99BsuC/MPh1RMqeI1SDlZthtHS2eOgFw7L8j92whovAlgtwpyF5PALRp0u6VQ+JVPPQX2TXrtz4XPv/H7T/POq65WLuE4ZPjSKkk4ZPrA6dwfaUDy1/gBK+mEwrc+ry7bdN3eAspBePoRHDZUAAGu3HBYEkfRnmHmPlw3NwkMh86+czvPweNhlIxiCJMeOHa8ibD5e2bpO1sXYPwsU+lDxWbSISGBmPYwwqOLijD6GcYpJcNmpwjTrhC87XrY70rcjtSexPRf6u+PBmkalNi5DJGDPPG9c/779P3rro5tuv++m2+8DAPz5x8rPnrkTztNwQbduNset/vOPhPiEi+de/Px/ny0pqzpaEKZfybETcJJcSVklGNlD8ZTVals4bTTg2drawLIoEstX701MiAcEb6urZzuAdEZXTwWPv7ZSmgScEm/45jf4g5DISIMTwFb8uitOb2xg7EuuuOqe2/8hPfvgXVcpzpZ/orR3Tp833/l0zsyJFQ775MHKeTIb8qELvlkWMka8i7NwmKbw0sqjoUN4yZrBobTce+o0Ko9bGhWK8nSwPG5oWj0STdS0Cb3kRN6mfkMnKFdbjwwB15TX+k1b4B7kx5w5ZqTbVVtSF9wNQQX0cfALBMf7dAAUVOyTijGjSGfs7DoVZq15iBmGdI+dODZx5kS0446ld167+Lq9a787uH0D2mO1Nnz1zbKvvlm2dd32bet3jJkwakiyWDIVCgHXa24ylrp6NQk6pnqOGt0XAPDCmz+FHrnsly2AIGz19Ww8/EwNdaDllbAGV62iGhj7f/790tTzpn741gv5uXspmr7rgX8//9rn158/gBVaK2zfL/ZMePOdT+Fs3fKaKWeKSxBKpKUnAFiRAS666Myff94uf0ouqd+s2WMh4W/Ur+//c+WPgV+eVRv2n7TDwcGzi2adqoI3FrZqAtOpaXRC56vLtqGN0CSSuE0Q4Od1B2ecOxg9jI8zGnTq+rAT1oNkN2i8H72thli4JNtz4HinLMS5bN7lD93/8JgJcJV5xOtv/++scWfd9+LHk4f1oJIolUrldov6/sAj97/6n9dgFLUGrlmvwFIo/kS+2SH8lQMwf7z4I6CSdN/uzZ8/ckCk26BSDeErD9qdGX2zThSe7JvTS9rz/Bs/AkDMnDLum2NBn2Rf/gJ/4k5rQyT1VDD1vKmfvf9Kfu5eAMD+vIKXn33wpbeW3/fiJy/cs0RSzz/Xb/78s0+uuvraUPXcf+QUOHIK1pYN7iepZ+jv6Ndr9khxBoV6wpVsjClIQ3ummk5V2aSPikSTyuIvQw4rrDeHi2u/K1SkySsoFHy/bl8VL4uYYdqe2Cd0RszCiyfyz0GHSXlV+Mk/HGzjFtBdabvUIq7p1uVZv3HdQ/c/rNg5aMDgg3t3AgB++eZXs9k8YZr4F/LEw0/a7WKGKhYkJRUf7gusnNrUUEM7c0JWTnusqOySmWedKAiMHtB2XV1dfEICEwd/Dx9cOHf3fhgsqqwOE6NwOBykER42fPTZy1Z8A71kgqG6unbiueP6UOLiV06Hc/GSqxnGV18eprM1VE8ArA22WRNHuD0++S/616t3LZgxFm0v/2MnSVIW0i2P0grquQ/9ZSH1fOHDVf+8YTbSUHSAXD1Dy9HCqufyX3ckELoFU0dFSq1+/+fe9mzuJSXiUF6u0dRccgzpuw47ue6nzfvMHD1lovJTtkmQEWciATBsQA/pYV29shoJMa4fX2d1obLQg7vXoQWFQDfDUmepqa3ZvWnvx+98Om3KtGsWX7tzI7Qtt191MQBgzmUX6LS6ld+uyumds+aXP+Pj4ifPEkf63QeKDxQOK7CRor16fsXKRs/zyH03jxxz1u9rtzz/4v/69BsEK8Ac4sudDidp1v7jvn/ec+PlbjcMizscdulLUk/OpK6prZM+w75evUuhnoInYBXqKbQSZwHgkXrC+Wk68oUP4SovKfH6mdNGhr1byZyGVc+vVkILvGBqYOASir9rYsdiypD+6AvEQEVJteILnG5+2rzvp81wnouVDCzxGzvS+D3sXHhxe/kqOIrJzohHDz2ELmx/I7fDmpJgOHZStAwsxzndXbYxe3pWSqQf/8x504cNOePT9z4bPuxFmHZbv/afj96PpkbxPD9mwqj77/7nay++/uXyLz758mPQ1Vl9IiQ/lmb+eS8ckUhRaQSVJGvrFRVkP0+dLLh2wYxb7354/DmTR46CqldbU/OPS8fDI4zqHRt2zZw3vbeBDvMhV2+jCcCZ1OOyM1CCsaYG/tKeP7r3uj1FaBuezVKvIvlpE0fKzTLCqFcXNAQacksaetdVM31wlYLwZOsSi10Wi1BTkZgc8GLLftkG4FSI0VHe8rdr9gAC1PCt0Oy9Vfhx+c89s3qi7WP5B9995Wm0PWVI/935p/oOHzI4M41jGRa2XOcstfAt78kvmji6v7SWGuK8s4dL2z+v31/qDP83tXRO4FPn7V8DU+9aCJJO+cN554b//IsF+u89RRNHi02ON+6vvHRyH5liBqokVbyH58W5kjpjnMvuj4aq9BoVhSbVJKelf/0bDE51T3LzDsrDoApefPWFF199oRmn/fGNeaE7L749TDamc7FaSIUrWHMY7qwoj2hScsuh0i194G70cOCAQcs++uq91bk3zRj29mvvllWU1dTW9DYInQqCIc2wmRZsK1NcPrZnuvyp8/2///DXmeS55MCcYDnHCopD5b5BRx46WnjoaGGDfGqJjAHpupHxg0QRl5Wm1dc3xMfHWfzCHRZx4eyOMaLbvWkvx3GvP/vPk0UFaRk97n/y1Zfe+xrNtQOAv+6G/7v62uvlx99365IdBwtoupFGlP+8afqdry6Lrp7yh+u3wlK2w5ZaKenXpNoJhXqK5/w7N/aBvNx+QgE9We2RxpPCWh2B51R8oOuSjwj86tTUO4wqcf/HP+7S+D8hayojJKkxMXDHEii+iy8PmosVXVW7gIw2CbVavXXddvmn1JGjh6++6arP3vsc9iIYA33o7k2Bj/B3l/02/aJpFuGPLbSaooQPl3JD6tmUYfN3B45fNjxaR+Gf9x6WMoFybFQjY7XqmtoOEto2m2GZy7hJY2aOgB8GleWl9900/6X3vhkzccbuv+FCjXMvmudw2B6/f2nQy3jgsDlWbt8398yRBp1KbkLlxBPmet4aSTolVqzakZpgCC2ZkDb6Dehx/9swOH7nzHM9/jFBhTuQf0vX6hWaKwVqyuqjfZJFgfbJZvIkmnXSAkaLZo88UlgtPbx8+hkH8wvRNkEQPqFQHM627DDji/ahSRHxZBL69JrMlEYdJSYW0PSEUSNG790vZslhTmkoHA8WWTxjJwaGw6NHjH7vjQ/GTRojNYhpEqsFIwxijjZ8d0B5/LQcZRlvM+gg6ik0goA1JHPGniGvuf7w9eeO7Ich40MnyhITk55/ApY/y+EBYITplCu375sxOrzFyz9WJV+KN4p6QuG22JFnv/faCx0u5cfP8aOlqZRpycxRU2fA34Sff4EvkUjXBlr/pJLGKs5+50XTfax44cz45Bg19D83nA9raT5chx7SF04OfDaajVrJgf60etfVl06o8edDN+8tqql3ITuu5nBRhZIHF4SZXvnBq/DvCitmK7Jh04YP3vzQYqm9+8G7WYb992PP5PTOuf3qS0ImucHt5qlnq7C2sDRKKQUqSoP79wfVVMwfEbFG7fTi8XhcbtdTr37Gsuy/7rq+ogzGuKvWryWssBQvIxVmSnL6DX7wiZeEOHXhw/ctTU0JCneTBFHpb2J5XJgt0q+vGDlZeMF4NJFXUs8/NsKB9sxJI+XqKcRAoBwRyaZQ9YQVu38fVFPg5VUbeQCcHiaSekr4GE4+kzuWgfzQ5Lg6h08uo/Qfm48uninOgbtgwsA6iyUrI6mkvHbRRWeXV9tyj4u1ipbqqphW1ZDhKq/X+XNQ3Uc0Jc6alPnBq42cYdvGMungsAfIB+lYiAEA/3jw7oH9B37x4TI0bPd6vdMvnOothMVPXQCFnnYoJkw75x/X3XjldUv/8wacpMDz/G3XXuoQ/BUK9I0cc+bV82fp9YZ3Pv3u069/u2bBBXaHnTSItY+WOmUNOZJRBF/rIZI0BQVBswDRw9825ScnJzTAFsBQOgEAd8+ejJYXCqXEi0rKgkIw6Vr9d2v3LJw+ivGvdsEwPlh/FBwoX3zheY2aUKSesOTD7TtSXDMkI56+4dJxboesM4hKh9Yf+uy7Vkt7dTfpjKSSUWhGNLO7BUAljhw7Mm5SUKQ4NOmzZ9+eKAm9SCg+orrtdzgSL3/8wR+/fg8HvFnZT/znrbc+/eGxGy48eewQz/NXz5+FjnE6HVfPn/XZN7/37JVzSLaKeFPpnxXoSdFQV8/GB1ornTxVRssWgkSsWLMvu0dgCftaf3ZOBUCt3SnfuW5vULdfOY0O5GsanAeOVRwprna4RSWl5er50Yr1oGWgpsuMD569a9vPRtm2sezGu69sxRNKf88/vjEP/223kDuWjGo0X9c9M3Vh+X7ZD4Uni+791z2HqmCfmkNVljXTztm6bvtTH/5yzaQ++0+FsQhZA88A2wIJ6x0yd2mhgkIr0wf1I6JWs8nVMwp++ym0kgihslrMJdbU1pMpulnDBp2QWWCJbXlF0kBeUQFxpNB2sryupjJIYcNUzDWVUKFUtgnBtCpd/k+6qXNXwibrEDju0SqoVOrJEyYrdp4qKe7bp1+RxbM7d6/D4Zg0c4L82dz8WCcioto1GPrMDyhaQb5o7EghWCznh4OwLZmc+SMHkCmBKOc3+45eHhxNPm9ktuIlfLgWbrC8rC6Q4FG43KOH8jmO88ckWiyg3dxgYk4LOFl3Wrhw/pzdm/ZuXrt17uWz6+phXuSiOfP69ul3y5VzAACokkytUnt9MLdz9SK44MK2HUEttdqU0PnN30aOJpMpUJH/yIOtDmcMUtafkUnqsKX1P23eF3Ymuyigi+aOWbay8X5FWDQxnTFZ1yRrrzCtOGCCmDDtnE1rt6xdKZbvCG1x7ss/CNPlh/Jyj584tnX9dpfbpdNCeZp7OVyTp+MTtl4tEmwCXJ5DQUwOFOtmW/PggnHPfw1L6jCKb0tTvyGNpuxQwUOUMMjFt/8k19AuHzCJEZfbFZqXM/o3Fl6zgKKo4cNGeL2eQ/mNrCDQ8Yl9fmdEAcWiielcybpYqh2aKohYPWOHZVn5BIdGiRSe7iDfc7mGhp0AGl5AO7Vuhnb6iLEr12lvw4XtZ4eig/wNtz+B3/kylJfrF9Q1IJiKmP+m0DRliej1D52r+KEVsvDtSUfohYX0rhmjyyhna61TYTBtjfSb/8GrX4FEjbz+oYvVPMQykKd/2H2iC3hPOTdff0tSUtL6jeu37diay8UNE2pcECRJfvLuZ9k9s19+/b8///pTq6he85QUiyam4xPj7/aPXUs35UQZvHdKBxqdnRt3o5WQGYa5bN7lAIDX3np1z/L/oWeHDxv+8TuflleUb962+fF/PXH/XfdPnBFUudY8sBRiMApC49FhJysrxumdUYi7joCuXbmOJEl5A54Xn3nprlvvvv6bN1kWdpz6+J1Pf/j5+3//B3aBfeTJh3b9vWf8mPE7dge1bMFgMA8uGIf64ETnxruvhKP42NJ32zaWNWo1FPUPnYKmdgjpuCTEJ/z860/yBjz3Pwz7a81ccAN6FgCA1BO1QuB5/r/Pv3L67heD6fTcePeVbTFfubNkkLqUA/X6vBfNmffkc0/Id46ZMArFQIcMGqI4/uvvVyy87Ir2vceuRjNyek2qi7j75VWzx+VID2dMDNOWuEngeEvH5+LOo55dyoFOv3Aqaki+c+PuVd//PqBf0GRYaS0XiS3bO+USyt2NVTvFNt4tB6snptXpOg7UbrePmTBq+vkznn/qhbTUtK8+WQGXVi4vfWhh+CUwXa7u1Uv/tEAQxOiRY2ZNn3UoL/e3Nb95PJ6wh/XIzLJaG2x2sZtOJA3dU25vUnYYKyamrek6AopYs271mnVwkRYAwKMPPn7x3Iuf/2LNg0umH8xT9obJzAjfwxjTWvz7sWcumCHOib70osseffBxjuOuOy+kfQNJ/vz1L/989P6162Fb8ljAyojpIHSRITxFUffc/g/FzqeffzLvcF5Gdl/U70BqnY04fzJsyo9pI1JTUi+YMfvxZx4bM2EU+poy+zySJJ9470f5YQRB7NwIu9iQZNhmNxhMh6aLCCjLskuuuOrW/7tNsR8l34ss4shx1IjAlLJzzjw3N+9g+95mN+Lyi+fD1cR++0XaY7U2bNy8IWdQYE3wn1b8suvvPTuFSjIyZCFiDKbj03WG8MUlxTdccyNFU6+/LVbOX37x5RnpGc88eCd6uHnb5vff+HDSzAkOh+Pxfz1B0/T1S687rbfcldmfux+u85qYJF9V+J5/3i2fGJbVI2v6hVMtdRa4FjEW0LYhSrVDpG4Pz3+9M2hBOExkiNkLp4POQ/Taly8/WjZoQFCly6dffvLRcw/DFFN8KlwP+n/vjR0N8w8cx81beGFZeVkHaSbSSYn+49j19x4UM1n5+8rPv/rs+AnYSFwS0FwOLjWO2L1p76NPP7Lqj1+jXw7/RFr9ZxQJ9GPK5eLkZWQxMiOk2qwLx6y7jgOFXV6uXwRdT0Kiz+eTUrryFcduvvOmVrlQ6HyJzlW81j6MnTh64WVX3H37PXNnzZ07a270ogg8hO84yEcJYcvIoqvq811XLru4gCIsdXDdq9YixrllnasHV7ux4rvlK75bjrJ8b73y9tjR4z7dWHDNpD4wKh3cu4ZAi8Fi2p0tf27TaAJrum3dvuWd+5fID5gxdeb9d90fH59wqqT4gUcfOH7iWPTi3PTuNHTrggIanUg/3U43CbeDEx8XH2eOO3lKXEKWZdmb77ypR2bWz1//YjDFAUuV4niSwAJ6Gti9aS/HcVfduDjvcB4A4K5b77560TVnCx9y6IBV3/+elpq28rdfdu3dNf+SBSs+/frt99/64NP3T8fNdkQ6mYA26cMNlVujfgc33j07emMYTOvy56/r6xvqp86ZIt9ZWlYC49FUHABKASVInIVvb3pl9wYAjJ88lufFJSpfe+vVXXt2/u+lNwymOIetwWAwpKWmLb5+0eGj+QCAX1b9/Pp/31z6f7diAe2sAhqJRqemYNFsZ1554+V7bv/HogWLl339pbRz+adfAwBOFiiXpVWU6GLaB0oIm2g0GrfbLe3csn3L7ys+RNuDBw6pqKxA6om46/47dm7crdPqXG48ka/zC2gsuhnLko3yflyxNC6MNOTHKzhKfLH88yVXXHXvnffde+d9drudpEi9Dq7cfcNl4as+8BC+/SkoKoDlfWu3frH881feeBnt5Hn+qzefQdu79uycc9kF8pdcfSVcshirZ6cX0LZYrxERYyIobO9CnESSM+viGUaj8ZpF155z1jk8D9tf/fzrT8Z65eAdLSxeVlYay7cd07qMnzx2x4ZdS664askVVwEADh46+PAT/yrKr+otrNWhQKvV3rH0Tqs1kKPHdLI6UIlWXJKohUUYkox2Q/VsRo2hJKCoMrfZEfDoSb9u+LNoyc+IIIjFC5dcs/jaxIREAEB1Zfl9889V1OrqtLpNa7ewLDt+8tjoZ0vHWXhM7OC/1bZGWtMx+mqOcnAspUnwPP/F8s+/WP45AOC5J5+fMXXm6AnT92xaIx3Qu1fv7778gWGYM89rK+PSSemsQ3hMR6AZXiPZX6QtLeUoBxeTtSe7N+1taGg4f8558p3/evzBGVNnLr7zse9/Xolqdf/vuptuuWFpWXnZhfPntOv9dQawgGLaNcYiLbbT1lqJTWijuN3uuLjAIF1O8ckitPHsE8/PnDbzxVdfWP4tnBCBUYAFtBU4uOtIa5ym0zMkG0bQEM+8sSrsMSdO1KON625f1lrX3bgLlpdOGpul2H9Q2N/NcTvDN7EGAEyeNXH7Xzt3b9r73EvPfPvjtwCAnN45yz6GQnnPDQtgo+uMHjOnzXzljZexekYCJ5FaYSYvFlCFekYiOVW//W+xqPDM4JYTNVXOSC8J1cqwhApolIO7D1EEtGefDL1Ov+GPv0nZPFqO4+acPdjpgM3/1+wpVKvVoS8cMyHQFjIUnETCYFpfPZFK9h3Y68SRk30H9oqkmKEvyStuTnMDrJ6x4HQ5x00KpOZIkuQ4ziioJwDg7PPPlB+s0+qSkpKSk7rRVPdGoeePDzS4jZ1vdhxoxqswGABA34G92uL7ICnmpLFZWD2bh3xV8FBcbldJaUlJaay+/sfIYe4uU7vSzBho82S346twt+rE1VXB6ilHq9dEGcW3bp1Z7NVmXSbFdzqTSC1R4RNFnqTklHijtWvrZnZScNM3ANSyzmPHyyrb/Y4wnQ+tXhN7sPLBBeNQ/52wpWa4zqzrZOFra6pra1p0hqRk+PvRMy0OgA4qoKF4ZSsDh8prdLD4YuQx60an87WpXP7YJUxoJxbQllNbA2e51dZUN9sLIwl+zwLzIfsLToFOK77FtWJ1EaYLE2OuL3a2bSyLsc8Z0squZ2A7gYDyfMddcAxJcF2DrRl+UO4KDVp1p5BgTJdXz0gNzCL14tkm7JfLaBfwlV1KQKOrJw8AEVl2O7L4yl0h+rd5EtxyxG66GIwgiDfefWWTGpiBZvUw6xo62wkENDpEBElFutlU9URSQkTW5WbT6idc/dNjb73/+48rdyh2wkVs5j2Ftr1eZu78Z+UHvPPqzX1y0qYLBwAATEbtVx/9IyHeAADw+djnXvr+g2//Ulxo4pmD3n3p/+bf+OqhI0EGOe/v/0rblP8bvWP30ef++/UtN8yeOXV06D2/8+GqP/7co9j5/JPXxZn1S+95U7E/Id740Vt3L7r+P4r9eX//d8jEe+V73n7hhjnTxMsdzC+es+QFEIG/f3oyKVG+zKAIOqH8HcEaSadnX+7JG+99V3Hwx68unTpxGNref+jkhVcH7pAgiEMbX/p0xYYX3vhZcc8bt+Tf8s8PFJcIvYcuz8VdQje7joCGQjRL2kTZDT5JK6peq/tgvU6joqnQnfJtvU5zyYVn/vDLdmmnTqvWCeECuKJDz5RlH95tqbNfftVLHMc/fP9lj/1r/sQJgx95WkzCIr58647yirqVnz8w6YJHAQCn/NFSo15bXFqz96A4aTrOAE979Dj0LGoVrdNp/vpbWaZWWdWESCtBEDqd5oevHs0ec5t8v1GvlT8s3g2V974nv/hzU25Komn1ioeLd7855bKnThSFKVEw6DVGvfbnP3ajFkSKZ4167R0PfyxeHRCzpoyYPW1U8e435TeALnf7Qx9t3nEkLSXu968eKt795jkXPlZSViud5LbrZioE1KjXarUquNLJxlxp57xZY3/6fVfs3xBMB6QLCmjzpI1oiuq1up1sO+6946LvZQIq14xlH95ts7suXPgceuLOBz68YPqoR+6/XP5yioKT/BZc89+///g32nNGThbgwcEiWE29fc/xe5+APdDCxtqef/mbZt92XrEl1cmi7Y0/PjHp4ifCHobkTBK4Woste8xta75+eP13jylkV87tD30U6Sm5ov34O6zNKN795tgRfXbtLwhzuTp79pjb/vr+8S2/PKW4nEJ2JSSBRgIqf4jpjOClEJtDx1dPJJRogL9+5ZORDnO5vPKHv63Z+4+HPpHv+c9TV/E8zwlm7elHYGis3mqvt9l7CuFao1bdMyle8WVzcF4GHm9zcKFfFKWHX2TQDHe3j80rtsi/pKfOmv1I754p6alhosM9MqBS9z3rLsX+6QvgihRPPQDbYbQKk88ZAi/UKw0A0OfMOxXPnncp/PY+cNtF0h50zB03zGqtG8B0WLCAdkHkNnPmpU+r1fTcWWG6iD/1wjepKXF///70kEE9pc+E7buCVnw7a9yAtz74Awb+vlx//iQx8NcSoP4KEixJKsPyKooKFeLMBDO0wAzf0ODc8dsz0n7ognvDviG3XzdTCN0yoVex2lzXLpzc8ru99doZAID/vr0Srqd2E1wdiGFEXyzH7nTffj28GQTDsG989Mf9t16oUuERXheHbqP5lK0y1xPT8ky63e7+bc3ef/3jklWr9yhmOv/x576aWutrL1z//v9uQXtWr9v/5PNw4Ux5RHXZN3/DPp6frr1u8ZR+fTKOF5RLB1wwffQF0wPJIrfbO/WigNvdvFpcm0zi3BkPN+NnOnv+M5tXP/PRm7def9tbaA+ywDMmw98xJKkK8o+UnDm2f+hTpJDsQiNxidAQp5x/v/I92pgwflCkOzxyrGzMCHEhdcR/3vz5lmumn9j2WpRIAqYL0FafkG03zx1LswQfWwXSv1/89oLpo9atfOK82TBHL2f3voIJMx+Bnm5I9t23zp1x/ogZ54+QZG7NT49ZbYHVa21216fv3C4XwXUbDr785i+B++FbQS7D8n93vP3+60t7Z6cUFQdW+HG6Ik7xlhJlYZm7IKgyQa6zDzwKQ7omk+7RBy4vKq5efOOr0gEet0/aljJpCG24yw04+66CHa/fc9PsV94L3xoV0wXofEOM2KW560mtQi7jhfIjBW4P/DtXMH3eU2t+emzm1JHynah3Gdo+eKj4htvfoihy429PX3jB2F9+E3MpZpNOYSQJgpDy1x4vU1fvAG1P3pGS0jLLlx/cLRflz77a8OgDQSkvieHDeoUdayOi3PPm7YfRxu9r925eDZ2vdMVvf9p611JxTQuFtx06MMvHsD2T4tHq9tKzK77ffM/Nc779bgsUWZVK2q/RaHAfA8TNF0x997c/Qael8wloG7ngDqu2UTzmxXPGv//JWsXOjZvzQo90ujwbNh167J/zTxQGinv+/v3pRTe8evJUwNOxLNTT7KxkGP67cWaoi9y8+plfv35o9nzl2LwdWHDtfzevfubbz++T9vy+du+jD1w+b/a4n1YFtTLQamDB0K33vt/CK06Z+/j6lU8u/+ieK65/Bf46/QgF9PzJZ6zbcFB+mE4H7ee1S98IPcP/3lk1/+KzQ0MZHo8HiWnY+EP4yuRgNBqNXqs+0JSpa3nFllafytlyamuqLx83vBnzpxGnXXy7soB2dmMbfXj+ybL11y6aEmfWN1jFzsSXzTsLAPDia+ELlR96atnm1c/0zUmT/CPP88s+vHvCzEfgHuFPdNSIHADAh5+vg03JFkwKNbMFRZV9esNk9Gnhmlve+PSd2+V7vv5hywN3X3zocIkUmSUJ4s9fnuB5/lB+S+fFer3MofxTQwf3RA95nl+1es/TD19x5FhpaZlYJ0CS5NqfHud5vuhkmPXuAQ8mzno0VEDRU+K/RFN+6v5nPW6Px+3pmdi0qWs2eyAIrtFq6uzmbNhJB9gcpzpRJ7NaYf40oknim5Sc8vF3G7z6MFnHZoMFtBWkNlInjmZMzYx9VuX7n6y9fN7Zq74Ncohffr0xSmTw3BkPy/+YJ8x8ZONvT2/yF3giPvjsT7fbi5ItUy9UVl9eddP/Nq9+5vxJw9bJCsIjEVY4WhIYPV5QXlFZn54W+K6+9vavKppSqKrN7pp1adCbajY33fWOfCD/zEvfkSTx9SdBU4YaGpwBS674+QniuOybvxfNnxjm2bAvaa/Jth63p7a6ura68VXjI5GUAp3gM9ddCgB4+GMx1daRqa2pbl31hD/hj/8b6wQyWhf31QaYkMW0+ppIjQ/lInPZRWfl9ErdtuvYpq35zT5Dn95pm7cf3rL9NKyO99oL18fHGa655fWWnGTYkOwZ54/IP1Ly25q9oO0ZPqzXtMnDc/OLV6/b33lbCdy4cPrpvYGklJSWKHgz+HZnKye3m+BAGVdD2NHreWeFd9G3/e+LFtxY90LK6jZDSb/7eVsLrw7P0IkmV4UjN684N6+4lU8aIf4IADhw8OSBgydb+XLdj9r2Vc+2oBWG8H9tOxBWUt+8c0mTzoMFF9MeRP+oCLWTndZgYkJRxExRFALx7qo/O0oMNFRSoxhVCSS4HBfTL+wdb3wJuiKKAsMWju47BXv3FxoM4decaDX4qBqKJbK7UlvdSD6q0SE/8dFL96K+maj5m7wWuq07aUaS1Bg1tEm0qeC26brw7SOgSLi7lFhjWezwMdBOAaqaykiGXRCf+PynJiSR5D2JxZ5vfJC2ymW3VdRWLqltIaNh2bjjYAunUXUZAW0fGnlHkj0MO9aW/1KgLoTSNhbNJoIFtOVEG8LLexKLPd+Cf6Glh0hh5a2IlYMkv9Q2Y+zf/jSp0lNriH+gNQQ0rKxQtJplghomdQHaR6ybWiOJwZzOGKiiFbFCKiNJZ+fN/UrWx+Woj90k0rS6sDJMxXWkM3Q99Ww3TlnayVNjpe7OnOZC+s6unk2FYbyxzt7rvN+dbkarKDVW4U4KnonUCrrZuWNJ0fU6+Fm1Lo6iiM/XbWyne8OE0NSZ45g2pQkzkbohfHd7P62dilHpzTRJfr4eC+7pAattW4MFtKuLZqd6S7TOTFPkF1hw2x0stc0DC2gnlZru806iQevMyzduOt130e3Aagv8/D/JmMXUoyojBwAAAABJRU5ErkJggg=="}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "The final preview confirms full front glyphs for both ALLY A and ALLY B, while HOSTILE sees the neutral dark back. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-ab1d22c1b1a9", "created_at_unix_s": 1788843285.930547, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 2, "requested_group_seed": 2, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [2], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 6, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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ECCiBQGiT+Rno5y2Znz4+akjBPcczn7hjenZuiVS+yfMgM7c4MDw8UI00x+bAFO53YYR2E4iAEgiENsSOeK6q3ozNz9p6o4KhkPcTwu2H0m+YPVLaOTO3OCYm3FcJ67UNVXrbQe5+Aq3iVyyfZjAZqzRayQjtLhABJRAI7UNSXChFQUbwfvKAP33pmnRXYp8oCEF1jSY0LDRAzchX8Y+/8Ale5oeGBktGaHd5S4iAEgiENpmfof6of1JtvfG2G6dA1A9E1FC5SlIQVtfUhYUGAc5CU1RZdb3DwE5shOIOI20yQjs3jkQElEAgtJIqq3oCAJKTozf8fmLlikk4152hKR7wkkqWV9ZGhgfjWxUV1YEhYZBWSMfBRugNy6eh/iHYCO0mEAElEAitND9DreppsnAXM/MBAD9uObly+SRkhyIVtVmDcdGhqC4eguKSyv6JUSE+FI0y5IGDhkpGaHd5S4iAEgiE1tQdVdWb8ZXaemNEdOjj98+XNPSGpRMAtJsLDyGkaSqvoHxg32iagjQNFQytUIn6i3n8hU8kI7S7vCVEQAkEQsuRmZ8WlqMg3Hs0Y82988wW5PrctP3U5ElDBYekaGZCCLJyigYlC/lJENAUpWRoNB/Jnsdf+AQboW6eedzLyFbtIhABJRAILe9ZB0XpqK03JsSFKRQqCOGB45nzpw1B3T8AOJKWFRIZIT0wPbtwUEoMElSrW5Sm4NWCsgq9eBwp3ISMUBKFJxAIPZUqrS3NKDYmhKEprabOS+UNIdx9OH3WxFRWmA2nrauTvKBIPQUkx2fmleKg8Mg+4WJ1vBRuQkaoLFG0i0MsUAKB0DLzU1q8czyvoGme5xmGrtPUhQYGQGEtP3NCKl7L2w2SwwhCmXWlePjo4WH+DENT3r5iBzxp3/1pnTEPrl0gAkogEFqDpt54z/LJFIUC7izL0RQsKq0IDw6EEO47ljln8mBBPeWheJHM3OJho4brNFVQaLTM4E50AmwHT+Bod4iAEgiE1kzsCPBVbT10cf7EgZQQUud4nqGp4tLKyNAgioL7jmXMmzrEOas9I6fo0Qdv9FWjCR8AIsXMvVZaZUY5oRCifPvu9WYQASUQCM0wbZA44VJCU28MDPAFAOw4kj533ABkh1LQYkHh+Pyi8pT4SAjhzkOXHMzPah3/1CM37z2YVlNehk3TnKslyoCQxAhxFY9j+t3o/SACSiAQmqG+othhS2JcKIDALNRr7j6eOWN0P4pCdijLcQxNZV0pGpoSK29al5FTpDXBEF96x54TorFJwezcYv+wiKRIHwCBys+3OxqhREAJBEKLzc/4pFg8+ZIVDMb9aVlThidLdihNwbPpeWpvW//5+MR4fy87ZczMKQqJjo4KRmt5CkIUTlKh/WHHzyJuR4iAEgiElpmfSX2j8grKwmMjtQbUOh7r3ZGzOROHJck7iVTXaMRaJAh8BZ+nRMbloqiEuHB/BgqhfAiAimYKytHIJFQB2n2MUCKgBAKhBeYnzl6iKVheXjUotU+d3oKzPgEAx87ljhqYQFGQpigzK7RYdqWEWD2DhM7KeA8KUFn51QPj/K1GaMvekesot0RACQSCp2gajMHhwTwvlm8WFpUnJkXXG1hkcgrG5tmMvBED4rCGSj7QhKgQ6QgZl4tSByZg9cR9myge5hTV9ovxpSno7dmY4LqCg27u/ePCfp32jhIBJRB6OGMURWMURa144LRB0Q7r90BfVLJJC75OjkN2aHVlTURUWIORlaLn5zLzU+IjIAQBwTbdlKundDMqNBDysLxGlxLlIyzcYZ3eovSy6zDSCjozD58IKIFAcI2DemoajGW1aH4chCh5nhfy3imK0jXUx8UjfygOyiPJTo1hGJV79QQAlFXWaeqNUYFK7EXV6tlQPyVDUXgh35b1e6cZoURACd3JJiK04qVur8x5AMAbT65IzyoQ29NREFdqMjSF6jgjQmiasliQHbpu26kbZ6S6V8+M7EKUTCqs5SkKGiwgQE1DFI6nSkrKQDcxQomAEggER+aOTHTYomkw9u0T8cOWk288uQJvgUhDKVawHikIjbpGr8BAioLYDv1p1xn5QHhn9YyJigjwFqceszzlrRBdppevlVpU7dAPtHOMUCKgBALBkarCqw5bEuLCzIJ1+cOWk7fPHyXKB4WSljiOhxSyQ3UNDZSPH02jwZxuXlM79QSABRRD8bgwKfNKMfQJSoqyFSa1gvnjUzvNCO2lAkqWkASC54v32nojTVEUBXGCp1xDoeAPZQVtpSA0G3QmxpsR1vKB/qIrs56VlSRlF6YmRWL1zLlWygKKBkL0CSL1VAaEJkX5KmTtRVrjCVVynWaE9lIBJRAInvPimsX9+8ZQEAXfjSZRQ2+T2aE0TbEcGiGHdJYzmhTeNE2hwLr1CFC4YPXEW3KulaYkRonqCUA6KkyKSYz0gQDQzQmo/LDX1wglAkogENyZnxzPr9+aNnpgTGQkGvuOvJxCr891Mg0V7VCWw/M4Gd6sgypJ3pCOQtSUPjXRTj2lp0jPKYrukxAZqMTloZ6UctpJp33m/bWiyk4zQomAEgiEJtWzrsEUEOiPTc5Zo5PUPn4MTQlaifZc58oORWt9tIcRyqJItRojUk/oWj1jE6XUenTBQ0GapSnzMzEqptOMUCKgBAKhSRLiw7BDE2voihmpal8/KCQwXRRmcK7bcvLWeTI7VLBPHboo+1Iw0FuUGlfq2SfAei8QIvu5aeebeUvcFnvW1tV3mhHqWeVUt2J4EGpJ4J44Gq1BzGzze56r9Wun8yIQup/5GRIqSpXJzCoVNJLLhWNf/s//BibHpPaLv5iZP2RgwvqtJ29ZMGbDzjM4jgQoyiJ0lr+cmYMfW683of9gU+ppU0PkJ72M8kybB0J5uzw5QT7eQQF+AJjmj0/dfjxDMEI7qly+V1ugSXSNy+3nav2kS6efFIHQVZCW0niqOw4frd968sVHlmTmFlMUHJgSh+1Qmoa3zB1p3RlN3JTlgIqwgBLVk0eX9JyiQamJcvVMz8pPz8oflBLr6fk1ZYeqWGCx2BmhCzrKCO2BFqgnqqdQ1Ik7m4lEEgiuzc/kxIhF04ZuOXABmZUA0jRKY1Iq6E27zjx+z9z3vt45oG90/+TYi5n5PwBw++KxN80e/tPuc6hBCKrzdDwgzdtazafnIvWU35uehYTYE/V0DB+JKu9okQqeUNP8canbT2TsP9VRRmivtkAJBEJTJPUJR4OP/LxmTUDRGJyihOxQ3IX+aPqdK6ZkXSlhaOrZhxcjDf39pFJBL502tNmXtC3qKQDd26FllRqbEaricDSpg4xQIqAEQm/H2fzkeSDWHf1+MiLEd+LIftK8DdqaTn/m0tWbF03IulLy+4ELa+6em55d+MPvJ33ViuT4SMCL9idVW0TVFmmuZUpHzsgtdlbP6Jjolqzc3W28duFipH+IPByPEYzQ9ocIKIFAsGPc6NSQ0EBJKH/4/WSfqMBJo0QNFbKYxLvSLxfMnT4iK7dk/4msB26ZgTV0TGqMWu3jtIJHt2vMVGpqH2f1tDZXbgeuFVU5GqHKDjRCiYASCL0aB/Nz3OjUHfvPhkeFIWPTOpzjhy0nEyIDRw9JwqYeTSM7lBXS3YuKK8aPTs26UnLsbO5NiyakZxeu23Jy+bSBRoutoTJGyzHBPnS7qKcbdyayOlVspH/IucxCmxHKd5QRSgSU0EMg/Q3aRT137j/Xt2/0tNFJgWEhaNYbTeG6ox+2nOwXH5qaEgeEhTyq2OR43ES5TlM3anh/CEH65cJbl04CAGzYlnbr3CHyIzfwSoehck2p54DkOE/OHNeGugBZnVWVtXWRoX7YCAWssuM8oURACT0B0l20vYhLQLGjn7adWjFrRGRMuFC7adPQYf2iUhKjxbU8LYTbeXRd19Cg8FJDCM9l5I8ZigzVbQcvyg/ra99eub1W7i4LOhOjYsIifbEntEGnq62rv5xX1kFGKBFQAqGX4tL8pIU0eADAlz/tWzR1WHhUOF7L45jSui0nR6fGJiVERVA1UXRtFFMbBqoiqJpIumbBAEUQ0EQxtXkX06Ymq6Lomjjv+rgAFl8iYLV0MZdmj+vjNcBXE0HVyC+CU7UoTVh63zHClm/vuYZCAHIKyoHFUlRWW1mrLavU+Aapgny8+8XFd5ARSgSUQCAg9cTzioV5cGI7D4amFk4ZEpsQhfswYX/oui0nxw6KZSL6l3PBFXwwpMTR8CcuXJs8PJ5Dj4aZuSVN9QMpKqkKCgr0UblQnvTLRQlJcX5edn5SZ9zmc8KUJOT0jA2LDIv06QQjlAho8241sjwk9IbUpZ37z0njOiih2v2T9buVCmbuhEGx8UhDadTLDnVKXr81bcqIPpevlgMAKvmQcj6kyBRYzgX/fl47Y86sSjqmgg8pY4MK9H6FdXRhHV0fOaJc2G1vZkODb1KDIrScC3a47M3SJSTF+boS1uYzQeXXaKNkhAIV62iEjmtnI5QIKIHQ27lhEYr8JMWHvv7oEqyhWEZ5AD5Zv1vB0DPGDoyKjaAoimFovJZfvzXtpTWLsIYKa3yIt2/cceqmWYMUXi6mwrnxe2Lb01P1FM/PTkalq3lFVZIRCgBwNEJBOxuh1MXvH2uvYxEIHcHwoPpmL5KvzZOdPWk309vMz+17z+JhcHYaKvz7yfrd8dEhN80dgzRU6PuJ1/Ibtp36y+qFl6+WOyTY/7j91IoZqRo9njjXAerp4PV0gFMA2ggYxqURmlVc3L5GaA+shSf0MEhzg04wPxPjQowmi0rJYA198b+/DRoQL+3zxgebXliz4s7FEzZsP1mQVwIgCjQxNPXTjtN/Wb3wpx2npRlzRhOrUtIbt5+6Ze6Qn34/hh8eqKQl9cy6Ujygb0yr1XPsSx87tlu2th3F/zMMnVdU1ScqJqegXDBFjYIRyjbodGYeuSaAClnK7VUdT/39X+uIEUog9Gbz87N1e3A7ZDFMZG+HYt744H9vf/bbyvljY+Mjcd94s7Bm/2nH6RvmoD5MQh87NJQYH2TvsUxJn65VGzxXT1wX3zJkShgbEtAnKgbQRqSeViMUGGmbEXq1dO7Ewe1lhKJT//u/1rX9QAQCoTuq55cb9qYkRfZPSaDRiE2Ic5iwhjo//J3Pfrtr6aS+ybGChgJcjLRp15mlM4fLiuWRHSpv8dEi9UyI96Qo3p3xWFRenVeMCjpzCsqxJxSDPaEos5WxzJ04eP+p7LZrqHj2t40KbeOBCARCt+OGRZOSk1DG5Z0rJg9JTUKiCICkobfNHibfGTs131r7y8r54+KE3Cae43HP0M17zy0U+jBJdijuqSzhuXrilHsX7fA8iCKhfxgGGaGRNiMUsCpnI3Tn0UvYDm0jtj+AaCiB0PvMz31oBhxDv/zO+luWTEzt34cWEpjwDA8HDYUQshza/tbaX/IKyihvH1oYCn8hMw8AsPXAhQVTh4hFSkINqPTAQG+lJ+o5KEVUT4mmNNQvdrJ/7BT/uCkBcVMD4qeJlwT0L6CNlTV1ghFaLaY0WcEdRmq1DRQF508dsPPoJaVS0UYjlASRCISeT5rZxbr4hkWTTp67zAMAOWBkuZfeXvfXP97yzSY2J7eQ43me4yEFkYYuGL1utzikiIJojU/TSGR1DfU8rfKmzKkp8Rcy84YO7LPt4MWZ41P3Hs9wWGFr8EgPd+pZIG9nFz5+yfhnH8LXsVEswQkK3iQQGHQgLMoXGGkUiAdWIxSogApFk7KySwsrtZCyXC3QoNTXNtuhdn8GMUIJhO6FJwleTZmfX2zYG+jjE+TjE+znGxMWHBUcuParHQ11uojQYKHZEofbKa3bdioq0M968UcaauGEPiP0tNFJuUVamqaee3hJRm4JyrsM8Z0xfmBTZ4vUM9FRPbWNlkEpNvvUE2DTXlAxx5Nhisqr82VG6K6Dl1HP5tSoxMjAeRNHLp01CO+/8+il55aJ11sBsUAJhB5OXABrZuvjve0EVGewxIcx2sKLpVWaqNBAAECB3n68jXV3HoCqaq1Lzdqx82SIN6ip1ny76VBEoLq6SvP+1zudT6C6Cpl7zz95q7QFVzTt3Y/m0BWyrkeTyWlRwpFBB7zUxtiQAMkI3XksHQJ+14nMRTNGpA6MBCYWcKolswb9tifdqqGD3/oVXW+rgN42KnTdaRTAIhAIPSBJFo//ogJ9C/R224cOSLyQdQ3po9oP33U5r7RfHxRQwgnwsAkvJG6/1ArefHc9viIp6czp4hw6Of/+6GcQHNtszbub+/afyp4/tT9gGI5nKVYFaOPsSclejA/Pg9KK2qjwIKA0AhOAjGrxzNTf92a0RUOJBUog9DrqGow1NbXDUqLPXS7hAY+nvaUkRLEcDwHILShDYzkEm5PneYoR1Yqz8NgjyXH8Dx/9+c41b/NodAfvMAW+2VW2pKRS5Fz+v0uQo9Zp4/G/PujqcV6SEUoZaY5m9xzJnTN+EKCMkFNFhQdhDc0rLu0TE0YxXhzHo+x6AO5evRzFx1qoocyWw+mLJtu5AIgRSmgLnhRKxtHIMWdmm9mTjJXuIFRK0XIaP7q/WNgDkSzmFNQEBvgk9E2QpG33vuP9hTwnJGFWy/PZJ28DSvq7n1+/88YXgSiqLbZMceGQ8C+SYJwj5XyAuoKD8of4xU5B0vk3JJ2eGKG33zwDAHAlr2RgUjRQGus0FqyhfRKD865V9okJWz5r1P92n6ZpeOddr3/37Yst1VBigRKuW+UlmSl9XdCbLJLNaNDWH00vkq/US8rwLSRoHM+HhgZjmYMAJMSFns8urqhpfOqlT8ekxoYG+gxLiQ4PD4KC+OIeThQEWJC/2LB3SGpiHXJ+gpBAHzG1k+dR9igAtZpG3AB5zMTR00b3HTYkAQT53nnTS/g8TPX5dQUHxVO0qrKH4sxyPJpKbzVC733yP1+9+8QPP+7OvIo0NCAQ1GkakYaW2TR0/IjYE+eKKErU0JYJ6EN3zf7k293ECCUQegkuV9zO8MJSXdBScZWOUpucsojOZKAkUGeGDpKN3rQatBDA6WNR3iXH8XqT5fP1e4t/3fvLr3uxHI8ekqQ3WjieP50v6G6rKCitTYwJthmhANz75H8AAHMnDkIaOiCivEYb4O/joKHL5wRuFgJKaG7o9hdS57/h4dNRxddKHdSTQHr2EHoqlwsdQ94D40M1NRrpUlejqavV1NXW1WvqdPX1hoYGNLhDUD+LhdM3NDhEli6kX8OXfolRyCXqvn4Igv1pl/enXT54OufE+auJiTHy3Xcfyzh0Ovvw6cuuHucpfaKDcSUVNkKl89l5ND0xISIzq7xfUvDlvFIAAIomcSqkocWVwETjxKY773oda2jLlvA9zBParBvODAKS6BpP3HAEQs/GV62886ZZZgvLcnyD3qQ3mjmOZzmuvtFEUcDC8pqqcqxfeqOZCowCVRWJiTFGGsVqAABDUhNxvCj7aqlc6Xge1OU5SqGw0hdjU2YLd6WknpLFh5DY8W4V0zMdpayrfpwTygl+A6yhcycOAiYaaehVMeVA1FDBDl06a9DmPel4IX9x3RNDbkOmqwcCSoNF0wYBMd+2J9Bebrgxwj4EQs/gTCVMCvEvKKlA2ZellT7eqHBy0ezRJeXVeAejGaVnBvkjl2Wwr0KYcclprHZUSERoEqybNH/owdO5APDF9WyMHxosh8FXpkwYfujYOYfnleJLQgcSHLACRjOr1+l8vBR9EsUs+jGpcUdya00mi7dbxTzxN7FIySX4CaTY+owx/felZUs3sYZ6eaChX7/35HPLUt/6FSU5ucFWErDlsKPrlBQmEQg9gzOVsK7BiCZwVNdFhAXfs3Ti3UsmPLB80qyxyEuo9lb6eCul/hwabaNG21ir1dVqddpGvSRi3l5MkL/3gVO540ekDOqHhg9jH6kcST3lEXkIrbml1gYgPBC7kCQK6ontWW8ls2xG6uhBsZ5lQ4k4+wyQz0FmhOKbUm+TnUfTUYI91lBhLY/K5Mt14lqeU0lFSnfcOue5Zaj7skdR+B5mhBIIBDlX9OKiGwCQW1gJAAgNQIM38oqq0rPtWn8CABbPGZNXWOGQgplfVGWxcJNH94OcZcqwBIamCq9cC/b1rmlAIltcq8OGHpJLjgcQSGM9SjU6lkUBKME5wCeE+zXqzdpGo5hKNSIlI6foUqE2NdbPYjIF+6uuiaM37Glh9j7L8jRti+RTAFXx4xYnm/edWTpjJDAByQ6NDAsqK0cxJWCyAAZp6D2PvwsA+Pq9J4XEpibtUOr3Y7bxIMQIJRB6PEjdpHojgUH94wf1j3/x0eVL5o4dPCBh8IAEq3qCi0KnJQyEUMEggSwqqVi3NW3BmAS5pYnUU8hkotG/lP2oNzTwo19iuEJB0zQlpN8jPn3rwYkj+zG8ZdjA+OkTBnoFBebmlReXiv6E1lFUhmIbEIKwUP/gIL/gINFHh3uVsqz4VxuMxsyrJdgOFV8WHtmhQMmi5iNWO/Sex991b4cKFqj1lVw0dRBw2+uEQCB0X8IpvV7Khxf+ZTn+UpYokdKVpsjIvGbhmfAAb4PREhPu/8nPR+X3MjRF08jJySMlBSjbU44Qyuc43sKygOdZwRiEEJQUl2AbNy4+JkewPO1GKcke7iEzx/a7XFAFKDvpj4lEo+WEUaPiFuwMxcmhqLJTLFKqAZwKF3piO3TznvR7Hn/XjR3qmEjvHI4nEAg9A72u0UGMKAiTYiNMlFKpUvE8r1TQLMsyNOXjrVQwNOC5ipoGU6MW75wUF3G5oDoqJrqksHjisMTfD9kJioIRAirQuuaXOUFxIqngi0Q3WGEhr1LQHMenpReOSY3T6QxI39X0PhSeAkExyMHaVIGoeyCEjUbWx5seMywlwN/XaGbr9eb8sjo1RB4DnPMvDyjZa2hwaUVNVHgwLvQEjAqHv/By/rllg5w1lHn4hgnFKAYHXRqh3TeTiUAgNAuEYMigRI7naYqysCxFQQVNmy2sSkGzqCco761icnJFAU2MRQIqBILg7wcz4hJiCq+gdiQYk9I6PANF7zGia5X3juYBuFINgTqGp4wGk4aCUKWgUZO9ATE84NXeon8WAt5ES4kxrelZAiEMD/JpNBiG9IvF7Z8tFi4hwl/baLx8pRDboazgbYCwSQ3tkxh8/mLhsP5xy2aO/HXvGfxbgKJPTqdEfbwJT87jpYvkCSXqSSD0MMKDfB226PQGg8Go0+lNRpNBb6xv0NXWNZRV1VVU11XUaLX1OmlPjRZl0dMUVVtvGJwsymW11r7Lk0BBnt1gOGEGHZKe8eNHmS0WfMts4WgK+UblgfbJYwalxKLeeq7wqNQeCsExCEBOfvmVwsqrhZUFpdXVtVqj0RgtLOSx3W11w1oT7AV/KKCQlYo0tKxx2JDo89mFwGJZNnuQ5Dl17qlKPXz7dA/OikAg9AicRGjP0XQxu0i64B0hPJ9ZkHWlWNrzrFC1efJ0ptFkrqxtMDUIWdLW/c3aQkpfAhqLzXUF8WEM32h7oOBs5ViWO3DwOM+jXHqW480sx7J8tdYwODlqcF9RjnmO9fNqcYMOB1XzRq1SYNqlvFOXRDeoMHIZKhk6JipUCihJ45FtGgpAjlikFAw4laihJnrF3MGShjoElFycq9Y7gtieBEIvYcbIpN1HhEWntTmSWo3null7itjD8cih6aVDchPINTC0OK5D6WemhHxPluOwk1HJi3mRFpBt4TmVUlFvNmSb1Jy3N3ZEWljOYERaDAA4nFGREsIwFCUs7duUUCmfApJm1VA5If4oVR8nh+LOI1KRUkpScM7V0hRrgj3SUGEtv2Lu4E07LzI0tfNoutwZSmXmFDc3/Y5AIPRkOKMeXUzCv2ZDQ10dumi1DVqtrt5W68yZ0QoXew+d89dp1IoJQgqt8YVkUDtbF0LopWIoCvbz08WqtAnq+gR1fV3+pSimRtVYjOUYAHDwVPaZdJtf1XOcRWxo/xhDY4PDBf8kVNXpoEIpJjbJ7FADy4oaak2wl9uhN8wdgn9NBA0V7VBmYEpMUX4JPkar+kwTCITuzb23zkGN5ihoNCEHJd5oMrMsx1XV6q5eQ7EXAMDgwf3PZRZgmTD4RAte0ZpqFvlAIQBVRiSvKIoiZNHzPFDiZnYAmBX+gAcWyv9KI9TpTbEUCo7zPAjoM7jsaFmUcCgAUOYpZzE7nJvnouTQdHnIgD5DBiTgnlJmC2s0sWYLa2E5nYnLKagGZp2UYC+3Q3GCfVN26LLZKLHJqqHIDrWb7uTBLGYCgdCjoL28i8trS8o1RaU1FdXa8qo6fKnRNNRpdRyLquMxPioUN3fJHTfNwlekiJBcywTLFCWGckKfJF5QZ47nL2QXOwjkoMEDguL7tPpvkctXUVlNUVltcVktTVFeSoW/jyokUB0W5NMnwm/aiLiRg/pAhVC9KtihUgOnzfvOcLTCjR0qm0aH7FDq4x/2t/p0CQRC98JkspjMFvQvvmK2NGUz4UW4fLy72kux9vV7x44cWFRQ/NuutN92pbl7JifTEQdtcBYRdgM4W5dJkc20AaJl5+OM3uyiEEhQ0priciSmpRWakgpNVU29wWAckBSZ0jfWOaZkMhlwsTzW0NKKWjsNlRXL7zyazjx88+QioTsLgUDohexB6Y3uiA4VRe3AyawbF0/8+ZfdLz9+E96yY99ZfEWlUtryQK0orHmgJmVkYGBASa2G8VMaqwsAD5QKGmWZOj1XfaOLpCgP1/JGC1dab1QLy240x/jwJed95kwebIvLo9JUqFT7mnQNuEgJp83jgNK1/PKBSdEpSSgcjxr1V9REhwcPGxItFcvjtTwZ6UEg9GomDo4/fCbH7S5+8iDSk/ctEFLg+e+3ng1Qupa1woLCuHihmkigvkFMJuUBsLAsTaFyTwjgoPjA1LjxZpbLLa0HoIJSoGqo1v0VJpYrrTflaQxSkhE+W2TzKoSkgqZVNSrEVyxSEhIMXBYpRYcHl5TXREcES4WeeCoyEVACoVcDAbh92WSGpiwsZ7FwjXqThUMp7nqjxWhmK2oaDDrchZ5P6J9iMnMb91wampqoZJjBqUmFuWLE/PPvt7H1jZRvjDD2g4+Lj5NWxHjdbTabIYW6J8ekjKC0VyEPWY7TaOp+O5zjpWIGDUB+T85sPHDmakB0jItTdAvL8ZWN5mu1+uQRg0yopSl6wPKFE1mON5osSqWirsHICQX41Rpdbb2+qlgMi2GKy4xisbywkG+q0DM6wk5DIaOaP3UAEVACoRvjZviChzNNedTo0+vzH/fgm39YOUPboOfEungkQPrGehyaDwlQUxCMHJJMUXBEYuClQrHEU8KsLeyXEHa1qJoHICYiUGONwUB9GaNk/CKSS/MyKjWF0cE+LMsVVTSYWS51QB8FQ41ICtp7Ep2KZDZ6Dmp9b7Dk1Rqqdea+su2Bvl7SWCdfa3J+VAjqFc0NjdMbzTzPm8wsTVONepOm3tBQi9s4wfFDEo9fvOZOQ6N8UNMmE1AyaiKg7j6FePqu55M/yBheQlf7VMsnSHuZTGovlGMUoUAJRn48cjj6+Kr1pVnLZo/Gj6qsqTcYUS4RJ+hLo96IDcD4qBCdxULTlKayHEKwvajoZEbRmH5WjyMqeI+iIbxWCylfFJkpaeClfqBJg8ZV5p3zslTQqM0dxwqNQbHFp6koM1vYPVViGGbKtLGX8lrWf0NnZvM1hopGk5TRiYCgpq7Rdm5WKxZ7CH7beVx+hNtWTNcbzWmigIJjF65dLazoGx/epIaWIg09n40Sm5j4iOBeGEQaHlTv4WhyAqEr0+xvtnx0jZ/BGOjrhZI0fVG9uQUiidt8BC/DXTYxRkRHBKD/eHAh/Sq4aWpRlU6lRItuOgd1ZZagUVm7rAeo7HpJSZkSwDPpheEhviwHTWaxLhJCWFJj8FIxdTrRScqxjnmg7tfyJgtXpDUWa40mp2yC3/acAQAsmTVS/jgIgZdKsXIpGi6P4VHrZZah4ahhKafO5fAA9RlJigt3KFJy1lCcHMoUlNi9Cr2Ec7V+nsxEwvuQCeaEHsy4wQnHz6Emck2B1Qd3NoIQLJ7SH3UkgWCz/W6MsYzn+ZSEsKuF1WigfJCvtMJXmsvRY8VIN280swaTmF5675KRLMurVMw7609Sfn6MpQVFnCzHVzSaCzQGHcpech19wjLqOZHBfqhxKU6wt/a0FxPsLeicc/JQgj3yhHJg2JBosoR3R5o5doyiqEVvAIHQveA4fuHscRaW0zYaIQRBft71OqPRzGobjXmlGmnwBgrC0D4cxxfUmM9lZJiclA4KOfQW60JaGo2Jwd2POA6t3y2saJ9yHH/2mga1d6pA9i9bX3/wfF5gpM0t4AaeBxrk+tTXGSwuY/dxSX10elNVoV1fKJdAxtaAr6ymPiLYDxvTU0clHzydgzNPDUajl0qFi5Su5pUnxUcIL4qKCCihs2dKOzjm3EB8yp1AtNCgSEFB3xDUBQ5A4K3w4ng+UK1QUHxNrZBGDlBsfWBCMJbF0KhomoJxfeILrFF4sUccBF5KhkFDO3gvmbZIpfNmC2c2s0qKUgiqRFNw83bUTvOGRZNAPvow8E6lnE3RKLg+KxtRSZPzvRCA4f3C0RTlobE8z+NCI18fNcfzeoNZ6HTKN+hMOoNJ22BkWa5Bb2rQGSGACgVVVlYcERbE0NSRs1dGDIw/l1VIUVAc5yloaFKfQElD0R+55cjlRZP6tfV9IBDadaY0obPgra3a7cp4FAzlp1bWYP0EwGCyBPr7Z+ZVIu1xbBWCGDB2XtbJHUJDEXQ8h5IhLHMmC2u2cJSX+OD/frsLX0mNC0BReADComNspaNNY7RwxXXGEq3RZC3BdEbBiCPlgoICcFepz3/YCQBYNn8sQJVQlLdCxfkrYYQfx6FwWYPOaDSxBpNZ65OoM5hoix5CeOFyUUpC5JVC5IJw1tC+8RHMD7/ZBaQIBEKv4mqR66g3claa5Ot0WF6lYVlu6IB4luMD/VBHOMkChQBcOHceKCMvFQNAo/h1bhWQxrujHneC0uF/ecH2BACsWDYLy21GIfpB5Xg+MdI/p6Lx6YfmOZ0OtHd9mgrqsOvTNWYLm5tnDYsJV1L6RN64cLx8H4qylfYrFbRKyRhNlvpGo5AqAOoaOD9gyimqySmqoaw/Bh/9fER6yFN3TrqSV868/sIdT776nbsXmEAg9Aggz+Ku8hlG1MQzRBhrXF2nGzoQzdcUsVsQCy1BBfXyUjENBouCodFFuK+gpAo7H+XG6Jw5U3ftOujwvGZVpIWy+IcG6vRGL5U3ZSlXCsVMKYnRPM8HB6ghhBGhASeOnvXzVQFgSz9yxmyx1OhR1qemCdcnAOBykYvAeI6kp01z8IzdeGcvykIzNNtUXMtEA9rMZFwu6s1uuDiQf5UV55q6Bn3eCISewOB+CXHh/igPNBgVL/oKjZMNRvb7LU0uQ2Mig/CcOC8FXVBU9o8PflZ7Kf750n1ffvu7WqWQr9MtFpZh6J07D6DBnLiq3ArWufHjRu7df0zXWB/ki4I0PMd6qRhp2gcAoNbAGutt89mc4Vi+rLwmX6OvajTLK51ccv6Si76iwwYnunmIgkMRM5qxazolmZ8OvLMetWFierkbrllP3BjrbgRCd+diTuHlPPSV7xuDjIbQQJzqDu9eMRm3oMMNPkxmVtNoKqrQFlfVA2DEJihNU5Q6GBiQcbfuxx3SMaH1CmUoBRRMjgstKtPwPB8e6l9tHeoBdcXQzB07jkZvUhSF5n0KPPbipx+88RC+fuzwaZ7nzWbWjSjVNxoulVwp0ZrcuD4lZk0cuveI2OsEw0PapaoCAEYPTb6QW2GhvBgOnaTn9EwBJRAILsEThuVbLl6rGDxAXMJjY9BbhSLpgT6KEH/VleJaYMG9h2FUiDqnxvmAAEpz4VHnTdR2nufFoZvSTizH1dcWqJRIOxnaTy8UOwEA/vzalyP7R6tVyCtAsZZrBaXiTGMXZw5KK7VXrtXrkci6Qfzr4sN971kxRcwB4F15JXjU+tnbS3X8XPbBtGxUkjRvFGoxdeji3PEpO4/bGqzMmTwEAPC/3S5SSt011yMQCD0NIZVdaiXHAzB9dL/SyjqHS1Vtfa1WB3kuKToIPw4CkBQT1MQhwZhhKUIlEth2GCkRhHaNRFHMm4L+PiqGRp06FAySWtxgVH4cvdHsH+rYE096dpZli8s0jbom+90BANKvlGbl2eoqpZajeCQ9vlAQUBT+FzI0NJtNOoOo5ht3nNY3CtlU9t7VXYcvmg2Ny2aOcH5GYoESCL0LudsxNMS1JmL1YWiqXmcU/ZkQmEyukjR5dNfJ8zmhvqrls0Y8/9+teLPcPYo7FqO8IpbrnxDCsrxLN2ff5LigOn1OuYsABg9AXb2htLKO52296ZxZMQtp3PnsosJKTUmFp863C7koSwmz/zRa45fXNqLWdshWt51paVnF2FTHTlFEQAmEXkG8D19rsCtRR7ZV092U50wcRFGC6lm3KBkXC1YehY84hXDXL3vOjhsYNXvi4J/2nN2blpcom5BM01TfUBS54jhebzK7DABZzGa1yvWa2GJhC0pq6rR6oXy/Sc5loTA6mmznMd8fqnI4JA9Aox4l2wPUd0XMuKqocR1NJgJKIPR8RoYhyQpVhw7pbzdxaG4oKkOS2HnkgrO2RoWhkXAAgCOnLzsclhe208KAYo5DHfAAALuPXvrbn+4EALz859dQW7lEW5EO7vpOU5TRlR+TglC+8Jc/qqq28WpBldnCoTFGTSOMswPjRqQU7kpz89sgJyR4gL5CnAmKgUIRAQQQ+YuFDAQ3/UiZRKGQi0Ag9AYuZruYky4RFervYqugkt4q9dwJaB7G9sM2kVX7+ql9URJLcXklBXmp+8a/PtzQ1FNg96g0xM0Bhwp6jMlsycmrqKiutwsGuX44Mj+vFFYPH5DkZjfpd6LM4AsMRQwTzgPgFxBkqUEOXBwQQ6t3wdPgvpszc62g+fxSwhhFUZoZdTkkELoR7dgKR1LemDD/cmk9W2qt9HQFDsNHh4c532VhOS8v38xcu04fuwWbMcC+mQgPQHVt47XCavuyqOZpbGy86iqjXv47UVaEdNxiMSpV3prqsgD/OByisoSNhTm/ItuzuWb4ZAlPIPR8zlTCAX6o+kh07VHQzKE5w0FqRsp5t09+7wybNyYMCZkkx1FCer/YGdQKy3J5RTXaer1Zp1V4e9o8obHRXTmTMyajXXD/9MkTgLHLB6gAYnJVaHgUVXFC2k4ElEDoFQiDMFGCDo4joVQiCE0cX6czR4T5AYCGFFXVGbxoJLI8D8ws8POiUEMjABqNHA1xP1BosvA+vio1ZG2Cy6OH4FFC7U5dvT6/uBp3I22W0mqkxV5eYqS+zoiaSAV4oQ4n+McDX2nqp4LjOWEH3D3K9Z9TVVEKQLx0kwgogdArwO3mcF4OD4CPtxJCJJp6E+fjreQ4lFWu1Zl9VDTLcRBAnYlVCy3pIASNRoPaS0FRaOaambX4+ahKKusjgn1UKlVNjRapGw8Gp/bNzbmKBFcQp1q9Baf8lDei/kqQohXCZPgQtZhgL5Mz8SFOuobkr7CktrZOx9RXmWl3CUzOXCmsVHsp0CA5nTksUI37mHAQVGv03krUMwq3KJXw9fHhOR795VDIAxXul6d8uYQIKIHQOxDiyuIVFBRSCPYopfbiGAZNaYeCqnorhMgJhIyC81Yg2w0LjZ9QOM9ynJkFfX3qfQ0NYb68rw9bzel4Dh2zj6qGChBqlgDkeL6WMQVzyGsZoNRDADgAVRQMU+lPayMggJpGY4i/Fy9MweR5vrbBpFag6yhjVAq086BBZ7pWVG22sAwAygB3cSEHrhZVQoj+RrScN1i8VQyWaeRR1ej91Epscc8d7n/i4jWO4+r5QKtU4j9fjFcJSmqXDeoAk9zHo/7PBAKhWyN1NcZiIKy4kWQE+nohS1As4lSoGNESpBlGiXRPlBIvFcMjBypt4SBFIWuOoWkfb0U9Q2OB8VWjQk0sPXLTTqVg+sRGXCko81JQDdCvf4Ce53ktYwr245CCc+i5aoC+BqLBwhDafKBmC1dUWisE30Gjmffy4G/keT4xJiwiLgIJqFCTKnQ2Qe4LLIO4xMhqcfOhgWo0yhgAP6gBjZrgQJ9qTSPlF2e1icU2+26ekcnNK33o5sm9cK4cgdCbEUQTXQnwVaFiI4GIYF99ow7fCvFX12kaAOAHJ0fmFtehRa+QohQWqK4EvkxYaCXgAwMiqivEMNS4xEFVuSh0gx9+uV4TY0EeSdovrG94iqYM6CHyk/I84ABfYdTx/r5IkIV8odz6mrhwBSWIr07oqGyysNU1KPfTaES3fb0UmpIM3+jUZv8o3NAZa55CsKxxWpJgSgqJnbLtSoVQU2oN7+NHcfWyqfHolwUd0btRj4sQdOoU+TOSJTyB0KvxUtJYpJBoBqqLGnXY+PJVq+rqGnDlJ06xBEKfOj+1sk7cHyoUFC1oEwDA38dLXANTdk1FGYYK8FVFhfrVaRqwPcdA6KNWUtgpa00VUiloCNHaH1hQy+SyCm1+cU1VbQNWOm8vRaPRoziSfCCoYFjbrgMAhqZEZ+dVCaWlFA94tUohb4tnts9OlUJkVjNcOJNGXE0g/Pwg/ScQCL2MPtG28hl5kMRLybjPaXeAEh6MGnOgjsj+su4dtn1UCqZvXNjgfn1oCrUUQboJYZDQ0N7qORAOJRwL103WaBqzrpTlF9fo9GL1vZKhg/w9WcQ7MiTFhYsSPZOgrQxDyUcqSammwo8FuiK4I/iJQxM4HkW0WCHUhpMZMC4s0FvHhHglzgU8+GrjulacMYFwvdpjezKoDkPG1bmmCc3sFxeIo0x4/QusFqsAdhY2KbgUBZUKJjE25Pgp28bgAO/aOrtUTZmO85fzKorL68wWVipYghD6q1U6T21QRGJMWJ21aZ6cAQnBzn5hDH466USwZQ0BtFispyFutp2rTUCjxz1UcuIT8Xp0NADgL0/+CQDw93ffacFZEwiE9iMxoTVml40S2XEahfCM9NV3JXcsK9cTl9MuUVe6KuusYztkBxyUFJFeIBYpQQi+/Hn/fTdORy3xkyMv5ZY5mLrOXMmvMuNBGrLaTfSQljU7bh50eNlTsBaLGDvCSUxi5yqg9lbUCe4HGULeFeSZiNEPIuexsC1q6Qso1d7ftmt6+qWnH3oAAPD2J5+387kTCJ0+X6AbSF4HYzdnw0lDHQZzunw87hjSUtIu2jUi2X86v398YFM7a+v1SiUjmp+ofyjNc7zzJPq2g1yucuTqKdiZQnG90JLKlX0NAWQK8nKlnxp8tHVH98+cOrHdz5VA6Jp0cclrX9zXa7qZM9Sk5QpdmqpgcHJ4bZZHfTaGJEdl51XbHVI4RbxwhhCqlIq22J79ZWt2B5yak6BnFjeKblIcQGs6D9RBPT17GgKhi0mefK1KcIXGx9b38lydrOLcYGs8nFsvbk9Wq/ZdLUu27xjfJBD9896X29zsIq/zHJISlV2gcbMzg7rJibqDDEAF1ZSAWsxmRuHZSXpggSKdtEbb3abPy05V/N92HIcCJ+txCR4wPKieRCdc4qhrgt4Rseuy5OqQlzNXmHWRmy7LiwQgN8s6Kw6A3DNXpOv9fLzcGFpmwZxsyhTrH+/QGB81GMXXeJ5XNtZyEBrNFgdFc1NkWaP2rqlFWai55+1ameSevyb/VUAxdXtE16cn2mkTUJtmimGn3kBcgEdx25aGd3sMRN0ILeJyo81KzM0oCi3JxddztMiqzT13Ff17EfWvyz2fR4d65+qMyUJ5qDOoHqmhXqFCJjMe34SqPClYfnILroDCuzEUHDB13MWDJxyM0N/PiU/tEvyrIF7PLbW7zxp2b8ov4VpAyQId49J47DrRiWYhkkfolnbuBTsjUYWEUWbE8eCKmDXFeg8bpD9v6zOfPHY46onXP7k4O7ctC3nZU8msWs/sSHkygbD6t1fTTz7+5J4HH3v/sw9Bj6Owjr7uykgkj0BwRsxhag7RY6tGOfkOK/fW0QpfJfPFx+/f99BjX336HwDAHx6+BwAwdfJwk0WoaaWC77p/Dcdya+5b9cGXa0HPIs0c24p+3UTyCIR2gdVQyK3phCpIjL97jveIIfqzF9tuhEqlm57DPLDq8b27N2PptG5Dk/Eg6tLcsj+ja+JO8jwJZZDwLoHQBmA99/CtKIseYzKzWdcqT2cU1ZtqGQAs0IXqlZVpgwNps1GH3aAuUQweIL/pPWJIG9+l5LiQa8U1dmfegiW81KxUTCW9nhArj0DoZKKUQflltUprxqSximWh4wRMdQjbWGaElE3yKF4YEyLbUx3uor3G2nX7LBbODJThwb6xEQFl1fV1DXpO0ByGRW5QZxmt0bDBga3J2G81ntT+O2NfUGWnni04HJE8AqHVrF40p6m7Ptqyy2FLmXVQmhooxw1NdH7IvgvZsb4ByfHhaP6Edf00wD9wf0YOqjNUqk0my8jUBKfH8UP7BE4fOwjp3e4jyKbiTX5BqFhIoxGTQ3XVSNF4ToxiQ0ohl06KN3FQqatwWraaDRao4JHWmGqqa8qr7bJZLE4yWluLRFmOrpJTh3V426PWxdIZD9WTSCSB4CGrZ09y3viRoErOWvnxul0HTqLI8rSxLppdrl40W5DR3Q7b1aDJAemLxg4+n1GYW4A6/MqLJaenpgwfmvjdz4eVCiY7r3TyuIGV5Y7Z7PuFM1klnP8HPx2GZoOXSnH/3TOqahtPZxbmFFSaAFpTe5mq9FAtV1LUpE6pRn4/Mws5o1xYGy1ePowBAN4knLMCIH0025+/s4w6oKtEutyhMir1tXOVwsR7ZIH2jgRQAqFj2X8KjRefPrq/fOPq2ZM+2n1k28FzAIAFU4dJ2x++bfaBkxkAAPxvUzI6b+rA5c++L23RAZMaKE9cuCY3Qv38A15562Pp5mMP36vNLJBurrx7lUGve+rJkXp9Y87VooP7d00eN1BYLOtYo6PRBwBYc9NkAMCG3ec2bDmGmrQtmjh/4kCxVr1R/+263/B1SUmNLPoXUgreae3faEFhBh9azyPpVDQroy41FMsodOUAbRdw49GWggR09jz0KycDfv7BB4889Zzam3QLJXQDVs2e+NM3Yu70qtmObRzW7j56vWTUWUMBANtOXN528LxcQ7FoupfRHQczf/nHY3IN5QGaESdpKEVRr7z18eH923/d+DXP8zGpY9//+Kuv33lp76/f493HTZ4pP2B53sXDJzJNZjFhaEgKHgFnPbaVW2aj89yw+/y6LUdvWyS+tv4+3mv+sBJ7GLxlo+H1UA0EMQ0MDwdOdfCNrJhvpKDF4LtbGTUAgPYvL3csYLGwrLkDDFLHxiKeAUePmUgBw/2rnkY9qH0dCwMsetH9wXLszzvwO9FzwMUSVdHJze7T7G6Edn/lnaEbwIMrpzpsXLv1oFcj9K1DDj4AwE133+38wM7XUKa6MaUv8jM6aChm2wmxNZFcRpEeffVLamo/6aZLa3T/sQv7LqTbL+T5cUOTFi67bea8ZU8/cqu055RFty5btPyeqUkadfzCpTc8uGrNn1fZ7sUcOH4pOMRWRollFHtCUV+hbSdvWzBWunf97vOAB7ctnujSUcsJSkpB2ADUFODjObEAtIBpchicpKTOMop6hvpDhUpdXVZtgUp/JUtBiOtB5fDBTboyPKQ6R6xMTYgKKrL6NKThUTivfs745IJS18X78L8f22Qx0Nem6BZZihYrTNcjAtrGt6oHU1ZUOX2g+PrszxR/cmYMFb+HcvZdSB8QHsZUoX3CBowGAFzIQdm4Q1Ni7XYTIh5yImPDaCFF2llDv9p4yL2AXl8NdS+jDhoqyWhdgx5ndi+ZOVJ+7/5jF6Tr+y6kq4UVMQDgkT/cc9u9a7CA7jmWMWsCEt/KSycBABl1Xpu37aqr0/zx8VUDEyOdz8RBRh+7Y678XrmMrt91HgDeWUOB3FFbhrQmlKvCt7SR6F3WVNllCDmgog0MI1u2Cz2RVGqlQqXWlFUwjMLWJKnDBDQ+MrC4os61gI5LLhD+KPcCygf60g4CyrKs5BklAgq6KquEUIMza52CDxIhtO/K+XbfTPy58Vcr/m/jnuHhUeOHOEZ41+5BGtTXJ3DO+FSXwQfbTUFDAxifuZOR16yqRu9SQC2hyVGhtjiHeyWVBNSlhv702RfuBbQLaujiBaPXvPqD2cItnTnC4a6PtuymavRRMTHWgT5gyYyRzgKKOXFBtGcPHDxGUTTHcRfOHLvvwdUzJ6RWCQJ6iQs4ffhsTU11cDCafAkAeHbN7RazuSkNTU6IBoBfMNmWWbluGzqOTEaRJ3egYwcQcKyw2HnpVk6Lku3jrTKYXGTOQxQ44hmFEmskRaHpHhaWVaHhw6CxtsFOWztMQGMjAkortbYjywR09ti+heW2nlVy5E4EuPF/W+3Uk7OpJ6EzWTV70qrZk1ZbL3jj6kWzHS6o9Xd4zKDwmJYKK4Z3umCeuXkWAOD4xWsO+2955wn8qJ3HM5L62cmcA9gatbDc1gPpWw+k33jbfW9/+B2+pB08nX3FVgBWWqUprUK/7ZNmzNu57yhWT6ykmBmpKTNS0RzEsqJKyIqxjk9/PNjUU3/16zEXf5hwWTVr4qpZndro1hLik6mpyrmCOmjsEyJLcn7fduqDV28HAGzee9bhrtWLZj9815JifU1Jkfj1/n3fmaaeZdzQftHBqBx52tQJf7hzeW5O1vDRk86evfDOh+tpxhYl/ud//jlmWOrCOVM2rF/3jw9+aKhvkF9GDepTbdbXVGOHJn+1oKy6qlq6zB3TF2mNtKW6tlrcs3noCF83BZp4LBINODENE3Uvpihh/GeztF097Y7mbOFKsz2afpTdEn7Xru03r1iIBRQv2+UQCxR0PDiDxIG1u48k+qO0vnlThsq3L5k/9JnXNuDrU8aIEVJP7NBgygdCeLOTBQpkFii+6WyHAkFAvZSKe2+dgb5pl4udLVCJtKziz7/4KiWl38fvvZWTdUnl5fXcq+/4+QfcMxX5xS7Woi/AwJR4AMD3m3YCAJ5+CIUmmiJTq2HqjBytwokmD8nsUMkCbQhAanvv0glujoNN6c6EqW5MtpqiM5xM0c1HsrxVitmTBuObDuLx8Te/xcTFQAiv5ZdMGdn3QhaSY2euFpZbeD45TpwWl1tUe/zYSZWX1z1Tky5xAdJuVL3RwsOzF9K/+fjfR/aj11ziVKEY94lWqsyAvnWui48HZuO5bLbKOHlEkicWaFuCB15maEAppB3F1cuWAFiOr0eH+ctzVIXGImJfu1lj+xY1YYG6mEziUj17GNgS+elb9B47WCVKBfPe9iYNnA5lUHD4oTM5U0Y6fuBWzZ644yQ61R2HLsg19LftF2ZPGLL72EUAwKG0TJcyOm9K/3lTBqx47r8enoNWh1Z25ypKJQ11Zu741J3HMtZ+vZMHYJ7ga2uKMQNiUlL6LZg/JzUJTY0xGAyvPrfmnQ+/u3XNX9Z/8HcYgMKsmTkFp85nfPjf/zzy6BOlVRqTmU2IEleaDj/++Nfl05/28gzS0E9/PPDgymkOz3jvzVO+3Hjwy1+P3resSWNz1ayJnayhlhCf3Cv5yUlIQ//7/Y7Jw/rI740PgIcv5Wfn5jtLEgrOhHgX6mpglQ4Ad5U5aWcvTZ0yXr7lzpvnbfztgOPR/FChNvqcq5qsYObC/eXPtPFctgu7MlQll8vuyJXLdv4EV9M9pPuaPIijgBqNxh5jbK5aOHX/8UsOG/dfyFaaabnF5Gw9ScUe6IeUctwYGRvWQSecXlORGhx+8EzuVCcNnTc2GWuoA0aLJb2imK4xDBiAFlmH0jIdNHTHIfTpX7Votht/qEuwhh6/eM2lEXpVp1HU6GJjo3ccS583wUWwSM60adO3bvl9zFB0hgCAPz1yZ9UlpF/Thw7afyH93x99XFxSrNEjB2f2lcLE+Oj80mqcolggZIM78OBNKB3nk18ON6Wh99089YsfD37x6xGeBw8sd2HRAwBuHo6C3RvP2Q3q6VAsIT5ZdVVsueteQSrGftKlE3yoWhK13BoXARlNnWb/gaNjpo7CNykfmgpGP1p5NUYQCE4fPjtqsp2n9cN13zc22s3FdMClbnYyho40PxU8cs038v7xffs3NOg4zpYz64C7JfzCW5osI+s0YtQhS2Y4+tHxF35mclK/PpHyIECit/8862JHvt1B+NCPpImaMhot6Ow2UvBQGopLTByaWJV9Gklkf/EzJ8HQ9O5L4qdnMCWa7tI6qOMEFACQGoyW6s4CisEa6rCQx68V1tC6BgMEYJHTiynf07qER5+Km+ejCKkDCpp6f/N+fH1AIFoSTh/Vz8Wh9hwVNDQK8CAnr2RwMprkKnHyUt7YwcjOmj5v6ZKb7gIAbN++7cyx3dWVaMWEBTR08ESVl9ff//XZqMkjblh64wvPvDhq8ghYbxiYgiy1oYNE4S4rFeO5UnqNxKe/HDGbjLfMH7HvFzGve8byJfjKL3syDHrdrXObfCk6WUAl2PIGBwv0WJn4B06IQ+5s95adw4dczunDyJdqMplOnz01YRwywF979tHdW/7XEBh++vBZjuMmzZ7go/bZ9dserVY7c5GtwYcD7fIJD+3y+X8F2bYFO8ejBKnps+bwPK/TGXJO75B3vJ85pq8UoG+ZgK5a5BjuxPiqqLc3oe+YS688Xh8l+4XMHusie0OCpqkPdh5uSkC9lcy//7d9ZjJa1EgaitlxBNmVzjIq/2zRJmQ6ygWUUSieffmdgMAQmqbfevONyqvn5AJ6631rRo2fAiH1+0/f7t/5m6ShnSygSEODBA0d1aSGWlh+0fRhLpVRqbVEREYACBZNb0ZDQ2g14MHNC2wCunbd/lW3TXcQUFFDeTB9tGsNFX7SAi7noaLrIU4aCgAYO7iPQqFc88xf4/qIf1F5WckzK1GVS+jgie98+N3zT96/89TZ5UtWvPTsy5KVBOsNrDiHHNE/Hr3mw/rFOZ/DwUsFLgV0/e9oDHkXFNA24kZA0cc1dcgjD62JjY45d/H8y6+96KtBJnxDIPpEvf7yGwvmogjHfz9+/8tvRZexM+318Q7tVgIqTPdwYWhOn40yuiAA+nykVM5Q7gO1LvEVvChP3zB9ZKSL+O9i2XfSPax17Al6uUPEUJ0DOGnjcl7Z5TxbZcO8SYPnTRqMZVRi1eyJs4fY9JpV2uXc3nz7g//4z7deXuozaYezM88/9/wL73y6Ubr3zQ++Gz1h2jOrbnv2kduX3Hz306+8bWGvmxc4oxZ96A+edlywL54/erErgxGD30eTv+iT2br/nJs9Vy2a3VCHMjY2bjtlTcSzfXrMsvcFIdyz/5SoNVUyFgpZ39f04m/MxdyShsYG6ZKaiKzXhsaGWk3N6395bOyo4SOGD/33//0tIjL6wy3IVnr+r/9Kv3juytW8QdHRDi4o3s+L8nc970GOm1G3dLgP6H1cyrj4yJOrlq5c8vJrLzrc9eLfXhg1ecSoySPcqGevIr6/vJO667j//t079+/eeencmSu1apcXuPmbfwgWh108rlnzE/lQvt87IDFu7CDnti5gwsTB97z2SbJf8MN3oW/1lWx3fYvX7jkaxiDj7sZ5Y1waoVQlchtNnyCuWx2sUbu/VvBm7s/IdV7Cv/Ph+vfffjnvqqgCR0/nHjtxavf/vv3236+EDxzzz483/OnBm/FdQcGhL/7joz89eDNNU3suXe58C9RqhPJTRzn6H1C9s7dy/d50ZwtUbl3G+YQIE1lBTLC7VqfZRTVVVTXjh6Ag+PhR/f/z3YFxA8XX9kSxnfORq0QFaStnowkKcnRmsDUDvaRlRZWcFuUYjR2MjibBqsI++/Kbh29fgIK8BWVcA5sSH7rsxtvuuOfBe6YmfX0QzclxwMFVh55da5Qs0MMlFd3U3ukcC9QBuQXqIb3HArW3Q11boJiwUDG3wRlm7mRktTkLaITK9dh7yLONBvbrnw/pmq6UP3b00pq5k3ccQ1W9ReW23FSXrJk7+YOdh7GGuoQL85VM0ekThmJT1I2MTk9NljT00KkcSUMl9URZh6OT3/33v0pOonUfx3GSeoq1A+KV69ZPOqO2IjUo7ODpHGcNrdebvFSKPccyQn2azJUrbKxmq1AwMCZYjNu4pJqzzbY9ftpdxIAKU2MNdbePvxJrqBwvHv38eHmrDUJNMOVLX6mpjU5EZ5VXYxw9xZYos3jBklf/8ldn9RSOrMrRoE9RfRPq2atokcCFCj//VdGePuSX/y5r0cksf/RX0M3xpdBHq4HzdxMrcjGp2ApjMLFeShfpEStmO0Yq5EjqeTI936URylAgt75m7TfIIJozzl3rFKxTzQbb5DI6dsTA4SPQz9q5s7luNJRVctgTilutvPPh+i2//LB352acsrBh/ff9VI7JwMkDBq/+0ys7f/sJ35w1uF95Rhq4HmTUVnIVjQEqFy9Mdg5yOIYOdxEcl6BDVVhDWwTLcTTl+neRClP/dL4ZjyHlrzxV4NhDoqCo4D+f/3w59/LTf/kTTVFr3/s4Ijxy7qi+Dv1veK4njD+4Xjy30nH1hvns3dxWyKLn/CIcuWfIKKQo1iJmQjTw/sDDNKaDQmD3kUWzP5SluYQr7R9vz0dbj0iHNbqqzWqWjbvO3DzHZn2smTt5w16UzOiA3ungWEb9fb2/Wrfv3ttmDB+RrDeYszNdpBZPT0UKe+jcVWyEPr3mtrc/WLdo+e2Llt8uFvw++oj2wnb0YjWImSUffb8F17cFBAVLG68jVBNePCpEyVW76D/mAB2qOlrQzNAnGOxlW637U6es8e52ZMWty2ZNn/V/r7+9+Udk72u1dVPmTqaMthG4bWkm1ptpSjQlxk+N/uzdzjqbnoUvtFs3c43GAJRFJq756imbS0SMOch//d2oJ+TtfPbNqueVhpq+PsG7jmfNGW9nhN46b+T6HTYN5YW8/Z92pN3k5AZ1mcC4M+vy3AH9tmxPmznd0SvnwJThSfvTshvqkZtj1d2LcWrCtFkLb7lr1fv//TDz7LG3nrhD2nn1HYvwlY/XbSu4lrt76//A9UZKcHGAClF2ozTmPfv3OKzNnSOGW3Zs2bIDTf0mdALHD1pb1VsZP9Uug4Igh6YgZV2ZQdSmWnRDQyhLpJdL1Q1zbMKkgKyZt67xefDRNtH89Bw3HgSJKktdqOAGPX8BVdTImdU/ZU+2Y2+enVmXuYqGX/eg9fXU4WL9RnHTPjKFUmk2IauN5/n9u7fs371lwJCxTz3zSlP7z11yU1cQUAKh1Rw/WPKHJ29zKZcud37rx2a8VR3nCugKuHEiBfrYFRzhxZJQxQ/t7hA1VCZ3Cohsw7PnsvDNkUNdxIWrqlxYSVqduMa80liT5BNUXe24Dw+AtFHTgBbOVZXVzurpBioc2TFchbu1NloY+tAjx05++LHnH75LNDAx323+5alnXsmrMY7s5/vvzzY+fNsC+aOw2hI6KHTb0uBGs9/hP9zwTg/zyrVx/d4RLH/0156noTwEQk2JO6SMeqye8ulzzIWLojhiZqQk7T+VLSnm2BEpVbW2eq9cIV9aIqOmnK8ypMa5CzchDc0ruZJXsnKO68wb1MQk17Hxjxxn81MOFe4rpbaUCbFaZz7Z8OPDjz3/4Vebx06z5VEe33fSLHg8D2YhL/C/P/9pylyU3b1g7kII4fwbF2Kv3GDSlb8Dvu04uPHcyjEdtHLsJTLa+eCXVJLR5d3/FQ5JFpPZa6x97dzjMLvTsRaeZTk+QGHthodssY27Lo5KEZs77Mp0p3Rydubk2Z4yzJuvtHWExMwaaZcwiAhiOs6vN2Xu5EM7D+NaN4lpg8XXbtTkEacOnZHuvfnOG0lMo+PsIym40dF+t1/+u6wHfMO7ID3yVQ22G2oiUpWN+uqbgl3ndLoQ0INXbcIHAJ92LmdEcjAvLIVdzk2GoV7NZrdgDb2+ZXM6XeOoySMUCsWAfgMaGxvzC/NZlpWHMuQ5iYTutbQkEDqI0P4uqoflMC4zXU4Ut6DaoRthNpsvprvIlyJ02eCGg6Hq0vbpeY45QnfBRT/QXoibGEgVCMNFaZ7ESZy/yT1ysdPJkNfQmbd+TOtMY79F9aNN9YGU6IRi6E6jRwlo62LBANT9YeVC6XPpLIg4wts6M4dEMzqB5Y/+Gtr7ZLd9NbTZHKZOk+PIbiWvPUpAW1foJl8k/jK1QxaDJJrRyak2PV49HVSv1UraXrr5+Oon7rnjXnz9/tX3nb94zqEFT1BgkMlsct/CuTvSkwXUw1gwoYfRS9TzetmPzpwW0lcysjIqKsunTpr2xUdfGo3Gh+aIkxF8fHwO7kDNNDmOoyiK5/meFLDtmQLqyQ+yFMH4w5O3eRjNIBAIDnz0n48dmhAqlcpje08suOUP2zZ8hhJ7dhyurqmeu1TsO3z68NmTB07JM7K7NT1TQD0Ex4I9pNlaNxILJvRCUvo6VieaTCae5xfftRoLKABg4Q3zpXtvvGPFz9/3nCLpXi2gnVbo1gsXlW1h5yFbRe/WtGttjPk2S/eKWnQ1Hn/60W8/+/6dN//9zItPS710R08ZKXUid+gjkzqgmRGE3QsioB1Y6Cbf2EvARnq7RIc9lE7C9SUjK+PnX3+6cdlNJw+g8TCNjY1zls7Cw31d8tpLrxcUNTn/sttBBLT96VWK6RIHX4fneoof2KwVSVHU8KHDlyxYWl1T/eOmDRWVrrtwQQgVCoWJ9IXpYP7+zzf+/s83BvQb+MVHX/r4+Bzdc5zjuPumu5jkkXYQjXFccWvPKXwgAkroZjHi+XMWvPHK33FUF0J43133AwDundbXuX3BqUNnCosKl9+6tB2fndAUWZczJ84aDwCIjYn9dcNvn+3O/MPsgfLfPKyeLqe2dF9IryFCN+ONV/5+4PCBUZNHjJk6avSUkWOmoqnUX+53HO6Cv66EDgVCePrwWW8vb/nGouKi/Yf2KZS2oarjxoxPO3i6sqqyh6knEVBCNyN1QCoA4I/PPSlt4ThuzR8fkXds/Purb50+fJas3DsBXrD6fxWGtcgZPWIMz/N5NcgTunTRsg///dF7H/1n/nI0Y72H0TMt0I7IK3ZzzLKiyqYu7X4avZysy6hT7R/ueVC+8fjJY/dMFacSAADmzZ730GMPTpo94XqcYK/jwUcfCAkOOX347LAhqOGvSqXa9MP/fH19H7kTeU4oinrl+Ve/+v7Lr7//CvRE4MJb5oAeinPsQp4f4yFb067hhAypKK1FkBSZVuD+h+fbz77Hduj5i+e//v7LA4cPCH2vxaQZ+dt0+vBZT3yg5D1q4zvi7x/w64bN/n62WWp3P3hn/rF9AIDvd5yIjYl1foibtXz3ejt6soA6MzLKeZpZ83kzLr+cHtK9Pg1dhGYt937J/f711rtRkVH4JsdxD88bbBImfRIBvS7viDO+GpQa0RBoG2DZI78yvSsK32xqYVho2NeffBsRHoFvauo0sxfPlO+QEJfw8w//wx43rbZuxsLpHXm+BNdczr28+CbUQAtC+MxTz6684ZZPd2W4DMR7MtCQQGg1vUtA3TNu9LgP312rN+g//fKT/ML8oYOHrrzhllOHzkj+NQjhpnW/nEg7/shTq2OiYjZv/H3vlv0zFxEN7TxGDh8ZGBC098AefJPn+X/8661//AtFjSbMWfbD+g2gyeELhNbjuVVY1il+/64zlIkIqI0P311rMpkmz56Ib27buRV/Mx9//aP3XlyNJtd/twkA8MhT6HpxafGcJbN2/SZ+kwmdw+svvxERHunSg2ZRtcZJTeiwuYHrhHYTC90HYNvYQeK699slAmrHj5s2OLxA5y+eq6koxdf7JPTZtXendFdNbQ0A4J9vvP3nF57uhLeKAAB44JH7f/9p61cff3Pvw3dLL8jrL7+BvkvrvyYvUefgeWnZeKHTbge12ZW4jh1giYDaceetd23avCm/wDZZ7/7V90lBJADAuQt2cz0BABPGiRYroRMoLStd/9P6W2+69fThsxaLheM4pVKJcj//8oSp6fprQidL5/he02yXCKiN8TPGHtt7YtMPqNcWy7JZ2ZmP/HF1Q0MDvhdnBZeVlzu8gl4qr858wwj/fPcf/3r/7RlTZ6y84RYvL+9ff/9l0+affWod3xcAwAt//YteryOvWBfnuNCNt6NnXHcQREBtmM3m0VNGJsT3+cvTfxk9csyg1MEHth/ief6+6clSeJdhaIdXUF4DQ+gcWJbdvW/37n273e+2fdc28o50i8Grctwvxrta110ioI7kF+Q9/PhD+PqkCZPf++f7X+7PvXdaX7wlNqaZOdEEAqEVXPd4eusgAioSGhL6p8effv6V5+SvzpFjh+99+O6vPv7Gy1sNAFrCL1mw9KvvvpTvk5GV0ZlvWG+gFanU1gGroCq6lWnYLTJtuum3vae2LV9OovDXHb1BP3fWvN+3/Xbk+BH5dtxk+3KpFhmnhfnxcfEOi/e33kGt1QhdZMAq3qGjHWrXPXumN7Pc+rJ3hXehd5Vyus/yPbE/jWGYR55afSLtON4SFBi0+/e9AIBpg2MAAHRCyt4t+7/f8N2/3keT4g/tOqL2Vo+aPKLwqpjn5IyX2tbUi9A6UuODm93nSnY+vjJuiq0HZUdz8FQR6M4MGd2/pQ9px0n0XWqwaKshS3gb46aPOXng1If//kj+Alksltkj+uDrdXV1L7z6/BuvvnnHLXfiLbiDLOH6qmdouPpKNnkTOpyOU88uzqNLkJUpLwo2GC34ChFQO8ZOG80wzNKFy4YNGXYt/9rX33/F87yvrMJ6++7t23dv76T3rdfjiXoiv2eFrm//hCvZ+X37J1RVeJq3FBqu7vUvMAGsEcTRQRYlGBo1/DRbOJfba6oriYA6YrFYNm3+edPmn8mHq3vRt39CRyzAp4520Y2td/LWj2ndywhdI4ijsyxKCih8323iqNVUt/QpiIB2IMQBSri+JEeLfcVc8vDCWdL1j7fuuY4a+lbLHaAPzJ3R1F2SeVjhVPbS7vSuIFLrCC0RI7w4N9j5zb54Krt9jZfuHp3o5CV868goQK0MPMH5few2b5CJT+4T2dSdT/7hRnylpgoFVzeevNCiYz/ngZJqdW46vKAsFgtrtzp2uGl3qJabh50AsUBbkCLTVGeE+x79oX3fFfyN7Tbf0p5Ot34jcvPK4qOCXN6FddNzHlk8W35Ta/U2s6ybpqt8U7LYNQWxpfR2Ae1ePh0CoeN4eIFtRe8SB6G02MtizxDEltJ7BZT0lekNmCwK541Kxgx6IslRoseTB4DlOLOZLS5rsahJstg7BbGl9F4BbWlbBDedEZxTZzxJkTl4qohEeNuRjNxqN/4+OSaLokdpqIlLToiy2YkcX1JZ16g3oa93Sxrd1FR34yGyPADQ+m9nQgS0Q+jWXjP3PHjnrBeeWEFR6INaUFw1/Ya/WSyo2rXVnN/7fwoFnTrlTw7bj215LTjAZ8Xtr3t+qNy8MklDf/rmj15eyjnL/irf4YnVixbPHzVn2d9MRpu9Jj62tMMjth2FksrNL42PEgNuPA9UDDBSfK+aCAVl/3YmPXMuPKGDOPTrX1966oZPv98zYfFLc1a+ER0ZfPXEe74+beqIOnzWs75qr32bXpZvHDUsKSYyeMQcu94uTWFmlWYWtVUGABitqL2Vam9xo4RSyai9UXGts7A46Gn3heU4ncHs6k8ktD/EAr2eNLWK77IGbEJsaOKYx1hOdJMljX3s6on3Mg6+Ez9qTauPyfP8+59vf+yB+fhmSgwyIf/3xZ8ysotjgoPNLOCssQuXnVc5ji+t0tTrjB4tV2WqYjT1qA72BaU18VHBPA+MJgvbdDIQoX0hAnqd6bJa2RQKBc0abd/Pe5/4qLq2vtlHYVmUSxjH2cRs06aTjz0wv+D0BzMX/43n+O8/ewwVIP/pc5e66Sh8PFCrKIPBeaSxK3p682se8HqjuY1L9873JHZfiIASWoDeYLp89N0DxzKffe37kvJa9ANwPDMlOjIlOtJZFuXyxzexHS+6Ue/qOS8c2fXG048v/nb9gajIoNnL/mo0oTBIs/A836g39SJvn0uUFDChXzWO453fgpZC1NNziIAS3IGVUfpOLrzxrVefv2na5NTjW8XYzsuv/3j4eJa0v6kN6+L3Ptry+OpFC+eO/OHHQ3qdyaPvNA+MZovJ7KLYGXNkJxrY2UsoKK2JiQgiI2YkWlpb1QqIgCJq6/09ebGC/FBb5Z5BSnRkU6aK/BtoMBgc7v3LX7/HzaSXLBj9x0cX/+3FlXkFlXc88K5CwfRPsbUxvpRRMDjV1nz6UkZBs6e0YdPRx1cv4jjug09t/a4cDoJuQrtjGowWNybXpLkvyG/++YllyxeNxec/aGCcw5HD68SKndMXroHuSW8zHjd2vES6hwiop+qJdbaTNVRaGrtH7v9z4wqUm4fOyugJAf7qOqGCj+f5zVvTNm9NWzBnxIvP3IRSX4P9Pv7Pw9Kek+a84HDTw6cwme2Sotwf08JyBmNrIs4UBT9+V3aouS/Ib7YlJnbdV/E9jI3XWyLdQwQUfL5x3wM3z6j2rC44yE/V6i4V7tXQZRAEb2xKE9uyXm4Fj69aeMuNkxykcNuusy8+cxOEsLS81uEuz0XTDe6PaTSZ3bSfcAPLcg7GabcUTVd4FEzrMmzs2vrYLL1XQHFTJVzQ6aF6OqBtFDvNmC1Kud41lVHoIJHOH/ROFsSW8t7arbfcOGn35ldmL7Vlp/+y7tmm1L8TaHO8pKdRVF7rpXLMfr2+bOzmEume3iugmLd+TGNaUk1QW+8nu8W5X0F3cUFsBUtveWvzhueO7LKLzExbYJcD35nQFBQr+AjXj409WiLdA3/96q2Pt3nUS7W78/CCmdJ1k5ndl5aTea2c4/gAL7B8xuC2HPmzDbtAbyLAX33bTZOVSubrdfvr6jwdodER6AymGq1O/AET/omLDGpdVKWwWgN6ACbOS6U0mKyV/sJr0lQ7uz941gu4B+vj7CHDvFUKmqYYmqZpSFMURUGaghBCioJQGLyLh+/if50/U73IAv3q5/333jgdX2cYOsQH+ipYDhXV9KIXoV2o0+rWfrETdAv4psWU75lx61anxHYLoRwRn+LjpfDxVnmpGCx8SoZWMDTSO5nQie9qcyldzd7dbMtUprqqEptmLl/1T7ahob49A6Oaq66qwtctLFdeXS+oJ6HX4PIj3sOW/0rKaDQ3KxydiTfrz9AUw1DeKoWfWuXtpVQqkOSpvZRqL4VKgXSQpiiaphSMqIIOFp8V2zpj2e12rZ1t1Ld+PfTV15tb8Sim532EPKFWq6vSNFzvsyB0FQpresT6HaO0+vRNrfxm62sZSpAwhqGUCkbB0Iygbt4qpa+PSqWglQxSPYWCVtCU2kvppVJgG5CmoHAPWgKL9p2jDvLOV/H/mhrRuHHg3kdvbcGp+6k919DWKaYLAa2x2mW9BI7jiyvqjKY2NWEjELoUFjFgaRMsBrZMQG+eNIGioIKmVEoGm4AURIdo0hgUqK2uAqyZZQELQEf0WP3qv+tbJqxNaGi7yKUzvcv991PahZvGDLWwXKPe2Cst755Ia9/GHmV1AkBDXqVSCY3sWou5kQPACIDxesYF20NYBQ1tqWLyPDKX8b+e07sEFHO9khZ7IZ0hUmhmB2zLirXHYDQa4yMCqjSNtuYA3fYl4XmrogEgNO9uq7A2K5pi9KmF3mN3AvpTWjeIyrUGlDvYhbzsXYoeZpcRum97OkihU0dpvvY/A6jVPs8LWUYt+LvQQXDSsPWKJJdtCbn1Ogv0p7QLS4cP6kpRyk6F6GOPhJdb366sTodt8LoPL+KbkS3xXmg37EhMPEOPxU0SHf8uJKwye9XZwGyjXDrT6wQU48ov3no8TEjuHNyvGwKTesjgCmegBkUFb5wzwsP9e9ICi6tFa/aV80bq9Kaj567VNejF7UKi3s3zRrp8lKQ+sFXyx7fQXSh/Ls8fKB92ZE1xauLcrMJq09w2G5jN0hsFVP5z1O3oSV97Qoe4DgFQMLS5JZP+XHpKXX5F2mvl2xHYbEzZvx1NbxTQ7UcyI0LkJe1dCKKPncbDC2b1yCJmH2+l3uBRM39C2+mNAmpUc+27hG8RRCIlon0ihHIUZaCfV0igj8qasy1UJAt+FlfvEgSwrkG/41hWUZlGbkL5tvCzDCFYtXDW2q09SkOlxXvbcR/A72LWJ7he/D/dmCugGXxE7gAAAABJRU5ErkJggg=="}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant": "I will use the centrally located S4 socket, which has clear coverage in both ally feeds and can be oriented away from the hostile view.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSOCKET S4 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "assistant": "The default S4 preview is front-facing in Ally A and edge-on in Ally B. A 45-degree yaw offset should bring the face into both ally viewing cones; the hostile will be excluded using the height-tier screen.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y1 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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0SkmTJOmnVoaH+m/OqfV0AZsO2rs0TRszuJ2vBYPBYLpOQGW9n2gH9FIi8zMpJD0xNCMxjCCAkiLUCpLm8+HRIpx3ZkInKUHAf5UK2t9PyUsnr60k4a9VRYUH+PvBGnkBcT69f3AA2kBGKNrG0SQMBtPTBdS7+ZkumJ8Fusziepi1SRA0r4wuZxFuEYCiSLVaoVLSSGJpmlSrFMGBmqgIu0q6Ex4cFBkXibbREh6DwWB6uoBOilN7MT/t3s+kkPQEaH4ChiEEe9P1POKbJEEoaEoo2SQIgiQJtUoRHRlcUl4r/ASryZKSqpJi+DM4QlVZVCks5LERisFgeoGAJkX6283PBLv5mZEY5jA/rWLvZ2ZxfVpCiNeTOZI9+YW8Y6d9g6bIkCA/mQeJpPeS8cloAxuhGAympwvopFiR+Xnebn6itTnv/STF3k/YMrm4oT2955RKeytlKdCstW8KKfeEo+8y9oRiMJieKKBJUf4S7ycyP+F9hFXi/UyDU4/ErT6lFiRSPi+jjggAgsLDvN0tejBKCEVgDcVgMD0rD9TF/HTkfmbCfEzY4IND5mdyCD/1iGAYNqu4IT0+pKyuyKtIykC49kwWa2iA2mmT7jpZOG0sXMJzHFdWUNGGV4TBYDBdZIE6zU+R9xNwfMMksfeTNz8JEhAsm1XaqAvxdVixLAEK58/+E+fdD2BZjmFk7FxshGIwmJ5igYrNT4JTOczPGg7wuUcy5mdjWnwIoWIJK1V+HHYSadHklBiePsLH7smBCVHlhZWtfzQGg8F0vgUqmJ+pCcFZ+VUO8xNlbrp4P0/azU8mu7QR7QFtorwSzlbyBZLPyBdSmgSwEYrBYLrfAhWbnxRQyXo/05NDgNj7mRAClND8zCqW0UG7scn5trtFCPi/oED7ZBEMBoPpQRao3fysN6QlBGfmCeanQpz7mYXMzyK79zO7tBaZn1ar1ZenaF2YSY7Q8JCYgdKZINgIxWAw3SmgTvMTPow3P1nk/XTmfqY7So/GDgrLKm5MjQ9B2aCAov7z1HW+6iPRLjHVapSbD5xt00MxGAymcwTUg/np4v2E5mch9H6arVaCZXLKWmd+dgg0BV8UNkIxGExP8YFKzE+OZgkbyCyp5jhFRlJoVn613ftpoQiSUFDUkXM16XzwHfDez/88sfTjz3/z8bmgB9RrJP66K6ZUV1Z5unf08IGbqzy2v8NgMJiutkAF8zM1ISgzr4pg6dS4YJT7mVVY6WJ+FiLzk80uq0X+0JceXdyua2yrW5QhpK8Oe0IxGExXW6ATReYnBdRA0QCscOgmAaD5yVptWcX1HEtmJITKmp8yZxQZmO2PGnmCIIi4pLjicy7Jp3jmBwaD6VILdJCb9zM1LphwmJ+whwhpI+A2DL4j8zPL1fxE6/f29BNpAwzDckZD3KC4rn1aDAbTX6BbZX7ag+9WNqdcB4AiPTGUE8zPxFCCIBQUeeRcbWpcCKV2mp9aP8cZUJmRLK0qP5K0ZPYASdkPixvkYodiIxSDwXSRBSqYn6ki81PwfgLB/CzQZULz00awbE653fx8Z8UNWj/1W+/9IHNeVw30jxrr+c42AtPqAeCMzomeGAwG03UC6ur9tOd+ZkPzE6QnhqZGh2UX13M2Mj0hdESSve9nWnxIhiP3EwAgo56+5YJ6OeqnP/aD1vD52u3hMQPFe3A0CYPBdPoSXur9BERqXHBOWRPH8cH3mHBofrIkHLpJUWkJIdmFddlltcAGddki1wPU83qda/O6nvT8KpDPICneRT27gPGKEgDAIWtsFz8vBoPpKRaojPcTAN77CTKSQkfERUDvp43kaFYwP1PjQ9Id5ucHL90iv3h3w9FK3qcrbsPqXkFTKsKGjVAMBtN1Amo3P+sMgKWh+WmF5mdafBTyfjIsi7yfhI08WWRvO5/jCL5brNaW1ZNoZ5TII6OHJ5aW60iWKS3XlZTrEiPUpaXVoMtBdiimC37P+FeN6Rbols1PAmQkhmUWVgIrAXM/ORh8J1n2ZGEdYKH5OTIu4uT5WtR2HuZ+WqiskoYPXrhZIqCUxjFEXgZHeF68Znddv9+yfB6IsE+mW87/+8KD76KbCaGKkvNlshEkMTVl5ZI9OByPwWA6RUAF76fd/ATEsNggSqnMLtRlFlamR4cB0gZYUjA/swvrsspqCRvJAWBlWXfz02ZsRhuER/emN5fnCy+v9vY6fLNYw2MG1pS6yCjWUAwG08FLeLH3MyMpDCi4YbFBpyrqsgt16YmhhI3MLmkQez+zSxo5kkLBd4KikPiKsTIMx7YgfHIa6PNKnnP8iHYNS0uOih0YFeuMINW6GaEYDKadjO/HLhTSq/dTL3g/AQCC9xMOeSdtMHPeYX4ChiFIq+D9lHkawn1JLcFhmHrJtPeN4Wkpw9NShqWlNOnqaMZMM+aYAaExMRExMRHRMRGSUBIA4M+3lvl6agwGg/G+hBd5Pwnk/RwWFZxbUYe8n1n51dnFDRxLZpbUpCeEwZV7YR0gyYykIFR6NCjCT3JChmX5WRueV+xcy2v42IH2kZwEADtOFE0fkyS+VxjMCQAwGI0Hs0rGJYeLD9h+OP+icfAABSfV96++/cvDc2J6BLZQ+zvriUoaOsdtNmkXbXdoXX7HXRcGI2eBSr2fVoIDIJ03PzMLK9PinebnpXMvBBSslyQcjeiR+clynNlqsznyQJVabdTAsNjYSPnft1RY7UaoF4uTgvPj2ojs0CRshGIwmA6wQG+cFGW1Mmg7I8lufp4q16PSI2h+ljQAlswqqUlLCHv7w/XwOIpKTwpCwfdBEX42ljUYLY3NFn+1IshfTarV/n5KBQ2rkpCX0l38Tn/5Suqtz7jZn22bzgnl28u9BAGWXzW5FM5xwvQOWjQbo3gHXBEuW8B0u4Ai9SypM6QmBp3MqyIAND/TEkLtwff4sOyy2vT48KwC3YxLLshe9SecC09A7ycqPbIxikaDubreUK83hwVp/DVKf7VCo1I4RFOqn+5pS/y/9t2+KyjDSA/cdCBv9sQUT8fHDAwt5SsCEF99+9efby2b8/Aa354Ng+kvtOg/Af3YhTImgqPlzU8C9v0kFA2ezE9AKT74bANUROT9dJifeqOlok5fU99sstiUCupsrXlSJAwg+Yogma22Pu1HkwTh3QJFbNxzetSgiFY9AaZXkPNOEdpIfTC+u6+lLyCWPFkxHUS3cVx5H/SBCuanS/A9IdQefOe9n7BM00b+3/I5sPg9PsTu/SyE3k+W4/RGS2FpTbPZxnHAbLVlpESqlHTLCgrdqM76o5ZcoDKINZMAYHxaTIsPcR+alPntva16UkyPVU/JNqZDoHX57j9RdZnoR/ZeyU9feiPGRHAuAoqC7xxvyjlzP8v12YW6kYPCAZ/7CUdvQvOTguYnw2SXNqIxnIDP/eQ4zmCyKpQqApafk6ERoSTfoN77dbjHkOz/JeV1lORnxrWKwAj3+JXUSsWx+N4OVkxMF6uni4Ci4HtpfTPgnMF3ZH5aOZOs+Ym8n8j85NOVuGazjeU4lYJSKiiNWqFVK31Zv7scwon2yD2WZTw2eRKjqi/a8fe2HX9vE59WgOZlXWKEvvLWd9gI7aWsfl3mrwJLKqazISW5n6kJQdD8pKH5edphfubkNXgxPzmaReJrtTFWK1y80zQJlGqrlWk2WTbtb/WIdhish7ho6KjRw4GbqmblVdgf0kHTQrCG9hn1RGANxXSe+emMwjtzP3U0YBoBIDjOHny3csa0+BAh+A4omLeUGhOIGi9B3XT89ZqsrNnKIJOTpki90VJe08SyMKjTju5KLuEkiiJZ+TajvD+CIBy660JslL0LiYTTBVVDEyPF4XikoW2+VkzX06JE5rxTNOgmaW2xgDq05dgxBuPNAnWan/FBgvfzdIVgfjaKzU+YtyRnfm45eIYmCYokSIIICQ+hKMJospZXN1ltrMlibdlAbJ8J6T3ynpN12su97nn1uF99D6ybNul07j8+GpjnVsNMEgymY81PuwXq1fw0pcaH5Phgfl40fojVagv2VxvNNriWt0HTs8lgJjimUmcIDdRoVAqOD1FxsNRIsEg57zanYFFGhQeCjoam4ctfvmCCxAjFdDbjFSWSdv0mXee+BedW673YoRhMG9QTWqCu3s9wu/fTaX425LiZnzly5idFAKWCCg/2U2n8+IQkqMI2hrXZ2MLy+oLy+romo/05peVG9iQmx03xfS5Lf8oef5dzB3AgNSUBtBVshPZGWmtXYjsU0+GQgvmZc96QmVcJYI95IfhuTBUF3+HhFJUqCr5bLfazXDR+CHJBBvgpCYJgWRb2q+dhWa6mvrm4op5TwCYjHB/vEVTTRT5l/aQOCT1wFnaiY1movv4aJdrp59jgAAgO8NKwuQViYnFSfS+jbWqINRTTgeZnTV0T6e79FHI/c/IaBfOT8+r9FM5o4UitmrLYnHEeDnBWG6M3WqrrDD6FyztkojEA5uBWFKIQBBGTJG1zhz2hPZb26CDW0FZ5q/tto08fgQLKAa7RYJKUHiHvp2B+Qr8kCc1PwtX83HLwzEXjh8COTDwKigwL0tisVsdy2w7HAYPRbq/KaqjVZvOkox2kqN5gWU4wmTE9nPYrINZQTIeYnzBlE7kaq2rMgAbDBtgr30cOCj9xtpKjWQKZn6SCYBgCgOySxoyUUGAmM0trBoVA81OrUQrqCWhFqEah1SjL6gxajVrX0MzwK26EYJXykSQ3VRQ6iCD4Lib23iJyCrrneMEFoxKl52hrOqhKSZEEEZ04oKzAnliKwAM/OrbrhNB4wr33BKeC/RIlEOW5naR9OKaEaQ8Tx6Vu3HRgcEqsPQ+U5bizhTXxwdq0hNC6RgP0fsYF55TDAR4cx2WXNKLgO6dgss7UC8H3fZkFEzOSBN3zU9FajdJPzWnVypjIwGaz1dBsEbSyuKg0XKjqRPtcJ3B4vtqWrVGNyjmDpA3QFCXT8BnTqt+hb5XOqPWce/c5mw9R+I61HLGGYtpsfm7cdIDjoz6ko/8xU8H6bzyUV98EzUbo/SzXARvJ0kxWSaMQfKdIUuz95F0A9lMrNSqKIkmCUNCwjjMyzF8I9UhwmomiKFL7K4n2H/eW7OkdoUEz9oT2WDpj3Y3X8pg2MHFcKtogCEB+d6TGbGWaDGa0a9OhvLAQRVwYjGgPCFISVhL22uS9n8MTtVln68XBd978tKOgKbWSJnkpIghCrVKolLTEYhTGvsmE4D0oqHg353AIjB48AHQQpwuq4PVw3hqU4GhSH6Yf1nraQpPFP919Ob3M/ESL97CQgGHDExdcNh4u4ddn1gtF8SzHfb/l1PjhUXHhmuIKS3kTdH0CFs7dzEj2T08IFbyf+zILJmfYvZBHztddMjZAQTtliCQANEddLyVt+VPZq15xl0re+2nf7aURaFU9VPnH7rsSAHCz+A4FfYvbwcuXXXbT4vvhQPnbFsAmKS+uEnehT08IKy3XqWFdAF1arosPVZWexwM7ey6dairmvFPUn5uHetJQT95qT4eBqjrQb2zPKl1j+ICwyNAQmaFyMFhUwdLl9ZePiiEVypPnatJiee+na+mRoJ7I/FTSLm5E2SlyX336wY13PM3nb6okdzXpm+mQDAIAi+4kcAslIczN8FP04mtfC/c/88QNkvO8+OKqFve0yKaDebMnuHSzx9GkbqQLFtr9SkPb6a32eBjwdlgf834OHZZgtTGffr9VRkABAWjO2mwjG5qMNsZAQPOzISMlMD0hNEvO/Gy0AZqSFA1B9I0WQ0OTpEodHcSyHL/Yl4nbqMJG8uprL/fk2zJxAQNQovtxycFiMe0wKDB7cgqwz4VqI4essTiBrne5KfuVhmLabH5yAAwbnshxHEESNoaVEVBB8U5U20KVXHy4xj9IUd/YXFJhsciZn/4UNzzav6LSacAnhWoqiytJxgRFVW7FznEcywISrvhdNVR29rEPmaCDBsFvP3Nz0y2P3gg4wFbWvfyGY8CRuKGz77JISQ9ugxFaGZLR4qCYPtamu1cHebCGYrybn4hZY1O2HMkjCI7gCKmAUhTNMDCn3Y9m9Y2NVTYyWE0CBWtjWECzyPxkYbd4x0R3jhOsRQnyiUH8wShhU6KhcqcABMwC5corqgcOiIgZECT7epB0Qr/nDfPRTdJD/zov8zwESqt0HThpC9NmcIgc03O4au6U0sr6jZsODEuJCQ/2ExRObgnPY7SRGpr1o9ndWYWBwcHxIX6C91NQT3gOmvBg2REoIO9igqI9BAxVoXiToKFi9ZQoKUHA3vi+cPvD78VEwKZNHADlNVBSfdfNVhmhvpiNeNZur1NPvITHeDc/h6bYh60tnTli7VYYrfEooBqaRRoKvZz19UUAXDAkXHqQ3LijEaOGovEekXEG9Zlyq2gCR+6pPEPlUegu5f+PAk0EQQRGj3Nbr0unH/tCR/Wlx3QvWD0xPYqr5k5B4aNBiQMUNLX2ryNLLxt7zcUZ32/LlBFQC6uorjVEhGmRehIABAQGDQySL/Xhl/AunThqGxr5Qe1sflFVYVk148MII335ERAwPGfVKyJb1P7ftFv/7dEb4EbHFxO5GaGYbueX8yPa/Ngrk0Q5HiKw7Ynxbn6u/PrvocnRHMeZLTaVkkYaumhGuoyA+ik4tZ+/oaGBYbnAkGBk/gmlR1L19GwdymYy8Xe4ncfTKQDI/vwltFGmocPHj5Hcu+b3Ay8/sBDatvmty+Jc8+se951PPHjNC++uv3TSIE+Pam0QCdMZ5qcnEWwzWD0xLUIAsPjyCev+OEhTpNXGCHao3BKeA1YjbDQSGBRk5PszDQyRabXJkd4WzLDIycs8eC+molynEU8smz+xVdIZlxyHNp58aKmws7gUFiPBpqhl1bcsuaC0kD+h4xq+O1KDKpGwevbJxTtWT4x38/OquVNWfv33qIxBP/59ZP7MMb9vPQpY3l3Iy5uLgNIUjR5qsTEkSdXUN3MAwHIdOZy2p7yQSu1Pm8FZ79TuHnWcycyYLDBbgCQJtZJW0l7l3E0rfXgG5+amg3m1VHAbrxTTg9Vz0E3+ajxTzueck/6ccme1QV/ehn9OXH7RqI3bjlsZFhmhMhYoTQGGhQLnpwA2m5UAxOnCikFRLpU5onnykDU/7kAbzz+wCAmnyWI7V9JgMHMM45AiRRDFNXu5xNTlT+XwhZ5Ey4YooVLCliVIovl/PVq7eceOp4weBXwg92wpACBQKa2Lx+rZV9Wzi5+xtyObvVfJp+udaynfubfor6z3c1TGII6F7kobw0wen1avN+0+kIucoTICSpIuw4HhYhwQ23N1Fw33+Dt68aEl4pun80tZjmOtZq0CWEU5SI6Wyui80pP4YkL+vOX4M3ddjraz88pglCnFGb/ae7wAtIPhg2GOQmlhWZc0ccZgegfehS+KL+X0Xu7Ze6nW2bMhCZKgCGC2cs++tfbZB5bUNRizTxVYrIxUQLV+GsZi4sPoDM03B6EpssFENNc3/rKv8crJsABp05GC2eNdmhmfzoe2mxgCENAHKp9K7zJGrgVcE0mvmjUK6aYDznHTSxOSNiA0jcJ0EYNu8u9KIxSbnxgfcz/rGw0loj5EAIBPv9qEDE2ZPNDGpiZ/tb2PJ8tY+W7ttJZhjPxQOSidY6F0cq594oeI0pjOONoa8RVKUtqjSc2c8lBm/uDEcAKA0sqGgRGBDMPFh/qxHAcLpXwQ0G8croa7brjMZmNJEqo8nGVPkvy/BE2RJYVl3p27mN6uoVg9Mb5QrWuKCA2ICA1AN4VleeZp50qXblHeDCYGAG5UYuD5inoLA/48mEdRqPwIuIum+FQwCO9qgmr8NC7t5X1UU4dxqavnm+FbWRTiZzmO4seJkAShlF6RPNcvuABtNDXK1Cm1KREA08s0FKsnxkfzc+HFMHay53g+Crmjzh6Sg0hP8SakuIKOGC02ZzjINzzmgTrul+7gWjjOpEmMGTLWqk2y+CUFxmSYNUlGtYsn4dT5iqO5RUdzi47lFi9fMqNVV+t+Ldj+7BY6VeCwemJ8VM9qXVNBaU1Bac0IPsrCshyM68Cu7pC8wgrUKE4miGR1jH9DQz/4TYLlH4eMRq3WT/Jsa37ZJX9dkjMT0jagvuKbQfjpm/fAWNCwOAC4U6elbtk2gDW0L9mhWD0xvuPvb09+Jwhi0tghaBoGx3FnimCzoYuTE+AcTEkzkRrOPxjUSzp4IuqaTDaGJUU958UsuwLWiorhOK6wouH4mQqUrYlwV2tXG9WjXgkHlZ45MiQRprVX1erDQ7Rwl1ojrLr37j5UoLMlH4Nz6hkzDIVheikdrqFYPTGtMj8D/FHLJci2/af4/zpa0AEwNGE4ukn6aPAZzK1cwCNt7FBXotroOUuJf6LKWoOGswCjATTrq+QGDKj9tBaWRD+yp/HRlMZ0AVjyMN2CRD0BAFPSpEla2/fnbt9/atv+XJklPEXRLGtDaivrxIwdENbiRTj6LbV8uXYfgT4XBNjnjThcBTIkx9mfOjLMnxUNnUecL62NDg88XwIr1strZcJEEQGqyABXN4LrEz1194K9R9G3Dabv2KFYizFeOFpNeOqdLDA4JuRQ1nkAQHCIsysxx3loZyfMIuI3pJwqrIkPjfT+fCg47qXViPjJQDsi4t9vPXXNzGH2YznuiD48JAAu6uvVRivf/pm/w/4F4iUjOEPjkuqF6TNg9cT4rqFC5rwErVpx2ayJ9U0mvdFSW99cXlSIlvAe+4HaQe2SWVtggH9jox6oA4Gh5Ybt9ofKzEnqMPJs0chX2xiszDRGoZ0VmpZeDqafGaFYPTGt1VAGBnuUhcWlxeXQqNKqlXNnjSMBaTEZ/RRg3PCkZpMVTBnCASBTiYTkkuSFkyQIkiDE/TC1CrYRAH97on0LoG7JLRyDDnQ+xJmtyufquzzcpEm02jgFn/I50HENFfVnMzT2fid6E5zxHe0Pm9KXm5rMddDkBgCkJNoPiNHg1hH9SEO9q6c6FP8xYFw4Wk0AANVDY2yODA+5dBJc2gYHaCqbTH5q6PprNplz8krMFrsohgT5yflASd4Hyhdx0hTBtLWjsFDJaW2GLZa9LdB9NlSRenYSJ87BL5wwrXx8CdO7NDT1wXjcaQnTHvKLYfO28GCtKsCf4LXKT62qb2qGdh4vXnUNzfIWqJqmTVabfeaGD9mQX/+6F208de/VaIPjQDNXYwbVehsDlD6NV1PzGt+xNe0Svv1930tPLfN0b2oGbDazacvBTnt+TKsR7MTUB0Nz3oErDF/AXT4x7YFjucFxYUL8pbC0FnbyvmQ0y3KvfPTb3JnjSYI4X1wBZ8LJPl7pp1UCYGrWW232kxgM3tIqb7hqmnvDTVOzMUAJrEquY9OYUPkmqg1wvb9l4/S6xTNyzsLmMZgOx5dJpeJhpZKBz7TcwyV9gFIfjPdFQ7F6YtpDsK2B4Et+BEHxo8DB7MKsU4Xo5obNThvLW9QFdgXlGEA6Oyo31tUTBGw4AkDLUXh3FyhjNrSn0NykcSnc1JjEaaG4eL2b8bHbI+p+1uYGaD5qKAbTLhxyEh4Mq3VoigwJCmhsbHJPhm8hbC2Uw2u1ahiFB4CmSaHWs6VrkCaCUiqtT1ftAbVaZTKZhZtGdaKrhvqAlxRTTC/Bu4Zi8xPT4aiUVEICTPshSYJhWIVSabHYlApSvqGygNYf6p3BYBDv1KgUVpt586HCS8Yn+JJL34rLtKfUe3SChoaGfvnl2tmzLxT28M1EClthfrZSPZOHDAaHoPuj85a3vaJNd2/RUKyemM6AIskQf8XAyGCW5RiWIwhgMtsUNOkxkb6xsSEw0JlwTymchU1qJdXooqjyfLN+t6e7fBvaIUNZWblYPbuA/DNn2+zyQ84+iacP03kauvRB/NvtCm91//zup2mqRpRj32yyT9eAAvrw3fftP5J5Jq+AYVhWV09RNMPYImPiqkqL7YczFqNFhUxDCjVidmXIoGjJzhceWlJW07T+n9zCCtjEE+Fnn1nXRsjGfJqCuan2m61fjAf4+TU1e5vL5Dst/vX07VEHPU1DsXp2lxBXOgyFvq2nW/dmAwBmTUmT7KcTImMNZiIpJiwiRJuZfaraNIgywz9Kp3ryiU0KFlhl+ifZOXNOPGbDTqPBTLAWN9EkZIxP/naLcugfO8pc2a6Z4EmxkedLXAZzIj39+qedwp6LxrmOz8P0YFIfjB8BcEypO8N9Uf3JUNjCyyi0BTUqpwX654afrlu6pKyimiNVlMofOOM0wIBUEwClRqvUAL2VcC1NslNSVhUbLY3LE3whUwdefWNjE9AkSXZOHQt8zx5tbGwKC3SZcR8WqCEI4tl7rhL27NrfLo3GYPoSh/qHMooJC5RpW3zhqMTtB08LN/VWu0rafaAkSQQH+S+84orGJv3R44POFxY3MGwQe06rAAagBcYGeJDV6K+AckP5BXPmlstCvAeR2pzNtGnTDoknVDjP5JHxX/1+uLYOuirq9eY4t5LTguLKNj0npuf67GpAsPNI0EKfsL69zMR0HvGJMTclwqm9NsfADn7ckdUuoGu++Z4B1MwZkxsamxpqSxijDvafV8C7tArQaOQPUkDbjSSAH80aRFaqJ2A7UJ80snVaKqin1j/AoJe0TiHWfvTIgMExgOMq8soff+D5Fs+2Zv0etCF0ABybCn9NmJ6Dd9ULFy3hy0B8l1wRpi9jscJCdrPVplI4Y+xllbC/cOyAULGoaVQKurCqJCHSbqU3GBoBBaZOSDlf6JeVe05nVoeq7AVIBgunVcJHUhQFgMwq/mu3VsTLF11stAC9meukuW1u6gl5/D9r46KcJoknhidHo42XH16MUl1POSYzl5S5OEkxGEz/xGy1ITGFqaD8sr64gu9Fxzn/gRLLsBTFezYPHzq25KqrzJw6qDk4rF5VX9/YZLMF2PJ4O5QzWIBW6dGt+eSdV8y+9hK4peANV7iKNnDafd+u2yQzIgQ9uU/tQp1oLdDWsDm6m8heSUZ8SBQ/hrSm3uCMgrmRmy8T9cJgMBhZtu7Lkd1PTxk7PffYrsAw+7r1h582XDpramgAiAiiayt0DKGtJ5JIcBxpKAAE6aFXUUl5zRdvfyfeY7bYjp+t6NSpwUxIBlWXKQwr2XscFiY16ZtblS319S8uKasXTRzSMReHwfR1B/QgWlcJfM137r0OaDhQ0woTPwmFNK5CV1cXxSbGBwVHFhRVIV2rra1NHdgYk07MSB+MDvpw1XHHmViSoA16PSB86PnWYhTeg5J6eoxB6ebhMja5xNR908oV9y9iOM5ssVEkQVPUigcWiYNdO/aeaM0pMZi+jHfVi2pfZ4NexNVzJiloymS2NjTbmpotDMvW603n887BJTxJEvnniynKHrxPHQjbd+blFTrjNtOc6aMpKQkzXU99LtcZ3ZcGkUi+oYhoDf/VN3/feP2l3VWb/twDi9FGYZGcaYzBYDBy/PTnAQDALYsvDNAyAGgb9aaYMM2wuDE0AQiKIpbPHyQ+uqCghKYpScweIRZWOwo1/ArSGVJSXKrjlQBMiYo6t25HR/WzC878qz7jMujQNcJW88i8fea+a7746vcWHum4gvzC8lY9IwHAfbcuVwfEvv7ui22+bAwG09u5YtYYGFnhUyTh0tdsA4BjGI5eNGMgUsz2PwfS1pSUBLSBJLjJSnpNcoeTO3yPJmktRUIQKSIo8K6b5rSsnq1EciUqf+gdfuyBZ7CGYjD9lt+2Hb/i4lGu+6Dnj/Yunb7Ee8SGZ0FBiWC9IgKUQNz4WG/29hQ+FhSteO52+J+6pg5XT3fKi04r+HSwm5dc++UPLlEyDAbTH0A+yN+2OUJBvvcDJVoWTa4F67V1MzpaOPrpZ24RtltUT2VUoqUSNQyVOacy6XK08f0Pv9x59z3CfqEi9c8/fp48cSIcCZ1kd5BfeMGE5ffjnj8YTD+DAINiw/MLZQInvs4BTklxRMB5LeqQJb8ARbpYqRLEgabPfoV5rbctoL/477fuR54qrhsWB5Mq4i+4MzEeuia+/jec0TR50bO//Lxu8uSJEWEuOfZlJdDVcNX8i4sLYK6r8+XxT1pVUWVphq2ksrOdDaUwGEy/gwMZQ+PSh8RZrEyAvx/DslYba7LY6pqMHgW0tKrhgvGpws2CAnuhTmuR2LCUaEBIG7AYYUHVTzsTg0bDVXx4oEs21ct8kec77386wNR49oxzFOjZM6fT00ewFn1ZCV/U7xHnSOWOYryipB+2Y8D0dsY78pMwEBSkIYBSQTI22GBJQQJSSbB+ClqIs6ck223MgsLSxISYxISYgsI2iqYYLiS1iZ8FgiCCOiClvrIMTZejGqpkRt2plTLfCgcP7L1o+kTvVyr8F22hyaX8RbfhGjEYTB+h0cQ0ltQADiTFhtsY50AjmEgu3MjLh4WSKP7TTulsNlqUCgqdKsSfsjTb60k9wXHg69+K77wdJgq0H9gGxQMURTHe59w71JMvNMXSicH0O+obDcGB8tPbzpfUiG8yLOtDQZFvVNQ0IWM2bPBci9UuUqro8TIN7NFYei/4bPH98fc2mUd7Tc3nm6G0rJ6+XgEGg+lbjBiWVN9oyD1XkXuuIjuvLDuvLPNUIfw5XSjVCQBo2VogjuN8mQenbzb7+8H6pTMF1YH+MJ3e95agHVcQ70KmjqtRhGfX2RW8MiQDRoHqmHNmVWiNYHsTLOsyWNTKMgBwqcFIW529VjAYTL8ilGmIDY+MnZoRGerPDwGC+sYBrqbeuPGfo4KGCtCy+uZFPU1mq1oF+y0NHDrr7LGNNhtD01SD3uQUUA6oHF5IU+khwtXr6RX4tJ7W3/UmW7Da6XDIbeAAsNWqojJ18AHbtmwV7ooYGL9j+2a0HcN3htqxfXNoeMSWTZsk55w+aw4/9JlS8MpJk3yvKgeXzBxrsUHvLUfa2/SyIvcHBoPpk2w9eDo5JrSgtJqfCw/bBHMcSBuWcP2CaVAEOI6i6HqDObegOv9MXstpTEdyStKSo5BoHskuSUuJQvsJgrDaWDXfncRktmk1ji52/Ox4YdtLP5H2GKHpofQXH79z6x03wLEjHG0bFjdq9GjR/aN279o1bvykg2v+DQCYv+wl8WM3btxwxRUL3M+5ZeuuWbNn8a/NfmG8rhJ//bX54oskDQAwGEzfxD2IklVQnT4sge/uASj4HyZIQ6clhCYNGC3vAy2vbnRWwYvONnLoQFaUsSnEu1mWE7o3S2QRdmRy3G6oyG+okGnuQhCgvlGmO7I7IcHqnKy9BFtrY+pvvH25laOtfEtTKOImq/jHy0k4wJlNMuF7iXqiS5Mc83/L7/LlOjEYTG9ERwUBAuSX6sSOzhnjBpfXNMCfaviDGjAFalUhARp5C5QVabBkOS8OCtGUXX85wAn7dXkbxccbTVRtndku6qoYhUKQbBelpQnLys/WT2rp5bF8Vz6WlHblIzhZ/fXoyOQAMJtMKrXTb7t9+5aZsyZ7V08MBtNvkj4JdDPMtfqGty/t6eQs6zDfaJoSN14Sy0hwoEt0iHKIJqEM8KUTXVCAf3hIMMvx3kOOoym2PU3p0Ul8PlT+3HyLPYhEQz09VrJ+T0rw9BDI+UIZ27Y/4L3zrgAaI97nW/BieiNXjh6QV6ord23ZtnlvrrA9a8pw8V0EIcoDFaNWOfcPig2zumgr7HFH01SzwSelIEnSXuAuK3xt8IN2RQPRtj+FU17LfBLcFum3iozBdCVjIrjCknIFAPEDoMk5zFG8Pjs8HA4D3gMnX2wRiSmClpUyP7UzItTOpSyJ1vaezUaa6gBBHJqW3trEozWrPxG3Lxk0JPWzD95Hd93/KAw9QTjoUfZT83ZyNyU2tVN/u0yOfbQW+08Pc0yvJvM06kNkZ2A4nLSWMTRRvHPTnkx5C5Rym3yErE5PT+Yl7Ym/q9O15++NG8vLYZk8gBmdkH/d/7Bwrx+fzAVfhQVO3LxywTSrtWn5rdfCy6NVLGOvOpg+/SLYkeRUFrBZnNcPX7uzkqqFQqYeT2+RYwymqxFUimiFqjoF1OVRrTQKFaK8JQksI1PHWVVVFxVp94UJNFXlgZbcYs1WKjXdXtKedzZv5Kh0tH31khtQUOuvv7dNmnQBPLLZ2XkUbedknxg/0X68jfP4yR82zH6M/UiHevZ26ewpcuzZs4HlGNONGC02go+foxA6SZIaUVcNTyaivAXa2kW1H0oHlePwPpdqS5JgAZCu3d77JoeklIQ2igAtJDPZrAxBQPeCQgmfkTE3i++F5UQOC9QdofrIaPTVbsLq2Qus40L5M2A5xrTBChSSQBUK2miFDTFYjlNqVDDTh+PMNlYL7BYVqvrxtR+oOyHBapiqz6PVeBTQqRfPyM49Z7PZamthnY9MaTwAoUF8uj8gCyMuji1x1hRVNpmiAjx+qMS5VgLi7C2Bg2v+PWHZS3q9cL1Ofv1l4/wrlkh2rvvlj8VXXo7UExuevRTsrMC0ClTyA0NBBMEBoFUpKF4dbQyr5vWNYTi1mtA3NjMsy7IcSVEKgm27gLbYQQ5BkPZapLAwtD63C+gfu2rmTo9wORKWSrrUSnpxnVotlvT0kYB1NspznKQF61lifmZkeGsBhdUTg8By3Pch7PKJNtUqBcnCZSvLkUqVguM4luX8tWrWaoUd7TigUilYq7XtAmoyOJfbpOdIOixEcvEfoDabnKxPgWVZIeRU1WQed8tjAICSdf/z8IKJ3//cMn8OXz7kG74v3s1ml+FNP/39je/PgsF4B8txD0VQHwKu6G02jiIpju/7ycHpxSAsyK+hXo96LY0fnXHk6InWCSjDcpTcGtxLwbt7gL6qqm7XkTr/gGB3K5OPOEmj9rGL7yv90a6hcUA0g94Cbp2dAsPqrWfD5nXCds753dK7KayYmF5Dt8vxob6YkUYSBMWnsKcOisou0fGbUFWVCgp1sAsP1ioVdOsE1GS2enF3yrJv956A0CiXPTlGAKyyktuKIiMAGJsZACJSVSneuXTmIMCe/+jPHXfPhZbpQcf+BE1JwsWpANR5SgwAANw9d7pkz0cbd7bigjCYPiPHPpaBlLVwTK8M5RGOVpz8f0TNPIBGreBTPGGoKTE2HI41btWZG/QmWQH1pXmoEw4kDErR1ThnFkFp19bCJTYL/xX4Y+tPUNdmXZCweMnedT+4SiegtM4a1XmzYe7Rhr+OoZtIPd3R6w1+MKDWsnQiHlpo3//2+p13zZoifR0268p/DrX0ajGY/ktSd1vHrYXwuouyR3Sg4AVqVQTwUMrpCV2DMToi0H3/gPAAcSm9mKABJADVEtWtqK3mLytMrJ6Ik6ExGbqSqiZzZULy3bNgRidiyuIlOb//jNRTLJ2IXzYcQb7fz9duufOGue6XMeuCwev/Po7U8/O1m0Cw0otuijrzQZqbbHfNnCL4Ff45mD0yPeF45rkdp87PGJ4iewYAgND7v6Kk+qIRaZJ7SYLdeiJX5jcZ6xJYw2AwPUCOCXcxRUXqrRNQvdFZovPT39+Mj7ErYNv4dc/xhVNHebp3QlyijWGFhk+wXvOyuYzVTPm7qKfVgn4LxOc/bAEAyKonAGD938eVCspqs32xZTcIVkqkU/AcMPzZmiwgQAvfqv99+ddtVzuN2X8OZsOiz+ToJqOVY8E1MydVlrnMSJFlycUTqmsMbsmwdi5KHSxsb885Oywk8qKxzj0fbdnjfsK7LpfqPmOzfLppP7xrptRMdsLaPt4uuDQwmP5OkkSODS5J5SZoEHpbWG/+Zx9obR6oJzOzzbj7PJurWHRRh3JKlDSVmhQ5/7KxW7YeMZqslH8w7EnieFkWswlZnV/8sA0A7s5l8tKJUCqo1T9uu/36WXfPnY5OIgbppsAX63YQgLvt6lli9UQQgMs7U8wnHlAS9UwbOe6Wex5H23/9uva700fQdnWNQaVWv/z2Z3whgPWpB26CWrk3C2iptIFweL3AuMR4sXoCAG6eNv7LXYe8SCd/TjMBCG/SCesLYJUFBoPxBtfqYzsmD7RDgNIpwmC01NuIkaOSf92aQ9Na0t/l1VnMUPI4AFb9sO3OZZe3ePLVP2677fpZ7r8fd+kEANx+tUz/eRIwaBoeKTeW7tk3VgYFh6759L+ns48lDBp6231PXrZg6U3TB0Ht5tXzgzdXFJ4/e+PtD77+wbfjR49gtZ5n2/G4X6q7etqs9lwryvNwQI6xdkn/Ko/cJfLDAAB+/CqvheMd/uuPN8IlBQbTXcBsSy9GKG/K0a1qGiJmckJkRxmkd86cUlBet58tqtDpOZarajKX+CcoOPbg8fNZp0unjExKSx5IOVJNrbx0AgBWrYNFol7Us0nvXDjfdj38WNKc1cZXgkqkE+mmF+lEG5zbb5NjGYKEv66g4NBH7ljC8PH93FPZj95z4xvvf4WOeeXtz/LP5hSePwsAWL781kPHTkYlx5ZXlLsLpb+fSnyTY2wqvrxM1vC0WswwW4319maxjFW44s9+2gFCNaBb1dPlLtdAH8kxH/6xfXTsIE8H+CKpd7l6Zj7GSRQYn2nQwnpI+Fl2rOUJr0raFgt0xvhkT6KZlVeZ7hiX1Aot5q/iRPYZlmVD1YyRtlhsTKV2oALYVBqV1Wpmzaas7NONuip/PxUcSUJSwwbHf8lLJxuinBIfeyLzlKdzD0lx9p5C0mkjFLImp2fpRHmp4OPvt91xzSXu0onUE6FWawyGJlIF3waWZR/+1zK0/8E7l6IMsu17s5DhWVllT70aOyq5qNAlDct5fl6LCY3KXT2R21fB0V5WHCxjH2rCAfDFTztsoZouVk9H0gLn/qIkzOczKDb+fWJ07KCJI+0C+s++k5LDCMAODw3fflIaeYNdxhz8sy9L2FZSYERE6KZjMHFYLtbHnWpyyQPBYMRK2uAYC5SXDRswpgTY5VWSdElLetG7I5kA7BGivXlXkixQlYJmGJZloNnHT3Oy8+W6bWyIPai/P7NoUoa976k7+eehs1Lg6HH42fPX2nVkx0H40WKC4c1FIwfX1LhUykeG25tFIekUqyfqVyJIJ8oqZRjmpf+tBgB8t/rjE0cPWvnRIwJ7DucyGnLV12tGpI9Yv2E9+pXeOOcC1Gja9apFw1TkMq6c6ukZxuZMs/3i551QPbsW95QvT+oJY4kbjwICrFq3PV0UTxOTNhh+MeecLWdcp6LOGDEUAFB6DEbPxChF39qzRw+18OEAMSMzEk5kwr5k3h3H8N3futeTOawgwXu/YydDfyGvCZqlecfPO/fkFKeoHZPgJFAUKfyxGoxWjahBvSfaMJtDYF+FIxoT7JQMNUGbLFbA2AeUIA4czxPUE4pXkEffHwBg1/lC2EuaxyDKH0DqiaTTE6fyS6FFc/gMG66trbVfnt6g99NoCHg2I3QRGMxRYXBo864jp6+98pKExEHPvfrutTfdde1NcPDcvp2bP/73XkE9AwODNm39OyQ4ZOG8ha+99cqiGeOsNracH53qDscwsupp4dVT2YJ6WvjOMWDVL7x09iT1JPykOXAM/03z5brt1kCn7InNz8FJ4YJ67syG33nLlt7w4D0PCQfccOFQ6M0QPYuZAQmJg+5/8vUJYzOmjRkmecbklGjUdCc11CWC5w7L2u64aMIn2w++fveSc0UuI6+FyV6zhgxOiXdmnn28Ze/ggNCZE51P+vEWuwRLkHXLfPzHzhZlXdB0TLeTZ/JQC2+xMhRJoDV4bX1zbJRM7qcEZPKgh/g+p+PXPcc93VXFmYE/CQDZAJjMGofCitQTsbfI3uHcO6u222PiE2IiDpZWA6/qidhWUArC7b2WBZp56RSorG04U1DBBUCxKyw4t/zaK9D+62+6fcHV1+6ZeGHmgR2MBn7aGhsbvlv37Xfrvt237cD+7QeXzrtQOElMdKTkWWr1jUDaJgXU1huS4gc01tSjm+H8pAE36URwq37Z1fWGZxvV80e7egrrdzFnz9ewrA1wAKnn6y+9OXPGzM8+/E9u1jGKpu9/7KWvd5y+floKI2p6vfvwmbdX/ggAcFdPmP2XX8qw3I5T59a8em/eqSJPLwQ+KQCf8IlfntQTvbi8oqqUePs7eNesKVsPuPiU7po1xV1DZdUTLWW8qycBfFsOYroKu4BabYxY8uqbjGF8izk+ed4nAe2uiRetBaqnGz+egBGeNnC6tg4EqK5esOipR59ecsXFwv5vVn86YvT4R95YdWF6DBVGKRQKk2OQ8mP/fvTd1/8rPklpmbOgPysHavEAuQRbDnCZp4sSwuzvRY3wpeKgtgEGzXYdhWHunqCecy+Fna62bDlqVsn8/UjU032NjLBarBRFIPWcMWLozBkzV3/6Tm4WLDljbLa3X3nirQ/XPvnO6pfuvR4dv/vwmd37Dq9Z+/WypbDHtgTYFAKAHafOzRie4kU9XV6U64UJ6onW71vO5M0a4uJgnTlx2NYDp8RG6K0zJn3+z/6W1NPasnpybLcPipW8xT+2lFPRLwSU44CusVlQTPjhrHfebPRteFy3v7Wdx7rjZ9BGRYn8inv7zm1PPfp0vo6fKeIgaVBK5jGYwvn7uo2BgYFTHX95zz39vF6vzyvVpcTI99+HzbLksFiZQyUVgoB6oidIJ2LLlqNwQU27ed95OI5b/fMO7+qJYEUeTwDA+EkXZh/bywD7kn/pZGeocPfhM0tvvslms9YX2t8yGfU8dHrGOBnLVNb8dFNP++JK4v0UG6FIQ10eRZOChsqqJ2Oz+uICayGrpiMQ/sLDHd8T4r/5GakpqJbE/VEz3GrtKILbeiInNij8tqUu0VcxfSBTDQqo1cboGpwmJxRNfatrTrs117Cb0dXpamprjuw+djLr5Dfffx0THXvPnfcCAO65YSE0xK6ec3jX0Q0//nHvI//3yXufBQcFj506Gmjps/UyUeAmZI1WtlzgJMv2Aui67WLoBuZEllSwFApF2uC442ft1zNyhMs8WADAVz/vsAbAj+mU+NiTrqkUJSUwxys2FvooCYrYnX0aqScFmHdefuTBp998/cN1Z09nbfx5TVFBXvSIcbW5UKmz2CBSSz5078MP3744KWWYJ/WMjJRa9xxjJShn9S7LWABBuqsn/x3GKhWURD3djVB3lHxBnax6Oi4CkC6LQPcDmO5d58UFSGfwCET5S02BUekxJ7NLYoPCZRMqZkwegTb+b96sDzZs8SUjTYxWSbz1iz15pkcIaJPBLFFMs8UZuPQP8viLE6MQpZcnJ8Wdc0TAJydE7itsW8+5HseA2AhPRuilCy5JT81Y/clXI9LfgB+q7Vsef+ZRf/4ujuPGTh396AOP//eN975Zu+bLb1a15xp+PCFjW3UvtiCZZDWO4z5Z98+EUfbA+gm39CMLr55eQDJK8ZmwRcWw7U9SXFR5aeGSK2Y88NiKKVMveuDJ16DjorbmoSsnoIcc3HH4+UfukJVO6NTOyQdaeqgo5mMvNBAB1ZPPtHBXT44fiiUbeTdamNFjBh872hZHEAr6UV7Vk+Psd3+0TZpy0DXEBYQkx4U9+dJ74ZED0J4zuSdXvvMi2h4+aCBBEEnJQ4xGI8vYGIYhaX+WbUHsZ08dsnXPmVapp1bp/C3JujucWRNy93IAnCvV7TpRYDZbuUC7xBlqXf56tWFMqwU097xUFGgN+uxDwvx9yqsPjQgXvh9JkkhJji8o7AZrqBvJysmEpqUH3nj3P2+8+x/QR9lXUTN5gEtQa1dJBQiTX7y3CnHqkq7RqFbSHMe+858VH77zMrRPVaFff/vDJ5uy7pid/tF/V9ZWV5aVwm9u5HHW62EkzmC0nCmsYrTwT31c/AC9Ae701/oL6onMT7t0ArDql923LpvnfiUqJSU2LAQuSx1+5bwJRkfaoCw2hpEvw+XjfrR329Oxel+57QDoJjgA3vpkHcdxb77wSHlJYdTAmMeef/etT9ahWjtoQMy9+pLL4XpL4G3+sxAbFJ5XWJmSEOWunp6ey5N68m8WjCF/8PWWsUOk8cZ5c8YiP+JHW/dmhMvnVxAAJMeEJseENputX+2G7jW2kZZY9YZayk9FEf7wfTGIaiO1kaTsHvhXZWQVkjcwPTkKOGYnVTZY46RRYhkmpMcBAJBo9jfpxLjkoonYVyz9S5CmDsjlUVToXKLeAgaj6bM1WxbPt3/AcitrAai98Y4bvvrka/gXOBbaoSu/+1M4fuV3fz5613UGYyVSTzF6g762tiY0OEhX34D21NTDRL/dx/LGjUh2t5fVahjc2372nPtVqRTUH38fGSjXpUzAYmObHYnGYeEiE9iHhBWUd7x6/R4Q2D2F17EBISOGJQAAHrnTPj2ssrz04TsWv/XJurHTZh/ZtQkAMGnqRQZ907OP3S2+ZgRMRnRj0+4z08Yn29wm3XpVTyid8IT+CndXxoY/7Wk2AIC6pubkoXH5p13SwMX4qRT+Vo1eYaRIIikhsqCo0r0TsbHKipZILO9tN1SxSpJRksDiqP1DYgrfkrgBwRU1LuMwzxbVjE4OFk+UlyUl2Z7EXlBY2k9Es1Xt5sJJ+OGsicYd6trLuTrn32dQ/OBjJ6DTEzEiDTrUCnTmcdOc463GjBzzyfufjZ8+dkxMZG6NvBxDk9ahnojtBSUgpNUN0349kQVqmqeOcMay3DmYV71ght0tW+tQ0toGKNk7D567crrUQSwmNBR+Ek3dpJ4Iq4W3lBUKm9WuhiPTE7/84LXTJ+ydbgICg1597hF39SxpqHGPLyF2HconWqOeCJO/YuzAuPGp0FxzR8GL2/23XuZFPRE3zBn90Za9kaEBV16YarEMOX669HBOsdHsfCJNpMJYBW+SgEEaiqRTSTKChtoFNCFS0+Qaam9oct4cmhShr6/rz6LpC08sGe++87N3YZLH+vcXiHfu32lv9v3aD7gZc6vZsXvHZx98rtPVPvDEA4yNeenZl5MSk+658UrxQFphOCvLsoeLK8QPP1zkvFniYQI2sponx8VIDERZ87NFOD5Ia/GTdwiazPLVWWLyCsq3utbUdSWxASEpcWFWq8ViNv/ng29Zlnl9xYOhvNf7dNZRfaNTGTJGjpt3JaxaListmn+N/W8+XAMrTTxhsXG+q+fKv/YBf+hsMVttXtQTpk+s2TpzvEcXgTtKJT0hI2FCRkJpZf0/h84IeUeaSAVda2xiaKShBO+ctHEuLk0ooMmJ0XqXEWpg1rgYob4zKtR/QJh/Xj58CxMTorFoehdNgUnToz97V34/2lg/3UVYfWThPb+CfsxDTzwwdPDQNZ9/i5btFovlkvkzLec99kNoM+7OB2+E++0WJfP6zt6C0hYNy25UTzFP3rvsgosuW3jN8idehAPKOI5b4Viwo4EUqRmjH/7XMrXG7+W3Pjm088j46XanpBeUjmm+A9XOSLWCdx5aUYalWD150sMHTh2Z5OmE723a26pchSsvcRnKGxMVvGzuuA9/cA5Js4VpAkQayrhFxujEBPh5njQiPv+ch/eJAAWFZagtSEGhY1RKv8e7enYeyJ7tzzJ6+uxp9OEUcEY8HRw9ftRLQK9bWHcw11MOwzpHZsXikVK7SdfoUvnW9ZSUV6tIu8W3ds3qtWtgw4eExOTn3/jghTc+fvbW+YVns4uKS4UqEpKiH7rr2rc//m5w8uCz+Wcrq3VBGmkU+mSms57w9ssu/vSvbfFh9LHjubFx0dFhWi/q6QXB/JTVTwKw67aeXDTT0b6dYwBBRSplErHhDCQ+giTRUAOrIAFr46SpIzQyLRHJyXH5+cUJCdGFDsWEFYptFU3fO+P1SfXcv7PstgeuFa/ZPZmibWD9+wv6s4b2VQQl7Wm8u3JNaUnRGy8+hW7uOnBs2uwL9m878MLnvwuBeIEdWXBBcOHUGWfzW87rOpvnbM9RUlwWHTZY32zJOQMfGOAPxXTPsfPAz2nIDosJEGrwJNXMyPxMCXCmA3lB7KgVI1vEYgvTsNUMCbiQCKi5ddXQq04AQFOkywIinxfTNismos9kfXY2grC2R0n7JK0dDIWDdZ2IP53f0AQoetzEC+AGQg0VraK8NC4hqUBn3rFzm7G5+aalLrlfWbmwtSDZUhKkpO7OyrA5ZwqFm3uOnTeK1FOCuJq53pnJzuUVlI5OdimXCA8LlZQkfLztgKf6t3/NnfWhW4mUJoIyVjPiSjDo3WbYjgntYdFsv5JKYkqS0BPGi/mPgnXu+3GYrqOYv3jukd3H9mzZN2/R5XV8SPmKuQviEpLuuhbO0UGZZMVNBgvf3+DG6+DQmv0H7evuPLekNH2RVFUPlFZPjIHfmtV1RnHgzot6StiSb+9skNfkMtlX4KJRsaitmq7RtPUcPNg9WQ2h0chnYoQH+dlM+vABUXXVtYKJSwuXK8lI4ziuxWHFWDcxPSdY527Ity1MJwb7SQSmzpqye8veLRtgL3PEY/9+JDcTdlPLzsnKyz+7b/sBo8moUcNWDPMWtTxlR8IBvssP+teBbN+ftng5amo9prL5jkFpZqsMxU31QQra6shgpQWNlIilJ/XEotk1LLznVy9GaD/5YHdXpE4Ah+wEjCaje1zO37FxzU1LKIoakT7SYjFn58o0HOlSwtRCAyAfEy1Q1hpi25l8Tw8hI7VslYER9Zj3aQmPRbNbkKgk/jC3NljXUc5lHLLzBYZhxAUOvQvfs9aghopu0n1JN907fch25eqkUEZ78MVb10+szg6k8yS1r+L8my9DRXQp9t4hclS0/jPV9+hBY419oYe8Z6/9cKgDV5c41tFl7N9ZJvvbxiE73xH+8j979zsQqurnxcr0oVL5oFXv5c5b7goLC9u+c/v+g/uy2KB0viAdQZLklyu/io+Lf/u9t37b2C6DTvgctllJsW5iejg+/m2vb2XGSF9aS/UyC9Q7h3YeIUmS4zibzXb1gkUAgP9++O7Rtf+zd51IH7Hq49XlFeV79u9Z8eRzj97/6LTZU9v/pFgH+zx96QPfE1jvs+D2/N98C01texFbNmwjSXL89LHjpo2ZdNGEsVNHb9ux9f5/PUA5Oj2v+nj1L7/9PG/R5U8/9+S4aWP8/LSoARoGs/CeX4WfXvcZ7i7D8zY+dteprH9/QQ/3rvQdCzQkOOS3jb+Kp9g/+vQjR3Yfu3TJrX9890lIMOxW8NLr9h7aHM9br70z7ZILuu+SMT2RfqiYbeY2r8XKHRW468lZEH1HQC1WyxVzFzz/6nPinWOnjkY+0NRhqZLjf/j5+2uuht23MF0Z02tVXsQDb/9x+Xhn653Z07z1zcT0TPbz8tpX8x/6joBeMn/mjr92Hdl9jGXZ6prqBx6770yeM5k2LlbagXXvgb1YQHs+fxyCzSbEMtpmsLe6B+Y/IHr4Or1fCKherx87dfQlF89+7YX/REVGfffl93DkennpU9dMkz3eaOzmRmH9AYIgxowae9kll2XnZP25+U+z2bXvrIOY6NjGxoYmvctYBHcZPVqub1XaA1ZMTGfTdwQUsXnbps3b4JAWAMAzT6xYOG/ha2s2P7Hskswc2BtGTPTAvrmm6Dm89OzLc2bba6KvuuLqZ55YwbLs8hnSCcAkSf72w++PP/Polu0+TQnHstj3WNhrC5f7iIBSFHXf3fe/8/7b4p0vvvb8kJQhyPuZzQsoQRDC4IeLL7S3gMV0BpERkXNmX77i5Wc3/Pk72hMYGLT9j3+e+2T9c3c4xzcSBHFoJxwHRpJ9s3UsxkfEKimIaU+Wzj4loAzDLFt6g9ls/vDTD8T7UfC9QGcG/Ii80SNHHz1uL9edMvGCrJzM7rncfsCihYv5WYl29QQANDY27NyzY/oFFwp7fv3+99iY2ENHDo4fO4FsqfUXpv+wsMfrZl8TUABAUUnRrTfdRtHUex/ZM+cXLVw0cMDAl5+4D93cs3/Pp+9/Pv3SqQaDYcWTz9E0fcvdy7v1kvsyJ7JO8BMlw3Q6Z6nbg48/IC4Mi42JvWT+TF2d7sjuYwALaOfgJdvBU7eH13445D59up281kdHKBKXX3MJ6D14z3355otvhw1xyXRZ/c2XX7z6NAwxBcPZ9iv/98m4MTAEwbLsgmvml5WX9ZBmIr0U72/H4V1HUVPEDX9t+Pq7r/L4AQ+CgGaxznmNR3Yfe+bFf//x90bvT4ffkQ5/jzyB3qYsNqgN+Q+z3bLN+qp69ikLFABw/S3XoWW7zWYTQrriiWN33ndHd11bP2TctDHXXL30gXsenHfZvHmXzfOeFIGX8D0H8SoB5T9I8K6qr/VduezjAopAIwcwPYHvf1r7/U9rUZTvw3c+Gjdm/Oqd526aPkjwSgsQZN+pKu5d7N26X6WCTZUQ+w7s/fjRZeIDZs+89NH7Hw0ODikuKXrsmcfy8s/Kqmr/XCj0QQH1Tr96d7uR4KDgoMCgwmL7gDCGYe68746Y6NjffvhdGxAEdNJWsySBBbQbQIUnN9x2fc6pHADA/f964MbrbprMf8mhA/74+a+oyKgNf/5++NjhxVcu+X71Dx99+uFnqz/tjovtidB9WP5QxjXsWgiLdi/vzwuNrmfrxu31DfUz514k3llaVgL90VQQAFIBJUgche9qEuITAQATLhwn5Pb998N3Dx899L8339cGBBmaGrRabVRk1PW3XHfqDJy/9vsfv7331gd33/4vLKC9VUA90WJ1iqeJY+I+CFhSO5B33n/7wXseum7J9d/+8I2wc+3qH+Dc7HMy48JbnGCI6XAo3m2iUqlMJpO4xPmv7z9H28OHplZUViD1RNz/6L2Hdh7RqDVGEy7k6/0C6otuopGNXg5ozxDHXpSw1sWsWfv1sqU3PHzfIw/f94herycp0k/jBwC49Wr5rA+8hO96zhWcg+l9W/atWfu1UITCcdx3H7yMtg8fPTT36jnih9x4LRxZjNWz1wtot89rROBBb164bOFsf3//m667ecqkKRwH21/9tvFX/3qZQVs33nFDWZmvU70wHciEC8cd3HF42dIbli29AQCQmZ359HNPFuRWJfKzOiSo1ep7776vsdEZo8f0sjzQDhTQju2v1T9N0TbkGAoCijJzWwUOAHbSe0QQxPXXLLvp+ptDQ0LhOPbK8kcWXyDJ1dWoNbu37GUYZsKF4/Db1Ost0PaDRzZiMAiO49as/XrN2q8BAK8+/9rsmZeOmXrJ0d2bhd9PYkLiT9/8YrPZJs7oESu/nkP/FVBPkuoeSuq9zQo7mzaYhOGOJO3+PMqxh3Bk97GGhoaL584Q73xyxROzZ156/X3P/vzbBpSre/vyO+669e6y8rL5i+d227X2VLCAtgBWz471sXz2bp73A3AuRJdhMpmCgpyLdDFFhQVo45XnXrt01qVvvPuftT/CggiMhP7rA/VEGyzQ5fd8u/MwzHDs56TGQw+ad/JP21PrJ7ZvPgf+hfuIqVm+iTUAIGlI3IF/4F/7q2++/OP6H+GexKRvV61VKpQXpscAAIKGj/5t3YZ33n8bre59ZEB/qlXBAtoBGpo86XXQv/FFOuH6PdLvwK7cDhFQ72B59UVA4wYN9NP47fh7Fymqo2VZdu7k4c0G2Px/89HzSqXS/YFjp4728ssf0J8ElL5zzkzJLopqIaX5ww0+dQ7vS0iC7L2o4WuPoqaqOXloQv7pwuShCTVVzeGRMDO0M5g+LhbLqC80G5vHTx8r3CRJkmVZf149AQCTL54oPlij1oSFhYWH9SN9bBG6tkaa5RAUEub9Me6aaz8XRfIVYVyvFllffHBYN9tD8tAEtFFT1ezpmA7R1unjYrEp2irEU8HdMZqMJaUlJaXYW+U1iNRQ5+yA22YWjR/RohB7Qq2UXtUHG5wZFYjXfjjUGW5QHyMYd17u8hXyblm9pyPzyio74rr6HUhbO89E7T+o/VReVvGYnhuFb7MQSwodwsIjZG3eOtcZjjbG25enVKA9eCn+bz4MqX3wu1SvxSwaP6K2GprtYRHe1jK1dY0+Xg/Gu4zmFOk8LdIxvqD2U/norBT338GpZn0kjcndydA2wsIjfJRaTz4KlHKsq62xXxgvowAAs8nDN7y9xw0G0xMjfi2u4XCqWV8Q0J4jxBzHsRyHetj4cDT8J2VglKf7K6rr9DZLOy+p37LzcAk2QjspWaLFcufWtt1Z2Hdjrf1IQNsPw3INTcawYK1PR/NeArNZ3jita/QYP+mlWKwK2f1KhbVbNBSHj9qmni02MGsD671mAfZqecUC6iscx5ktttoGg68C6sMJQV9XT/e7dPVNAyLUHfW8EpUU9BSrZ5vZv7PstgeuFbY7tueOLOvfX9B7NRQLqK8wLNdssjTona1n2wPHcQams0yzzubNFcuWXDEZbWefLr7jno9lDe3Nvz57013v1+oMwh5dvWvgD4DBydGvv3jL48+uOpPn0s4uJNj/iw8f+PeLX2fn2iuXAADPPbIoMEDz0AqPVTGCbm5d9++BA0JSpz0sOSBn11vuOzGt6rkjZlIHyWvvbQuJB9H4CsOwBqPFYOyYjJDe24D97L53l1wx+fEXv5k895krb3krbWjcns32/rtiflrzqJ+fiiQIswiZs+WXaTSq/71xl2T/d6seb9Ibs3MLhRD8kOSBt1zrMiDEC35+Kn8/GTtXdiemzezfWYZ++u3vEFugPsHB2D1jMlstVgb0YxQKWqVUJIy7J3lApIZQNFQZLp73wrYNz7796s0PPfmlcNjAASEDolynbnrmsiuf+euXFz/537133Pce2vPD6ifhVOTLnhaO2f7zs8kJHsNxmG5noVfjsQ935MEWqG9wHMNyNoZlO8Jx2XsjSIEBGjiMLDxEMCebms2vvvXzOx9sEB/249ePXHCJU/4kWKwKyc8TK76Nj4sMD4+EDlNCExjoN3/JfwZHD0A/P3/2cEJM+Mz5L3b+68NgWge2QFsX8+mtC+8OolbXxHHczr9e/HH9vi++3lbXYGhoNGz464j4mN2bXvpj01FPZ9BqlJK1vFKpOnA4T28wrfn03ovmvvDXz0+VlNYaRMUzjz2zRtgO0Gi8ZIYJjhEFRUEHa/SAtr5QDMYnsID6Tj8XTzsJ4+75c80TixZOXrQQxpEYlr3l7g/zzpWje5ddM50giJff+Mn3E1osUCsvXfjins0vr/n0HgDANTfbB5y5wzKMp8wwpVIlSWzY+vsz7ofJ6q/EJX22rML368f4uMD3spDvjeEjBP3joZOgR7Jo/AjQkyDgx6yDzsVxHXeubmDOstfiQoNJgrhywaSH/m/e6pX3fPvDrg8+/YsgiLtvu9TL4t07/37xu5eeuXbmvOfa9nAkxAikpO5Xsmfzy0h/01PjhZ1ZOUWSm8kD2uJvJUWj7XupBNc1Bfp+cEhA64qVxSopFtPeq5492gL1Rdm7VGThp4PokOg515ut2bAQ/9o62O7MyjAffbHp3ZV/fPHfu65bMu2DT//a+dcLdfWGt16Bk28RTz5ylclkefip1b6c+WQWzFgymbsiu2vlf+8Uti+45GnJTbEWt424UBhDU6lUXr5D26zOHuEPOVvaC7R7YW8Wzd4hoD1QZDvKaiQA6KVFnJt/eHpocnT82P8TS8BX3/0z6yL4SyZJMiRYO2n8EOH4URmJoEciMU7bbDV7x5O3oVUgFfap7MJr9bCF/2ISn6a63l/S/6y2xtfmYUWVjRdPwo1BAR2uDDeaLEazldUYQV+kY0SWtzw7zGzstev3Jbe/e2Lb6x+/fvtdj30aExyE7PEvV95rszHuMrRn88vX3PR2SVkHdEfsz3SICgtCLP7TE1qnWxmmtKphw87sxTOcX37eYVmurilAuHnnnIvF9678cxvoH9BXzRwhNA3i+N8Ly7IMy3EczNoxW2wGk0XfbNY3my1WxmK16ZvNBqNFT0h6zvVufHQEp4VH9Vbl6yDqGgwr3lj3/KOLi458IOxkOe7COc9263Vh2iLEuhp7XzGGZc/klVNMK0wokiT6TiVyOyC+eNNjZVtIWLj9IJnH2dcLvNLCDYZlLVbGbLE1mywGo8VqY8xWm95gPlNTBPoW7ubqZ9/L9A/V8zVL/hqpF6yusbmXrt/FDEuJvnnRhU0G06vv/hIT4WvOvMFo0WpkZuxguoXbHAMlGw2mfSfO65vNV88a5eNjiyob46M8Rpx6bGi6A0FNL735QOscjS99ISQsXEFTWo0yNMjZRZwAYAZIEW4KX1ksyzEsa2NYk9kKy3tsjNXKbM/JBH2OYp3HZvW9mlN5Ze+t/KO23sCy2BDp3XAcV1nb1GzqsNjd8TP2nLb+QIcFkdzVdvk917Ti8Y3Nj4ArAAC/btwnq7bQpLXYUDX6zlPZHXTVmLZTWtegJnp3EBID58qZLEUVdd6nIbUKyseGuX2iv3AnfgBWvf99K8Q00A/wBY4L5k72EqxB8josURpn5FC3Y77a0mK1mSzQV6tvNu/PP9XOV4HB9G0qa5saO6jHGOJ0XT+aA9aRAspyHOkhviwW01YbpyKgvHrm1437aJpUq+hAACJDYYrGiCGOdluiQBnHcQzDWm2syQIdCFYb1Nw+6UDAYLzDcpy+2Yz9MF4QJxhwHDhbVJ1zrsLP4crvSAFF6skhreL/8ZR23jrjVPLY1b+3dzISQVAkqVQArcbe65cDYHCCKA/D8RpYlkPyWttgOFNYXVCmazb2+vhPB9Jr07EwLlNq8PQuL9Q6chXgOjk4tKymoaBMx7Ds2JSwTlnCE2gFTjiEiBP2+mSc9pzJSGHhESQFaIrSqBT+fiq1Eqrt+VKc1ejEyNo66W3CdBl9aDBCp/DjoZNC4k1xSVlZaYUfZQWwWQ2kc4MA7rIJs574QvCe31FYIrWM2RKi4Zq0rbvsEI0mLNie7YH+UPPL+5aHSNG6X4jBaNEZO7ebHyqjxPiIUOPkew6TFzLz+taftzRdobHZ5LIG7eooKiwmdzVOe76Simj1pfprVCiBWaVW1bsNtOjt2DqmRqa/pI71RGXnOtgCJXrRp7mV6JvNxZX1knSFbktDERun9labPf53T7SUkCwmvwS6TpJj7cUIAIDC8joAwKGSYtAnuGL8+PXbTzabLDfPyfDxIedL4XyOnSftEyC04R2WOtNn6OImZLz7syMV9JSub1qgHMfVNhgMbiGQHpHH1/OlEyl+r/A8dBmErTk2VFle3eoWCg8um8HyI07NfLZZvd7YqDfV1BuMZmtDk9FktvLlbS4tq/qP1PpYw9OBOot9oC3y46GTV43LkDWve4SA9g74dnbdfRG9nqSYUJ2o5sKPBn7BiuhgRejIJOmh4l82B2uFWQ4YTRazxVbXZGwymOoam6vr9E3NZouFsTIMMqT6idR2VB8y/qsKR5FahiQImpIpEMAC2graaX0mDAxBq3iMO2JV9Q4FwOB42EithXeDgLaVjWEYhms0mJpNlsqapnq9sbbeUK83Wq2MJrSPzwf0RWQXjE7D+gl8Q/bL5v8BDp8hijCLaGUAAAAASUVORK5CYII="}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "assistant": "Use the lowest tier to place the hostile sightline behind its socket screen while keeping the ally views clear.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H0 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant": "The low tier remains exposed to the hostile; the high tier keeps both ally frames clear while placing the hostile projection into the cottage/screen obstruction.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H2 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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AaJf56evlLpmfHh5qSME9xzIevXV6Vk6xVL7J8yAjp8g3ONhXjTTH4sAU7ndghPYQiIASCIR2xI54rrLeiM3Pmnq9gqGQ9xPC7QfTrps9Uto5I6coIiLYUwnr6xoqtZaD3PEoWsWvWD5NZ9BXauokI7SnQASUcI1Jfy8//b38a30WhA4gLiqQoiAjeD95wJ+6eFW6K7ZfGISgqloTGBToo2bkq/hHnv0cL/MDA/0lI7SnvB9EQAnXEkk6iYb2XPMz0Bv1T6qp1998/RSI+oGIGipXSQrCquraoEA/wJloiiqtqrcZ2ImNUNxhpF1GaNfGkYiAEroLREN7HJVm9QQAJCSEr99y/MYVk3CuO0NTPOAllSyrqAkN9se3ysurfAOCIK2QjoON0OuWT0P9Q7AR2kMgAkq4ZhDF7OnmZ6BZPQ0m7kJGHgDgl60nblw+CdmhSEUt1mBUeCCqi4egqLhiQGxYgAdFowx5YKOhkhEKeghEQAndSD2JpPaguqPKeiO+UlOvDwkPfOSe+ZKGXrd0AoBWc+EhhDRN5eaXDYoPpylI01DB0AqVqL+YR579XDJCQQ+BCCihe0E0tGcgMz9NLEdBuPdI+pq75hlNyPX52/aTkycNFRySopkJIcjMLkxOEPKTIKApSsnQaD6SNY88+zk2Qp0887gXkK3aTSACSuh2KqmrrpZfuvC8CC73rIOidNTU62OighQKFYRw/7GM+dOGoO4fABxOzQwIDZEemJZVkJwYgQTV7BalKXglv7RcKx5HCjchI5RE4QkEGyRBbNHGvPJdA3n1ujOVdZY0o8iIAIam6jS1bip3COHuQ2mzJiaxwmy4utpayQuK1FNAcnxmXC7yCw7tFyxWx0vhJmSEyhJFuznEAiV0JDbGY5sNSaKh3dn8lBbvHM8raJrneYahazW1gb4+UFjLz5yQhNfyVoPkMIJQZl4uGj56eJA3w9CUu6fYAU/aNyW1K+bBdQhEQAntpVUq6boyEg3t5mjq9Xcun0xRKODOshxNwcKS8mB/XwjhvqMZcyYPFtRTHooXycgpGjZqeJOmEgqNlhnciU6A7eQJHB0OEVBC10E0sTdN7PDxVP158ML8iYMoIaTO8TxDU0UlFaGBfhQF9x1Nnzd1iH1We3p24UP3Xu+pRhM+AESKmXO1pNKIckIhRPn2oEdBBJTQfSGC202YlixOuJTQ1Ot9fTwBADsOp80dNxDZoRQ0mVA4Pq+wLDE6FEK48+BFG/Ozqol//MGVew+kVpeVYtM0+0qx0icgNkRcxeOYPug5EAEldGs1JBraHagvL7LZEhsVCCAwCvWau49lzBjdn6KQHcpyHENTmZcLhyZGypvWpWcX1hlggCe9Y89x0dikYFZOkXdQSFyoB4BA5eXZE41QIqCE7g7JDO2G5md0XCSefMkKBmNKauaU4QmSHUpT8Exartrd0n8+Ojba281KGTOyCwPCw8P80VqeghCFk1Rof9j5s4g7ECKghK6AGJK9yfyMiw/LzS8Njgyt06HW8VjvDp/JnjgsTt5JpKpaI9YiQeAp+Dwl0i8VhsVEBXszUAjlQwBUNJNfhkYmoQrQnmOEEgEl9AD1JEZo9zE/cfYSTcGyssrkpH61WhPO+gQAHD2bM2pQDEVBmqKMrNBi2ZESYvX0Ezor4z0oQGXmVQ2K8jYboa07yWsot0RACT0DoqHdAU2D3j/Yn+fF8s2CwrLYuPB6HYtMTsHYPJOeO2JgFNZQyQcaExYgHSH9UmHSoBisnrhvE8XD7MKa/hGeNAXdXRsTXJt/wMm9/7ewP+gqiIASOheyeL/mjFEUjlEUtuGB05LDbdbvvp6oZJMWfJ0ch+zQqorqkLCgBj0rRc/PZuQlRodACHz8LbopV0/pZligL+RhWXVTYpiHsHCHtVqT0s2qw0gb6Mo8fCKghB6jnsQI7WJs1FPToC+tQfPjIETJ87yQ905RVFNDfVQ08ofioDyS7KQIhlE5V08AQGlFraZeH+arxF7UOi0b6KVkKAov5Nuzfu8yI5QIKKEnQTS0tbTN9nSYOQ8AePWxFWmZqJUBtkN5YZHO0BSq4wwJoGnKZEJ26NptJ6+fkeRcPdOzClAyqbCWpyioMwEfNQ1ROJ4qLi4FPcQIJQJK6CzI4r3nMndkrM0WTYM+vl/Iz1tPvPrYCrwFIg2lWMF6pCDUNzW6+fpSFMR26MZdp+UD4e3VMyIsxMddnHrM8pS7QnSZXrpaYlJ1QD/QrjFCiYASCARbKguu2GyJiQoyCtblz1tP3DJ/lCgfFEpa4jgeUsgObWpooDy8aBoN5nTymlqpJwAsoBiKx4VJGZeLoIdfXJilMKkNzB+f1GVGaB8V0Da71QnX1vxMejyavAWdjf3ivaZeT1MURUGc4CnXUCj4Q1lBWykIjbomA+POCGt5X2/RlVnPykqSsgqS4kKxemZfLWEBRQMh+gSReip9AuPCPBWy9iJt8YQquS4zQvuogBI6FaKevYzn1iweEB9BQRR81xtEDb1ZZofSNMVyaIQc0llOb1C40zSFAuvmI0DhgtUTb8m+WpIYGyaqJwBpqDApIjbUAwJAtySg8sNeWyOUCCiBQHBmfnI8v+7P1NGDIkJD0dh35OUUen2ulWmoaIeyHJ7HyfDGJqiS5A3pKERN6ZNirdRTeoq07MLwfjGhvkpcHupKKaeVdFpn3l8trOgyI5QIKKGDIeZnb1LP2gaDj683NjlnjY5Te3gxNCVoJdpzrSM7FK310R56KIsi1Wj0SD2hY/WMjJVS69EFDwVpkebMz9iwiC4zQomAEjoSop69jJjoIOzQxBq6YkaS2tMLCglMF4QZnGu3nlg1T2aHCvapTRdlTwr6uotS40g9+/mY7wVCZD8n9VwLp+W02LOmtr7LjFDXKqd6FMP9UEsC50TRaA1iZFve82yNVwedF4HQ88zPgEBRqgxGVqmgkVwuHPvCB/8blBCR1D/6QkbekEEx6/48cdOCMet3nsZxJEBRJqGz/KWMbPzYeq0B/Q82p54WNUR+0kstjMwy7wrl7fLk+Hm4+/l4AWCYPz5p+7F0wQjtrHL5Pm2BxtGOR1CcrfGSLl1+Uj0YYn72MqSlNJ7qjsNH6/488dyDSzJyiigKDkqMwnYoTcOb5o4074wmbspyQEVYQInqyaNLWnZhclKsXD3TMvPSMvOSEyNdPb/m7FAVC0wmKyN0QWcZob3QAnVF9RSKWnFnI5HIjoGkzfc+8zMhNmTRtKFb959HZiWANI3SmJQK+rddpx+5c+5/vts5MD58QELkhYy8nwG4ZfHYG2YP37j7LGoQguo8bQ9I85ZW82k5SD3l96ZlIiF2RT1tw0eiyttapIIn1DB/XNL24+kpJzvLCO3TFiih+xN3J2pUTrgGr3y/YDT4yMtt1gQUjcEpSsgOxV3oj6TdtmJK5uVihqaeun8x0tAtJ5QKeum0oS0euT3qKQCd26GlFRqLEaricDSpk4xQIqCE7qt0RD2vlfnJ80CsO9pyIiTAc+LI/tK8DdqcTn/64pWViyZkXi7esv/8mjvmpmUV/LzlhKdakRAdCnjR/qRqCqmaQs3VDOnI6TlF9uoZHhHempW7s41Xz18I9Q6Qh+MxghHa8RABJXQYRO96B+NGJwUE+kpC+fOWE/3CfCeNEjVUyGIS70q7lD93+ojMnOKU45l/uWkG1tAxSRFqtYfdCh7drjZSSUn97NXT3Fy5A7haWGlrhCo70QglAkrophpK5PiamJ/jRiftSDkTHBaEjE3zcI6ft56ICfUdPSQOm3o0jexQVkh3LywqHz86KfNy8dEzOTcsmpCWVbB264nl0wbpTZaGypg6jvH3oDtEPZ24M5HVqWJDvQPOZhRYjFC+s4xQIqCEXgLpb9Ah6rkz5Wx8fPi00XG+QQFo1htN4bqjn7ee6B8dmJQYBYSFPKrY5HjcRLlWUztq+AAIQdqlglVLJwEA1m9LXTV3iPzIDbzSZqhcc+o5MCHKlTPHtaEOQFZnZUVNbWigFzZCAavsPE8oEVBCB9MhlmNrD0Jaw3QUUTEodrRx28kVs0aERgQLtZsWDR3WPywxNlxcy9NCuJ1H15saGhRuagjh2fS8MUORobrtwAX5YT2t2yt31MrdYUFnbFhEUKgn9oQ2NDXV1NZfyi3tJCOUCCih22koWbxfQ/OTFtLgAQDfbNy3aOqw4LBgvJbHMaW1W0+MToqMiwkLoarD6JowpiYIVIZQ1aF09YKBCj+gCWNqci+kTk1QhdHVUe71UT4svoTAKuliLMka189toKcmhKqWXwSnamGqsPS+dYQl3951DYUAZOeXAZOpsLSmoqautELj6afy83DvHxXdSUYoEVBCp0BEsGcxbjQSl7j4MGEenNjOg6GphVOGRMaE4T5M2B+6duuJscmRTMiAMs6/nPeHlDga/vj5q5OHR3Po0TAjp7i5fiCFxZV+fr4eKgfKk3apMCYuysvNyk9qj9N8TpgYh5yekUGhQaEeXWCEEgFt2a1GloddCVHea5W6tDPlrDSugxKq3T9ft1upYOZOSI6MRhpKo152qFPyuj9Tp4zod+lKGQCggg8o4wMKDb5lnP+Wc3Uz5syqoCPK+YBS1i9f61VQSxfU0vWhI8qE3fZmNDR4xjUoAss4f5vL3symmLgoT0fC2nImqPwarZeMUKBibY3QcR1shBIBJXQjKSTqeU24bhGK/MRFB77y0BKsoVhGeQA+X7dbwdAzxg4KiwyhKIphaLyWX/dn6vNrFmENFdb4EG/fsOPkDbOSFW4OpsI58Xti29NV9RTPz0pGpau5hZWSEQoAsDVCQQcbodSFnx7uqGMRCB0liG7+/tJluF99ixfJ1+bKzq60m+lr5uf2vWfwMDgrDRX+/Xzd7ujwgBvmjkEaKvT9xGv59dtOPvPAwktXymwS7H/ZfnLFjCSNFk+c6wT1tPF62sApAK0HDOPQCM0sKupYI7QX1sITuhVxd3q6WCaf9Hi0m7+D7aS5QReYn7FRAXqDSaVksIY+998/kgdapqe8+tFvz65ZcdviCeu3n8jPLQYQBZoYmtq449QzDyzcuOOUNGNOb2BVSnrD9pM3zR2ycctR/HBfJS2pZ+blooHxEW1Wz7HPf2bbbtncdhT/n2Ho3MLKfmER2fllgimqF4xQtqGpycgj1wRQIUu5o6rjqdfeXUuMUAKhL5ufX67dg9shi2EiazsU8+pH/3v7yz9unD82MjoU9403Cmv2jTtOXTcH9WES+tihocT4IHuPZkj6dLVK57p64rr41iFTwsgAn35hEYDWI/U0G6FAT1uM0CslcycO7igjFJ36a++ubf+BCITm1uCuTIIj0+KulXp+s35vYlzogMQYGo3YhDiHCWuo/cPf+fKP25dOik+IFDQU4GKk33adXjpzuKxYHtmh8hYfrVLPmGhXiuKdGY+FZVW5RaigMzu/DHtCMdgTijJbGdPciYNTTma1X0PFs795VGA7D0Tos8i1Urq0Sh+Jel4rrls0KSEOZVzetmLykKQ4JIoASBp68+xh8p2xU/ONT3+/cf64KCG3ied43DN0896zC4U+TJIdinsqS7iunjjl3kE7PBeiSOgfhkFGaKjFCAWsyt4I3XnkIrZD24nlDyAaSuhUiEp2S/NzH5oBx9AvvLPupiUTkwb0o4UEJjzDw0ZDIYQsh7a/8envufmllLsHLQyFP5+RCwD4c//5BVOHiEVKQg2o9EBfd6Ur6pmcKKqnRHMa6hU52TtyinfUFJ+oqT7R08RLDPoX0PqK6lrBCK0SU5rM4A4jNXUNFAXnTx2488hFpVLRTiOUBJEIXUfS49Hp79kObCDC2gWkGh2si69bNOnE2Us8AJADepZ7/u21L/3fTd//xmbnFHA8z3M8pCDS0AWj1+4WhxRREK3xaRqJbFNDPU+r3CljUmL0+YzcoYP6bTtwYeb4pL3H0m1W2Bo80sOZeubL29kFj18y/qn78HVsFEtwgoI3CwS6JhAU5gn0NArEA7MRClRAhaJJmVklBRV1kDJdydeg1Nd226FWfwYxQgldDFHPduJKgldz5ufX6/f6enj4eXj4e3lGBPmH+ft++u2OhtqmkEB/odkSh9sprd12MszXy3zxRhpq4oQ+I/S00XE5hXU0TT19/5L0nGKUdxngOWP8oObOFqlnrK161jWakhMt9qkrwOa9oGKOJ8MUllXlyYzQXQcuoZ7NSWGxob7zJo5cOisZ77/zyMWnl4nX2wCxQAnX3ggldCpRPqyRrY92txLQJp0pOoipK7hQUqkJC/QFAORrrcfbmHfnAaisqnOoWTt2nghwB9VVmh9+Oxjiq66q1Hz43U77E6iqRObePx5bJW3BFU17U9AcugLW8WgyOa1KONI1ATe1PjLARzJCdx5Ng4DfdTxj0YwRSYNCgYEFnGrJrOQ/9qSZNXTwG5vQ9fYK6M2jAteeQgEsAqGTWPU4WPeeeIXQTlpMksXjvyhfz3yt1fahA2PPZ15F+qj2wnddyi3p3w8FlHACPGzGC4nbL7WB199fh69ISjpzujiHTs57n/wK/CNbrHl3cl/Kyaz5UwcAhuF4lmJVgNbPnpTgxnjwPCgprwkL9gNKPTAAyKgWz0zasje9PRpKLFDCNYBI57WltkFfXV0zLDH87KViHvB42ltiTBjL8RCAnPxSNJZDsDl5nqcYUa04E489khzH//zJ329b8zaPRnfwNlPgW1xlS0oqRc7l/3cIctTabTz20r2OHucmGaGUnuZods/hnDnjkwGlh5wqLNgPa2huUUm/iCCKceM4HmXXA3DHA8tRfKyVGspsPZS2aLKVC4AYoYT20GKhZBSwxAEo4GxnMla6k1ApRctp/OgBYmEPRLKYnV/t6+MREx8jSdvufccGCHlOSMLMludTj90MlPSPv75y2/XPAVFUW22Z4sIh4V8kwThHyv4AtfkH5A/xipyCpPNfSDpdMUJvWTkDAHA5t3hQXDhQ6ms1Jqyh/WL9c69W9IsIWj5r1P92n6JpeNvtr/z4w3Ot1VBigRI6mBZVjwM10vXzgIyV7mq0BpNkM+rq6o+kFcpX6sWl+BYSNI7nAwP9scxBAGKiAs9lFZVXNz7+/BdjkiIDfT2GJYYHB/tBQXxxDycKAizIX6/fOyQpthY5P0GAr4eY2snzKHsUgBpNI26APGbi6Gmj44cNiQF+nrfd8Dw+D0N9Xm3+AfEUzarsojizHI+m0puN0Lse++Db9x/9+ZfdGVeQhvr4glpNI9LQUouGjh8RefxsIUWJGto6Ab3v9tmf/7CbGKEEQh/B4YrbHl5YqgtaKq7SUWqTXRbR6XSUBGrP0GTZ6E2zQQsBnD4W5V1yHK81mL5at7do097fN+3Fcjx6SJxWb+J4/lSeoLttIr+kJjbC32KEAnDXYx8AAOZOTEYaOjCkrLrOx9vDRkOXz/HdLASU0NzQ7c8mzX/Vxaejiq6W2KgngfTsIfRWLhXYhrwHRQdqqjXSpbZaU1ujqa2prdfUNtXX6xoa0OAOQf1MJk7b0GATWTqfdhVf+seGIZeo8/ohCFJSL6WkXjpwKvv4uSuxsRHy3XcfTT94KuvQqUuOHucq/cL9cSUVNkKl89l5JC02JiQjs6x/nP+l3BIAAIomcSqkoUUVwEDjxKbbbn8Fa2jrlvC9zBPaohvOCHziaPRJMrJ9vbMZoY/jqVbedsMso4llOb5Ba9DqjRzHsxxX32igKGBieU1lGdYvrd5I+YaByvLY2Ag9jWI1AIAhSbE4XpR1pUSudDwPanNtpVBY6YuxKaOJu1xcT8niQ0jseKeK6ZqOUuZVP84J5QS/AdbQuROTgYFGGnpFTDkQNVSwQ5fOSt68Jw0v5C+sfXTIzch0dUFAabBoWjIQ8217A643QDtrdLbnGGEfAqF3cLoCxgV45xeXo+zLkgoPd1Q4uWj26OKyKryD3ojSM/28kcvS31MhzLjkNGY7KiAkMA7WTpo/9MCpHAD4ono2wgsNlsPgK1MmDD949KzN80rxJaEDCQ5YAb2R1TY1ebgp+sWKWfRjkqIO59QYDCZ3p4p5/F9ikZJD8BNIsfUZYwbsS82SbmINdXNBQ7/7z2NPL0t6YxNKcnKCpSRg6yFb1ykpTCIQegenK2Btgx5N4KiqDQnyv3PpxDuWTPjL8kmzxiIvodpd6eGulPpzaOoaNXWNNXVNNXVNdY1aScTc3Rg/b/f9J3PGj0hM7o+GD2MfqRxJPeUReQjNuaXmBiA8ELuQxArqie1ZdyWzbEbS6ORI17KhROx9BsjnIDNC8U2pt8nOI2kowR5rqLCWR2XyZU3iWp5TSUVKt66a8/Qy1H3ZpSh8LzNCCQSCnMtacdENAMgpqAAABPqgwRu5hZVpWba1YYvnjMktKLdJwcwrrDSZuMmj+0PONGVYDENTBZev+nu6VzcgkS2qacKGHpJLjgcQSGM9SjRNLIsCUIJzgI8J9mrUGusa9WIq1YjE9OzCiwV1SZFeJoPB31t1VRy9YU0rs/dZlqdpSySfAqiKH7c42bzv9NIZI4EBSHZoaJBfaRmKKQGDCTBIQ+985H0AwHf/eUxIbGrWDqW2HLWMByFGKIHQ60HqJtUbCSQPiE4eEP3cQ8uXzB07eGDM4IExZvUEF4ROSxgIoYJBAllYXL72z9QFY2LkliZSTyGTiUb/Utaj3tDAj/6xwQoFTdOUkH6P+OKNeyeO7M/wpmGDoqdPGOTm55uTW1ZUIvoT2kZhKYptQAiCAr39/bz8/UQfHe5VyrLiX63T6zOuFGM7VHxZeGSHAiWLmo+Y7dA7H3nfuR0qWKDmV3LR1GRZjjOBQOhVBFNarZQPL/zLcvzFTFEipSvNkZ5x1cQzwT7uOr0pItj781+PyO9laIqmkZOTR0oKULanHCGUz3G8iWUBz7OCMQghKC4qxjZuVHREtmB5Wo1Skj3cRWaO7X8pvxJQVtIfEYpGywmjRsUt2BmKk0NRZadYpFQNOBUu9MR26OY9aXc+8r4TO9Q2kd4+HE8gEHoH2qZGGzGiIIyLDDFQSqVKxfO8UkGzLMvQlIe7UsHQgOfKqxsMjXV457iokEv5VWER4cUFRROHxW45aCUoCkYIqEDzml/mBMWJpIIvEt1ghYW8SkFzHJ+aVjAmKaqpSYf0XU3vQ+Ep4BeBHKzNFYg6B0LYqGc93OkxwxJ9vD31RrZea8wrrVVD5DHAOf/ygJK1hvqXlFeHBfvjQk/AqHD4Cy/nn16WbK+hzP3XTShCMTjo0AjtuZlMBAKhRSAEQ5JjOZ6nKcrEshQFFTRtNLEqBc2inqC8u4rJzhEFNDYSCagQCIJbDqRHxUQUXEbtSDAGpXl4BoreY0TXKu8ezgNwuQoCdQRP6XUGDQWhSkGjJnsDI3jAq91F/ywEvIGWEmPa0rMEQhjs59Go0w3pH4nbP5tMXEyId12j/tLlAmyHsoK3AcJmNbRfrP+5CwXDBkQtmzly097T+LcARZ/sTon67Dc8OY+XLpInlKgngdDLCPazHTTdpNXpdPqmJq1Bb9Bp9fUNTTW1DaWVteVVteXVdXX1TdKemjqURU9TVE29bnCCKJdVddZdngTyc60Gwwkz6JD0jB8/ymgy4VtGE0dTyDcqD7RPHpOcGIl66znCpVJ7KATHIADZeWWXCyquFFTkl1RV1dTp9fpwYSGP7W6zG9acYC/4QwGFrFSkoaWNw4aEn8sqACbTstnJkufUvqcqdf8t0104KwKB0CuwE6E9R9LE7CLpgneE8FxGfublImnPM0LV5olTGXqDsaKmwdAgZEmb9zfWFVDaYtBYZKzNjw5i+EbLAwVnK8ey3P4Dx3ge5dKzHG9kOZblq+p0gxPCBseLcsxzrJdbqxt02KiaO2qVAlMv5p68KLpBhZHLUMnQEWGBUkBJGo9s0VAAssUiJX/AqUQNNdAr5g6WNNQmoOTgXOvcQ4jtSSD0EWaMjNt9WFh0mpsjqdV4rpu5p4g1HI8cmm5NSG58uQaGFsd1KL2MlJDvyXIcdjIqeTEv0gSyTDynUirqjbosg5pzd8eOSBPL6fRIiwEAh9LLEwMYhqKEpX27EirlU0BSzRoqJ8Abperj5FDceUQqUkqM88++UpJoTrBHGiqs5VfMHfzbzgsMTe08kiZ3hlIZ2UUtTb8jEAi9GU6vRReD8K9R11Bbiy51dQ11dU31llpnzohWuNh7aJ+/TqNWTBBSaI0vJINa2boQQjcVQ1Gwv1dTpKouRl0fo66vzbsYxlSrGouwHAMADpzMOp1m8au6jr2IDR0QoWtssLngn4TK2iaoUIqJTTI7VMeyooaaE+zlduh1c4fgXxNBQ0U7lBmUGFGYV4yP0aY+0wQCoWdz16o5qNEcBfUG5KDEGw1GluW4ypqmK1dR7AUAMHjwgLMZ+VgmdB7hgle0uopFPlAIQKUeySuKoghZ9DwPlLiZHQBGhTfggYnyvtwIm7SGSAoFx3ke+PQbXHqkNEw4FAAo85QzGW3OzXVRsmm6PGRgvyEDY3BPKaOJ1RtYo4k1sVyTgcvOrwLGJinBXm6H4gT75uzQZbNRYpNZQ5EdajXdyYVZzAQCoVdBu7kXldUUl2kKS6rLq+rKKmvxpVrTUFvXxLGoOh7joUJxc4fcesMsfEWKCMm1TLBMUWIoJ/RJ4gV15nj+fFaRjUAmDx7oF92vzX+LXL4KS6sLS2uKSmtoinJTKrw9VAG+6iA/j34hXtNGRI1M7gcVQvWqYIdKDZw27zvN0QondqhsGh2yQ6nPfk5p8+kSCISehcFgMhhN6F98xWhqzmbCi3D5eHe1m+LTV+4aO3JQYX7RH7tS/9iV6uyZ7ExHHLTBWUTYDWBvXcaFttAGiJadjz1ao4NCIEFJq4vKkJiWlGuKyzWV1fU6nX5gXGhifKR9TMlg0OFieayhJeU1VhoqK5bfeSSNuX/l5EKhOwuBQOiD7EHpjc4IDxRFbf+JzOsXT/z1990vPHID3rJj3xl8RaVSWvJAzSjMeaAGZaivr09xjYbxUuqr8gEPlAoaZZnaPVd9o4OkKBfX8noTV1KvVwvLbjTH+NBF+33mTB5sicuj0lSoVHsamhpwkRJOm8cBpat5ZYPiwhPjUDgeNeovrw4P9h82JFwqlsdreTLSg0Do00wcHH3odLbTXbzkQaTH7l4gpMDzP/15xkfpWNYK8guiooVqIoH6BjGZlAfAxLI0hco9IYDJ0b5JUeONLJdTUg9AOaVA1VBt+ysMLFdSb8jV6KQkI3y2yOZVCEkFzatqWICnWKQkJBg4LFIKD/YvLqsOD/GXCj3xVGQioARCnwYCcMuyyQxNmVjOZOIatQYTh1LctXqT3siWVzfomnAXej5mQKLByG3Yc3FoUqySYQYnxRXkiBHzr37axtY3Up4RwtgPPio6SloR43W30WiEFOqeHJE4gqq7AnnIcpxGU/vHoWw3FZM8EPk9OaN+/+krPuERDk7RKSzHVzQar9ZoE0YkG1BLU/SA5QsnshyvN5iUSkVtg54TCvCrNE019drKIjEshikq1YvF8sJCvrlCz/AQKw2FjGr+1IFEQAmEHoyT4QsuzjTlUaNPt69+2YNv/vXGGXUNWk6si0cCpG2sx6H5AB81BcHIIQkUBUfE+l4sEEs8JYx1Bf1jgq4UVvEARIT4aswxGKgtZZSMV0hCSW56haYg3N+DZbnC8gYjyyUN7KdgqBFxfntPoFORzEbXQa3vdabcGl1VkzFett3X000a6+RpTs4PC0C9ormhUVq9ked5g5GlaapRa9DU6xpqcBsnOH5I7LELV51paJgHatpkAEpGTQTU2acwihYTel2c/EHG8BK626caf4bxB9jNYFC7oRyjEAVKMPLikcPRw1OtLclcNns0flRFdb1Oj3KJOEFfGrV6bABGhwU0mUw0TWkqyiAE2wsLT6QXjulv9jiigvcwGsKrNZDyRJGZ4gZe6gcalzyuIvesm6mcRm3uOFZoDIotPk15qdHE7qkUwzBTpo29mNu6/htNRjZPoytvNEgZnQgIqmsbLedmtmKxh+CPncfkR7h5xXSt3pgqCig4ev7qlYLy+OjgZjW0BGnouSyU2MREh/j3wSDScL96+QeLQOihtPibLR9d46XT+3q6oSRNT1RvboJI4jYfxstwh02MEeEhPuh/PDifdgXcMLWwskmlRItuOht1ZZagUVm7rAeo7HpxcakSwNNpBcEBniwHDUaxLhJCWFytc1MxtU2ik5RjbfNAna/lDSausE5fVKc32GUT/LHnNABgyayR8sdBCNxUihuXouHyGB61XmYZGo4alnjybDYPUJ+RuKhgmyIlew3FyaFMfrHVq9BHOFvj5cpMJLxPi7sRCD2XcYNjjp1FTeSaA6sP7mwEIVg8ZQDqSALBZuvdGH0pz/OJMUFXCqrQQHk/T2mFrzSWoceKkW5eb2R1BjG99K4lI1mWV6mYd9adoLy8GFMrijhZji9vNOZrdE0oe8lx9AnLqOuE+nuhxqU4wd7c015MsDehc87ORQn2yBPKgWFDwskS3hmpxsgxisJWvQEEQs+C4/iFs8eZWK6uUQ8h8PNyr2/S641sXaM+t0QjDd5AQRjag+P4/Grj2fR0g53SQSGH3mReSEujMTG4+xHHofW7iRXtU47jz1zVoPZO5cj+ZevrD5zL9Q21uAWcwPNAg1yf2lqdyWHsPiquX5PWUFlg1RfKIZCxNOArra4P8ffCxvTUUQkHTmXjzFOdXu+mUuEipSu5ZXHRIcKLoiICSujqmdJRso6zFHC2M/EpdwHhQoMiBQU9A1AXOACBu8KN43lftUJB8dU1Qho5QLH1QTH+WBYDw8JpCkb1i843R+HFHnEQuCkZBg3t4N1k2iKVzhtNnNHIKilKIagSTcHN21E7zesWTQJ56JPA25VyNkej4PqsaEQlTfb3QgCG9w9GU5SHRvI8jwuNPD3UHM9rdUah0ynf0GRo0hnqGvQsyzVoDQ1NegigQkGVlhaFBPkxNHX4zOURg6LPZhZQFBTHeQoaGtfPV9JQ9EduPXxp0aT+7X0fCATXVI8D+DuJOG/OMSRcO3hzq3arMh4FQ3mpldXm90pnMPl6e2fkViDtsW0Vghg4dl7miR1CQxF0PJuSISxzBhNrNHGUm/jg//6wC19JivJBUXgAgsIjLKWjzaM3cUW1+uI6vcFcgmmPghFHyvn5+eCuUl/9vBMAsGz+WIAqoSh3hYrzVsIQL45D4bKGJr3ewOoMxjqP2CadgTZpIYTnLxUmxoReLkAuCHsNjY8OYX7+wyogRSAQ+hRXCh1HvZGz0iBfp8OySg3LckMHRrMc7+uFOsJJFigE4PzZc0AZerEIABrFr3MqgTTeHfW4E5QO/8sLticAYMWyWVhu0wtQsIHj+dhQ7+zyxifum2d3OtDa9WnIr8WuT8cYTWxOrjksJlxJ7Bd6/cLx8n0oylLar1TQKiWjN5jqG/VCqgCobeC8gCG7sDq7sJoy/xh88uth6SGP3zbpcm4Z88qztz724o/OXmACgdArgDyLu8qn61ETzwBhrHFVbdPQQWi+pojVglhoCSqol5uKadCZFAyNLsJ9+cWV2PkoN0bnzJm6a9cBm+c1qkJNlMk70LdJq3dTuVOmMqVQzJQYG87zvL+PGkIYEuhz/MgZL08VAJb0I3uMJlO1FmV9appxfQIALhU6CIxnS3raPAdOW413dqNMNEOzzcW1DDSgjUz6pcI+6IaT8uOiQN4VVpxr6hj0eSMQegOD+8dEBXujPFB/VLzoKTRO1unZn7Y2uwyNCPXDc+LcFHR+YembH/2qdlO89fzd3/ywRa1SyNfpJhPLMPTOnfvRYE5cVW4G69z4cSP3phxtaqz380RBGp5j3VSMNO0DAFCjY/X1lvls9nAsX1pWnafRVjYa5ZVODjl30UFf0WGDY508RMGhiBnNWDWdksxPG95Zh9ow9c4gkivBBxdTlMaYdyMQejoXsgsu5aKvfHwEMhoCfXGqO7xjxWTcgg43+DAYWU2jobC8rqiyHgA9NkFpmqLU/kCHjLu1v+yQjgnNVyhdCaBgQlRgYamG5/ngQO8q81AP2FQEjdzRY2j0JkVRaN6nwMPPffHRq/fh60cPneJ53mhknYhSfaPuYvHl4jqDE9enxKyJQ/ceFnudYHhIO1RVAMDooQnnc8pNlBvDoZN0nd4poAQCwSF4wrB8y4Wr5YMHikt4bAy6q1Ak3ddDEeCtulxUA0y49zAMC1BnV9sfEEBpLjzqvInazvO8OHRT2onluPqafJUSaSdDe2mFYicAwN9f/mbkgHC1CnkFKNZ0Nb9EnGns4MxBSUXd5av1WiSyThD/uuhgzztXTBFzAHhHXgketX52d1MdO5t1IDULlSTNG4VaTB28MHd84s5jlgYrcyYPAQD8b7eDlFJnzfUIBEJvQ0hll1rJ8QBMH92/pKLW5lJZU19T1wR5Li7cDz8OAhAX4dfMIcGYYYlCJRLYdggpEYRWjURRzJuC3h4qhkadOhQMklrcYFR+HK3e6B1o2xNPenaWZYtKNY1Nzfa7AwCkXS7JzLXUVUotR/FIenyhIKAo/C9kaGg0Gpp0oppv2HFK2yhkU1l7V3cdumDUNS6bOcL+GYkFSiD0LeRux8AAx5qI1YehqfomvejPhMBgcJSkyaO7TpzLDvRULZ814h///RNvlrtHccdilFfEcgNiAliWd+jmjE+I8qvVZpc5CGDwANTW60oqanne0pvOnhWzkMadyyosqNAUl7vqfDufg7KUMCmn0Bq/rKYRtbZDtrrlTEtKy8cm2XaKIgJKIPQJoj34Gp1ViTqyrZrvpjxnYjJFCapn3qJkHCxYeRQ+4hTCXb/vOTNuUNjsiYM37jmzNzU3VjYhmaap+EAUueI4XmswOgwAmYxGtcrxmthkYvOLq2vrtEL5frOczURhdDTZzmV+Olhpc0gegEYtSrYHqO+KmHFVXu04mkwElEDo/YwMQpIVqA4cMsBq4tDcQFSGJLHz8Hl7bQ0LQiPhAACHT12yOSwvbKeFAcUchzrgAQB2H7n4r7/dBgB44e8vo7ZysZYiHdz1naYovSM/JgWhfOEvf1RlTeOV/EqjiUNjjJpHGGcHxo1ILNiV6uS3QU6A/0BtuTgTFAOFIgIIIPIXCxkITvqRMrFCIReBQOgLXMhyMCddIizQ28FWQSXdVeq5E9A8jO2HLCKr9vRSe6IklqKyCgryUveNdz9e39xTYPeoNMTNBpsKeozBaMrOLS+vqrcKBjl+ODI/LxdUDR8Y52Q36XeiVOcJdIUME8wD4OXjZ6pGDlwcEEOrd8HT4LybM3M1v+X8UsIYRWGqEXU5JBB6EB3YCkdS3ogg7zJpPVtiqcq1B4fhw4OD7O8ysZybm2dGjlWnj92Czehj3UyEB6CqpvFqQZV1WVTLNDY2XnGUUS//nSgtRDpuMumVKndNVamPdxQOUZmCxsLsTcj2bKkZPlnCEwi9n9MVcKAXqj4SXXsUNHJozrCfmpFy3q2T37vC5o0IQkImyXGYkN4vdgY1w7JcbmF1Xb3W2FSncHe1c0Jjo7NyJnsMeqvg/qkTxwFjlQ9QDsTkqsDgMKr8uLSdCCiB0CcQBmGiBB0cR0KpRBAaOL62yRgS5AUAGlJUWatzo5HI8jwwssDLjUINjQBo1HM0xP1AocHEe3iq1JC1CC6PHoJHCXU4tfXavKIq3I20RUqqkBa7uYmR+lo9aiLl44Y6nOAfD3yluZ8KjueEHXD3KMd/TmV5CQDR0k0ioARCnwC3m8N5OTwAHu5KCJFoag2ch7uS41BWeV2T0UNFsxwHAWwysGqhJR2EoFGvU7spKArNXDOyJi8PVXFFfYi/h0qlqq6uQ+rGg8FJ8TnZV5DgCuJUozXhlJ+yRtRfCVK0QpgMH6AWE+xlciY+xE7XkPwVFNfU1DYx9ZVG2lkCkz2XCyrUbgo0SK7JGOSrxn1MOAiqNFp3JeoZhVuUSnh6ePAcj/5yKOSBCvfLU74cQgSUQOgbCHFl8QoKCikEe5RSu3EMg6a0Q0FV3RVC5ARCRsG5K5DthoXGSyicZznOyIJ4j3pPXUOQJ+/pwVZxTTyHjtlPVU35CDVLAHI8X8MY/DnktfRRaiHqYQhVFAxSaU/VhUAANY36AG83XpiCyfN8TYNBrUDXUcaoFGjnQUOT4WphldHEMgAofZzFhWy4UlgBIfob0XJeZ3JXMVimkUdVo/VSK7HFPXe49/ELVzmOq+d9zVKJ/3wxXiUoqVU2qA1MQj+X+j8TCIQejdTVGIuBsOJGkuHr6YYsQbGIU6FiREuQZhgl0j1RStxUDI8cqLSJgxSFrDmGpj3cFfUMjQXGU40KNbH0yE07lYLpFxlyOb/UTUE1QK8BPlqe5+sYg78XhxScQ89VDbTVEA0WhtDiAzWauMKSGiH4DhqNvJsLfyPP87ERQSFRIUhAhZpUobMJcl9gGcQlRmaLmw/0VaNRxgB4QQ1o1Pj7elRpGimvKLNNLLbZd/KMTE5uyX0rJ/fBuXKEa8V5EI2jwySx4RoiiCa64uOpQsVGAiH+ntrGJnwrwFtdq2kAgB+cEJpTVIsWvUKKUpCvugJ4MkGBFYD39QmpKhfDUONikytzUOgGP/xSvSbChDyStFdQfHCiphRoIfKT8jzgAF+ub+K9PZEgC/lCOfXVUcEKShDfJqGjssHEVlWj3E+9Ht32dFNoitM9w5Na/KNwQ2eseQrBssZpSYIpKSR2yrYrFUJNqTm8jx/F1cumxqNfFnRE90YtLkJoUifKn5Es4QmEPo2bksYihUTTV13Y2ISNL0+1qra2AVd+4hRLIPSp81Ira8X9oUJB0YI2AQC8PdzENTBl1VSUYSgfT1VYoFetpgHbcwyEHmolhZ2y5lQhlYKGEK39gQm1TC4tr8srqq6sacBK5+6maNS7FEeSDwQVDGvLdQDA0MTwrNxKobSU4gGvVinkbfGM1tmpUojMbIYLZ9KIqwmEnx+k/wQCoY/RL9xSPiMPkrgpGec57TZQwoNRYw7UEdlb1r3Dso9KwcRHBQ3u34+mUEsRpJsQ+gkN7c2eA+FQwrFw3WS1pjHzcmleUXWTVqy+VzK0n7cri3hbhiQ6cFGiZxK0lWEo+UglKdVU+LFAVwR3BD9xaAzHo4gWK4TacDIDxoEFumpMgFvsXMCDbzesbcMZEwjXqj22YES0vDMZV9cszWhm/yhfHGXC619gtlgFsLOwWcGlKKhUMLGRAcdOWjb6+7jX1Fqlasp0nL+UW15UVms0sVLBEoTQW61qctUGRcRGBNWam+bJGRjjb+8XxuCnk04EW9YQQJPJfBriZsu5WgQ0fNx9xcc/F6+HhwMAnnnsbwCA195/pxVnTSAQOo7YmLaYXRaKZcdpFMIz0lffkdyxrFxPHE67RF3pKs2zjq2QHTA5LiQtXyxSghB882vK3ddPRy3xE0Iv5pTamLr2XM6rNOJBGrLaTfSQ1jU7bhl0eNlTsCaTGDvCSUxi5yqgdlfUCu4HGULeFeSZkNH3IuexsC1s6bMo1d7bsmta2sUn7vsLAODtz7/q4HMnELp8vkAPkLxOxmrOhp2G2gzmdPh43DGktaResGpEknIqb0C0b3M719VrlUpGND9R/1Ca53j7SfTtB7lc5cjVU7AzheJ6oSWVI/saAsjk5+ZIPzX4aGuPpMycOrHDz5VA6J50c8nrWJzXazqZM9Ss5QodmqpgcEJwTaZLfTaGJIRl5VZZHVI4RbxwhhCqlIr22J4DZGt2G+yak6BnFjeKblIcQGs+D9RGPV17GgKhm0mefK1KcITGw9L38mytrOJcZ2k8nFMvbk9Qq/ZdKU2w7hjfLBD9859vtjnZRV7nOSQxLCtf42RnBnWTE3UHGYAKqjkBNRmNjMK1k3TBAkU6aY62O02fl52q+H/LcWwKnMzHJbjAcL96V9abfRBbXRP0johdtyWnCXk5c4RZFzlpsrxIAHIyzbPiAMg5fVm63t/DzYmhZRTMyeZMsQHRNo3xUYNRfI3neWVjDQeh3miyUTQnRZbVavfqGpSFmnPOqpVJzrmr8l8FFFO3RnR9uqKdFgG1aKYYduoLRPm4FLdtbXi310DUjdAqLjVarMSc9MLA4hx8PbsOWbU5Z6+gfy+g/nU553LpQPecJn2CUB5qD6pHaqhXqJDJjMc3oSpPCpad2IoroPBuDAUHTh134cBxGyN0y1nxqR2CfxXE6zklVveZw+7N+SUcCyhZoGMcGo/dJzrRIkTyCD3Szj1vZSSqkDDKjDgeXBazplj3Ycnac5Y+8wljh6OeeAMSirJy2rOQlz2VzKp1zY6UJxMIq39rNf38s8/vvPfhD7/8GPQ6Cmrpa66MRPIIBHvEHKaWED22apSTb7Nybxtt8FUyX3/24d33PfztFx8AAP56/50AgKmThxtMQk0r5X/7PWs4lltz9+qPvvkU9C5SjZFt6NdNJI9A6BBYDYXcmnao/MT4u+u4jxiiPXOh/UaoVLrpOsxfVj+yd/dmLJ3mbWgyHkRdmlv3Z3RPnEmeK6EMEt4lENoBrOfuX4Wy6DEGI5t5teJUemG9oYYBwAQdqF5paZ2/L23UN2E3qEMUgwfKb7qPGNLOdykhKuBqUbXVmbdiCS81KxVTSa8lxMojELqYMKVfXmmN0pwxqa9kWWg7AVMdwDaW6iFlkTyKF8aEyPZUBztor/Hp2n0mE2cEymB/z8gQn9Kq+toGLSdoDsMiN6i9jFZrWH/ftmTstxlXav/tsS6oslLPVhyOSB6B0GYeWDSnubs+2brLZkupeVCaGijHDY21f8i+81mRnj4J0cFo/oR5/TTQ2zclPRvVGSrVBoNpZFKM3eP4of18p49NRnq3+zCyqXiDlx8qFtJoxOTQpiqkaDwnRrEhpZBLJ8UbOKhsKrdbthp1JqjgkdYYqquqy6qssllMdjJaU4NEWU5TBacO6vS2R22LpTMuqieRSALBRR6YPcl+4yeCKtlr5Wdrd+0/gSLL08Y6aHb5wKLZgozuttmuBs0OSF80dvC59IKcfNThV14sOT0pcfjQ2B9/PaRUMFm5JZPHDaoos81mTxHOZLVw/h9tPASNOjeV4p47ZlTWNJ7KKMjOrzAAtKZ2M1RqoVqupKhJnVKN/H5GFnJ6ubA2mtw8GB0AvEE4ZwVA+mi0Pn97GbWhqQLpcqfKqNTXzlEKE++SBdo3EkAJhM4l5SQaLz599AD5xgdmT/pk9+FtB84CABZMHSZtv//m2ftPpAMA8L/Nyei8qYOWP/WhtKUJGNRAefz8VbkR6uXt8883PpNuPnz/XXUZ+dLNG+9YrdM2Pf7YSK22MftK4YGUXZPHDRIWy02s3tboAwCsuWEyAGD97rPrtx5FTdoWTZw/cZBYq96o/WHtH/i6pKR6Fv0LKQVvt/ZvNKEwgwet5ZF0KlqUUYcaimUUOnKAdgi48WhrQQI6ex76lZMBv/roowcff1rtTrqFEnoAq2dP3Pi9mDu9erZtG4dPdx+5VjJqr6EAgG3HL207cE6uoVg0ncvojgMZv7/5sFxDeYBmxEkaSlHUP9/47FDK9k0bvuN5PiJp7IefffvdO8/v3fQT3n3c5JnyA5blXjh0PMNgFBOGhiTiEXDmY5u5aTY6z/W7z63deuTmReJr6+3hvuavN2IPg7tsNLwWqoEgpr7BwcCuDr6RFfONFLQYfHcqozoA0P5lZbYFLCaWNXaCQWrbWMQ14OgxEymgu2f1E6gHtadtYYBJK7o/WI79dQd+J3oPuFiiMjyhxX1a3I3Q4a+8PXQDuPfGqTYbP/3zgFsj9KxFDj4AwA133GH/wK7XUKaqMTEe+RltNBSz7bjYmkguo0iPvv09Kam/dNOhNZpy9Py+82nWC3l+3NC4hctunjlv2RMPrpL2nLJo1bJFy++cGqdRRy9cet29q9f8fbXlXsz+Yxf9AyxllFhGsScU9RXaduLmBWOle9ftPgd4cPPiiQ4dtZygpBSEDUBNAT6aEwtA85lmh8FJSmovo6hnqDdUqNRVpVUmqPRWshSEuB5UDu/frCvDRaqyxcrUmDC/QrNPQxoehfPq54xPyC9xXLwP//uZRRZ9PS2KbpKlaLHCdD0ioO18q3oxpYUV0weJr09KhviTM2Oo+D2Us+982sDgIKYS7RM0cDQakZSNsnGHJkZa7SZEPOSERgbRQoq0vYZ+u+GgcwG9thrqXEZtNFSS0doGLc7sXjJzpPzelKPnpev7zqephRUxAODBv955811rsIDuOZo+awIS34qLJwAA6bVum7ftqq3V/N8jqwfFhtqfiY2MPnzrXPm9chldt+scALy9hgK5o7YUaU0gV4lv1YWid1lTaZUhZIOK1jGMbNku9ERSqZUKlVpTWs4wCkuTpE4T0OhQ36LyWscCOi4hX/ijnAso7+tJ2wgoy7KSZ5QIKOiurBZCDfZ8ahd8kAigPW+cb/XNxJ8bb7Xi3xv2DA8OGz/ENsL76R6kQfEevnPGJzkMPlhuChrqw3jMnYy8ZpXVWocCagpMCAu0xDmcK6kkoA41dOOXXzsX0G6ooYsXjF7z4s9GE7d05gibuz7Zupuq1oZFRJgH+oAlM0baCyjm+HnRnt1/4ChF0RzHnT999O57H5g5IalSENCLnM+pQ2eqq6v8/dHkSwDAU2tuMRmNzWloQkw4APyCyZbMyrXb0HFkMoo8uYNsO4CAowVF9ku3MlqUbA93lc7gIHMeosARzyiUWCMpCk33MLGsCg0fBo01DVba2mkCGhniU1JRZzmyTEBnj40vKLP0rJIjdyLADf/700o9OYt6ErqS1bMnrZ496QHzBW98YNFsmwtq/R0ckRwc0VphxfB2F8yTK2cBAI5duGqz/9Z3HsWP2nksPa6/lczZgK1RE8v9uT/tz/1p199899sf/4gvqQdOZV22FICVVGpKKtFv+6QZ83buO4LVEyspZkZS4owkNAextLACsmKs44tfDjT31N9uOurgDxMuq2dNXD2rSxvdmgI8MjSV2ZdRB419QmRJzpZtJz968RYAwOa9Z2zuemDR7PtvX1KkrS4uFL/eW/adbu5Zxg3tH+6PypGnTZ3w19uW52RnDh896cyZ8+98vI5mLFHitz54a8ywpIVzpqxft/bNj35uqG+QX0Yl96syaqursEOTv5JfWlVZJV3mjolHWiNtqaqpEvdsGTrE00mBJh6LRANOTMNE3YspShj/2SLtV0+ro9lbuNJsj+YfZbWE37Vr+8oVC7GA4mW7HGKBgs4HZ5DY8Onuw7HeKK1v3pSh8u1L5g998uX1+PqUMWKE1BU71J/ygBCutLNAgcwCxTft7VAgCKibUnHXqhnom3apyN4ClUjNLPrq628TE/t/9p83sjMvqtzcnn7xHS9vnzunIr/YhRr0BRiUGA0A+Om3nQCAJ+5DoYnmyKjTMLV6jlbhRJP7ZHaoZIE2+CC1vWvpBCfHwaZ0V8JUNSaYTdEZdqbo5sOZ7irF7EmD8U0b8fjs+z8ioiIghFfziqeMjD+fieTYnisFZSaeT4gSp8XlFNYcO3pC5eZ259S4i5yPtBtVrzfx8Mz5tO8/e+9wCnrNJU4WiHGfcKXKCOhVcx18PDAbzmaxlfrJI+JcsUDbEzxwM0IdSiHtLK5cMvnAMnw9PMhbnqMqNBYR+9rNGhtf2IwF6mAyiUP17GVgS2TjD+g9trFKlArmP9ubNXA6lWT/4IOns6eMtP3ArZ49cccJdKo7Dp6Xa+gf28/PnjBk99ELAICDqRkOZXTelAHzpgxc8fR/XTyHuia0sjtbXiJpqD1zxyftPJr+6Xc7eQDmCb625hgzMCIxsf+C+XOS4tDUGJ1O9+LTa975+MdVa55Z99Fr0AeFWTOy80+eS//4vx88+NCjJZUag5GNCRNXmjY//vjX5YuNe3kGaegXv+y/98ZpNs9418op32w48M2mI3cva9bYXD1rYhdrqCnAI+dyXkIc0tD//rRj8rB+8nujfeChi3lZOXn2koSCMwHuBU3VsLIJAGeVOalnLk6dMl6+5baV8zb8sd/2aF6oUBt9zlXNVjBzwd7yZ9pwNsuBXRmokstlT+TyJSt/gqPpHtJ9zR7EVkD1en2vMTZXL5yacuyizcaU81lKIy23mOytJ6nYA/2QUrYbQyODOumE06rLk/yDD5zOmWqnofPGJmANtUFvMqWVF9HVuoED0SLrYGqGjYbuOIg+/asXzXbiD3UI1tBjF646NEKvNGkU1U2RkeE7jqbNm+AgWCRn2rTpf27dMmYoOkMAwN8evK3yItKv6UOTU86nvffJZ0XFRRotcnBmXS6IjQ7PK6nCKYr5Qja4DffegNJxPv/9UHMaevfKqV//cuDrTYd5HvxluQOLHgCwcjgKdm84azWop1MxBXhk1layZY57BakY60mXdvCBaknUcqodBGQ0tZqU/UfGTB2Fb1IeNOWPfrRyq/XAF5w6dGbUZCtP68drf2pstJqLaYND3exidJ1pfip45Jpv5L2j4wc0NDRxnCVn1gZnS/iFNzVbRtZlRKgDlsyw9aPjL/zMhLj+/ULlQYBYd+955sWOfLuN8KEfSQM1ZTRa0FltpODBVBSXmDg0tjLrFJLIAeJnToKh6d0XxU/PYEo03aV1UOcJKAAgyR8t1e0FFIM11GYhj18rrKG1DToIwCK7F1O+p3kJjz4VK+ejCKkNCpr6cHMKvj7QFy0Jp4/q7+BQe44IGhoGeJCdWzw4AU1ylThxMXfsYGRnTZ+3dMkNtwMAtm/fdvro7qoKtGLCAho4eKLKze21d78cNXnEdUuvf/bJ50ZNHgHrdYMSkaU2NFkU7tISMZ4rpddIfPH7YaNBf9P8Eft+F/O6Zyxfgq/8viddp21aNbfZl6KLBVSCLWuwsUCPlop/4IQo5M52btnZfMjlnDqEfKkGg+HUmZMTxiED/OWnHtq99X8NvsGnDp3hOG7S7Akeao9df+ypq6ubucjS4MOGDvmEB3b7/L/8LMuCneNRgtT0WXN4nm9q0mWf2iHveD9zTLwUoG+dgK5eZBvuxHiqqLd/Q98xh155vD5K8AqYPdZB9oYETVMf7TzUnIC6K5n3/rd9ZgJa1EgaitlxGNmV9jIq/2zRBmQ6ygWUUSieeuEdH98AmqbfeP3Viitn5QK66u41o8ZPgZDasvGHlJ1/SBraxQKKNNRP0NBRzWqoieUXTR/mUBmVdaaQ0BAAwaLpLWhoAK0GPFi5wCKgn65NWX3zdBsBFTWUB9NHO9ZQ4SfN51IuKroeYqehAICxg/spFMo1T74U1U/8i8pKi5+8EVW5BA6e+M7HP/7jsXt2njyzfMmK5596QbKSYL2OFeeQIwZEo9d8WP8o+3M4cDHfoYCu24LGkHdDAW0nTgQUfVyThjx435rI8IizF8698PJznhpkwjf4ok/UKy+8umAuinD897MPv/lBdBnb01Ef78AeJaDCdA8Hhub02SijCwKgzUNKZQ/lPFDrEE/Bi/LEddNHhjqI/y6WfSedw5rHnqCXO0AM1dmAkzYu5ZZeyrVUNsybNHjepMFYRiVWz544e4hFr1mlVc7tylvuffODH9zc1KdTD2VlnHv6H8++88UG6d7XP/px9IRpT66++akHb1my8o4n/vm2ib1mXuD0GvShP3DKdsG+eP7oxY4MRgx+Hw3eok/mz5SzTvZcvWh2Qy3K2Niw7aQ5Ec/y6THK3heEcE/KSVFrKmUsFLK+r2rF35gLOcUNjQ3SJSkWWa8NjQ01mupXnnl47KjhI4YPfe/f/woJDf94K7KV/vHSu2kXzl6+kpscHm7jguK93Chvx/Me5DgZdUsHe4C+x8X0Cw8+tnrpjUteePk5m7ue+9ezoyaPGDV5hBP17FNED5B3Uncc90/ZvTNl986LZ09frlE7vMDN378pWBxW8bgWzU/kQ/lp78DYqLHJ9m1dwISJg+98+fMEL//7b0ff6stZzvoWf7rnSBCDjLvr541xaIRSFchtNH2CuG61sUat/lrBm5mSnmO/hH/n43Ufvv1C7hVRBY6cyjl6/OTu//3ww3v/DB405q3P1v/t3pX4Lj//wOfe/ORv966kaWrPxUtdb4GajVB+6ihb/wOqd3ZXrtubZm+Byq3LKI8AYSIriPB31uo0q7C6srJ6/BAUBB8/asAHP+4fN0h8bY8XWTkfuQpUkHbjbDRBQU6TEfyZjl7S0sIKrg7lGI0djI4mwaqCvvzm+/tvWYCCvPmlXAObGB247Pqbb73z3junxn13AM3JscHGVYeevU4vWaCHist7qL3TNRaoDXIL1EX6jgVqbYc6tkAxQYFiboM9zNzJyGqzF9AQleOx95BnG3Xsd78ebGq+Uv7okYtr5k7ecRRV9RaWWXJTHbJm7uSPdh7CGuoQLshTMkWnTxiKTVEnMjo9KUHS0IMnsyUNldQTZR2OTnj/vXeLT6B1H8dxknqKtQPilWvWTzq9pjzJL+jAqWx7Da3XGtxUij1H0wM9ms2VK2isYitRMDDCX4zbOKSKs8y2PXbKWcSAClJjDXW2j7cSa6gcNx79/Li5q3VCTTDlSV+urgmPRWeVW60fPcWSKLN4wZIXn3nJXj2FI6uyNehTVN+MevYpWiVwgcLPf2W4qw/5/b/LWnUyyx/aBHo4nhT6aDVw3k5iRQ4mFZthdAbWTekgPWLFbNtIhRxJPU+k5Tk0QhkK5NRXf/o9MojmjHPWOgXrVIvBNrmMjh0xaPgI9LN29kyOEw1llRz2hOJWK+98vG7r7z/v3bkZpyysX/dTf5VtMnDCwMEP/O2fO//YiG/OGty/LD0VXAvSayq48kYflYMXJisbORwDhzsIjkvQgSqsoa2C5Tiacvy7SAWpN55rwWNIeStP5tv2kMgvzP/gq18v5Vx64pm/0RT16X8+CwkOnTsq3qb/Dc/1hvEH14qnb7RdvWG+fD+nDbLoOr8LR+4dMgopijWJmRANvDdwMY3pgBDYfXDR7I9laS7BSuvHW/PJn4elw+od1Wa1yIZdp1fOsVgfa+ZOXr8XJTPaoLU7OJZRb0/3b9fuu+vmGcNHJGh1xqwMB6nF05OQwh48ewUboU+sufntj9YuWn7LouW3iAW/Dz1Yd347erEaxMyST37aiuvbfPz8pY3XEKoZLx4VoOSqHPQfs4EOVB3Jb2HoE/R3s6zWvamT5nh3B7Ji1bJZ02f9+5W3N/+C7P26utopcydTessI3PY0E+vLNCeaEuOnhn/5fledTe/CE1qtm7lGvQ/KIhPXfPWUxSUixhzkv/5O1BPyVj77FtXzckN1vIf/rmOZc8ZbGaGr5o1ct8OiobyQt79xR+oNdm5QhwmMOzMvzR3Yf+v21JnTbb1yNkwZHpeSmtVQj9wcq+9YjFMTps1aeNPtqz/878cZZ46+8eit0s4P3LoIX/ls7bb8qzm7//wfuNZICS42UAHKHpTGvCdlj83a3D5iuHXH1q070NRvQhdw7IC5Vb2Z8VOtMigIcmgKUuaVGURtqkU3NISyRHq5VF03xyJMCsgaefManwefbBPNT9dx4kGQqDTVBgpu0HPnUUWNnFkDEvdk2fbm2Zl5iStv2LQHra+nDhfrN4qa95EplEqjAVltPM+n7N6asnvrwCFjH3/yn83tP3fJDd1BQAmENnPsQPFfH7vZoVw63PmNX1rwVnWeK6A74MSJ5OthVXCEF0tCFT+0ukPUUJncKSCyDc+czcQ3Rw51EBeurHRgJdU1iWvMy43VcR5+VVW2+/AASBs1DWjhXFlRZa+eTqCCkR3DlTtba6OFoQc9cuzk+x/+x/23iwYm5sfNvz/+5D9zq/Uj+3u+9+WG+29eIH8UVltCJ4VuWxvcaPE7/Nfr3ullXrl2rt87g+UPbep9GspDINSUOEPKqMfqKZ8+x5y/IIojZkZiXMrJLEkxx45IrKyx1HvlCPnSEunVZXylLinKWbgJaWhu8eXc4hvnOM68QU1Mcmwb/8ixNz/lUMGeUmpLqRCrtefz9b/c//A/Pv5289hpljzKY/tOGAWP54FM5AV+76uNU+ai7O4FcxdCCOdfvxB75QaTrvyd8G3HwY2nbxzTSSvHPiKjXQ9+SSUZXd7zX+GABDGZvdrc1845NrM7bWvhWZbjfRTmbnjIFtuw68KoRLG5w64MZ0onZ2d2ruUpg9z5CktHSMyskVYJgwg/pvP8elPmTj648xCudZOYNlh87UZNHnHy4Gnp3pW3XU9iGp1nH0nBjc72u/3+32W94BveDemVr6q/1VATkcos1Fff4O84p9OBgB64YhE+APjUs9kjEvx5YSnscG4yDHRrMbsFa+i1LZtramocNXmEQqEY2H9gY2NjXkEey7LyUIY8J5HQs5aWBEInETjAQfWwHMZhpsvxolZUO/QgjEbjhTQH+VKEbhvcsDFUHdo+vc8xR+gpOOgH2gdxEgOpBEG4KM2VOIn9N7lXLna6GPIa2vPGL6ldaey3qn60uT6QEl1QDN1l9CoBbVssGIDav964UPpc2gsijvC2zcwh0YwuYPlDmwL7nux2rIa2mMPUZXIc2qPktVcJaNsK3eSLxN+ndspikEQzujjVpterp43qtVlJO0o3H3ng0TtvvQtfv+eBu89dOGvTgsfP189gNDhv4dwT6c0C6mIsmNDL6CPqea3sR3tOCekr6Znp5RVlUydN+/qTb/R6/X1zxMkIHh4eB3agZpocx1EUxfN8bwrY9k4BdeUHWYpg/PWxm12MZhAIBBs++eAzmyaESqXy6N7jC27667b1X6LEnh2Hqqqr5i4V+w6fOnTmxP6T8ozsHk3vFFAXwbFgF2mx1o3Eggl9kMR42+pEg8HA8/zi2x/AAgoAWHjdfOne629d8etPvadIuk8LaJcVuvXBRWV72HnQUtH7Z+rVdsZ8W6RnRS26G4888dAPX/70zuvvPfncE1Iv3dFTRkqdyG36yCQNbGEEYc+CCGgnFrrJN/YRsJHeIdFhF6WTcG1Jz0z/ddPG65fdcGI/Gg/T2Ng4Z+ksPNzXIS8//0p+YbPzL3scREA7nj6lmA6x8XW4rqf4gS1akRRFDR86fMmCpVXVVb/8tr68wnEXLgihQqEwkL4wncxrb7362luvDuw/6OtPvvHw8Diy5xjHcXdPdzDJI/UAGuO4YlXvKXwgAkroYTHi+XMWvPrP13BUF0J49+33AADumhZv377g5MHTBYUFy1ct7cBnJzRH5qWMibPGAwAiIyI3rf/jy90Zf509SP6bh9XT4dSWngvpNUToYbz6z9f2H9o/avKIMVNHjZ4ycsxUNJX6mxTb4S7460roVCCEpw6dcXdzl28sLCpMObhPobQMVR03ZnzqgVMVlRW9TD2JgBJ6GEkDkwAA//f0Y9IWjuPW/N+D8o6Nr734xqlDZ8jKvQvgBat/kzCsRc7oEWN4ns+tRp7QpYuWffzeJ//55IP5y9GM9V5G77RAOyOv2MkxSwsrmrt0+Gn0cTIvoU61f73zXvnGYyeO3jlVnEoAAJg3e959D987afaEa3GCfY57H/pLgH/AqUNnhg1BDX9VKtVvP//P09PzwduQ54SiqH/+48Vvf/rmu5++Bb0RuPCmOaCXYh+7kOfHuMifqVdxQoZUlNYqSIpMG3D+w/PDlz9hO/TchXPf/fTN/kP7hb7XYtKM/G06deiMKz5Q8h618x3x9vbZtH6zt5dlltod996Wd3QfAOCnHccjIyLtH+JkLd+z3o7eLKD2jAyzn2bWct6Mwy+ni/SsT0M3oUXLvX9C/3ffeD8sNAzf5Dju/nmDDcKkTyKg1+QdscdTg1IjGnwtAyx75Vemb0XhW0wtDAoM+u7zH0KCQ/BNTa1m9uKZ8h1iomJ+/fl/2ONWV1c7Y+H0zjxfgmMu5VxafANqoAUhfPLxp2687qYvdqU7DMS7MtCQQGgzfUtAnTNu9LiP3/9Uq9N+8c3neQV5QwcPvfG6m04ePC351yCEv639/XjqsQcffyAiLGLzhi17t6bMXEQ0tOsYOXykr4/f3v178E2e5998940330VRowlzlv28bj1odvgCoe24bhWWdonfv/sMZSICauHj9z81GAyTZ0/EN7ft/BN/Mx955ZP/PPcAmlz/428AgAcfR9eLSormLJm16w/xm0zoGl554dWQ4FCHHjSTqi1OakKnzQ1cK7SbWOg8ANvODhLXvN8uEVArfvltvc0LdO7C2eryEny9X0y/XXt3SndV11QDAN569e2/P/tEF7xVBADAXx68Z8vGP7/97Pu77r9DekFeeeFV9F1a9x15iboG10vLxguddjupza7ENewASwTUittW3f7b5t/y8i2T9e554G4piAQAOHveaq4nAGDCONFiJXQBJaUl6zauW3XDqlOHzphMJo7jlEolyv185lFD8/XXhC6WzvF9ptkuEVAL42eMPbr3+G8/o15bLMtmZmU8+H8PNDQ04HtxVnBpWZnNK+imcuvKN4zw1vtvvvvh2zOmzrjxupvc3Nw3bfn9t82/etTYvi8AgGdfekarbSKvWDfnmNCNt7NnXHcSREAtGI3G0VNGxkT3e+aJZ0aPHJOcNHj/9oM8z989PUEK7zIMbfMKymtgCF0Dy7K79+3evW+3892279pG3pEeMXhVjvPFeHfruksE1Ja8/Nz7H7kPX580YfJ/3vrwm5Scu6bF4y2RES3MiSYQCG3gmsfT2wYRUJHAgMC/PfLEP/75tPzVOXz00F333/HtZ9+7uasBQEv4JQuWfvvjN/J90jPTu/IN6wu0IZXaPGAVVIa3MQ27VaZND/2299a25ctJFP6ao9Vp586at2XbH4ePHZZvx022L5XUIeO0IC86Ktpm8f7GO6i1GqGbDFjFO3S2Q+2aZ8/0ZZabX/bu8C70rVJO51m+x1NSGYZ58PEHjqcew1v8fP12b9kLAJg2OAIAQMck7t2a8tP6H9/9EE2KP7jrsNpdPWryiIIrYp6TPW5qS1MvQttIivZvcZ/LWXn4yrgplh6Unc2Bk4WgJzNk9IDWPqQDJ9F3q8GibYYs4S2Mmz7mxP6TH7/3ifwFMplMs0f0w9dra2ufffEfr774+q033Ya34A6yhGurnoHB6stZ5E3odDpPPbs5Dy1BVqa8KFinN+ErRECtGDttNMMwSxcuGzZk2NW8q9/99C3P856yCuvtu7dv3729i963Po8r6on8nuVN8QNiLmflxQ+IqSx3NW8pMFjd519gAlgjiKONLEowNGr4aTRxDrdXV1UQAbXFZDL9tvnX3zb/Sj5cPYv4ATGdsQCfOtpBN7a+yRu/pPYsI3SNII72sigpoPB9t4hjnaaqtU9BBLQTIQ5QwrUlIVzsK+aQ+xfOkq5/9ueea6ihb7TeAfqXuTOau0syD8vtyl46nL4VRGobgcVihBfnBtu/2RdOZnWs8dLToxNdvIRvG+n5qJWBK9i/jz3mDTLwCf1Cm7vzsb9ej69UV6Lg6oYT51t17KddUNK6JicdXlAWi4m1Wh3b3LQ6VOvNwy6AWKCtSJFprjPC3Q/93LHvCv7G9phvaW+nR78RObml0WF+Du/Cuuk6Dy6eLb9ZZ/Y2s6yTpqt8c7LYPQWxtfR1Ae1ZPh0CofO4f4FlRe8QG6E0Wcti7xDE1tJ3BZT0lekLGEwK+41Kxgh6IwlhoseTB4DlOKORLSpttahJstg3BbG19F0BbW1bBCedEexTZ1xJkTlwspBEeDuQ9JwqJ/4+OQaToldpqIFLiAmz2IkcX1xR26g1oK93axrdVFf14CGyPADQ/G9XQgS0U+jRXjNX2L72maT+qDqL5/lfNh/7+79+tN/H28v92J+vJE35m/NDHd3yso+P2n63z966d/qEQdfd+qp80tG23/614LoXmjtUTm6ppKFbfnn63MXcJ5616rK8axN67Jxl/zLokb02cdyAvz+6xNPDLTe/YsYN/wI9FCWVk1cSHSYG3HgeqBigp/g+NREKyv7tSnrnXHhCp5J/6qOk/hGLbntzwqLnX3n/fzctm3D52Af2u11MedtT3XKz1AmLn/dUu53f92/5Rk+124KZw5/4109y9fz8Pw87bB5oZJVGFrVVBgDozajdlUqFrX2gdlep3VFxLQ/Au6/f8crzN73+7u+r7v7A18cj/9RHUm5gj4bluCad0bpwhtBZ9IZPTO8zVA+cLOy2NuxTD6Ex69Gj1lzIyC8qrf7ixz0JEx5VKJj4GKuUw4ObXqqqrnfxmItue9PX22PCqEQAQGJEaGJEaPrBd+rqtZlpRUZWqTco9AbFtt9ejo5CM3INRoX8otMzhaWajCutzvgbPqTfdbe9dfBIWnFp5dJVrwMA9v7arG3b/ckvQVlZPA/0BhPbfDIQoWMhS/hrTLcVyuYI9Pe22WIwmB565uuS8hppS3REYHREwORl/zy8WVwXJ0aYvZNmw4jjLCZSU42+orJu/eePzVz8L57jX31hFeqyc/Nb+F4Iwd4tL1TXNNxw+7t7t7ygN1iP7uCBWkXpdPYjjVvg6x/2lpVrpJu5+RX9onvSRHKH8IDX6o3tXLp3vSex50IsUELreOqVn/AqfsHM4RSFvmiJ4aEZF4oi/PwTw0PjQ0PiQ0MObX5p3orXYgIDcWg4ISyE53jxYgZCpIwQAoNBbzDol9/8JgDgp68e1uv1E8b2X3nnO3qDXrpMmvvskptet5VOAZ7nG7WGNkjGVz9Y1d70iw76eu2+nvppUIpfZI7j5b9MbYOop+sQC5TgjMRwZDnafCfnr3jtfz8/8dlb9+KbJaU1d67+2GRCjVMNBv3hXa/uP5TW2Njk7YW8jXqXZ73d+/CnX3y4OmXbv8rKNcXF1S59p3mgN5oMRgfFzgCAUcPjDu9EAztbZM8fLwIAXnx7I+jJ5JdUR4T4kREzEq2trWoDREARNfW2y1KH+Hmhtsq9g8Tw0OZMFfk3UKfT2e9gMOhnL30JABAc5HP/PXPmzx6x8/dnF93wmqa28YblEwAAz7zUltKs9MyCsnJNSLDvdbeKi3cLzRtVOr2pOZPr1Nkrjzz5lXyLQz1N2fqSQsFMmvss6Pn0NeNxQ+dLpHOIgLqqnlhnu1hDsQHYInL/nxNXoEG2BHaojC6iUDBGwegrr6h9+c2NL7+58fCuV3/76e8zF7/4+JrFx1IvvfPanQAAlQolsb/z2p1Hjmf9uknsUe2cqur6kGDfFnaS/YEmltPp2xVxPrTjFR6ASXOfLaiy+EN7JEoKGHph7GjDtZZI5xABBV9t2PeXlTOqXKsL9hOWpW3rUuFcDR0GQfDG5jRRLohdxuFdr+7ce+6l13+Rbzyfljc0WewmN35Mf/ld48f0Ly3rLG3SG4xO2k+0yOGdKMl0yrznQC+itcG0a8uG7q2PLdJ3BRQ3VcIFnS6qpw11jWKnGaNJKdc7qaLOuUTaf9CviSC2lk1bU5ctGvP5N7tKSsWwO0VRQ5NjPvoC9ZmeNMeyEA4L9dv4wxPyLR1Oe+Ilh3a8wvU69Swsq3FTiZ/GbsKGHi6Rzum7Aop545dUpjWpCDX1XrJbnPMVdI8QxNby7/d/nzhuwMYfnpBvrKqu//mXg11/MjQFxQq+VjJp3EAoJOXbeEWjR63pyPPrG2zo1RLpHLjp2zc+2+ZSL9Wezv0LZkrXDUZ2X2p2xtUyjuN93MDyGYPbc+Qv1+8CfY/pU5JHDY/PzSv/dbNL/s3OoElnqK5rEn/AhH+iQv3aFlXp8T5QjIFzUyl1BnOlv/CaNNfO7q+u9QLuxfo4e8gwd5WCpimGpmka0hRFUZCm0G8rRQlZdvg/8whe+89UH7JAv/015a7rp+PrDEMHeEBPBctxXJ96ETqQlINpKQfTQDeHb15M+d4Zt25zFn2PEMoR0YkebgoPd5WbisHCp2RoBUMjvZMJnfiutpTS1eLdLbZMZaoqK7Bp5vBV/3wbGurbO9CruarKSnzdxHJlVfWCehL6DA4/4j0q5NIySkqvN7YoHF2JO+vN0BTDUO4qhZda5e6mVCqQ5KndlGo3hUqBdJCmKJqmFIyogjYWnxnLOmPZLVatnS3UuzpS0J5vv9vchkcxve8j5Ao1dU2VmoZrfRaE7kJBda9Yv1tXJQFDG7/Z2hqGEiSMYSilglEwNCOom7tK6emhUiloJYNUT6GgFTSldlO6qRTYBqQpKNyD3cuCfWerg7z9Vfx/TbVo3Nhw10OortdVvNSua2jbFNOBgFab7bI+AsfxReW1egOqnCEQegcmMWBpESwGtk5AV06aQFFQQVMqJYNNQAqiQzRrDArUVFUC1siygAWgM3qsfvvfda0T1mY0tEPk0p6+5f7bmHr+hjFDTSzXqNX3Scu7N9LWt7FXWZ0A0JBXqVRCI7u2YmzkANADoG/7OrjrcCasgoa2VjF5HpnL+F/X6VsCiml13x4C6MYihcqdYHtWrL0GvV4fHeJTqWm0NAfosS8Jz5sVDQChZU17hbVF0RSjT630HjsT0I2pPSAq1xZQ7mA38rJ3K3qZXUboue3pIIVOHaX5Wv8MoFb7PC9kGbXi70IHwUnD5iuSXLYn5NbnLNCNqeeXDk/uTlHKLoXoY6+El1vfjqxOm23wmg8v4luQLfFeaDXsSEw8Q4+FDjO2kLDK7FV7A7OdcmlPnxNQjMPJEG3GxYTkrsH5usE3znGZaS8AalBU8Po5I1zcvzctsLgatGa/cd7IJq3hyNmrtQ1acbuQqLdy3kiHj5LUB7ZJ/vhWugvlz+X6A+XDjswpTs2cm1lYLZrbbgOzRfqigMp/jnocvelrT+gU1yEACoY2Cu1ZXX2Uo40OvyIdtfLtDCw2puzfzqYvCuj2wxkhAfKS9m4E0ccu4/4Fs3plEbOHu1KrQzONCV1AXxRQvZrr2CV8qyASKRHuESKUoyh9vdwCfD1U5pxtoSJZ8LM4epcggLUN2h1HMwtLNXITyrOVn2UIweqFsz79s1dpqLR4bz/OA/jdzPoE14r/BykHYaLwaluoAAAAAElFTkSuQmCC"}], 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"}]], 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CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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jNUzzRsKan55YWtXGF6F8TUpTkoLMGKQlmaeMCOXcrxAxMcmTsSQJCMvcjKQDFYwIzYtEMpZ5KSNpUovQFV7Ndu/syXVNERWoiPGNDqZjbFKyY071iRNNob8MpVxKEFCjlFmBkwjF45JKjqLexg8/85FMSp5eOMVksmTZxygdqmlEnEiDI5VotAYjJ+NK61tw97l1fzwfryzIiccvMSn4rGgtyImfnR1H0yA/OwHL1Lm5idzNzYlQQIMDNUiEzs1NKC1nRKgUZZVOZqzgCU9/ou1OqpP30YkEKmIcY/++/QlEUyuF2JMgHN/rU09yxFJCID9XnjpXo5LJZRIJSZDYlcmIUG4bCnMfAE/ce1VNW39suMpmo2gaWfHIbM9JwFb57Kz42VkJpeWoV3xBNrLKd/22G0BQmJdQUovotfRoB+cnzc+OLznacai6vbSmsbSqFRPigYpm5nxIN5U2IFn62D1XAFqKXqXBgWPopyXf7iWobUYxqJzkyMlmxa/wZbZ7kp+u7fpEAhUxvrG9uDohKXV0r0Euk4RrFHKZBEJIQBgfF8F/FYtQnOZ5z+Oop+K8vMS2Th1BQMyVB6uRCJ2TFXeoouVQdStfhB4o61j3x/OLyxE/MiIU5ufEFU5LpGlQWovIFCvd/JwEJEKBXYQybIqUaVbcfY99gNfnZyOPakFuQmkFM8yDnKQidMWQ2FMgPDmIBCpiHGPX4SN5BcsI3EeDEaGc/T5CItRVfs6fN81ssaoUUolde9qYLvFobhfvnis+1sGJ0OJqZNEX5KAIEhahtN0qn50VPysrAdPi7Iw4VoQyW9hFaGfx8RYcNSrIjodMtL20upH1mSIRyrgCKAnv5DSgpKX1aD3TwR6xMGbP2pbJJUJX+MeeAnjpEy0SqIjxjWPlVa0trdyjjYfQXIBaKYuOUHPsiRyMEiIsnM243FZSz4lQiidCkWP0OOJ6LEIhYYUQGdeHKloOV7ceZFYePoHewoGyjv/edz3iOwjm2j2hSLRyBZqMC7UgJ94uQmlOhJbWtyJPqI39gSnITjhY3sydFCVCTcCRwz7Y0x+nJyc/PQlPDiKBihjfSElKOHqsHC83N7dwRHa0vi4E8jM5JV4hl8plJHOrsesJCBUyJOggTa8ozOC2P1DGaw7JXCcnQudhq7yGGbLAhN3nZMdhxQoAqGtm+I4GeABS6dFOJiKUVFrXWZAdD6xorkcJEqGts7PiORH6v4f+xDPSaeRIrUPsWYDUKyNCmaD9JPGErrCzp88tMXv6pE4MkUBFjG/sPnykocv3cJegY3b+VIYGiZgIlVTC0CXvVb4g3XusmRWhBJidzdTD4BR6uyYtLe/ef6wFEiiAjyI8zKDZgxXdbKISAC++/j0WoaX1LZwIPVCF2o/iYD3TQBTF4g8xzgGaRiL09ofe5UJJwEYVH+vC7FlS3sSp1yfuv3oyeEJX+M2eGP7PdhIJVMREwLHjVc3NbBpQaOQnWnPuIpTApJC6DlODECanIK6kKWrBjBRufekxB9fPzUEGNd8TijZgRCgbGsqO4yLmLOwi1HFAxnOK6o4gKK1pBIAuyEkUXM68vERXEXqwoqcgJ+6eJ97jPKETNal+xQVh/ucqKZR837FviAQqYiJgIVHrVv2NEGbnT338xa8MJqtUQgY0ipIC9Bx7KOlADasWOadkYW4SCpRnx0NGhJZiEcqUbN71MJKTBXnxOBaErHh7PhOy4u0iFAlMJp+UCyVxp37sniuxCJ3LilCIqRiJUAbYUTsh2RP4ApM9Qag0MiLAL49IoCImCFYuTY0IMx+qqTlUUzOi8hMb7+edMS9MLecnn7qCE6GQIByhJIYccSipEGW2M5mbDIoreVY5g/wc5AnFa/573/UlFW1OSfUMSph8poI8tu5zwfRUJprEhpJoGuyvbpqXl4jzmQrzEg7UsiIUs/Y9jzMitHkCitAVXtkTkyZ+IEtCzf7YBMShIoGKmAjAQzLiox3NNEcUj7/4lUohlUuZkZvOSGJ6I/FpUYCS451znPOZcPQcr0ShIWyVM6Hz0nIkQnFS/cdfrBfkMzkl1TMRqpLqxj1HT5RWtdI0m1QP+ZN3aRqLUHQQRoTifCbOEzqRROgKD+zJJ00OHHsGyqEigYqYKIAgjtekfUTl5xXnLVbK3bg+vYhQRygJTQB2iFCObQ8y+e0HGBHKd33ipHpBPpNAhOKkeqxcC3ISFkxPxZc2NzexIDeRBkiEzs1JwOEpRNZMsN4hQp9wEqHjHSsuCFt1pVo4f9gdb3qBnxwqEqiICQIIQGQY6vc+0nj8xa+6e4xcyaafcAollXfOyYl1m8+E15UwoaQ5WWxx0YIZSbMy0fYff+lGhM7NRV1UcD4TJ0LxjgcqmksqWudNTQCQV+pKSQ82MEn1yP0KC7Acvv8aToSOayt+hbPw9Ic3BfIzIA4V+4GKmAi4LG4wddWqMI3kgRe3jJz8TJ6SGh2hPlR63L7CuW8duw79o9SEcfp0UNcn0KqVTT3zZ8aXlDOp/rSETXTHtyvOcKIl+RmxqLiI1ySUNkN0HCaxCfEmv0kobirq3J+JpoGjSSi0oH24JqGEZe6UpANVrWiGMjo+++p4bxK6ws6e/itNT+zJgbKXIbiFqEBFTBwEFBAfGh5/8SunE7pmUTIrDPr+QV0ffjheYUJJGPuO8Aql3IlQ51L3uAUzkmdlMk7SPFaW4pfsIjSR9ZxaUD4TbhKKrwyH42nnfKa5ufEH6ppx1J45NTryfx64dlxXdq66Uq1Q0QHZ6T7Z06cOFQlUhIgAcj9XLZkuWANdadSFVHGuEm13QWJPaOFUTF6Y1IANpy4xTkkmgI5CQ2w+U3n3nrKmI41MZecRFHdCpjrjGLUn1Z9wzWfiLmNubiKkJQ6xbKMOHGfOYk+qL2F63N39+Dvj96uw5uow/3nTf/b0yaEigYoQ4ReSp6S+/uEmebjT2NuZs3PxY9bsvFmz88Jj7ITrSQrzRKjJgozNEob7CnMTDlW0zLWL0AOVJxyl7gxwvIhZQ7MilLYzph04nwkn1WMROhO1I0EHKUCMTLOhJAaFUxMOOCo7HfEoLEJpAArHjwhdc3VYoPaH/+zpnUNFAhUxgRDU1hi/vny960qNSt6oMyenxOLHNz/t79Hpfiup6NHpvti8XyulUxKjWRp1ezvbRSjSlcc7sAhFGaC4BNPd6A4unwkdUorCPliE3nXLxSV1wiahfEotyEk4UtvOhZIgLeW6phbkJBQftSfVMz1NWLIm2cvLHT/5TGsY9gyBD8cth4oEKkKEe7z21vf8p69/uOmS809GTT89bM/dvFop3dlvSkmJS0lFj+PNfSmZSYnpTiPjsRVvwQEKJnBUmMtyGebKEkaECvKZcJ9PnA//31e/YBgQNQnFTkxOhD54xxXcXthFMDc3MZ9ZwCIUn4jtVM9WdvYyVwL4+UyF8WNahK65OozPnv5zaKDy0wuHigQqQoRv4KGbNjSa2PPd5bUqyfVFGoDio+1z81jN6NQk1JHg6SxCaQAYZybWqs88eOMBJp+phAkl0QwRF2THP/rUp4xzAFnx9srO5tLKFppX2cnU1UswY2I+ZU8qYRstIxE6hvuMrHGhTj8xZPZ0y6EigYoQ4Zf9vmJ5gQIlzw/VlwBh8hQnEYrNZLwl5j5Hk1DcNJ5Z6SRCc9lGyAeOtBfmJdz5zze5JqEFOXH42nDdJxKhzCROnghNmssk1WPXJyJlvANTa+8Yl8SAay8yLtkTwhFiT1cOFQlUhAgf9juWn3jqkefwkBOg3zf23sMotg59JdU/8NdLuF1wGRILGmARirs0cZWgrAitdhKhJZUtyOtKI9cn3rswjw3Ws+OS7PlM+NXc5MixFkpa485s58AkIHj8qIPCngIOFQlUhAjf8jM6Plarlku8Guk+QdN0cgabtISBzWRcBc81CeV7QgFkO9X/87+fclY8jh0VM5mkzPAQJEIPHusssDcJReOPAHjor1dyQXaMublJcgIVa9lFqI3rz8Qmk+J8pn/z8pnGUsv6Nf5QJwdnDg0ie/I5VCRQESJ8Y+lJM1F0wocB70F3cgsQMiOJhDBbHWU/mMsEIhRzw0N3XsquZAZ4AAAL8xLQFfHbN7FJ9SiU9MiTn3AilAOaBcoc0C5Caa4/04FjaEvHuKQxFkpa45U93WOECysIkpeVJkKECCw/BfZ7cnqKRiVXSIPhPoOgpnuQvyY3OfJQeRcOJRXkJOImoVynetYqZ/KZHnnqE0eMnjHbi5l8pv8+cAPWpCVHsQjF4XunfCa8cKC82WK14aR6zhOKRCgzMt4uQrudQkkpkWOZPWmfEpnhUH/lJwz4IRKoCBE+EK6RZyZHweHZ7/ZbFMokbm5mi5Xikurn5SXyO9Wz/ZmykBp9+P/sItSuVQvzEu7611tCxyjXn8nCthdxEAQAC2ek4XZQc5jaUH4+E6NlHUlRXChpFEXoGg9OT9/UaYdEiioL/HoEDpFARYhwgkB+5uRlaNWKuCh1sD4mgoBpWWmClQeOdXD5TPvtA4od3UpogEXow4xVjkUilpBIhDJTOUt4+UyAsM7JThAk1bMnqmj+vaxBQsgc9GOzAVqKRWgJE0piT8pE50dXhK7xLDz9hIRJ27WYvDUEGQ5EAhUhwoHi924TfBwKuSQ6XKmUozKeYME2qOc/xQxlwZ5Q51ASbhJq75rsnM/EsghdODUBkQQvnwntWNXKTlG+DYlWDKagk21/R9H0wYbWWRmOYn8sQlE+UwWTz8QcfxSbhK4ZpvCUQMyeGBYTNRI0KhKoCBEO/Pf5z7jl9UVlKVNSZBIyKlzlf/fP61dsuX7FludvPep9M3citNNXfyZhZScjEiE3mJOfVI/qjhhfwRPPfeHIZ2I6M+FO9TMyEXWigchYn9qT6nE+05ysWPSU5wkNsRW/xoU96cCFpyuCzqEigYoQ4bH3klopS44P9z9//voVjm6k1y/f7GXLmmq25zFfhBbkestnmpOdIBShDA4csY+bd4jQ+NLqEwerWucwnlMsQrEVX5iH5Ofc3KRjNUhaHjzRZj8RRPzENBudm5t4sHLUROgad07PgJKpPLFn8DmUFglUhAh3xjtOnlcrZeEahfcazaEBQjee0L32VnVu85mQVc5kQd3z5wu5zXC0nW1KYs9nKmHaiyAeZDynfBFazIwPOVDRvGhmGrbx5zC97tmxdAyYfCYmlITfdwhF6JrhCU+f7Bl0c15UoCJECI13DG1ERFSYUiZDrCF1FzrnEBGGQkwf3FkhWO9FhEKIPKF8DsUilNWV0IrzmVgRyrTvzM9JhFIpR4hYmZaySfVsM6eSuhZOhArzmWgnEWoyWwkg5UQosNlK7E1CMfmWYOVr788UAqxxZs9AqdNP9uQQFA4VCVTEhAEMovw8bfGc6IiwmEg1zpX2eSdff/J69+s9cqj7qzWamOkdzHIh00AeiVCIrPvSKmSV43yme/9ykTCfCYeSnEWoUz4TdBKhGDSTWG8PJTFJ9UxwH4tQR5PQEbbi1zib7UOgzkDZM1gcGuTyJhEigov5UnTP77M4JrJ5wnCKQgTys90g/XQrigLtPcZ2ff/X69sBANetzAEAZMZHCXa//w87vRz8o7tr3v5tmtuXtu48lJMUwV9zuLK7YGrMkZrOubmJxUzMBwMTCjPgCBHcv5/9HJAEEqEQMHVEBBKhEmSGl5S3YGFUkBtfUtE2PzfdYjKhcUl2EVpS2cqFkgAthYT1cGM72gUn1ROIkQ9Uts7NTTRbB9lQEjNMidHIPcVttpEWniGgTg4WEyWVD/2rIxKoiMkOQeyI8X66T1p69+dK15Xv31Xl8xTXn7z+7d9Wu65fXphplarbGtiAUm5KZEUj2xKJaUovoQFdcrypYGpySXlLQV7cgYoGACT5WXGlTO87LEJLK9ox5RVOTSg+3oqHHpdUtmEq2odbfyKqgEBKMyKUvevn5iaiiUnMZrMyYg6jKD9SsIwIRZxypLqvIC+upBKdq7a5LyMJjZwLLtY4C08QWvbk69CAaZS5XJFARYjAd8NQ7sOa95wyOr3g+pPXZ16rwcubd9e63eaPK3MZv2eHVMZeDCRJdmYnbwQoTqpH6+0i1MprdG8fekwgK16KckgZEWpGMz7xeA+HCGUULi0BPBEKSAmA1rm5CciKJ0n2nCMjQtfY2XPIHUuGz57DlKKiD1TEpMblhTE0TZssNjzTbX1R2UVnLwg6e/q5/Ts/V/x2DLksZzJJmp7ymeZNTcaeUIz83LhD9YgQi4+0s+F4Xj4TFqG4hcjcXEdtkj2fyd4dyuEJRflMuL0I158p6J7QNTyn51hgzyG7REUCFTGpQdH0gNHS2j2gGzTjAIxUSowEewaEkuMdbvKZmFASqvU83uQQoXZiddskFIeSCnJQPlN+dtyBsg6XfKYWllghm1TPVHYiw5QVoQDMwcmnvMrOYeYzreFR59hhz6FlOIkEKmLy4vLCmEGjpblT19TR36Mz4LsZD8scIfb0Z8fcFBSkmpkZ7ZzPxJQS2UXofBcRyu/PxInQ/Ow4FEpikva5snpBPhPyhB5HXUEPnmhzrewszE08iCs7mWMOX4Seco5U12vQ9RqG02h0hNiTg28OtV+9SKBBxnxpI34E+8Aigg+apnt0hpZuvW7QrDeYN/x29MwVhXIpOaLa08/dD2GudIhQiJPqsQjdd7wJp8FzY+U5EcqWM9GoR31pVXtJmUOEOoYeVwlFqEAKItXJcFQx1yTUeczy8JPq9b0GwWMIFe4jBz91qEigIiYvbBTdozMaTBYIgcmMhGevzqDw2jckKJa7z4M8dveVNppyEaHIE8rx5qH6rvysOKdBnrxa+Dk5caVcUr1dhLKVoAwNOneqT8RmOxNKQp/LQbYVKSNU7Z5QyMyzG7n+THoXShWwamioMyBzXiRQEZMUlxfGWG1UU1sXAaFMSrZ19iSmpcZGqqWe+4YE0e/pz6FsvPb1nCf0YEVPQW4c7hyKyRQTIj+pPj837iCTe2Rv2EQ7tSNh4unYE4o7M7FgrHvWikf+1ngcSsIilG0vEvJO9Xo7kw72G0NMoBjeOVQkUBGTFyaLVaVWSyWkTIK4QEIClUIWsqiRlwO+99HGx+6+8lBFFw7HIzclI0L5oGlwqB75LgGTP8A2CWUor5QRoUh42jvVO3riYc8pQ0QFOQk4nf5ARQsrQqFDhJbwxiUhD2klMzheOqxO9aecM/SugFFxqlCa8H5yqEigIiZv8vyew3UWq40kYE9vf0xiAk3TfQNGVtaFJObu87A4MM4k1SOUHG8syGGr1CGfEBk4ZigBGoWVKto4xyhyYtI8EWoRilB+iIkToXNz43FlZwFjy8/JZtqO0KPQJJRw7ucSehp1Mud5XxCRQEVMBAzhZvp5z/E5eY4KUbVSarNRzW39BqYaPWQZS17w3rPrDjBpoSyQb4F9o2ykiJ9U79wkFItQe1I9o0nR1AorgDzOpcG8qclYhOLRxwIRiqPzc3McnepnZ8bglnej0iRUAEyjoWRSVykqEmiQ4U/VtogRAD2E2k2VXKpRyHQ6vVqrNVspk8XW0TPQ0TMQSvb0efwZGWwoiXmGKjv5+UyMd9KtCHUsc6EkTL+Yc/Nx3iibYJ/kCOjbRWh+Vhx6ka3sRG6EQ5W9h5gh9UNLqj9lqPa7z3aCoaRRyub0TRMJVMRkxM97jq9cMFUpl8ZGIM8ahJCmaauVMpgsjW19Idaebs/y3kcb8cJhPNWDwewsdninkwh136neATapnslnmpPFRJmy4kqPdgAI9jMT69yK0NLqdpQo6pxUPzsrdnZmDN/LUTg2hh6HQJCSEkgyB6comhtPLRKoiMnbOoQk4fHaloz0JIgKcCgm05Hu7B0MveXu6VzvPbuOn890kOFKflL9rMw4yLfKAZg3jQ37sFY8oynn5sTPyYqbmxt/sLaRF51HvtF5U5O5pHpHVikjQg8ca8RUaR+XxIpQQorW1rb05aZEzuDl3o8EhtDNOrg0inkTUycfmEZFAhUxSeUn4zlFd0UMI0KtzGBhHJofFb+n6xkfe/pj16T6Aqek+vjDTNgdEyKag2QHl880IyMaN/08WN2OSznnZMVzSfWlle37K+q5pHosQrksejaKZePGJbEilCmXZzuPltV2jh0RGkQa9cSbAogEKmLSyU+1Uo6fri8qO//0udHhKoKAzNg4piUxPWpRI08caqVYEQoJawmTz4SLgkqYIM+c7ARU6p4VV8q4R/czwpPLZyqr7SIIyHWqRwlJtXhsXJxAhGIwqaaIrw+faEfheBubt4Sj+YV5SIQKkupn+CFCh+YAHf4wlSHY9f7wJgeRQEVMLuw6VLt4dgb3VC4lYyNUcpkkLlpDkui2+enfjobtoUfNe/rkRKeGze89uw7N/7A73WhUyR5fWt7MthfJiz/IZCNxbsk5WfHYiueLUCbrHr3KNZl3EqHlSIRiYBGKjsbjkDnZcTiltPgobi/CitBaJpRUhp2wwQafPXES6HDgk0b9lJwCiAQqYsIABto4+apzFsplEoVMIpeSU5IiVQrpT/+ytx8eM8Ai9GA5m1Q/Ly+ppMaR24SlKBKh9Wwo6WB1m6sIxZWdDscoP3zPcKVAhELnys5SXmUnM61eWNlZOCateD8F6RB4k4NIoCImr/zsGzDabBSEUC6VxESovvpHDRht4KbLAhGamxLFZNQjwiqubMBik18Fj0XobPt8TX4oCSfVH6xsp2ja3m3Ebstz7UXsKHRX2YkjVFxlJ0OjUCBCg46RmITKh0QCCXJY1IkhEqiISYHLC6Ndc0VVcqkStw6B4NbTfgKjDa5lvVscLu9mGy2zSfX2fCb7OGK2spPJZ8IilJ9UD7luI2UogUmQQ4qt+GJ7PhM6Mi+fCVIU9rei2SG8pHoopSsYj6r3fKbhVHCOKPj9pYYGkUBFTCIsnj0FK6v1RWVLFsxQyqVY6Xx4t/sZG2OHPZFSpFGiFVvEibOLcD4TQ22uSfXzpiVw9JCfGwchxCI0n8lnchKhFmKOcz4TG2JnROhMLEJpUFHV3NrVFxulzojXckn1iwuyAKCDm8800vIziBwqEujEx2RoUTpr7cOB7qJSSKUS9vt/1X8cdv0YYc/yph6BFT81LfoQ5kpGT87JioNMPtPsrHhOhLKEaB+jBHndQNDdDmFJbUspzmdyFqEHq9hQEs5nKq1srahuTIlRAQiOMCI0PzcxNzNtd1Vndx+q1IqLJLMSwyqqmnv0Rmps5zO5Bb8WYDgcKhKoiIkACKA6vsCr/e7A+qKjp83PldjMza3dTS1dTS1deckRd78xE4wl7emqwY43dNkA7ZJU33Souu0A0zmJFljlTqGkDlzZySbVZzIilDkHJkScz1RR3ZgcrcEiNC5Ou21PVUVVc1ZMRHZSeOnxpqNMOn1Nx8CvR5p0g+aNO48TUqXFymSZ+p3PNKbk5/A5VCRQERMEEAJtsl/z4M5YOFVi1558nH6XE8+OuuWenJHoOuqj5BiixdnZsTTXgj4ncU52/Ny8eEw8uMowPydxVqY9n4kJ++CyeghAR4/+YE07F74/0WMwU6aUWHVpVbs8LLxvwNjVi9JR+4yEXBsp10aaLLYNvx09ysyt43C8ccDKzPTl2G5jUdn7v6P+T0F3gA4/h2nkOFQcayxiIoDx2UE/5eeqZdOZkmYw9v2ebgARDx6q6oAEOFDdAZn3fqiaYS4I8rMTSqta5+CketwZGuJCTAKFkpga+YZeXWVtS2qSkpGfiCtbeq11JyrlWtTis+xEj6msDmXIhrFT4Lftq5CHR1h1OgCA0UaGa+QGo8U0gILvZoulf9Bktth+3XesV8mbaje25afbjoWYQyFuIOg3RAUqYiLAf/WwGrOnM2bnT509J2/WrNzFf2YHRo4ie9Y093L3c0J6vKsIPWj3hELIJNUfR0n1uM9IKZPPxIJGSfXp8egshUwKZ0V1c6RGWlndLA8P37q7sr7NYDBa87MT0mO1mD0x5GHhmD3T4sKSo9W0NsYwyPYHgACYBvSEzcRt3KMz/rqvIijsORaAuiEEIkVFBTq+kWfwPYor2swyRp7N98blSiRMxh30rcWquLn+yM/Vy6bj5eQMR85jV1+/yWxt7+qeNi37hpfBW7ceGEXtKYf2EBCErgJnalp0WU1nfmbskZquwtwknHhE00iEFuTFl1S0YRFaUdu4YuH0g4wyVUuk2NGZm57y28EmtTYCnYX5V2cwfb55Pyc2OaTFhVW0Dla0srypVKkGGAUqhxYUo4IO3milNEDpTUdv/8HipxU/Kt7PYUpRkUDHPXxSXhjTOwdtaRmX5BhErFo2HX8Wnm6OE82IcTKv1YxoObxPy72ppTs3ObKJSdsU4HhDFymB2NHJJNWjRPDS8mZAEiXlXQBiESqRh0cYzdaK6kZ5GNLU0WHKitomebiQKCta+vnsKQUWSh4+YDRj6lQwTe9dQdLu13vCvp+b56/kpeiPKjzZ70PjUJFAxzfGqWD0X0Rj+eyPdi57658KYAHAow3Ol5/uwbuzRo5Dh+L35CE3JYog4IFjnfNmxB2p6UT3OEkAG1VR3Thvdsr+w02L5+R16ozpsWH7q9sxewIAtuwtd2VPxJi0RQeYrxDjQ5YoVRRFK5jG9UHEoNFRIjX25WdAHCr6QEVMZHxSbE+cDDnTBe2YLrmVxxu6CBJSzCw5AK00DY7Wt8rDwvYfrpdrI/sGjBkJ4RV8ZygAfPY00lIoU4VFRspUGpsCrVdAq4KwKqDVahykzb5/qwxGY4CxFrD9B2T7jyP2xPDpDxUVqIgxLaKx/8Ev54PHODzC+qKjuCMRd6u+9tGvz/zjUv42kVrNGx+PYEGnn+yZnBjV1OJoAIpAAoCm1juJ0O5+xHRzcmJLjrMb40BQVVt/WSVrufNhGHQwI20e7Dez/k0FjwtNtESrkpsNjqEmKRTqJ9JIoBxSUqE1MpehlMvMFgvvisaNIe+n/e6/DhUJVMQExyfFXTiU1K+Mh6j/BYu1lywpr3b0XrLaqF2HGswSzDt9QTfkA9Ke/GYiTW3OZMrgeEOXRkVkJoUdrOw4XieceIzZU0Jb9JRCpZDJpWSvzgjlGtqE3ouCCQQJfnmMlERBWOXQaja48W/iKLwnl6ifGDTGuuVQ7/IzNEmgQ+ZQ0YQXMfHxSXFXv0ueTW5GQm5GwobthzZsP8QlQikltNLepS2IhnwQDuJ8p+amROkHqU07j9W3D/IcmlaakBttJH7oKQXSXKZBg14nhxbk2fQImiNHKbCYbETfgLVXb+nVebS7uaFAQXeGjin56dOWFxWoiEmKilpUWrPqlNmOVR7ur2Hq0GGzp3vfxNS06OZeg8XEpmSaKYlSozEbPRKlkWnx6f4lK+NnxSeBJG3Uq0nCZPbmtRwyFe1zFqFj1vvpiUMFUlQkUBEi7HeI509iyBw6BPb86Ptd3PKV5y7mXZrTrXu8oSspQlndzHo2ZcBi0FvcspGJlsghihEJwkNmG2kwIP6laYtMSqpk0EHYgQaJhm3IjxcIzHmRQEVMUnz83a4rzsP0JER2cphez9KlRqMxmyxmk2nxurBdL/aHQHteecmpAIDUZLYnSFObPZGAoAHFlG4yyEuN+uVgo1KhGBhwGmTPwUhLsBqVO2tSk42gjGiXCI3CQhnxStoMgEzNbWOx2tQqBcPYvoPyQwDm0FDKz+HY7144VCTQ4Bf/+Jm6OAFSOMcUAr0/rlizANhsebyGHeEa1ZufbUNMpdS63WXhbZrfX/JXh769Zfn9V6OFf32wHwSOE03C0BD/XW4oKlOFoVIig5FlQFdg9jTRJA0gyVAVSZgoG8hITaiurIYQSnGxvDt4UaCEzTxgHa4IVSkcU0lKdpzw7r2YucCpqUp8aujKbX1yqEigIYJIlyOKISuZcnu1D03TpZWthFxtsEJgdrBxX1+fVkaYAzzDo18soOjB1EgUQb7vqnn1TT34fmvu7F9UmMPUGrEMkpzGssO/Xt9+/02neD/sj7sr1izK3VBUpmTY0wtMNDk4iMxzhQwopSzxUiRhsYDmRpR7ICGgVi3DEX6lQkZInKot3XarMgIUlQKkQikxeyFu/w15mdJzQxe2pbObH8a2E/1jgU8xh4oEOlKlk2LdZIgxfGuQC8Sj4nqzY71ZFpaXl3qstBQA+ox74n56wq02dOCyv4LMFVNkyjBAVfp/drfsmZEa21TvNCV0/e4KrD3dQmcCcgmKCJmtNilJQusgZTINmoFK7TDP0XtUKowGY6/epFbJUUsSl5C6lCR0AwxF8pwQCsj7UIIBs4GQKmz8Px1agugPgdo949U0OLKnRSBCvfCpW0odsv1eWeqmQR9GXAqTKza044oQMXmgIGloGZg+I4ei6OaWdu8cetlf7UuU+yj2a58Xrb1kiePpx78+fueF/375W+RVuGCpYOPaEx1ui/ddfy2MFLqX87LiayqrE6M1je0OxyjHngIekdhNeIKw9y8BwGBAvCnwTirlsgFeHn5QMMga8m68BU5rmGXvHOqnRPXChgEBUyeGSKAixiICHkASjGiEF5VSVo78dDRNRWgVJAH/9FzOG3dUemNPAMwmA5Szy+nJkd9uPYj6b+qMz9xzGUkQnAm/9uIldSfaHrnjImwV2ijaaqXMVpvZYjVbbGZ7vY8glQnVsFMK2uJgSZzI2dzYRNtHwPt8s4NGywBj5oepFZh/0Qcp1yigWSGTWFWKFMJprEjQMWiMVYEOb4Y8DwFxKB9tJ/p7Ox3Zsp6APjP+5+YI1zHPmBKMeB51YogEKkIEdw+5Jx69Tk8CQJLoHpKQRIRGHhuhOu2uqG3/7fbEnipTPUoblLPenlaD7IdPH0G5R8cbf9t90FVz8WuivN3h7BJlAaSCsAC5zGByY1b36IzINmfu+wFsiTOwOTOr0Wx1rIdOFjrhNY1pmA5QAcwGwk8OHRp6OwcF7lSuIE2odD3rfL7q5EOsRBIxEcB81QPzc334/e6AtpeQhFIuDVfLM5Ii4qOQUfzolwvcsqcrZJaBd9787t2Pt37z3a9cdGQI8FNn2yiKZDyhbsuHjDaJ0YaUk81GqVUKtVphsVEce5ppicFoHjC68T8YaQl+KBVMNCkY8L82iWacoYEeH2tP5FFleBM/uDX+IC4lzBN7igQqYvLiH+suSE1L5B5p6UnHKuqtBh1+uG4PIVTIyMQYTVK0Vq2QIUJRTMEvPfqFj1lMer2egBAyTTre/OK3kXlDwuvllggb8iZwslFBmBUEokvaXV2mjEl+GnQmUKxzUdMm5sGt75cFYYrUoDHWbPCt5DDxDYFDhwbMm16oE0M04UVMUggSLQeNltiE+B4dYpkBCwQmlkMlmih6EPUWgSi0QsRGqBRyx11z749MPydkqe/xfrqaJjQMrrlDGDJWqJCY7dejEI2M8MuSdZVOaucgOwSgvx+RtVqNpOIA0yFFqVLyGzIh3rTZGAaArgntFE0PDBr5IfiRhtlvQ95/Z6g/rk9X+CRNPkQCFTG2BpAIxpCwO/rRUHn44GSYWkrr7VN/1FIKp86nGSxKG61q7rN1odvysfOXbS1p4PaNE2ioZmQs2jrYSJGiF9FZj84YCUCm2Ykm4sLk3L8AgMdf/QEvXHn+yYFeP2EzDTAZrIIgktJr+tHAAHKYGgF7AS69mkKBQefU+tFCQNSJIRKoCBEsZHKFuc9MWnzUGuHw7Gsfb82eFoRR8g1NTsRx5TkL05L5bkHY2Owj7RSZ2DjeL5EDCaJL44AhLExtszpsbRMtxeOMBMBReAlBMM38eWcFAJVyuqvjlNAWnSnQXqDBFKHYGepThAYkP4dAnRgigYoYi+VY3BynUE5zMpuMyIiWMw0oXWi0QSUNU8lypkSTasRWDdUtWYkUF7pvr3G68zUZaahmR85OGzY2SQCEPYz9XtOrS8tI5tKYfFKqW5A2s57E9aZmV43pGqXis6eBlgOTjh9mt9oodgay11JOAxN6Sk9Li4KgvqoCjJII5ZyhQ8tqChZ1YogEKmKSIjcziaUJGoWt//n8V+jmVLivgmcjLX7Hzx1pTC5EdNOVyxsah5XRjRiQkCsZTvTs2nC6VoOFAPak0SlpCW2NFqvZbAM2XIlkNttkzkF7CEGfEaQ49zJWMqGn9kY0NX4kuoAMooi8v2mhw5efw6RODDGNacykgosILSpqmsurm9CjpqmqruUft/0hd1quwQrxg9usr4ed0m61UUip8WBQTDEophiV6CE4+KAqD/hBMz8WlQ/hypWERUlY/E9pNVgItItcxiaWN57gaUz0LzteiTHPcSWSRimT2Bs1ucXINVIy+xGRRxhSVhOGP+F1PyEqUBEiECpqmvv7dFw7emzA22y2cK1WP4DkDEXRJovNbGXcf87ssXnzthzBnWQzGlCSk2+lqYmNTo4LR1n6DO5/7CO8MGNGFl6IVrsnFIK2GPFATWcYBpAq1ekGuSi8W7bl/xjQSNVKGd0pUyqRzDRTQK1Rj1Avu+BEkzwb8p7kZ7BIkw+RQEUEjKtOzUpPZSdkPPa+o/vvqGLogoip4aMpGsH7ljaKGjBYYnnjgTdv3nbmmaedeeZpNVs/41Zmrrh08+ZfzjwTtfX0CX2/rlzn8LdecTFqKTI9N4Vb89PPe93uSEGpDFhNkC1jR0Y6oKekJddV+dXBhKJprVqhGzDefOOFr775lTrA4HsQ22sO05D3xxk6EtSJIRLohMLVp2WnpbCNeDn89MnneOG+a9z3D8agAaiv88vtQNss9Sfa0hgOvfeaxf8eKxw6RDA1fBAVL/pBwlxxOlJ2xrrzT8kkzI5kJg5e2PP1j3597C/nNjXYzU934ZqjFW7+EJk52eA3p9CNwWjEjT6xkc6Y5/X4Ja1WxaR5uoGBkuIsVylTrfTBp5sYsem3+5WRtCPGn8Ot7xTIz5GjTgyRQCcauC+2KxvW+cePgWKYHOo+OdTenTJ6wK/u1EPA618U/XPduY+/xuZdAgCSUnxHdZmxOK7rnFbJDbVsObnCjYl904WL6nlxdu9Tcxude9lhOBp/yDTAivjCi0tUrVZKaKsOqrnSANzOjmlNj+10f2Gg5T59r6FPC/WU1TTS1IkhEuhEw4nG9lRGhKZPSamqbvDSdXyYaLCL0HGKmy5YtPKylSsvWwmkTATaYLl53Yu1Lb6TQDkwXk4OgVXWuz2gn+CPF3Y/BYlBfFomQcCqo0cJdQQK2SsUuJoTk6CMBGajwSTRus78wDDSMsHF8dnTk7wNvQhFvz52DsXyMzTUiSES6ERGr84QHa5yq5e4bjSenvLvnfQpyR7OQNfXoTZCVy5L+2iHG0t2yMmh8+0LXeoR7E799jMfc8t9emNbe7fann+qt3OF3kpMnTX7WMl+1P/N6tbgFsKkzECpmjK1Jtas7/Cl+kcmmq3TDeqOH0MKFGlMoWw00RKTFcilSjmwCkmTQWoK89PY5Eb/8hEVnt7dxzoNRkKEBmrIh5I6MUQCnYA40dhOWVEsNTZCnZ6eXPU7uz49HfFgfX0T/7Z1JLO4e4qBWXISwI1jTyMDVps1Z3Y+QRApsVpbPxqG7A9s5oEn39l165ps722iZman1zejsXG6Qf0Q2BQ6eycRFGyKqJeNBZLTYPcJ4ExPAEBHE0p1ckSyeNDDBK1Ws2JFfkxcumOtFfkH3vj4fTBKEXm5Ulp1uDM+dYjV+9EJQ9xRJNAJDkyXrssi/Efl8aq8KbEAghN13clRcn4QyXtG5HlL2OG9R6paaJru7kee3MNVLWo5sXwBShSFAPT26WLClf986ZtbrjoLb8wwqRtYnLNQWUA7e0LgiTc9gcebfk2JG1RkpqTEnz1vVmRkmMlo/OzTjyWkikleoAlnJRtE+CNCFSrkMqdp0NU6EJ0QgFeXQ1frUGZWiwQqYvLi0/V7L1t9kj9bWq3WI1XNJEHERahSoh0E6glyQy224lefvrh0fykA4Kuv/gUgbK1EFvGGn3alJMXuKT6KN25sRiLrqnMWxYSzbgq88M8XvwEALJ/HZoMKq9EptiMn9lMGFNvxkzcHFdOAcSdevv72/2zYuDEmOjJrShIwdr7/Lhd5o2kUBmNmGI2SIY/Z00M6w4hDVKATCh9sq7r6tGz+Gtp7wNgD/Nxs3IH/pi47d9GU1AT+qxu3H0JvnTceHUPKpFpCYLWYTVv2VGVdMJ8fRJLI1XIdy4Yc5IZaAsLS/WwfkAceeIem6ROtqKjplLnpmDSRn5QX4qttcHIOXHXuIoZe3XQSScjMSyaJ+kqc0uT7D2WkJDRNAcsgphifenNQmUmSZHJ8BKhm13z0wTvITdzZVF1+hNssIy2jobF1GO2h/QIhkVC8riie2JPDkEXo0CAS6ASHz2Fk/uw1DjCkK6474eAsg8kSGRfb2GUEzuHlvr4+Errp7s5BSsIBWToAvwuCSAxY+stN0EIIopWkY6gGj3emZSff9/QXePmq87zl6mKkxofv3l2Ml+VS0jEXyRkGG8fOFCJNucxo9jFW08RUoEJTR0H+1JSk2KN2AnULiwXL3hFmUAZuRagre4aeQ0UCnWiwWc2khI2luuKjHQ1XnZpF2xwW30c7Gq5chloHHW/onZoWwV/jug0FSYJ2EEx9S296oo8B5eMLjig80w+UJEmNSqXXs7nZEIALT3O0sFMa61A5vAeXpR3ogJX1bRBC1E0ZgkXRwn4lx6qaMG9Oy2azHbg0dVcFqlYpOfbkkuH5MNhIjtSEYtOF6/QwMTkpfsH8grCoZDshAjlhb4bK35VnlUAAaUATjHzGNjyAwDKIQmEhE6Fu2dNqoSXSkP76iwQa/K7AXDNgn+nf/rd38x8QfY1M3FCwXYebYwQbQOhWM/jzvRMU63T0DGACHXIOU2hw/sqzpmQgVdhWygg9d++f9k9McY62bZ888PJbP3Tp6wJoqwH5wzPcnOtYlbco34maGnfXA3m8iQ7rpz90QD6V/aNLwxWaOKvF0tpYvut35LGNjo7q7e7wFIXnQBLEQHeDhCStUnV4RLyVIoAlOHOD+dDrIzWaHkIiMRusnAj1pD1DL0JFAh0pdKnp0HSx9HJTfbSjISPS3z+xl3oYTxg0+DAJRx3v/O9ZvFBZi/IxMxZfGxntlNPa0tbxxWco+eacucqAjNG33/vu1hvOQ90AXnBUwfMrkR66/bK33/+eLyeZoiM0Fz5cKVlamIlXfvitY0TSVYF3ocd/M4ON9D+OlDLr1Ln501QqZV0tus6EhJwvP/8UQrh4YT7ewOY1Sd6kbzebTdq4qVZaooxIoYFJAoDJMJThGYGBIMwGEBbtzZ0Seg4VCTRg+JSNgmbAocfQbJgoexTYCwgC8ix4YBvpiuhho+yII+iBZOPOXYsXL+fPsN29+7ebb71285Zdn+2ttlhj8Q0RZkVZkD4/2bff++76a8+778EbUEhqpcMH+tDDN4IePWZPAMCB423RWhn7E0WDP/1hIUHApjbW4H2ImQiPQdM0RaEHTdNSKQndtaM3MLM3SAJKSIL5+BFv+mTPQUkyhDA9Kyk5MVGhkHe2dGiU5qKdqE3JovnYOqatuNEUAGYPrlLkQiVkCk1cpFqj0xsaT3j4lEYOcMy13xQJdILDdcA3fwQjH5tLWwWuT98YM/ypSZgHgKOqPSBUHD/W3FAvhWapFAyaIUEQ2tS5dL/FZDRZbLQO0WZJYDqdx55CQGHeaHVdYE0t2WRPGuDMS+guwGXRTuf/tEkk5PwZM6dNzRnQ60kJ+eN3qLlMbmYsX2/STsJT+B6tFj0hQf3uFBxh0FTI6Iy14gmCoqjBfqDyu9ooBCJUJNAJDrlM+Cc2mCxKHx6k8Qjaz7Z1OEfL/h/7AtdrXiWjpRLY2VQlU8ZJICWRACstAbFzIYRWY8+f73tAYg/aNDS2REaEf3UILXd39/BP5IE9R+TXRoYL+XlQJJ+8YOa03r7+tqaKjPTkktIjwALKSncdPMBqZAkJ0vp6lLXGdGZyJ3n8eHp/D4BAU1eb3t+Njmkc0On1JElG21gmlWqAebCPstE4ZiU3GQwGE/YH08CqUMiNzMTmjpGP39hsgaXYjTSH+iDQM05e+tNvbDKtiPGICK3C4hwd7e4bTIxWuipTV0BSyo/Xj3fgtnXcUx5/CrfLzMlrbGQ5UcJUPZIksXjpkrqaCr753zboxJscZDl/QIZwJcqEH2ngCfXh6YsXFM5KTIjF8tYGVbr+/ul5Wc0NZThiLtirITwSJkfU29CkZXlicv2AFUAQNyWjvsfQ39+pVUgsMEwqU0iYUiuU7yVXAYWUtgGpRJLW3+NqxNhoSiaTjlg5kgM0DQZ1NpXWoyc0xIF4UYFOcESGKQVetO4+Q4RaolZ6THWa4HASn3iNgGGg29bKNTXVqSmemqo4ITWZ6cSRfDN++nPlu+dfvpZ7te4Estl/2/Ph4ikB9+jDVyWLX7D05JOSEtFZenqb5ilk0IK8lr09bT29KJizY0cRiokDsGThLOYNuZG/3BgPAIGpH11Sa1eKUqOmgZscJgyZXEbbqIawSEhKrHKbob8FyKMVCrlKpQxL07S2tYE+w4ha8fb34m8cKQQi1DeBiiJ0XEOlEJrrg0ZzZ++gTCYZfqe7EZuLEzB6GxyxbD6gPOy1F/6LmtfddBNeY+dOB4cyVj09nGwEAaornSYdLVt6SlXlce5pds7Up59+6q9/QfS64FzHZp1dvRHRbO18cwubD5SYDFr2vem4NgBWX35XVBRKHTMy6XRKedRA9/EfN7PN9AjgRC4dncgkF4J56wMDxowpWb19AxYbcdKyM2urynU9nd5dOzKp1GRD9GroawNybXpOblubXqUg5RLY2Rb8BCZXYDeojaIHdZQXERpKDvVBoG3toz/tXsSQceWyNJvL3MCqLgsBbGqlNCZiWF8pmolOjOW/zm1/vHlwwHD19bchxbd3/8L5OacszQegD7/685adZ5190fLlp738/OPcLmFxU1Co3F12AbPSVb4GjKrK41dctLr5BGsdc/js6x/OO+9Ch1akQVpGznfffWU0TjnjdJQ5AGiwDpcp0YaWlmYagPUbtzHzP2LwJanUSuOA83xj5/dh7GsiSMIkjw0LU3f3Grp761NTU2prUDJTuFbupFSFFWzIk6M30DYbSZAEqQojSbKzqxcTyMgNmHMLmgLOPxOjiTGXFjABsM/iPQE5pHCrpwaNltbOIbaf4WCjKNcI1ZjF3LnzzBRhtjkeTL6OxWy2nHPBtVFJM2zyRKs0Udfb39tnpCiJ0WToH9D163W6rjp994lQdapwsCeasFJb2d7avHxxfk5GCnpkokdLa2tLa8umzT/39bLjQuvqkQFOoybHQt6vqLZn5kMAoFkRnizTJMqlEq2S/cMRBIHfGhoBQlEDAwNuKybMJltURHRebqYUlbXSYWFhfL8HDHEbD8aKH9QF1tG5q9VTyeuw4O0GeOmxB2rrTvz8qyPBTcS4g9uvNkXRvfrhuqv0g2aJfZbk2AdN03K5jJ/hyEVCKCbxMjIckYKuzxQdrlZICY1SppLju0M1OGiIbj6RpUgjj5f98vMmtI6hjGXLV3BHi2OG8ba71Gt/88OmVat4hrodJEl6zFd3CfqUlTmyWTdt2sy9I8cCkqBuZjoZ+1ukkQk9vT3aMOf6URfK45JA7RuAfl0XN1hFpVJBACrKmRgaJJgeKGw9m81G9fb2g5GHww3K/O2IwEXoSBjy40ZBiBgiPFCc1TqUiV18dHTrR6eD2JDgqpI4wxOFU5iKbs9hJRCmVUdGRNAUvXzFmUePHi2YM40JPUOc1rNx/Y9xhINGzaX7Gpta09Jzf92yKSkyhj58UHg1hbM8cqjTmb1lPuEB7q7bDvQhd2RcSrpGpe2QyWgAhOzp7mOhadDZjHqH2BQoNqVQKGKUCqCrc3sp/f067ntlsVgISLT0hcjXh92gFI1sZ0/h+FAG4r0RKGUb+awEESOMj3+tme8uNDD8GqKu3kGlS4RqLCAjne2V6QqZzEmEOjs3eXBmW5lC06xUZk2dQUSgicYzZ+XLFE4/P+fMnPHDi/dwT/NmzuonCEvezBVz8h13Pil9+flnMe/8mSFQt/CTPV0LLg09TTExc5OTwmqrwYwZGUeP1w4OGiFUouInF3Bvj5OxbV39qogkg8kaoZZlZ6aVV9Q6ORop92K5QxeK2JF70CSAtlEXoX4p0Kyk1OrmkJdtiQgVhhYVMZqto0KgXvhR8GpkpIJjj/89/RRlM9/053WuHApRRQ30/vMCIRp87Iiy8D8yGnzzw0a3N5LRyEqQfft+nz9/4XU3sIlNFMlWzZptlndeQ9X6a9feMqTeJgj9RnlEeFj6lLzG+uqeLpTaSVtZcUoD2u2wZnxg00BXcyuhUinM9kp2qVQGIZRJJa5j43icS0EA2npHqveSv1Y8jZJAvOeEhoBDRRN+MoIJNDueGoyWIVDhCNXBswzYHABpeoJR7xjke/v//Q0vUBYTZUNHW3H6OSQBXnv5eTuHAk20x0pWgiDwNhg/bPr1nLPZyPgQMDDAOqBJkrzuT3cgLqPAJZdejldaTW1vvf7+tTf82fuxaQBIiRUChYyAVrOxtaVFYJij3iXATa94k661yaTWatXxKWmd3X1yiZOaNpos7j0zzMrRlJzOkEgJygqGIEKDC/cEuvmTN++498mQX4yIEIEkoJUXse3oGUiKC9E4w6FR4fBhHNDJZGztgNXKvncrsF134211DeUlB493dHSaulF/5ea21o4WlNnDQR2VzghQF1rxzHB502d4fpXpn8kczWQ0yhXsB2I2oVShrVt/WnnGyTfcdA1Ot1p11kKLFYWP337zQ7wZSUKoSlLKbTp9l81KSZGzDx3KaDRICDejAbmrMPS2U/J4tZKWh0UxJWqy9m50CuZK0FZSucJqMENC5kqfLX0dIfNy+ukGdS2vGhUR6p5A+3U6k5EtSFi5fOE367cG5WQixgiUCpl+0FFw0tEzEKbxPepn+PyY3KkH/qVPaQac7mL50JKuuGHNDMxmM8ehroiORrQCAFQqFXFRkU0t7d39g/gI+gHdoGGwta1Jo4wAAL771kfpObOff+FFAMBf1q0L/LIckRtMfAqFow8W65QkHX8OCEyfffbdNTewJ/r8S1QhqlaQtZ67kNjNdjjY22y1mORhiYkJMZaoJLlC3tLM/jZYzKx7wWyjCGZ7lNKEsixtWGy29HWkjEUXNwsIoEIrMehtQ7Dig8ihHk34eQXTh390EWMTGpWM38oT1Sb1BNzPkS/I/NSVTTH+Do+NMTsNCOr0e8chuHidg0g0QRDdPcjRFhWmsh+AkMukHR3tU6agDkZX3XAtAGDpsgLmo+uG0rizVp2z5ZXtgV8Zrqp3ujitxoW0SLnHBtAARIar+nTCjLQ9u3ZLtCkymVypjY6Kjm7v6NFq1FU1zIR6+9kGURAfMSZF2QiI8+HR0w5d2+aisWKne3eDegklhSwQLybST0bIpKiehHtqo+ie/kEvbOiWH12HSYxd2BnIbDZv/bXI+7YEQfT1OpmrEFAEQUzPy3Hd2EyRFrPFYnZquaJVR2nVUeHh8vBwuVIpf/dNpFXfe/tVT2c0GR0JSYtOXuD0GnTvkJSQJH6hvasDDTEysUcYMIPwqDhlZJZcQkDKwsldiUQolYxGp0haS19HTXPt2HFx+gQ24ZVa9HsTaFJ9EFPr3SvQpian338REwaYClFAxH5bZqQrTO16g8maka72X8ZRNB2uUdi4hhRjEl/99BG3fOEZV6L/uSOjmOgUmnbUqpPuqgMgYDu5+QOrkf01stG02Sa77kZUSzp//gKlEk1vx3jl+aeZ/zt+xm5Y+2fXs0IAPv3o8wsvukbwilwuA/auH33ttVAVIZOSGk2ktX+wq7OT4GWZY13pmnBqsVplUtDR776h1JitrHPE9IIx+bC5lq3mwkjKCHjAlxiFnzhwqxPRSueItlsIGpTZKJRKzm81abWh5BVujcFkjYlUtXcNtx50VMh0w7ZKbnnVadf5k14AIRJxDXUtaVOcJiEHBC71/WjZoVtvv8Vq0lssLK999vl3WCPabMp3X3vmj7fcIrTtBdcIaHNfM1AmSaXSmIQ4vZHiJ3Uiq9zOzC2tKLHJaA9pIAdOJGPyR5IAGDSRitp654T8MQ+9Oyt+aJ5QpUZm0Js98akn8HnWI4FGRWhffuer1af7HrIqIgTw5GREYRkOzLLM2S2T3KnnAjKy+k68fQRJQp1RVo9ureROvUWPbiFiwCQDhDkdzaDTDZoADSIjHKGMfr0RDeaVsWu6egeiI1TtXSNSXxxKbNj2Liq84blYTVSfOhpYBoRBdrPZTZ+3Pbt2LTr5rEBPiolOItdYaQd5mUwsmV51w18tZhChIQAzXfX6G25C6ZzMOJDePmNmZmpNXaPeIFVHpVuYOJDDfes5s8zAEKgmim2k4varNc6YFLL/V2qlBv1QTHhiqIXIfJ6VrP/w9e1F+5589Q3X7U5ZlJ+emljPtC8UMaGSe9x9c5DotKOtS09AEBnhKAFs7x6gaTo6MtxexzkQH60dU/Pja9u7p8UHLRlLqnYkit9yOp711p2HPg8n71b6yZleKky2/rpz6VJHvTwH7NY08lyf7mFlxZGNYUqJQhURrtBq1BJgHdT3Qc5Ot//hBowGt39aTVSfke7T4CwDzxhnTErzlplELIEIDU0cSTThxzc/usa10xkJ6dgAUFxEOzo9pgkwXkuZrBlAvGUToMz2QvBsZs3SvMyiPUf5sYvcmLiKqnr+Ydt1yKIfa9iuiBvajlqraml+RlpCuE/PmtWMyEWijeTWpIITr6zf4rHa3EO6oi/2dOxlxTqTca3abLayI2XCTe3b2tD4ADZPSxHhKCIY+tfVD+fPqECvj9RqHVZ8TKKys9kQGvnpnkD/fvOf3IpQDmI1Z2D82Dw6SeNLslJSk2Pr6x3jxUkjWU2kZlGsTlqYFturM9S39Jrst+Xi9Li9OtOg0cLdh1t2MoN+eDh8FE0kN1KODJtwJXIUyOwW/djHYLsw3gUhzEqOWjJnSnQ4m67Ep0gBS3KQyJVrVs7Ayz9sKsVMd/NZyz56DXUqwoiVtr26fouXi5l30kxfBAoF7Oll1DATynPQgVs7fTjISB+LflLa+YcpJglx6NA8ocOBpLq6trqmjm++iRgjxTNBQXfvYFy0BtjTcho6zZX1PZCA07IcZYs2UmmiYXQ4G4W3QHl0hGre9BRgY01IpTY8Py8xMyWqpZU1bFPiw/sHrWOqGdPy6y79/Jdi7umixNg2PThW12GzOagzIjoCQBAdrjprcZ4CjZl0A4nckdZuNjnpGqxPf/yZ1YCQREdwe+fcvPp0vPDq+vdcX12z8mLBmgULlrluhtnT+5R23A8JQBkbGgIjMlRjnFn3oZKfrAJlOg5MXoxrfnTFiaaO9PRkToSuvaBAP2j+fcMx/HR6Vvwp8zK7+gb3H2kEkaz9UTg9eX9Z04A9MfCP5xVWNHTtPtSwaAYbcY6NVO88UFd1omtWButIa+o2ZiVKO/oteWCMAgLY290RrgTdBilJEtMzExbOnqJVy12/6ibkOuTvyEKmUMp4ZMqHgDRPX5T90cuOpzYLith8vRlNXXfrEvjx5y9cV5bV7BRkBXinTpqyaaK4EOJIDSMaR0waMyQRKgjEBwp0C0EAKewac8ELb3x67lmngPGMCcaPgeKLbeXzZzjy+H7cfixMLZ8/M7UgN8YwyN51csJ28Yo8mqabW9EInfaegezUaMfQMQCWzp2Skxa9r4wpZWEQE6GOjYnMn57e3Nyanp6cnp78yQ7H8HSfaG30UVgdQwS2/e59x7nNElJQvdCulvZUhbxX16eRSGdkJWjUQEbYTPa2Q9xkTblCJedVUnrBgjmpeEGjQo6LbzYfUClZD8bmHU5+SYkUNRu+eI1TSjwE4JX1O/zMChCATWLlQRExyglko86ken0kSfYq1IgrB/ppdRjkODRk8tPhAw11U/7gYZLzo1ucaHLQTVpCRITWQRDXnVfI/a05lZqWEm8PQKGpk/X1TS2DOq0U0HYF1MwUVhRmR3JTjnMSlcA62NyM+Ijvch0t7Dte5+ZzsJlAlEKiowaNFq1KcbyadQT/tu/Y0vnTZs90qGdXM7yvz6nLukoh/f0g6jfMQRvm8Yvn1qinkV2/TCUFz3zrm0a9JLEqAs71Dt0NWDs2ZCknQkMQiHcThX/uobt9ul1CBpEfh4nEcLK/xzGasaHBTWB1LDAgh307inHxDABg/47NL9zvaJS5a+vvcjkSfbfecfOe/Xv8PKBVS1Tre+OjUJz8t32sKwMAcOgIOzszLRV5KiQkqdY4Wo2Eh/v1w2yzR5wuu/Gqb977GADwh2uvsJmNWrW33W9egdKrX926C0wsZIy2LPVThI54GlNHV6/ZYsV9VUcOIjkGHUXVjUuyQl14N8wplXwUF5WUHSu75k9XzSRQKPm9HTV3P/fRf+5ABmxxUcnmLZvvffgeCOH+nQdOXbW8vz+AcPOuBiQ/iTg3TUlq6xtioyOS8TB3d+QYKEhKMqizemdPvEBZTa9vdwS+xhRimqvGOJP29UUo1O6ztTx5QoNrv/NMeAAuOOP0r39icy/Kq0/QlDUmmrUW/rB6xRA62on8OEZAD48QffJjcL+S1/zpKm75vmvPeuw9Zogbg3sfRmMzaJo2W8z/95e7HvrXA0M+y2/1rDl/cjrr2Sw9VI59mlnpPoo1SUri/6vz5jlakMRFqTf8VMp/lZDIJ6oaDQ2TGgds2A06HBE6nDgS98fmjYQGoLG5nSRoBWMueYLIj+NFhAZKcMKR4CCkePSBfz34z/vxcmNtxbXLMvFy4RLUPg5DJpV9/BnbYHiYwEy6kLemur41NzUAFQ9pp1ZMAuzf76i79wJOlopMGhTEJCkHdeaRlp8OAuVnMlE0PWgw0pS1q6fv5AVzuE1FxhQhwK7DTYtnobhTsHDq2af8snH76jNXoy4YDdWP3nzhoN4pmFNchGL9z774THkl68EMCn4/gdq4nZabRtP0L78fi1ayuikmmi1dFYCGUrfLfsJiNgrMAhnTJd45gdRbKv74RUZQNekQrPiRMuEBgPi9WSwWiqJsVqqvXx8Zro12U44hYkyjqLpxcTpb1Lirvh0RAW85mAiTBveY/br+wiUFJEnedPZpN97z5CsbShtryu+77mxug8IlBRKJZM+v+05dduoNt14fxFMDALZVNKD/RWv8IUec7ElKPVppFhcXqtTOklKFU+0THwTNOk9vXbX8/W926uUjEs516kHjAVwPGj/HATQF2PR6pK17LEK5QPxIyE+BCc/CbLbiuiSr1YabuIgYjwg+XY48wrRh/bp+m822c8OXOzd8KVMo3/hJWPpttVoLlxRgKTpC+OIgqsu8eE5uVxeqvIoI07gSJW7oa7W43Px2cvTEkhxFegdlNl+12pFJ+uqWYDpJ/SG7vJ4QNVfOGB6TunWDhkyEunGHO6Y1QWTOf79pu4VoAWN4OoqIicGel1102d/uuJvv6zQzNUIQwvi4+A1fb+K/FAJ8cbDi4nyUK9rbrwdA396NTMWcKQkSLRKSEs8qkn/xiFHlPm6eF975HgBw25VObfGamD6eSC0mIPvhZl5jyeCSqSd0qekgzFMZVU3KeUJHSH46ESjHm1ztMJomMG4T7Mc4XEtrcP3MZManX376tzvu3rG5aNmZS/CaZ7/8Dd1OXUa9DamhnKycymoUk9m3o7i9IxS/EF+UlmMO5VBZ12phOgvMnsWuJwGysg32QliON10pUgCOMbmFptZOTJccb2L06xxG9Ec/7oGxfpVOjV9kBMKkXtygqAJCKzMbvUX5hhmId6NAmWlTCNDDPJbJCZ/VhK6ViAHtIgIl/Sydu3/nAc48NwzoTpnJBqlOOWvp9k07WaJpbjz3knNC84l9USqMVi1OSwWAPnT4uEzGSssZeVO4V9/4aIPwEMxd9M5zd+xn6k29gCNNi8lksM9Z0kaHa3mxrJuvPUOw12sb2I9l4iEjGJp05OSnewL9chMqqAAE6BoAXdWV4tw5PzF96nSZTF56yI1vLjsrR61SHTx8cJh/rQkPmqaxnY4T6fnQ6/UhNuE9YVfDCcyh3Jrn3/oWAMAl4pMJ4S/+37VHDjolMHliT1rmxkknkak8NRi12OfHsawAwVp7ChSH1yZcVmmG14JR44BNHeY+Obe2jBExNEjNG5FouNhQOQj4/osfkxOdsnm47EW1Wr1jc5Fer5fL5VKpdIxQgIhhAtc1cSATGIXYjO7V265COQMC9nTLkp7Aen7xEwilzvEHwVO3ZRIcpU48JgXuZGlfX0R0ot4jezI4UY4aMAedRiXPvv1scI842bDqzNXJicl8ZiwuKnn68x3/dwnq8Lhjc9ETTz/+xTefo7qav9+/f+eBeUvnjur1jgn4dvg2swp0ShQKf3cmjRsH8ZFjqOfIzGlZXf09UTFO0wH44LtKZdBRg+/61E9QVpOgb+mXP++396efjAZ+LY89uTqhE+U9weVQUYEOF1dffk1fn5O9ed/D//jbuju4p5g9AQCPPfmvC869cNgnnMi455L5eOHN56rcrufjic/3gbGKI8eqE5OjujtRJL2zR5+eEi8nhsuSOPNUWBvm7N/jRpIg9pw0yEhXDPTrcEc7N+zpjOBKUZFAh4sXXvnfC0+/dO7q875f/x1es2nLpsZtnyFrS+omfyUuNi40EeTxArfkKMDCZUmoXmhHs6cdxw6ZFjWhhIElvNYkMWrlQE+/PNqjGnXfvoTzcfqNVz/5CS/ExkSDSQYjzw3qnj2ditXd0GjAgXimSYRIoMPFrj273nz3jYf+8fBD/3gYxzouvvpC0IU0aVICuu0FiI2JFQk0IPZ88+v/wwsLlyUJOJR/nLHDoZhGL052GoPZ3cXmdYZpvaVVQmETF6deLq99ylKkJ5BJiDod7QsnDaIiEgDo9K49XTEsi575s4gEGgS88ubLr7yJRjpcdvHla/+4duM3m7vbW/560ckmd5PERQTEnjfecfnvO5qxAvUkRccmvtjL9h69OD+Xv76GqbsHAGSmChvoIYr8zG0JPC2gSBGu8E2dziI0KBa9SKBBAEEQeADGp1988ukXn8jl8l1bf4cQtrY5zRDHqKhyjG/ko7WxQ8yl9xPjiEaZTNKKi+fkutrir7vnSgQyydcQdxFBBabRmGS1wycw6FfFrUigw0VxUcknX3z83/89xa0xmZDwbOy3Acefw3mGoogAwReh4xG4uJ5To1t2HRZZMuiQyxUme5JsQCKUQ2cTaqCijgxgWLfz7C4RgeOhfz1w+cVXLDvZMXpv07fIUWWxWDS97QdKD+zbwbYc/3XTDoO7Oj8R3rFwWZJbyTkeKfWL0opf6prIpChRYwYdickZcrlCLh/WkDQagIGeADxvogIdLn7c9GNmRtaz/3mOW2M2m7kCxD+tu+GXjdtxbaLVal2w3LfXT4QnlhyPjDlOcfOKxV9+wGaS3bxi8fhq8yz3LkW9ilDMofoek59SVCTQIOD5V/73/Cv/46/hx1lPPXt8z4UesxhpH2igrQyG1gBhFB3fXBt8P7cc+zSamJzR0lTrrznvnxT1TqMigYoY0xjL3k+ZVEaSpNFkpHED3bEK/4mS6XDqYsDK2cZ9N686/dUNWyYVh0JfNCoS6LDgST7E2HthjKMaxNADZ276TGbilCbHpK7aM8RJoNOnTv/gTcesdgDAynNWAOeCNADAI/f/c81Za0LTACEgluQTpUTjlMEjsdOlEBRKMr/5rGVfbtxz0eql3OpXxx6l+uZQX1Y8B7yVFxoVCTQ4ppaABbhKxHFXgxh68D8N72Qq4M1R/Bg/ePOjN99745U3XmYTkl588+cftnIdZDDkcvmas9aMxNkXJcbOme4Y9umKxYumRoQ5eoZu3XIAJYdI1D6J0mLm+pi4ZF3ZS1H57AkYWToGmTSIOpRfzOBKoyKBjmASuD/7imTKB/dpxIzhzycvB7VS5tgTAHDTuhuLi0pOWX3J9vWfO0rUtv5+38P/eOzhx4N+AbtbWDfrnOk5tI2tPoSqCMepDzDDnTg4UyeKc5oMngrqpbKAuzXrdP0qpfxPZyx946ed44ZD/RahGPyCsIEeE8ehIoGOIHXiGkTvsY6xVoMowifwQNB5c+fvP+D4wxUuKeD3MP320+937tqhG/BrHNuQcfBoJZDK58yc6mkDs2nQU0E9DYBM7hdXrliczX/67eYDKqWTJatSyr/evNfDeSagDuVLUZFARwQ33nE5v/pwvNTMiPATP2784bXnX0eOQYq6856/7ty1g//qnFn5qSmp5192Lj87OLjY3dKxKJH1KR08gls1w6nZqYLN8A0vUwQmKhfmo+N86YEx8bLVaqMBeOvjjagF6vVrLl59kttDvTra3fK9cWiAIpTvEsUY6DGJBBoKiBw6wfDQYw8+/O+HbrjmxrU33PzckyiDra6+7qGrT8Mj8N5+5R3c9ZUgRrBQBRvyi9JS7Cvo41WONs+/7T+2dP60WTOcBjp5Ql9/P15QKWQA0Dv2OFUba7VuAtCkBLWIvu16H07em1ctHXUa5XMoUygYTCkqEmiIIHLoBANN02++98ab772BmtctXvq/J5+/9JZ7Pnvlif07D/zv5edwYpPbfobBxe6GRmcaRdTJLR8uYwc68Zm0r98pWyA8TBEexqdIOLRofpjaYwlQNE12wREZcB8ohwqlaOAiVMChIoFOxgRGEcPB/p0Hunu6zzj3dEfzul07y46Vrbr8pv889ggA4PZb77j9VkdH7eKiklUXnNXW3jbSNIpBxLlpl3e4rNxmM+fPRpX44WEs00lZUkP/WmgygMxQ/zCgY9s+XHS6k4H/2tbdYOxw6FCBiVck0CBDJM0Jj12//3byInbwMocZ02a0tTThwaJ4DYTw3NXnPXD3g/OXFeJmXSHGb/XIoj853eEYLT1UERsTkZwUh59aaJJHjn41HxKApEhXuvQCyopyBv50SuFXPxd3h3zcSGJyBgBA6BIdqgjFEAlUhIjA8Je//bm4qKS4qGTLL1s2/rQ+LCwc99K+9IwF2LTHm9E0LZUgE35U2NOVRklSZmNynkoPVahUyGbPTE8IlCX9xLx5jhaocVHqDT+xo2oJCcuaF5+9aLTUKEejQdGhIoFOavgs3BZblLpF4ZKCyy++4q7b/3b6qciQ37N/z6133KxxKegMgQ80IBpdmOLUwrmmHvWr3bb76NpLVnrb2bNAI2g3utVose7f577prSvWnsYwKQw1k3I0ijh0GCIUrrrU62cnIvA8ULdWvKdMpiEkgQbaroIbs36EYqbvBoLRItCYZraUqzPJKQkxlAj0c9b0olFX+gjWQB6zn/DClHisQLftPooclKchn0N0tLDR/VcffoAXLr7ycsFLFAyO8LI4j2x4uwhVTN28asWrG7aCUIHl0KESqKhAg1/B7WfW54jmz6vV6rjYuP7+/q7urpE7i4jxiN8b7eGsaEe4qaurq7tvANVZZSQFnS4tnmfbyMIclfg3r1ohWAgBk2IpWldzbGgcKhLoiFdwu91l5BAfl7Dha5TezOGLbz7/8dl7BJvdeN2fbrnx1tD0uRinCFQb4g4y47p9TE5mGuU1/sP3Y/pPlHyW9AKCyQcYFUzJnMYt11U78sB8QiTQIGBM1WJu+Hpj2dEj19x0NX562cWX/+32vzfu+al01zZumztu++vVl18zetc4juHlxxJ3kBl37WO+ZMaNXDQHhX26GXslIkzruhnHm9x4ej4oGrGkTOGhk9OQiJIymfktpkLWjXRKloNMffKpSKATCvPmoruXY0885+5vt//9loeeX3vmTJxbs2vr7zKZ7KHHHnzkvkdH9WInmuP7zec8VkyM/fYxmEbxyKbefh3/JQFjSrT+DrAkAlGUL7zzPSptuuIswfqmNnYc9KiQqYBPXclUJNAJhfIKVBYdFhbez6s24fpcaHrbB6MSyo4dufG2G04qdF+8LGL47WO8Dw3FRxubNIoGiDoPYQYAVNa3mpnhaXNmeiwMNXvIB1LIpa4sKQAmTT51YtJMjo/h/uWjX6+/YsHsj/ccAiGHK5mKBDqhoNPrLBbLLxt+BQB89tWnn3zx8YlGR300zkm88bYbUEWwmXVpiQgKft/RfOMdl4/f2cscvih1SFGMbXvKULnqglkHj6DCULlMOiN3CveqweT+i/TGRxvQ/6CbwlBXmYnR1NbphTT5Tz9evxfEDGt4XLDIVCTQiYaFp5605qw19/7t/ksvvOzSCy8DANTW1T58DRvW5GCxiAQaivrd8UujmMM6k7IFafQms6Wsog4vz7AzKUuXziAT2LS5N+75Iypp3ccW5nsCnzdxPMpgdlQ3ffjdjlv+eA5eXnu1++TL1zaGumuJSKATED9u+vHHTT/ibkB/XXfnFZdc+e6vVdctd0qoFBVoKDEBWsm4di3ZWlS6tajUlS4xYxpNtrJDbDKvT+rkGFOiYcNQEhla4AezOPZ0t68RC90bVsx/84udMErBpei/tm1k8/NFAp1QmDd3fkx0zKaf2TQmiqKefv6/73307ubvfhZsafacmidiJJrITAAOFXQtIXmMKZjI5JMxaZmwPBQzpndYzEJPq5RGDCbl8djNF53qptJpxJhUJNAJhUfv/2d8XDxHoBidXcglX9ftxJhm0Qc6MuBs9knVVubIcVZszpya7Z0ofcJsQnOZ+JABNhLFJ8pAgZmUtlle374fBA8igU4oXHn95Vt+3Pbx259ccb0joLFvR7HrliKBjgT4Ywgmqgj1k0kTklBtaHSMMBzkiSg5luQW/ERZZSWuIZqZk+OU9g9BeS2aDZWXkcZU28Mvf2ao010dQHMFKsMFACTlBlaJKxLohEJPb899j9z72EP/Li5i+99gLJ/lcF1hiD5QEUFEURMioCXJQvbp6kTWT1evPt3ex0QOh0iUriirrMStTn74BaU0kS4R/wNlqHPdgaO182aigamzc+N+rWOJUgClhBIwqeMlkjLYGB0NQVKOsMxMJNCJhk0/b9z088aoqOiczGyjyXTk6GGbzebaKGhwcECs4wwxJrz8LGJo1C2T6rv7XfuV+Ak2k99Bj/BoBdvt6ceicgFdOm3JLPT1m5vbe8KUiK/nRSNPq9lq6+wbbGrX9Q2YKIqOykpwZVIHaKAkUUWAwUY0V7TLoTU6x2FeiAQ6MdHd3bVHbCMSJDzx+T4/c+n9dH2GOIt+CO27mNJ+dt8h9Isqamq/iDHh+ejqYvvaeGJS19b3HqaLuG/7YbbaJCRBQKEQjYhQY/Z0HBYCtBkMbHqJkkTcarBJsErFxr5IoCJEAP8pzyeTcjKTz6R45disPhoJ/k1Iif1yH1v1eNH8aW6ZNDzMzegRL+PbAA1e+2wLXpyekxAVEQYAWH3K7PXb2ZIks8VGEgSfFLHdNWgwWylKwhvwR0AoJQkJSQTKoai2ilGjHEQCHX/wRxEI7vM3n/sEL9x4xyrBluP0rh4tuP24XFkVbfY5GEeYMyv/jBVnGo2GV9582WodyngPT8BMipuV8FFzgu2ql5nq1OYZAPC6nSi9g6RRMcjqU2bjp0XF5RRNkwBi3qRptgxKLpMIWJKAUCIhZFKSJAgbZeupQY2lhwaRQCddvbaXHUUyHRrG9eeWlZH1+QfcEHhw3VWoaujcS84BrY52CoKYJG6wAIba80mAmhNtW3YddrsXmRTl6YDdvf2x4QqORgEAp8zNsEE2xH74eB1SlwxxRmoUBOFMoRDVmCB7X7DePxhtDtoUCXQSNQrCrS68hDLGcp8LESOEzz/40mw2LzoNDXRi+3Vt+/37z3+4dhmKXHPT7f/15D8jIyIjIyKjIqMiI/xtyOSdRvm86YUrBchTe2xIypFpV78hJpzNzFcppK6WOgx0fLM79hQJdDIKT5/ZiPdcMl/k0EmC+DgUgD7lrKXcGpqmF526oLioZNaCUw7v2Q4A+OeDjwEAvvn+62Cd9EuGRgMizeEgMQ7RvWFgQKlW4wXsXfW/A72RqXfCkEEU6TLTMl1DlzYt2uFYFTHu4H9o+MY7LsfyE2NSFcmI8IL2DuSI/OcD/xKsXz4r5cjeHXj5rNPPMo+Z1jOpCYV+bnnSzLT86VPyp7PtTjB7ohyASLVEphywwE69zWCy0L5gpCVS2sQ9BNlOIoFOUogcKgLrzb3Fe08/dWVxUcl//vlURHgE/lgwd3Af0YeffIAnORcXlTx87yOj9dGlJBRy12Q0epxIvG1f9eqTpw5aoNVGcw/u1UEzsNook9lmsVL++EA50nQLkUAnC1zNdpFDRaAuR7evveKPlw0ODpx+6ulb1/9SXFTy9SffCj6Z66+54ew/nLli9am/7vzlnFXnfvre6GQYDPT38p754D61lOY/5ATFPbQKGKlVSkhCSgZcqs9HdYWFuGz5acM5hIhxBJFDRbhFeWX50jOWFC4puOTqi8ory9NT07cfaSIlrONv3tK5hUsK2jvae/t6/+8fd37xzec5WTmhH3kfqcpCMpIGA5pZvunTK0iCiA2TGExWhVwytEC8kwIVOXTyYMJXE4oICGHasIR4RyFjdW31FX+8bNGpKCL/+Ps/4ZV8Wx4A8OyLzwAAYmNGZ/ioXseK0JR4RzO9QAEBkElIqYRQK6RDFqHtRpTJL1HJkDf0suWnffqrY2qjiIkEgak+2TqtifCC77/4UavRCpI6ccgoLjm9rtski5Pl5eQdLnPkG9lsqBSnX9c/Wh+sXtcri5xZY6YzpdVDPghBwJS4MJI2DxgtFpuNogKbCm+zsbUGEhzMV0kpkUOHjMP7fTfcHgk81u67xi4mTvWai8OKv6azfdB1r6MN3WBUITewgyFNzaPz2U4kGAfdh0FSMxPve+Te55964cbr/vTmu29w60lGlH381ksAgKsuu/q2m9bxGfaKS65ELOY8pCiUiIpE2lMhg80A9R41mhGFBUqmEAA5ScskUrVCajRZTVQAxVf9IB5SVgLYIKSIAV2/VII+L5WUWneO+0kjIsYmRp3mRIx3/La7qLau9pYbby0uKvnyw6+f/c9z29b/unf7fovF8vpzjwMA3n7/LQDA3u37cTr93Py5t996x649oZsq7BMKGURkCrObYXaNJQv4DZmUlEtJjUqmkKEeJN437gfx6EFF66hoSFkhpCBEtjt879m7UcOSyEiLlS2SVymkL/4gnAAhYgwqUNRVIc13KnJMnMdhCWNTfjopUCV/Lo6IICtQvBAZEfnT91swRQIAnn7+v9+//ixe1kfECUo5N/604f5H7/N+xoTAGzj5G0SyK1Av4N4IB4OJTducpqrnVlY29ihlxKDR0t4z0NVnMFmQCO2jHI34COAYaUfT7BEomiAY6sRgCRRzKJo6wtCoyKHjhUD9p1FXJnVlz7FAnRgigYaSQF2h6W3nE2igSBgBAkUcpcoaGoF6Qk9Pr81G6fUDXV09Ov0Adu+6BUegAjhKlHp7eiIiI6US0mK1DRot169crlLIXvyBjcSJGMvgE58XMg09Y3q6dX1CDod1BIVKPrTzihjL6B2sxhwaRBAEoVDIlSqF0WSiKEqQcuATToXxHIdiKTpoNDNeUSjS6DjC2FGRIsYIPP2ceNGJMUSfYArhGIHaUA0i5wbxgBACiUSiUioNg0aLxepFhLqFsBtTb08PNucxjQ4akRdg3TlniBwqYgQBQVpKmlQibWlrGRx045YVEeJeCm8+VzUlSu62gezo9uuiKGrgxP5+/WBOSpReOzOoIlQ5BBHqvp0dlqKoDpS16EUpKmJEACE8Y8UZbONGALKz0WDF+oa6lgq2zTjKUMnIzMl29JG0mC3btm8V/x4j7TEfqz1kIU1TVitlo2itvgxndEIIepXTA3WACkWoSmEwyP0XoTEKw/+tPd9jP1A+h4pSVMQI4YzTzwQ02LxlE9dcbMnipelpU/qb6wf0qKGvXC7Pyc7t6Gg/UHqAJdzTzzxt+Yptv4ocGgrq5Mbcj5EesiQBKQqqFBIbRaGJHAz90TSIGDxKM63m+9QzhihC5XKVUmk0ehOhWtjDbo9SZVExqze2xuY8hp1GzevOOWPdOWcM4RJFiBBAJkOdcfcW7+G3ZizatRMAMGvhKfjp4oUno4GLDHvissK6utrQF2JPKnCFalwLxIXLkrxXr90z1IEIgQJCQJJQpZBKJSRF0zaapiiaIzwa0Ed2b4wyHo00HI0yHos2HYsyoke0iX14OaxEIlGqFAqFnCAILezGjzCiRwO6NKBLC3s49uTDR0d6ziXKcajFapNKCNErOo5wdOfThSv/YTA6tXQ8uvPp19/f+twbG/By0Z7ym+56nb/B4V+eMhjNJ53NZvytWDrz9af+JJWiL0xHV/8p5z+iHxT2Eyv6/pGoSM30pY7Goxg1+190vaqTVz/Q1TPw8uPnEPAcwQ8+hHDlqn/g5UOHS8PC2LSV9mNvnvaHR/r0BrQNAWl7+d3f/3zezdc614AwLgF8JUd3Ps1/xWy21ta3/+H6p4UNhy4/9cE7L8QJ1U0t3ade+KjR5EgDPLrz6a5u/dLzHuLvsuXz+xPiImYuv+urt+6clpvs+h65axhfwA1kh6BGQwOCgCSBKtlpGqASTAgoG22lUW86SKOfWAqvBzQeGod0Ks36iDgO/XlvXX5OrNxsRZPlICQJaKNoqc1qg4MEabLQjKz1BZvF6INAMXUKYLFSFqtN5NDxAo1KIZz0yqyUydi/vkalOOvUOYIN1GoFSbIGyr/uufSai5dt2FLyz+e+Jgli25cPHN359HnXPlVypI6/S1pyDADg0b9f8uCTTtWjGrVi647D/QzxcTAazWazWSmXSiRE0Z7aji6nslS9jk2k7+ru7up2yiuYmjcVaQ1e8bJcJtGoFd9s2CsoRuTe3T2PfTLIpENBCKZmJ91y3RkNxS+lFd7GbVO1+38ymeSz73Y/9fIPMplk25cPVOx67rk3Njzz6nruIBqVYsmCqUV7jjs+IpVco0ZjefaVVre0sU0uzj2zsKy8sbqOnZg2ruG2bcKo0ygEICZCzU4nJtE3m4aIRhGf0jSEkKJoG0Uh7mRndEKKppgxxuxPK74b2H7JZookoFRCQghwexGDyep/cbzks98PXLpwrk9PqAA4uLTunJVizdKEgYBT+Ljm4mV3PfLh59/vxk+zF91+bOcz3733N/72eVnovlp379sv/vt6AYECAB5+6ovyKje3HEVRABCvfVSMn9I0XVNbU1VdGQ7dt0BPT59ipYhDhw+6vrT2/173lLizcWtJTx8a5IDx+AvfNRS/tOPbh5ed/zB69eN7ZDIJ/73kLr7j6ouWPvaPy9755Ff+jh+//Ge3H9ETL3zHLZ97ZuE3G/a+/uEEcdF6aj0zKjSa6sHjiLkRT3snGB6U0ASFrXtUOYRkKPrFZbQoUpYMg5qt1KDRiox3uRSvJyBUyCRKmcRisVE05VOEEpSEmDcl57PfWQeTT0+oABYrdc1py8QK+gmA9HnrAACv/OcGTxvYENM5sPyCR8679in+mp8/v+/I8RPfb0ZUOHNqakBn3/zzprKjZRazGUKYlZl15sqzZDKk7AbMwvKP5MTkjo72ltYWMGxoNUq8MCMv9ep1QifDB1/upCj64LYnuTX4V+HYTtTMzTtYrTNR4IUlF47Btl52MmV0JZpdLCEJkkRzjJF/Bs3jRAY7Eo8kVCulChlqCYpHJEEIUXW8UiaTIUHqD4i8KfGnTp/lk0N/Lz7i9jFgMPUPmEQOHe+gafqOB95bffpctbuk6+NVzc8+cs1Pn92XkcYW9rV19AnsdwDAqiufQN+W/oH1H7L1wf6jsenEtu3bNv+86TcmiDR3mTBQOa8QhSla21u5gNKQkRSPjKrXP9jCcf323W7CC59++5tgzaLVD6hV8sLZGWCSYZxxqAdgiYqZMS5KLSFRMxG5jJQyrenRgyFclUKqkkslSLj6INHurk6kV5Niw1bOmuPdlveEw1UtU5KiaFr7x5WnvPMzmuEnYpzi6w17n3zgymM7n3G1Us+49LF7b//Dzdecvv0bNory4JOfv/uZ48/9zdv/xwWCLrrx2S2f3y8hCavNoR93/vAo/4CbtpZes+7FZUuW4ay99mNvCs549V++lMmVA2bWfF552komcQTU1HjsWiY4CP9d8IUkRmlZ3SvvoY45s6eleTrgxm2lV1ywhL+mqbV7/8Gab965y5OvY6KCM9g9mfO/h9CWlxCBVVu6BRahBEEQ2Ka3cyWEQC4j1UrGE2pEI+d8XAwkoFIhS4oLX1NY+NnvxYFyaE//oFYl16rkEVrl2rNPe22j2JV53MD1y5F78h11+1585pGr73zoA8FL//7fN//+3zcAgDUr5/7n/ise/fslf127es5pf8evFs7JfPZ1FNAHAFRUI/v6jafX/vGOV7jdz73qP1V1rdxTs9nK7ykZN+1G/rliYmIKC+b1DRj57Lnztx0AXOTl7Uxf8lduWa500tF/vu8d/YBRLpM8+cBVGrUia8FfOI+E0ewItQsQHuami9UF1z/dUPxS5e7nchbd4WnHiWXBOzSmdxodj2CtfW6Z+b+EJFQKqUZptVhtZnuPOk8gzr/8dAhRg/u4KO2FixZ4t+X5oGmwsHDGBafNnjc9NSZCLWEitmvPPk0058cgOH8fH/06g0tIh77nsU8uWrMwTOu0vUaFPJIYP/58YMYpd5120T8jw9USJrMtOlIDAPjrTasail/CD5z2xD9CT99AZ5eOe+BTlx4uxa8qFI7jy+XywoJ5zMWg727+7HyCJHf8tt1niSf/+F3dbBAfY8fuY1t3HtmwtXTm8rs6u/tr973AvbT5FxSPcuu4uO7S5W5PNOe0v8tl0nlzMsGkxMRgT5LXABQ6T1hiPKESjUqmUkhJX3VN6OXzLz/9/CtOJ0kYGaa6YtnJXjgUJwrgf7FvFIeYaN7DbLXddLY4qG5sYcUSN1XDRXvddOH7+Osio8ly5Nf/cmvmzsoQpFKivJ9aJCfjotFYmG1fPYhNZu6BQ1JZ6fHer4qyUUwUHpyydPmZK8/Cj+XLTqVpeusGdjBkPDOxZ9nJp5y58iycV483w9w9BMw7814AQN0+Nmo0wKQ3bfvyQTdbzsncVnTEdX1P30DdiY6v3x5/CZ6To9EM7c9GOEXP7aYQeQmgSi7VqmRymbev2ZcHK+38OmA8/9wlEAKlQnrZ0sWNXQa3NIryUtncVPQvRm9Pz6Zf9goe/rwHEaGB2Wx94v4r+GtKtvwHAFBWfsLt9rmLkXGKTQpUBXS4Fjkf9zpUGwDgizeQvdzchn4+I8PVOB+IA0pmpuhfvnbDSm6xr3hvW1urXq9ra2vdvWfXT1s2cy/tP7CPfRSjYsGyY2X7i/fuL95r4zlYA8XclfcQBFx3/Zn46fILHkmMj3j2kWv422CGve52hxeCD/x+kxOHW1Q+LjjUH//mE6PXXkQI/xykuAzU4wEglEpJtUKmVcq9O2SEifQEAbUqeVZqjEaV7zasNLHcO5MC2Ytur9v3IrasOcw/CwkxT7jv8U8f+8dl3NNpS+88tvMZwREKTkeh9rvXnYvo5oRwOtO6e99++QmPSVECHP9NGOR59sVvn3ruS5RI39XFX9/X1yvIqx8COrt1Vhv199vOffFtxNQ19e1nX/HExo/vuXANmkaJYbVRUwqRjvaEvJP/Wv4b27Z9knCoJ+P9/HWONNgJAwJCuYwM08jbuqGXUBKkjXuB3VsPAPj2+yJsqhtNltbO/mgNy7DHa5pcd56ayX6gu0srnV6gQYNp1GZOTcKO9P5AIiFvu+4MrVb51Y97jlW6+Wv6hEIuvfmaleFhqg++3FFTz3YsH6GGyhIL+520Sh3uUVzKeeT4iRFqqBwXE37jlad19+hefR9lOE14zJqXN4Sq9ic+3/fti+eNCnWmMkaRhKCj3MX3MHYfblhaMMXnoWqa+8JUUreKkCNLplIJHKxstdhsAmFLkGRkJnIuSd5/5atrrlnNvYBS9gENIbpb4qK1NpMwzuAd+JSBdnUWEQJYrbb/vblxOEcwmiy4dt5/BERk/F5B1eXs7JqsPKexE3KlbOS6zbd39uFMg8kMf4zxCSA5CV9T5OwpTRKmTB7iqI8rkMB8/31U8Itp9LxzT/7u+98wh6oVsn6/CZTGPVHQWeylUiJEBLsrZWZCOG0YnGRRERHBhyOG4wxnmkQpTSQBCTSB0zOBYnA0ynFoQDQImSpU1E9K5E8RI9PNd3vRYf2Awe3RRCYVgeGP/c4oUH+2YiqXmNJPT/mgwiASptHwiAhsy3Prp2Ymu3WDOhygmD0xlYoCVMQwqDMmTlXNeJUXLJ3Gn4X3+NOfezm4yKGTAXSQjuMnSbHVn0wFqFvPpHse7uvt9aJyPZ4Mt5QSDXgRQYWXufbBnVQhYuwDjuTBaQ88i5s8uYVHIYs5NNALENWniOGjs30wKy8dy0+MmDiVnzQqYjJj9+GGoB8TiVACMpOXHCTKzUQgvBBxwByKs+sdHUtFiBg6XKfYixwqIljgM5SE6crsfWNn/nTALYH6S3/Ha9yUKIjcKWLkIHKoiOHj573CToyesO9oE47quIhQdsHHSI/AwHYlBZMNbhOSRQRl7pjb6hdPxYVjqKBQxFgHDCg8heYmQUi5RJICnqHsHWwm6OTjUBEjBLdcOTEaAokYR2BEqMARikBI5G4anfmPT7Yc5ZaZ9Ht4wjTQYJxEdZwiRhoih4oYXdAeQkmIQDUqhVQx9PhmQWbE0ROIMRuM+hOmgRP2FuIiRAwTeBb5qM+AFDGxsRd5Od3b72FqtmgYUyaeswRRoyaH5/P/AXvufpBdXbpyAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "The configuration is complete at S4, yaw 1, height 2; commit it.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-072300c0ac1f", "created_at_unix_s": 1788842938.511632, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 3, "requested_group_seed": 3, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [3], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant": "Socket S4 is centrally placed with clear lines from the two ally views; configure it first so the next decision can reveal its placement.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSOCKET S4 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], 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Use its authored-forward yaw.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y0 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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gMF1iCC/ZTyhhwcJNDUXTBkqvMsGUo6D9hBNETjjz09zyRLGTjKhGpbwe2vD5vr17dRqqqrKyqrKSAFylzH5KVNvENejr3RweyGMwmC4koEg9AeDnnTkSpS75fF6pa7Lb49Fb1WLhpsttNhqmDYQzPIpl4JA4IhfJy5Q0UlL3lJGtFV9KLU79D84040RQDAbTVYbwkv2UUpeE6CctTL4HD2I1UEZ1OqNeI7atE9hb7L19ICMp2lYA9pVTt+XYUQ86AMCZT7zbWLLx4w++lb/imFFDgNyKBpOnHPYmWhU9RwoA4GQpIwVH9DY3RwA8kMdgeiz3LVlyuJlgIKrrWbt1S4e80HlTz466/5c/N8fbD3S8uCKTmPsJWA2KftJqbch+CrmfTpfb7fWnoq7JtQ2ZaUlAFfDt3hfDEp7x+LtNJRsld0kA8L+v4M/lxsuHSf71k++LOI4tKi5ramo0qqG3pWD0M2wgr6JVyYnGhvpG1LL+aIU9SU+iYCjOCcVgehL3LVlSXVXZ0NAU9VF5YaTGfrCdr8Umjo66n7IdiGsIf98FQ9EGUs9qWfRTKNBskOyn0DdEA9VTAKknalvXInKFvenyYTfJ1BMQRHXVEVfZPkk9I0EHm/Tqftmi2A/ONJsMolfFKU0YTI9h8aWXVFdVdtrL8Xyz7TXiEtDQ3JFAWmKa1+eVCjetRq0Y/dRB3ayxCansgv30eL2hviHtJsBwVn2UuSMEqkHSqGmSJBoc4vdPRWMArqssgDUUg+kBLL70EtBlIOOzn7w4dyTYT6/Xq6XE4KnCfgKeRfYTDd51puCSRy0Ra84naEQ1BmOzh8BmTKKAEgQxqH8a2t83Rd/iS2MwmO7C4q6knnFNIqG+IdJqwdB+eiPsJxuynyj6iQbvcK7JDJczah+igpbXNNJqYQ0lAVb2MEkAkiR4YQjv8fgAIBISLQ02GB9RqVSBQEClgpn4OC0Ug+m+TBk7prioSL7HYk0sLDweeaQPtW8X0Hjr2vm6OQmgqKg4cj/pqCPjjH5eILefdJj9ZHykaD8BkOxnZNfkDkeeIEUSBFrPQ69Vo/0EEX0gj8Fguimb9u1XDFijqmeHU1xc0txDZFz2M+g/0xLToHpKC74DoLeqaVol2k+pa3LQfopniW8SKTqKPFJpd9CXwg0BJKBaNY0eIQgwsF+UgTyOhGIw3V1DiVP18opQIx9TQO+bN1TeNwTZT/ExjoNRziwz4wudQUpd6gT7CQiAop4EARvRUyQh9AIFKloSa0JuQtFAHm1jDcVgui+b9u3Py80NSZm8ICeMNhXT8NFuofPxiv2xYqBS+3doOQX7Ka0ZB4A4r+8PMDQNw4s19Y1o/C6PfkL1FLom0x1rP4U1PCiS5ITBO0mSNA17KgPA0lTYU+bNGrt1e37bXhyDwXRNbAybn5s7YhRcyxLa0ShSKe4qrK4EQLMoxe6SRUVbS1QFGWY/EEtA75s3FAkozP0E0H5a9BYacLSGkuwnbFunC7OflbUNGWlJIfuZLC7aEcpqbR+2Jp9ZSD71+YHFqM7ItCSadWa9Rq1C3ehBrc1TG1y3Dt6tsfEAUMic4tkkDKanYGPYvNzctEyhr1uE0SqsrpLfVVOE2JujNUil5MOaoFb6WQ7OtQhiIkHHYz+raxuj2k9UuCm3n1A9VQF4fMh+egxm2Gqk/bgcLgoAVxN0oGoV5Xf5mjiP10lVCu8ouERnlCc2OHyoNgnNxWMwmB6AjWFtJc1O78iRL2DR5gEwGt0qdpKxo5+C/RRTl2DfEFn0U7CfsG+I3H6iZ6Wh3E9BywxmEzqmvZcvg+eBP8C6fQGH2+f1M+iLQjxa9iQxUsHzJn1IN3EkFIPBtAESEGSEEJMx7CcUpmDhppYWCjeFRTsQYuGmQS9Ovkv2sw5mX0L76Yf2U8hz8mvGwqWQOhCeh4VJLm+gyQU1lONhpirH8Txc5hiudQz/A/8VHSnDiq5ZbkLxbBIGg2mPqSNj2E/Utg4N3lthP02JDXbnoiVXi4N3hvEGmq2/bBtDBmQOGZA5uH/mwL4ZfTJSDEY9rVajHHu0qhxaXI4gCD+l4dRaQqsjdbgkCYPBtJ2oQ2K6ubmjoP1sSkuE7eCD9pMLa1tn0Ett6yT7mWZKTEjWffrKKhT6bLQ709OS+Y64WIPJQKvVDMOU14srx6GGTBRJaFQ0TZMeH3/mmJzKuiZU1kkQwOHjaOE7giQJVkUFXMpQMq5NwmAwbabZPNCg/UxF9tPhdAOOK6uuh/ZT6hsSzJxX2s+/QvsJ7zOMT7CftrrQzHhs2hDs5Tg4cNcIGaAWk5amSHSjSNJs1Oi0Ko2GVqsovU7dGEwLxWAwmA4W0LDUJcF+osx52kCZNOJKwqG+IcHCTcl+ooojuf2EkirYz0Rrx0zER4WHjZrYgBDlpEgSxT2FITys7KQpUhWU1AEDMyKfjiOhGAymbcSqREpLTNXSNMP4nUH7mZVjjWE/YcKQ3SnNHQGGqRaOsdXZKmpCx0TFkjMNtAOO411emCjbYPd4fdD2otkjuHqoGBlFg/pQbRIGg8F0mIDGsJ9GwX5C9YzDfqLUJZn95BOtpsxMsclxxyKN93ko17BcSmeyOD1+h9vv8QV4Hug0cM5dqPgUjifA1AkDI8+DTSgG04OxuX2d7UCR/ZT6hpRV16P9kfbT4/Wi1KVI+8kD4Gyyx7CfNz/7UPQHoq4nJ3u4uV2l1Y0Ot8/l9jc5vV4/o9eq0FhempfSqOjEhCjxBKyhGAymjQLanP2UiGE/dSYKzR1J9vNERS2ynwDwRoMe2k9SyII6+bg8AX+A9foZpwdqqIqmFFJMoRQnDAaDOUkOtFX2s0awn5Cg/eybmiDYTx4wTFvsZxxQVLMt8ngehj4DDOd0+10evxgGDSIk2oOkRJhLoACbUAwG02oBVdrPOpn95LiC4krRfqKxuaxtHbKfqeH20+F0ifaTgQlMyH5W1ylXRuKHj4EN+u98BZwEeKEqyc+wjQ6v2xuQ+pmikqTB2ckAgCZnJ5liDAbTUwl3oKhtXYLMfgJg0ImraOiFzktS4SZEFUD2E6pn0H6atBrRfgJQUdPgdkMtTjMnKl74luvORxvrndqT9N44jm+wux1Crac0kPcHxKVAJp7Wd9SIAYqnYBOKwWDihwxvWweas58ut2fRkotl9tMG+34CgOwnOhwVbkr2s6KmITMzRW8kgZ+Maj9ltCIoSQcXFIkHu9PndPvtwoQS0lCvX6wr/XNf8Wah3/7JYJKqbJKq7CSdHIPBdBHIOO2nQa/79K3vJfuZmZYM7We90n5CCQ7aT8ADt9uLBu8NTaGFOfnhY5D9PCnjdzJMrN3eQIBh7S5fo8ODEpt2HxfjuQCAgTnJ2IRiMJg2Q0baT6lviGQ/AQAK+4menGpU2s/q+kZkP8urGzL7QPuJBu8JlvAViVMT4O3kw/j9qPed3elrsEMNHZQVLKkSqKqqO3kaik1o54D9PuZUQUbaT6ltHbKfDU2wc0ec9hNlzgMA+vSBc0c1Ue3n0oXQfi54As2Jt5Dx2WG971iHy2dr8sA5JZ5XhAwiNRSDwWBahJYt2tFk0ZulRTsKiiuHDk0FrCbBoln0t0s+ffNb0X7W2DLTkyX7GSo9Eu1nkmA/bX36wB5OqZL9DA9cIvVEtDEpU3wW36r+oQ6XDwDe5TEFmFBuU1VVXbrwjuLv0sQkRilnklNNi/6aYZSzZwpoW2esy4rBYE6aA9WwZZU2lPsZ3X6++W3IfqbHtp8QqJ6kHx0T1X4iGJaL1Wq/g6ypfNKJYaGGVttcdqikYeo7b/bkDnm51l0bVk9M92dSL54ypZH9VNGk1+ulAecJ+EwqU3P2s7zG1id++2lKaGhyivZTVomE7CfL8rBrZ0fXBJWVVsQ+gGH5ynqnS0iuCjAsRZIkQRg0lDxXtEUT2qLwpQU/TyWBrNZcPgaD6U7ACfeySltWagZaM84UsACOg/aT1TQ0ORIspk/f/FbqmtwnaD9TjbBrMvBTjXbn3x657tPXv05LS1LYz1RT2DQRsp9IPaG5FDrGEwDMMHrFfh+d9Z4LK50EAQxa9eHCaookVDRZUlmvUVEXzT3957U7O+sqMPESZ8CkxWgJtvyYDgcOb5F6MozfG/AZQ/YTJFhg040aW2Nqis5e73W4fQr72Wh3WhOtn77+dXV9Y2p89hPBcrxQk97qy7U1ShX67JAhfaX9flZ5rtIjec2dBBWBelweiiQokvAzRIDhOK3K7xdz7CVwv3oMBhMDWrSfqG2dYD/R4B3ZTyiFKXCcbjYazGYTUAVq6+0pxgRkP62J1kVLLo7Hfno9vGQ/OY4nSXHuaALtjLG0sgKXw6kogD+eH3XJ+/jgeZaD686h2CjL8d/+sm3BxVN+WrtDfhTW0FNOnAETHC3BdD60ZD9pUosm37OzTXqNpjn7mWJMaJv9lM+8ozWLYC0oBWextv3jL2c8/m6Mq4xqVdumnsj3islTPM8BQggo8D7AkF4iwCpNKKaLk/dyyfUPxOoLjjmp8RPQu0MopNcndk0GAKDBu15jRZPvcvuJop+19XZp8h3az79dIky+Jy2+fb5i8l2eOY/sJ9pmOV4aupuaamhS0sZOCoEO7p8xuH8GWtdzyIBMg0FnMOotCVadQa/Sar9fuzfyKbhAvsuS93LJqb6EHg5tOy7doh4wgI53ubMeCa2lQm3rYO2j2yPZT4fTZbLQ0uR7rc0u2k+zUbSfr36J7OfyN76Kx35ygnpKoU8dTVEnTTYNJlHBhYU8A5JAl9c7VQTBsgzqayfBw957rI8kfB7GahQLWDFdGayenUxUDU3r3QknobFPpP2E6glAWXU9sp+AB1LqUtB+8ih1KdJ+Ol0epJ5eD//HzgIprVNynA0nSg1qKp6JJHTE/bedZzAZDSajJSnRYDEJN7PFYjIY9dLNarVYEqzosNjnpEiCDq+aF8qiYGKTWgdLVxWM79fy8ARzatUT6ymm8yHl9hMh2U94x0cRgHB7vch+wj1C6hLMrn91VXV9E3Jx5dXB6njBfgIAjMJJjhRVyV9MLpe0MAMueyy4kKb81g6y+Fp0i/ooRZJaNa2mlQ6Y4+GEUpTVj12hFiSYU05zWok1FNPJCC5MVrgpPYAG79B+ZiTrTVSKISHCfoKwuaNo9jMnPQ3ZTwBAcUW9JImVTZ3fzFhc6xhBEECjpkx6tVoVrqE8D+MMqihDeGxCuwixVRJrKKYzIVHhJkIavIv2E4CsjBSgCtTZQnNHcvupOFcM+8nzoF8m7B8KAKhzx1uj2THZ9c2cgiRJg05tMWhVNKVoZc+wXNSF5zCnnHj0EWsoptOgFX1DFPYTCigAyRH289NXVynsZ219U4opIcFidLk8hgSNwn5OGz/wz70wAt0JvZfihldRpNqg4Xi+0c2ElkfmOBIQkZWdwFW/9OozX1yx7RRcaQ8CFU3vjJhw8NrimswdcL0yul24LNRsQSLv5ZIR9+a04zIxmLgIJbFLmfPi5LvMfiYbxMz54OT7qga7y5oIG2v26ZPqdntdPkdKcPLdAE/iP1JUlZOehs7McvzGPcfR8LnKGVBR4ov62bDl3hTE6T379cspLhZdCUGScXbHkw5SqyirUethvCwrtqznOejLWY5PSjDXN0DrLWfp1WdgDW0/k1RlkRraNiRJHXC94pG4FFmbiKcHMW2HRpnzkfaztLo+O5r9BExAVM+g/axtsum1MA4gT10Ks5/jBm7eX4jSmOC/VSUgKSPAiX3rIXzr1NNhHYyOogBfpUpwJIRWVaIomhV0uRqkoz12VQIrpMcTgECvQwsxUJ+admpotYoCauBXc9rKXK+PETvpCw6UB3yT02+RpTSt+33ruTPPbMdPG9Pl8NpsWEN7NjdnOOvFkGTHQ56oapCnLknRz+xmop/wruA9Jfup16qQ/ZROKo9+FpXXb9onqCfPV7vEkTKsQSIJDS1MYcmENLZ6WlLEKGpUmuvsFHWvRq1CL5rlP5HDlIzU1FpNWjihhJIBAJGVZoWmJkfZJ3Td71uXXn1GjMvAYDC9BF4awidYTB6vV6fVivazqj47M8x+osF7G+znwvMnbtlfFPnaFAF0KrJtI3dT41F0LA34dFMfbwMcwhMEwfM8rVIzQl4qR1EZjKDj2j59MtKKS8qEA+AOFUEYdGqa5M1AA9TwJ6DV0GaDlmX5JqeX5XiaJlhkkAmgMKFIQzuxdRQGc2qQWnx2VLClh4EcGDlnymhkP3Vabch+ZirtJ1TPCPsJSH90+xm0lEkJ5i37iwgChhRrZZPvhFAOj+o45fazWQ/ZjF4xbRIySqOR5oiE5CbAspyKJi0mrdmo1appWqjQR0dEmlBc3InB9HL44Ab98Y9bU61CGJ7nJ43OBD5rTPuJNkJ9Q6S5o6j2c3jf5E3CPAwBA6CyPh0EUFGEPI8+lnpGf1ha04OQ7GeMN6woeYJrMcnOiMKmGmFCiaZIhmUpoUhqSHbSkdL6qLNJuEtTG7pOxFjshNekRfmtVbaj2xYGc3KQCw1t1Ksrbfb0BNOEicOTDBzwheynNPkus59Weds6lLokPzWcfE8T/xISrWYU/eR5II9+IrQqMrKYUslJGygLU0ShnCommA+gUVMEqfb7WY2KDr04Hq/HQZyNdpornWbiS2PCYLoUpEZFXXL+GVdePoPyN6HoJ3pAsJ+O0NyRYD+F3plMWbBwM4r9TEv7Yxe0n0Xl9UNzQnM+NK1S+EE6OIRvlrhlq2+/UGdlCdipTiDg86MKJLlDDdpVcZcvEHLHaprSaVQaFQUIcdYeAFDbKK77JAd3acJgehuKcS70gBRF7s87MWb8mF93HswrqoL2swFGRUkNPFhuP9FGFrKftlAl0uK7LldMvo8f0W/LATh3xHF8VZPURj74qgRBx24kcjJNn9frVQz3JQeKoCgChUElBg7q0+Ts/PJTDKbjYRIHym/4Rxw/kVFCcRY+0aLLO1o26fQJNGAB8O8+VMqD0vMmnRYZ/SyrtkEBRZ2Vg/Zz+Rufo8E7sp/uAJicnbRDmJuKkEl4lyIJHdl8KzuidYJacqLVTSEVy9ELMdAwxSQFdyy9KsNw/Qek22qUw0wcCcV0d5rT0DiX5q4OHgZqGkCPJuociyiglBCOHJidRNPk+19s1gJw5ui+cvtpjbCfcGEPoWvy4rsuX/7G59IZi8rrxw3vt/1gMUHA5kbVEfYzfcqVjUXbJ9/29Mhh/aWJJAIAS840AEBT6caIiwzTsg5B4UBh0lI0vt1YMKI/nIV3efw6ja7R4bOaojS7w2B6T7S62cNAT8524lso5STgISdKqwEg/rpo+sffbLNozFHsZ5UtK6sF+5mRntLQ5BB9p3JmPLiSB03p9VrlNLyAJXuavJGd/Ai1KQ/UKtvKlZeVy2Ka8f840P9EWFk3v6ig7ssDBmXaquvjMaE7A1m9dqVsDKYnEVtZoPH8c/N+dGfEEPgdkn+krF9OWh3Lb8stBoD/ZVt+mHoGo5/ytnUSReW2CUMzkAAyLFdtVzbWFMp8AE2TBr22/VffXoJn98smkaKCgqTQJieHZR0g8GxSO8H9kzBdDbhQWhz6AwV07LABjnqHw+bIO1I+cijUUJMGcKQqITP9pw37sgdkK56D7Ke8bZ1kP8eN6HvwCHReMM0yWvQTLSdHEoRGo4r/nXQ84fWjUXovhSO9FcXkEqZDwJ2TMCePeldEf/SWRDN+zYFy4HKJBUj2evuhgrJRQ7M0alpL8bOmjKx3eUdkRrefrmj2c8ygdKSbLMfVOpTT1lKbeZoiqRiTSNHeWPzQNB2pidGG+HCXmNaaBFulRGVINkzGyjCHQp/WFGxCMRiMoDbox+B0uQthAFQUGiRtZeW1qM4dDrKKqvQGXWKSRop+Sm3r6ht8haXQfs45a8T6nUdIEhaSk4LVRPKEzia06YD4mwr1KspduZVvgCfng9NHInHJauigekfAqiebPEGB9IS+bbIEVUzPzvEyIL0PdNYFBTCziiQIo06tUusSLCZYeoR6mgjkFhSjjc11jTBAblFvjVbIH1kgj8FgeqmAFhRVDemfge5POX3kkJzUIyU1KANJo6K//n23UVhnzeF1sDavOPkuK9zMSQNo/F5QXEkQBMfzBAAOv7K2kiCgclHCEJgiCZVK7ANPANBUsjGh73S4QPxTf4m8xDOefF+yii6HC4Cw1lQ0AE6HrK2pwMDhw+B/SpVZR0OH9j+en09TJMmyHidT5ndVtWlIHlU9cUrTSYLPGN7OM8QuCcXdlzFthkbKlJFqQQ4UxjQLK4cNzDx8olpaOyirX9/sdOOfG/fTGsDz5XMnjxKPFOxnkxNW6cycPHTt9gKYusShPHlSsp9oUE+QBEpQ99vtpFW94499Z80cH88lbnvyFrRRs7cS6MRsqtgczz8M5VIo8T+eF/7HIxnjtiIlD/AtRU4xHUInVMRjDcXES/gfvei/SAKmgvoCbGVFrcPu3rn3mMMG0+AH94Uxzawk3bC+6TcunJ2elQPVU1a4OW6YuHBCUD15kiCOVnsrq+vdLo/X4/X7/BwTIP0eA8mZKN5AsHrS77I79+UXSlMznVlr3nf4GHmYmCBDN5hdH/zpwFr58Ftop/TfCPB0fPcFZwL0cnj5FFKMWzi0fA8lBC6dTpfRaACAcNjEJnUEAQ4fLx8+qE96khUATmY/vU1OZZ48HKqznpDxFKRZRcOgKCnEQNvzJlVG0YGq+SjF6RG4AQAGgzj/k9F/yMiBaQOzEvJzLH9u2CpJqPzKo24jdDodIKDI+nziW+ZJ6YeB6QlgHxq7VClGBv7O3tozFAYPh/bPqKiGc+sqmkrPTKmsqHU63YVlcAg/sC9srTRySNahI2X5x8qPlzWyGdYMi2g/peinZNAIgiisaERt3aEcC+pJkrDcnoIrzMPDOB66VNhQjufhEa3JgY9PN8PIIhrKeDhvXnr08LN3np89MO30mfdaLEaeolVqDeCl5eR4p5/TqmmLQZxWQoZUyByA8Vzhv1B1vcI0FWqH1+j0W8PjoTgSiulVRZ/VQiln7HLPVlVAdS9Csy+w/3oz9vDQkbJRQ7Jyj5SlW9VD+ib7A8zqNZuNWp3cfrIcRwlT72o1xfE8y0BvRpEkTcEbnD4iSJVOn5Rghs2NqqoFde3cPnE8//zLy81BQyrLYhJG5TwIBDiK5BiWU6ug+IuDdKEvvWRUpWtGPyqrWR1pQrGGdlNwOmobhC9NqLiLXe7ZgxFjoJlpFhTZ65+ZeKS4Kis9bPHYoQMzc4X0eKtJW1FVV1ltmzp22LhhfeWF5KiavrzeQZGwTx3sOU8S2Vmp6elJiUkJpE6nNhl0erWf8TOs35qScNtfLw/mN7VCRt0s5WYp3teWNaLkEQy4/nt4lgABDThJkgTDciiHlFAg7IjU/EbcpalHgNUT014HKgENI0m6vQF0t+B4xZABGUcKK1Edjoom9+YVSvZTiGyGnkvA5xKkEUYMaxpdKppSqyitBtpQkoQPoaNRuyPpSXFerp4Sai4pQ6vepIeFKVM0z4YsJ88zLAeXP5K9skpFaVQURQrhBQDH7NKl5RWEGj6lCevNSUSa0MjSeEwXB6tnr2XULY/FWY4YFRrF+pocfmTIikphBmhplUOhcbuF3vImM8xNV3GspJ4sC3Pm0XZFvRPwgOM4kibhWFeYNVLRpFpFqWiKFuaRJK0NF13QgXhYSod0Ngi662dD4VaPx0fSfL3HlZpqRcaSJ/hyB6/XEjoN1S/JOqhvcpJFj66YJMBZZ50mZUD98OPmUC6UcOXyLk1YPbsdWD17g0TyLcpMm1SIHtw3HXDA5RYnZ0YN7rNlzxG0raLFXPfKijphXh447B6TWbf38InQi5KhITgJM4HE0S6a0yeFmwoKqBAJjd2CvjVkJJmiRmwr6uwK9ZQRNmZ3UiaYUM/xdLCmdES2SauB4QeC9R4vLIsV+An/rir0aseb4C6snhhMN6VtCeJwCH+0tGpITgZSl9yjsDtcZpqlqLxWrndOp8sgaKjd7pHsJ8z6DB5TZXPSNMmh1doEBYXSSVMUSaKpJJIkCg6LhZJPPH5TO99tZb24kH2r87yC6CiWINiMlJQaW1Pbp7OCJ8TSicF0X+RF51EfaiEGmplmOVZSFf485RNdTldhGRzgS0gesKzODmfaxUQfniLJJj8B/Bzg/TAtlPRajdrUROPkSaelJxnNhrga2Z0MFNnvcOY9fDEPca6J4wkC5B4+sfjy6ZwwpYRK+1GV6h81ypagGAymG9FMZ6Hm1gWOLqG8chIJ7aDEPTwAZ40fUlXXCHiQkZlcWVEnHfn+f+569rUVR4qqqaD9FGwmwXAcDHoKeUtGFUeRBFygTavSqmmNmlbTrL2xydEEE053Hzy+4JrZiqvpjJmXiB8FI6RbyR9EXwoEQZw2vN/+vFCwQn4YniTCYLoprR+nxxJWKJfjhoflxwZ7yUs5kmDj9kM8yw/pl472fLhyLYp+IhITzTVNbmTvBL9J0DTJ2O0MAD4A0Eh72JC+KLv+5JGRbAbC0vOVtuhJTlHb/AVYMWAKR/HCW4Yb4dd5uCAko7zGkqIPnQcrKQbTvXBV7ekghYWKBwXU6XIDoA89jQCoMEni+oVzPvrk18w084mKOo8/tML72ZNHbN6Rh6qJhOE8PAecOCIIXUJqgknL+L1qFVwlGCYHCdVHgsgKOeooVtp6XL4ASVKcoJUURbOseD3HymHvJU/SMBDMYVWpxVitg4fR20CArdCYHJTGm6IhKSqghhVEZSoNRzIEIFLdxw+X2LVqWquh+6Zbc9ItVqNWraYDDDt8QB+NivIFWJ7n1Spq3aZ9bbhsDAZzSnC2pJhtnU2CyTtQQDMykisr66TxOPJV8hBokdCZSd5SXuLsySPyj5VJTgzVHREEoPmAz8tpVbRauI/sJ3zB4Eab1RNteJJgizOSJDlhOSOGtpK8+JAMb4yvFz9HKJK/0syU2aCmKQownsoqf6X0FNQjNVh02r9PclF5KKCBwWC6LM/fe15lNezt2yrEUXgcB0IB3bW/YOKYoaidXb/s1G278jOEOaVNuw4P6pcmJZlX1tgFiQwJHzKRQlU4mj6CSUsoYZ4QAqC0kP5JwSXgRVEO1b6HO9Cmko1WeVvlZmCCa6iqmsQpL4pQAT6gAuEzYKQGcL40uhTdCwi18B6Xu6Y8UAOAlgfGRNhnHgCQTOlIwArKHoxtRgSN0WVGdcy4nx0G01OJx4qGJpHGjh5cVQcjln0zk+Ezoz2VF0xf6AUENYTrc1BQdWDipyCVJEF47E0eqLfAwethcSdF6jQqi1FjNmhmnDsuI9XSWgcqXQ7FM6yg+8EHIo0nAFyUVVCGjhk7bfyAhfPGffbjruVfrRefLZ8SkrrxS23rJNEUpR/HPE8W2sSwbhRem7IZNgbTBlq5XG+rrWionZ1Bq8pMMReX1kQcLe7ISDUXCWlMW3fm989Jgct57jg0dfJIjYrSqVUBhoXTR0LfEJgEqtapaBj9zNTCUbxKegAw2zbsumrhnLa/JdahDsZAxX5OYW8WjeuFqaHwPvVkwL1l2779+/PqG0I5pIJGik/nYaJSqEA/bDYJK+cp1dMYjAahKtsdtrAeDhhMh+SExpBRUWOW3Hn5W69/pdPAuwQAt915zYZrn5SeMCg9YUvwFGedOap/TvrWrXAl5OsvmVZYUU/TpEZNURTs+SZUHgllSEIwlBJG9DCZXjCn0kW1uSsozTS07YnCekcl44ekD8+yHvR5C70BrbDck3w+3ahT8xzvCYZZEVWVtdL2wMEDEk06wHllS0dhZY3V66y57mcnqQFa4TKxg60cXKmJAUqz1UYZ5ZsT0Lde/2rJHZe99cbX0Glmpjz+yFtjRwyAkzYemBJ07EQVbFZEiZ3Zi1DKPQ8KS2sGZKdWVNURQvonmo9BE0aoCl4l9GVC01NwTMwJuUQ8z7IcWhypVW+mndCABSxTUlG/M/eEsHKnIKCyU3tZqm+6Raumkyy6BJNOJS42FyxNFX78W/cdNenFBqBtzSPomcQpdqj7Wac1QMPqiWkVfEwZjbSi9PBpN1Sseglq6BtfL7n7yrdeXQUASE1P2rYLLkQzsG9q1LNHUTSh6YaUPSp1XQo+KLlO+D3w5ee/LVh8Huh0Ii9bLoBaMuBy2L0kYW9qLI5XGnFafdcFqyemQ+Cbt6JhYcK3Xl01uH/60aIqkiAGDeqrVlE+d3BMRBAllQ65X0ObhUL3pgHZqWhDEk2VkPoZEJoTh/ZHJqm3EmkWHpAgwBKU0O6EBKHUVNggitACsQvK0Wg/iuCWF0ZCqQQ9WpcD9q9D00YxrjBSgOHhLWtojOFtj2zT3RXA6olpLTFGus1ZUZrj+RlX3Auny4/+BCWnqErQ0MpEs87uEvMoN+zK14glnvymzQemnD26xUsReroLjrTdw+/mTgBDCjLpZHmKIoTsej5K+mckXg3MZKIpgmejyR8P19wM5q6K70LuSmsNgyThZBKJ9ix1gOlwsHpiTgaRMkp7GZWoR/3nB4q+C2poxtGiSotRW+eF67Jdf8XMFd9syMkwHRWin0tunf/4E+/GfiXU1a796hkVSSvlSHsoWNEZJcAqW8IDoqMYYeJLpWg1IqqkEJFA3h2FH+psQloWAGhRkBTXMUlAy2xRXq6XL3VwShhxbw5eGA4jp6ommEXfUlCOb5OM0rn7thFCaufIMcNVA84NFK4TNBSW4aD+83qturi0Wh4+fet1GCcFAGzcdXjaxGFRX6OyQsxsZ/XJGcmmkQNTc9ItOrWoVllDshXHE615P5HqKYcFpDhZT8c+J89zwOtnNJTQxBQQx+v8APgJgjAbtVlp5iSrASVmGfVqFUXlDIAtmrRquuIE7PgnzIbhSaQuB/aemDbQBqeH/BM994Lp5bJOS4KG/h553hGDcqK/qCZsTUqJ6xbMUavhehkkSWzZXVBXW1dXKyt/3H0YAKBoyNQii8ZlvHcMKqNUCx+OLA80vhNyPHB7A7SOQuVVJg0vhG5Js4GkeL/bwaL1OBsbxNCtkFwPzOqQ3+QB0JpTQC1ucIfpmfSY9YqJk3BOGAM9kn+ksb7K6YRD9bHjpsDCx2gJTy5FwxEZmVlpFcIayHL2HoJLgJxsvHQGHa3gnebiTBflvX7WqCWlFZBQib58xXih9Yl0eGiWrG39qzEdnmFaB0JLVDFArNBtDjxlh4mkPX/IcJR79tnQCVYH+wSbB8+zH/1RcW7UEjTUcKRNr8nz4OCh4zcsmAXXPW7TEk7yWXjh6r2AoHieU9SvM3RCnCn3ijb1sgfEak4phQH9N7TYHKaziK16ybJKpAoQOU7CYE4i0HwdPVJgc/IqfaI7oEI3ut8lkYearGbxI5vawve8HGGxZPEGADht5MA9h4r25RUfyI/Sq7hVUHCVTUAAUT01cFK+BYSorniY2wdv0uLvoWb1QW8J4w+EsKyTMCOG/m13IhYGg+k50EgPKkqLMrP7H8nbjSaUBPr214oaNyQn5UhJrUGnnnvupLLKeoNOHX+9VHNzX22u4eEJDcHDXiEsIWR7cnCxTYKkfOHZSIJXjVLYJ6Gnof4KzfCCZ4Z3OQJ2im5ZiwXnit0oBtPtaU84TszuBACs+/Xbiy+9prJKvurROFD1PgDgSFHVkP7pR0pq7XanRk2dkK2MNLR/n4IiOCutYF9ho0ZFadR0slWfaNbptWqdljZoofI63L6+g7OzM4KD8VaC1FOoi0cJCkLukbYP7464jMhZ+GBaKsMwAGiinR1NFmEwmLhaHFTHne/clQPQcTcAVUIbDCkBj9h//uiR/MFDhksayhOOnLMWlH/zljk1ucrFDM5OOVpaO3po9tHiUPNNNuCPet5r543RaVQEAbbvO8b43Haf2y5rcl9dU9dvwaw2NxOBi77JuuoJPe2iL+OhIDIvVd5MJCM5gWfFTiJut98NQIJVLz/4YO4xGDIeP1i+k1JHE2IMpkcQW/UmdW5ngy5oRWmf10+rjcAj2jqStvfJ0paV14qnKv/jzMnDPE4ofsVOoNKQxZU15qQEc1LC0JFQR2ocPrPFbLaYUSmnxL68YtSWKepQHcUWQUfgpTOErnxeEJxfYgmtJiC1k4+1Kmc4xJGyBpKAiZ9Wky47zZyTbk0062DafPBKzx43CMroIfEjhfrzuevKxCktDAbTyzSUrqkusyamSck6+YfyU4kSXfDhD1bvRhsLL5wgPQfpqYIZU0ahDZ3R8sGKdYkJwYbEgtCg9eKRaKIJG5blaZgn2jr0Dps9IWxMIU9j4kkNwfko3hucrHfGcKBuDsYTNMGAAAAg3Uyb9GqSJHUamuJ85RXV5RVRrsGsE5v+ieA5JQymt2qoGCZMIQ6ljKQByP1gJaxEai06owVtfLAi9HRURw7/L6RPBpcLhqEBQADYxL6jqzmJYCN6glTxXESnepY5Ulzt9sJungNHja0uhwt+wCXpxCwlMV+pudmtA4cKR4+ELf7CXxIrKAbT04g/JEoPSa7/YIVYmhkDh8tvMqhbFM2wixC0Uq5Hh/KLwh5tnhYvPXY1ZxT1BGDQaWMunj7iigsn/L4x1+MNvPweFFCapqQX0+h0VqvRF2BSk0ywFTRFMizncPmMBm1OmmX66cP/2JYfao4SSqjHYDC9dU2k5uRPgd3lTU2Dy3ggWnxWcY0nwaRV09SAPgmpCQadBrZWPmP0AJlyRh8Et9gbjmYaIieRwlbyCFKrz0gJra0JaH/TH5t2f796Pc9zGr1J9oriT8nn8TQSLE2RNbUBFU1JF2rzemz1DUJL01BfPiydmK5P/77a1j6l6ERczcx6CURLGhqR6RPOzVefizY+WLFuw47I9prN4nI5BqRqb7nufJg8PzhT2n/3Y2Ibp637ji5YNBdtv/j2T0tvnwc6GpvdkSL7/Bw8UjGif8rkEdkl1Y1lNQ6vj9EKS5iEqoyE+ihY/h5+HtRWiuX4wwXFAIAzxg3G6onpFmrY5lfpYTLKt+MvNraG0rFFM05/2hzvf7wadDRogogFWg0TtpSxm0ylybDfuqKaU03xKSZVRbWNBuB4SRVN0wRBy382jgabI7z+kyAIVpeo16qMOrVWTU+cOLpPqrmmSln4H4NJqrIe044B072ksD1I+UkYEHtRuYUXTvgsONWOaKdoNovQjxN1fBc8Hs9xHFokWc/GlcUph6Z4RtZFQpqOJygNz/qauQBld2eKFCe40F2vyqpWUSa9pm+6ZVB2UnqySVhLVJwP+2NbnsflOFYUWtETgYOgvZmuL4Ud9QZ7mCdtA7EWlesQLpwxatv+EtR4+LT+KWHWWaw4R/2ORMVCiaJo/2uvffHYi9Pif63mVFLaTzMNGp0J8GGZTAoFdQWgPEoVUTlJGpNBraIpwHgLT1QUxl2sbzb2o0iiwR6aIsN0O3q8FLaTHjm0b7+GtktA+6Uazpo8bMfB0mMnYBa9TqMyGTR1tvAXlG3I1jWWWseJQKcXn5cLDsyFNebpkAlVc3b5JJJGZ+rbLxsInaElFA5URzIUSXAcCSgSVbbHaKGPHuLg/FWU98dyfIK5P9bQrgCWwk742fZaJSWEf6MvKhcPCy+cUFnn2JlbhlqIAgAMerGWcfO2PEuyWBJb4wJJFh3L8majxqiDto7jeLUa5gxxPK+mqUlnjWjm2lqg34CBVVV1XrddVg4vIEQDPEnDVWptcgrsF3W4piox4TQY3OQNsM9pgC1RWdHiTt4UDUlRjJomCdJNAY0aZqWmuo9DHeZ4kiI4XmwUIm+PQhBiLYDih8h4YCdABgA9ZXSzsTqYYDpeCmUDBSydnYb4o64AvdyKxiWg2ZlJGcmmHQfgHLSwyIdKo6IknTAFBRRuC+sFwfCi305xJEUAr8vvjRbhLCqpQB3p//P2T1Lh0xlPvr/1yVtiX0xteUl6n5w6zVgekI2NjYxf/Cak1dqExARjcCUSW03YFBNCYTBh+yWeYznAcWSo1Wl4eIEgiKzRFxqtsGBUweZvXkAb99x5m7TzX6+8GPv6ey2KPzksdpgeoKHRBfSq88cfOFKpVlEHDsPJOJIgNOrQkWoVrdXC1Y3Ovfweb+kvUjIQIADqtySevpVXEz+15SUpfQAPqGQdAIL6klA0PcDvQQfUlBxHeZ5pBBzvB3gY5/R4A32SMkwUvMLjtcdpmuYSxkHR9+cadWqNikIhhSkX3U6R4rLIiNKyMpUqyg/qnKsepnmHSiXpP+SRe5Y+09M1FGsfJupHoqiXjevhmkjy+7QKyiITCFAkYTZokJVDAUqpByjPAxVNItfpY+FOeVsQmlbmt0t93aV1OqO00RRy2Z9Z+rdHXnwzzkuvLS9J7tO/xAbH5pGE9VAKu5iQBb3uzn+hHAAAFkAZ1Ya5U+m9Cx+OnH379ztcfun54n+F/8yYdjo6bOPGjWhj9plnrdm6BXQHsBRiTsbHqajXKGl0B0oQcEZIWNgX3SU06pApo0jSoAutBixfJig08pVKHcWl1cH4uXfu/e31WC3dUfemlq7YrRO7lpTWOgngYKOppVM9XPguAMANhezs867vl5Np0GkB4MsqqpNS+gwRU11DNDRUpySnAwA2bRJ1UI7RZImqnkg3u8LynFgKMV2H/l1MSYmTKqAoFXTetJG/bT0iPUBRBKwTF4DLWpBkVkZiWaWN43mSJGCuDwDVlcWpUDRD1ymJ6cyrH418sXFz7vT6Q2tzHhBmyGfPvw7K1ldvjZt5Pdofu3aKhf0/EFqac+jU4WXvBKFWqQwGfZ3NdsUlF+xZAQV05ND+W7aELGFhUWgVHYldu7afMWl8czpYXlFtMlnQzJLgoUXJ5Hk+wDAURQlrlvBBS9uujsxYCjE9hv49fWgfcqAK7YCuMxj4E5YDEp0mw3AatZhfnpOd6S05IInm3Gvuq8z9RjxvUHzlbN20YchAizEpSmXOjAV3NNY5duxueVZPT0sCCvzRuzkL18wHCvJz0bZcPSOzQdH1nzdnRmNDkyiMwfJ4NAXP87zH69UbTGhJJCG5VVRPwPMUharm5W1Twn6UWBAxvZz+XcyQniQBDf3Z7ysl+hgINrhgEAlH5nBVNRj39DNQQIXtvlnpBSUh5VWrKOkkWzb9Ee3liMLCE4yYm6k0e2az8djxYpAyd2Ltr+1+X/zEiad7nPXRNJP3en00TQeFT1RJSfEZlkPrx4mOWvjmSEywCjZTdhZUTRUWAibCPjG9NcMDg+k9hjQ0VaJcDI4kahtcYQ5UUAqvLyAdnJsLLZ4U09y8aVOMUCAspOS5gmMlgQATbgGbW1m47eh0evnb4eASyqKnPJh3hERfCGI0VtRKmha/S8RdYu8l8QxGgz5MN2X/YjCYVtG/rxbdQM8S0PBRKAFKq8Q0dSSdyHW6PFBAke1a9dk7irE/QRDJSWaZcsFO9OhftEergS3fJUGVHjSbjIAHQnJUa64+ul7DWKT8vQj3QlEIGpa4C9cgk0ii2Yp5iEql2rbhJ+kIuYZu3bQe3bZvXp/Vb0h2/6HZ/Ye27m1gML2S/t1fRsOG8Aad2udnmEBg3c7cG2cNaLCLaZXBhdHhtt0l9OwIk9pgSbswxyJOxAfb0IvSFaS61tknB9ZNSk8L/kf4B75Ge22dcO4wAfUESGQw9279Dc4XbSmfNnu+Wq3R63VSQ2UOgNTMnOOFJ0rLqlMSDUOHjVSroZxzwTjG/KtuKC4uSU/TqGiV/OSZl14cCMCwbG2dv/BY1113EIMBADRq+ll9YkVMF6F/d46Qhk0ipSebGuwej8ebrvbCTm6sPAYq+lCPLxBgOLn1k1U6hlweVLGINeKlZyiaKZvNxuAZSNipTsYntgHXJha26i15bCcO1lUeLqDT08TCUhUFjS4A4PxLFkkXzbP+ygrl2nMsE0iwGJlAoLwsSjuvvLwDGZlnMygegAyo+ANCbfGan9LCYLoMjZp+LR7DJojlIQ5NWjyHUUA0W71NScMcaFYaXKWjuhaO3JH00SoVEwgIIUNxCO8PsCzHKQb78pOgu4kJxoYGp6AwLXQJ2fbHGukM02ddPKn2V8mCfrqn3GZNBeErTtuaYGQ21JBeKBRFZzDrSIKmaFOyNTwNwLlnpX7cQgBAZWWolWdGWpSVrEmCUNG0z+vz+3xqDSwWQO9AaYvl6hlcJ48VuvOhwERW8kRvBVwDGYPpUsRpP5MbxE8vpfO0TYJ7yVyTTEABSDDrpGG7IobIsjwSUK+PCQTY5nJ25IFUpC+EYEZ3bf7tsoW3kATRr7/4o2cYlI1EXL5osFEPL+P40aNMILA/fdboqrXSCfUa8mPbwOsSQ0PjBCu0q4zPqdUpU+ilunj08i3FAsRUJJ6Da4eiy1apVCRJMQF4Hp/XS6vUQvmU+NZGDs+Rzi6UVIX69G38c2//fv1UKhUN1wI51Vn1GEynqHByUGfrdHDF746lWxjSkIB+vWZ/TmZSeoopqoA22N2MMAr2+hm3N0DJliSC1lXIsU9Kyyb89UhnzUbCbDTBGSJUuTRrnq1e3ucuDMbL19dHWSpZPD8gljcMWpzQOkMXXT1lNUMVVXVigFa2PihFwfklg9HAsfB9MYxfrdZKXxEJZuGHg6RTbNYk5NUTIDHBqlar0D4sn5heRV1mx6vnqTKkrV38I6yUkyCDU0DhZyEJorii0SMkMDEM6/YGzMGuS+hQ9KzKisrEFFGgistgh1AJDa1Tk5rIgsfa2loYSbFqvT6fWqy+FKXv0z3liWPPc1fWkgLxvBmP16U3mmNJaLS5r4hDiMP5BUOGoJl0IuAXx/IwhUCrVuRfIS3leWAyGRSJohgMptsZ0ta6nzABJQlCSgtXONDqenEpC5bjnW6fxRhKPpD1mBfvRLwKX3CkcPiIUYrypNpaQWR54HS5kRBBoeRlryt09NAIlU8/sOMvovagV2h5dC6+bByHRdSxS3cO7dss7Zxx3mUatZoTXr24pLS2zpaRnmI2ia1MOJ5PTEokSZKmqcQEqx4W3YM9++K6RgwG030jpOECKnegBBx6o2IkgiB8fiYYNAQOl08xiYQm6OVpTDC0yHJwv6BQTU218qZNQe+JcpeA2WRwu4KxatmZWZYlCGAyGoRKIXoDNQ2AlWq1yu/3E+Ed51pk5+Y14nskwBWLYMtRhmFYjrPbHTqtVquFHvObb76ef9EMmJmUcbZGoxo54gqO4yuqbWZDYp3NLq2FVHy8oKqqwqwd5bTxYhtRgvC4PfKRfYAJJAePryqDLjtO0rNCa0djMJguHiFVOlB5TlKCWedwwzmjoPRBgWACfrdX2iMcGcyxJ2RiCuMBgn9EJxwz7jRJmqWRu4RKBQsrFVc2fO6iispamqZNJqOQhUpQFDV41vW2hiZbQyMAOmddkYcQPaBeZ3R7WZrS2R1ibxGNVqMVnCDi5tv/BgBYsfKrESNOKyoKNTHned7pEHtDX3T+WUaLuEKS/Jgmu8NudxiNhsLjBeitpaSmw/dFit7b5UbqL0pn634DGAym2ypphAMNDeFBToa1stbhcnlIkmACAdQtFM0jhY3Tgzn2SCzRtjB3jSawg+rLeQkKZo398evX8rH1qAlTaViWLnbp2LZp3ehBMAA6aA5dbXNyLDhy7Ditov3OGr3ZatTr1Bq1VsO5PX5dYqbUytjrZRMsYjIpGlMbtMSArBR7MBJ7OC9feINUIMA0N1Eu5czL1RMAcORYkU6nh3n1gptWqWiSJVwuj8kYlgYwedLERx6+H20v/3TFmnfEMfz000bodLpnXnpf6H7iP/Nc2D90xojB6NH1eUfld8+ZPPKttaHQgcTt88JW3Hv7p1DPvdtnniXu/L17NCHFYHrM0D58EkkweuI2IFITjQzLlVbWK+TGH2CZYI69aDwlRxr0sHu2wGYiFEmdNfNSFQ2hBAgCTDxjYlJKCuABy3KoyFKjJVK1JqfDn5rep9+AAQcKC0+bByV3xKA+HMf7GZZjuUJnY7IQZxQmyuF0jcfrjRHkZJiwhHwJh9NpMsIJHzHhP3gKnudZLuxtogp6WKBlgNLs8/k5niMJakw2zfM0QfBVXnh8htYJdODxF96xWBOXv/fqTxv3Dhs69PFHH1686Orrpw0QCvOhet5955K8vNwnnvzn7k17/7UUNu6zCRljSDqNOpVTqJEFACyZdbZCQxXqCZMcOJhfdc2sCWGHBZUUAPDl2j11RJcIEmEwPdWQ0nab0oHKF1BTqymjXug5Hy6gHMs1ODyRs/ADB2YRrlq0PXrCNI/Hl5GR7nA4Ffq7a8/RyZN1Sl1zeY8dOTJpfJgiaNRqnud1WhiBHTFsqNddq9ElkiShVtO8ECH1+aOPlwnYVEkhoMGiIaHCShJQFMFEyo+60CP7OWtwaPj/Xflhnx5KIcfxY7NRXALuz9A6d+QWZ4yCma0Wa+LUMyei82WamAfuuO7fb3yMnv7MS+/v3783Ly+3ob7hnrv+tmtv7pbi2rP6hcU6JfVE9FGby/32qNK5/PutBAEWzZkEmmfVmt0EQaQAbS3AGorppfTvlCr7iFl4WXMNngcofyi0V4AHoLIG/nkHjxR9a2lx8fDU4LqVMGRJ1trsWrU6bJo75sy41H8TWT+xsAfwtNCZ1O8hVTSNJvqNeh0TYNgmR1SnSVEUG1AuHI9eWUiVD4s/yDbh9rhEGBKtrxcDoyt+25uVnmw7vjVj+DlDkvxOp1ivmVcMJ8FG9Et2Op2HimCkwGyxDB8grj3HcdzSvy5G29OmnaVRUQ31DbxRjIFU11T/Ul5909Vwwir3UJTuzlGlE6knALHUc+Vvu8SvBJ6vI5Q/gVPFkllnS9urPg7l8y65YJa0/daPa+U7/9h6QHpo/YFDMU4u5i0L4Pk6TCcTJqCwzadsMM7DESuIdKA8D+qbxDWNhSNDCitPaYLzQgRczEP+5LCWmrL9RoN24ODBer3G40F/9kL/JxLW0wsrFIEAwx07UTduHBz7AwLOO5nNRoZh7Q6nMNZWwgkqLN9DEGD0yCF2NyvZTzm7dm2/6PyziopOjIuo8IQFoGnmStueLCI00zVCZiFdbi/DMD/+8jsA4N03XtyxdaNfaPWMXkOnUdV57MCo+vDtZaNHjf529bccx80+8zSWBfmHo6snVPA65dz9T5thtHTeJOiFo7Li150wx4sAdptQs5AcfcGoUyWdyodk6jlsYNKrdy149u1fbr3ynNpG5SKuM0aPjEdJhSPD+mCtP4Am/TCYjueVz7YtvXCIMpFePoRHDZUAAGu3HBYEkQzOMPM+Pxs5Cw+FLLhyOs/D42GXjXAIkpw4cbKKcAR4Zes6WRfjYBUo9KHio2gRkVBlPYwwqCwWY4Bh3OIkuOxUUZp1wqcdq9jd3I8jNZvYngv93bFwTaNSW5YhErCnnzNp8MDB//vv/269Y+mtdywFAPz+6+qPn7kL1ml4oFs3my2//f5rgjXhkgsvee4//yqrqDlSGKVfydHjsEiurKIajO2jeMhudyyYNR7wbH19aFkUiRW/7U1MsAKCdzQ0sl1AOmOrp4InXl0tFQGnWA2rfoa/CImMNOgyV/64y6I3NjHOxVdfe+8df5cevXb60IA/zGvnHy/v13/Am28vu2Du1CqXc/pwZZ3Mhnzogm+ThYwR7+BZOExreHH1kcghvGTN4FBa7j11GpXPK31SRXk6WGkZmdaIRBM1bUJPOZ63adDIKcrV1puHgGvKa4OmLXQN8mNOnzDW66kvawjvhqACegu8gfB4nw6Awqp9UjJmDOmMn12lUdaah5hhSPfo8aNT505FO+5cctcNi27cu/arg9s3oD12e9Pnqz77fNVnW9dt37Z+x4Qp40YkiylTkRBwveZWY2toVJOga6rnuPEDAQDPv/ld5JGf/bAFEISjsZG1wu/USAdaWQ1zcNUqqolx/vufL848Z+YH/30+P3cvRdN3P/DPTzYULJo6iBVaK2zfL/ZMePPtZbBat7JuxuniEoQSaekJAGZkgIsvPv3777fLH5JL6qo1e2wk/ET9+N6Dq78NfXh+2rD/hBMODv618LzSGnhhUbMmMN2aFgs6X/lsG9qInEQStwkCfL/u4Jyzh6O7VovRoFM3Ri1YD5PdsPF+7LYaYuKSbM+BY90yEefy+Vf83/2PTJgCV5lHvP7Wa2dMOmPpCx9OH9WHSqJUKpXXK+r7A4/e/8q/X4VR1Dq4Zr0CW5H4G1m1Q/grB+DKyeKvgErSfbk3/8qxQ5q7DCrVED3zoNOZMzDreNGJgf37Snuee+NbAIi5MyatOhr2TfbpD/A37rY3NaeeCmaeM/Pj917Oz90LANifV/jSvx568b8rHn552T/vDDYqBOD39Zs/+fija6+7IVI99xeUgoJSmFs2fJCknpGf0S/W7JHiDAr1hCvZGFOQhmanmkprHNJXRaJJZQumIUcV1tuixbXfETLS5BkUCr5et6+Gl0XMMCef+As6m52FF08UrEGHk/Kq6MU/HGzjFtJdabvcJq7p1uNZv3Hd/93/iGLnsCHDD+7dCQD4YdWPZrN5yizxL+TJR55yOsUZKok7F4viu+oP5bheUlLx7r7QyqmtDTV0Msdl6bRHiysunXvG8cLQ6AFtNzQ0WBMSGAv8HD604MLd+2GwqLo2SozC5XKRRnjY6PFnfrZyFfSSCYba2vqrzwy1VnO73IsWX8cwgcbKKJ2toXoCYG9ynDd1jNcXkH/Qv/ht11VzJqLtFb/uJEnKRnrlUVpBPfehvyykns9/8NODN89DGooOkKtnZDpaVPVc8eOOBEJ31cxxzU2tfv373s5s7iXNwiWTcc3LJZMtH9Nli+u+27zPzNEzpiq/ZVsF2WwlEgCjhvSR7jY0KrOREJMG8Q12D0oLPbh7HVpQCPQybA22uvq63Zv2fvj2slkzZl2/6IadG6FtuePaSwAAF1x+vk6rW/3lT/379V/zw+9Wi3X6eVPl0impJwDg2zfmf/vGfNDjoPhQ4rACBynaq+dWrm7xPI8uvW3shDN+WbvluRdeGzBoGACgwGPQqEiNijzqMZBm7d+XPnjvLVd4vTAs7nI5pZuknpxJXVffIH2HffHbLoV6Cp6AVain0EqcBYBH6gnr03Tk8x/AVV5SrPq5s8ZGvVrJnEZVz89XQwt81czQbz+SYNfErsWMEYPRDcRBVVmt4gZONd9t3vfdZljnYidDS/zGjzR+j1oLL26v+AmOYnIyrOiuj9BF7W/kddlTEgxHT4iWgeU4t7fHNmZPz0pp7tc/d/7sUSNOW/bux6NHvQCn3davffCx+1FpFM/zE6aMu/+eB1994fVPVyz/6NMP0VPkuqng2zfmX3JHlHBhd+G34xHzY2nm7/fCEYkUlUZQSbK2XjFB9rP0ROENV8356z2PTD5r+thxUPXq6+r+ftlkeIRRvWPDrrnzZ/cz0FG+5BodNAE4k3pSTgaaYKyrgx/ac8f3W7enGG3Ds9kaVSQ/a+pYuVlGGPXqwqZQQ25JQ+++dm4ArlIQnRxdYonHZhNyKhKTQ17ssx+2AVgKMT7GW/5yzR5AgDq+A5q9dwjfrvg+OysbbR/NP/jOy0+j7RkjBu/OLx04esTwzDSOZVjYcp2z1cO3vCe/eOr4wdJaaohzzhwtbX+/fn+5O/rf1JILQt86b/0YKr1rJ0g65Xfnnx39+y8e6D/3FE8dLw6CNu6vvmz6AJlihrIkVbyP58VaSZ3R4nEGo6EqvUZFoaKa5LT0L36GwaneSW7eQXkYVMELrzz/wivPx9bNnqShLfKbMBWuYM1huLOqslmTklsJlW7JA/egu0OHDPvsf5+/+1vurXNGvfXqOxVVFXX1df0MQqeCcEgzbKYF28qUVE7MTpc/dG7w8w8/ziTPJYdqguUcLSyJlPsmHXnoSNGhI0VN8tISGUPSdWOtw0QRl6WmNTY2Wa0WW1C4oyIunN01RnS7N+3lOO71fz14orgwLaPP/U+98uK7X6BaOwD4G2/+y3U33CQ/fulfF+84WEjTLTSifPDW2Xe98lls9ZTfXb8VprIdttVLk36typ1QqKd4zj9z4x/Iy+0nFNATtT5pPCms1RF6TMWHui4FiNBHp67RZVSJ+z/8dpcm+A1ZV93MJDVGILZ0vr58r/yAnq2ebUOtVm9dt13+LVVw5PB1t1778bufwF4EE6AP3b0p9BX+zmc/z754lk34Y4vMpijjo025IfVszbD5qwPHLh8dq6Pw93sPSzOBchxUC2O12rr6LhLaNpthmsukaRPmjoFfBtWV5UtvvfLFd1dNmDpn959wocYLL57vcjmeuH9J2NN44HK4Vm/fd+HpYw06ldyEyrES5kbe3px0Sqz8aUdqgiEyZULaGDSkz/1vweD4XXPP9gXHBFXe0Pxbulav0FwpUFPRGOubLAZ0QFbJk2jWSQsYLZw3tqCoVrp7xezTDuYXoW2CIAJCojistuwy44vOoVUR8WQS+vS6zJQYYU1JKNOzUlb9Ubbqj7IeGQPtEFB5wrgx4/fuF2fJ4ZzSSDgeLLb5Jk4NDYfHjxn/7hvvT5o2QWoQI305vb685UHSb4IRBnFHG746oDx+Vn9lGm8b6CLqKTSCgDkkF0w8TZ5z/cHrzxbshyHjQ8crEhOTnnsSpj/L4QFghHLK1dv3zRkf3eLlH62RL8UbQz2hcNucyLPfd8NFLo/y6+fYkfJUyrR47riZc+An4fsf4FMk0rWh1j+ppLGGc9518ewAK75wpjU5Tg39983nwlyaD9ahu/RF00PfjWajVnKg3/2267rLptQF50M37y2ua/QgO67mcFKFkoeuilJe+f4r8O9KEsRtGyvkjz73BfzwRYK9Zww2bNrw/psf2Gz19zx0D8uw/3z8mf79+t9x3aURRW5wG6mnwviju/HIaHtYW1QeI5UCJaXB/fvDciquHNNsjtqpxefzebyef7zyMcuyD999U1UFjHHXrF9L2GEqXkYqnCnpP2j4Q0++KMSpix5ZuiQ1JSzcTRJEdbCJ5TGhWmTQQDFysuD8yaiQV1LPXzfCgfbcaWPl6inEQKAcEcmmSPWEGbt/HlRT4KWfNvIAuH1Mc+opEWA4eSV3PAP5kcmWBldALqP0r5uPLJorFsCdP2Vog82WlZFUVlm/8OIzK2sducfEXEVbbU1cq2rI8FQ26oJzUL1HNCXOmJb5/isgqnRGPr05McUo+PtD9wwdPHT5B5+hYbvf75990Ux/EUx+alXM5M7F4yIzxk45Cj3tUkyZddbfb7zlmhuX/PsNWKTA8/zfbrjMJfgrFOgbO+H06648T683vL3sq2Vf/Hz9Vec7XU7SIOY+2hqUOeRIRhF8vY9I0hQWhlUBors/b8pPTk5ogi2AoXQCAO6ZNx0tLxRJmR+llIWFYNK1+q/W7lkwexwTXO2CYQIw/yg8UL7oonNaNKFIPWHCnDdQUFI3IsNK33zZJK9L1hlEpUPrD338VYdNe/U26VQQVT0xbabgaMGkaWFduyInfc4exX/0dticRi+cputwXvrw/V9//BoOeLNynvz3f/+77JvHb77oxNFDPM9fd+V56Bi323Xdled9vOqX7L79D8lWEW8tg7NCPSmaGhpZa6i10onSClq2ECRi5Zp9OX1CS9jXB2fnVADUO93ynev2hnX7ldPiQL6uyX3gaFVBSa3LKyopLVfP/61cD9oHarrMBODZe7b9bJFtGytuueeaU30VvY7mXCcas8eZAoFR8PVn3xSdKL7v4XsP1cA+NYdqbGtmnbV13fZ/fPDD9dMG7C+NYhGyhp4GtoUmrHfI3KWNCksKnj1sEBEzm02unjEI2k+hlUQE1bXiXGJdfSOZojtv1LDjMgsssS2vWBrIKzIgCoocJyob6qrDFDZKxlxriRRKZZsQDKY1tLZ2pcXJukvu+A6dEw3b0WHYfsaPSqWePmW6YmdpWcnAAYOKbb7duXtdLte0uVPkjw7M9Ny5eFw8sWaUuwZDn/khRSvMF40dKQSL5XxzELYlk3Pl2CFkSijKuWrfkSvCo8nnjM1RPIWP1sINppc1hCZ4FC73yKF8juOCMYl2C2gvN5iYrj9Zd8a0zKgqiaWztVx05QW7N+3dvHbrhVfMa2iE8yIXXzB/4IBBt19zAQAAZZKpVWp/AM7tvPPiwzAxqPRQp03ZRdY3f9l8NJlMgYr8ax5sdThnmDL/jExSR02t/27zvqiV7KKALrxwwmerW+5XhEUT040m67ZtrMAzdR3FlFlnbVq7Ze1qMX1HaIuzNP8gnC4/lJd77PjRreu3e7wenRbK05efiouDITpHRttA1Hy15mAT4PIcCuJyoFg3TzYPXTUJT8RH/bF04GQdOhv+ObcNj9cTWWhnDG4suP6qu6+bkJI2iGUDdTViwriCOEf0XYH46zubFVAsmphuAZ6s6wrcuXgcx3HVlS2kYXVTDY1aABpdQLu1bkZ2+oizK9cpb8OFbRGmKxD6zFegeblBYV0DwqkK/k1F5jbIVVL+aHdRz/jpgFn4zqQr9MJCeteq0WWLZ+uoU2EwJxvpk//+K5+DRJh+tOiKCVFzHhDygoUu2xu0zQN5+pvdx3uA95Rz2023JyUlrd+4ftuOrbmcZZSQ44IgSfKjdz7Oyc556fX/fP/jdx2iem1TUiyamK5P/J/tbbIAtPSsHvAhjzF475YONDY7N+5GKyEzDHP5/CsAAK/+95U9K15Dj44eNfrDt5dVVlVu3rb5iYefvP/u+6fOCctcaxs94FOCwbSHbc1M3/WGFIjWFrh3XdauXkeS5KRpEyZOHX/GjMkTpoxbt+H3u/96D1xdWeDDt5d98/3XF14x75EnH544dbxeb0AN0DAYTBuM5y2tKbTrwJBXl6LnCGiCNeH7H7+T2pcBAO5/BPbXmnvVzehRAMA//y320OYF/vPcy6fuejGYbs8t91zTy+uVe84Q3h/wX3zB/KeefVK+c8KUcSgGOmLYCMXxX3y9csHlV3fuNfY02jCn16q8iHte+mnepP7S3TlTo7QlbhU9dSCJOVX0HAc6+6KZqCH5zo27f/r6lyGDwophpbVcJLZs75ZLKPc2ftoZPSu7DWD1xHQ4PceBOp3OCVPGzT53znP/eD4tNe3zj1bCpZUry/9vQWgJTDkeT+/qpX9KIAhi/NgJ580+71Be7s9rfvb5fFEP65OZZbc3OZxiN53mNHRPpbNVATWsmJiTTc8RUMSadb+tWQcXaQEAPPbQE5dceMlzy9c8tHj2wTzlIvWZGZmn4gJ7Ef98/Jnz58xD25ddfPljDz3BcdyN50S0byDJ77/44cHH7l+7HrYljwesjJguQg8ZwlMUde8df1fsfPq5p/IO52XkDET9DqTW2Yhzp8Om/JiTRGpK6vlz5j3xzOMTpoxDtxnzziFJ8sl3v5UfRhDEzo2wiw1JRm12g8F0aXqIA2VZdvHV1/p8vv++96Z8P5p8L7b5gFAlMG7MuD37xPXIzjr97Ny8g5GnkirPUH0Fbn3WNq645Eq4mtjPP0h77PamjZs3TDs71Fbyu5U/ZPXJ2rl7x6QJk8mIhYgxmK5PDxFQAEBJWcnN199C0dTrb4mZ81dcckVGesYzD92F7m7etvm9Nz6YNneKy+V64uEnaZq+acmN8jNEbVeOF35oG/tz98N1XhOT5KsK3/vgPfLCsKw+WbMvmmlrsMG1iLGAnhxiZDs0V1j53Bc7wxaEwzQPMW/BbNB9iJ378un/Phs2JCzTZdmnH/3v2UfgFJM1FbZ6fe3diePh/APHcfMXXFRRWRFjmQd5hS/2oW34dez6cw+Kmaz+ZfUnn3987DhsJC4JaC4HlxpH7N6097GnH/3p1x9jnK3bVVJ36/YR6NeUy1nkaWRxMici26wHx6x7lIAiEhMSA4GANKVrbKyRBFRB7BVyIlsktP56ezgt/joWXH71PXfcq1aJnWjlSREKAX3in4+t/mV17LNhAT0Zv6NIon7JyYmtqnuCyRK9gZ4zhJewNcB1r2ITWzpR0y2FgGLawMqvVqz8agWa5fvvy29NHD9p2cbC66cNkKLSEgRaDBbT6Wz5fZtGE1rTbev2LW/fv1h+wJyZc++/+36rNaG0rOSBxx44dvxo7OTc9N40UOiBAhqbbd/eEuNRZDPRJ+CSO76TltnB9rO1WC1Wi9lyolRcQpZl2dvuurVPZtb3X/xgMFmADQ4L5JAEFtBTwO5NezmOu/aWRXmH8wAAd//1nusWXn+m8CWHDvjp61/SUtNW//zDrr27rrz0qpXLvnjrvf++v+y9U3GxXZFuJqCt+nJD6dawayEA738dSzebk0ism23m9x/XNzY1zrxghnxneQVsDemkLAAoBZQgiRhDhJ7XiLcr0DenHwBg8vSJPC8uUfnqf1/ZtWfnay++YTBZXI4mg8GQlpq26KaFh4/kAwB++On71//z5pK//BULaHcV0JO3eA5elahjefmNl+694+8Lr1r02RefSjtXLPsCAHCiULksrSJFNzLAgvbIW/Ni2g8lhE00Go3X65V2btm+5ZeVH6Dt4UNHVFVXIfVE3H3/nTs37tZpdR4vLuTr/gLaom7Kl2xscd0xvOhYB7J8xSeLr772vruW3nfXUqfTSVKkXgdX7r758uiTlmgIHzs2jVPKOpbC4kKY3rd26/IVn7z8xktoJ8/zn7/5DNretWfnBZefL3/KdddcjxaY6+BL6bZ018DTQ1dNaq3rbHHVRkzHct4lc6afN/V/H39QVlF6ouTEU88+OWHKuGMFMNam4Lpbrx2QWhNbPRFSVBrTIUyePhEAsPjqa3dv2rt7096P3vm4T0YfOMUXDa1We+eSu+z2UCYvppulMXVCf9YenLPWFVJkUFaZPLEshm6+vnyv/FFcG3aSfkcEQSxasPj6RTckJiQCAGqrK5deebYijUmn1W1au4VlWaS5MUjvTbPw3dWBYnoGzann68v3ookjaQNz8uB5fvmKT2ZfNHPClHG//f5rSlrG+Clhvqpf336b1m5hGKZF9extYAeqBDvQk0pyxTG08f7X9zV3DE5+6BwHunvT3qampnMvOCdyf11V+eWzJqNRwl9uvPX2m5dUVFZcdOUF8q+915v5YutVDrR7TyJhul2M5f1XRAGVUMSm8RdYp+H1ei2W6LVGJSeK0ca/nnxu7qy5L7zy/IovVyhGDHcGt3vzEAE7UCVt+AM+uKugo34f3ZoROTCCFpvjBWJq/elTh9fVuGMfnFfSclEZJjZed/QZIQBA/yHZ2/+An/ZnX3zmy2+/hHv69f/swxVqlXr6qD4AAMvwcd+vWv3yGy8tX/FJnPV7oJc5UCygSrCAnjz1TE7Vb/9TTCocOLRvPKfFGnryBDR7QIZep9/w65+krI6W47gLzhzudsF69jV7itRqsY+BnI/evimGhqb3JgHFQ3hMJ6knAKCuxj1waN/jBSfiVE90ZqyhJw+3xz1pWqjnA0mSHMcZBfUEAJx57uloA3lPmtbo9Ba9Pvqov3dCXzl5dBuetmrHgZNwMZheQfzqielk5KuCI+TDdobxOew1DnuNNFq/M3xQf+ficb0tHtpGB9o22e36KoxnMDA9DK1eE2MUH5uoQU+5RL4ubPfm2aRTOYRvjwofL/YlJadYjfaerZs5SeFN3wBQyzqPHauo7vQrwnQ/tPrQZ0ZO1GDlQ1dNQv13onZ0vOSO7yKftSrYo6BXRT+7fQy0vq62vq5dZ0hKhr/v7DQLAF1UQCPxy1YGjpTX2GDxxchj1vGU8ymSzHC/iJ4joO2nvg7mGNfX1bbZCyMJftcG03H2F5aCbiu+JfWNp+KKMF1xrk+iufYRcuV9rquO3jqHbiCgPN91FxxDEtzQ5GiDH5S7QoNW3S0kGNPj1VPRwKxFHhLEtNfKaDcQ0NjqyQNANC+7XVl85a4Q/ds2CW4/YjddDEZwnbfccw3+SfSWZiJEM3KAdLO16skHn873aJHiI97pwIy0QZlpYwZkx/P0/zx5bcnuN9Ht588ebu6wbz57NCMtIfapBg7I+OazR4cMgnUvchKsxm8+e3TEsBx096ZrZhRsfrlg88tLbpgTzxWu+/KxvD//E7k/6k4Mpic70NbSWseJPCz6V3ouEdPedsJVnTz4aNvQAhPA5/Nlt+SC161+UqNWPfj0p39szctITfj2o6Ulu9/MmfA3xQjxk/eW6nQaxUIdkRwvrNTpNK+9cPt5lz4m3//5hw9W1zTmHS7JK7F98sYd088cPv3Sp9Qqes0Xj0w/Y9jVt78W+7Q6ndqo10buj7oTg+m9DrT9oD/xqH/oRHewk/H7Tcl1thkVTWnUqguvePbzb7dUVjfuOViUM+FvUC7fuCOvxIZuMJ0lLSElOd56FSSd775+l7Tni4+hq510/iPobNPPHD7m3AeKSmoKjlecNuP+syYNbd+bwGA6DCyg3dtONkdHKaYCoxE6OLvLnZ1klW7/+s/Xb737s3TX4eI+evvvZ895BADgcvMOFye/UZQe3ki4vIfEdXe+mZOV4uapvBJbYbXDbNKPmCo2uzt3yihhms6F7jbZYcLDRXPwitOYLgEW0J5GG0Uzvu+EhkYXz/Mbf3763r9eaDGLIvjjr7tLy+ulYzb9+s/Vv+5u7gyNdie8OZxyVT1eUOF0ejd++2R2kvXIlldKyuoSdFokx5fNhR185XoNALhs7slajwCDaRX0Saqn7JBaT0z8tNdpxh3unTL30Xdfvf2KS8684pIzUfX0jX/977HCSvTotVdPJwji2f983drXn3vZ05t/e2bVMmg8r7npZWl/UpIp8mCLRR87VksL7YVKdr8Z+VB2krUUJ71iuvgk0smrc8fSfMrjsLfe/bawjiax8KqpS26eu+ztOz5b9eeb7/1CEsTtN81Bg/c28OjTn//zsWtmXvSkfCfHRnmjDMPGc8LIK9n8G1xvMjvJOmqEOL8PV/7JK1HcbfHMWIIx3XUWPn5p7nlS2xUmr6wWQ6MQkeR4fvnKjctXbnzq/xYsvHLqm+/9suHnfzQ0ul761w3Swf+39DKvN/D3//sonjMfOAR7LXt9AfnO0vIo5bonSlu9mJ2Cd165Tdo+e84jirstPr3FXIXYaDQa3McAcdv5M9/5+XfQbel+AnqSXHCXVduuIJoSn7x714B+aQqJ+fjzP2adA396JEkmWA2nTxwsPTRmVL92vuK3P+5YeOVUxc4169s7vlG8hTa75rYRT7pYeFazcrdGo9Fr1QdaU7qWV2JrbSlnJ1BfV3vFpNFtqJ9GnHLx7ckC2t2NbadKZ3wB0DuWvv/Tl48889jCR57+TNr50dt3ojG1QoY2//bMghteKqsIzS+1gfIKmMl09hnDNm87DAsNJw0BAOQXiO1/ujoxIsvSb5dozW89+KjP6/N5fdmJrTPCDmeo3adGq2lwmnNgJx3gcLW9hvi5Ti/iRPXTiFaJb1JyyodfbfDrmQ68GCygHSC1zXXiaENp5inzm/FNIjXZ3S//d/W9f70QBRMRHM9Pn/f4ybu0ux744LV/3yzdve+RZS0+hThVBbwKwxiPOPKn5mPh8/rqa2vra9seDElKgU7wmRsvAwA88mGrpw07n/q62o5VT/gb/vA/za4uq4DWWT7f8GfHvnzPoP2LyrUzptYBtLLoamD/tPNnj2cY9t0P13B8Z8j+jYvPBQB8uHwd6Gp0qSBLa7hlQdji751PUkpKexS8DXy588ApE9DmOOeM6C76b68tB72DDlyV85QpaQdWrfYYmok/hh7q5pxyAe18OlxAO2AI/8e2A1El9c27FrfqPL1HcDGnkthfFXzP1EpM1JgpikIg3vnp91PjQNtpVBVwXFwf2Dvf+BR0GU7quvCd5El7tgON/EzJ32xvlche6EA73LHSUt9M1PxNHs7q2EB8c0ZVAUkS8Wjo63csatWrdynB7YKU2hq7RCi2A4lvOhuDicEVk0ajrKmMZCMA4MlPvmuFA5X3JBZ7vvFh2iqX3Q5RW7mkxmlF28/GHQfbWUbVAxxoZ5bWtPCOJC8c1RTLPxSoC6G0jWWxlWAHenJjoPKexGLPt/APtHQXKaz0kZb+lZCktrUu9ZTQqkxPrcH6QEcIaFRZoWg1y/hBz6JzxLq1OZIYzKnMA1Wkvimksjnp7L6RN8n6eFyN8ZtEmlYXVddE7m/uDD1PPTs5KNEJYKXuzZziRPrurp6thWH8LattczUqmJ6r1FiFuym4EqkDdLN7x5Ji63X4o2qdhaKIT9Zt7KRrw0TQ2spxzEml89KYuiN8b3s/HT0Vo9KbaZL8ZD0W3FMDVtuTDRbQni6a3eot0TozTZHLseB2Olhq2wYW0G4qNb3nncSC1plXbNx0qq+i14HVFgT5f/qTJERgoP+HAAAAAElFTkSuQmCC"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "assistant": "At S4, the displayed glyph is clipped through the mid-height private screens in both ally views. Move it to the upper height tier, which clears the apparent screen bands while remaining in frame.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":2}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H2 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "With S4/Y0/H2, both ally feeds show the full front panel and glyph line, while the hostile feed sees only its narrow side behind the building. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-2f3ab5dc55b4", "created_at_unix_s": 1788843242.32412, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 4, "requested_group_seed": 4, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [4], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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2F0USGo2KEuQSgWSXYaHsjhwzXPBIwFOezi44XWAFZXCccre4BI1GI68jkMDqicFgfNXTzXCU4KusQ0DrrkOSIay165UT5dkz4DEoIB74DIJawtukKZKEGQNALswUECVYdiGlVucJMopINMH0AxG3HYTvAwaDwbQj25NASlK3QCiyKYPrqV9DsjEQdW1CXk/4Tmj4ptANyO+iXs5Qr5wErJ4YDMafep49X9A3FVZg1iGgoZufLQkhOBBoigdC1qrPqxgMBhMW9SQJj3qiFKN2AkUSyALFgonBYJrI9szK9agnx4cWhW9dBmmgODoByHAmL2EwmI5OupSxxHI5ebAthqSeLMuTQt5lMNqYHLWx28VgMG3D9sxWqCfHEyTIzitsP0t4DAaDaYqMJWR7PjR/stwTmpMH0887YpoOtlMxGEwo6nnmfAFKJn/g1kmb9p0U1ZMkkEEaER1DtzEHKAaDwTTXyh3+hyDunTdh8/5TCtszKiaGIOrygTYburgBzXMhbH5iMJhQ1DMnHwrl3TeM33EIjiliOZ4iCbQxOjYWqmcLCmjOutXNf1GsnhgMJnT1vHPO2N+PnEVdPQkAsvOKAK2JiI4mCFh4Cf8FdSN0PwJtHqyeGAwmdPVcMu/q3cfgVEqG5XgA4EZaQ+hMHCc22YANPUDLIesy2hzXCvgMg8F0bNJ91HPpzRO2HzqD+mSSBHFOUE9DhMmgBlaOqnKJGtJafKDNCFZPDAYTTD379kjedhD6PYWexaLtaTJoUAeRSB1JU2S1iwQ83yEE1Ku1CAaDwfitNcovmj9zVN8eydIWnudz8ototc4YaXISGoJxkSRB02RchCouWm/h6PYvoFgyMRhMnbYnQRJ3zR13KEMcUslyPEmSSD1N0RGw1zANOJXeyQCSgAEko5rokWSk27cEYdcnBoOpuzsySUy8vO+OQ2c9lZoErNTU6PRavd5sYziej9DTWppQ0So3T2nUpEpFqVVUSBZoU8wobnba/jvAYDBN0WOJgk3ckHryPMxYQtnyGp1ebzRQFGnSkhoVbXcK3UNIMlJPRUWZLl4sgU3cg1xjL5sE2jLY9YnBYHw59d3f0QNUWSS0wZQmXcLZlNl5hTqDQW80EARBkwRFEjoVr1eDGhtTVGFX0xTHuPr0SomKiqjbAm2j/TXb5l1jMJim5dR3f1+w9NVaL6engTAMGAkzNbLzCg1Go91qJQjYZRgme8KZl3BPgwpoVNTZ/CqaJCmKtJgt7bOZCHZ9YjAYvyD15ITsTsk65IRhbgQBsnIL9QajwaBjKG2VhYmP0ghz0nk4eJggKIrUaUhTp6gLxZZqq/OyPgmhzEQS5623TVrmzkeoLgIADrk7t8jVMRhMENcnnPPmvbaG83p5qJ4GozEy0uhys0Y14LVas51xutjYCBUFF/KkRg1HrwHW3SlG27NzbKXZHmwJv2xGf9BM8M3v+jTaS8N4UQwG0yZcn5KjU/Fqdl6h0WTS6rSA52mSoClSTXKRWtJkUDsZOHJNRVNqmtKqaRVN0RTJuB09kqODCejb3+4S/ttGzc9gtz2oX5dmvBNM0zJCdRGZ/BhMna5Pwc+pFIesXKieFrMZAOByswQBA0ew0IgkIrSkSae2OjmNmlLRpKCk0HdKEkRFZTXZCszPFqOpjVD8V43BtCr1RE1AFPqZlVtoijABtcYQE1flBLExEXxtIr1aRWlUVISO7JFgYDnyUrmNoqC2ShJM1mV+tlX7M0TwQh6D6TC2J+ABzJCXg/ye5hpoexIA6FQgu7Cm1MpGRRqhvUnBNbuapiiSjDep0lITKs1u+exfsm7zs00pKNEWbxqDacuMaMUuFLl6ogx5+atZuXAyh9ViMcbEoS08DzUUymiRWavTqmjYN4SmYSITQQDG6ezeKUqtUpttbDABRebnI4vGgQ7AsG5RLX0LGAymiW1Pn8ARUk+4DI2NU9hdJEFEaImCKkd+pcvmho3ohUPh/60Wq8vhSIo16rRa/wLaEt7PljQYz57OasGrYzCYpuDU/uVy21Ph+vSoJ7I9YcRI+C/KmCcJiqIitGRyQkS1g6uyMYS3RlktVpIgDDodGcz8ZNWg+dKXiBZcv2MjFINpb+p53ZOoJR3LcgBlyXurJx0RRxhirE5ObC8vQArxd5okoQNURZE8kxClNkQYi6xstZ2RK5bVYoUyqriw0VEmPX7/h71S3AqDwWDalno63axUiKlQz+i4eIMKRAgWpM0Jp28KqfWwUR3M91TBrE+NikZ5SxTP9O0Rn9gpptouuj4lfCqRBL18ZNG4FZ/veuiG8dvXb+0I4Zizp7OG9et9NK+qpW8Eg2ldMDE969ynmI6GezIxde5JV+SAJiY9ASD1dDMcDZ2XflbukbFxsHZT2B6po2ocnNXBRag5QKgpCiqmWk2p4AMYQYIdmAiiqqqGAERqatK5c0WROlpqUUcHMj/1Gh3QoLHITfhuifCPq+sIgo/BNBNyyfMrpql0RettUhdADSRJ5XlAkiBSR/Kkymx1ci4mMUanUpFaoWSTpklBPz0RpPLy6tTUThzHVRaX+7NAa81PwKrvmnkFcDble62vhhJhFk6DQWe12uVbhnaLOoaNUAymPvZjYm0CU35Ld36Q6txZDjap83Z7esxPwhBTbecitFAahQZMgKZIjYqIMRiq7VxZlSM1JQIVbqJ6I0FZkEohDa2KjY2KSYo7fx6aoqTf4Dv0fmq4ld/83gJTOgN9NTE4Fo/BtF3SRfXkYZslwWj0Vc+ULsmJyUl6rYogiBoHbF9HkoRaaCyvUsFGISmx2iF9kix2Nv9CiVhxBLUHnchzuvLyqoqK6tTUpMiEGNJv8F0wP5tvXBLRBGfK3b0VfQXZ22DQyZ9mnc4ainNCMZi2qZ4cx3M8DKPL+9QhsnILEzoluRmOIAgdDWKNsJq92s4BllEJ/UGkeBHJMV3i9Gn9uldUuxSx+1rEBXN5eTUsUFKYnys/3yWZnw/+bSJoOxoaFl8q1lAMpm2RXuv3FFp8+mkAD9UzKVG0T4VhRwRBxBppo05d7SIvlds1alqtgg2WIiONqOKIdTtTO8fo1NrMsxeDOB3Ly6toP7mfDIO8nyu+3DlI59m9CQI+Lay/Kpry9YRmnc4Cuvhw3RgGg2meocRo4a7YAdmeHMdJcSMUWlKraKOO6pYcXVRmzrpUMyQ1liAJq8UmncBcY46IMF0xtA/L8Va73VdvYFk9IMgg5udDt4wXr1krnGF2RYbDuVl7joafy2TQK7bgDiMds24a04bVkxTLLX2jRmVmF5JWgoDLfALATE+tmtaqaZJzd0swDOmTVFTlzM4tlk6QcSYfAFBTY66pMVMkEeEjEbViyJNy8/PBm6+RvJ8rvtyJ9uNba5ZQrX+34dLJsDBPi2u9hnW7BWsoJrwj3RX5npJ66qLhfuUWN5JOAOBYDpjmqRKSPWGiEkHxbL/u8QP6dis3M+jYtL5dI0xG9LhGaNQUYdD7LOfh2ehlM/q//S1si7ny8106je6uG0asXLsbeT9XfrUzDdbLtzrOu+PrFM3S6EHoAQd39oMlpi8BCDjtBNYlAOLin/JXjfZSC17IYzCtXj1JgiB91AA1qaO1WgJwapI0O/kKKxNnUglNkWFjeZjmKeQpIePUYrGYTMZuyVEuF+tywgV7jdkSYTLuO3wqrW/XmtrlPNxutdVeREiBCuT9fOiW8Txg/bo+w+MM5Rtyukau1n0xVpwBAMTERl4I1xkxGEyzqadQauQ1xketO3v2nNFkojUaOPtISPaM0BI2N1FmdgPg7pMSKQTcPeqJMJst0KFnMqrVxiPHs/umJtWYLWl9u5pMBrPZKtqhEaYIg77GapUuJwaR/JqfD90wbvt6/2lAjdJQviGnq5VOSKqq7k7yqWmJKIepuyrR77kuVpwhCAJdluOBIcJorYHfQQlshGI6MpKbpVXNRkxX2J7e63ZJPSm1Bv51i+OPYBTHqCbUak21jc0qgCEjscLIxxIzm6Epmj6kl9PFuJ0OYYvVZDLul5miEQbDwWNn+/dJkbez4xXeTx6wdSTS87KvOtOIFDv7QoTi6wwn0UJmA4JhlD0CMBhMayNdXqnpk+0pqaed0IiTigXpJACcEEfDlTvZOU7XIzk6t9jqN+EJYTZbLGaLVk3r9AZpy5XDB+j0eimyNHJonwiDwWOBsgyAwXdv8xNo2O3f7wjpnYWiofUnUClrfc9SJ24hmoSN0KbuOiE1nmg9vScwbQ43o1RPQg0rYs6ePWeKMPEqjY4HFjfcHqGGQQ5CKDeihTx5tYqGMto7qdJsLy63dE8UJdIXZIrqDYZjJ3L6piahp1qdziGkNFXXmCMjIyKNRk8t/OtrfqcIYsWXOwnA8gSAmUwLRte+KNaBNhuNlc56Ho36rWIaQ4hi13pKpzFt0fxkkN/TRz2hz81k4mloe8LguwokJyfn5BVE60jUYAn2B4EzjkiKIuw2m44GaT0Ta6wOkhcj775Iy/kai5Pk3ehpbFLs1t1/9e+dUl1dExkZ4RFQiy7e6CilhMARMj+Bk+o5dyngAcdxZG3NEiLn+9X1X4u3RukE3pmu2AjFYFoh6bWNQlDUSNJPST3h4j02Tqr54XlQWFDQp0dKVm4BzzM9EjTICKVImLeEjrZYLDFRETzPV1fDAJFfUGQpwmR0uii302E2W8xmy5Txl7ncbFFJRXV1DQ11UzbdFzYyociVX+98cOGYlV/vvG5hH3g3PuftOXcprDsNsGrPWfd2W5FOjZp2uhiaoNxAMPr9gaNJmHbsbGn9fpL0Wtcnmmsk6p9KK+moqJ610olyQgmSKC4qGtKvOwD8yawL/TpHqMTZRh6lqaqqiYqKiIqKYFjOImilXyyC7UlT+rIqm0EN8i+VdkuJ75Icf6GgFFqgioRHDrbSg3H5B2+6Oi/I2xJC535Vq+ecJUGOy1m3Gj3I+u6t3jfcBxpPQ4XXYhFTuhTVnBhMxyGQ8zpEb3Vx7W6gpLJJbU+4DhbsTwVnz54j9NFWO6PX0qJ0CiJLkXAmR1FxYVq/XuNG9Dtz/lJljaV/SoRCLqqqagAAUVEREREmlKvkF7R+T4wxWuwuwLryLkGjs1tKvE9HeoHrZ159JvvSim92zV4Ac0cDIt0KH5Kgob16zlnq91Vd3AAAgL3sVLArBrqBsKLXaW12mMEggY1QTLshvN7qRGk30LlJbU+vWk2VWOFz9uy52Pg4iiTLzG6bgzHqaEJoIE+SBBRQmtSoVXn5F64Y2j8xth8PwMmsfDXvZ60pmqKREVXVUE/9IiWKspzKJqSC5l0SLFBfjBrCYDISPBdUpmTyGUBJAx0QBCSjaF+/+9vKToZ6robdAwFsDi/1bACH3J1xtSIGE55Sd2Geu1/1TEhM4IQeoLFG2uzkLXY2UgtUWi0Ng0VIQCkVRZ3IzCZJ4qr0tDHpfSmSOJqZ6zArjU3Pcp7hLBZLndH5KrNDTbL+BZQkiXlThhAEgexB6Bgl4e3r4wbCl9H8T6B46K2kDc9q8pzCb+xfvIcA2MpPhnTqUO7C+/4bYIQWRw+qM1mn9XugMJhWkPIJFGRlnY9LiIdtloTXNGraqCNVGm1BmbmmytE1GXo8aRqqp4oWjFKCOHjs1Jypo0gCXE73PH3uYkmRZ4JRA5bzMZE6i82lFNCpY9I27s4APOxH0r9bVGG1S96NSbT+AqCLlaRNqavNht5zDwCArQrBtXvfv6KhMsJqs4dx0hYGg2mU7QlNN5l8CuZnVtZ5whBDwE5AsIMyTZFqFaWBPT25PikRTpa8UFLdNTmShuPdSeQ5RXONfti0DwAwd+pV8TH9dx/O9NXQ+i7n/VuggAAD+3Q5eRYWiIvtNkLAjqw/PvDaPNiZiKZyb8rsWJ1MTNUmAhQVN8wIDcVsRL4hnO2IwYSt3KhWPSNgxhJfaedZjkuMgDM51DTUUJSlZKTB0D6dyqpsuUUlfXp28p2O9P1GUUYBAH5lVNJQN8taLZ7KdwVmsyWAgAJw8uyFtL5dCv/IEWL+9bEhA+xuLzvlu9kQnxboHOFE6giNwWDaiO1JKWxPAaSeSGN0Kj6la9ez2Rd6JapVNEWT0O+Jmos47fbEGENclL6symoyqf1eS5LRXYczSwMt5yMjIiNNQRJF/QjonGuGV5XWRMVHZJxpaIsimX9UsZkPqm0537/le7Jec+9rMRltzV34Owx1Okxa1VxyTBOVugOVNivrfHR8PAfHwcFsT4LnSwouXZnejwDgwLEzQ3tEywcZ24S+c0mxRoblnbBW3T/fb9wHNfTQKZ4HZSXisGKJquqaqEhoitqdbqfDHpKAOuyO6OjIj77YBl2i08fIxyjXD3/qE0CRgmlbtj9Vbbi8yi/lklr7QThTgooNFn/HHUJbhDpVD3tL2pV6MhxJ+dieMvWUhJUUenrm5+bq1Kp5067Y88ep5Ei1wm1ntdj0Rr2BUh05mduvN+yfFMgUjY+LikuMLasd+C4ZnkhDdRqVWkUh16cc/0v4z3/ZCwBYfOP4Qlvj2hTVQ0MbGGwKJq9B5ooKl7JroM2iI8W8MJKx+75bqVILqycG0xzDOSh/I92zzvM8UPEsIAFJgJjYuJJKGwHcKpqCAzVVZGZm1rzpIwEAG3ccM6m8jrVZbHqDPn1gdwAIqwtGxf1SWlYFAIhLjOU4vqK0orraHBUVsefAibR+3VA0ye9y3ktAp45Jk0Ts2lEDq6utgtc2/IvY5lwZB1LYknKbLr5TKGfQabWlhCnc94XBtDEXShM5QNJ9GiT7DueIi4VeGh7wHMsTFFlZUW4QusoTBE8wLhcDXAD89Is4hQjZkEP6p16s9Wyi5bzBqDeo1UE0FA5QKquKi4uKTYgpL6moqqpJ69ctKtK052AGklGkoS43a7fZAlqgv+7JgP/h4Voehb2gx6Exmet8S2qoi2TUXMBYmRe8f3MbqycGE8gZXSw4oM812gHtZlg4YsNHPXlAzJqQ7nAxuYVV1TVmXhgMJ0gn3C8rt7BPD/9m0PHMc4ot5ZVeaUlD+qeiBxcrqpUaGhsVlxCLXKJV1ebRV6TJNVSjommTES3nQ1OWRhJYQ1sXBNXSd4DBtDqCC19i49L1kPnJ8XztgCKvV+OjjaWV1sxzhRzPXyqtMerUyECF44yEWR29uiWhLHUe8Nm5RYHENESFlSgXPKFIYS+WViANhQ1HLFZpOY+S7ZUCmpwgpn9v2ndy+phBHuVrZEdQHM7GYDB+1VPo6+ZHPaOMYkG3EHInCKLG6hw0oIdaRUWZtOcuVijO1q9/L/TgdGZ2r+5JkjGL+jMNHtDjr1PnQX2QK2y54CFVPB3SP1UpoB+u2y093rD7xNTpo72LitqVhhoMRoblXG7WzbAsnHoKaDpI4AmDwYRZPVlBPX1zluKijGgLlA1hfC4MzBNEbn5h7TQ4YT8eTB03WKyWFHrZ8Rw/sE9nof4Hxuu//2knMkv/yjwPo8q1EnQ2v6x3Z6EDnnAWdDrULi/0t3A881wdS/iNG/ZcO3102NbarUxDrRYxoEYDICqn2D4Fg8E0LUdKwDCht4TveKKs3ML4ob2VBxCwR4fV5jQaxGYi6KCNv/+lzOAR1BM9XHzzRBTC2XtYaPMmn98p62q/dvUyAPgLBZU8z//jPx8hSRWcA56lN5qwpEjrDM3gCqO3stU5PjEYTMtwtBTZnl6ikJVbOHXMYPkmwfaE9UUkAfcvLq+x1tRYqmvMtbXqQayy9dv/XL/j+Podf4p78jz0GAgHlFbZ+vfuMvKyXgNSkx5+6i2xAxxB3L5wJrolqbWoDDgXU2hOiiZ+hhxECudQpFZmh2IwmJbiaKlXJlNWbiEAYPO+TFaoHRo1VHRr1iJOIu+f1o8iSRfD1licRfm5inPK23cYTRHiNnS8rB09D/jfD52mSGLMZTBYdN2i/6DtsGUyADU256MLJjAsZ7a5vtsA8+JTOsVLaorsU4LwFtBvDxUb/b1JjuMpWBsQPrCGYjCYWo6UQA3Nyi2s0UN/5e2ju2rUJIodRZoM638/BncSvKVzZ4xyuRlJyPpf0YvnBzIsa3O47Q63zeGutjqtDrfLxVRbnTC2wfI2h0uIUwnVnLJazyqz3UCD8aO82nGIMX0ejBjQ+cDxHHgdHgzu310yIsWYv/Bw9LA+IVmgDSzlDG61tk4NdQbsvILBYJqOIyUACOoJAKisNtdY7UhASiqt3TsnAgAulVSPGNidJEmtVo10xeF0Z2ZfQodQtVMvU2J1AHjaVCKNSUmM/e5XaEUqiO7cDaaylte6AgTnqfAvsDsZmG0qSVjto9rIFvx379EsPz7Qqd6S7DmqXoQyKb6l/aE6nUGj1ZO0lqS1KrWOUuki4uqRR4bBYJoCcb3row+HTubmF5RJm7UaFfoS/aTCV5XZLvuyoY0FtRXucm6ePWr0ZTB91UVpXZQ2NiEuLjEOrfEJADJyityM4OqEgX2eZaHzFLk+0QN0h34tUD48Fqj8ZM2llYXRHvVnSLGyiOb8ZMhbVE6zBvlHIMaKM9Dx4WyWu8RgMAFITxTqOeXr19rHMIIDJxGJBZrpg1J5nv/0+511zvSdNHaI70bkBzh1odJkMiA5djjdcYlxlrwC0c5EQSR0do8liv4vxPYB32SVSIoVejiDUEqhxGAw7YYIgybgQDQAjmbkampFK+M0DB+l9euePig1JSFq4+/HffdHwlNSu0iXuHxo75xc2E89Jjbmsn6doUUpXHHv4TPduyXn5RWg3Vg0MkSWH8oLE49snNZWUxlhkO5FxsZ9yrkdRFgkMKwa2qlSKNgPiosUmwD6rYW3ldv4mNrgHwaDaR1EGbWSUJRUCjEJmfXHA2B1wmztSB3FCOvojNO5SEmD07lTsGr9fimm8hobTdNXpfchCeKOG8c+8sInh09eGNQnmRa8qwQBNCoK3YqN02h4uyYCelr9KMu0UWliP5FGyl/rDBMFJiUhCpYo5OIZ8RhMi6EKWg2IROiWWVfpNKoaq7OkwlJtcdjdPMNwsZE6jZoqqbAW5tWtp38cy9JroWdv8KABv2zaN3RAV7Rdo9XGJ0gVSoAWWo6SgkfhRHYxpaZpODeEYFmuotqakhjlrxvTXh/1lHLwQSM0lGh1wzb0UVE8x9NCXyyW4yqcLMviUiQMpoUpq6ojGcbpdLmcrpSk6NhIvdXuqrY4zDanQad2OBmTXhUzqC/DcGhEkkGnUtFQKM9mK1XV5mBj4uEa9LIBXWElNwHO5JaYbW6Wz3l66Uy0DxzoCT2ytXF3wSfKC61PxB383t/ieePdbnbtD566+IbTiu1Qm89saEyDCWVSqXxYKZ7AgWkYc6akIzm7VFSJojkUASJ0NEnylIYkgap3t0SOByQJrT7UtwnwfgR0/swrnS4mNiHuQv5FVOlUaWWuTOt86IRnT5uTMWhVQod8iE6jQnom1ekHDCJVmu1TR6dtFNbyYdDAls5YwjQ1IXbbRd3P8LxSTINZ99sRk0GDHk8ePYggCAqWyUORYxi3WkXlF5RJSe9SQxLf81TVWMpsIDYBdO6Skp93ERDgyjQ482P2tFHSPgzLuWGbZ9glH63oUbwf9UAJ1g80JkJf6qgdECSs4BuugVg9MRhM+OBdTkINNXTznhPy7WTtytqXpFiYq+QXDe/s3TU+K7+M54HJpBeW7LUXEjo9q2hRQSkTzBuFhi0BukV0CSagNEV26hR3y6xRlY2bitSm6d+v16Fcr27VYV/e4jmRGEx9sbjBdROGFpVbTh73Cthwak0QDZVj1Kp3HjyTNqgvdLlWwt7yZptsoGR3MT8H5cxzHI9UFSUzZZ8+1av/ALSDfwH96Nudi28cD3i45q+0BZwI2u7JPJ0NtELLraactYvBtEVvNd00I5IcTsbpYjTqYCnqDyycRBCgW5JpRP9OHMdRFImmdZIkYFhepVIxLFdtcVSZHU4Xs3//EeEgLwvU4nAZtWpF/xGehzJ6+NSFken90HZki3K1IXTaCXud9OrX3+1yq1Q07I0PAHjm0celE73x32VTRw/cuOfkvU8seGf5Wun07Y+esfrsRhyOZ+1iOjiMIMSSoRBePXW6GOlf8XJShgzPF5RUSuqWkhQD40e1nk6YrAl4mia0UbrEKJiqObjntTwAG7YfVlwiMTlZPJ/Qsln0lQrOzUde+AS95AnAi1cWypNsBRTH5eZWdkuOoR9afHtMfJdn/vGY6OTUwYU9AOCdl2rVs/3SOTHmYjEcDMB7D4jHYNox4Q33JTZuJlIDmHvtCPnTS0UVDYi63DtvnIthT12ADjqvjiHedetCDqh3Xzqhg6lK0GmKFCzQ/Oxj/3ru6bIKcehH9r7PpAP69kwuOJHfmlORGoOLJaKjoliOc7mNboaFsz1YFoAwOD0xGEwT8f2mQyaj2JRewRQ4xs0PJ057TUM6m1c2sFc10rSeicZj5WJxvS9uhpU7EzgORuQpMSYvjLeTXkvt3uXseXii7lfede+VQvjp/I9ncsSa0HZJSanXN46CHykA97NrbYTR3YxDdvXlUDOalqFjtjhMWrhshypGe1yZv+32CspLJMeb/GwVVPCV936aONpPqxH5PtCZQOpRS1JJO9E/ooBeyDvvYOhPP/Natt81PSRncxtF7LfnFBfvBIlnGrdSsLsZo2DGhGHRJi1qSs8ITecKym1Wh8ticxVfylfsLFdYiS17YcePtEH9/vnQDcdyygkCpMWKFZxpA3q9veZX6Wj0f3mFotwvSq/46OOH71iMnix/6WkegCceewY9fffnjHtmteemRySt5kgVRdMwBddVWwLv7LhZBxhMm6BzYjRFEmu+2wHb1984jmG4SKOGAISLYdz9k1QU6Ram7RIEcLlZkiAyc5SqanG4jbCPKHh+9a+zp1xRUVoenxh/6VLRwUMneTU0NhGSB7RP98TsrPOUqTNSh7TBg/sMB5l/bIIWqN1Nayk3AODM6bODBwx5dNkLXpc69mVTfzvC3uwulAtCg5yBw09YF3zvGAymTfC3GVeYzTBz87pJwwEAFbLCeR6A2Ag9QRAw8UhQFZaF/egyfcJm0yaNqLE6GTdPAr53J8Mrm/bNnhZ38M/sXj1Tsi7AED9CPgRJfnjGX3AUKNAm0/feds/Xaz9YuHAheuHY8TPyhfxddz/g+wb6d40PmDiZL8yIat0o4mE8HuOBwTTIAZ1KVxSD5nZAf7H+oFEnWI8C100cJj0WOtKHlFHTOTHKzbCZOaUcIF75eEvPLnH5eQWTxo+w1lTLBVTm7fQPzTJVDob58NMvb/3bgtqb4F7490sXLsBhI2qt2KKd8FbPX7cpk6oQ0ybCz4TWLK9y9XTx8MfA0ZHyMX7Arey9isF0WIKrXmILdTbghZmdUJco+qdtRwPtNvsaj7YqKC2vRtnx44b3ikuILyku3XP4zLA0cDTjPCA84RBpdEfeuXPyjTlnMnv1648qkaA2cpzoJEVCotXae/eOKSmsAkArtzff/2LrntrHKYmwe6YCv8J6qVhMkBodKNpFtIztqSaExbt39AhH4dsKPbrV5rIUeJ6ez5MV5GHaKSPS01Q0BWdwWhxVFkdlsTJZiKCgc/LHraK2piT5EStkVf5+OHvcCGLP4TNzpo9et2FPz85x2Zc8FiiqQZImNPHWi2qaYnm+W7JRz1dWVZtpyfhaK6zcF8yHdmhJhhiEev+LrYHeA8NyLMupaAqOu5e6hRLE9FojNMixrYUQklsNlI7WJ1WbvfLIMC2smPXcB6tqO6NznAGmEFEGVJ7Ecl20GnWlxcEwXJXF7ma4ihq72eaqKYWVl37ZLMz/GDSwDwBg1x+nZ0+96tKloqfunLL/RL5cQBmGAxqZjadPcQuT6UkVW+FiVKZIWM4JAPj7ostrD8mMUNWEon1llVa9VgW7jfLErMni4e9/sbXeutlC5meIULpEnucijN1qLHlhvyVMI+UyRLCqticWzBqJZr3BtHZBPUiKYBh3lAE264wxqeHkTD7KxbCAT+F4niDp4z5RJKvNqdfAtiMkwV8/bXRpSWlycuL2wzkH/8yWL+FZeCo/9wAbKqtowAM6iboYqXHWV/Xu+ts16AE6MMTDd/9+bMy4oW1IPaFH1FqEHugIrZ3Hy8O2oZgNvhy2VVucw6fyrXZnWi+xUN2XnPwSRVCnd7cEpKfoqTBNE2jVVExUxPtfbYNL+E5iYxSJsSMHGnS6Sovzn0umn8it3H3ozJzp8Qf/zL5+2qh1Gw9Iuwk1mLBZvYitgKZJGOHn+W6dYvMuldGhS6dcNBu8PN/0655rp40GzYKao9FcuUDqScdfFhMV6bCZC0pg4IgkCY1arY8k3Qyj1Whs1kqVxgAAwbpt6GNIAyKqrCXNc/PtnmaWyxDBqtqyPDFLbBNnszs//WW/JzbDB5u1k5Xn/6+yDyBuuBYujvcfz1K8FB8FmzNVWsCJXLhgn3XtVQCA66eN+vHn7UDlyQP1OEBlis2wHCyIFzykxNVjvSrzg1uajYRnOasVBmkkDdXHDVTsk/P9ahA+kICq/E3lhNfSiz8tlvPTQ9Dtdmg1yhqGYrNv5wL/jBAClK2zEq4FtSmuQOyBVZbcK1xnRucM4wnrpCMYqqH8Ao+ojcKH6/f86u6aSiEPacww6J0028QsoGqL+A2vtjosdnf+RaVzc3D/7kFOW17llVozclg/nucJAFJTu3RLiTv4Z47LzdA0LHHnOWhISZVIXZJjIvQavValosm/sori4iKkIZ3QeenbD7Rha/MgxEYby4V+pQi9wWizWiQ71FZ20ldD2xMjVBc7poa2TgMzXLR7Q1VSxuYn2qSvNNt2Hz0Lk42qPEmdNRavcblL/nZNjc1ltjpLq6wZJ07/lelvEicPBg/o7lutc/Z8IVqdp6Z24XkwLK2H0IfUzXK8WkXlXvRkW6L2THJ/gTgNCfYehSWddLjW5r6MHj3k3NlcaXiIXStm5FIGQLLuLQeqJ42MBE1Je2wg1Xpp34oZCu1eVZsHvnbiEEEQyHXmm8X+wPxrCALEmtSxJnX3JNOIfkk86jVH0U43U1ZlK66wHj9+igPgr1NQWFPQUHiPIohn3L794O0LZqCxndL1kY2JENvbSzegT2Zq78YNQN/+/aGANl40CYLkeS4lMYqHvU6r/UqYivOMwHTY3VqdYefB8vFXxIKmAatnU4MVs8HfJayqgdiR67y6uwaujlGkRvgz5nneUruWh8XvN4zXaNQqmvpm48Hp44aI3ZGEGAbgOS1NpsQZOscZh/VJ4HmoqiqV6tffvZLtD/yZbdBA0Uzv3/Wt99fdd9cchmHRefIulf9v1bfSnsIsuICR7jOZmcH65gcnJTFKypBHXCquSk7wMirFd6XEs2XnwfJp00HYweoZdrBcNvU3E6sqAOC2Md3zLhSi6knUy0MYteFRjMEDeuReLKmsgq2YLuvX9VJxpW+fkdr28nD1TdIEz/sZ6zZyRFphuQ0AGCN56/11+UWilJXI/I09OsehjvSeyxNQkQmSyD5zundfOPaj3gI6evSQ81mhZkQqKvBl25uqf0dhdJpcQFkSfu8ozn+rOosGTjEVCrHgfRpqWszv0wrBitkavuEdRFXThRlug/pCf2W3Lp0qT+XUtt0EhDBwA2HUa87lFnS9oj/lxywTuegjqX7p2Tm2Z+fY096NlhW4WVZNCyPhpZYi1gJ06V4pRreboVVCwVNwblq4eM9v6zieL5StzZXUtqyXK+aePcdTEqN83qhyw68bdk2bPhY0SCgbaX6abVDH0YccDNlXlyPfMOwvKCwezDaYM0CQ4jiqaFO7dfNhuWyddChVPXFGDARRlGjxJMVGIFVlWO6nbYctNqdRr9l5MNPv4VNG+29H78vcKSPKK2s6J8UElw4CELQwwcNro5DETxAUcBXTLEkHX5vftGgZy9U94oIiSaY2YysUNDpDI5fZnSq9xplKKM7qDprGZNZ6qbBdTWr1Rt/dXE5OIwyhbk9gxWxPP7v2KqlApqp9uomjhiVbFcHzYPNeWJf5m/eM+LpUlbhYVIetGhup06ioXqldcs57rU3hsp4k1ATF8bxHWYpc2sfuuWfHr2sUl/now0/7d9EGrxjSaWgzU3cfYkuZeB/GuC7WKlj/z6mjwuivbOSp/KqngjhDXJk14ASVVguWy/ZNWzdUj5SAwTGcw+U26qClAvMxpeEZQVUVkRwfqVBVQU89qopiM9L0zUBIrxEE6Nk1Aa1Hz+VeQoGcwb2TkhLjWJbLPA+1i6JIwHmbZq+8+256F229vJkInVZlttbx06qsYYG6E03xUcJAqIiYJOmlH9b9ev2caaBF1dPpsKiFH177ACsmpm2pqoomWY5yuGCakBA9h42QWY4vLKvp3SVW5igMKEV+VVUS1r/O5iIFhWPpfjtk1MM/9mF9AhaMalSin1V+taJiaDyl9ewMCJCZWyhO5QzC+x+847uxR+9uijiSPHMqENEmPQA8RfpZ6et1XrVTzaae9KU/mBSpi0pAWv/6Hcslpq2rKsfzJEFQQswGEAQnFAeitslWF6xGR42XeJ7XQO0jtDwTiq0qCatYkSkoRZ8ucZK5mudT1IRQq2iSJITG9tBulQfii0qgjF41KLUhUXiUe9RN4X4N/g5CULnvv980d+61DbiZjpm0hBUT07S/Ts0+jZckUMSmNoEJKqlYPGSA/efhlA40n6O4rLprlwSWo2FGEQE7zpVUwEpNLa2KNcDoE7JRYbFmYF1SmKtRJp2Pa5W/WFyKzlAbI/fyAGRmXwgooFIcadGixSrCuXerZyzSzoPloB0huGLhN4fWRjorRRetS5Uo7UDAT0X4cyJJ2IXFHYKrtynAiolpDb94TWqrkijtU27sCRi0ati2AxqhorGk1QiDIAU95Ti+pKLWkeiGCYmskEZjtTujDFqCAHrY5U4abQT/DWS0KlQVoXCtFhSXylf1dVigwX2udcKyHHS1hsaGDbunTx8DmhdDdBzgazuGaGEQyWYzRxvhx5F/G1ejLvZUVDUVWC4xrRPlb2ZBuC/gT29UNNQQluOFpEz4x6imKUAAmqZQTxCEUa+G/Y+F7u46Q9T5vHyDTi2YkqLUFpdDQ9Vk0HIcz/EgWk/D1siSkRkAv6oqCWtdAtq4fp12F2PQiuOfaPdFRuW/rYa1qhioI+2VytGjLbJ+1+tN3mdtDg8BVkxMm6ZHUxqqsHsxXAJ6/h7F+kZosHokSqeheTU0P6HZJ+xg0KnQMp9leTScQ9gNKhLDcg4WimtljTUuHg78KBNqkJIjYCFpKHYjElbatyKzMSjaN9sdbvhZUYu1qoAihCASQblpIUaGLHY6Em7WxP628ffma0kWEk2inlguMS1FlaZ7lNO/SdVqQ1WEGFhCaoEqg0RTtNY76QllqwDJsjxLEKndu2lIuzgVjhYblPTsHMty0GhlOR5pa2WNTa9VEQRRhrJCtTo3yxWXweR3rVo0/mL1lFBR6qcwvd5BJHuV4CiMUeRvis9QpEzCYNTbnZ4uAC5Cq1ZReoOedTMGP6Ht5p0NL8AxLpJSNv202h0GnTaM6okVE9N6qNIEa52JYKNFL5ZZkxjKbhTw6jXXRKoqFwiPmNZuoSlhRU8AKKRqDWzG7HLAsL4QSScFC1ZFU9JAZKSkyHkKAOjbPSE+LqaopFyyVfVaFQpb1big2iYkxJ3Puxht1PE8z3JchBaqah0C+tbHbz94++3yLSYDcFCdALCz3lqJsDu8itzzikGMzhN1iY4ywuYmyCb3A/yeHAfdh4B6f0Lm6Af0tJ0K9KqbZAIVIxFk+KszxV+UWvcQVk9M6yFE8zOuUux7TensDZPgEPH8ddj9K+mVQ2CzzlAQKizFynVeiPrKMjnFlT4qzRSGJgGC4NEOahpKIQFAZVWVWgXbKiMMOjXLwngUMg1RFbxOA+fIMSzHCMJctwW68uOPFdn1QgQMOFyyeHStrWZ3KoPUrHeJJ0rbCnI5g94APE1U6+Z8qYXrdrnUXj6IjNYTPvRG9FgiMR1TheNqdbZM19y+N4WIDOnVSfmqbA/pIdJUpLNkbas6NKIOEIDiYZN5uVOVFabKCSn9Yjwcrvdh1qrgbCX8JdL7zZ/ytTcVxqZfuYQBGcZP0FoadeJ7cQCIY0zCUDqk0UPnykT1lDhF9xzAKCfwBaHc5t//azEHmw+PFRODkWjOYSqh4jb/dbZgSE+l/8G7t7xXbhN6We5UpUhSyiHiOBIJqEaFzFUCRvJ5QPtO7WSEae+KjV72JrpDhjXotVabt+EdgtuQALzb7VAHLO/hSTqkwqS9bBKIBsDHNtwHYJHoVZQ4TbPxYLnEYJqaCp02xmcVfza/7MpoU1jOfzzLU3HklWwKIWTihYJVEJoi0Z48z8P57YL1CmWUh3VTNEnB9vW+U49tDrdJr/RR2p0+9iYPHK4Q2nrKJb0WjU/bDsUaeS9IGhVUAbM13RIBKLY5E40Gr/NYRMsxP6IPSRCJ5pOg/mDFxGCanwoYufUirm/nLIe7d20qZBNx/GTekIHdhIeKznXic8FtCvQ6zYih/Vw1lUKWlPgKrVh0C8OVGLVKaYG63X66OnO1hQGBKLOWdZJ9T8osXrPxGkxJTB/KDE91/5zRNEVxHMdwfNaFQrPVSeY7C63Mc4uv5XmeIsn1n9YtoD26aZfOGO+7/bWPN8FUhnjlD7VINnMqCHFk/fZHJHWOD31nDKYjkOXtLdR3iWvkCYMEYWBgKHCE6ujxs2ndxauLUSlF4hHyY9p8/JtyQ7XYXNE5yn/wmqQI1nutT5KEv3B93exl/Rih58osjz6+5PW1Wx65c5Z8++sfi2NICQB+fuXuM+eVB97+5AMfL1/le5WX77tW8U19/eNN8je7dLKnOGrHQdiE9MKpQlUE9B1fPRBOXvVlx0k4UBCDwTQRWTVeIQp9p+gsoRtcb0Mjk2pkw0MULwj/sqzSjqRdvqYlD1zuuiu+UR8UBVo1bWW8TkiRhB/bNQRevmvSjx+tlW85V2bhk3oCjc5HPTeKVyeZJQtn+aonAOC5FT8Sui4Gio2zeFefyXRTccijt07yPY8h0oR8zw8vmn78cJb8pYFDht++dBl6vHr16oNfvYIeXz0kTaPVvvjq+7BpntN51cSRcvFFais9vfrygau37PG97tKZXjby6l92+kr86s27fQ/EYDoOWbKmmvqUmPos/4MtplEWvRxP8qnTJzqUnBBRUFL3WluloqD4ep9Xp1VZZfPz6mwkGoiuWmbV2o1AFdfFLXYvzqET1Z1hdOjdb/fqtCqDTmMyaE6eEUea6EiGB2DedROtDpeapnwb8dO0Sss77po/GQDwxicbuzmKA+mmGrjvu9VPc9LX1m4pLYfFChOv6EeRpEI9n33p7ciomM8/WPn+Z98PHjx45cpVS5cuXTQWNrxC6rlg4cLjx/967bVXj+w59spTd8ClfYVFks74KL00AnvppNEKDVWoJyxJKIdLhIdvmeC1m8xYfv2zrZqE1t6FD4NptuU/Wvijp72EVKQQkcxEa7WfDvZ+lvAhxoL0WjXLOmDxvgzf8H1YuKCK1RMMI7QHdFnKXRaAxoygJCwVSXAx2pFdu3zxwzbFgVIWA88DB+Ne9ckGFkDf5EUtcjV6upqq4ZxnsGDWBI0atsmS12y9tnaL+O4Ac/WVgzmOtznc1RZHSYWluLyCJ1QEAV6JihkyZBD6ZFKzVY8/sOj/Vom9/V989f1Dhw8fP/6XmmSffOzhw8dO7MwqHN/bK21NUk+EzqGya91+pfONT7b4SqeC1z+Dc6qdJU6soRiML9kyF2Vwj2qdWUW0YpcjFxx+m9L7Tj1SqyiNmmZqF+ydEiILS6rDXox5QYXeHn/OrurqFW8XUVEEFw1NrQP5sD1fIOgojhVsWQpwSEPhW0jpBgd3AvF9EXGmNT96lsayDyBaS7gJACoAuW2/OIuJRSVV0OnsYgXPSWRkVI3VybDs5n2ZBr3m+lnXRgh7Dhk6lOd4NcnyBjHrtrikeGNx2YIboDhmnPI/4tRXOkNRzzcE6UQfdq1HPZdMGi09/nZNtmf7DLggQLy9fjN6sFTYuGM/nMeA2HHc//wrRbyuXsE6HK/DyMmq8bJg9MnRUGdtzp6yKRV+xZQO0d7UqCnGuxUmSRAaNS0t2H1yUj1nagD5DlqRVBSonksu6lRcSH1hKdlBJOCIuDoSzbSEGySY4PejzOPZgNIpOyHDMLt2QRfks888vWXzbw5LTa6lZrDQL4Uk4VhPHtAfv7NmcNrgH9f/yHHctWOGUmr6+J8Bc/5XfSI6diVY4YdFsa7g6gl//BT8STFA06qkU/mSTD2rzDW3jBv5ybq9779w28lzRYrfmquHpAVR0quH9C098Qd6oHhpx/EzjX4HmA5Njt3LIalPiQlJQGmKdAv2ZpLaUeSCqqRW0Vbgda5AJe1J8RFFpXIXKkHTlGSotmAOJhVnqHufhLr38YUE/BXjR/Tu2fv9Nz/8939e/Pd/XgQAfPP1Vxve+gd8VQelNiIicvO236KjomfPmL381f8ezsg+u2Gv76kyzsKefhRF+37yULz7jllXffjj3lWfKf22EFgiC10ceoplYhvyLppTPeV5ywCANT/sg43JhOcDU5Pe+vw3+X56PXxfv2w7zvGANoD5Ny945P5HpVeffHiB4ryb95zs3bv399+tGzxkkMpE+yZLoMDdUp/b8xu+w2CC5FQJf6jCb7KklULDZ5XbOxAvzirxJkB8iGivWetkhLJvk5ysnKzxU8UB9w8sefC2W2/PP/zbiYO/oy01NdVffvvFl99+sX/7wQM7/kgfPTTYleJ0oNxPE4eLQYN7FO/W0CQT29gBU02hnsYYaOb/e+U6WUdY9JvHr/wUyqWeZplIKJSCBeqFzQZ/ZXmepw3g//7zysTxEz98+6XMjGMUTT+07D/L31i7eEIfVlgebdkL034JAnz/3TphZcBcPRB2SJDD1lbnTZ87asP3Xp9hckld+dVOOhaaFxveeXz9+mPS9jfW/KaJhWd47fZZWRfhdIa3t/j5IMR0EGiS8JNmBMcUwxCxf55ZNAr+Ym09LG2ZNnG4y81W1tiPHD8jl88E6PVztVG5DJ25193wj2VPyzVx1dsrR44Y+djLH49LS6FiKZVK5XCICRaPP7Psjf9bIcixP19zouhPoGLFRmFsrZJScfqt2eeC3AaVaGyZeSM+sMWWNz/bdP98z5Crf6+EivboHdd9897q2m3wc3vlp9D1aaA5d4R/9VQwcfzETz94PTMDKtqmnX+yzJOvvPXVYy9/8tIj85F6AgAO/XFk7dpPFiy4beLIwYrDN++HvWZUEdAsVainnJVfia7wJTMmKdQTRufKOaShvTvHZl0sXzIJ/jkAALonRuQWez7hfIXVb73G6vU7/ZrDEiu+3KGKa9pSHEyDoWma9JMdGnQOxwdfwmB3p9qpoQCAjduP+O4WYdS53C7U7qRfv56nT+ckqR0AtEMB3bFr+z+WPf3AfJmAfnasX5/+J44dgmvPbzdERESMvuYq9NLzT79gsQT+dPJBUtLGOBlahDdlrgYVAPNmjF71yc9SXsSbn/yCPOkcoXYLhQlvPbzo3mfehUG2Uj/5IkKFMtSR7n2GvbrqQ+Q52bbnxJDLBl0WIaZV0wR36/yFDOOuLvTzSYPUU0NyCydekVfkdYk3Pt/28K0Tax9vJSmajqWXzPBKBEbqCc8gqOdLH2x44s7pSEPRdrl6Cjb4KLmG+lXPlWvhn9KD88YF+h6u+HIHaEakQFyIdXRxIYTv2ndxHek38UieRPnMolGXiqvkdqWL1LtIfZ6QiogoqOLKbISV8Vrh0roIQmN0AS3DMKdP16NDUpvj1und7Lbq2+79aPr1T3dPHZ522dTDu4/CStMFs4Wl4lSdVrf+u197dO+x5ZdtUZFR465t7tFPLQ5N8BzvP2FOFSP+ct33hpj4FYR5N14/duy448cz3nvvg7S0QUJTMvG0NMGRJu2jf39i1MjLj56ECcLb9mdIX5J6ckb1Rz/slrIp3vh8m0I9hRplRqGeKJVYUk+o8hT70gcb4JTw1KQp1wzze7eScdow9WyZHuMhcPWA3ugLhEDRxVLFF2hHEHNmjqusFkP4RS7tM4tGIXNSPufjzlsm/vTbH26GrRKC/Rwg7hYyaX7e/EdJuVm+5dftR3iOn3L1sE++8f/JqesEx480G3EF2eHtuOX3x49sz/iE1OlznkFbcs8d3rl59cdvQpvFEpUAAFj28BNjRo1Z9+P3n3z+cYf9uJb/RIrVnX0TJ9gyRyALlDRBK5Uzu0iS/N9LL0+eLPoHysvKHp0DWxpmcJGHdx+9dvbkkvMFQ4cO+2jN2qGDB8pOwNMkwRnVl3dLPnACtrNMjBN/FVlAU8Dj/FCTwAX8WBWUxgBMyrVaAkM+d9/13Qd0X7/+gN/3u/bXIxaVA/lhHljk8WmsXLu9TvVcKZifdDOu36Vf7zQSZVrD76r06o9f/dylcxf0OCvzxLuv/7v0NPTjxfcbvv1Q1rCrhvVPTuRYhmVZjuMqyuGptu4/uWAWLL0rLPW0tRx/5RDp8Ztfbqci/EeYl0z3fHPe3iDGElobxA3XXV0uTFNCQomW5/JBSSpDlMvNJhiBJKAuQg9H4nH2pFgjEtDxoy4z25xHjmXWeb12LKC+yAU0RDqOgAb5iYRupPTt0++Lj750Omx3T0677/WvUpJTZt04g6txDRuW/uEnn8oFVB4A5GpccgGVoyYBG2dAOu6Lnzw5AK5Khm6J3cc8+a0K7rrl6ve/9a2y5XlSQ7iDDbTgSXXrEdAje45xHPfl2y8eO34isVPKshdeh2PPhVq7+H7D+w6/5q6775af6rGl87ceOAl4EFxAY6K1L34GnTlB1FPOG2ug01wTp5EClW+3aO6E0Fy+FqvddeucsSqa2vK7x3G+aFY6AOCn32CqHWLpzVcCAM7klhr1arWK/mnTgZ17/2zm+27NrPrsWCBJxTQGtVq9f/tBebDuzNnTC+9e8Ol7sGfC5enQDj2yx/Ore+yvk5NmXVNRIfooFWLq15vcgL4N+wqKkYYG4qPtnnCrHDqaYoP2DSc4VytxeUdEQBkdMTZ94QT4TS4uvPTY3fNeee+b9DGTj+yGijZnzhy7zfLsY/fKjyIAoab5L7Yd+NvEkZ3iTXINlYB5GDUUITNCA0nnyrVb5Y3YpTQP6QGjVr2/Adrsb909J+N8sO+spLlLpisdNW9vEMsOQ8QrD7S8yrZpJ3TedUn02O3vfXcQuK2JMZ4OnnaH+/MfdoEOiV8L8dYb0n2fzr7/p+4x8HOyLLmdG5XNgPwD6T/Lbnrm5a+lp4MHwlB7boVz+BiPI3LYkGHvvfnBiLHpgWcfBMOvpRmEfQXFUvoEgi32Ixb1vo3WoZ5CExxoKd8+eVTuRY8wfbjqf2eOwzDp5gOZr8TFLX/+Md8DGaGY44ttB+bXuoMVVFY65O2Ng6inUJNC0hy8k3fvW/DnWWWRDO1yg2pi9RNzug1J7T605/p1+wO9Hb4KEFHgrYcWZGQrsz7clYQqmn/1thmwEumi6Ep6e+ve5bdemyf7mb69da8fASVJ4oZpIzVqetf+E55vAcMBQldQzalY0VXaYdUzELPv/+nJeSMUG39887o757wKM719XkIs/wb+8mHqpZ4Xcv+cet2T4ycv3bZxBc9xacMX9uje4/6F1ytGaqOujg1Tz7Cg0FNFKkUgiWRLgg2SaUGcTqfdYb//n2+yLDt39sy8POhE3nYgY3AUlLMrBsEZc7qolFeehx3Iss6enXfj3ImjhgiJ4uIPhee4bXuPy6tF7l80Ez2975YJyO6T1HOFkPDw0KIpcvWEaiWoJxFn8lVPyWW89J3v3773hr9y6h5I4VuaubK2lu9/7/3y1N0ze3UWCjovVi65ZhRSz26JJvRgyTXw8+DtrXu9Kl7UKuq7Xw9cNcKTe3z9tCt/+FUQ8ob1pIMBfRi15IVQaTM7QJuaQMqIOLBL/BmPHJssPQ50OBbTENm2aWVMXNeZc/85Y86zQn9G96SZE13nT4N2QesxOX0Zfc1Vj9x+x99uX/LjL7D3LsdxE8aPBqBIStoZM3bc0CGDjEbj7r37jxw7nj50CIAdecRw3P6jyrLaN9fIXZ88Fad7c43Y1RcdJT6FrYxVOgoKCCOUY7x3303Hzl7ye5O1CbNKbVz11Y43l91w5oLozGEZNw1UJ33MT7Va7XJB/3iZmvjfe/D25DIqjBn2WlgsuWaUlwX63eYTgNDtOnxezYvGpqie9aedaWW9pBMTRny9yRVl+WvevdPztLJCOR8GgKN/Hq2j1gtTf17/+MNXV8A6iB6pqZ998c3OXfv+ecfMvKyTNVbHUNiKDGKxWIYOGXTs+Ineffrk+MvGDRFKZrLpKI6J9DR22PlHxtoNnpAMQhizKcYJeQDe9K4GBhR8CW1EoTm/3DtvrOQDLVPDUyIZfe6+OZOvuQw6K7Yqgz1eAirpZsNo36LZAO58+Ba5KYoJO6s+84SMME3Hui9+OJ+X+/enHiEjoJDllV0aM3nU/u0H//XhL4vGpmaVlqIkMzlXT56Ys8YjoOcqK6R9fHPXqDj/Lg6EXD2DINVr7Tzgp+nMA8u/EK/Ou4l4/U0jh3qbwB7kt4dklOX4nMz8/kN7z5hxxfr1B+U702VCW9/GgHUT03SRum93XvzxzesC7fntzotCPQzMucHBunDxwPyhO74Qi1kfmD901WfHVCr1uNHK8M6Fi/k9U3vlVjiPZByzWq1jp3hVo2Zk1tGE0FewpHY/wZsKf3HopK/vmJRt+eZPpbuA8Olhlp1fj3z+hz/88arkBLDl4O5jOaG1s6sLLJqYZmP2/T9JGqow59H2QME67FkOi8/kgflDN//0zG33frR36/4ZN0yrrILewFnTr+uZ2uveW6YDAFAmmVqlhqXbACz82yL4k/qjgd6/sPiO6/QmHym4SMVpfRN+AyVg7CsogRrqQ/0EFOsmpvl5ct6IQG6QA7sKRo5NDhSsU0gq1tPG8NkHS+bf+fbW9bCACvH4M49lnoA+wZOnMrJzsvbvOGh32HVauBifcYOfoTitkHrlq+0rKFEkV4QkoFg0Ma05WBe6ixmdDctoEIIUgDCM85N3FiuczsbaBzctmkdR1OC0IS6X82Rm3bPE2w3+BRSLJqYNgYN1rQGWZY8dh2U4HQq6Pemmbw11iF25OmZZOgajQArZoSDSB+v+7ruD9NiS0YEszUDQbUs0W0kvrOXfHApjKiheVGJaD/KQnS/yl+6cI/WF6bimRgOj8K2ZexbfGxsbu2PXjgN/7M/gIqW+MqiW9pN3P+3apetrq179ecNPYVG9Bisp1k1M62T2/T/FBfUvS4E7REcuVm5XAnpo1xGSJHmeZxhm7nU3wKLa1W8c/WolenVw2uCP31lTWFS498De5556ftlDy8ZMDj71LCQ6wm8JBiNHIawjO3CxcrDRHW2Lreu3kyQ5Ymz68DHDRl59efroodt/3/bQ0ocpSkzL/fidNT/8vG7GDdOefv6p4WOG6fUG1AANg8HICXFRdadQaBdiIkR7rX5uPwIaHRX984af5A14lj0N+2tNmXcHehV2Qvu/f6OXeIFXl8OmsBgMpmHc+fAtkox2TNrPEt7lds2aft0L/3tevjF99FDkAx3QTzne9pt1X9809+bmvcf2RgNievXKi9i822vGweQx/UHjaK8LSUxL0X4s0Ekz4VywI3uOHdp15Nd1m/r06iN/VZrlIrHv4L7mvUFMvfn10PlfD8HxcGEBqycm7LQfC9RisaSPHjppwuTl/3opMSHxy09g0/JLhZf+cZP/EZh2uzhvHdN0EAQx7LL0aydde/JUxsYtG51Op9/dUpI719RUmy3+u7hLGnq00FIvhxpWTExT034EFLFl++Yt2+GQFgDAs08+N3vG7OWfbXly/qQTp5S9YZI7eaViYMLOf/754tTJYk30nFlzn33yOY7jbh+vnCVHkuTP3/zyxLPLtu4Q+4HXCVZGTCuhnSzhKYp65P5HFRv/vfyFU6dPderaE/U7UMy7nzAOzmHGNBEJ8QlTJ0977sV/po8eir6unjaeJMnn3/tRvhtBEId2wTHaJBm8hxkG0xppJxYoy7Lzb17gcrlImzjsCdE1BY5LzK1wAqHeauiQoUf/FMt1r7piVMYpz+gnTHi5YfaNAID1Gz09a2tqqnft/X3sKE9byZ++/qVzSudDR/4YkX45Kftsw2DaCu1EQGGX7At5ixfeceJYpyMHv0Nb+g4YbzTFvvjkg+jp3gN733/zw7FTRlut1ueeep6m6cVLbm/RW27PHM+AE8RiYmLlU4UfeeJheWFY55TOk2ZOrKisgLOIsYA2DUGyHQJ1e1j+zSFUiRRGlrfT/If2I6A7Nrwwc+4/Bw2dNmiopxdhxp8bN6//HvXpevCx+99d+d6u3+BIaI7jZt44nWUbOioPUxd79+/heX7Lz9CtuX7T+rVffpqdk6XYRz62iCTbiTepfWgryjZ7+DU41m3aiB71Ou1kn2yz9qqe7UpAAQC/fP8vAIBWF8FxjMvpZ77TPQ/e3RL31UEZPmbYTXNvfvj+R2ZcO2PGtTOCJ0XgJXzrQb5KkGdBSASX1OXtVy7buYAiHPYaadyY1CAWTx9rEb7+/quvv/8KRflWv/728GEj1uw6t2hsquSVliCwBdpC7Nt2QKPxTG3bf3DfO8vmy3eYPHHKsoeWRUVFX7iY//izj2fnZAVPzk3qSH0g24+ArvrsmLyfNnq86rNjxg78021Blt055pvf8vMu5KGnLMve8+DdKcmdf/7mF4MpElSUKPYnCbyEbwGO7DnGcdyCO289dfoUAOChpQ8v/NuiK4UPObTDr+s2JSYkrt/4y+Fjh2+8ft7Xa755+/3VH6x5vyVutjXSxgQ0uPzdekO6/OmBXQVvPNrpgze+FIp2p3XkhUazIX2G3Xzbytk3W7765EG5+X+pALbjtVCRACgFlCBxFL656da1OwDg8nHDeZ5HW1asfuPw0UMrX3nTYIq0mqsNBkNiQuKti/92+iysqf3l159XvfrWkruWYgFtqwIaCFSaUq8J7HiIY5NyaP/XI668acDgSaf+2iKp6qhJT8F8iXPKaJIiRRfTPFCC20Sj0TgcnuGU+w7u2/T1h+hx/74DioqLkHoiHlr2wKFdR3Rand2BC/navoCG3iMrUMtC37Nhy7TByF0oJ4//NnDwlMuvuuXyq25xuewEQapU0NF2x9xJfo/FS/jm51zuOZgvsXX/Z1+tff3N19BGnue/fOtF9Pjw0UPT506VH7LwFjiyGKtnmxfQ9tpesD3xzdpH1Wpd2mVTU7oOAjx/+uT2B59ZZaxSLt7RYPGCgkstcY8dncvHDf/j98Pzb14w/+YFAIATJ088/fxTuZkl3WM8YSUJrVb7wJIHa2q8YvQdnLYqoKGDRza2IK9+tO+B+fajf6wLvhsqtMU0PyzLpo8eShDErTfNX3TrbYMGDvr52/WlxYWP3ThKsadOq9uzdR/LsldPG49/UhI49IkJG765YvI0MpxJ1mrhef6zr9ZOmjkxffTQzdt+i0/sNGy0l6ele7fue7buYxjm8nHDW+42WyPt3wLFNGdShCIRQrEdTiurTdLuyKMcW9Y9jVj12bEje45VV1dPmO5lUT713JOTJ0659cF/rvt5PcrVvev2u++9Y0lBYcHMG6c36023BbCAYppkKK7fkN2Pb15355xX0eOOPMqxxdUTbWQYV2RkpN9D8vNy0YP/Pr98yjVTXn7jpa++gwURGAVYQMPAicNnwnGaNs+ArjEAgJnzPgi4x5sgJ6cKPSx+E9ZZB+dUfkU4769D4rA5P/jUk6UkhyTvW3j3+0f2HNu/69P7/wHng/Xo3uOLj6FQPnLHPNjoulPKlGumvP7ma1g9A4EFFBNO9QyduAR9WYktlHNiGW0iOI79/MMlf1u8+sqxC4/sWVi7kZt6RV/0+LvPYfTvkfsfVTTblXeB6eBgAcU0q3r27Nst50xez77d6lRPTFjo0x3Oow2E2+1c8y4cW4tCfCRJchxntIqjU66ccIV8Z51WFxsbGxeLndceaLbcBeoPFatuwFEYDNJQ/H1oNs7mVgbXUHn6hHwquC92h/3ipYsXL8F6XEyj0pjYclfjv0AjaKJQA45gYNoZWr3mbG5lA6JMmNa+hG+khtbYoiP0dfxmtHXd5J3Kls99eqRIj7MKipr9jjBtD61ek19iGzu8c5CcM+nxgV0FqP8OTjVr5z7QVWvWN+g43vPBuwh2+a2qET0+bYKz5xte8ojFtyOz6zBceiMZRQ5oRarZyLFwTu3IsckfvCFuwalm7VlAG6CYYZJgoJDg5fnlgOfzSj3Df9qc+BIaPBSz/TOga0yQ2B3SUySjwZEL6/LWunprHugOqJthBElw7oXiRlqFLjcDQBuQYEy7z5SQm6W4h1lHFtD2vyTHYJqu/059G+x2THAzEQwkxmBKiopOia4j3wUO5ltwTe6hN/OPvJV/5K19v/yLphu79v/m06d++OIZ3+1r33/M7/YQObL5f6d2i2WjEi8su9F3I6bxPNlR20u2AwHlA3xh6kF5RVVhcdnFojLeyQb52vvzC08/fP27a7deMe3pq+f+Kykx+tzBlSajrjHFQjctWq7TaT5c/bB844B+XePjIm9Z/H8NPrNBrzXqtYqNahXtuxGD6bACioWyWemSEnfVpGe/+GpPYXFVTm5x6uUPMAx78vdXUMEl+qrvOWEL9G93pnSKlW987X93HT1x/uhZnKfVNljeUUNJbcgHirWyVVBWWW13eBJ4Fy55s7zCLM9XPZlVGuTw1K5J6IFO4znkieXf3nLj+E0//Ltr+n0AgD0/vwAbO90GdRmDac20IQHFtDw2u/Ps4ZW792cu++faS4XQ2Ny93zNxLBTO5fs3Kruk3Xsh451Xn52/+sPfuqbE9bv8Id8iArkEny8OJtOYRrZ6wYQI3URV7Y2sMsK0TvqOeGj1K3fOvHb4gS3/RVvueeTdX7coG9E3jBde+va5J268cfaVb3+02Wpz1leC/S5Z8o+8FZZ7w2D8Qky7yf+UxFYLWx7wT6ulOHk6B3QwCIK4cfaV/33mFo1GlZNbPH7Gc2o1Pah/V2mHI8fPpQ9JlT8N5bQXMt5hOa774KXSFsVJQj/nmUMr9DpNl7R75Rv/989b588b0yXtXoIghg3u0eC7bXN1B34t0Kfvnxau8y/HPtC2AhXrZ15gW5Hatk5MtLGiEla+8jz/zQ/7vvlh35wZV6xYfjsAID424sfPH5f27JJ2r+JpiJdwuRj507Cc0xeCaNSZA7kXQqRPj5Rm7mNwKr+ida7i7512zTu/bgVtlvbsAw1darHahsKzj829+7ZJCnFZt/7giuW3EwRxqbBC8VJjBC7QScJyTqFvG99EZ26e6ok+PVJcbne9Stdap4a+tXZDfQ+5b4FnNFOLi297FtB6gQ3bOvn3K9/ffduk7COreqU/IG08suMlZJA27Y8HEw4JlidI9OmRUm2NrKqx8DzfPTlYG9DWtnh/q/6ai7hvwfSPv9/p1HktcRoJFtAwSG0gj1gjF3qtkGHjHz+68/8uZLwj39jjMph7hGlbnD1/6c1PG6hEiPsXQkvwltEjecB/tecgaPW8tXZDvValoUBMuWZciLs+ePust37eFN7Ltw8aP1SubUltdJThroXXqNX06g9/Qy5RTFtkYL+eLXsD9y+c3kgRry8tKaCB6N2jCy8s4eTrOAKAXEtHydQL41TOtqWkmDZNiwto8xN2AQ3DEj7r/AXFltRuKTwA3YzK4VOE8rnXhlxzSeNvBoPBYELMzEFeCMTbG7a2Fh/ouTw/Hm6kql7wvFxBuxvjAUH4DUcQBID/k3G+naqtrzsV26QYTBNRpwOhTou1+YJIvqoaSFKVEkoIWwherqE9TAn1unp7FdzwCjcWawxGDlvuXDxvsk6rstnsBCDW7NjTiqLw3pIKVbNH185QJAWd9JiiPDQ/4RNvDa0XQQQ3O/dCUztK2grNU2CDZRrThvjom81BXm0pAfWfNng+v1WMnK5XCdN9C2fcF44gkl9ZMRiMVmd7C3Njmca0G1pEQNtV0vVbn64P3aTq1T05p9DPAKVAZ7Ba25t6NhvNVq6ODeqODE6kb1ayc/GQmfZGWJQaq3AbBQtoGOg4+XQsHUFTJGm0t/SNdFxwi5yOJqDtasHewaGYGp4BbMgu4kFpA6JN2r1nTzbtbXUkcIucdi+gWDExIicyTtXrezH9mlFOl3vHyeP4OxgWcIucpiYMpZwysHRimpURwwYfzQtbHS0mRLAbAdTy/yvOnVjsXaIaAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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+7PANG6Tsdlakq2dSli6lM6E69UzydVRF9cpUEn5FXRPaZ4vqZyT3mVfh8lhdHuumtfjPp9NjUT1KBZRSBhBXBVmGaQ5YgQ8vXdvbWhnA743gIdj5tZ9k8a5vP1UmlBc/aDr1TAT91FCingmlS98TpKeoUkkJCU4uqqcmFKQJfRLp1A/TUAGllDzyKAwI4cnHjktZy+efjPZTpxhAv55JLqpXmlB1PRObrqieSDCTzoQm8u/JRfV92YTO0Fq8q6ST2E+CyoRSAaWUERAwOM2CiYW6IsHOXrSfKhMqp5L065nkonrF95RUVC8d1DKh8vnKSlIpL8+K2SdFPZOOCe3L6ulKdZ0y8THRSg2lAkopK0wcyzBMVVWiLWbe2bBhy5D+ONiaFcpUkmxCteqZ9FblmiY0tahe/lwj0Y1K10F6RfVKE9pHUkkzFOpJdFNTOiX7GVdPgqyhfe5vDqW84VjstSxsh581599+jhsu9jJm1sf3R6bD4amO8ohjiZDhEitW0V5EkJsoQ17aty5+PrEfhDw5iFfluLZJOoindWAHySaahIoT3sV6JkYVm1PVM0lXYEnvEUGeK6e8vmhCi2QXQkHVM63fzITTY/HRrZyUciEuKz0sAgaT7/7OtoivPdDZGuhslQ+SVBIhxisWf+lNqHJVrjohtaheVc8knaB4Uc16Jvm/I8eM7DvtRdav2qW3Wte1n0roEp5CyaL2U7m9PVuUJjRVMfXrmfCTlAVMcoW8oqheFTlNuq1MJaUU1ZO7cky27DngCGTEeCpzR5o4PRa6hKeUAYXobjl+3PAN9VscHre/0ycfHDd2WNJJAl+/aafORXjAyDJKwNqHpAU1hOJ/jazK4/VM+Ez5UgyHBVeAeDM9uax8Jr4hppIExREWty0l5yTOZDgy7cMeai/LaR+TT8bhS19XyOgT0ttPGgOllAM9JJ+nHDps0WIND+LwuHCjJZFdja3i9sr4e4jxDodt8IDqdDKqVM8Yz8jjkiTtUyCtyokJhWJ7EcjjACgeZ4QjoSNHjcKeUQ6MypHTuKSmFNWTkKtY7xUPjMqRUAnx+upAbdmpJwDA6bbqa2hG+0mgS3hKuZBvHV20eLny7ob6Lek618WRdGhnY5vTYXO4HIkvj9PhSdpUmlRxmdSKSa+eSXlbLsVPjZwq24jIgQJlPZNcva85Lqlcd3ZOjqsnwem2dtN+UgGlUAyhmhOXL5T1TDpNQhWR0KQedHhvklZR/ZaNDbIEyyaUTPRMennNTvXx65dZUf3kZPXU19CE/cyUk6QOlELR4JRDk4ObAGSyn5nJaELTNQlNnCULq8CmM6FJszmT9zhJJpQMXJIvS8YjpzGhZaye3Vy8E6iAUigZ1u89ZD8JMV5hKdPXM40cnRB0paQq0/HKeqZ0JlTdDFR+FYUJHTlGigCURz3TZF31NLSQTw8VUAols/20d9t+6phQSaB1TagkiCQ8qly2i5C98Kp6JqUJlU7DNpNVmlC5nklpQvtOPVOqhmZlP6mAUiiGGDQg672bWSGbUOV6XL0qF01ookRUq/VnOhOqeVrifKLIWjM7Sz2VNNnY4j03H8qZpK1mFApFWb2UtH7vfvRTXQWlgEF8jGeICU1sKEouqk8SxBQTqkwl6RfVi0v4+NBjXRNaHqmkydmEPp1u69aN7ZyJ5TiG41h8I9MXdaCUMqAQVfQFACXPN1aN7pBW5VomVJlKSlwtzc5OkaSeTNL56Wd2lqgJnZyNegKAttS32p1m8bcJF8oahDpQSglzgktjykI36VH7SRg/Tj2JSDahql3wMvomVKtJKC641zSh+NtK3tlJ7qq6O5e0CZ1sVD3J7EG0pV4ai213mbPqqUIFlEJJcNtlxxXgx7FjZ1O+6pmSDGNSYFT6aI+ItxDVyO+zkEkxoWVQzzQ5C/XUAPtQw1ABpZQ2+V3AP/D4Gz1tP3VMqJF6pnRF9alNQhGDL9Igdt5T1TPJl0Up3UbSFdWXyip+cmb1lFynfF+2nzloKBVQCiVt76UCm9BM9UxpVuXJ9UyKyClD7hITqh4coowDyGJS4kX1k/XUU62b6dQzCw1V9gqkUPoyqsV7jxbPGzGhWvVM8VX56MRIes16JlWT0DQmVJ1K0iyqlyl+Ezo5rXpq6Ka+euppqKzD4iWpgFIo6sW79PkxPPgI58yzZ8fOJqWGqkwo0T6lCVWvytOkkpJNqEbkNP7feD0Ta6ieqciZrKGe2pYzK+xOs5SaT3MxKqAUiob9nDB+TIjvnaVrOhOatCoXTahOUX1CUlPqmVLXnXIqKWNRfSmADOqmvv1UglPzaYATjjg6mzdHoRSUSSbcWHNZdLDmoye4vCgewBs49Vi5gG/Zyvr1u/Ekotyinwzu95nWW1iclRyUJsRBqSKddASFIBZTHFdcgVStSyROw8M//InJ9aQFp4nFrw6x2jEACRCKBfAwihQHxcswiYCpqI+AQVIPUPEuZHjyFCCITUJJfJNcQYnyCpDBTUJ5hKct4W8LX4E8SrosC5Atwi7LkyX7mYXZNK6eMgFvYiygTAn9YaFQeoSs1BMAEPbtDSvujh4/Vr7NmcwIoUiUt5g5CMH2Bly2qc9++45dvWaD9NJiG2MiA6SlPDGDuFM9MmENFTvVp3ZNTgyYS+lUj48wjKpTvfK58ZcWBPkpZPycGCUgwk1MKNHQYmPyyeZs1+mSembq9anC7jKnaigV0D7BzcMWkRt3b5sKyp7CDpfctF6Sv9yQ1TOpqJ6NfxNYMdXpLI1O9aoTRAWUO9VjE6rXqZ4FkBd3gZIaKVa/U31RjfqYjNUzOxLeM/vfk1QNpTHQPqSeFCP20+ZykTbyvfLjklNJKH09E+7smZwaUu6Flw4qAqPKYUrKZ6mOlFxR/eTs1bP7qOKhVED7lnpSMTWOE4/icCm+nM6UyRx5Yb99E0GA1KHHqfVMuLNnSlG9ZpNQZT0TaXmXsag+dWdncRbVT85JPXMIfepraHH9SaHkF025vHnYoj6xkM9r5TxUFJs7PU68wJWOx9saibKnOB/4FNkhmXFjhibuiJes37RDtYonkVAWCDzOHYn1TETL8JsQF+yaq3JxxKa0bCcHJUlF6WZ8Jv4rXhbv7OTVkVD5UemJxREJHTzKtz3es3ToKEPxBARAQz7UU7WWpwLaI1ljncRxwdAxm+Wpod0OfUp7N+Pi2M2XTwoCxEV2Z3O7SmrJaX6vumaAT5ZCOUdE/qvsLZ9YoSenkkSzKdaVkux8ytDjpEio+puJx0nlKZ7JpxVPJHT7Zj07PCQur3lUT6WG0iV8KRFqb1d9pTsz41KdruVzQL9gfshQI38yta+gCrmOHzdS7B0vRUKVRfXxq8RX5cpBnqplu4Gi+sRtcdmOg6jl1SR0x+Z28rVnZ1feL253mamAlgyacpkqqfrCWvYaKkXtDK/fIZIWpAwQ6gZ3Z9Fg0LdmYW+Vpyq1TBZEJaqiek1fmVpUn9rT3nhRvb0IIqEGiYax4u/Z2ZV3GaVL+DLk9oNXGzwzndRaq4pidVYAEEx8BLzejJ8uRb+kFC3csTupP4gRBg8ZuHPHblynmcyG9RvHjx+zvn6LicWRUEZegGeqZ1IWMKkDo5DHHjVNPZM0LklRz4QAvqB8vhQJVdQzCWLZf8lBNLTf4Pz02aIOtO+qp87JObvaUiHVfua3c10OYVTNwqlEUT25IXaqN9gkVC6q1+iJl6UJVc/yLLJ6JiP2U0W+3Ggp/SCKgXFBjdSqkq5g9QgWa804PsOZ9TYb6FX1lJ9yy8r9QFmQ6gr1TkYxpf0svjcIxo0bWV+/hZF9nqKoPmEeU4vqRZMop+NVRfU6JhTfT2dC40X1UkOTXi2qHzzKVzxulApoflCqodsklqdE86yP1qoqfRuYg3qWn4bmVjxvdzgyCxvOR2engDJDBw3cvmt3xtMcDptyazxBrmeSmicl1zOlE0SVCVVm54nmpp6p2tkpzmhiiryeqddllApodvSEbcwLOatnmWkoLF77mR0b1m8cM35MxnomHRMqX0odGCV758W7GvVMCRMK5cCobELjJfdsEdYzGV+/50VG25tx5RmNgZYD3VTPPF6kJPduOvK2a3Mo2zGU7cj9+SnZpHHp65nkJqHpIqGqTvX4KyV9j2UyuVN94r0oZswpd3aK3UnEt8NwDOIL0Hm6+zQ3Gl31G4mNtjf7iXpSAS0H+ojwaSKmQrLuaWzEfg4e1D/bNzMUteq/rNbyP6X83pFY4mxcv5HcwCZUr0mofC0h9TQGxDfU4wlySZ3qcagUm1LtnZ0JNMclxREAU7T1TMbtpwodDZWlk0AdaGmTX/UsOS0e9auZ+H8oW/vJ9IT97AmIhqaaUIKiU33aonpBXG7LRfWJ5LsiH6VO34tXEMjMTvm09EX1TCmY0GxJtaJK4ylDBbSE6Qm9KzkNzRZlykX/xCyPa1BRUUFuZJV4UppQsopXvwNRKzVNaNKqPMWEatYzySY0/lbT7OxMX1QvFFBG8piCNy6jmtJJoAJaqvSc0pWWho761TW9FP3MrIodHd2Ihyab0CjPEBOaWIArSBwUsz2pJlTZ8s7QoPn0OzsTjfWY4urPlK/1eyqhQDTdQ1RAKaWuodC4QUxnP4ttcJrKhCr3niuL6hML9kxb3TMW1ac+K/7SUu/R8iiqz7uGUgEtSQogcCWjodCQCc125vvOXY2g8Ah6wURewD3upDvinwGVlqU2CZVq6RUzO9OZUFUqSWlCxW54CRMqp+YhXrsjE5KUpahMaB7tJx8TdDSUCmjpUTLS1vME2tYS+6TUUB0PKttP0nk+uxfDVfTdobs2l0iXTqf6DKvy5HomlQlV1TNpmlATiuKIKhQYEJPqmQCIIFjGqSQVqRpKBbTEKKR6lqZSF3YiUvfYuWOX8VU8g3jNeiZlGl1pQpWnyeCNS/F6JqXmptYzmUCUEXsFEBMKgRAFVpvHTX6+DIwwgDeBaCHrmQqcQTLyx5AKaClReEUrfg0Ntq0l3tBes2868dScu+n3+v3+YOKr0yd9ef3ky+f1+zL0MygoAmQ1i+rlrJF6VZ6cSkpnQpU3TCjCgBg5wkPEArxCZ3iBQbzodrFcWl1OmwvPNWGBEIklBKR4TGge1+9pTWh8tmmfCACXB73VvrNsdnnKGC+7gRGvX2MYeBry2c4J4/A4/Z0Jz0U6yJGdnWJeSNoFH1fApB6gyoNy6owBPMnIC5CDYm0Tvs2YGXHMvQBYs9sR9PpI0snmlL6fYKeXxU9Jem82p9vv9SrjEhxEHdZaUF7IAVAloUDUYjORqcjUgZYGvdv8uMh9aLB1Lblhr97XiP3ERrUnF/p+f3DgkCEDBw0aOGjQAPFL62OYI8SE4r8BiqJ62YQqi+o165nEJTkrRjNJLzv8X5vTzSLB4cRFXTaXExtPxdqcBYLZ4yG3QzGWfPk6cZE5JHVOknrWgXK3nzLhIP75IEQdaClQlq3j84RU2B1oW+usxTbZXrNvsHWNzhPkoWlKxo0dJl6Hqa9vkA7BpFcYMW48z9q2r11p8G1tFIfFjxmfGLc5YMDA1NMad+zwxziHOUlfXU6H1+fPaEKltykWfkIojdUkzUFkxPCo6DpFE2pCUR4yLMKCGPF22lzuoNcPgeD3hhwuqeYfAGCOp9eG1VXwfGwTz/k627HTFbEyWDuUwUiuaNSzkISD2IfSJXxpq2e09b3cLmuqOcP4ycW7hM+U2daMfpLbygHFO/dIE8fGjRtBbtRviCspADYHPhPGAsPHjm3ZtU3zhWKcC6BdmjKaEX84+a4/qMogJX0LiAeQIz3ulJ3qxdqjqNSlieFMQpiHrAA4hMyMINolAKLIDBBvc2M7aXZJppJDMbNbHX0YVldR3+irb/R5XFYTx1o9ZMEuqWcsvjseAOCzFW7ZvnOzs3jySERDqYCWvPfMSgpzoHjVUwEEYNPbT4z+1UyAkK1mXwDWG7efSnY2qctFHQ6bgGc64gtCCOx2i+YTuZhX7vHmcGicM2DYqE1rpVBDRlK7gsrgQRp4FY+R/aaySSg5aPZgd2lCEbPbBYAl2OkzoajZ4/Z3JQmQLbkNvgUIEZMjGIrWi+2LrGwsHPClDu7g4nVUhadlh792iKMw63fNAKgKKqClTRmop36T/+oI1Gnvb9q0S/JWowYq3ajZNRSAdaqTlfazO4R4/KmxCX5k0hbTVIaNmcDHYiPGjUMIbd2wITVEYByyK4kXOJbhcaPleKd6E4rwDGCRwAOTgOckh5UzQsQ4pkaSywx4zlnZ5QvZrKZAKMqaOYiQlS3qTsktuhpaYKiAUsqHTf97YvRZYn+mOD5bbbZ7kDJC9AWxRtUTALB146aAH3s6CKUCT3/AcIVUShu8ZBMqlhsB3ux2RbydZpcn6A0wgGexzibeoVI9Q4LJauZMHGvi2EAoGg5GOBCJhiImAPiIoZoDn7fX1tHhSD+LeU9W9tN4M9AcoAJa1Ny9bWovZpAKs3jXb/KvPx/lsNGDknaGEw09+3epZ6aWKAa8fsaqNmV2pN10p5sEA2kvCxlot1kzLOGTNVS5NR6HQeOxAxLWtLnskS6vuHJPwteJg5gQwgqnLRToisZIoj1pf1UUmCpdNp9iOukQAf/52cHgQCdjcQVFb+p02H3+AOg9iseE0jImSgmHPtOQZNjkLEeXrZ/yuN1ht7Ix1VcAOsiXfFogGOrWW4mEUCTrKzgcNvlL8wQyUpjFGXYGxHejyxD1NCM+hkxmi8NscQDGavdUk0dDAWWbS5y6xydb8bdsAlGlesqQoiUbEyNfoFfhOFyb1bKjR/7UZQsV0GLn7m1TC/+ipayeYNNbT6iO+Gy1KvXEYUTE8IgBsQj+iiMrabYvOniAdjIamq3QrDaYSlLtpzbJhasM4qM8yyLB6bTLB81AcLk8UcFkszmjgolzVMR4IRL0RYI+EAsyQljn0pGQpEcWIISiTKePJ1+g+PAHpBFMSg3Ne/mnkQwSFdDSoMAaWtLqSdjz/byM57BQYKEAODP+6jY7G41GWmEkCCJ52CLKIN7s8pCFOQ4OxjjG5g4EIyYQCfq9JhAJ+LqsCrcY4lPT6RJmszUcY1jOAhlzWAAxfwcb83NCkBN6c52e0YQa9KEo1oN/BmgMtDQoWDC0DNSzUOS4mckfg5zDA2JSr+VAMMQ4KuVHzSaO9SW0GJnS+lMBsl2dAXkJaWGiIX+anpUmqxANWVlelUxhWIvXF+JYaOaQhWP4fMh6YfAHqpSpJKX9bN7RqToZJmsoLqLNH1RAS4YCaGifUk8mFhK4zMtnu8Oe3KXTqG5GBBYBYOEDILnayY4X3VFZ6uy42EiR++YjyIbDkQJrs1vNLAP9gZ3pUklON67w98e3LangzLaYqIlCNCkIG+HZiLdjZARfqxbXQSEgao7FKr3PkPiQ1YrFucqPv9+REQQhssSDrdvtkgEsBlp2+CPhdNGJzIa0m3pKY6ClRI8KXImrZ9Z+UGDMQBBCMUb+4kxWJhZmYvo5H5J1wV0w/eGoPxwN8+pKzsH9cJDOzPAWhlepp3EYPhjyd/q92kNBWCAQ9dSpKCLqCTmLwFgiiOMZ6Z0MH9ytbZcs4uv2+kdG0MiwMDIsjAsG5S/QG6t4I+i1iI3xyq90AVAGj5bS+KIOlFL66tmNVsXKKGEsHMPLXbvTZjHJti7gD7gsrKY2RK1DTKEd4l5M0ZrF0zn125vJDYclz58vKHZWYoFgS9l8qQIx5i5vCEDAsWGHBZghAIL0ne7etRsAsAUfwhck++mJHBN8PHbETtFmInHT/RYBQgaydhfRIisM+7w+sjvea9arQivMKt5ssWRlQnUgGsoY/pWiDrTE6AmlK231JOSvu1Io4Nu7d2/SEdYxZsJ4LEBC0uqPiXp5tiKMnDxbwbOJZhzkrrlyTPffDFbLZCADddSzurLKH2GtVifHcgzLcHwQRHBvU9Vpslw67FaHXTuO4Q+E/IEkM26FESsMW2F+pKp4TGh3oAJaeuRX78pBPXsYE4wxscC4cSNTJwzr4O2UGpSoCPiSUtsBrz/Cc2wszMY0hIkxvuucs8aAyWy3olBna3NT194WJhZv6ZS84V0fX/JmeYLTkSiWKk7MlsyhEtK7L78rGrqEL0luWblfXnp0Fq16TjLFMye5kP8hm+s27RZwF3fgdiRcD8vjACXHcbGYRt0oJ7aYI9jtZskGQuisrrNbEyECO9amqF+wMQxjYVmHzcxAGIrEQuFYjOdZRtvimIEAra62FsWmxliIA6BRXJ4bx+/Hy3wxp28REBo2oGKLYLKAsM8XcNitQ5g8jGXuOfyBKoe9PRZLO3O4p20oApAKaN/V0KJVz6xI2SmefwK+AIMQGYNpBCKsGE76fI0bN7ZTbFoHIWrctduXnBYXC/h5XNofjcoPMUAMXGp+b4KAg5QBr86WSrvNDPEuTYgl0gBRXrCA8K5dTZbi3guvg4FIKE79oQz/ikZ/m8h16BK+j1Ie6tktY5GnskdN+6liw6btWzY1cEKofv1m0NtAzuZ2SxFbh0OKgVqAJD0RxPmSgwwyIZ6LIFNYMDnFmUhFtSupkA4UASirMBXQUsJaVWWtquq+CJaVeuYKctZEGGsIWDi72+L0hKEVS6r8ZQBl4kgfv7cryvNrNu5MjYEWAE6IuiqrZduIIv6ujrTjM81Q4+8B8blWNmaGUYvYU5ngNdeAYkol5TUSmkE6pVfP+VqU3kKpob3brqkYyPnTAGNBvIWTAbFQNAaABQIgNtQQW9XZQGcruc06q3hve0YNFTuN6ikjR9pBibVDyuOD+1fL3aR27slxLDAZZ6QkEMCrfIcDu0veXtXeGXB6XPK+T4I8pUMTnIIvFqNZsJCP3m+T5tqfCmjJk62G3r1tqrWqKtTesyO8NTFea036KCeemL6hMvmVj44eFD+Wh4goVLycBYaJLuKtOxBYgGTQqqqrd7UklQdVx6T3PDwmFQbZeCn+GIngKW/kAwgBalI8a+NOXDLFQOi0W5xOl39vs9RkPvv6Sk6Idvg0goAo4tOu9xFfiARJQ8BS5baHIjEhUhRdjnJOJWWKhEqj9DJFQlXPSXsyFdC+paFyXxKljdVEpbAZzy8DIgIuujTzhpbYe9valR2Lc0buDhcNRqNBAFgLZ3WEI7zZxEIIPE5rup2aSgSTw2oxBcPR0UPcTY1Nvg6v3U6SSPETGCsjaGST5BSTlYkGfEm7yMXYqMZTzIBv95bMrvlsHKj2cX2ppQLahzR03rbhAAw1eMGeUEz93smpfZQTT8zYUDkfmBmx6JJseQyrQ4F46w4EDrv0kYEA+sJJ+mKzS+9lc1hyoG5OukgATyOWPokQQLfdDdLLYizkxy3mRRfVFu8yp4JDMZOnVo5OMnwgEsB9lZsatdVfqZ4Wp6etqRFkT4jHfexd/apd/cG2jYbm5RVfPVN8mqkBE2rEpVIB7SuI6klJEEUmXtEmxAGwq+LZPLS2Sy1jUmJ3O2KhpFhktjgrqlo7AtZkw6iPzepo2SNFDjwVnnDASwrmSZDU59NwlF0hRt6cSrDCCOBB2+7ulOgWgrzs7DS4xqdZ+OIsBc9z21CqnqmYYNTK8vKXH9ginJM128ICp2ydGUjZB6nasil/KY8DxxAj/2TKVbZxfF0dVsbQ8CJCKMIGfV3ybqLm9ApoQry8E4kt1mag3QOmSccjnVS7DlRAywpNDaXqaQQry7MowkeCFiYmlrVjINQYWKRBPFmkOIC3yRt53TBjiTDWKGdlbE5odbB8hHwN6ldBvtI9kUM8DzWSQ74ObG8DgYg/Pm/eiNqGkWXgwP5BxuF0O8lXama/mPEHqrKtZ9IsacoquUSX8OUfDC28et5+5bHKu7c88yUoY/gQYK34S/O4ASzxJJIg9gjlLTja63C5g4jjeSGMK6y0iUGWVUxDMplsJpZxVnh8HfUG3ztCyB8IDRgx1h/YYIHh3bub+kZED6bki1AO0knoGz+xcuT2K48DAGnKk6yhd2+bekD35C8jj7/4kerIR1/8OOO4g3pkR3ovbeXMEOtMsZ/iY2nVM9Dl91RVA6C3G9Lv7UoNHIwfN7J+Q4PyiFwbz7LWaNAXxVX60kg4VRZeidXhbm1MbJnvatll3GwGEXZ2Ntb49vOCouxLYDASCgESurFDiQpoiaEQOJSqd7KcPds4CQDQz5zU7Fw8EsxWEHPgoy9+nH7cQUTY/nHFsbfO/TLr4tD4aqxa7IiuUwdKfhCKOlCjBPwBi7OCCydSMTmUXspETf0AaDCURLJbo2ly66n4tXojhXjO5bAIAnJV2bzteP4HH057QYfDZgYC46pubdolFWB5Q8run8YJIkvRSieQcvHaHbB0gAwDk0cOpELrQMuHhV/9NO2YA/IueXkEl4dA8ITiXfUzgz2R3mm7q4PdZq3tXwtALYpnAnbskHyZwdSOMsqZfuZl/rGysWgo8wb82oG4ZG3rxo0RV5XZ3+l0OYljtYkhUQvLRELBEIvbR0elvQJJhJBF5cyU6qm/i6n3gFmZUEiaXZFfWZ3T0i9yqAMtMVT/kqGftw7166UIPLGkPOFpR0zQv751/+Hd1OWGHY3dLw7F/lmkzYH060C7Eyto2YX7ycvYFD8q2dEFkXmfgyasWbFUP2keE7s1KYXVVdVv/2G2n39O6phVMNUJBCJbN26MNwNVi0hYYMM8sJgsFsAr1ROLpsiQgeLYD8VKXwv4jxv/cut9/wSlaUIl9eweVEBLDEFAC7/6adNWaTmGceiVLvaLJC1Pnv1hXYYXyHhCH8PGxoLB4Ijx+5C7zbu2aZ6m7P5J8Lbv+WYvcLEZ1HNw/1ogLiF3Nme9/FRi91T5gxG3wxJlbKYUxVSBhzUpCPKcx2kVBGSNhwJaGvGfFs1SrM6YOEPUUmmy1z34+H/djqEH7Dch4MOtA35YswL0PjCXmtBMJjQdVEBLDJwz6a2kSVFSgB/GtoYdowd78K3kz1gi1pkGJtYBWPEjJvBirgI/PRgI2M0J77NxRzvHMqZY1+DBg8mRdEoqhxpSMTs8nb6QCYW8nQHjYy5Cgqm20tHa4bex0Ugwc4vPgBU35DfhnBUbi4QtTCdA+I93/dofWCZDGLFgODxmfyfK1GMQj3jKy8tRAS0xIAN5vlh+WYuBnLPwJhSLGmsoHg6HVm+S1rkVzswCxfIdZBV/6CEHrluzBgAwYsI+KJ6paG7cBUx2EJGGv9tY8aPOWbuCMauFw5Wndpc/GIGhtOpsQnwQ2qrcdrOJIWHNSNBrXDfDgmlgrbutM2CNBb2dHbgNlRZB274gKNXDjT3819u3Y+sdi4aDXbvcnorEMGAIio8M7ymPAVwqoCUGFDv39Pa7KAeirDksmMQuSRJusR9dqqqaII8AIj/1QCBit0sxE6KS7ur+/ub1qqcQc7pujWTrzDEfQkgOoaKoRtKmq7OjSykAJrEQSiu97uo/tNrENm3favA79XgqzRxs2Lqd3LUw0bY2vXBByIbH4UFFAceOzWsgNp4CxwJrZQ0vDvj0+4IO3fBRr9Dc6Kzr7/N3JUyoahWfVj1zWsVTAS0xvt7QPnlszzZGUmbM+5mDo4YN3Lxtt+ZDmk/sZw7abM6ggSVhr6PsDYylE5iDPBc3tNJ3F0gzM0OmrbXZJKXjAynZeemHsHpTE0KSA61wqxNiUYExxeI/LnPm8W0mE9e0PakgVBOrzREIRSNR3i3wLS0dOvM/Eu/EgXOMoXYizSgf3aZ6AwhsTrO3Q2MVn/fiASqgpYfqV0BH1PZEbP3SXERfCrsJSl5VF2ENUzqkBXU8C48QcthtAcV0X9l+ykt1U0r6KBWlh9V4lBGIbkLWjBASBMRweBsSL2gEJxx22x5d9YwiU4wXPA5LhzdkZaJWBnvbdCd3xqQ/xv2GjA/4/U6bdW9Hp6dmeDgi2s+wIlepBDI4pJtMLFBEE+ggo1FUn049GZYVeD43E0oFNP9dgeVmwOnKv3No76YkKQVPMYAdWkxikTwfkiKP3aff4KGRnY2RcEe24z10QDyWLZwqisVYAFgIlA65vn6TTh1VRODqKp1N7V4rDJsYEAli6dQhZNvHbrcPtttisVhHZ1co4PW1b0cOjwUAPgKsxpJCoa4mjmP9PMdydqe9CkTz9uPtJhBCq8Pk64z1dOEqFdCeos2B0lUvUvJOug/HMYcdTW4cd+wUsQhM2LqjdW9H5+aGbY1NLdFo1GS2hrpwgMLNZldF1LRz25DBw3D3z82bVRl5cvuA/ffdvEFjW3rAF/DYFSkf5fylXLdChXnOAiMde9utBlQi4hjLmbhh/Wu2b90aCkBesJhhmLQfNULU1xaJhoJMLQMsDk8lACGxQKHogqEMA1mOFQQpys0wrCAkFW9pk6UJpQKaNRlto6oZcB8k3TTzwrN+3RrljUDAZ7G6yRFs8RgQ6GplrHhfY4gTyxtFrMFNRi7etHNb/8HDRo4ahSUs4AOd0sp63LixAEBZPRGu/IkkbeVUXCTIuixmtqbCYeLYTm8oEIpEebH7choijM1lt3j9oZGDqxt2t5PAqwVm6LfkBeL0Nwew2N0gFub5SP2qpS5PBUAg5O8wO0X7nOnX1ufzRZGp0lUd7PJWOjPHanszjzTAh02o3RzAM07wN8ZynBAxIKBZQgWUkn+sZs5f2Mk6L8//4YRcJ6BJOxTFzkZBHntDn2kIV4UtFeRs2JkF8DYkfSx2J4jvqnc6rOvXrJUf0jc0OOrKx9rbJAfIkPF28aek/hTNIBwOhM0Q7NzVaErdYyQS8+wPxb2qMV6IRqM11ZWhpnaHw86xIBQKwyge1gLJkDtjdHa1eDzYaFa65LySUOzNMCFgWQgZAPgsC92yMaFUQCkZkFPwxrHbTG14VFrpQcTU7+0wOcW9jHw0ikxM1f7iUruDtVbKimYy2yHgWyNWn88PIAshxLU/Ikr1LDymfkdYxSrNll31ACCXw9He1GEFIOL1EpvKKjZHxYlPvIvT4cXKPCQuD253rbotjc6TiwYIocXKhQIxsvnE6Co+G6iAliSjhg3avK14U0mmRJl1SeJwVcqmxQSlBbfJ7kgSkRi+HfaKFezJDqdyxDFtezuYjlXkbgF2jvGe/a0Wc1WVJxyOtLd3cmart7PTUz2kq9VorajKbDIg4nB65JlyGfUxEAy5c+rwVAgTKs4/Aj1jQjUEdOI++69Y+3OWL0gpLMX5F7/3QT33w2VNJl7seZyRxqZGl9MBaw/2BYIQgFCgw1w9Uv4Qhzm7zWYVmhuYoFTZni1szaEQwBjPRyIRkwPreKATD5Nrb0YWm8dsNgmBTivLRINGx36Evc14Tc5WdAZCZKlu6FldjRHGifPbLMMyDCkhKKowKEklma1sOMgjhHoiDEodaB/Vz37mYAmVZ+ZMdd2AHRtXir02+hfsRa1MJBqIyJlpxmrHRfrxf7NYLNDZ3Gh2VLLumliMD8VFnzNZQqEIhMBsNtfU1Hp9fr8/wNoA37JMeXHn4CmCIHR0dkXCIZOJs1hse/fUuz1SERULYyDDNvAEXb4Yx5kjgjkCzDUeC8kK8Sjz0y2s0N7RjhBX7XYEA50Ou6t4N8dhE8pAyGeXizdsQrUFdET/IQ1NSZ2+KJSiZcWKpJZxMjWOmuamPRbXQJyG9nkj4qI7GMHlSgzDsCAaBS48W6g9EeR1VGXdmDlbOBizOG145LoAOEUaJuBts5EKgVikvQm7J7utsqO9CVmHOSrwBGOIm12y7XtbKz3uYCcO4NhYFx8MMPF0ENYI3Y98uKsRsmwIOQFElRU4yNvl9Va6iOXUzSmJWf5wzBTuxLFRxAtVFS5QCkDIMiwv1s/2SCRFQ0D3tu8t2j8nJcGy6ODCj+Sk6GO3u+zJtT5+b0eFVIszAqdNfAHsOfxBXuqGLxkxNroXQNbuTrelS812a7ZTVNISC+5lUNjlcjIsikajPC9ExBBCe2vI43J0eTPNRkbA5qjASWgCjFjcA4gvDgY7VduK/L4uh9OdrnNwMBgVYjEEzJVuPPxDEEopxg0hsNi5oC+qUxyWM3QJX26MGDqwYXvCUv0Ehh4Acoy1lRnikiztR0gUUxQIRCucqh3g/aMxUNuyh7g7TiB5eHzbbE/afVQdg6T9sxJl5WnOCBEfqVrNdhJy2LvH7q5p39vmqTAa2ZTp6GgF8cFOLqers2NvpdsmNuVTVy/BojRcchhULEzG3ZPJdk3NVXxiN2eWq3gqoOVGn2p2t/SbZYdNlrvXdxM9e2K3yqpqjkSCVguWFj8OWYpF2jGxSYcZT3Dyd+K/XgwQELSYUBCxFi6q/hfZ6emupOoQ6MIZoShjh4Jgd/aLCcCgegY79+ANBWyV+LfGUldbBbzacbzUXZFNnd3qBt3TQAaYrVzIH2U5jjccIzYCFdByIxzJ5+9HkVO/lz0sPyZUTz0Zht3mSuxTAqBS6h+skEEeRS3ifLpmC44t+rs6HK46r6L7kYCAiZf61bmUz8wToc49EbYKgViVu65lb0elS8wQGl6z8tDi8Li7fEGTWLvkdCaFOBHSTry0ePHmd19xqyeB4xjIQCT2Z8ljKilJQIf3H7q1iS73SptQuA8JqD6VVR4BoeaWNrznPdopCGFr5SD9hXxuBAJei01jjrHLIe13DAS6XA43ANLd9i5JWAUE7WBvN4sEYkzF3i4BwNoal7Qh3ZxpZychEuiICcDn93qcNrcDW2yHwxn0x9ujxNLUbIl9mFq8iihqKYDLG3BRfZ6nilIHWm5EU0M5fZWuDlGbzCwws74QLmbxB6Ieh63TF4QQQtYVCAZBGO9rJC7D7sE5FiN4fT6XM/e6cU/SRnKxTZQAglEuKDZaRkLQBsNWNy4e0Ke9o7WyosbjlLXbUPQm7GuJILbL70OI9bhtIWyTEwVtdmdlwJd2GxmxnKUFhCx20BBwuN9/9vFaXROaJKByPQTe6zJg8OZGmkouQfpYJ5OX5/8woX/mVkBOqwlnfiw4w+60K/TLNpDU6LR3eEOdbWaTKRAKoHBSgtsqFkJlhcON+3RkRYULv6tgoMvmdnt9/qi/IxbDfwtDe8U/A1DaFcRxpgByC7wfIVBV6ZYTyxmFIdjVanIM5NiIxYV71vk6252epM7cXV4f3n2ktcO9qRPPjAuUmno2K/JIpKOd2coihPhYLF/bOrlS295KKQ2sNkcoWNiGIpnR+60mhY2IpNpx6Tvo8EmaFQtI34ggNni1wShgHP6OJiyUFT1Sn+9y4mohwt6OLjMTs7qkAlUIoQcAX1ensyLzYAI+2BaLhmOcGwoxZ9WIzk6vWIaUhN3twQHalHBnU1fbEOODlkoBiE0oY7hvnwIkBS4kFM3G6BKeQlF8xsQFm8VqC4eCFYmlMVZVX8DvdEipFVFYcNAQb8iBNgC4WKgpHuHsFg6XQ4iPnyNUVLgDXWKcQYuAr8NOmtEpMIGo3x9FAFU4K8Jen9Ph9Hs7UDTsxu301eFRgY+yijFQLeK2Tl9XCeSFcoBhIcPA7jpPxT8QFdByg+mZztvZAot0BYOMFGY2N+/wuGuUzwIAOu0OXmx5p8TjcZHRvgAMBREsc74AMptNNpxTQoFAMByOcNEOYHYGOpuMZIpU6qkXBhAL4B0uO5b8+LG9vojZZLJXjrSj7JxWk6iYAVE9yxgIgcXGRcUNCXlZxasFdMLwMeFQnifkUAqJxcwVwzi3Yiiu3uNNGLd+rvQLXmQmYVBdjAa1PA7Rnsbw8t9uZi2cPZ58d3v9AVkQsaomY69QR1rJyYGudodLbx9U2LsnBCw2iykWibpT3Kg+RpLpy6LSwPpSpDk5DErqmTgTF4saL1YxVsY0euBQQeDD4cT8LErRojMWyWIqilVFLunOQomp149TIoQah9JpYnSKnILBiM2W++wKZVWTrbKuo6MT4AWlGHUFYX8wJAgCBMgi7DW5+2VMQUW6msKgEgHAIFThwVeOkZ2eZFUeD9OJeSEt0WTYYKmVInU3ES/dBpyJzUZA9Uj5sBWgeSGlJ+GUDSp6j17Rz9VbG/cbbrQUidCqENMEkNHau6Px0fB37dW3hzpUVHjktvD+rrAjUUnqAAhwLNrbJboZ1h0IR1iW4TjcZtUfCjEQIs7tcDvMMfyWfF2ZNsXHafGVWBq9R4DAbGFDYiWuchWvvZsz07Ij1a3Q/HtpU2TOr9B85nOf4JQ2/OTLsSqxZTM9zR/wWa1puxZlHKpR6bYqbY03GMH/Q8iJc0EaCXJ3ZQWvVSdO8kIBqp5xGBYKgsDkY3JXUSz3KHml1xT0H1ccI/a7FD5dUvINuR+6ZjoeuBaJLV2zffn6nZEo9iZ7AuFWX7I6M2xQ9LCpoQB9shhJFF8UumycfmyEx/36zMqkEEUT/CPM06eECmi5sW1nY1Z6BwB44qUFRo6TNsw6FyTnjxw6YOrh+7z1UeZBbPliXRNOAX3my3otjVUyDYKAtu/p+O7nbTuaO4WwH1hw4r6fC/u+PV6NLF2rv3WEScO6WnHLUTU2HAw1JqDZh9ToOj1jHolATKhuLt5AQ+URw+LLhBTzn3iogDRso1ksPY4aW3XS0fs/+fICI6ImPZQmw/zESwtmXowV5NzTp67e2Lhm3UZynBcQK9ZCzbx4uj8Q+fanbRs24NHnAICzTp3SrzqxhTEaE5555WP5LhI3sWzevqfIO0INtwuTDt7n0AlDcApbXMdVV+D6cp8/8UFiABg+3DF8uFS+zqPIstU7tu5u27a1sb+NNdtcONkkIF5Auzu7so2x1jjVdVQOd61GiDUb9Sy5zenlQdE50F5R7fyzW+N7ydffBgjB1RdOe+o/C8ndU048fNiASrKwi8aEnzc2frdMWkGfdMyho4fWvP3sy5rXOeTACeFIzGLmqivsx0waJQvoU69/u9+Y/oftN8RmNTns5hOPGCML6GsfrXTZLUceOGzsiDqWgab0CasoMB1y4D4AoB9WrSvY+JA9NeMMnrl05dqlP20AAIwfN3bKIaOU0nnvv99LPX/2VWccsf+oI/bHI+Dxn6RIbOP2lu9+avAHI0OrKziW2dKcttY9FXUcQIHxUIAg8C2+tr6WT89LIj5+CPCCwHYvEsrBcA2yJP2RFFvmiYWmkRrBrJWjpPTe3wYBoY8X/XzyVDxol/De56udoqiNG1Fn4phDJgySBfTDxeusFlO6T6TDZn75gxX+YMRptxxxwDD5+DXnHbllR/sbC1d1eIMWM3fIhMGX/Pr4F9/8HG9xEfwhn/+Lr9u/+FovrrdtF+4s+cOqtJriE9t86FGb5flKnEb6pSMUDUOTZX39hnXr1kfiLdZnX3XG7KvO0HlajA/XVDoBMNXVDJ3/+VKyBzIMwFAn1J7RniWajjW1iJWGOPMCWaKnWcXD1G0LGlc49czziYCy0TpyFZYz8bEoiVVTAc2Nmt2b8Idh4GiQbwZY7DClDjTKOExSp3QN+kUSxSt7zMnKVCjsA5T9NEHTzgz1NPsxSZZqtZBFQ/X+g2sDq9bYD9w345lCO45uILL5R0xgQw5v0Lz+8jOUuZpUTzr7qhmpV3vl8acAAL+ZedXulq77X1kMSh8ymaakC+ll6gb4lA60rVmq/UIIsAxDpC+5jCklAKoloEW3hKfoA7EJUv9D6qhnybFs0Qq5vmT5ooWP/u0a+aFvPvvOYsECN/OGq79f/r3ORYyoJ7YeVThnzbeG2BorzvcAILRhSZ3zLFbMyYfve/D4wSYTqymXKu5/eh4Jl973zHz8d04smbfWFm88iiYbdPNERpP0VEBLj2LdZp4HVixZuWbdmouvvJA40BcXbZn98H/vveEC8tDCTxfefNtNEMLli3847pRju7ryE/gT1VODb5f+tOi7n0wMiPC5/MBvvAQn6BBCrR3+V79dDkotoERWUaWYlmjIMtkgRkJz3BdPBbT0KJJ2IT3ExVdeKN/+6yXT73oxUW9w8203EUmKRCN/uu7Pt97597y/OlMt1VESK0rgxPY9MZQIrc4SxVHFK3PnyrqJQ8yVrr2deGXw7NuLIABmRmD75d6DmWIcTcX3R9K0iOjewOO0AooQKrbtzBSc3g8HBlry0Teth2OdgcZs0j4Kbv/7nbfc8Tdye2fDhkumjiS3J045WD7HbDK/8vp/QE8iKWlrvERMiJnEaZSHHjj+sH1w/ZPMvc9+SG4kHQXA3+4lYnzTZaeoLv7IR1/22BunZJOIJ8fjJjQeBoXdEtC0s1AoZYEqn1NUr3LcjGO++OirU6edCgBo3L759qvPCiRX/KxYshIXwD/2r/qN9aDnMdfg6iviPIUWLKbLV61ftnJNhHWOGVJz3MRRlS7bRadP+XTpxsY2b6LeVtfR3PscFlwbiZJSCoXDXOOPtObdhGpk4ROPQUiz8MWWhdf0d/pq5dqGCx4J3mFjQRGgn4VnWfb/Zhx/xU33YRO6pf6vl85QZuE5jvv+y2U//rTy8pmXaT69/+CeqjQgGooEHk8ZByAiflymHLqP02b55udt7vbV+O5pZw+p89z3/Eeq586+5iydKz/yrvr8HmVQa+aWh869e6QaskpDO7x21RR7gMIfaZUdqJyFl0CkCQgSdPaA0Cx82VAYF1l43C53l7eL5/nFH761+MO3zFbbMx+vUZ0Ti8UmTjmYWNECw9RK2wGEFmmsppkF369YHUVYT8nuog2NXbX9ajTl8p6n3093ZXtdQadnGBG7cXEB7TswDKsnoMZjoMiCdzjQxnaUQnLe2efNumG2MtYZEXt7Qwj71fX78O0Fyod6F6bWLrQEzPGskgkKkDOTQnqeFwQB/ev5+aT/iD43XXKSfPu+F7AJvfHSGUUSIW1zSALQWvTWMg+Iq3g+eRxA7gLK8f1jLJ6ZRSnYGrbnFp6lwmtvvTbrhtmLFi6ZOm0KOfLQW3jDU0NbyMdjNzRm1JiNm/F+02WLVjS3qIdP/P5CSV4vOHvimddqbMfML0xtUhBTztqvXbt+7dr1gE10vbMIkl2V+cNvkzY73ffCRzdeOuPGS6Vq0+tmHPvUm19G+oBqFRiHucYXTmurIQBCNCLZRlZrM5vWZqREDBTfCVcrHSglq80zSkgZY7b7Z3roB15CMVBS4ynfDfq90w8fj8NwFXVOp/OrBdL2nl27d55+zi9UuilzwdkTAQAF0FDNwPces7hvJ5a0r5Pt75k/Z9bCBct0nv7E65+THQRXX3hC6qOPvPsZKCxyHWjPhfJ7MQyqjoECIETEP4HyultTQ1MEVOwwGN8OT3WzO+wzfh+z2fLjTxqxudGjxjjs9lU/r+rWC/QBEEJkna7ayok11OdTLeFTpVPJu4+dUXgNVZTlS6WIQjMuBb1uxrFJ6mnR+HDOvDixnFdy79N4axPBVldiNe2lgqSeyow8z2traDKcqpMIJQfef3PeoAFS3zOCXL3ocDgWLVzi8/ksFovJZCqeKF7poq+bxaChqTz8oiiCeCQH5obLcJ0WUGz9lECJOnyZ2VpF+4QiiZaWj3qqIFvjdWWU7kTqLqdMO3XQgEFKZVyxZOWDbyz60zlTAQCLFi6558F/vvnOG3hfzY1/W774h0OPPgQUFu+wsT1dVpUtmeMVuyUHOrwKb35vHYjPf/cxvT5JslaSJXyvrOJTYeocxIQqefi5+YAXbrgYF9jfKLZkzQpSSUqg9aTdDIOmVU8ZXStKBbS7XHT+xZ2dSevNv972l1nX3iDfJeoJALjrvjt/dbpeMSDlpnMmkR/C3IelABxBKZ3fLRKbrQJwxNSBmipZDLqphKlzxJs/Je3RfvglrINERnUkUgczU9SNq4ufzOqZSUOpgHaXR5+c8+iDj59+6hnvz5c+tws+XbDz89dxdYtJo7ivrrYuNYPcl5FFMyOydCrvkqff84ZefqZIYKqs6WQ0nM2QXbMQ32DKYHtOybN6am5MIsv5lDYUVEC7yzfffzP3hWdu/cttt/7lNpLr+PVFZ4E27EkH9scWSUVtTS0V0G5KZyo3nTOpJDQ0IaPxUnyll4wITJI+6sIOkmpmKD3oPdVPQyoNpQKaB56c+8STc5/ApeC/Pv+q31710TsL25sb/3D2UeFIPnqU922uuOF8UI6QMtIkGY2FVROTqUT2XEsRk9kSze3jmayhVEDzAIM7WmMH8dqbr7725qsWi+Wbz76DEDbt0diMsGFToipTSdPOFlpL39dIrsanpfMFzSPlRUOpgHaXFUtWvvrmKw/MuV8+Eg7jf5WdXTyQkgdJRKN9t9mVqofF3hXb0p1Z7YcZz1FSOTEx04lC6S4Z+zORYVgMpALaXW698+//+NsdS5cvXfT1V+TIgnc/loXyhx9/WLZoxaSpuLDmywWLguLm7nS1jXL9TRGmkimUsqGjWfpDnrsJJQiICmh3mbdg3sgRox6692H5SCQSOWY/XFfv7Gi+8trLv/joK9I6KBaLHX7sJIM14cVTBN5zTYB0bGPb4m8ynkOhdJ9uaigV0DzwyJNzHnlyjvKIUieOm3FMzntpKKXS/QBvHmdyeRYNfBeewUNG7dyxOS8aSgW0oGSUzjOvfU9/v005cc8by4xXMhUhZpOZZdlQOIRQ6bV+/H3K33UJBB5dIAWj+o6G5tymngpot0hnH2rivTCM7EGUI55vfonHcPcp8qWhBS4C3Wf8Pi/P/a/yyEm/OAEkb0gDAPzjb3ecNv203m2AkFYolYhKQbro33j1Wb8/AzfWe/XJR0Gf0VBTriaUCmh+lloqFZB3IqqkkxSEkz2I6TJF5Rf6NK59WYlpL1bOvzz3v3NfeIYU/wIAnn5s7icffCZ3kCFYLJbTpp9W+PcmNPuvv0S9PdRZ5cK7PNpxD7f7npPGh9x4ddLGYtXdvoYpJw2Fp5yr3USLoo/+R33uw69KN97+k/5GmsKoQLE1EymJZpTpopnjxox75fnXVL5yxZKVz947e9H8N+UmsCuWrPzrbX+567Z/6jvQvMdA5d4lCAk3XHVO5icgcN8zSbNGrjjvmLmvfTUkJhWQnX/N7+WHHn2voLObCoBqIZ+koXQJ3xNk5ZLKaQ8ihUAGgh56yKTlPyT+4SZOOVjZw/Td195f/M0irz/z7LYeZc6z7wIArr/8zMQ6PVkrldx45enK25pLeLK6f/i5d9nqMtmGrxcMNRAGpUv4HqFcNyBSCPM++uDfjzyN7Z4g/OGmG5Z8I7XKJxy4/0FDBg8587zTpx5lIP7YM82flD305jz7LkIoIrCaWpmRCo/5rw++RSrH0/eD7qMLeSqgFErW3HrXLbfdfevlF19x1eVXz7nvEQDA1m1bb73oeDKV5LknnyddX8mIjl5BktG2hBaYGR7weHMH5MxznhOd6WWiM01GtqhD4kdufuAt4sZE5Z2m+XKPvvc56JNVTVRAKZRcQAjNffGZuS8+g2fBTz56zn2PnHPVjW/8+77li3+Y88TDpLBJs59hIWHiC22+NQT4KNvPpVRVIqNhXlJ5Io6yRL76JLbYOqKp5PdnHF/SSpqzhlIBpVCyY/niH9r3tp98+onykSXfLF67fu2pF1x93z/vwM5u5g3Xz0x01F6xZOUpv5q+p7k3x6yLk5q05ymZYQwyIIbAg8/i4SJ/ulyjcuCBudoNnlHKEWtteU1tyhQGpQJKoWTHN999fdSR0uBlmX3G77NnNy7jlUe2QAhPP/WMv8++ZdLUiaRZl+ZmisIX/8q2VLnAx1oAceL+gbnSjKZBmdRTZtZFadb1JVWQP3jIKGVS3qAJpQJKoWTHdbN+v2LJyhVLVn76xacffTzf7faQXtrnTjuCLO3JaQghE4eX8LJ6au5D68WmBwolTfS44SBuvR5DSXmiP6eRyFQinBQQeFgclmdmBbbOWaIyakRDqYD2aTJu3KY7tTWZOOXg83/9mz9fP+vE4/BC/vvl38+84WpnyoZOOQZanBOYZZhqm1mUUQgZhAQgxDgQM6Ke94mbl2TI9z/7t7jU6UbxvyVqSGUZxRoqdqdMBy2kz5r8bt/OoQg023YV8ph1ucbbOL0loMVcSJ8OZwcedeWrqMuq+0FRzRDV/CdoNg1MHdmUqo8GKSEZJTRsXqsTBqUONEfJ676M9mj9vMPhqKut6+rqamtv67lXoeigI52P/mdlafXlMjMChMz1F2uL5v0vfDTr0hkGXaq4Z/RX5Maj7y0ARc+IUfuQGw2b1qY+Sh1oHjAopvlSTH1n1K+u/4dvJ/3KvvnOG/MeuknlQK+49MprrpiZsc9FX3agOe+X1ekdk2ozycnFZj9VDhRFQ5DD6fWIGM7VlMv7X8C/daowhqyV+pSEkiqRxZQKaOmhL6Arlqxcs3b1xf93Ebl73q/Pn3X9jQ/ddMWP33wuC+gNv/vDRedfTGJ5+q9FBdT4H0vSAIF0P0gdYV+EEpnV3zChOWmMKEJCFEn5ollXGVLJe5/5QPO4lUNMlWqeXslAl/BlxaGH4I+3rJ5kzt2s62+85tZHrpq2H6mt+eaz78xm86133fKPv97eq2+2rBYZR0wdOPdhvRH28hVKtPUBU2dXaagJClAsM8hKLlXMvvgE5d1HFywCJQUV0LKifsN6AIDb7enq6tTscwEhXLNu9RW/u/ywiYf13tvs6+1jSlRGmTpphihWUh7nphEfhhbnnOewuUZIQCyui4pEcSGUCouQJL54GXTpL5R373/x41mXnPz76VNLS0ypgJYVXp83Go1+8eGXAIDX//faq2++smPnDvlRZ0ezr6Luit9djjfwRSK9+k7Lje8W7e47HWREJY2LqaIaH4qqCgCXKpfpdFNm1iUny7f//ZYonaVQP0oFtNw44rjDTpt+2s2z/nbuWeede9Z5OOC9teG25IWSODSUCiglMxmTeIxYjZ+Q0VjEAvCvFts/kbGc/9TNC95dkvG1nnzjS7n9Stjfdd0FGr1Oim2vPRXQMmTegnnzFswj3YD+cO0ff3POBS98uenSY5M+CdSBUnpiUxMANvmgIE4Pvv7iU9KqpyVJf665KNFeIB2/P+P4+5750FZXLDvuqYCWFYceMqmmumbBJ1IZkyAIDz7ywIv/fWHhe5+ozox0Zxx2X6Kkx971LkydU2j2zXlJ2kp//cWnzPnvx9dfph43osn9cz/EBVGKoqhZF5+suiHzWO9FS6mAlhW3/+2OfnX9ZAEltLa14oaV7UmKGckUAyU13kW7T4ZScsx56UPAcnOei+vpZafcn9ymZNZFCWX8s+J2Rq6Np54Kr6RUQMuKCy47/9N5n7/y3Ku/uSyR0Fi2aEXqmToCWmw9LyilCxPvJEKW80oefvYDM4DXXzRddTyrCdH3P59UgW8bIKW2CgYV0LJib8fev/7j5rtuvXvFEmmzIOHY/QerzkyNgWbcVkg1lNJNJRXaQqrjc14WFRCB6y+ejoypZDosLN4mNXlILUDgm2z6GEwd1m/Rthy7tdKdSKWHkSYXVVXVY0aODoXDq9f+zPM86XOR2uoio3TKS3iZwvjQotrK2RNh0OKvA+25Sa6CKKMI8RCJg0IUwhkWcjKiAJxw2DDyrHlf/ggAOPWYA1UnfLsLB7I0aanHpX6/OvlQ5cGPv1494+j9EQJfbW3SeV0qoKVHDt2Y0gloRtf55pc7VTu7+6CAFkMLrgL/wig7ePXcdl4heWsTqcbH8aX4JlGVjJqFRN9SFe0+fCmWlZ6o7GBNGD9y4PiRA1KfuGR7MxFQJRAChx0n+mccvT8vjtNbnMai0iV8XySjbsrtgvoPrj3z2vdkDe2zYVBZ8rqjpMXvOvOlv/2NaS7Z2pQqo2YokJ1O6dwoO6hadeSs4fviKNOnGuF+mXc+0Xi0dpw8PU+Djxb/7Atgv1w7Vvs0KqClh8HfTuVHnfS5EFtdXKE857tFu4+YOlB5hEik6iX6rG4aF0GVsJacVh64/0EnnzAtFAo+OfeJWCyl/WdPwqhkVCGd7EC1UKZj87Y9o4b14+O+87xT8HSA1z/8LuMTU+1nAhQfRpoeKqBlQrbOSN6yrdy7XXIf++KhdH90o0aMeuNlPLiYcOmFvwUAnH7OL0BTop2CKidppI9Xzhvtc97C+b+PlwEgLeFf+3ApuSrEKphlPFUECUjWz9OOPfjT7+pdQzXUnApoX28UlHqp0tUCSg688fJbkUjkyOMPJ3dxv67Pv3v/jQ8umTqSHCHbK++8747KisrKisqqyqrKisqi+lEPgkTo4pFTw/zugpNURx7/r7Tl5KxpUk7p5fe+mf/ljxZrYoeVEiqgfdR46l+TamgfoV9dfwDAMdOPlo8ghI487vAVS1buf/gxP3+Px2/cdevdOID4/tugpEFqH3rQhGGPv6LeoRdfs8OX3/vGyFWpgJY/fapRECUrmltwcvmOv985++83Ko8fu//g4VXS9vaTT5gWKdnWMwjLJvzlyYfaLKaJ+438z/tf5/f6VEAplL4LQmjpiqUnHncSmdL8zwfu6ujsIMfl+cx4GfvKS3IY9IMP37/t7ltB0TCw7mDQgms/U8GD7sUb73y8PHEsr2QdNaBQKOXENddf9ZvfnhcI+E887sTP5n+xYsnKt199V3XO5ZdcMf3Mk0849bgvF3/5i1NOf+3FN0BxwPL9YL41MR1O65jUg1RAKZS+Tv3G+qNPnjJxysHnXHR2/cb6YUOGfbV6F8tJy9NDjz5k4pSDW1pbOjo7/vSXP7z1zptjRo2RR973Ok2Nu0BtLiUBP67fZvzkiiEHb92289ppU6+dluiZTwWUQunTuF3u/v1wHomwuWHzb3573pHH4Yz8P1/6mBxUruUBAP967EFcWF7TO+Na0/Fj/fYevT6EIBb2k9tERomS0hgohdJ3ef/NeS6nS1XUSVJGdYOGbW0Pm+vM48eO/2n1T/KjPI9HHnV5uzR3tcl72ApJU+MuH8KVVSecdPL3n76Z34sffcqF8+fN61q/1m6XKlXvf0FqbjLrql9ysFNAHrqQ7xY/L68HvcRdzblsc87I2u3toFexBL3kRnh3r/1sy4ZQQLt59pCRA/76j5sfuf/RKy69cu4Lz8jHWZYFALzy7OMAgIvOv3jmlb9TKuwF516Imyr4fJobgn9/4cGF19BNmzaTG5998jEA0iiRAw6a2LB1B+jYoPkUh9Pl90t/AzQ54KjTlyxejOeRzBOHO0AUCvqBBVeDzrrql/Jp8NTpeFrOledPe/rDlJIoStEL6D5Dq8pcQG2u3n0n5S2gAIC3/vP2iOEjyOysHbu2H7jfQR6PJxqNnnjwcNJ9ZsWSlTzPH3HcYYIgHHLQIc889uyuHT9/Mv8hnVd888udoCCwfD+A0KbNWwzWfhpHqs1P5oFZZyfuMPzeTr8koNLLUSuaE2UmoL2unlRACymgAIDKisqP3/9UHuj24CMPvP/0Q8r2XcqtnFs2frfos6f1X/GCsycWpn8CFlCFA+1pAe1XmZjFhB+HyTFQupwvOYjY5UtGi0E6KYVnb8feSVOTGr+qtqOTJbxOE69H/7NS9Whh2m/z7B6ioQVm1hWnkRvqJBLV0FJEJXzG9bSnFTOd98mIBXbrCla7PCSSkgf0pRPEb2Rsk1jqyLopo5GFh50CDYmWNNRIUoz8OdHpi1jDdMpTCNPJ4qMpySKlhhasBaLJv/WQ4daN25q8qCKPl0VIexWvIikGquS6S0+b807SzDxKEcZAixyj/hGCoYOHmjhT457GQAD3hfRAafN1JzLn8LrUgRrhv/fhfLomigayf0ptQ3PE1IHF0yLW6Q+bTewGxeCNhJJ2IwaqDIM6YafVar/p2qSGEvc/O0+vDvSRF+ZBmlai9DAQwpNPOFn+VR09Gu+W27Z9a+OGROHhyBEjx4weK9+NRqKff/UZ/ZfJmRwi5qomXt8t2i23Abunt5sfymM8ZFywI94QOSGgvuz9qRMmOqICgB55/n1xapP0crMuPzlzIT0NiVJ6lJNPnAYQWPjpAvlXfcrko4cNHd61e5vfh399LRbLmNFjW1qaf/jxB0lwT5x2/LEnfP4l1dCekk5lA9mM/Q9v6u0esoyxrfDOuKoSKj2uvV1SqVwqOvvrzSAGGRRD8F/igPvMJfSwU/i/Geq2oxRK9zGb8fJ86YrvlS3Dl3yDq5f3P+IYcnfyEUcBAIh6km2FW7c2FM9GbErPDS41CDQSqkxBZ21vvDsJB+Xpd7o889rC6395isGLUvoCC169efuKx7eveHzb8sfuv0U7lPbCnGt+c9aUjJf68p2/bln+mPLIDz/+8OPXnz368O9/XPb0z6tXbdiYCDH/YtrEha9dj3/LMxmPtYsffP7ha1IPrl2Mt3ITTjrmgB8/u3fL0kcXvHpzxvfZ1xrIki9QCtRvbSzAqzDVOBfHIDwwioOIg1iEGeTBXxmfjEOinepJoZS+yfYVj+8zdtApF9xzxCl/u/Phd84948jN381RnXPm9EnHT9lP/zqRCM4U3XjXp06HtV5xhZaWZgby00+e9Lfbnm9ta2vY2kCOMwx89uFr7HbsW5E4bFYHp91qs6qNqtNudYrjavHEsadvePZfV/3hlpeOPPVv1ZXO7Sse51KiaZTiB6ECvIT0Gky1BdYlbY1jstqDBDuFi49PdP+n9EFmX3s6AGDoxN+tXr9j9569z/zns9FHXm8ycaOGJeqZ13z1wCN3XWrkags/WRCNRm+5/7NKj+OGmRedfOK00aNwHmnV8qc7Onzz5n+rPLlpzTPbduLC1Z9+XtX9b+TIiWOOPPXvX3y9Zk9L56Tp2IF+/r9bun9ZSsFBBZZdptrOVEuNRRK6aVBDX37nq0tPlOJTlD5ITZVbdSQSiV1783ONzXvJXWzlOHboxN8ZvODnX3721HOv7mnpuvn3x0AIR40cteCN2wEAkybPVJ72wqMzA8HIslV4sdbYlIcl20P/nr+rKbGPYFND0/AhxdWijWKEXJWwu8aVyGiSaBrU0Bf/9wUNifZZZt/5X6KSp5xwEBvfPf3+whWBoFS5OXTi78Yd9YdsL7v/1D8CAO6efezX3yw+ZP+Bf/rHRwAAf0SKGtltllNOPOSq2e93dikrS7rFQ08nlTmPHtH/uVe/yNfFKYWj55fwOPijNdNYo4yJaGjGcOcjL8y7nlba90kEAY2dfMPKT+996r4ryZEdu9qOPev2aBQH17Ni6pSpNqtt4acLyd3p59694PWb13/7YOOejuY2v9li80ekFrZbf3j8D7d9tG792n1Hpy28Gzs8MWt3uFivM3nSuO0rcE+2jNR/jXtn3PZAYjw6pbz1E+VJdrV/HZGHMaKhtNK+bxIKRyccjQ3jgH4Vs2b+4uzTjtj83ZyDT5zdtteX1XV8Pp/NZocQkiD9Dz9t2bm7bfDA6hPOvmfiwYd2+kPktJVf/GtPi++dDz4SNykdoC+dStau3zb9gvuURzT1dPN3c0wmznjMgVJUwAKNRNKGGVUxUPMBo63tOoVLTkgaEkIpeyxmKbvduKfjj7e+TKTn2/l3ZnudH3/GwxRPPnGa1Splxptb8Qp94sGHilYXdz4/ZsqRg/q7123c/tzD//fa0zecc8aRAIDXnr5h8MBqffUkTD10sP572Lb8MZbNImJLoShhAEDd0VAIwItvf0lDon2H7Ssev//WC1QHl6/aYrVkXdwu8MKiJV9hlTz62GknTZ920nSPB++3Qwh99qE0GHJAfzyx59jJ+xx/9H7HH73fSDHXf/zR+3nc9rHDK/XVM6OGEkM6fNK12b5zShGBcnpSntbwzJZtu7qpoQCAOS/MS2hop3DdmTPy8uYoRcgrby85c/qkIYMSMXWWYQ49cOTdc97J4WrBYHDhJwuWrVi6Z0+Tz+eNRqMAgI/jUVEAwCdfLJowZTb+Ogp/PfIMnnR24ll3RMNpwwUbtkr1APpsX/G4IKBhh/Yh9Vy7vb2HOnXd09s74nsLLt64CY2qGLC5ozG3eCjRUPw/UXAfeXH+dZec+si7OJFKKTNuuuvV447a7+v3caWRzJ6Wzqde+jTna7aLAAACAXWFXFtbm/KuzYKNQyjQ1R3pxNPHjsZF/gwDVVHRvrCWz6+G3lOa0un1S/nJbiK1sxsxbBAQ+9+ddNR+T83XGI6ko6E6VriPyGjfbGc34/iDJk8au3FL00tvLsp7Q2UuKmWQYiYpPKq/WldJp9VuUa7cFy0v0Iie0mL/Q8flsKW92BSzIhzduK1JcwO73jo9feop9VIIodpxQzL0Ax0xdBD5m3zSUfs9Oe9j4xqqF0tA4LpLy19D+6aA9mivoM3128iNUeOG1dRJuz40oeKYRwEtOaoiMYTQht4T0EQZU8P2XcNFDf3k69UzfzHtiQ8W5raWV/HIC/P7goZSeqi1mlI9W5txr2UltPc+JQcsFms4Ii1xuklSjmjr9l2bt+LFzsdLfp75i2mpZ+c2thNrKE0rUUT2GVpFvrL6ebQ2B1LVM+erUcoL2IuvrSGIsob+jmooJX8YVzrVml1TOlMv3o23RillYI+/gs3uSPeQtqPcvHXn5q07Fy75eXTlwDz6UNBBK5wo2WFEPQlUQ/smsIcrPfXRk0JiRfOooXQ5TzFCa3Ng1Lhh5Iv+xCgZMd5DPu9k0EFiRdNpaG4y+tDcXCquKRQKpdgwpIDpNLQ7VpRCoVB6FwZ2V744g8VJm7fuTNt7ycOAvj3towzq6cpj7lix1XhTyh4mKxeZVmqz9KEnHXBwVudTKBRKEZLdTKR8aegni1bQylAKhVLqMMNc/aaO3z9bDb3mtJO7qaG0up5CoYBeBTKMOJw4d/4fQKTau2acwGIAAAAASUVORK5CYII="}], 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"}], 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"}]], 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+7PANG6Tsdlakq2dSli6lM6E69UzydVRF9cpUEn5FXRPaZ4vqZyT3mVfh8lhdHuumtfjPp9NjUT1KBZRSBhBXBVmGaQ5YgQ8vXdvbWhnA743gIdj5tZ9k8a5vP1UmlBc/aDr1TAT91FCingmlS98TpKeoUkkJCU4uqqcmFKQJfRLp1A/TUAGllDzyKAwI4cnHjktZy+efjPZTpxhAv55JLqpXmlB1PRObrqieSDCTzoQm8u/JRfV92YTO0Fq8q6ST2E+CyoRSAaWUERAwOM2CiYW6IsHOXrSfKhMqp5L065nkonrF95RUVC8d1DKh8vnKSlIpL8+K2SdFPZOOCe3L6ulKdZ0y8THRSg2lAkopK0wcyzBMVVWiLWbe2bBhy5D+ONiaFcpUkmxCteqZ9FblmiY0tahe/lwj0Y1K10F6RfVKE9pHUkkzFOpJdFNTOiX7GVdPgqyhfe5vDqW84VjstSxsh581599+jhsu9jJm1sf3R6bD4amO8ohjiZDhEitW0V5EkJsoQ17aty5+PrEfhDw5iFfluLZJOoindWAHySaahIoT3sV6JkYVm1PVM0lXYEnvEUGeK6e8vmhCi2QXQkHVM63fzITTY/HRrZyUciEuKz0sAgaT7/7OtoivPdDZGuhslQ+SVBIhxisWf+lNqHJVrjohtaheVc8knaB4Uc16Jvm/I8eM7DvtRdav2qW3Wte1n0roEp5CyaL2U7m9PVuUJjRVMfXrmfCTlAVMcoW8oqheFTlNuq1MJaUU1ZO7cky27DngCGTEeCpzR5o4PRa6hKeUAYXobjl+3PAN9VscHre/0ycfHDd2WNJJAl+/aafORXjAyDJKwNqHpAU1hOJ/jazK4/VM+Ez5UgyHBVeAeDM9uax8Jr4hppIExREWty0l5yTOZDgy7cMeai/LaR+TT8bhS19XyOgT0ttPGgOllAM9JJ+nHDps0WIND+LwuHCjJZFdja3i9sr4e4jxDodt8IDqdDKqVM8Yz8jjkiTtUyCtyokJhWJ7EcjjACgeZ4QjoSNHjcKeUQ6MypHTuKSmFNWTkKtY7xUPjMqRUAnx+upAbdmpJwDA6bbqa2hG+0mgS3hKuZBvHV20eLny7ob6Lek618WRdGhnY5vTYXO4HIkvj9PhSdpUmlRxmdSKSa+eSXlbLsVPjZwq24jIgQJlPZNcva85Lqlcd3ZOjqsnwem2dtN+UgGlUAyhmhOXL5T1TDpNQhWR0KQedHhvklZR/ZaNDbIEyyaUTPRMennNTvXx65dZUf3kZPXU19CE/cyUk6QOlELR4JRDk4ObAGSyn5nJaELTNQlNnCULq8CmM6FJszmT9zhJJpQMXJIvS8YjpzGhZaye3Vy8E6iAUigZ1u89ZD8JMV5hKdPXM40cnRB0paQq0/HKeqZ0JlTdDFR+FYUJHTlGigCURz3TZF31NLSQTw8VUAols/20d9t+6phQSaB1TagkiCQ8qly2i5C98Kp6JqUJlU7DNpNVmlC5nklpQvtOPVOqhmZlP6mAUiiGGDQg672bWSGbUOV6XL0qF01ookRUq/VnOhOqeVrifKLIWjM7Sz2VNNnY4j03H8qZpK1mFApFWb2UtH7vfvRTXQWlgEF8jGeICU1sKEouqk8SxBQTqkwl6RfVi0v4+NBjXRNaHqmkydmEPp1u69aN7ZyJ5TiG41h8I9MXdaCUMqAQVfQFACXPN1aN7pBW5VomVJlKSlwtzc5OkaSeTNL56Wd2lqgJnZyNegKAttS32p1m8bcJF8oahDpQSglzgktjykI36VH7SRg/Tj2JSDahql3wMvomVKtJKC641zSh+NtK3tlJ7qq6O5e0CZ1sVD3J7EG0pV4ai213mbPqqUIFlEJJcNtlxxXgx7FjZ1O+6pmSDGNSYFT6aI+ItxDVyO+zkEkxoWVQzzQ5C/XUAPtQw1ABpZQ2+V3AP/D4Gz1tP3VMqJF6pnRF9alNQhGDL9Igdt5T1TPJl0Up3UbSFdWXyip+cmb1lFynfF+2nzloKBVQCiVt76UCm9BM9UxpVuXJ9UyKyClD7hITqh4coowDyGJS4kX1k/XUU62b6dQzCw1V9gqkUPoyqsV7jxbPGzGhWvVM8VX56MRIes16JlWT0DQmVJ1K0iyqlyl+Ezo5rXpq6Ka+euppqKzD4iWpgFIo6sW79PkxPPgI58yzZ8fOJqWGqkwo0T6lCVWvytOkkpJNqEbkNP7feD0Ta6ieqciZrKGe2pYzK+xOs5SaT3MxKqAUiob9nDB+TIjvnaVrOhOatCoXTahOUX1CUlPqmVLXnXIqKWNRfSmADOqmvv1UglPzaYATjjg6mzdHoRSUSSbcWHNZdLDmoye4vCgewBs49Vi5gG/Zyvr1u/Ekotyinwzu95nWW1iclRyUJsRBqSKddASFIBZTHFdcgVStSyROw8M//InJ9aQFp4nFrw6x2jEACRCKBfAwihQHxcswiYCpqI+AQVIPUPEuZHjyFCCITUJJfJNcQYnyCpDBTUJ5hKct4W8LX4E8SrosC5Atwi7LkyX7mYXZNK6eMgFvYiygTAn9YaFQeoSs1BMAEPbtDSvujh4/Vr7NmcwIoUiUt5g5CMH2Bly2qc9++45dvWaD9NJiG2MiA6SlPDGDuFM9MmENFTvVp3ZNTgyYS+lUj48wjKpTvfK58ZcWBPkpZPycGCUgwk1MKNHQYmPyyeZs1+mSembq9anC7jKnaigV0D7BzcMWkRt3b5sKyp7CDpfctF6Sv9yQ1TOpqJ6NfxNYMdXpLI1O9aoTRAWUO9VjE6rXqZ4FkBd3gZIaKVa/U31RjfqYjNUzOxLeM/vfk1QNpTHQPqSeFCP20+ZykTbyvfLjklNJKH09E+7smZwaUu6Flw4qAqPKYUrKZ6mOlFxR/eTs1bP7qOKhVED7lnpSMTWOE4/icCm+nM6UyRx5Yb99E0GA1KHHqfVMuLNnSlG9ZpNQZT0TaXmXsag+dWdncRbVT85JPXMIfepraHH9SaHkF025vHnYoj6xkM9r5TxUFJs7PU68wJWOx9saibKnOB/4FNkhmXFjhibuiJes37RDtYonkVAWCDzOHYn1TETL8JsQF+yaq3JxxKa0bCcHJUlF6WZ8Jv4rXhbv7OTVkVD5UemJxREJHTzKtz3es3ToKEPxBARAQz7UU7WWpwLaI1ljncRxwdAxm+Wpod0OfUp7N+Pi2M2XTwoCxEV2Z3O7SmrJaX6vumaAT5ZCOUdE/qvsLZ9YoSenkkSzKdaVkux8ytDjpEio+puJx0nlKZ7JpxVPJHT7Zj07PCQur3lUT6WG0iV8KRFqb1d9pTsz41KdruVzQL9gfshQI38yta+gCrmOHzdS7B0vRUKVRfXxq8RX5cpBnqplu4Gi+sRtcdmOg6jl1SR0x+Z28rVnZ1feL253mamAlgyacpkqqfrCWvYaKkXtDK/fIZIWpAwQ6gZ3Z9Fg0LdmYW+Vpyq1TBZEJaqiek1fmVpUn9rT3nhRvb0IIqEGiYax4u/Z2ZV3GaVL+DLk9oNXGzwzndRaq4pidVYAEEx8BLzejJ8uRb+kFC3csTupP4gRBg8ZuHPHblynmcyG9RvHjx+zvn6LicWRUEZegGeqZ1IWMKkDo5DHHjVNPZM0LklRz4QAvqB8vhQJVdQzCWLZf8lBNLTf4Pz02aIOtO+qp87JObvaUiHVfua3c10OYVTNwqlEUT25IXaqN9gkVC6q1+iJl6UJVc/yLLJ6JiP2U0W+3Ggp/SCKgXFBjdSqkq5g9QgWa804PsOZ9TYb6FX1lJ9yy8r9QFmQ6gr1TkYxpf0svjcIxo0bWV+/hZF9nqKoPmEeU4vqRZMop+NVRfU6JhTfT2dC40X1UkOTXi2qHzzKVzxulApoflCqodsklqdE86yP1qoqfRuYg3qWn4bmVjxvdzgyCxvOR2engDJDBw3cvmt3xtMcDptyazxBrmeSmicl1zOlE0SVCVVm54nmpp6p2tkpzmhiiryeqddllApodvSEbcwLOatnmWkoLF77mR0b1m8cM35MxnomHRMqX0odGCV758W7GvVMCRMK5cCobELjJfdsEdYzGV+/50VG25tx5RmNgZYD3VTPPF6kJPduOvK2a3Mo2zGU7cj9+SnZpHHp65nkJqHpIqGqTvX4KyV9j2UyuVN94r0oZswpd3aK3UnEt8NwDOIL0Hm6+zQ3Gl31G4mNtjf7iXpSAS0H+ojwaSKmQrLuaWzEfg4e1D/bNzMUteq/rNbyP6X83pFY4mxcv5HcwCZUr0mofC0h9TQGxDfU4wlySZ3qcagUm1LtnZ0JNMclxREAU7T1TMbtpwodDZWlk0AdaGmTX/UsOS0e9auZ+H8oW/vJ9IT97AmIhqaaUIKiU33aonpBXG7LRfWJ5LsiH6VO34tXEMjMTvm09EX1TCmY0GxJtaJK4ylDBbSE6Qm9KzkNzRZlykX/xCyPa1BRUUFuZJV4UppQsopXvwNRKzVNaNKqPMWEatYzySY0/lbT7OxMX1QvFFBG8piCNy6jmtJJoAJaqvSc0pWWho761TW9FP3MrIodHd2Ihyab0CjPEBOaWIArSBwUsz2pJlTZ8s7QoPn0OzsTjfWY4urPlK/1eyqhQDTdQ1RAKaWuodC4QUxnP4ttcJrKhCr3niuL6hML9kxb3TMW1ac+K/7SUu/R8iiqz7uGUgEtSQogcCWjodCQCc125vvOXY2g8Ah6wURewD3upDvinwGVlqU2CZVq6RUzO9OZUFUqSWlCxW54CRMqp+YhXrsjE5KUpahMaB7tJx8TdDSUCmjpUTLS1vME2tYS+6TUUB0PKttP0nk+uxfDVfTdobs2l0iXTqf6DKvy5HomlQlV1TNpmlATiuKIKhQYEJPqmQCIIFjGqSQVqRpKBbTEKKR6lqZSF3YiUvfYuWOX8VU8g3jNeiZlGl1pQpWnyeCNS/F6JqXmptYzmUCUEXsFEBMKgRAFVpvHTX6+DIwwgDeBaCHrmQqcQTLyx5AKaClReEUrfg0Ntq0l3tBes2868dScu+n3+v3+YOKr0yd9ef3ky+f1+zL0MygoAmQ1i+rlrJF6VZ6cSkpnQpU3TCjCgBg5wkPEArxCZ3iBQbzodrFcWl1OmwvPNWGBEIklBKR4TGge1+9pTWh8tmmfCACXB73VvrNsdnnKGC+7gRGvX2MYeBry2c4J4/A4/Z0Jz0U6yJGdnWJeSNoFH1fApB6gyoNy6owBPMnIC5CDYm0Tvs2YGXHMvQBYs9sR9PpI0snmlL6fYKeXxU9Jem82p9vv9SrjEhxEHdZaUF7IAVAloUDUYjORqcjUgZYGvdv8uMh9aLB1Lblhr97XiP3ERrUnF/p+f3DgkCEDBw0aOGjQAPFL62OYI8SE4r8BiqJ62YQqi+o165nEJTkrRjNJLzv8X5vTzSLB4cRFXTaXExtPxdqcBYLZ4yG3QzGWfPk6cZE5JHVOknrWgXK3nzLhIP75IEQdaClQlq3j84RU2B1oW+usxTbZXrNvsHWNzhPkoWlKxo0dJl6Hqa9vkA7BpFcYMW48z9q2r11p8G1tFIfFjxmfGLc5YMDA1NMad+zwxziHOUlfXU6H1+fPaEKltykWfkIojdUkzUFkxPCo6DpFE2pCUR4yLMKCGPF22lzuoNcPgeD3hhwuqeYfAGCOp9eG1VXwfGwTz/k627HTFbEyWDuUwUiuaNSzkISD2IfSJXxpq2e09b3cLmuqOcP4ycW7hM+U2daMfpLbygHFO/dIE8fGjRtBbtRviCspADYHPhPGAsPHjm3ZtU3zhWKcC6BdmjKaEX84+a4/qMogJX0LiAeQIz3ulJ3qxdqjqNSlieFMQpiHrAA4hMyMINolAKLIDBBvc2M7aXZJppJDMbNbHX0YVldR3+irb/R5XFYTx1o9ZMEuqWcsvjseAOCzFW7ZvnOzs3jySERDqYCWvPfMSgpzoHjVUwEEYNPbT4z+1UyAkK1mXwDWG7efSnY2qctFHQ6bgGc64gtCCOx2i+YTuZhX7vHmcGicM2DYqE1rpVBDRlK7gsrgQRp4FY+R/aaySSg5aPZgd2lCEbPbBYAl2OkzoajZ4/Z3JQmQLbkNvgUIEZMjGIrWi+2LrGwsHPClDu7g4nVUhadlh792iKMw63fNAKgKKqClTRmop36T/+oI1Gnvb9q0S/JWowYq3ajZNRSAdaqTlfazO4R4/KmxCX5k0hbTVIaNmcDHYiPGjUMIbd2wITVEYByyK4kXOJbhcaPleKd6E4rwDGCRwAOTgOckh5UzQsQ4pkaSywx4zlnZ5QvZrKZAKMqaOYiQlS3qTsktuhpaYKiAUsqHTf97YvRZYn+mOD5bbbZ7kDJC9AWxRtUTALB146aAH3s6CKUCT3/AcIVUShu8ZBMqlhsB3ux2RbydZpcn6A0wgGexzibeoVI9Q4LJauZMHGvi2EAoGg5GOBCJhiImAPiIoZoDn7fX1tHhSD+LeU9W9tN4M9AcoAJa1Ny9bWovZpAKs3jXb/KvPx/lsNGDknaGEw09+3epZ6aWKAa8fsaqNmV2pN10p5sEA2kvCxlot1kzLOGTNVS5NR6HQeOxAxLWtLnskS6vuHJPwteJg5gQwgqnLRToisZIoj1pf1UUmCpdNp9iOukQAf/52cHgQCdjcQVFb+p02H3+AOg9iseE0jImSgmHPtOQZNjkLEeXrZ/yuN1ht7Ix1VcAOsiXfFogGOrWW4mEUCTrKzgcNvlL8wQyUpjFGXYGxHejyxD1NCM+hkxmi8NscQDGavdUk0dDAWWbS5y6xydb8bdsAlGlesqQoiUbEyNfoFfhOFyb1bKjR/7UZQsV0GLn7m1TC/+ipayeYNNbT6iO+Gy1KvXEYUTE8IgBsQj+iiMrabYvOniAdjIamq3QrDaYSlLtpzbJhasM4qM8yyLB6bTLB81AcLk8UcFkszmjgolzVMR4IRL0RYI+EAsyQljn0pGQpEcWIISiTKePJ1+g+PAHpBFMSg3Ne/mnkQwSFdDSoMAaWtLqSdjz/byM57BQYKEAODP+6jY7G41GWmEkCCJ52CLKIN7s8pCFOQ4OxjjG5g4EIyYQCfq9JhAJ+LqsCrcY4lPT6RJmszUcY1jOAhlzWAAxfwcb83NCkBN6c52e0YQa9KEo1oN/BmgMtDQoWDC0DNSzUOS4mckfg5zDA2JSr+VAMMQ4KuVHzSaO9SW0GJnS+lMBsl2dAXkJaWGiIX+anpUmqxANWVlelUxhWIvXF+JYaOaQhWP4fMh6YfAHqpSpJKX9bN7RqToZJmsoLqLNH1RAS4YCaGifUk8mFhK4zMtnu8Oe3KXTqG5GBBYBYOEDILnayY4X3VFZ6uy42EiR++YjyIbDkQJrs1vNLAP9gZ3pUklON67w98e3LangzLaYqIlCNCkIG+HZiLdjZARfqxbXQSEgao7FKr3PkPiQ1YrFucqPv9+REQQhssSDrdvtkgEsBlp2+CPhdNGJzIa0m3pKY6ClRI8KXImrZ9Z+UGDMQBBCMUb+4kxWJhZmYvo5H5J1wV0w/eGoPxwN8+pKzsH9cJDOzPAWhlepp3EYPhjyd/q92kNBWCAQ9dSpKCLqCTmLwFgiiOMZ6Z0MH9ytbZcs4uv2+kdG0MiwMDIsjAsG5S/QG6t4I+i1iI3xyq90AVAGj5bS+KIOlFL66tmNVsXKKGEsHMPLXbvTZjHJti7gD7gsrKY2RK1DTKEd4l5M0ZrF0zn125vJDYclz58vKHZWYoFgS9l8qQIx5i5vCEDAsWGHBZghAIL0ne7etRsAsAUfwhck++mJHBN8PHbETtFmInHT/RYBQgaydhfRIisM+7w+sjvea9arQivMKt5ssWRlQnUgGsoY/pWiDrTE6AmlK231JOSvu1Io4Nu7d2/SEdYxZsJ4LEBC0uqPiXp5tiKMnDxbwbOJZhzkrrlyTPffDFbLZCADddSzurLKH2GtVifHcgzLcHwQRHBvU9Vpslw67FaHXTuO4Q+E/IEkM26FESsMW2F+pKp4TGh3oAJaeuRX78pBPXsYE4wxscC4cSNTJwzr4O2UGpSoCPiSUtsBrz/Cc2wszMY0hIkxvuucs8aAyWy3olBna3NT194WJhZv6ZS84V0fX/JmeYLTkSiWKk7MlsyhEtK7L78rGrqEL0luWblfXnp0Fq16TjLFMye5kP8hm+s27RZwF3fgdiRcD8vjACXHcbGYRt0oJ7aYI9jtZskGQuisrrNbEyECO9amqF+wMQxjYVmHzcxAGIrEQuFYjOdZRtvimIEAra62FsWmxliIA6BRXJ4bx+/Hy3wxp28REBo2oGKLYLKAsM8XcNitQ5g8jGXuOfyBKoe9PRZLO3O4p20oApAKaN/V0KJVz6xI2SmefwK+AIMQGYNpBCKsGE76fI0bN7ZTbFoHIWrctduXnBYXC/h5XNofjcoPMUAMXGp+b4KAg5QBr86WSrvNDPEuTYgl0gBRXrCA8K5dTZbi3guvg4FIKE79oQz/ikZ/m8h16BK+j1Ie6tktY5GnskdN+6liw6btWzY1cEKofv1m0NtAzuZ2SxFbh0OKgVqAJD0RxPmSgwwyIZ6LIFNYMDnFmUhFtSupkA4UASirMBXQUsJaVWWtquq+CJaVeuYKctZEGGsIWDi72+L0hKEVS6r8ZQBl4kgfv7cryvNrNu5MjYEWAE6IuiqrZduIIv6ujrTjM81Q4+8B8blWNmaGUYvYU5ngNdeAYkol5TUSmkE6pVfP+VqU3kKpob3brqkYyPnTAGNBvIWTAbFQNAaABQIgNtQQW9XZQGcruc06q3hve0YNFTuN6ikjR9pBibVDyuOD+1fL3aR27slxLDAZZ6QkEMCrfIcDu0veXtXeGXB6XPK+T4I8pUMTnIIvFqNZsJCP3m+T5tqfCmjJk62G3r1tqrWqKtTesyO8NTFea036KCeemL6hMvmVj44eFD+Wh4goVLycBYaJLuKtOxBYgGTQqqqrd7UklQdVx6T3PDwmFQbZeCn+GIngKW/kAwgBalI8a+NOXDLFQOi0W5xOl39vs9RkPvv6Sk6Idvg0goAo4tOu9xFfiARJQ8BS5baHIjEhUhRdjnJOJWWKhEqj9DJFQlXPSXsyFdC+paFyXxKljdVEpbAZzy8DIgIuujTzhpbYe9valR2Lc0buDhcNRqNBAFgLZ3WEI7zZxEIIPE5rup2aSgSTw2oxBcPR0UPcTY1Nvg6v3U6SSPETGCsjaGST5BSTlYkGfEm7yMXYqMZTzIBv95bMrvlsHKj2cX2ppQLahzR03rbhAAw1eMGeUEz93smpfZQTT8zYUDkfmBmx6JJseQyrQ4F46w4EDrv0kYEA+sJJ+mKzS+9lc1hyoG5OukgATyOWPokQQLfdDdLLYizkxy3mRRfVFu8yp4JDMZOnVo5OMnwgEsB9lZsatdVfqZ4Wp6etqRFkT4jHfexd/apd/cG2jYbm5RVfPVN8mqkBE2rEpVIB7SuI6klJEEUmXtEmxAGwq+LZPLS2Sy1jUmJ3O2KhpFhktjgrqlo7AtZkw6iPzepo2SNFDjwVnnDASwrmSZDU59NwlF0hRt6cSrDCCOBB2+7ulOgWgrzs7DS4xqdZ+OIsBc9z21CqnqmYYNTK8vKXH9ginJM128ICp2ydGUjZB6nasil/KY8DxxAj/2TKVbZxfF0dVsbQ8CJCKMIGfV3ybqLm9ApoQry8E4kt1mag3QOmSccjnVS7DlRAywpNDaXqaQQry7MowkeCFiYmlrVjINQYWKRBPFmkOIC3yRt53TBjiTDWKGdlbE5odbB8hHwN6ldBvtI9kUM8DzWSQ74ObG8DgYg/Pm/eiNqGkWXgwP5BxuF0O8lXama/mPEHqrKtZ9IsacoquUSX8OUfDC28et5+5bHKu7c88yUoY/gQYK34S/O4ASzxJJIg9gjlLTja63C5g4jjeSGMK6y0iUGWVUxDMplsJpZxVnh8HfUG3ztCyB8IDRgx1h/YYIHh3bub+kZED6bki1AO0knoGz+xcuT2K48DAGnKk6yhd2+bekD35C8jj7/4kerIR1/8OOO4g3pkR3ovbeXMEOtMsZ/iY2nVM9Dl91RVA6C3G9Lv7UoNHIwfN7J+Q4PyiFwbz7LWaNAXxVX60kg4VRZeidXhbm1MbJnvatll3GwGEXZ2Ntb49vOCouxLYDASCgESurFDiQpoiaEQOJSqd7KcPds4CQDQz5zU7Fw8EsxWEHPgoy9+nH7cQUTY/nHFsbfO/TLr4tD4aqxa7IiuUwdKfhCKOlCjBPwBi7OCCydSMTmUXspETf0AaDCURLJbo2ly66n4tXojhXjO5bAIAnJV2bzteP4HH057QYfDZgYC46pubdolFWB5Q8run8YJIkvRSieQcvHaHbB0gAwDk0cOpELrQMuHhV/9NO2YA/IueXkEl4dA8ITiXfUzgz2R3mm7q4PdZq3tXwtALYpnAnbskHyZwdSOMsqZfuZl/rGysWgo8wb82oG4ZG3rxo0RV5XZ3+l0OYljtYkhUQvLRELBEIvbR0elvQJJhJBF5cyU6qm/i6n3gFmZUEiaXZFfWZ3T0i9yqAMtMVT/kqGftw7166UIPLGkPOFpR0zQv751/+Hd1OWGHY3dLw7F/lmkzYH060C7Eyto2YX7ycvYFD8q2dEFkXmfgyasWbFUP2keE7s1KYXVVdVv/2G2n39O6phVMNUJBCJbN26MNwNVi0hYYMM8sJgsFsAr1ROLpsiQgeLYD8VKXwv4jxv/cut9/wSlaUIl9eweVEBLDEFAC7/6adNWaTmGceiVLvaLJC1Pnv1hXYYXyHhCH8PGxoLB4Ijx+5C7zbu2aZ6m7P5J8Lbv+WYvcLEZ1HNw/1ogLiF3Nme9/FRi91T5gxG3wxJlbKYUxVSBhzUpCPKcx2kVBGSNhwJaGvGfFs1SrM6YOEPUUmmy1z34+H/djqEH7Dch4MOtA35YswL0PjCXmtBMJjQdVEBLDJwz6a2kSVFSgB/GtoYdowd78K3kz1gi1pkGJtYBWPEjJvBirgI/PRgI2M0J77NxRzvHMqZY1+DBg8mRdEoqhxpSMTs8nb6QCYW8nQHjYy5Cgqm20tHa4bex0Ugwc4vPgBU35DfhnBUbi4QtTCdA+I93/dofWCZDGLFgODxmfyfK1GMQj3jKy8tRAS0xIAN5vlh+WYuBnLPwJhSLGmsoHg6HVm+S1rkVzswCxfIdZBV/6CEHrluzBgAwYsI+KJ6paG7cBUx2EJGGv9tY8aPOWbuCMauFw5Wndpc/GIGhtOpsQnwQ2qrcdrOJIWHNSNBrXDfDgmlgrbutM2CNBb2dHbgNlRZB274gKNXDjT3819u3Y+sdi4aDXbvcnorEMGAIio8M7ymPAVwqoCUGFDv39Pa7KAeirDksmMQuSRJusR9dqqqaII8AIj/1QCBit0sxE6KS7ur+/ub1qqcQc7pujWTrzDEfQkgOoaKoRtKmq7OjSykAJrEQSiu97uo/tNrENm3favA79XgqzRxs2Lqd3LUw0bY2vXBByIbH4UFFAceOzWsgNp4CxwJrZQ0vDvj0+4IO3fBRr9Dc6Kzr7/N3JUyoahWfVj1zWsVTAS0xvt7QPnlszzZGUmbM+5mDo4YN3Lxtt+ZDmk/sZw7abM6ggSVhr6PsDYylE5iDPBc3tNJ3F0gzM0OmrbXZJKXjAynZeemHsHpTE0KSA61wqxNiUYExxeI/LnPm8W0mE9e0PakgVBOrzREIRSNR3i3wLS0dOvM/Eu/EgXOMoXYizSgf3aZ6AwhsTrO3Q2MVn/fiASqgpYfqV0BH1PZEbP3SXERfCrsJSl5VF2ENUzqkBXU8C48QcthtAcV0X9l+ykt1U0r6KBWlh9V4lBGIbkLWjBASBMRweBsSL2gEJxx22x5d9YwiU4wXPA5LhzdkZaJWBnvbdCd3xqQ/xv2GjA/4/U6bdW9Hp6dmeDgi2s+wIlepBDI4pJtMLFBEE+ggo1FUn049GZYVeD43E0oFNP9dgeVmwOnKv3No76YkKQVPMYAdWkxikTwfkiKP3aff4KGRnY2RcEe24z10QDyWLZwqisVYAFgIlA65vn6TTh1VRODqKp1N7V4rDJsYEAli6dQhZNvHbrcPtttisVhHZ1co4PW1b0cOjwUAPgKsxpJCoa4mjmP9PMdydqe9CkTz9uPtJhBCq8Pk64z1dOEqFdCeos2B0lUvUvJOug/HMYcdTW4cd+wUsQhM2LqjdW9H5+aGbY1NLdFo1GS2hrpwgMLNZldF1LRz25DBw3D3z82bVRl5cvuA/ffdvEFjW3rAF/DYFSkf5fylXLdChXnOAiMde9utBlQi4hjLmbhh/Wu2b90aCkBesJhhmLQfNULU1xaJhoJMLQMsDk8lACGxQKHogqEMA1mOFQQpys0wrCAkFW9pk6UJpQKaNRlto6oZcB8k3TTzwrN+3RrljUDAZ7G6yRFs8RgQ6GplrHhfY4gTyxtFrMFNRi7etHNb/8HDRo4ahSUs4AOd0sp63LixAEBZPRGu/IkkbeVUXCTIuixmtqbCYeLYTm8oEIpEebH7choijM1lt3j9oZGDqxt2t5PAqwVm6LfkBeL0Nwew2N0gFub5SP2qpS5PBUAg5O8wO0X7nOnX1ufzRZGp0lUd7PJWOjPHanszjzTAh02o3RzAM07wN8ZynBAxIKBZQgWUkn+sZs5f2Mk6L8//4YRcJ6BJOxTFzkZBHntDn2kIV4UtFeRs2JkF8DYkfSx2J4jvqnc6rOvXrJUf0jc0OOrKx9rbJAfIkPF28aek/hTNIBwOhM0Q7NzVaErdYyQS8+wPxb2qMV6IRqM11ZWhpnaHw86xIBQKwyge1gLJkDtjdHa1eDzYaFa65LySUOzNMCFgWQgZAPgsC92yMaFUQCkZkFPwxrHbTG14VFrpQcTU7+0wOcW9jHw0ikxM1f7iUruDtVbKimYy2yHgWyNWn88PIAshxLU/Ikr1LDymfkdYxSrNll31ACCXw9He1GEFIOL1EpvKKjZHxYlPvIvT4cXKPCQuD253rbotjc6TiwYIocXKhQIxsvnE6Co+G6iAliSjhg3avK14U0mmRJl1SeJwVcqmxQSlBbfJ7kgSkRi+HfaKFezJDqdyxDFtezuYjlXkbgF2jvGe/a0Wc1WVJxyOtLd3cmart7PTUz2kq9VorajKbDIg4nB65JlyGfUxEAy5c+rwVAgTKs4/Aj1jQjUEdOI++69Y+3OWL0gpLMX5F7/3QT33w2VNJl7seZyRxqZGl9MBaw/2BYIQgFCgw1w9Uv4Qhzm7zWYVmhuYoFTZni1szaEQwBjPRyIRkwPreKATD5Nrb0YWm8dsNgmBTivLRINGx36Evc14Tc5WdAZCZKlu6FldjRHGifPbLMMyDCkhKKowKEklma1sOMgjhHoiDEodaB/Vz37mYAmVZ+ZMdd2AHRtXir02+hfsRa1MJBqIyJlpxmrHRfrxf7NYLNDZ3Gh2VLLumliMD8VFnzNZQqEIhMBsNtfU1Hp9fr8/wNoA37JMeXHn4CmCIHR0dkXCIZOJs1hse/fUuz1SERULYyDDNvAEXb4Yx5kjgjkCzDUeC8kK8Sjz0y2s0N7RjhBX7XYEA50Ou6t4N8dhE8pAyGeXizdsQrUFdET/IQ1NSZ2+KJSiZcWKpJZxMjWOmuamPRbXQJyG9nkj4qI7GMHlSgzDsCAaBS48W6g9EeR1VGXdmDlbOBizOG145LoAOEUaJuBts5EKgVikvQm7J7utsqO9CVmHOSrwBGOIm12y7XtbKz3uYCcO4NhYFx8MMPF0ENYI3Y98uKsRsmwIOQFElRU4yNvl9Va6iOXUzSmJWf5wzBTuxLFRxAtVFS5QCkDIMiwv1s/2SCRFQ0D3tu8t2j8nJcGy6ODCj+Sk6GO3u+zJtT5+b0eFVIszAqdNfAHsOfxBXuqGLxkxNroXQNbuTrelS812a7ZTVNISC+5lUNjlcjIsikajPC9ExBBCe2vI43J0eTPNRkbA5qjASWgCjFjcA4gvDgY7VduK/L4uh9OdrnNwMBgVYjEEzJVuPPxDEEopxg0hsNi5oC+qUxyWM3QJX26MGDqwYXvCUv0Ehh4Acoy1lRnikiztR0gUUxQIRCucqh3g/aMxUNuyh7g7TiB5eHzbbE/afVQdg6T9sxJl5WnOCBEfqVrNdhJy2LvH7q5p39vmqTAa2ZTp6GgF8cFOLqers2NvpdsmNuVTVy/BojRcchhULEzG3ZPJdk3NVXxiN2eWq3gqoOVGn2p2t/SbZYdNlrvXdxM9e2K3yqpqjkSCVguWFj8OWYpF2jGxSYcZT3Dyd+K/XgwQELSYUBCxFi6q/hfZ6emupOoQ6MIZoShjh4Jgd/aLCcCgegY79+ANBWyV+LfGUldbBbzacbzUXZFNnd3qBt3TQAaYrVzIH2U5jjccIzYCFdByIxzJ5+9HkVO/lz0sPyZUTz0Zht3mSuxTAqBS6h+skEEeRS3ifLpmC44t+rs6HK46r6L7kYCAiZf61bmUz8wToc49EbYKgViVu65lb0elS8wQGl6z8tDi8Li7fEGTWLvkdCaFOBHSTry0ePHmd19xqyeB4xjIQCT2Z8ljKilJQIf3H7q1iS73SptQuA8JqD6VVR4BoeaWNrznPdopCGFr5SD9hXxuBAJei01jjrHLIe13DAS6XA43ANLd9i5JWAUE7WBvN4sEYkzF3i4BwNoal7Qh3ZxpZychEuiICcDn93qcNrcDW2yHwxn0x9ujxNLUbIl9mFq8iihqKYDLG3BRfZ6nilIHWm5EU0M5fZWuDlGbzCwws74QLmbxB6Ieh63TF4QQQtYVCAZBGO9rJC7D7sE5FiN4fT6XM/e6cU/SRnKxTZQAglEuKDZaRkLQBsNWNy4e0Ke9o7WyosbjlLXbUPQm7GuJILbL70OI9bhtIWyTEwVtdmdlwJd2GxmxnKUFhCx20BBwuN9/9vFaXROaJKByPQTe6zJg8OZGmkouQfpYJ5OX5/8woX/mVkBOqwlnfiw4w+60K/TLNpDU6LR3eEOdbWaTKRAKoHBSgtsqFkJlhcON+3RkRYULv6tgoMvmdnt9/qi/IxbDfwtDe8U/A1DaFcRxpgByC7wfIVBV6ZYTyxmFIdjVanIM5NiIxYV71vk6252epM7cXV4f3n2ktcO9qRPPjAuUmno2K/JIpKOd2coihPhYLF/bOrlS295KKQ2sNkcoWNiGIpnR+60mhY2IpNpx6Tvo8EmaFQtI34ggNni1wShgHP6OJiyUFT1Sn+9y4mohwt6OLjMTs7qkAlUIoQcAX1ensyLzYAI+2BaLhmOcGwoxZ9WIzk6vWIaUhN3twQHalHBnU1fbEOODlkoBiE0oY7hvnwIkBS4kFM3G6BKeQlF8xsQFm8VqC4eCFYmlMVZVX8DvdEipFVFYcNAQb8iBNgC4WKgpHuHsFg6XQ4iPnyNUVLgDXWKcQYuAr8NOmtEpMIGo3x9FAFU4K8Jen9Ph9Hs7UDTsxu301eFRgY+yijFQLeK2Tl9XCeSFcoBhIcPA7jpPxT8QFdByg+mZztvZAot0BYOMFGY2N+/wuGuUzwIAOu0OXmx5p8TjcZHRvgAMBREsc74AMptNNpxTQoFAMByOcNEOYHYGOpuMZIpU6qkXBhAL4B0uO5b8+LG9vojZZLJXjrSj7JxWk6iYAVE9yxgIgcXGRcUNCXlZxasFdMLwMeFQnifkUAqJxcwVwzi3Yiiu3uNNGLd+rvQLXmQmYVBdjAa1PA7Rnsbw8t9uZi2cPZ58d3v9AVkQsaomY69QR1rJyYGudodLbx9U2LsnBCw2iykWibpT3Kg+RpLpy6LSwPpSpDk5DErqmTgTF4saL1YxVsY0euBQQeDD4cT8LErRojMWyWIqilVFLunOQomp149TIoQah9JpYnSKnILBiM2W++wKZVWTrbKuo6MT4AWlGHUFYX8wJAgCBMgi7DW5+2VMQUW6msKgEgHAIFThwVeOkZ2eZFUeD9OJeSEt0WTYYKmVInU3ES/dBpyJzUZA9Uj5sBWgeSGlJ+GUDSp6j17Rz9VbG/cbbrQUidCqENMEkNHau6Px0fB37dW3hzpUVHjktvD+rrAjUUnqAAhwLNrbJboZ1h0IR1iW4TjcZtUfCjEQIs7tcDvMMfyWfF2ZNsXHafGVWBq9R4DAbGFDYiWuchWvvZsz07Ij1a3Q/HtpU2TOr9B85nOf4JQ2/OTLsSqxZTM9zR/wWa1puxZlHKpR6bYqbY03GMH/Q8iJc0EaCXJ3ZQWvVSdO8kIBqp5xGBYKgsDkY3JXUSz3KHml1xT0H1ccI/a7FD5dUvINuR+6ZjoeuBaJLV2zffn6nZEo9iZ7AuFWX7I6M2xQ9LCpoQB9shhJFF8UumycfmyEx/36zMqkEEUT/CPM06eECmi5sW1nY1Z6BwB44qUFRo6TNsw6FyTnjxw6YOrh+7z1UeZBbPliXRNOAX3my3otjVUyDYKAtu/p+O7nbTuaO4WwH1hw4r6fC/u+PV6NLF2rv3WEScO6WnHLUTU2HAw1JqDZh9ToOj1jHolATKhuLt5AQ+URw+LLhBTzn3iogDRso1ksPY4aW3XS0fs/+fICI6ImPZQmw/zESwtmXowV5NzTp67e2Lhm3UZynBcQK9ZCzbx4uj8Q+fanbRs24NHnAICzTp3SrzqxhTEaE5555WP5LhI3sWzevqfIO0INtwuTDt7n0AlDcApbXMdVV+D6cp8/8UFiABg+3DF8uFS+zqPIstU7tu5u27a1sb+NNdtcONkkIF5Auzu7so2x1jjVdVQOd61GiDUb9Sy5zenlQdE50F5R7fyzW+N7ydffBgjB1RdOe+o/C8ndU048fNiASrKwi8aEnzc2frdMWkGfdMyho4fWvP3sy5rXOeTACeFIzGLmqivsx0waJQvoU69/u9+Y/oftN8RmNTns5hOPGCML6GsfrXTZLUceOGzsiDqWgab0CasoMB1y4D4AoB9WrSvY+JA9NeMMnrl05dqlP20AAIwfN3bKIaOU0nnvv99LPX/2VWccsf+oI/bHI+Dxn6RIbOP2lu9+avAHI0OrKziW2dKcttY9FXUcQIHxUIAg8C2+tr6WT89LIj5+CPCCwHYvEsrBcA2yJP2RFFvmiYWmkRrBrJWjpPTe3wYBoY8X/XzyVDxol/De56udoqiNG1Fn4phDJgySBfTDxeusFlO6T6TDZn75gxX+YMRptxxxwDD5+DXnHbllR/sbC1d1eIMWM3fIhMGX/Pr4F9/8HG9xEfwhn/+Lr9u/+FovrrdtF+4s+cOqtJriE9t86FGb5flKnEb6pSMUDUOTZX39hnXr1kfiLdZnX3XG7KvO0HlajA/XVDoBMNXVDJ3/+VKyBzIMwFAn1J7RniWajjW1iJWGOPMCWaKnWcXD1G0LGlc49czziYCy0TpyFZYz8bEoiVVTAc2Nmt2b8Idh4GiQbwZY7DClDjTKOExSp3QN+kUSxSt7zMnKVCjsA5T9NEHTzgz1NPsxSZZqtZBFQ/X+g2sDq9bYD9w345lCO45uILL5R0xgQw5v0Lz+8jOUuZpUTzr7qhmpV3vl8acAAL+ZedXulq77X1kMSh8ymaakC+ll6gb4lA60rVmq/UIIsAxDpC+5jCklAKoloEW3hKfoA7EJUv9D6qhnybFs0Qq5vmT5ooWP/u0a+aFvPvvOYsECN/OGq79f/r3ORYyoJ7YeVThnzbeG2BorzvcAILRhSZ3zLFbMyYfve/D4wSYTqymXKu5/eh4Jl973zHz8d04smbfWFm88iiYbdPNERpP0VEBLj2LdZp4HVixZuWbdmouvvJA40BcXbZn98H/vveEC8tDCTxfefNtNEMLli3847pRju7ryE/gT1VODb5f+tOi7n0wMiPC5/MBvvAQn6BBCrR3+V79dDkotoERWUaWYlmjIMtkgRkJz3BdPBbT0KJJ2IT3ExVdeKN/+6yXT73oxUW9w8203EUmKRCN/uu7Pt97597y/OlMt1VESK0rgxPY9MZQIrc4SxVHFK3PnyrqJQ8yVrr2deGXw7NuLIABmRmD75d6DmWIcTcX3R9K0iOjewOO0AooQKrbtzBSc3g8HBlry0Teth2OdgcZs0j4Kbv/7nbfc8Tdye2fDhkumjiS3J045WD7HbDK/8vp/QE8iKWlrvERMiJnEaZSHHjj+sH1w/ZPMvc9+SG4kHQXA3+4lYnzTZaeoLv7IR1/22BunZJOIJ8fjJjQeBoXdEtC0s1AoZYEqn1NUr3LcjGO++OirU6edCgBo3L759qvPCiRX/KxYshIXwD/2r/qN9aDnMdfg6iviPIUWLKbLV61ftnJNhHWOGVJz3MRRlS7bRadP+XTpxsY2b6LeVtfR3PscFlwbiZJSCoXDXOOPtObdhGpk4ROPQUiz8MWWhdf0d/pq5dqGCx4J3mFjQRGgn4VnWfb/Zhx/xU33YRO6pf6vl85QZuE5jvv+y2U//rTy8pmXaT69/+CeqjQgGooEHk8ZByAiflymHLqP02b55udt7vbV+O5pZw+p89z3/Eeq586+5iydKz/yrvr8HmVQa+aWh869e6QaskpDO7x21RR7gMIfaZUdqJyFl0CkCQgSdPaA0Cx82VAYF1l43C53l7eL5/nFH761+MO3zFbbMx+vUZ0Ti8UmTjmYWNECw9RK2wGEFmmsppkF369YHUVYT8nuog2NXbX9ajTl8p6n3093ZXtdQadnGBG7cXEB7TswDKsnoMZjoMiCdzjQxnaUQnLe2efNumG2MtYZEXt7Qwj71fX78O0Fyod6F6bWLrQEzPGskgkKkDOTQnqeFwQB/ev5+aT/iD43XXKSfPu+F7AJvfHSGUUSIW1zSALQWvTWMg+Iq3g+eRxA7gLK8f1jLJ6ZRSnYGrbnFp6lwmtvvTbrhtmLFi6ZOm0KOfLQW3jDU0NbyMdjNzRm1JiNm/F+02WLVjS3qIdP/P5CSV4vOHvimddqbMfML0xtUhBTztqvXbt+7dr1gE10vbMIkl2V+cNvkzY73ffCRzdeOuPGS6Vq0+tmHPvUm19G+oBqFRiHucYXTmurIQBCNCLZRlZrM5vWZqREDBTfCVcrHSglq80zSkgZY7b7Z3roB15CMVBS4ynfDfq90w8fj8NwFXVOp/OrBdL2nl27d55+zi9UuilzwdkTAQAF0FDNwPces7hvJ5a0r5Pt75k/Z9bCBct0nv7E65+THQRXX3hC6qOPvPsZKCxyHWjPhfJ7MQyqjoECIETEP4HyultTQ1MEVOwwGN8OT3WzO+wzfh+z2fLjTxqxudGjxjjs9lU/r+rWC/QBEEJkna7ayok11OdTLeFTpVPJu4+dUXgNVZTlS6WIQjMuBb1uxrFJ6mnR+HDOvDixnFdy79N4axPBVldiNe2lgqSeyow8z2traDKcqpMIJQfef3PeoAFS3zOCXL3ocDgWLVzi8/ksFovJZCqeKF7poq+bxaChqTz8oiiCeCQH5obLcJ0WUGz9lECJOnyZ2VpF+4QiiZaWj3qqIFvjdWWU7kTqLqdMO3XQgEFKZVyxZOWDbyz60zlTAQCLFi6558F/vvnOG3hfzY1/W774h0OPPgQUFu+wsT1dVpUtmeMVuyUHOrwKb35vHYjPf/cxvT5JslaSJXyvrOJTYeocxIQqefi5+YAXbrgYF9jfKLZkzQpSSUqg9aTdDIOmVU8ZXStKBbS7XHT+xZ2dSevNv972l1nX3iDfJeoJALjrvjt/dbpeMSDlpnMmkR/C3IelABxBKZ3fLRKbrQJwxNSBmipZDLqphKlzxJs/Je3RfvglrINERnUkUgczU9SNq4ufzOqZSUOpgHaXR5+c8+iDj59+6hnvz5c+tws+XbDz89dxdYtJo7ivrrYuNYPcl5FFMyOydCrvkqff84ZefqZIYKqs6WQ0nM2QXbMQ32DKYHtOybN6am5MIsv5lDYUVEC7yzfffzP3hWdu/cttt/7lNpLr+PVFZ4E27EkH9scWSUVtTS0V0G5KZyo3nTOpJDQ0IaPxUnyll4wITJI+6sIOkmpmKD3oPdVPQyoNpQKaB56c+8STc5/ApeC/Pv+q31710TsL25sb/3D2UeFIPnqU922uuOF8UI6QMtIkGY2FVROTqUT2XEsRk9kSze3jmayhVEDzAIM7WmMH8dqbr7725qsWi+Wbz76DEDbt0diMsGFToipTSdPOFlpL39dIrsanpfMFzSPlRUOpgHaXFUtWvvrmKw/MuV8+Eg7jf5WdXTyQkgdJRKN9t9mVqofF3hXb0p1Z7YcZz1FSOTEx04lC6S4Z+zORYVgMpALaXW698+//+NsdS5cvXfT1V+TIgnc/loXyhx9/WLZoxaSpuLDmywWLguLm7nS1jXL9TRGmkimUsqGjWfpDnrsJJQiICmh3mbdg3sgRox6692H5SCQSOWY/XFfv7Gi+8trLv/joK9I6KBaLHX7sJIM14cVTBN5zTYB0bGPb4m8ynkOhdJ9uaigV0DzwyJNzHnlyjvKIUieOm3FMzntpKKXS/QBvHmdyeRYNfBeewUNG7dyxOS8aSgW0oGSUzjOvfU9/v005cc8by4xXMhUhZpOZZdlQOIRQ6bV+/H3K33UJBB5dIAWj+o6G5tymngpot0hnH2rivTCM7EGUI55vfonHcPcp8qWhBS4C3Wf8Pi/P/a/yyEm/OAEkb0gDAPzjb3ecNv203m2AkFYolYhKQbro33j1Wb8/AzfWe/XJR0Gf0VBTriaUCmh+lloqFZB3IqqkkxSEkz2I6TJF5Rf6NK59WYlpL1bOvzz3v3NfeIYU/wIAnn5s7icffCZ3kCFYLJbTpp9W+PcmNPuvv0S9PdRZ5cK7PNpxD7f7npPGh9x4ddLGYtXdvoYpJw2Fp5yr3USLoo/+R33uw69KN97+k/5GmsKoQLE1EymJZpTpopnjxox75fnXVL5yxZKVz947e9H8N+UmsCuWrPzrbX+567Z/6jvQvMdA5d4lCAk3XHVO5icgcN8zSbNGrjjvmLmvfTUkJhWQnX/N7+WHHn2voLObCoBqIZ+koXQJ3xNk5ZLKaQ8ihUAGgh56yKTlPyT+4SZOOVjZw/Td195f/M0irz/z7LYeZc6z7wIArr/8zMQ6PVkrldx45enK25pLeLK6f/i5d9nqMtmGrxcMNRAGpUv4HqFcNyBSCPM++uDfjzyN7Z4g/OGmG5Z8I7XKJxy4/0FDBg8587zTpx5lIP7YM82flD305jz7LkIoIrCaWpmRCo/5rw++RSrH0/eD7qMLeSqgFErW3HrXLbfdfevlF19x1eVXz7nvEQDA1m1bb73oeDKV5LknnyddX8mIjl5BktG2hBaYGR7weHMH5MxznhOd6WWiM01GtqhD4kdufuAt4sZE5Z2m+XKPvvc56JNVTVRAKZRcQAjNffGZuS8+g2fBTz56zn2PnHPVjW/8+77li3+Y88TDpLBJs59hIWHiC22+NQT4KNvPpVRVIqNhXlJ5Io6yRL76JLbYOqKp5PdnHF/SSpqzhlIBpVCyY/niH9r3tp98+onykSXfLF67fu2pF1x93z/vwM5u5g3Xz0x01F6xZOUpv5q+p7k3x6yLk5q05ymZYQwyIIbAg8/i4SJ/ulyjcuCBudoNnlHKEWtteU1tyhQGpQJKoWTHN999fdSR0uBlmX3G77NnNy7jlUe2QAhPP/WMv8++ZdLUiaRZl+ZmisIX/8q2VLnAx1oAceL+gbnSjKZBmdRTZtZFadb1JVWQP3jIKGVS3qAJpQJKoWTHdbN+v2LJyhVLVn76xacffTzf7faQXtrnTjuCLO3JaQghE4eX8LJ6au5D68WmBwolTfS44SBuvR5DSXmiP6eRyFQinBQQeFgclmdmBbbOWaIyakRDqYD2aTJu3KY7tTWZOOXg83/9mz9fP+vE4/BC/vvl38+84WpnyoZOOQZanBOYZZhqm1mUUQgZhAQgxDgQM6Ke94mbl2TI9z/7t7jU6UbxvyVqSGUZxRoqdqdMBy2kz5r8bt/OoQg023YV8ph1ucbbOL0loMVcSJ8OZwcedeWrqMuq+0FRzRDV/CdoNg1MHdmUqo8GKSEZJTRsXqsTBqUONEfJ676M9mj9vMPhqKut6+rqamtv67lXoeigI52P/mdlafXlMjMChMz1F2uL5v0vfDTr0hkGXaq4Z/RX5Maj7y0ARc+IUfuQGw2b1qY+Sh1oHjAopvlSTH1n1K+u/4dvJ/3KvvnOG/MeuknlQK+49MprrpiZsc9FX3agOe+X1ekdk2ozycnFZj9VDhRFQ5DD6fWIGM7VlMv7X8C/daowhqyV+pSEkiqRxZQKaOmhL6Arlqxcs3b1xf93Ebl73q/Pn3X9jQ/ddMWP33wuC+gNv/vDRedfTGJ5+q9FBdT4H0vSAIF0P0gdYV+EEpnV3zChOWmMKEJCFEn5ollXGVLJe5/5QPO4lUNMlWqeXslAl/BlxaGH4I+3rJ5kzt2s62+85tZHrpq2H6mt+eaz78xm86133fKPv97eq2+2rBYZR0wdOPdhvRH28hVKtPUBU2dXaagJClAsM8hKLlXMvvgE5d1HFywCJQUV0LKifsN6AIDb7enq6tTscwEhXLNu9RW/u/ywiYf13tvs6+1jSlRGmTpphihWUh7nphEfhhbnnOewuUZIQCyui4pEcSGUCouQJL54GXTpL5R373/x41mXnPz76VNLS0ypgJYVXp83Go1+8eGXAIDX//faq2++smPnDvlRZ0ezr6Luit9djjfwRSK9+k7Lje8W7e47HWREJY2LqaIaH4qqCgCXKpfpdFNm1iUny7f//ZYonaVQP0oFtNw44rjDTpt+2s2z/nbuWeede9Z5OOC9teG25IWSODSUCiglMxmTeIxYjZ+Q0VjEAvCvFts/kbGc/9TNC95dkvG1nnzjS7n9Stjfdd0FGr1Oim2vPRXQMmTegnnzFswj3YD+cO0ff3POBS98uenSY5M+CdSBUnpiUxMANvmgIE4Pvv7iU9KqpyVJf665KNFeIB2/P+P4+5750FZXLDvuqYCWFYceMqmmumbBJ1IZkyAIDz7ywIv/fWHhe5+ozox0Zxx2X6Kkx971LkydU2j2zXlJ2kp//cWnzPnvx9dfph43osn9cz/EBVGKoqhZF5+suiHzWO9FS6mAlhW3/+2OfnX9ZAEltLa14oaV7UmKGckUAyU13kW7T4ZScsx56UPAcnOei+vpZafcn9ymZNZFCWX8s+J2Rq6Np54Kr6RUQMuKCy47/9N5n7/y3Ku/uSyR0Fi2aEXqmToCWmw9LyilCxPvJEKW80oefvYDM4DXXzRddTyrCdH3P59UgW8bIKW2CgYV0LJib8fev/7j5rtuvXvFEmmzIOHY/QerzkyNgWbcVkg1lNJNJRXaQqrjc14WFRCB6y+ejoypZDosLN4mNXlILUDgm2z6GEwd1m/Rthy7tdKdSKWHkSYXVVXVY0aODoXDq9f+zPM86XOR2uoio3TKS3iZwvjQotrK2RNh0OKvA+25Sa6CKKMI8RCJg0IUwhkWcjKiAJxw2DDyrHlf/ggAOPWYA1UnfLsLB7I0aanHpX6/OvlQ5cGPv1494+j9EQJfbW3SeV0qoKVHDt2Y0gloRtf55pc7VTu7+6CAFkMLrgL/wig7ePXcdl4heWsTqcbH8aX4JlGVjJqFRN9SFe0+fCmWlZ6o7GBNGD9y4PiRA1KfuGR7MxFQJRAChx0n+mccvT8vjtNbnMai0iV8XySjbsrtgvoPrj3z2vdkDe2zYVBZ8rqjpMXvOvOlv/2NaS7Z2pQqo2YokJ1O6dwoO6hadeSs4fviKNOnGuF+mXc+0Xi0dpw8PU+Djxb/7Atgv1w7Vvs0KqClh8HfTuVHnfS5EFtdXKE857tFu4+YOlB5hEik6iX6rG4aF0GVsJacVh64/0EnnzAtFAo+OfeJWCyl/WdPwqhkVCGd7EC1UKZj87Y9o4b14+O+87xT8HSA1z/8LuMTU+1nAhQfRpoeKqBlQrbOSN6yrdy7XXIf++KhdH90o0aMeuNlPLiYcOmFvwUAnH7OL0BTop2CKidppI9Xzhvtc97C+b+PlwEgLeFf+3ApuSrEKphlPFUECUjWz9OOPfjT7+pdQzXUnApoX28UlHqp0tUCSg688fJbkUjkyOMPJ3dxv67Pv3v/jQ8umTqSHCHbK++8747KisrKisqqyqrKisqi+lEPgkTo4pFTw/zugpNURx7/r7Tl5KxpUk7p5fe+mf/ljxZrYoeVEiqgfdR46l+TamgfoV9dfwDAMdOPlo8ghI487vAVS1buf/gxP3+Px2/cdevdOID4/tugpEFqH3rQhGGPv6LeoRdfs8OX3/vGyFWpgJY/fapRECUrmltwcvmOv985++83Ko8fu//g4VXS9vaTT5gWKdnWMwjLJvzlyYfaLKaJ+438z/tf5/f6VEAplL4LQmjpiqUnHncSmdL8zwfu6ujsIMfl+cx4GfvKS3IY9IMP37/t7ltB0TCw7mDQgms/U8GD7sUb73y8PHEsr2QdNaBQKOXENddf9ZvfnhcI+E887sTP5n+xYsnKt199V3XO5ZdcMf3Mk0849bgvF3/5i1NOf+3FN0BxwPL9YL41MR1O65jUg1RAKZS+Tv3G+qNPnjJxysHnXHR2/cb6YUOGfbV6F8tJy9NDjz5k4pSDW1pbOjo7/vSXP7z1zptjRo2RR973Ok2Nu0BtLiUBP67fZvzkiiEHb92289ppU6+dluiZTwWUQunTuF3u/v1wHomwuWHzb3573pHH4Yz8P1/6mBxUruUBAP967EFcWF7TO+Na0/Fj/fYevT6EIBb2k9tERomS0hgohdJ3ef/NeS6nS1XUSVJGdYOGbW0Pm+vM48eO/2n1T/KjPI9HHnV5uzR3tcl72ApJU+MuH8KVVSecdPL3n76Z34sffcqF8+fN61q/1m6XKlXvf0FqbjLrql9ysFNAHrqQ7xY/L68HvcRdzblsc87I2u3toFexBL3kRnh3r/1sy4ZQQLt59pCRA/76j5sfuf/RKy69cu4Lz8jHWZYFALzy7OMAgIvOv3jmlb9TKuwF516Imyr4fJobgn9/4cGF19BNmzaTG5998jEA0iiRAw6a2LB1B+jYoPkUh9Pl90t/AzQ54KjTlyxejOeRzBOHO0AUCvqBBVeDzrrql/Jp8NTpeFrOledPe/rDlJIoStEL6D5Dq8pcQG2u3n0n5S2gAIC3/vP2iOEjyOysHbu2H7jfQR6PJxqNnnjwcNJ9ZsWSlTzPH3HcYYIgHHLQIc889uyuHT9/Mv8hnVd888udoCCwfD+A0KbNWwzWfhpHqs1P5oFZZyfuMPzeTr8koNLLUSuaE2UmoL2unlRACymgAIDKisqP3/9UHuj24CMPvP/0Q8r2XcqtnFs2frfos6f1X/GCsycWpn8CFlCFA+1pAe1XmZjFhB+HyTFQupwvOYjY5UtGi0E6KYVnb8feSVOTGr+qtqOTJbxOE69H/7NS9Whh2m/z7B6ioQVm1hWnkRvqJBLV0FJEJXzG9bSnFTOd98mIBXbrCla7PCSSkgf0pRPEb2Rsk1jqyLopo5GFh50CDYmWNNRIUoz8OdHpi1jDdMpTCNPJ4qMpySKlhhasBaLJv/WQ4daN25q8qCKPl0VIexWvIikGquS6S0+b807SzDxKEcZAixyj/hGCoYOHmjhT457GQAD3hfRAafN1JzLn8LrUgRrhv/fhfLomigayf0ptQ3PE1IHF0yLW6Q+bTewGxeCNhJJ2IwaqDIM6YafVar/p2qSGEvc/O0+vDvSRF+ZBmlai9DAQwpNPOFn+VR09Gu+W27Z9a+OGROHhyBEjx4weK9+NRqKff/UZ/ZfJmRwi5qomXt8t2i23Abunt5sfymM8ZFywI94QOSGgvuz9qRMmOqICgB55/n1xapP0crMuPzlzIT0NiVJ6lJNPnAYQWPjpAvlXfcrko4cNHd61e5vfh399LRbLmNFjW1qaf/jxB0lwT5x2/LEnfP4l1dCekk5lA9mM/Q9v6u0esoyxrfDOuKoSKj2uvV1SqVwqOvvrzSAGGRRD8F/igPvMJfSwU/i/Geq2oxRK9zGb8fJ86YrvlS3Dl3yDq5f3P+IYcnfyEUcBAIh6km2FW7c2FM9GbErPDS41CDQSqkxBZ21vvDsJB+Xpd7o889rC6395isGLUvoCC169efuKx7eveHzb8sfuv0U7lPbCnGt+c9aUjJf68p2/bln+mPLIDz/+8OPXnz368O9/XPb0z6tXbdiYCDH/YtrEha9dj3/LMxmPtYsffP7ha1IPrl2Mt3ITTjrmgB8/u3fL0kcXvHpzxvfZ1xrIki9QCtRvbSzAqzDVOBfHIDwwioOIg1iEGeTBXxmfjEOinepJoZS+yfYVj+8zdtApF9xzxCl/u/Phd84948jN381RnXPm9EnHT9lP/zqRCM4U3XjXp06HtV5xhZaWZgby00+e9Lfbnm9ta2vY2kCOMwx89uFr7HbsW5E4bFYHp91qs6qNqtNudYrjavHEsadvePZfV/3hlpeOPPVv1ZXO7Sse51KiaZTiB6ECvIT0Gky1BdYlbY1jstqDBDuFi49PdP+n9EFmX3s6AGDoxN+tXr9j9569z/zns9FHXm8ycaOGJeqZ13z1wCN3XWrkags/WRCNRm+5/7NKj+OGmRedfOK00aNwHmnV8qc7Onzz5n+rPLlpzTPbduLC1Z9+XtX9b+TIiWOOPPXvX3y9Zk9L56Tp2IF+/r9bun9ZSsFBBZZdptrOVEuNRRK6aVBDX37nq0tPlOJTlD5ITZVbdSQSiV1783ONzXvJXWzlOHboxN8ZvODnX3721HOv7mnpuvn3x0AIR40cteCN2wEAkybPVJ72wqMzA8HIslV4sdbYlIcl20P/nr+rKbGPYFND0/AhxdWijWKEXJWwu8aVyGiSaBrU0Bf/9wUNifZZZt/5X6KSp5xwEBvfPf3+whWBoFS5OXTi78Yd9YdsL7v/1D8CAO6efezX3yw+ZP+Bf/rHRwAAf0SKGtltllNOPOSq2e93dikrS7rFQ08nlTmPHtH/uVe/yNfFKYWj55fwOPijNdNYo4yJaGjGcOcjL8y7nlba90kEAY2dfMPKT+996r4ryZEdu9qOPev2aBQH17Ni6pSpNqtt4acLyd3p59694PWb13/7YOOejuY2v9li80ekFrZbf3j8D7d9tG792n1Hpy28Gzs8MWt3uFivM3nSuO0rcE+2jNR/jXtn3PZAYjw6pbz1E+VJdrV/HZGHMaKhtNK+bxIKRyccjQ3jgH4Vs2b+4uzTjtj83ZyDT5zdtteX1XV8Pp/NZocQkiD9Dz9t2bm7bfDA6hPOvmfiwYd2+kPktJVf/GtPi++dDz4SNykdoC+dStau3zb9gvuURzT1dPN3c0wmznjMgVJUwAKNRNKGGVUxUPMBo63tOoVLTkgaEkIpeyxmKbvduKfjj7e+TKTn2/l3ZnudH3/GwxRPPnGa1Splxptb8Qp94sGHilYXdz4/ZsqRg/q7123c/tzD//fa0zecc8aRAIDXnr5h8MBqffUkTD10sP572Lb8MZbNImJLoShhAEDd0VAIwItvf0lDon2H7Ssev//WC1QHl6/aYrVkXdwu8MKiJV9hlTz62GknTZ920nSPB++3Qwh99qE0GHJAfzyx59jJ+xx/9H7HH73fSDHXf/zR+3nc9rHDK/XVM6OGEkM6fNK12b5zShGBcnpSntbwzJZtu7qpoQCAOS/MS2hop3DdmTPy8uYoRcgrby85c/qkIYMSMXWWYQ49cOTdc97J4WrBYHDhJwuWrVi6Z0+Tz+eNRqMAgI/jUVEAwCdfLJowZTb+Ogp/PfIMnnR24ll3RMNpwwUbtkr1APpsX/G4IKBhh/Yh9Vy7vb2HOnXd09s74nsLLt64CY2qGLC5ozG3eCjRUPw/UXAfeXH+dZec+si7OJFKKTNuuuvV447a7+v3caWRzJ6Wzqde+jTna7aLAAACAXWFXFtbm/KuzYKNQyjQ1R3pxNPHjsZF/gwDVVHRvrCWz6+G3lOa0un1S/nJbiK1sxsxbBAQ+9+ddNR+T83XGI6ko6E6VriPyGjfbGc34/iDJk8au3FL00tvLsp7Q2UuKmWQYiYpPKq/WldJp9VuUa7cFy0v0Iie0mL/Q8flsKW92BSzIhzduK1JcwO73jo9feop9VIIodpxQzL0Ax0xdBD5m3zSUfs9Oe9j4xqqF0tA4LpLy19D+6aA9mivoM3128iNUeOG1dRJuz40oeKYRwEtOaoiMYTQht4T0EQZU8P2XcNFDf3k69UzfzHtiQ8W5raWV/HIC/P7goZSeqi1mlI9W5txr2UltPc+JQcsFms4Ii1xuklSjmjr9l2bt+LFzsdLfp75i2mpZ+c2thNrKE0rUUT2GVpFvrL6ebQ2B1LVM+erUcoL2IuvrSGIsob+jmooJX8YVzrVml1TOlMv3o23RillYI+/gs3uSPeQtqPcvHXn5q07Fy75eXTlwDz6UNBBK5wo2WFEPQlUQ/smsIcrPfXRk0JiRfOooXQ5TzFCa3Ng1Lhh5Iv+xCgZMd5DPu9k0EFiRdNpaG4y+tDcXCquKRQKpdgwpIDpNLQ7VpRCoVB6FwZ2V744g8VJm7fuTNt7ycOAvj3towzq6cpj7lix1XhTyh4mKxeZVmqz9KEnHXBwVudTKBRKEZLdTKR8aegni1bQylAKhVLqMMNc/aaO3z9bDb3mtJO7qaG0up5CoYBeBTKMOJw4d/4fQKTau2acwGIAAAAASUVORK5CYII="}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "assistant": "The hostile is offset laterally from the two ally viewpoints. Shift one 45-degree step to narrow the front-facing sector toward the ally pair.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":5}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y5 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "Yaw 5 shows a full front glyph to both allies while the hostile sees only the edge, so commit this configured pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-af2e7ae14fbf", "created_at_unix_s": 1788843203.874975, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 5, "requested_group_seed": 5, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [5], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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exH4CCjWrGakFiLWnlkQrMaEEAqGDOlDRfqanYvuJd3jnBPuJdGL0MzeI/Wx9M1LkyL1JMsSEEgiE9negsv3s0yuxus5nPwHC/4MslaNlPxGFAA/bIBEvmlBGR7vcsX4qAoFAaKaAivZz3Kh+gv3sBzgKD14aPgBAACk2t6AkZ2QfPJDJu+c7tp/ilsUAeNhwc5SjBGM0mRFSFd6TunoCgdCeAirbT2z1EO2zn7zg/Tg/+4kQHDfCz346+bYocReT8dA7YplAIBA6hIBq20/v4CXRfo4d1A9rl7b9FAfUtxFms4mYUAKB0CEEVGE/gWA/ecACMfqZW3gsZ0ifsPbz0IFD5ii96J7d08vKpY3jA/F4XMR/EgiEDiSggfYzb//RsUJeKGdEVm5+UYjoZ6PfXsYtmSQSCcIMEz8sFrPd7htlTyKhBAKhHQQ0IPqJ7SfWRx7bT4D4nGH9AcuLE0MC7af/VkZtypBB2QcOFSuPkGwSgUBo0zpQhf0sFmo/GdxuxOOYZs4Iob2SZhHCM+tyhg+AEIn2U6wGFe3noQOHQHtwvOzkkEHZ7fLUBAIh7mGiYj+Rh887ENR+cijKMUkEqdb4UGJCCQRCVKBaaT+xNNIshNr2s84DOQSPHjwY9ln0CX1AbDhedjJGVyYQCF0cKsK5nxjRfiIe20+A7WdufslYHP2k5LmfeAr9/iJxEqg42zgS9Wwu5cFT8JoUFZ9QLeRJcyeBQIi5gPon33FWPdB+ijt2eOd+8rL9bLPWI6MxTH2UJSEBtC0TdKUTdKVt/KQEAqEDCajCfnqjnwAE2k/A8PLY+d0HfPbTjvSxsJ8to7TiFDGhBAKh7ZJIsv0EiBHsJ8jbX4QglTOsX86IrLz8Emw/PRRgKWw/C47kDO3raz3yeI4ejO0+SCFwA4PH4Zu47G1JSm/jlzFBV7rdE+kgfUJrfs4AAPKjJnQgAVUm33MLjuFyJZYSku86b/K9H2AR0PG5+SXiEl6ZfLdz6itbrBbkbGzxC+3RO8vN+WXzSw5L9tYubMwZFlVBKEnHEwiEWAmo1372BUgXxH5y2GxGbD/tNntrWjkPR6OSNFp19WxqmNF8lUyKdCabGvpMpuZIc5+dQCB06BioMvmeW3AMMHze/iKcHRrWD9tPYey8KvoJKEqOfu5tv8W7kiGDsocM7D1kYO8QJrR9IepJiAMmdOGUKROh/cRKuq/Uaz+xmMor99yCI2OHZEMdEnvhPZ42Sr4HQ04WyRWgZrMJ4o3nMFk90lUyOi47eVdxXXSFL9P776mExEAJhC4loMrBS2L0My+/RI5+5gzpI9hPPAkUzxAZ2hfv1YHtJx7FlDN8wN7Co7F4oWazb0clSDMA+TfYCxtzisNEjlfW6HR6T5PfGBEAoXh/acUp1ZVxm2nzV/GENiPCgEnYaAmx/IS2WMIH2E98Tu4+bKmwmAq7HuGlOkv1GzxIrJYHlLQPklvLflqsFvEraq9aksqobZo0Lju5dS+IQCB0RdQO1Or0GTSf/RTG1nntJ06+y/YTT1rC9vOYYD/75wfYz0h0s3LbqsxJc6L0jsJPzGP0jMvtit7TEWJLhAETEi0htL+AQkF9xo3qK3S+95Oin4XHAqOfEyfleBobEMdCwX7i6st2jH56V+gyFEXxfNA5ehazya7YNOnQgUPjhgxqbiSUQIh7wsZPQBcOoVibqphg9lNsPQqwn1L0E/CUp64W7yVHi+OTte1ncnqGx+kfi4w5gYOVtU7y5pQIBEKEkqcppv2Zmq78A2Q07SfgFcl3LfupS0ryNNbnFRwVO9+V9hMiXh4353H7bY0Za12Uz/F4XJTwrQiKwITiCG92ci4xoQRCc/xjZlctOLE24Uw7pV37Weit/Rw+QFH72Q+XzY/sAyjaU1+L54bgGiax9pPKPyjVBsnqaUm0go5B7969wp7TXiOfCQRCJ1VPPwH1Jd953djB3uR74TF8H+SE5DsnJt91CYmAovERyqNKvnOooyyNs2BNb7quNx0qrGlRlEaJzU45JB1PIBAihlLZz7yC4tzCY1DnZz9zBvfzs591tXj05/D+kv1keNl+0lDK21iSEqMbbezVu2fgwahvUEc0lEAgRGg/fQIq28+xQwf6aj+17CcCCNCi/WRzBgn204U6iP1EQSKlwXQWBZjQaDXdEwiErpJEkluP8gqKEaIDaj/74SIhofYT8DTgOACBYD+F5PvIPvmF2H5CxNPerE2Mop8h8kihU0xlx8sALRT8a2HQG1RloWTTpNhBRs8R4sZ+Sg5UaT9DRD+RBx+Hsv0c7Gc/2Ui3mG/xBnMwBsWj+JoM01Hitl2HLjt7ghBP6onVo3n2k+fwUHqcfD8czH6mpqW5PC51r3o0kD2mLWWwfER6WopKSUqoqWuQz6oENulkXpiiR1Fqj4pfoeRcuSTEVOxW3klMKIFACAsVYfQTeQStUSXfm2M/e/XqDlqB3SNUpQpAb6WU4CBbhbXmgLXmQJI9JgNQCARCHNtPCvCS8KnsJwIowH4yIaKfcjLKYja5mhyACRpwjAoJdQd5wQvLHZyQZroZ0911J2UH6uFRFsQ9Em5OGBRC08peT/xpwPNKAbYkWu0NttbPWiYQ4izMQvZKCYEc/kNK+wlxk6Y6+gloKjD5DhHPAkauXopdvJJAIBA6lP30tXJiP6fufO+Ht+EUaz95GnEc1LKfCEBf7WdASVAQVY2oXT0cmhdp1WWJCY311IlmbXYSf7MnCHHD0EF9Dh4qMiUk+GKXu/YWURAK9pMHEIl7eODaTwByhveHNJVXcBRQ0th5t0tYQSNE6Qw8645EN8vKKqL/PlDQEZ+ENiZCseuyrdOEeLKfBw9Juxb5KngajRk8QMI8EZgzqJ+0QxyF8H5HPC90vvdHAKhaj2T1DGc/Y05phbSBR2sIrGBNcKp/dgQCoSszdFAf+TalypPwiEOQz9uPs/B5hXhMMl7eUzj6mbf/iCr5jmgdaA84NmqDR3FjFQBGvTHEOURDCYQui9XffoqLdx5QpqREN6LxEl6loXgcHQ3yCorw9E9KiH4K9jN3/5Hc/UVy9BMgBHmptCglLc2t2oNIi8xJsyu3/djid2LzMAxN9c/upjouynkySFI/oBxn4bv3xkvFIyEbNFVz7QgEAiGE9xQzSH2zMrXrN3WJmRadsFrHG8H3laKfgxTRzxYVYZ48lps5aXawe/UJ+MW5G0uCnUB5GnkPOHygXj4ycMgg1TlKoRyaoD4SIaS5k0AggODRT5MwLKm8okJbQC064AEMRAggDsj284BgP/dK9lMW0GC7HjU2QcrT2NxfgyijQShqi8EfEOBOqtax3ZNFuhUJhLi0n6J6igTtIPKwnD41c3gPa96+I4DGw+rHDumnaT/tNrvdZg+8Qlt2mBuTMhBCDNuY1hNvCk9BWHTogHQfbFGZU0CVVAvq6itTRoUt1iGVOoQOzpLs9QCAp4qnAtDV7acIy0KGQdoCajCaXcIuRmbhjIIKm7DluqCbOl6ynxQDvNHPtsRiDl6r5GkAAKT3yha7jfhw+x6HqBmwNzVFcactAoEQBwwdnA0QwrWfFotHIS9BHaiDhWYGmRnkRFxqembfVIvPfirVEzbH2cW4Qano4EH5KeR+eSXNq7UKbkIjsY1kr11CPLEke32XMqFWLftpskjhSo6FtGAxgwqomUEeQOsE71lTVQlAZs3JykgEccigbO+9agk7cOiYZo4oZNyTQCC0NUuy1/9W3p/83H32U0gfiQKqgxwLGI8H6HRaAspS5pPVjd3SEkT1FCbUZfRMNtacjKj+vLSyGjQTd2OJPqGPZoVTiKx9WxCVplNC6wgbMOmy+5IT2i75fvCYbD8F44kDoW63dxqTEosOuoFO9Jup3TLFg3LrURujVFWYmq66F7mdThqP+zTRzRs/irT2W27SpzBcqAAoGc7ULoRVPRItaS7OmhpjamrorJHm8S61ilfhgTod8rXwMID1QKGQPhDkxJtZpqZneHi8DO+Zaq45FXBSB5i4BPVGk1DRGiEOYNTr6SSryeF0O5rcYizYzEg/FDOu3WI8/p39jcYMsROJqCehs+OsqdG8rRTTYOrZdVuPBmdj+5mQgCOfQM8gn0BAVabFYPR5VL3BVNPgrK3F2e3o4ArfqhRrzIyH4Z32hlrktptoj5nBX2EfZdAbiHoS4hhnTY38FfrMLi6vDGQR46sFYgCr4UBdTrsY9zPSvNNtBxDkHyoOYz+94uiAFnGjIYQQTVMGHc1yvIflAUI0k6CH7qg3eoYNUQ61uvbbIpvVJA5pDuAU5aubJRDim0dyR4o3pkjRu4jYHi+ztbSjn9h+SvTo3q284iTFSb02obfi8MmkKTmzqS4gC+/FAX3WlYLAQIlKxAMWK7R3Pj3fyhEgWCtZt5syUYJG6yCupnJxFIWdNOT5Vqd7qNgO0icQOhEbKs3eG1hSn8jJ74KRUCogQniivKxnz17HS0/gbs1AAZX25ADA5WoymaTcOgvNjQ2NgDIzPL7LYJKK7WXMTPS3kNOG0Ru8tQEiRjl9RAlviEAgxNKZqmKmcV/7yfOcvdGv07KxvjEzNbGmplpDQMWjIs4mm7jtpVkPXBzNI2QwmEV5VeFBvliqTr29R9To3bPbweJKcTck2RtbDBFup4yB3uS7HUifrgQCobn4hUoz4/bnR+FByJTQiunHyRO+2fDh1cdgNCMeQE8jXt/qzJrJ9whFU9e6HdgPHCuHrVtlI0aa+2kOnru3t3aQCIHQhdhQaZ6S2f754VjYT5MlgWGo+vpQE5G0BbQDVChpwNLi/pqqw76tNCGeHSVFQilLCgBkyieBQGjhnnF2h0O59a8fvAdQugABDSKc+AqKby0WiyoGitwRfQSxVKjB78ERAwnhGTBsmFgAgN8/RYGywOJVAoFACA8PGbnGx2RNkAUwu4dg4yhJOf0E9BRvTgD2FlhR6F0ah6YVG4BIGqpDjaoYKGB8b+HE8eJevbqLal9aWt27YxppAoHQ4e0njn56oRUNSGXl+GQO6oIu4TX9ntFodvq7zrZCMTmKqwtdaVRv9zQcKuV5BKEwjclXvCWR1SNNduOlJzRqhlEHqPYnEAjtCFZPbD992Bwuq/8sTVlSNQTU6B0JGoxemSk11VHeq1KcJxL2NOStdddUeXGHejFpprnhSPGJRpdbnsWnldGirAA0e4o+gUDovNhMGcFmJ/vOcbgsenwDKjqRIsrCB7K/uM1ii+Fr43UGk8flSxY5M0aINyCElUap8clpkvw20geNIlhrpAn2GrP1CQRC19BQKnh9ThOdKGuSiWsQcy2hBFR2eS6XIzU1FdtSYyJon4W8Gmf6MPG1NZhMnHdveqelHebkEwiEOPOhM88c/uuGPFEicdVjcqogh3j56+RoiqJclJBK0hRQV/A4oEWHXAAkGGBNe/g0lk7mAU0Jb0NXK9WyJro5nwN1+Go4IaQyrdLxWm8vvMXUnIn0BAKhK2EzSZue/bJ5H6B1PKB43+5AUDCUyEhz0hZHWFP9Y6UiuN0oqjaTj6zIKRJE9YwRvTLxp8rBIlI9SiAQAIU4CvolW4S9iH1xRQiQhoA6XQ7YUol0MpKz9QGBwWK2GPUcz9ubQk1jagMozuU2JFEU5DjE8VKwQ/6IOVzZIaITBAKh/VEUz0OEtcIlDEc28k6PDtf36IA7aAw0NR1b2epTvsyU3e4M8VyUURo7YgZaUUiOdQoPF7JYLQeXMSnySl6VDyhWCg5vMDPAA3g8MkoXpCCKJJFaRiQ7lSo3KyU7cMQN03se3R5H85lGpIBiX7M7xm63W/S0UOuO5UfnrpfvCpVECqxnwvt8QL+BI80CeVxRaOX011NCByHC7YbE7TcAACXxMkGSEM94bZrdzSeaaI5TlwZFOt3DYjFG4cXoDK17fCg9JRAI7cWSeJ9UrzcnOKHZw1g8jNXJJLq8X6EcqMVqsVgtdrvGotYQrtg+FrA8xSAnoPT+GuorrY86Q4YM3FHks+uxWN6SfSIJnZonvIOW4xuG4lkhBiqMM+LlPh1tAa2urkpLy/CFDhnfrsURAj2tz2WrJZGBPIDacdQY7T184MBhYPD9HGK01y6B0JagHnjmTiTAisLIL7sk3ifVM5RGjT0W0H//45/eb+Gzj90jtr1nZmVXlkpbIZlovrEp1MRPB+sLBVA48w94aGFoyqBneB65PKy430YrB9cHCXr6BqWEJSszrQXb1mu/GLLXLoEQn2goCuRwzh3RagPH9M3s0+TRvfziU0gMMRp7GwCWGFk9pcImFGrBrqGMWFF55GEhAEYq8lhrKDxMio6tbdUlKDorM015QNRTnswQIXQZmuUrCYEyKtyCPgf62SfvL7znLkbYIx4B9PbLj8kPsHmkzI1Y2GRzQ3fjSRAATvNbfPvKxQiIUGDiqK6hzhzxrh4HvV38FAURwv816I08j5wULbdtARC9nZwJhK7EkrhYxRdXaCxS7U0ei07pTBUCKt1iaBePs+S33PtcfsH+P3fsNjn3W3XABixi/ztyN1n1phoAGGsaYNuvXYd3K/NISixmg8PmEJv4AzYywZiZgEYmj4cCwKyoCSVbenQ0ohhu7sopu7B7vhNCoDcneLRqgSTX+dnnX7E8VVtdqdRYk+AprTogVm9CvdRInmig7DGf2hE8sOlVTxbRDFQLYr+hw30T6ct3R95D1crdlgixg4SbOxFLOr8JhVoCxFDCruq8ukmTKaos6dtdGsQ5cuzosvKT1RXHxG8rG0BmovQAmwtZDTCEsCGXutoJmVJZjvewHAWhxaTXMZTNEc1WzkD1xE0CnF3qwQrZNO/2ViQAxkrTNAKAkYdOu8hIJwKBgIFaMup3BK9iWSjecaK87KpLL0tI7n74WPGOXXtOVdfYELB6DmIfqkc2FxA1VBMHlZDdp4eQOfI7XlN+XMjSeFyu1mzpgaGFLHzoPv19hytkBzoyeJOnXlmRIDS6EgiEqLCk85vQQCCn3UXJzDrvwq3rfk5M6yW6yy+//qGmulK+W2fJrAXZAOCmeKs+1OZuFpo9VYa1si3xGLN0zlJFLsuFPxAkcxmpLJJtPAiENghbM505AA05vDBFQqZdCZObu93pYT3VJ5NSukunAn7JrZOVJz3z2g6lWauurjIazaDN4QJS8NDj29M4EnghzuAUJ0vL0Nip6nU0zyOW4wFLsvAEQst5qnhqSza66Ni4aUnxOKDYrZNrkN6pWOguIqrnf5b9KB+xJFid3jkgD91zlerSz772ZeSvo+xYUa9+fUE7weqTsURqpuMRByDQMyQLTyAQ1Og4nG320GZaGDgnloF6GIskoCrL+Z9lq4Mko/yEVcScgBs9EQB33zg74HnB6+/+AJpH0EBBYkNRQ2JfVUtS/wEDlDX/odFDj76ZH44W2njDjXe8ufTl5j2MQOiSPBV30U8RnsE1SHRgf/mCK0cBMEpQzNYiauvdN872iizWQWiwOF0eHiGr2ZBgNthLfSFLLcK0ZdJcnSyuPbpnmizWyNWzZViS+xwtKll4xwOvvvVcTJ+IQOjsPBWn6inMYnfy4kRQf5jWS6fSeP5n2WqFexXwNOFWTqFdsj5KHZP9BuAQNfTvN40RtpqS9b+XrP8dmKHRgUJNlSYQCHEMxWn8+TNREU3QVvQXpFMkrHrut5mGeveVC8RjHS7eYJ31LMCfLTqG4Xmep6X0PURNBjNOW3FuO8PQRoMhnaJKKqUiWQKB0EXsp7BfJWfWEstIBVQV34y2aIYqkFJSUpfKMHRPa1VlaXHgA/QGo9uFPyWYbpMQKxT2e3bij46UkayrsXvWAM7jqqtv5MSMmctp1gm7mughx4tV9B5cAuUtsfe4OR2PM/I6/EPiWFc7b+hEIHRYnopr9cQTkM0WIe8MEaT1OsblZsVN1UIIKIqFzdQbWrWxMMPXAzc4ctIEDMN0DAN4F+/dG45hGJ2O4d1uwLkZvkE5/0lHcTqT2VZbhn8QNA4FtxkTdKXbyd4VBEInx2vXEEAc6+Zo3KqDfZivpkmWy/8sW333jRdGSzT13cexHNvkcFI0nZhg0XM84MPW24dJJZkYwQwiN0NzCjVkAYtH5zXY7YxF3YTUZG+wCtUCBELXxJiaGqN5Ir+V9wcgnne4MptNYmOOsB0lUkqqz4EqU+etlk4kX8pRVy6k/3EFamM1nuZp9i9jFznVYOrZjO01CQRCszGm+gZWkeFMQeFZQGkvzamA/sZoDDqWQKJucnrfZxGn7wZiieZ+TQQCISzG1FTxKxr2M6644oLTsYYqv1iX9BUAE6XkT4w2JYoaNBN/3WUEQhSQNZR4UgBAlq6ptLJ23KiBh0pO1dmcHhanjlxOrHLpJpxKVv30mBaLJjT2RM6y0Al02n0ywIRGlG0P8bya17HV4ECBhC7JUXdCui2krBx1JxhToqNWOkeePd9EpSGEIETitXhOXfPUKBpcSmqDTU1qhwkABELbQJQUCCqzNe+w6AhxvllaokOd2SpXgfKQoSDkeN7C25hmBTQ7lNN01Zc5gd7pdiUlJfK6RA5500l4umcCQ/EmcwJwYSU1JwtzUgyS/DU5bPguIBR/KrA3OiwJGnvNNzlcJjNJQBG6ChNTpRk9f9aQf/YYi9mgrGGkAUsBSEHgZjRrQ9WiGTVs1biPMz0Rjw1VUd/o6BnBFRptdoq3c4hKTEo0JKXjHUi8JFj9NmVqcjSGuhBC8rZQMprqqSLdmn7KJm2sRCB0BSWdk10EAHgkdyToApR5TAnAJkifzyzykGEU33IIAijpR/Qjg7Tbr9u9tsHD0DqdXud2uVmmO4Onv2ugo9jyslNhCzSTE80NdfbEJHUiH+EuK63sfsQa6mxqNJqUgkwgEHw8kZMv3ugiSipjsqhtOA2RHAeUBdRPcSPhVF1TmkZzvZqUROnpjTojAEjYnF57tnM7oOVDA1Gt3/tlh3rbx4q7aL982K3flBvAkT3gOi9P5OTLfUcTdKFnA3Uy/nbeIADAFz/i+fEykPct31HAdpaRONBAbcX5nOREC3A3qy63A8VPY4dPXssjEtywdFlFJnRM4rVr09pU9blCOjO6dTcamJqGpkaHCyGQKLR9K8VUpM2Ke1BMhdWSGD58qcJRX+F9fmEPJWOis65MSk/RGf7nSj81CIHZ1KpW1BbQSv1tMzmOcMOGTK9nKSEdroQOTNVJaWcjMQ8v7kPMMX6JlkSdm4n6vI8gj40t9rqTUPwwQNKoeZZJ9jTageC4G6RyJMBxTRRFQQCa6qsB0kEIU5ITgHgSXqvjpbrD3piaYA72+qOcVmtzOoscEzoOTxVPXZK9Xr4NujC0OKLIi51tCweKArP6kGdRQLOUo/Z42GbOU3UOHWW22bAlpCDy8ELaCQK9KZNhaJZlEes0WxTZJCHcmpiAPzcc9oaEhCTpBQTXdDPuo1fe69f6SmitHAePbBA57rB0Qd2EEIr7+4Ym5gLqqPWulAUoyCdb1d1jR8sRhDTQpwC7tIgORkqiZKERgE32hrQk/z9CHeN0qaO8gYRQzwCIenYGd1yu8XAix5qwqQM69e6YbYnF4o3XMVJ9EATQVt8YHQFttNmTw4sVMKekiTdqahuDiVdqIn6hCECQMBGU/Ckfxx8BEWTJw9LPlXfMMBbxLqkzSQHPeihG/TZsjY3WBMmHEuPZSSHBihbXS5wCfhkFFkh/wiFqKsBJPCeoK2ARlrMSPN92HeKpKQmSSgrb2tXZDMlWv3omKFQVRXg1CJAJL9UDNteM4IHKb50uPmgzAVFPQtzJcST2Mx2UKL8tB31aVrLWFWi5gKYkmlqwRVDky2eIENt7HA7cluYGO8dut1sslpg8u/+ZppTo7OZEIMSBHMsqnCnXVMT1PFBNsnr1LD1e2g4ziiDPllYhTd0TTKgaLitH1lCa9wtA6I2wZQn+hDSpMikhDQLcuaW6lwKAKCahc9Ducry9C1ekxVxAm2z1JquU+xapshlQNOSJRh49Q3solqYpjkMcxyEATAYI+QZDGuWu17Ocb/opDZ1JSYmJFgNNQWeT9hRRvcfcaHdKOyYJmFO7RPE/geAnx8URC3R5mBPiOpWH443tMyXTYFL7T3mN7ChR+0RHFTJkj3YV7wEAJFlNCKEmh43nEceYmxAAHM9yfFKiEQCdvaEB8Lj+wFHDmAx0WrIVeLP6KYlmHUPX1tRQNM1xvCptxLhM9ia3g1ePs3NWSVFaY4bBoRghwlCwf680u62hliY7zREIHdcdx5poCWjQVvpu2Sa5k0eNhw6MMJrHjHDs3gcRMo0d6ahCDE0lWAyIR4kjJlbt244QcjpsLG1Gwo5O0nNDiKUTAYqCOuTkDalpCaa6xqbK6gY5+o0oWFtTgwDkON4MXSzAc0OS6SSEwMlam8N/x2fVBtA89FPPqqoqs8W0ZWeFLpH2NIKUJPPQ7G4piSboXzBQXn5YvHGitGr6+LGqt74z/0iNS2NeVPcsVRMUgUDouHLccgE1pTgcfiWezcal4wxeDQ3EUYXnHJuMugSTXhRLpudABnjclInCx/Usx7k9HI3cFMCRUANwAx4kZmTVNTqqahrTU6w90hM5b8rRXlcnFvTrUyELDMlMMgXhiVMNLMfTFEwwG1we1u3hGN6FPwsoyAsLeT1yQWMqQ1N2p5vnQdUp3CqbkIjLCSAEeirBBRqr6x2b9uB5XxCAbqnWEf26W8x+BveWi88pLqtTHEA784/K30wfPUy+/duewmvPnJAhlCuIvPLjH4E/mXsvn6E68vGKTac4HJe4d855wX6eX/y8rcK/j4JA6Mr085fjqv1+95rNYcS6tAwvbzvcRhdsrTQDmqEpjuftTW7GaKqvqqRpmqXNNcJnhsmoz0ixVlaepIXGTb3Qb0Rb010etrKmIT3ZajHpq+vsJ6sbe3svixAwU247NCdCC01TJ2ts4rB+CvcbgCZbg/JnoUNuhHgnTHRCA3AJTbBeKMQ6GrAacoBu9JbUQgCsZsNVl81ZtPif4pEPly0t37NFvF1cVmswGB9/7m3cVO92z551Pp7yV1MLLMx5I4coLz64W6ZSPQEA0wYP/v3gwRDSKajnxtDSidXzp22kopVAiDodSEBd1XhKvlzaadAx1pQkD8u6nR69Jdnt4fQ0ZTbp3R7O4XQfL6sw6PGLN0K3HeAedqrJlZ6Mb1TX2U7WqOMJZsrdgIwA8SeqG0Os1nXADRBoQtZAuaGRi0JYTJU7KYvSObhPxmv//TApOfWLZW98/f2Pw4aP/L+nnrvhxltumIr32xLVc+HddxQU7Hv0sX//9vv603JGIYvWTz5kyipQPT/8boMw2RVcP+v0YI/6fDWWTvxu2ikftmj2ucpvP3n3cJjzL5O2137lW3GbWAKh48K0eGiIo0K5LG0VTacAQoimYLdUa5LFeLgMUT0HNTkanRxQ5sRZnqco6HHaGWGTZuhxGCEQ1VNoCkAna/zEUQd9Zfa6pLQUHjXWVTPGBJqmnG4Wepr8dFN8Jcgqby8qQyPpXh76flwMRWWkWOrqG6aM7UdTVFJy6pIF87cLa3OP/eSSe2966uX3xTMff+7t3btzCwr2YXW4564duXu7D+hdcUIj/JHdw6/JdcvuwyGM54ffbgAAXD87qHQCAD5bvVUUTx6h6qpTIM3cvuqp/LEu9AqlyKc/bDjFNtw5ZwYKcgIA4NWQkrrwsukB5//WkhdN6JJkDJXWq1X7j0e+R0dzHWiIucvaY0Mjuaiercf/x4H6qrr6KqDv3hsgD6Ao4Kn3e30csNfaGJ0R20aIUKqRq3IYAF59a6KcPVVfc4oxJerNiW6HjRVFxV86kT6Z43ggrOsDpdMI3HYoKTVDUT3SE6pr608blqV8gzv245jI+DGDBTXnH1r4V/H47AukxXVtTa1oPCu9k7IGD846clixHV6Aep45aeiZk4aq7vrgm/UAgr/OPjO0dHpvolMnT7Fp5jZWz0Wzz1V/EAHw1a/bA890s8IiAIE758zQ6XXiwd83qbsn9u7H9RmVdnXL4PnjR5wQ8nW/b9qtPP7zOly2wVjxcmH6GF+gWWTbnqPmXs3cwoDQxZRUiaNcu1eVacEs+vaF5px8qrT3hs5g9LiCptJozq/aFDmqOVwQSolvV/w/mI5lka20gaY66BtsBzioU0qnOBcPApCWZHE47DW19dMmDRdP+H1bgTBfnf11zVoAwPJP3t2bt93j8Ss8qKqq5kzUso8/HT1y9IpVK3gcqQC3XzYt9DsNlE4AwLJv1kMQRj0//XGrsG8q9vXVp6qxerYtCwX1BBGoJw4y/LANIWSvD7qgSU2SJhg4XX4/1fPHj9A8X5ROEdbGzTpLvQWFwZsfWKQZOFa8dDmJt/DSC1Vnrfhte7HNb3Q5IY4x9/R2/StwHD8VzIG2qarS3f0q7UW4Ko1iex0FZPXERi+RAsH/DdPpJnl4mgoKIFE6g8EADwCQATzqlqCvlFqVaOSyO/CPxuV2/bQhFwKo02Nh5dzOCePHDBo8+L33P7py/t+unP83AMCPq75fnrteVs/ExKRff/8lJTll7py5z7z41B2X4T/dwkK/pmOZLbsPa6vn14L3nBNaPbfgPlgAaqrawXiK6gmCqOe8i87+9PU85fGPvlsHIbDV17PJ0ieWyn5mpFo4jl+37YDT5akXqgjmX33dvQvuk094+O83yrd/3bAX/3IpOGDAwK+/+mb8mOHTJ6sLyLKyMqpqGk1G/d8vPp8VPsmC/Uv/7o9d2abk4qa6D//1t135fnPCVvwmfRjcMf0svc5XdPHq6rVXTxybme77Y3t1Nf5YDWRRQMBBCPv+Fhj3UJ/zo/YFCW2PuXc6E+0hys17iEsXdBoInaHxl89Hdlog+nSjuI0xV9VIZ4SdOypcuZsl8C/L4/YbgOJxuylcPGUEje5DBw9OnSxFJO9ZeN+NN91cuG7F3m3rOFx2BRoa6j//6rPPv/psyx/btq79M2eUTx9nTsEt/0o25RVuyitUHYSQSk1Nrq6sfv69HwAAi2++WHXCJ6u2iKWoCKHa6nYwnmHVU3X84xXrIYANdbVsEv53KK/flVTV2HfsOYIAqGfxx9hz/35h2jnTlr3zwv59eTTNLLj/8SdfWHbTeYM5lhXVU/ic477+6hu8cg9QT4DAN6u3mQy6Sqdt0ulDN23CqwdNvvtjF66gaMK+OJh6irg9bllDF154buUpv7XewgvPDdRQTfX8bOWWsOr51a87QtxLaHvgOVMneFVPlgvlFvDNva1yr9Dco9mbbUSRdG81+6meA1t8EbzAF6is9ltmQgCpRPyXc/klVyxZ/HDOGL8V5WdffD1s2LCzR/ZqSuuh0+mcTinUMOXMqa8896pSQJVkpmEzjhiNDzYIsRAj/yit/wn4F2Gg8X87gnqmdcdG7NWlq+df7gtWfPr6q+INlDUOAFRfK6mnMmWkdKD5B447Xe46D/4VnD9+xLOvfvLJstf25vlmHj776icFOzc/e+/8PTb8uUgjbvO2Xcu//XL+1dc9dOc1qle4bS/O8jUg1/TRw6adMTrEe5EFVLV4l9VTXr+rTCj+d3KqVmlCC46d+K2wMLR6fvrDFgDBtTMmhHhJy3/ZASAo94TcrzsaJBQf9BtcmY3D+iL3XHiO8q7Pli5t5R9XZ4dpmz2LOjVMprTYpxWJeyVr1/+xZPHDVILfX9GwYcPy87BfWPnVj4mJiZPPl9bdjz38uM1moxk9xza7DZROM8lqHoyOIJ0ir7z7IwRg/hW47jUQCEFdbV2gegaSYDXX1drkiOf4iVOef+XdC84ZI36bM27MaCvWFLEo+JqbbmRZT2OZxtA2UT1tNvv0M0aFfjuRq2egCcWfggr1BAAM79cdd0kIGqqpnp98vxl/GuPGkXbmRKnQKqJ1EH+AjR76x1bJ5qtOOH+c+tNo254jjaz97NHDZ54d9FPhte9Wg05OB6oD7bzU1Nacqj61c2Punvw9n375ca+eWQtuuxsAcNf8S3AW/vJZOzbsWvX16rv/ftc7/1manJQ8fnIOsNBUwHTnYOHgyKG7NWO4X7RIdNEvvbdCdRBSTPfM9IqT1S/99ysAwH23zVOdgL1nIk6Rc1UO8RwZcRU845wJAAGdjjleexKrJwI/r9t9dN7lX331ze7dew8f3Pf4v5/Kz8/3fc5bdBRF3Xf3/Q/deU3fAUOCqeeMyWNwuYWCpd+uu+UyX3jhzS//6JGBQ5+BiSOL2Wh3OFXq+dZvm+6YflboH5HN7gymnlLYC4FrLwhlP7/8BWs3CoxhtRWzx490efyaSmRmnjZWqOD2wfH4zLNH40TrHwEFFeedlSPeGJrRe3+VVs0QAPfM1f7cBQC8+P5PulSNaE8HFVAPZdXxYVyPirtuuPCND6XPFkdFXfuu4qNI96wM+dNYxcxLpo8cPurDdz4aPfJ5AMCatWse/Ndi0bgihMZPzlm86MFXn//Pp1988sGny6JihzsODQbtqMKpk1XePQ2BLJGZ3ns9gnqG4Nf/iXaPohKYX/+3U7jNHDx4cHzOiKeffWHGjFkff/Qp7puoPnXfpRPFh/y5bseT/7hTdZ1te46KQaYG5AIWJlA9ld+++SVOu1dU1S28Tq2ev27C5ksz8242Gk6bOHTrFnXYOpJl3McrNgEI518gvQVNvvh5uxjdPsE17y8xuur5wOMvp2VgNw0AOLR/79LXnhJvczwPIezbf7CzycFxHMdzqUmmbXnHQi9fe/cMWkYWTD1ffP8n+fbCCzUWPXK4WfNemZXr9xy1VXufa5byrtdW+J4icgFVBTFBUmpGfc1J8TbNRzR6Li2jR3WVVBzOctzE8SP/3IlLx7sO+QV7sbUMwvOvPPv8K8+24LIrXsc2VsXcBd+DjgTdPYk7IRTzeqG6WbBQaZVStPxZGJpjOZ7nH1x834MP/B2vjocN//Tzr975Nf/WGSPfevW/5eVlX/34PwDAOAd9OwA/b8QZfwghQ1G8UIHLNbjEgxcI+SVRPW+5bCoA6M0vpb893t10182XBz67sL+Mxir7H5fNGJ0z0GwMs7+Npv38aMUm3A0RUj29z95u6iny7JufI4ReefLBirKSbt173f/IC8+++bnYa4fzdbMunXaB3z/Ut5b/sG5Pwdmjhns8rE4V00egd69mq+erH0ltEQbILpl3UaPdL51rScOBh6EpmftrK++fcy4b0qpfNHW0KLULLla3qNwzd9bKtbnH6k/g27N8Ad/Xfvqf5hH8xs6dnLN1T3FTgyTJAIC+vVJ3ewV00IC+R474hl8EY2CftHpbE9uE/4r++8mvYc8nqLh7Phbfa68YH7mqdigZ1Qw+aNRIlIc/h3JpDz3R0WDXzr053qp4yqrff/zw9bde99E7HwMAJo7HMpS3x5dYz9tTMO3cKTU1NaJ6Kvl5Y15uYVFyclJdXf0L768UjC42y3oaujj00jtfq86HFPbLVJqvhE6G5fhdOw66PWF2l3nhHSnK8fdb58oHI9nE5ouf/wQA1tXWgcT2CbjNHj+S1uMKlofukpJyJ0+UPXjnX5598/PxU2bs3ID/2CeeeY7d3vjEPxaIJ2zN8zXs6rw7sik5XtYwoG+azeFslnoaII4M8CkasS+7okXb5WGnThm9aXPQEgsAQH9rmmhCZ84c+8svfqV1IrdPO9PtxlkKvVCqeM+sc05UVZ+oqu6eIe0QJYop/pVk90w5WWPbV+AT0KH9uu3eKw0nGTmwezABZUzJ2T1T9DqmcP/hbV3DbzZr3Fw6hT9OTvXMCOsoCaGhEvAfsBiBGz/5zNzdOMkjMnoETl8U1bhOU1SDjRsz7p3Xl06YOt5T10QnaqieSF2dv2vOMLPNd83Pf78GnGoKnRIcPnxQoJJSgij379lNVPBg9OqRCSBytpN6ingEKWF0OtbjEY9YkywPLb6vsVDKqiUkJj3/fw/gWwhs2+NTz3V7C87P0a52OFLkE5zIvCdeIvMppn/Ou0BlP2XqG/HvbuqUUaHVUwYhoKmeAIC3f998+zSc+HW73aKGitKp1FBJQN9fvnbe7DP3FfhqF9KSfbkIp1v6kYl4KOkuHe9gm+qPHPH7J9hleehKjQzA0lfwvySimFFk3cZ1S994r6ametFDiziW+/cjT/br22/B9ZeKS2z5NCT+sfG8Sj2V3waLJouOOLCPQ9N+RsgB73quBZyoPAUzNDxXmyHmjtxu15OvfsTz3ItPLEbI6XK7t2zZ1JfyFb2OGH3ahZdciZXxyJErb7hCPDhtrLoNTElSgqXUXhXhyh0mUryQdxUb+QJZ7i2S3bKlIMJqdCogJrNyrS/l9fbvm3W1rpuvOFfWUFlG/QR09rQJ6QrFxKH0j36Wb/+4rjAxKd1Rj0cK33rtjHc+JcvzUKIpc/rUnktfCfP727peWtCePrWn5gnKRToRYpzNf2jRkEFDPnnvM3HZ7na7p180zX3Mf5RjNIiwQUMi3URr1VSEhUo1KnVfk/ZVT5l/LfrrGWfPuHjeDYsfe0lUMf7hu49uwikXcbbEsJFjHrz7BqPJ9Phzb29fv3PC1PFh+2qmnT583wo8S/f+K2Z4vGHLF9/DI2Puv3m2v3pKl1ly+Qx7k5+lU1LBRlIkq5jFLsRtQuBJMbz39VqVhiqB1193eVFZDcfzOl4OPPkVyd82f0YrY5rtmIWPSiF9C9RT1MRbLnsR5ygW/SXs1Z5Zrt0nrkQloB0qABrd30iwUodgWOuwv7Mld2tlNKbdf8KBRb40RcFmSXnrEH/ygzi/ZqoDNvVo4f4DB33y2XKTyfTIzRcVH9q3p94n8RTFJCYlrv3fhqtvuPLQkUO8jQNCfa4SZZ3spNPG7ig+yFVh4dOZEl3CaAtBPZFXPaGsniEEVLSfooAuuvAcb2xZ0spl32++cnqOxSy9zne+2dAjI3nOtAm4GtmfP7btz68Q6gcU6GpdC+bPFOoN1M/LlBQXU95ik2lTxv++YeeVF01ZvnKD/MnRCvVsQSdo5yCseoruMhLpjBz573nF65d0UvXsONw9Pydsvq7tM3UdsEZN5LuVPxUdO3rvPXeJ35YdPz55+plbft/6xHsr5US8TIPQdHv25HMOHTkU9sp/7tpDpUkC7WlqABR2eS+/+x3WYhoXexp0jFtRbfnEm9/Kt++/8SLlpWT1FL4L4+srquog1NjT97xJQ/NXqAXUk2LQ6xi3hz39dNxAuHWrvOJBfpHp3zfgajtBPVtO3FR9tiUPXTkhEhMqEvfq2dyNoTSTdSIk7tF6qCST3mA4+5zzKO9YLNEBlhwvGThwUFGNKzf/qN1um3zmJOWj8gtxjwNlpflGrnlPx/vScVg9E4Jmz15c5ku+MQwDJCFGn67aXFHlZ6JTU32Tdt/5Butbkwlv4qvJyB79Ak3osyvWPDj3fLNJjoPjR6NodSIR0SS0o/0nybrYcYjGy+2L5s3euTF305otc664sLYOa9PFsy8ZOHDQ7X/BkUqxksyYanULUxyvv+YGbNP+lLa0UbU4a8aX6YwEcRVvMBg9TT4BDaGearw29pXV6zKARp/S21/+Lt7QMTok5ANffhdPnAmEohmYqhHu5Hh+48a9kyePUtrbVgko0U1Cp0jWNcvaq0wrCZiITD7/zI1rNq9Z5dvl8IF//r1wL64B2leQf/jIoS1rtzU5m0xG7FLnXKHu4wqLOCONB4i2JrQyyhGYlqupqQGt5oUf/uCr7M+9txJxbMsFlIgmoUPFmoMVNgRDLHgIEQaZu+B7pYbGfcAkQpqcTYGNdlbvjatuuJKm6dEjx7jdrn2F7VwSfor2hNBZpGWElVVrmvZThMqw8FV+XR4RCSgRTUKHRU7WhZXOFggiUc/I4ThO2eAQlmDh6Xb5mUdetUZlWCIV0E6nm4HlL+lUCytjWpDKaA2RZ5AIbUCX1U3p33xxbSR/BSci/psS25RlQtc/dMA25fgZZ9cCHYwFzyzf3oLVZYirRetSBEKskf/lL33lcyDsryPXP3TBmgfG3ENjs6ROzW033Z6WlrZ2/dqtf27J55NGCjUuIhRFffDfj/r07vPSf1784cfvo6J6LVZSopuEjk8k/7xXdD3d7KwONDTb1++kKAohxLLs5ZfgbtxX33xl1xevifeOHjl62dsfVpyo2LR106P/eGzxwsVTZkxu/ZMSHSQQVKji0ZF0KndSIY4fAV2z6g+KoiZMHS/PGnj+yRcW3rnopq/e4Dhcyrvs7Q+/++Hbfz/3fwCAfz6+ZMeGXRPHT/xzp293HQKBEDm3LPoLXsVHkL4TTwhrNVT1D52CML30nYiU5JQffvxeOall8cN45u7MK28W7wUAiOopTu5BCL34zMvt93oJhE7PLYv+Eot+5c6SQYorB+r2uC+efcnjTz+mPDh+co4YAx0+FG/PomT5t19edfnVbfsa440W5PSaVRex6KXVF07oJ387Y4o0SpkQx8ztPOoZVw50+kV479ydG3O3r9+5+tufBw8c7LcBS1Zv1fmbt21u2xdIaAmrtx9bvV3dmNwynlm+nQSsCdElfhyozWYbPzln+nkznnni2cxumZ9/8CUeG1NRtuSqKZrnNzVp71FMiCIQwnFjx18w/YJ9Bfk//faTy6U9SLxXz6yGhvpGW9BhjqKG7qqwNavsgcglIdbEj4CK/PbHr7/9Ic3f+9dDj86dM/eZT357aP70vQW+/W9FevbQzgwSosW/H3ly1gypJ/qyiy//10OP8jx/4znqMaAURf2wfOWD/1q8Zu2aSC5LZJHQcYiTJTxN0/cuuE918P+eebxgf0GPPgPEeQeiIZLvPe/s89r8ZXYhumV0mzXjwkeffGT85Bzx69wLz6Eo6jHvjkAiEMLt63fKGwQRCJ2LOBFQjuPmX33dnX+TBr7KiMn3ohpp5ZgzxtdSduaks/IL8E7fhFhwxdx5AIBVP/kmNjY01K/ftK7fUN8WY99/uXLHhl3bhUqywN1pCISOT/ws4UtKS26+4Raaof/zllQ5f8XcK3p07/HkQ/eI327auund19+bOnOy3W5/9B+PMQxz0x03tutLjmd25+8WBtmm1dT4Nl+898FFysawrF5Z0y+aVlNbs3NjbuDmCoSoEKLagUx7aD3xI6CXXn3Jp+9/9tdrb/zrtT5Z/PDTD35dJY1NvefvC/772jvrf9kobol10bzZYoE9IRZs2rIRIfTbDzisuernVR9//tHhgA0elOPRxG3ZCR1BW5XzOH/dUNjcy84IqDaL47B1/AgonvJy0zXY9aSkejweOaWrnAt42z23RuWJAvslOlfxWttw2pRxV11+9aIF9865YM6cC+aELoogS/iOiWYNmbI4N5A4lss4F1CRmtooTJ+WibC3rHPN4Gozvvzmiy+/+ULM8r358lunjZvw4fqjN0ztj6PS/rMSw24wS4gRm3/fajD4trzfsm3zq4uwEZGZMW3m4oWLk5NTjpeWPPCvBw4fORS6Mrd7G86BbHfiUEBDE+y32+macDs4yUnJSYlJxceLxW85jrvtnlt79cz6YflKS0ISqMEbESuhIBHQdmDnxlye56+75dqC/QUAgIV3Lrr+mhvO2FJ09Rl9xRNWf/tzZrfMVT+t3JG7Y96lV3754fK33n1z6YfvtseL7Yh0MgFt1oebWHEtzju4ZdGFYWfDEKLI7z+urauvmzb7XOXBsvJS3PJAJwGgFlBIkSRSW5PdB6vkxLNPQ95dhF5985Udu7a/9sLrloQke2O9yWLN7JZ57U3X7D+II6ErV//wnxffuONvdxIB7awCGoyw3SlENNuYl19/6d4F911z5bWfLf9UPvjFh8vxvPOjGtuFK0t0CW0DLYRNDAaD0+mUD27etvnHz5eKt/sPHXWi8oSoniILF9+9ff1Ok9HU5CSNfJ1fQCPRzUi2bFTO49KU2sDgpuaSn+zgKPPJFx/Pv/q6++/5+/33/N1ms1E0ZTbhbWduvny65q+ALOHbnqNFR3G9xJotn3zx8cuvvyQeRAh9/Nq/xdv7dm6Zffks5UOu/wvespiopwy88Crtf9AdnAgbok+f2vOWy14Ub0c4dyvyHCLJxYedqGS1Wm+45q9nnn4mQnj81Q8/fm+tkxbvtuRu8mkjho8sLy8TNxwPQZfKTrTN74im6T/X7ZC/3btv78OP/aO+MLevsFdHPp+kPNloNG5as6Whof7cC88Jcc3uXenXFOcC2gKaW4Qhy2gXTMG3YJydpoBGiPIvM3TSrwv+LlrzO4IQXnvV/Buu/WtqSioAoKqy4u/zzlIJqMlo2rhmM8dxE88+LfTVunclAe3cS/iOAPlbjTXyno6hd3NUQmIpzQIh9MkXH3/yxccAgKcff2bGtJnjJk/ftfE3+YS+2X2/+fQ7lmUnnRMr49JJIQJKaDkt8Brp3lZOeStHJaSYrC3ZuTG3vr7+vNl+6/F/PPrQjGkzr73nkW9/WCXW6v7txltvv/mO8oryi+bNbtPX1xkgAkpo0xjL0lcOt41WEhMaFqfTmZTkF+WUKSkuEm889dgzM8+f+fwrz37xNW6IIKggAhoF9u44EI3LdHqG98ERNJEnX1+tec6RI3XijRsXfBat512/A5eXTj0tS3V8r3C8i+N0aA+xBgCcfcGUbf/bvnNj7tMvPPn1iq8BAP369vtsGRbKe2++Eg+67tFr5vkzX379JaKewSBJpCjkkYiAKqUzBOndzNu8wykm+Y+cOHXSEewhgVqpSaCAhji56xBCQHv372E2mdf9skE5yYXn+dlnDHPY8fD/33Yd0+v1gQ9UToHp6kmkRZep4xoelhX+P2hh8xs//Bzrl0WIS06ddAwYkn3kQPGAIdnBFDPwIQUlLRluQNQzEhxNjglTfak5iqJ4nrcK6gkAOOO8ScqTTUZTWlpaeloX0sewMC+8I017k1ly11WhHxOouSIej6i8WHvf+OEX0DnpUrNk2oUBQ7JjcVlZMaeelkXUs2UodwUPpMnZVFpWWloWqa9fETzMHTe1K3DGNO3xYq0nrBAHw+bwNZYBgLt031wpbXMU61LQCNVz4WUXCq9L4vm38AZ2mhwo9fU4xTERLuFbRsscKCHsKr53/x5RqdW921tn1qxqs/jQ0BgKaLT4+62XR3KaXhdpQqzRHqaN961VGnotw56S9P3+v10WQkAPHC0TXlaXaPEmAtoZGXXakBDzd4RSM/UOgFGsnZhLBLRzEaEQhyUw6FFwAPcUq4DifDYioK2GONAYfdo9vMA3okyJLKBLv70fxJK5nV9Du1AZU6DwtQFDegcdnXfgSFkXkVdCvC4Utq4vj3DOmaiV8dco0YUEtF3Yf/i45nHJn8YRHG/UPE5Tyog2oaOrZ7ABZsqJZYHHT1fIaBz4ysghAkqIoXoG3nXgWNnwAWnkh95h2bq+XJ5bFkw0A5kbmWjOXfB9nA3fIQLafnTa9ftX7947aZyUXtiy89CVN7y4/4jPaD+4cO5f/3KOXs/sKSi+dP7zQwf0lu86cExIrCm469Y5558z9uobn3W5PMrjs6afduuNF8y77mm2RTun3nDl2Q/effHwKeoQXsGGF0efs5jlQhXrEGLK3LjQTRkioITmUbLzDQDAdQteP3CkYszw7HdfvPV4/tu9R94u3nto53+MBt20uU80NDi2rXlaeZfmznFvvLPqolmTvv/ikQsu/Zfy+MI7L1m7fg/LcS3LIOl0tNWsYYo1DxIILSbeInGEmJKYYAIA9Bl/14nj9Ul6c9HhqumXPAkAuPLSMwEASYlmo0HXe+TtBw+XnzhZlz36DgDAYw/hruoQiNL5wL1XyEd+WP4odpH3/pfk3wkdHOJA24HOm0Ea2Lc77m1wcfuPHJfX5vc9/OHGrfvF20s//l31kInexX6IgOnmbQfPmzrmmZdWIQTOOn2IXsfMvPTJQb3wcynAEY9DZRXRfUcEQmsgAkpoBrv24g3Bj+e//eSL3370xTpHE25xkQOg9Q2Ox5/9SvWQuxZLO5SJIJ5XBkxxdnhQn8ef+fqX75as/vqh2fOefewfV7z30Vqe194laUhWz0g6ETMSEwAAg3oKEtxZQ82ETgARUELz6DP+ro3fP/7w/Zc9fD9uxLI7XGfMWFJbZw88s2TvW243e6xYvYOxioJDJfiyo+4o2fvWe6/fZne4HnsuaGtsCIYP6kNRtHiborH4/r7SL64qMjirJxeQRFL1gB8qP9GCF0DogjBMegcNq8sdk4SOxuRLHkUuzmox3n3rrDtvnrln44tzrn5md740glekZO9bEMIB4xZEeE2E0Gdfb7zmisnKpNP4Mf3l2zt3H1V9qynEImdVDcK93opLiRzPf/vAkeMsx4e+8vBBfUDz8bC+goHDnVOCHe70yE8260/F8rV0DjquA41E2YnItj2pydaaOjzuzGZ3Pv3yd0+//N3eTS+u+uIhWa0SE8z7trxkszmHnb6oWVde/duua66YrDyy4tMH5Nu9R96u+rY17yL0lZVa3BqGDpBGlComz/jtyd4ydQ5NJ9XuTkrHFdA4F9nOWQR6eMurFE31n3i38uB7n/xx/10Xibd7dk/Ztubp39buuenuN1v/dCqVbKVots2Vlew/EoWJzkMEFaaoSP/BDM7SnrHE8mrhZlF31XidNz76LsJnMZks9984LsKT4xjmvivmiLde+noViEc6sch2PC6+4bmfP1/y16vO/uDLdcgleaL777pIzCYBALatefrbVdsWPrSsXV9mXHEgGiosCzGniPa6vQN8DXpdK9f798ydpbzrtRU/ga4Bs27HoQSLMcFivPrsKQYdw9AUTVOM8AUpCHHts8ZHX5ypbYSBYKKzBQfLduw++sQDVz7xgK+60+1mh0xYiMcAvvg3AMBlcyZdNsc3yfzAofLzL30iNr83QquE+KWlkfpNQkvmgeosqUaDzmrSJ1pMSQnG5ARzotVoMRlMBp2OoRmGwgKLqxrxf8Xb3qvGrdRqymiIcXYI8RrHO+f6Xcnp4wdNnzyquqbxzfd+QSgwxKcNpCgUcuY5oS0ZPsSXOmsuJpOlqUmj9EKkw6amo8jdl1wQJgbqsdd47KARgEhqlzN79k6wGJKtJlFnrWaD2ajX6+h75l6IhVZSjFDC8fI38Sa1+A0bpMKaOGPrzkNbNkvF8wSCEovZ4gItmWDQpZNIleXHK/2P9OjVm6KweFJCMMCLdK9kXqVvgMvt6WXFIe0rZ4bYDwDGsdR2OqCBlsOgBEJX4+X3VsQ2C19R5ms4ye7bDy9ooVTQASFECMsn8oqiQa9zuT0AgeW/7NQo+hBAAFwxfRxFQXFXO0GBw7yGl7/5MYrviEAghMVl7kIfq21UxlRchFsAZfoPGEBhDUWChkKoVEwt9ZSPff3bLrHoWvivcJeAeM7kcQOtJr1OR9MUhaO0NLXw0gt9pteLpvISqSUQCIHcPRfHOgPVKeYONARHjxyRb/fvPwAhRFHYkwqSioVVmZJSKWpJsV/Hi5KvFJ43GNOmTEywGC2CzjI01lm9jqYoLLVyYKEDbg1CIBDahZeXYqEUuffmuR2ukP7oUZ+YtgG/b/gzwjNnnnu6jqHNRr3ZpDfqGb1OKDsI8LNdGRIGJcQ9TLpRLrwRXaffve3xkjoHv6zdGoWruNHg/r2UtjbOdopvrobimcq62H4IkdRWO2LpSil4IqAxByH+gDC9zWAyu53C51jnLwJVdQ1Gq1Um7kvHiLLHH8SBxqogmabxz5bjWNURmBInP3MxagwAePHdbyN8iE6nBwAsuklqnCeJu/jrdnN1pRR8mE4kQmuIewFtwZ+6KKBNvN5k1FtN+gSLMSszuUdGUlqSxWTEuTzlybQw01OESG3rf/jRJVgnEhOnPUjBfuBx8sdM6Cx4PG4GuD12UGsHtadASbF03AUtqjNpCpqN+tQkS8+MpFnjTstIsVrNBlroHxYzeWJ5Bu0/uP7lb7tE5S8ZkdNBIA607RyoeLCLO1CPx936p5bVFkJg1OtSEs3d0xOTE0xpyZaMFKvFZGAYCirK0TRrJ17pGlIbIydLHCgQ+H+96l7KqfDtuwAAAABJRU5ErkJggg=="}], 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"}], 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"}]], 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gPwEJm9SM1AzE2lNLvBWbUAwG004dqGg/U5OR/UQ7vHOC/YQ6MfqZHcB+trwZKXzk3iQZbEIxGEzbO1DZfvbqEV9V67WfAKL/ESyZpWU/IQkBT7RCIl40obSOcrmj/VIYDAbTRAEV7eeYEZmC/cwEHIkGLw3tBwhAkGx2XnHW8F5oIJNnz3dkP8UtiwFg2FBzlCMEbTSZIVQV3uO6egwG05YCKttPZPUg5bWfvOD9OB/7CSExZpiP/XTyrVHiLibjCc+IZQwGg2kXAqptPz2Dl0T7OXpAJtIubfspDqhvJcxmEzahGAymXQiown4CwX7ygAVi9DM7/3jWoF4h7eeRQ0fMEXrT3bumlpZJG8f7wzAu7D8xGEw7ElB/+5lz8NhoIS+UNSwjO7cwSPSzwWcv4+ZMEgkHYYaJDxaL2W73jrLHkVAMBtMGAuoX/UT2E+kjj+wngHzWkL6A5cWJIf7203cro1Zl0IDeh44UKY/gbBIGg2nVOlCF/SwSaj9p1G7Eo5hm1jChvZJiIUQz67KG9iMIKNpPsRpUtJ9HDh0BbcGJ0lODBvRuk5fGYDAxDx0R+wkZPudQQPvJwQjHJCFBtsSHYhOKwWAiAtlC+4mkkWIJQtt+1jIEB4ljhw+HfBV9XC8QHU6UnorSlTEYTCeHDHPuJ0K0n5BH9hMg+5mdWzwaRT9Jee4nmkJ/sFCcBCrONg5HPZtKWeAUvCaFRSdVC3nc3InBYKIuoL7Jd5RV97ef4o4dnrmfvGw/W631yGgMUR9liYsDrcs4Xck4XUkrvygGg2lHAqqwn57oJwD+9hPQvDx2fu8hr/20Q3007GfzKCk/jU0oBoNpvSSSbD8BpAX7CXIOFkKCzBqSmTUsIye3GNlPhgQsiexnXkHW4D7e1iOGOXY4uvsgBcENDIzDO3HZ05KU2spvY5yuZCcT7iB9TEt+zgAA/KPGtCMBVSbfs/OOo3IllhSS7zpP8j0TsBDo+OzcYnEJr0y+2zn1lS1WC3Q2NPuNduuZ4eZ8svnFRyV7axc25gyJqiAUp+MxGEy0BNRjP/sAqAtgPzlkNsO2n3abvSWtnEcjUUkaqbp6NjnEaL4KOkk6k00OfiZdXdDUV8dgMO06BqpMvmfnHQc0n3OwEGWHhmQi+ymMnVdFPwFJytHP/W23eFcyaEDvQf17DurfM4gJbVuwemJigHGdOGVKh2k/kZIeKPHYTySm8so9O69g9KDehA6KvfAM00rJ90DIySK5AtRsNhFo4zlERrdUlYyO6Z24p6g2ssKX7vn3VIxjoBhMpxJQ5eAlMfqZk1ssRz+zBvUS7CeaBIpmiAzug/bqQPYTjWLKGtpvf/6xaLxRs9m7oxJB0QD6NtgLG3OKw0ROVFTrdHqm0WeMCCAI8fGS8tOqK6M206av4jGtRpgBk5DREmz5Ma2xhPezn+ic7APIUiExFXY9Qkt1lswcOECslgektA+SW8t+WqwW8Sti71qSyohtmjSmd2LL3hAGg+mMqB2o1ek1aF77KYyt89hPlHyX7SeatITs53HBfvbN9bOf4ehmxY416RNmReg7Cj0xj9bTLrcrci+HiS5hBkxwtATT9gJKCOozZkQfofM9U4p+5h/3j36On5DFNNRDjiUE+4mqL9sw+ulZocuQJMnzAefoWcwmu2LTpCOHjowZNKCpkVAMJuYJGT8BnTiEYm2spAPZT7H1yM9+StFPwJNMbQ3aS44Sxydr28/E1DTG6RuLjDr+g5W1TvLklDAYTJiSpymmfenqzvwDpDXtJ+AVyXct+6lLSGAa6nLyjomd70r7SUBeHjfHuH22xoy2LsrnMIyLFO6KwDBMKIrw9k7MxiYUg2mKf0zvrAUn1kaUaSe1az/zPbWfQ/spaj8zUdn88F6ApJi6GjQ3BNUwibWfZO5hqTZIVk9LvBW0D3r27BHynLYa+YzBYDqoevoIqDf5zutGD/Qk3/OPo8cITki+c2LyXRcXD0gKHSEZVfKdg+1laZxBVPekantSwcKaFkVplNjslIXT8RgMJmxIlf3MySvKzj9O6HzsZ9bATB/7WVuDRn8O7SvZT5qX7SdFSHkbS0J8ZKONPXp29z8Y8Q3qsIZiMJgw7adXQGX7OXpwf2/tp5b9hAACSrSfbNYAwX66YDuxnzBApDSQzkI/ExqppnsMBtNZkkhy61FOXhGElF/tZyYqEhJqPwFPAY4DBBDsp5B8H94rNx/ZTwLylCdrE6XoZ5A8UvAUU+mJUkAJBf9aGPQGVVko3jQpeuDRc5iYsZ+SA1XazyDRT8ig44RsPwf62E823C3mm73BHBGF4lF0TZpuL3HbzkOnnT2BiSX1ROrRNPvJc2goPUq+Hw1kP5NTUlyMS92rHglkj2lLGigfkV6WJJMS4qpr6+WzKoBNOpkXpuiRpNqjoncoOVcuAdLle5UPYhOKwWBCQoYZ/YSMoDWq5HtT7GePHl1BC7AzQlWqAOGplBIcZIuwVh+yVh9KsEdlAAoGg4lh+0kCXhI+lf2EAPrZTzpI9FNORlnMJlejA9ABA44RIa72MC94YbmDk6DoLsZUd+0p2YEyPMwgUI+EmxMGhVCUstcTfRrwvFKALfFWe72t5bOWMZgYC7PgvVKCIIf/oNJ+EqhJUx39BBTpn3wnIM8CWq5eil68EoPBYNqV/fS2ciI/p+58z0TbcIq1nzwFOY7Qsp8QEN7aT7+SoACqGla7eig0L9Kiy2ITGu2pE03a7CT2Zk9gYobBA3odPlJoiovzxi737C8kCUKwnzwgoLiHB6r9BCBraF+CInPyjgFSGjvvdgkraAhJnYFn3eHoZmlpeeS/DxhwxCemlQlT7Dpt6zQmluzn4SPSrkXeCp4GYxoPoDBPhMgakCntEEdCtN8Rzwud730hAKrWI1k9Q9nPqFNSLm3g0RL8K1jjnOqfHQaD6cwMHtBLvk2q8iQ85CDB5xxEWficfDQmGS3vSRT9zDlYoEq+Q0oH2gKOjdjgUdRYBYBRbwxyDtZQDKbTYvW1n+LinQekKSHeDSm0hFdpKBpHR4GcvEI0/ZMUop+C/cw+WJB9sFCOfgIICV4qLUpKSXGr9iDSIn3CzIodPzf7O7ExNE2RfXt3UR0X5TwRJKifUIay8F17oqViQdAGTdVcOwwGgwniPcUMUp+MdO36TV18ukUnrNbRRvB9pOjnAEX0s1lFmKeOZ6dPmBnoUX0cenPuhuJAJ5BMA8+Ao4fq5CP9Bw1QnaMUysFx6iNhgps7MRgMCBz9NAnDksrKy7UF1KIDDKAJCAHkgGw/Dwn2c79kP2UBDbTrUUMjQTINTf01iDIagMLWGPxBANRJ1TJ2Mhm4WxGDiUn7KaqnSMAOIobl9MnpQ7tZcw4UAAoNqx89KFPTftptdrvN7n+F1uwwNyakQQhptiGlO9oUniSIwiOHpMeIZpU5+VVJNaOuviJpRMhiHVypg8F0FPspwrIETUNtATUYzS5hFyOzcEZeuU3Ycl3QTR0v2U+SBp7oZ2tiMQeuVWLqAQCpPXqL3UZ8qH2Pg9QM2BsbI7jTFgbTEXm09ybxxrqyvm39XtoFgwf2BhCi2k+LhVHIS0AH6mAJMw3NNHRCLjk1vU+yxWs/lepJNMXZRblBqfDwYfkl5H55JU2rtQpsQsOxjXivXUxHV08AwNTux3YWTQadD6uW/TRZpHAlxxKUYDEDCqiZhgygdIL3rK6sACC9+lRFOII4aEBvz6NqCTt05Lhmjiho3BODwbSNemK89lNIH4kCqiM4FtAMA3Q6LQFlSfOpqoYuKXGiegoT6tK6JxqrT4VVf15SUQWaiLuhWB/XS7PCKUjWvjWISNMppmWEDJh02n3JW4dHe296rpOZUKt/8v3wcdl+CsYTBULdbs80JiUWHeEGOtFvJndJFw/KrUetjFJVieRU1aPQ7XRSaNyniWra+FGotd9yoz6J5oIFQPFwpjYhpOrhaEnzcFb7bOluTE7G9jMIDKHTQW8LDw1YhhAK6f2BTrSZZXJqGsOjZXj3ZHP1ab+T2sHEJUJvNAkVrWHiAEa9nkqwmhxOt6PRLcaCzbT0QzGj2i2a8e3sbzCmiZ1IWD0xsa+nnvBbZ8aqaj0a2BvZz7g4FPkEehp6BYJQZVoMRq9H1RtM1fXOmhqU3Y4MrtCtStHGTDM077TX10C33UQxZhp9hXyWQW/A6omJeZ7Jyg30EHamIjTBQtpbC0SLA0BVuJx2Me5npHiaQwWeuUeKQthPl0P8criJRoZsZEiHm3BxFCD1LKTRETfhpuP0hmBJ8OaFO6HiS5PB1rCr4nlO3LNExWnSWzeLwXQ29RRxVlerlvxKYmbosnb0E9lPiW5du/CUIcxqd69MmhKlYKgmDsIifglF7MBEcWaaNxAsYF00ZEwUZ6J5PdHS0lGkkqzbxVMMpBkoBR9cHMnwFAtoN9/iMfgkhb4wGEwAhRVlNLiYxhJiz7uSk2Wl3bt1YYA0R0kdA5X25ADA5Wo0maTcOkuYG+obAGmmefSQwSQV28uY6chvIacNrTd4agNEjHL6iAQRm9GEwXQmQtpPTZyChm4GKItrDF0B0VFrP3meszf4dFo21DWkJ8dXV1dpCKh4VMTZaBO3vTTrgYujeAgNBrMoryoY6HWyOvX2HhGjZ/cuh4sqxN2QZG9sMYS5nTKC8CTf7cJvHYPBhM8zWblPZA/XfMipMKTG5OSYsZ88IIVWTB9OnfTOhg+tPgajGfKAYBrQ4lZn1ky+hymaupbtwH7oeBnRsiU2pKW5n+bAuXt7SweJYDCxbz9jD6uf/TRZ4miarKsLNhFJW0DbQYWSBiwl7q+pOuzdSpNAs6OkZBJpSQIAT/nEYIKB1TPInnF2h0O59a8PPANInZ+ABhBOdAXFXYvFooqBQndYJUosGWzwe2DEQEJo+g0ZIkzaQ2+WJElQ6l+8isFgorKKjzF4gpZLe0zWOFkAe3cTbBwpKaePgJ7mzXHA3gwrSniWxsFpwQYgkobqYIMqBgpo77dw8kRRjx5dRbUvKanq2T6NNAbTPsD2M4j9RNFPD5SiAam0DJ3MEQGy8IH8ntFodvq6ztZCMTmKqw1eZlRnZ+qPlPA8JAhhGpO3eEsio1uK7MZLTmrUYcB2UO2PwbTnvqOYN6Ek4AX76cXmcFl9Z2nKkqohoEbPSNBA9EhPqq6K8F6V4jyRkKdBT6G7psqLO9SLSTPNDUeKTja43HI5qlZGi7QC0OQp+hhMhwPbTxmbKS3Q7GTvOQ6XRY9uEIpOpLCy8P4cLGq12GLoUUg6g4lxeZNFzrRh4g2CICqMUteq0yT5bagPGEWwVksT7DVm62MwsUULWzNjz4TaPBrqXzkv00jFy5pk4urFXEswAZVdnsvlSE5ORrbUGA/aZiGvxpk6RHxv9SYT59mb3mlpgzn5GAwmxnzo9LOH/r45R5RIVPWYmCzIIVr+OjmKJEkXKaSSNAXUFTgOaNFBFwBxBqK6LXwaSyXygCKFb0NXI9Wyxrs5rwN1eGs4CYJMt0rHa2yS67aYmjKRHoOJRSIyGSSQCTV25Cp6m0na9Oy3rQcApeMByXt3ByIEQwmNFCdtcYQ01TdWKoLajSJqM/nwipzCQVTPKNEjHX2qHC7E1aMYDAaQkCMJn2SLsBexN65IAM9IDiVOl4NorkQ6acnZeiGAwWK2GPUcz9sbfWdttjok53IbEkiS4DjI8VKwQ/6IOVrRLqITGEz0wIPpwkVRPE9ApBUuYTiykXcyOlTfowPugDHQ5FRkZatOezNTdrszyGuRRmnsiBloRSE51ik8XchiNR9UxqTIK3lU3q9YKTC8wUwDBvBoZJQuQEEUTiI1j3B2KlVuVop34Ojo6vlMzKWSRIYlgSJvszvCbrdb9JRQ647kR+eukx8KlkTyr2dC+3wQPgNHmgRkXBFo5fTVU0w7IczthsTtNwAAxbEyQRITy3hsmt3Nx5sojlOXBoU73cNiMUbgzegMLXt+MD3FYDCtvHh/ptMMItGb45yEmaEtDG110vEuz1cwB2qxWixWi92usag1hCq2jwYsT9LQCUi9r4Z6S+sjzqBB/XcVeu16NJa3eJ9IDKb9Q5M8K8RAhXFGvNynoy2gVVWVKSlp3tAh7d21OEwIpuW5bLUk0gQPCO04apT2Hj506CgweH8OUdprF4NpTfuZYwh3b+fRrn6dNhLqD01q1NgjAf3vv//juUu8+NS9Ytt7ekbvihJpKyQTxTc0Bpv46VDsrUSizD/gCQtNkQY9zfPQxbC8MGWuhYPrAwQ9vYNSQpKRntKMbeu13wzeaxeDiU00FIXgUM4dUmoDR/dJ79XI6F5/9TkohhiNPQ0ASYysnlJhEwy2YNdQRqSoPGRYAgAjGX6sNRgMnaRja1p0CZLKSE9RHhD1lMczRDAxzXNFk2UTGo6vxGgiyqhwi/A60C+Wf7Lg3rtpYY94CODi15+Sn2BjpMyNWNhkcxPuhlP+10Vpfot3S+QoQUDonziqra81h72rx2FPFz9JEhCi/zfojTwPnSQlt20BELmdnDGYWOeJmFu/F5VrLFLtjYxFp3SmCgGVbtGUi0dZ8lvveyk37+Dfu/aanAetOmADFrH/HbobrXpTNQC0NQWwbdeuw7uVeSQlFrPBYXOITfx+G5kgzLRfIxPDkACYFTWheEuP9kYEw804ZRdZnog59QyE3hzHaNUCSa7ziy+/YXmypqpCqbEmwVNadUCs3iT0UiN5vIG0R31qR+DApkc9WUjRhFoQMwcP9U6kL9sbfg9VC3dbwkQPHG6O+CoeEwRCS4BoEh1geXWTJl1YUdynqzSIc/jokaVlp6rKj4t3K+pBerz0BJsLWg1EEGGDLnW1EzQlsxzPsBxJEBaTXkeTNkckWzn91RM1CXB2qQcraNO821ORAGgrRVEQAFoeOu3CI50wmNA80QnsJ6Eloz5H0CqWJcQHTpaVXn35FXGJXY8eL9q1Z9/pqmobBFbmMPKhemhzAVFDNXGQcb17dRMyRz7Hq8tOCFkaxuVqyZYeCErIwgfv0z9wtFx2oMMDN3nqlRUJQqMrBhPzYBPabAhOu4uSnnHBxds3/hqf0kN0l19/+1N1VYX8sM6SXoMG/6OmeKs+2OZuFoo9XYq0sjVhjBk6Z4kil+VCHwiSuQxXFvE2HhhMpOwnG6M9IwSHFqZQyLQrobOzdzoZlqk6lZDUVToV8I/eNlF50gtv7VKataqqSqPRDFodzi8FTzDePY3DgRfiDE5xsrQMhZyqXkfxPGQ5HrA4C4/BNHPxTndklQyCm5IUjwOK3Tq5eklQxUJ3EVE93176s3zEEmd1euaAPHLv1apLv/jW1+G/j9LjhT0y+4A2gtUnIonUTMdDDhBAT+MsPCaWwav45qHjULaZocyUMHBOLANlaIskoCrL+fbStQGSUT7CKmKOQ42eEIB7bprp/8LvfPRTE99qwEBBfH1hfXwfVUtS3379lDX/wdETjL6Ju0BZKOMNN9353pLXm/Y0DCZG6Qy5I394GtUgUf795fOvGgHACEExW4qorffcNNMjskgHCYPF6WJ4CK1mQ5zZYC/xhiy1CNGWSXG1srh265pusljDV8/mYUnsdayweMGdD735/ktRfSEMpv2b0M6pngDNYnfy4kRQX+iWS6fSeL69dK3CvQowjaiVU2iXrItQx2RmPxSoJnz7TaOErbp40x/Fm/4AZsLogMGmSmMwmBiG5DT+/OmIiCZoLfoK0ikSUj0P2kyDPfvK+cNYh4o3WGcdC9Bni46meZ7nKSl9T8BGgxmlrTi3naYpo8GQSpLFFVKRLAbT2Uxop7WfAO1XyZm1xDJcAVXFNyMtmsEKpJQU1ybTNNXdWllRUuT/BL3B6HahTwm6ywTICoX9zG700ZE0nHU1dM3oxzGu2roGTsyYuZxmnbCriZ7geLGKnkElUJ4Se8bN6XiUkdehHxLHutp4QycMBtNWGMwWIe9MQILS62iXmxU3VQsioDAaNlNvaNHGwjRfB9yg4JQJGIboaBrwLt6zNxxN0zodzbvdgHPTfL1y/pOO5HQms62mFP0gKBQKbjXG6Up24r0rMB2fzmw/gdffQQA51s1RqFUH+TBvTZMsl28vXXvPTRdHSjT1XcewHNvocJIUFR9n0XM84EPW24dIJZlowQxCN01xCjVkAYtG59Xb7bRF3YTUaK+3CtUCGAymqav4Tq6eZrNJbMwRtqOESkn1OlBl6rzF0gnlSzlqy4T0P6pAbahC0zzNvmXsIqfrTd2bsL0mBoPBRAeeBaT20pz062+MxKBjCSjqJqf37rbI6buAaKK5XxMGgwmH54omh3NaZ7OfV150JtJQ5Rfrkr4ilYX3S/5EaVOiiEHRLfxOMZjOSGdTzwxdY0lFzZgR/Y8Un661ORkWpY5cTqRyqSaUSladTzdbNAljd+gsDZ5Ap9yn/ExoWNn2IK+reR1bNQoUSOgSHLUnpdtCyspRe5I2xTtqpHPk2fONZAqEkCCgeC2eU9c8NYgGl5TaYJMT2mACAAbThpHQ8cm2faCXs7q6k/wWIADbc46KjhDlm6UlOqEzW+UqUJ6gSYLgeN7C2+gmBTTbldN01ZU6gd7pdiUkxPO6eA560kloumccTfImcxxwISU1JwpzUgyS/DU6bOghIBR/KrA3OCxxGnvNNzpcJjNOQGE6HWuKpLEVxmTvtP/OI6YyFrNBWcNIAZYEBEkAN61ZG6oWzYhhq0J9nKnxaGyoiroGR/cwrtBgs5O8nYNkfEK8ISEV7UDiIc7qsylTo6Mh2IUglLeFktFUTxWp1tTTNmljJQymM6inCllMnbGopKWMKQ7YBOnzmkWeoGnFXQ4SgJD0I4JJJAnK7dPtXlPPNNiBk9HV26Cb7sob0jWfpSPZstLQwpQYj1xkfII6kQ+1uqxC4N1FDuFsDCq4GEznTiX5Y0xOnpTumJSO+rONycniF4g5TBb16pMiIAF58Ut2oD6KGw6naxtTNJrr1STFSy9v1BkBgMLm9NqzndsALR/qj2r9ntk72Ld9vKiT9suH3PpNuQEc3gOug9pPTSalO3YysSCd/7pgAADgq5/R/HgZgvcu36HfdpbhxED9tRXlcxLjLcDdJA/fjuKn0cMrr2VhCW5IOq0iY9oqldQk9YwZrI2VXyqkM61LV6OBrq5vbHC4IATxQtu3UkxFWq24B0ZVWC3xocOXKhx15Z7XF/ZQMsY7a0ul9BSV5nuu9FMjCGA2tagVtRm0UH9bTY7DHEWerpMiPMW4wxXTjqk8Je1sJEY5xX2IOdon0RKvc9MRn/cR4LnRxV57ihA/DKA0ap6lE1HwVXDc9VI5EuC4RpIkCQAa66oA1BEEkZQYB8ST0FodLdUd9obkOHOg9x/htFqr01HkGNOGkdCR+KcfAEocUeTBzraGA4X+WX2CZ6Ffs5Sj5kTIZs7TtQ4dabbZkCUkCcjwQukSAfSmdJqmWJaFrNNsUaSYhHBrfBz63HDY6+PiEqQ3EFjTzaiPXvmoT+srpqVyHDiygeUY034gCELc3zc4URdQR41npSxAEnyiVR1vPlYGCYIC+iRglxbRgUiKlyw0BESjvT4lwfePUEc7Xeoorz9B1NMPrJ4dwR2XaTwdy7EmbHK/WN33LeJYLJ54HS3VmBOAsNU1REZAG2z2xNBiBcxJKeKN6pqGQOKVHI/eKAQEiBsPiv+Wj6OPgDCy5CHJdOUcN4yGvEvqTFLAswxJq78NW0ODNU7yodh4dlBwsKLZ9RKngU9GgQXSn3CQmgpwCs0J6gxYhOWsBO8tY4o6yUlxkkoK29rV2gyJVp96JkKoKgrzagSAJrRU99tcM4wnKu86XXzAZgKsnpiYk+Nw7GcqKFbeLQO9mley1hlovoAmxZuasUVQ+MtnAkK25xgUuC3JDnSO3W63WCxReXXfM01JkdnNCYOJATmWVThdrqkA3hlsnYSMHt1LTpS0wYwigmdLKqGm7gkmVA2XkSVrKMX7BCD0RqJ5Cf64FKkyKS6FAKhzS/UoCQBWTEzHoM3leGcnrkiLuoA22upMVin3LVJpM8BIyBMFGT1NMSRLUSTHQY7jIAAmA0Hw9YYU0l2nZznv9FOKcCYkxMdbDBRJOBu1p4jqGXOD3SntmCRgTu4Uxf8YjI8cF4Ut0GUhTojpVB6KN7bNlEyDSe0/5TWyo1jtEx2V0NB7pKtoHwAgwWqCEDY6bDwPOdrcCAHgeJbjE+KNAOjs9fWAR/UHjmraZKBSEq3Ak9VPijfraKqmupqkKI7jVWkj2mWyN7odvHqcnbNSitIa0wwORac+TRJ9e6TYbfU1FN5pDoNpv+442kRKQAO20nfpbZI7edQwlH+E0TxqmGPvAQJC0+jhjkpIU2ScxQB5GD9sfOWBnRBCp8PGUmYo7OgkvTZBIOmEgCQJHXTyhuSUOFNtQ2NFVb0c/YYkUVNdDQHBcbyZcLEADXJKpBIgBKdqbA7fWSSqDaB5wkc9KysrzRbTtt3luniKaQBJCebBvbskxZsI34KBsrKj4o2TJZVTx45Wfeu7cwuqXRrjS7pmqJqgMBhM+5Xj5guoKcnh8CnxbDIuHWfwaKg/jko059hk1MWZ9KJY0t3704BxkyYSHdezHOdmOAq6SYAioQbgBjyIT8uobXBUVjekJlm7pcZznpSjvbZWLOjXJxMsMCTSiSRBnDxdz3I8RRJxZoOLYd0MR/Mu9FlAErywkNdDF2FMpinS7nTzPKg8jVpl4+JROQFBAD0Z5wINVXWOLfsKxc+QLsnWYZldLWYfg3vrpecVldYqDsDducfkO1NHDpFvr9uXf93Z49KEcgWRN37+0/8nc9+caaojn6/acppDcYn7Zl0Q6Of51a87yn37KDCYzkymrxxXHvR51GwOIdYlpWh52+42umBrpAl7NEVyPG9vdNNGU11lBUVRLGWuFj4zTEZ9WpK1ouIUJTRu6oV+I8qa6mLYiur61ESrxaSvqrWfqmro6bkshMBMuu2EOZ6wUBR5qtomDusnUb8BaLTVK38WOuiGkHcS8U7CAFxCE6wHErKOeqSGHKAaPCW1BABWs+HqK2YtXPQf8ciypUvK9m0TbxeV1hgMxqdfWoya6t3umTMuRFP+qmuAhb5g+CDlxQd2SVeqJwBgysCBfxw+HEQ6BfX8K7h0IvX8ZQeuaMVgIk47ElBXFZqSL5d2GnS0NSmBYVm3k9FbEt0Mp6dIs0nvZjiH032itNygR2/eSLjtAPWwk42u1ER0o6rWdqpaHU8wk+56aASQP1nVEGS1rgNuAEEjtPrLDQVdJERiqtxJWZTOgb3S3vpgWUJi8ldL3/32x5+HDB3+f8+9dMNNt94wuS/6RgT1XHDPnXl5B5586r/r/th0RtYIaNH6yQdNWfmr57IfNguTXcH1M84M9Kwv1yLpRN9NG+XDFs48X3l3+UdHQ5x/hbS99hvfi9vEYjDtF7rZQ0Mc5cplaYtoPA0ghBRJdEm2JliMR0sh2X1Ao6PByQFlTpzleZIkGKedFjZpJhiHkQCiegpNAfBUtY846ghvmb0uISWJhw21VbQxjqJIp5slmEYf3RTfCbTK24vKUFB6lCe8Py6aJNOSLLV19ZNGZ1IkmZCY/Oj8eTuFtTljP/XofTc/9/on4plPv7R4797svLwDSB3uvXtX9v6u/XqWn9QIf/Tu5tPkum3v0SDGc9n3mwEA188MKJ0AgC/WbhfFk4ewqvI0SDG3rXoqf6wLPEIpsuKnzafZ+rtmTYMBTgAAvBlUUhdcMdXv/HXNedOYTknaYGm9WnnwRPh7dDTVgQaZu6w9NjSci+rZOvQfDtRV1tZVAn3XngAygCQBU+fz/jhgr7HROiOyjQSEyUau0mEAaPWtiXL2VF31adoUrzfHux02VhQVX+mE+kSO44GwrveXTiNw2wlJqWmS7JYaV1VTd8aQDOU3uOsgiomMHTVQUHP+kQU3isdnXiQtrmuqa0TjWeGZlDVwYEbBUcV2eH7qefaEwWdPGKx66NPvNgEC3Djz7ODS6bkJT586zaaYW1k9F848X/1BBMA3v+/0P9PNCosACO6aNU2n14kH/9ii7p7YfxDVZ1TY1S2DF44ddlLI1/2xZa/y+K8bUdkGbUXLhamjvIFmkR37jpl7qPc1wGCAQkmVOMq0e1XpZsyib1sozsknS5sh6QxGxhUwlUZxPtWm0FHFoYJQUvx2xf8QqUgW2QobaKwlvIPtAEfolNIpzsUjAEhJsDgc9uqauikThoon/LEjT5ivzv6+fgMAYOXyj/bn7GQYn8KDysoqzkQu/XzFyOEjV61ZxaNIBbjjiinBv1N/6QQALP1uEwFCqOeKn7cL+6YiX191ugqpZ+uyQFBPEIZ6oiDDTzsghPa6gAua5ARpgoHT5fNTvXDsMM3zRekUYW3cjHPUG/MaPPmBhZqBY8Vbl5N4Cy6/WHXWqnU7i2w+o8sxMYy5u6frX4HjxOlADrRVVZXq6lNpL8JVahTb60ggqycyevEkCPxvmEo1ycPTVJAAitIZCBowABA04GGXOH2F1KpEQZfdgX40Lrfrl83ZBCB0eiSsnNs5buyoAQMHfvzJZ1fN+9dV8/4FAPh5zY8rszfJ6hkfn/D7H78lJSbNnjX7hVefu/MK9Kebn+/TdCyzbe9RbfX8VvCes4Kr5zbUBwtAdWUbGE9RPUEA9Zx7ybkr3slRHv/sh40EAWx1dWyi9Imlsp9pyRaO4zfuOOR0MXVCFcG8a/553/z75RMee/Am+fbvm/ejXy5J9OvX/9tvvhs7aujUieoCsoyMtMrqBpNR/+ClF7LCJ1mgf+k//LmntymxqLF22eP/2pPrMyds1Trpw+DOqefodd6iizfXbrhm/Oj0VO8f25tr0ceqPwv9Ag5C2Hedf9xDfc7P2hfEtD7mnql0pIcoN+0pLl3AaSBUmsZfPh/eaf7oU43iNsZcZQOVFnLuqHDlLhb/vyzG7TMAhXG7SVQ8ZQQN7iOHD0+eKEUk711w/00335K/cdX+HRs5VHYF6uvrvvzmiy+/+WLbnzu2b/g7a4RXH6dPQi3/Srbk5G/JyVcdJAgyOTmxqqLq5Y9/AgAsuuVS1QnL12wTS1EhhDVVbWA8Q6qn6vjnqzYRgKivrWET0L9Def2upLLavmtfAQSgjkUfYy/995Up501Z+uErBw/kUBQ9/4Gnn31l6c0XDORYVlRP4XOO+/ab79DK3U89AQTfrd1hMugqnLYJZw7esgWtHjT54c89qIKiEfniQOop4mbcsoYuuPj8itM+a70FF5/vr6Ga6vnF6m0h1fOb33cFeRTT+hDnTR7nUT1ZLpRbwDf1tsq9EuZuTd5sI4KkeqrZT3fv3+yLoAW+QEWVzzKTAAQZj/5y5lx25aOLHssa5bOi/OKrb4cMGXLu8B6NKd10Op3TKYUaJp09+Y2X3lQKqJL0FGTGIa3xwUYQSIihb5TW9wT0izBQ6P/bg3qmdEVG7M0la+fN8QYrVrzzpngDZowBANbVSOqpTBkpHWjuoRNOl7uWQb+CC8cOe/HN5cuXvrU/xzvz8MU3l+ft3vriffP22dDnIgW5rTv2rPz+63nX/PORu65VvcMd+1GWrx66po4cMuWsYJPXZQFVLd5l9ZTX7yoTiv6dnK5RmtC84yfX5ecHV88VP20DBLhu2rggb2nlb7sAAcqYqG8fG1d02GdwZW8U1he59+LzlA99sWRJC/+4Ojp06+xZ1KGh06XFPqVI3CvZsOnPRxc9Rsb5/BUNGTIkNwf5hdXf/BwfHz/xQmnd/dRjT9tsNorWc2yT20CpFJOs5oFoD9Ip8sZHPxMAzLsS1b36QxCgtqbWXz39ibOaa2tscsRz7PhJL7/x0UXnjRLvZo0ZNdKKNEUsCr725ptYlmko1RjaJqqnzWafetaI4N9O+Orpb0LRp6BCPQEAQzO7oi4JQUM11XP5j1vRpzFqHGljTpYIrSJaB9EH2MjBf26XbL7qhAvHqD+NduwraGDt544cOv3cgJ8Kb/2wFnRw2lEdaMeluqb6dNXp3X9l78vdt+Lrz3t0z5h/+z0AgLvnXYay8HNm7Nq8Z823a+958O4P316SmJA4dmIWsFCk33TnQOHg8KG6NGG4X6SId1GvfbxKdZAg6a7pqeWnql774BsAwP23z1WdgLxnPEqRc5UO8RwZcRU87bxxAAKdjj5RcwqpJwS/btx7bO6cb775bu/e/UcPH3j6v8/l5ubKz4IWHUmS99/zwCN3Xdun36BA6jlt4ihUbqFgyfcbb73CG1547+s/u6Wh0Kd/4shiNtodTpV6vr9uy51Tzwn+I7LZnYHUUwp7QXDdRcHs59e/Ie2G/jGs1mLm2OEuxqepRGb6GaOFCm4vHI/OPHckSrT+6VdQccE5WeKNwWk9D1Zq1QwBcO9s7c9dAMCrn/yiS9aI9rRTAWVIq44P4XpU3H3Dxe8ukz5bHOW1bbuKjyBdM9LkT2MV0y+bOnzoiGUffjZy+MsAgPUb1j/8+CLRuEIIx07MWrTw4TdffnvFV8s/XbE0Ina4/VBv0I4qnD5V6dnTEMgSme55lBHUMwi//0+0eyQZR//+v93Cbfrw4cNjs4Y9/+Ir06bN+PyzFahvour0/ZePF5/y98Zdz/77LtV1duw7JgaZ6qELWGh/9VTefe9rlHYvr6xd8E+1ev6+BZkvzcy72Wg4Y/zg7dvUYetwlnGfr9oCCGLeRdK3oMlXv+4Uo9snuab9JUZWPR96+vWUNOSmAQBHDu5f8tZz4m2O5wmC6NN3oLPRwXEcx3PJCaYdOceDL197dg9YRhZIPV/95Bf59oKLNRY9crhZ81GZ1Zv2HbNVeV5rhvKht1Z5XyJ8AVUFMUFCclpd9SnxNsWHNXouJa1bVaVUHM5y3Pixw//ejUrHOw+5efuRtQzAy2+8+PIbLzbjsqveQTZWxez5P4L2BNU1gTspFPN6ILtYkFBplVI0/1VoimM5nucfXnT/ww89iFbHQ4au+PKbD3/PvW3a8Pff/KCsrPSbn/8HABjjoO4A4Ne/UMafIAiaJHmhAperd4kHLxLyS6J63nrFZADge19Lf3u8u/HuW+b4v7qwv4zGKvvfV0wbmdXfbAyxv42m/fxs1RbUDRFUPT2v3mbqKfLie19CCN949uHy0uIuXXs88MQrL773pdhrh/J1My6fcpHPP9T3V/60cV/euSOGMgyrU8X0IejZo8nq+eZnUluEgWAfnXtJg90nnWtJQYGHwUnpB2sqHph1PhvUql8yeaQotfMvVbeo3Dt7xuoN2cfrTqLbM7wB37d++Z/mEfSNnT8xa/u+osZ6SZIBAH16JO/1COiAfn0KCrzDLwLRv1dKna2RbUR/RR8s/z3k+RgV98xD4nvdlWPDV9V2JaOawQeNGomy0OeQLu2hJzoK7Nm9P8tTFU9a9QdPHL3+tn9+9uHnAIDxY5EM5ezzJtZz9uVNOX9SdXW1qJ5Kfv0rJzu/MDExoba27pVPVgtGF5llPUW4OPjah9+qzidI5JfJFG8JnQzL8Xt2HXYzIXaXeeVDKcrx4G2z5YPhbGLz1a9/A0DU1tSC+LYJuM0cO5zSowqWR+6WknKnTpY+fNc/Xnzvy7GTpu3ejP7Yx599nt3e8My/54snbM/xNuzqPDuyKTlRWt+vT4rN4WySehoIFBngkzRiX3ZFi7aLYSdPGrlla8ASCwBAX2uKaEKnTx/9228+pXUid0w52+1GWQq9UKp474zzTlZWnays6pom7RAliin6lfTunnSq2nYgzyuggzO77N0vDScZ3r9rIAGlTYm9uyfpdXT+waM7OoffbNK4uVQSfZyc7p4W0lFigkPGoT9gMQI3duLZ2XtRkkdk5DCUviisdp2hqAYbM2rMh+8sGTd5LFPbSMVrqJ5Iba2va04zs013zS//uB6cbgyeEhw6dIC/kpKCKPft3kVU8ED06JYOCOhsI/UUYQQpoXU6lmHEI9YEyyOL7m/Il7JqcfEJL//fQ+gWBDv2edVz4/68C7O0qx0KCr2CE573REtkPsn0n7kXqeynTF0D+t1NnjQiuHrKQAg01RMAsPiPrXdMQYlft9staqgonUoNlQT0k5Ub5s48+0Cet3YhJdGbi3C6pR+ZCENKD+l4B9tYV1Dg80+w0/LIVRoZgCVvoH9JWDEjyMa/Ni559+Pq6qqFjyzkWO6/Tzyb2Sdz/vWXi0ts+TQo/rHxvEo9lXcDRZNFR+zfx6FpP8PkkGc91wxOVpwm0jQ8V6sh5o7cbtezb37G89yrzyyC0Olyu7dt29KH9Ba9Dht5xsWXXYWUsaDgqhuuFA9OGa1uA1OSEGcpsVeGuXIn4kleyLuKjXz+rPQUyW7blhdmNTrpF5NZvcGb8lr8x1ZdjeuWK8+XNVSWUR8BnTllXKpCMVEo/bNf5ds/b8yPT0h11KGRwrddN+3DFXh5Hkw0Zc6c3H3JGyF+f9s3SQvaMyd31zxBuUjHQoyy+Y8sHDRg0PKPvxCX7W63e+olU9zHfUc5RoIwGzQkUk2UVk1FSMhko1L3NWlb9ZR5fOGNZ5077dK5Nyx66jVRxfjH7jm2BaVcxNkSQ4aPevieG4wm09MvLd65afe4yWND9tVMOXPogVVolu4DV05jPGHLVz9GI2MeuGWmr3pKl3l0zjR7o4+lU1LOhlMkq5jFLsRtgsAkGT7+doNKQ5UQ1/9zTmFpNcfzOl4OPPkUyd8+b1oLY5ptmIWPSCF9M9RT1MRbr3gV5SgW/iPk1V5Yqd0nrkQloO0qABrZ30igUodAWGuRv7MldmlhNKbNf8L+Rb4USRJNkvKWIf7kB3A+zVSHbOrRwn37D1j+xUqTyfTELZcUHTmwr84r8SRJxyfEb/jf5mtuuOpIwRHexgGhPleJsk52whmjdxUd5iqR8OlM8S5htIWgntCjnoSsnkEEVLSfooAuvPg8T2xZ0sqlP269amqWxSy9zw+/29wtLXHWlHGoGtmXP3cczC0X6gcU6Gpc8+dNF+oN1K9LFxcVkZ5ikymTxv6xefdVl0xauXqz/MnRAvVsRidoxyCkeoruMhzpDB/573nVO5d1UPVsP9wzLytkvq71M3XtsEZN5IfVvxQeP3bfvXeLd0tPnJg49extf2x/5uPVciJepl5ouj134nlHCo6EvPLfe/aRKZJAM431gEQu7/WPfkBaTKFiT4OOdiuqLZ9573v59gM3XaK8lKyewr0Qvr68spYgNPb0vWDC4NxVagFlkgx6He1m2DPPRA2E27fLKx7oE5n+YzOqthPUs/nETNVna/LIVePCMaEiMa+eTd0YSjNZJ4LjHi2HTDDpDYZzz7uA9IzFEh1g8Yni/v0HFFa7snOP2e22iWdPUD4rNx/1OJBWim/gmvZyvDcdh9QzLmD27NWl3uQbTdNAEmK4Ys3W8kofE52c7J20++F3SN8aTWgTX02Gd8v0N6Evrlr/8OwLzSY5Do6eDSPViYRFE9OG9h8n66LHEQotty+ZO3P3X9lb1m+bdeXFNbVImy6deVn//gPu+AeKVIqVZMZkq1uY4nj9tTcgm/a3tKWNqsVZM75MpcWJq3iDwcg0egU0iHqq8djYN9ZuTAMafUqLv/5DvKGjdVDIB77+EZo44w9J0USyRriT4/m//to/ceIIpb1tkYBi3cR0iGRdk6y9yrTigInIxAvP/mv91vVrvLscPvSfB/P3oxqgA3m5RwuObNuwo9HZaDIilzrrSnUfV0jEGWk8gJQ1roVRDv+0XHV1NWgxr/z0J19pf+nj1ZBjmy+gWDQx7SrWHKiwIRBiwUOQMMjs+T8qNTTmAyZh0uhs9G+0s3puXH3DVRRFjRw+yu12Hchv45Lw0xQTRGehlhFWVq1p2k8RMs3CV/p0eYQloFg0Me0WOVkXUjqbIYhYPcOH4zhlg0NIAoWn2+RnHn7VGplmCVdAO5xu+pe/pJLNrIxpRiqjJYSfQcK0Ap1WN6V/80U14fwVnAz7b0psU5YJXv/QDtuUY2ecXTN0MBq8sHJnM1aXQa4WqUthMNFG/pe/5I0vgbC/jlz/0AlrHmhzN43Nkjo0t998R0pKyoZNG7b/vS2XTxgu1LiIkCT56Qef9erZ67W3X/3p5x8jonrNVlKsm5j2Tzj/vFd1Pt3sqA40ODs37SZJEkLIsuycy1A37pvvvbHnq7fER0cOH7l08bLyk+Vbtm958t9PLVqwaNK0iS1/UayDGIwKVTw6nE7lDirEsSOg69f8SZLkuMlj5VkDLz/7yoK7Ft78zbsch0p5ly5e9sNP3//3pf8DAPzn6Ud3bd4zfuz4v3d7d9fBYDDhc+vCf6BVfBjpO/GEkFZDVf/QIQjRS9+BSEpM+unnH5WTWhY9hmbuTr/qFvFRAIConuLkHgjhqy+83nbvF4Pp8Ny68B/R6FfuKBmkmHKgbsZ96czLnn7+KeXBsROzxBjo0MFoexYlK7//+uo517Tue4w1mpHTa1JdxMLX1l48LlO+O22SNEoZE8PM7jjqGVMOdOolaO/c3X9l79y0e+33vw7sP9BnA5aMnqrzt+7Y2rpvENMc1u48vnanujG5ebywcicOWGMiS+w4UJvNNnZi1tQLpr3wzIvpXdK//PRrNDamvPTRqydpnt/YqL1HMSaCEAQxZvTYi6ZedCAv95d1v7hc2oPEe3TPqK+va7AFHOYoauiecluTyh6wXGKiTewIqMi6P39f96c0f+/xR56cPWv2C8vXPTJv6v487/63It27aWcGMZHiv088O2Oa1BN9xaVzHn/kSZ7nbzpPPQaUJMmfVq5++PFF6zesD+eyWBYx7YcYWcJTFHXf/PtVB//vhafzDuZ169VPnHcgGiL50QvOvaDV32YnoktalxnTLn7y2SfGTswSv86/+DySJJ/y7AgkQhDEzk275Q2CMJiORYwIKMdx8675513/kga+yojJ98JqaeWYNcrbUnb2hHNy89BO35hocOXsuQCANb94JzbW19dt2rIxc7B3i7Efv169a/OenUIlmf/uNBhM+yd2lvDFJcW33HArRVNvvy9Vzl85+8puXbs9+8i94t0t27d89M7Hk6dPtNvtT/77KZqmb77zpjZ9y7HM3ty9wiDblOpq7+aL9z28UNkYltEjY+olU6prqnf/le2/uQImIgSpdsDTHlpO7Ajo5ddctuKTL2687qYbr/PK4rIVn/6+Rhqbeu+D8z9468NNv/0lbol1ydyZYoE9Jhps2fYXhHDdTyisuebXNZ9/+dlRvw0elOPRxG3ZMe1BW5XzOH/fnN/Uy07zqzaL4bB17AgomvJy87XI9SQlMwwjp3SVcwFvv/e2iLyQf79Exypeax3OmDTm6jnXLJx/36yLZs26aFbwogi8hG+faNaQKYtz/YlhuYxxARWpronA9GmZMHvLOtYMrlbj6++++vq7r8Qs33uvv3/GmHHLNh27YXJfFJX2nZUYcoNZTJTY+sd2g8G75f22HVvfXIiMiMy0KdMXLViUmJh0oqT4occfOlpwJHhlbtdWnAPZ5sSggAYn0G+3wzXhtnMSExIT4hOKThSJdzmOu/3e23p0z/hp5WpLXAKoRhsRKyEJLKBtwO6/snme/+et1+UdzAMALLhr4fXX3nDWtsJrzuojnrD2+1/Tu6Sv+WX1ruxdcy+/6utlK9//6L0lyz5qizfbHulgAtqkDzex4lqcd3DrwotDzobBRJA/ft5QW1c7Zeb5yoOlZSWo5YFKAEAtoASJk0itTe9eSCXHn3sG9Owi9OZ7b+zas/OtV96xxCXYG+pMFmt6l/Trbr724GEUCV299qe3X333zn/dhQW0owpoIEJ2p2DRbGVef+e1++bff+1V132xcoV88KtlK9G882Ma24UrS3QxrQMlhE0MBoPT6ZQPbt2x9ecvl4i3+w4ecbLipKieIgsW3bNz026T0dToxI18HV9Aw9HNcLZsVM7j0pRa/+Cm5pIf7+Aos/yrz+dd888H7n3wgXsftNlsJEWaTWjbmVvmTNX8FeAlfOtzrPAYqpdYv235V5+//s5r4kEI4edv/Ve8fWD3tplzZiifcv0/0JbFWD1liIuv1v4H3c4JsyH6zMndb73iVfF2mHO3ws8h4lx8yIlKVqv1hmtvPPvMsyFE469++vlHa620eLcldpFPGzZ0eFlZqbjheBA6VXaidX5HFEX9vXGXfHf/gf2PPfXvuvzsPsJeHbl8gvJko9G4Zf22+vq68y8+L8g1u3amX1OMC2gzaGoRhiyjnTAF34xxdpoCGibKv8zgSb9O+Ltoye+IIIjrrp53w3U3JiclAwAqK8ofnHuOSkBNRtNf67dyHDf+3DOCX61rZxLQjr2Ebw/gv9VoI+/pGHw3RyU4ltIkIITLv/p8+VefAwCef/qFaVOmj5k4dc9f6+QT+vTu892KH1iWnXBetIxLBwULKKb5NMNrpHpaOeWtHJXgYrLWZPdf2XV1dRfM9FmP//vJR6ZNmX7dvU98/9MasVb3Xzfddsctd5aVl10yd2arvr+OABZQTKvGWJa8cbR1tBKb0JA4nc6EBJ8op0xxUaF447mnXph+4fSX33jxq29RQwRGBRbQCLB/16FIXKbDM7QXiqCJPPvOWs1zCgpqxRs3zf8iUq+7aRcqL518Robq+H7heCfH6dAeYg0AOPeiSTv+t3P3X9nPv/Lst6u+BQBk9sn8YikSyvtuuQoNuu7WY/qF019/5zWsnoHASaQI5JGwgCqlMwipXcw7PMMpJviOnDh9yhHoKf5aqYm/gAY5ufMQREB79u1mNpk3/rZZOcmF5/mZZw1x2NHw/3V7juv1ev8nKqfAdPYk0sIr1HENhmWF/wYsbH73p1+j/bYwMcnpU45+g3oXHCrqN6h3IMX0f0pecXOGG2D1DAdHo2PcZG9qjiRJnuetgnoCAM66YILyZJPRlJKSkprSifQxJPQrH0rT3mQevfvq4M/x11wRhhGVF2nvuz/9BjomnWqWTJvQb1DvaFxWVszJZ2Rg9Wweyl3B/Wl0NpaUlpSUhuvrVwUOc8dM7QoxbYr2eLGWE1KIA2FzeBvLAEBduu+tlrY5inYpaJjqueCKi4X3JfHy+2gDO00OlXh7nGKYMJfwzaN5DhQTchXfs2+3iNTq3uOpM2tStVlsaGgUBTRSPHjbnHBO0+vCTYg12EO08b6/RkOvZdjTkr4/8K8rggjooWOlwtvqFC3eWEA7IiPOGBRk/o5QaqbeATCCtROzsYB2LMIU4pD4Bz3yDqGeYhWEOJ8NC2iLwQ40Sp92j833jihTIgvoku8fANFkdsfX0E5UxuQvfK3AoJ4BR+cdKijtJPKKidWFwvZNZWHOORO1MvYaJTqRgLYJB4+e0Dwu+dMYguONmscpUhnRxrR39Qw0wEw5scz/+JkKGY0BXxk+WEAxUVRP/4cOHS8d2i8F/9DbLds3lclzywKJpj+zwxPN2fN/jLHhO7HmgzoSHXb9/vrT1xfvflf8+uWLf0MXd7DghPw1dEj3bz5b+Mt3j7zx4vUHC3wM+KHjQmJNQb++3X744j8D+/dQHU9KtP7wxX+GDu6lPEjTVN5maThhcP789nHNM8N8OiZ6zJ7/o/gVGz9k7EAxTePotjf1enrRM8s3bsvv1iXxx2WLTuQu7jn8DvHRzWv/r0+vtLk3vnasqGLd94+fyF3cZ+RdnKe60H/nuIJj5SaT4a2X77jo8seVx79c+nDFqdq8g8XKDNKxHW+F+SZNJr3VrGGKNQ9iMM0GO1BME9DpaL2e7n3G/JzdRQl6s6PWPfWyZ9H8+Q/uFU/o0yut5/A7tu86fKqybtSkBwEAP6/8d/BritL54dvSFQAAKz9DTxk34zGlev68/GG329OpgcG0D7CAtgEdN4MUb0UOjmR45dr8gcc/e/w5aVTPWx/4zBApO1kzbHBP1UU43qj6evTpr3plpKWnd+N4I01b4+PMl1z94oAeXeWvEf16jhjSa/a1r7TKd4nBhAtewmOaQFWNDUJ4fO97n3218dV3V1fXoKZppZi+/PZPyvO7d0164nmfLgPI+4gvyg4P6LU755jN7ly2+K7plz+35puHT5RWOZ2scpekn1c+ctk/XiGFjecGZXQP2YmooygAwIDuXdGdjhpqxnQAsIBimkbvM+Z/t+T+66859/przhXsJD/r6udz8zWqtY7ufhsAsHTFhuAXzDtSDAAYMmHhidzFH719GwDg7OmPKU/Y8ut/9+cV79l/1GjQqfRaydABvUgS6SbwqOYfq33iqiKa+qvqAT9SdjL4e8ZgRGg6tZ2G1eWOSUx7Y86tr0EXR5LErddf+PiDc3755rEPPl3331d8+hSO57xL05ScXAqHO+7/cPFrtw08wxsMBQBcMHl4r4zUcK4jCrFyopj/s07kLhb1d+yovvLB3XuPqe4OHeCT/Q8ThuXk20c7pgQ73Knhn2zWn47me+kYtF8HGo6yY5FtfVKSrFXCyp3n4Yefrvvw03Vvv3jz7TdOVQroidzFLMs1ST0BAH/vQcPqG51u5cFl783fvffYig+RqlLCwvyLjxbk5p947rXvW/JdrFrxkHy75/A7VHeVWtwSBveTRpQqJs/47MnePHUOTgfV7g5K+xXQGBfZjlkEum7lY4P6de819m7lwXeX/DZ75nj57oncxXX1juFn3x+pF1XaQwDApLOGEEIwtCWoxL2pWh8mBwsiMNF5kKDCJBnutzwwQ3vGEsurhZuFXVXjdd797IcwX8Vksjxw05gwT45h6PuvnCXeeu3bNSAW6cAi2/6Ye+sb+za89OErt9324IfQJXmi37//D+vxRydyF3M8H0H1VEqb0aA7svvtKIldu+VQJFRYFmK5JhcA4PYM8DXoUXC5Jev9e2fPUD701qpfQOeA3rjrSJzFGGcxXnPuJIOOpimSokha+CJQ1pPQ/OiLMbUNMxCMdba23v7ESyufeeiq4t3vyj8WnoeZo5EnvfbKiaLHOZG7WPlzC1PyWu4rMU0S4teWhOs3Mc2ZB6qzJBsNOqtJH28xJcQZE+PM8VajxWQwGXQ6mqJpUiwrEf/di7c9V41ZqdWU0SDj7CDkNY53zPW7kkH9us+ZMY5huVfe/onn/UN82hAkCYPOPMe0JkMH+cRGmoTJZGlstAd6tN2mpiPIPZddFCIGytirGTtoAKA8jMuld+8ZZzEkWk2izlrNBrNRr9dR986+GAmtpBjBhOP172JNatE3bJALa2KKQwVlz77aojQOJlaxmC0uEG7Kq6MTsSRSRdmJCt8j3Xr0JEkknqQQDPAgPSqZV+kOcLmZHlYU0r5qepD9AIgYltoOB2Gg5DAoBtPZeP3jVdHNwpeXeguee/fJRAtaQiroIAgCQiSf0COKBr3O5WYABCt/261R9CEAAbhy6hiSJMRd7QQFDvEeXv/u5wh+RxgMJiQucyf6WG2lMqaiwuPKu3379SORhkJBQwlCqZha6ikf+3bdHiGwiA4I/4duS3cBmDimv9Wk1+koiiRRlJYiF1x+sX9qQlN5sdRiMBh/7pmNYp3+6hR1BxqEYwUF8u2+fftBCEkSeVJBUpGwKlNSKkUtLioMdNlvFJ43EFMmjY+zGC2CztIU0lm9jiJJJLVyYKEdbg2CwWDahNeXIKEUue+W2e2ukP7YMa+YtgJ/bP47zDOnn3+mjqbMRr3ZpDfqab1OKDvApTYKcBgUE/PQqUa58EZ0nT6PtsVb6hj8tmF7BK7ihgP79lDa2hjbKb6pGopmKuuiW8WFU1ttiKUzpeCxgEYdCPlDwvQKg8nsdgqfYx2/CFTVNRipVpmYLx3Dyh57YAcarYJkikI/W45jVUeIpBj5mYtRYwDAqx+FWxCq0+kBAAtvvkS8ixN3sdft5upMKfgQnUiYlhDzAtqMP3VRQBt5vcmot5r0cRZjRnpit7SElASLyYhyecqTKcp7F0tty3/4kSVQJxIdoz1IgX7gMfLHjOkoMIybBm7GDmrsoOY0KC6SjrsIi+pMiiTMRn1ygqV7WsKMMWekJVmtZgMl9A+LmTyxPIPy3R/l9e87ReUvHpHTTsAOtPUcqHiwkztQhvEZ99k8ZLUlCGDU65LizV1T4xPjTCmJlrQkq8VkoGmSUJSjadZOvNE5pDZKThY7UCDw/2bkwohQNaUtAAAAAElFTkSuQmCC"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "assistant": "At default yaw the purple front is facing HOSTILE, while both allies see the back/edge. Reverse the facing by 180 degrees.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y4 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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The middle height keeps it above both ally screens.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H1 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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Bj0Z2C24ACBrkSU0I1M3Hg4/qm9tr51YABBgdshjHedbIxDHbE4m2xwKNwlNMYjAooAdOLD8xY4SC4BbWkHH5qQaPQjcXhwTCfU6kxwa49E5AFjGviYv1ze1bmzswx2HGxC/tEHvK1QcptXaWpFsxz+5qr5tVvrCqpLa6UFag0oaAv05qazBpOpEeIPHbBYkcDaSTsgxTUoCHym3f1X5o2fQi0+qRnJ4Jyk9KoBSTHlqIdCYplWeCgIhQGcqZnRaFCMUcJ+pBIkIxIc4uWVhVipAsQkHdrPIAm0U8oSKlwoamjq1NmAEJ5+KqUADrm9vVIlTMgmpoabcg7FLAcrWlHcCgCAVAYwA2U6h/+fRBdfrYkxIoxeSGQ+CdyBrwSl08IEBwjEVogvKTWPFKETpvZmlDU5gIJUwXZpWLi/UtHQqzXRKhmExFRblgVtnC6lLpvbvaSbQ9igglqlMG0mArPqjRyZVoWKYwN2daidDqxNgzcVATnmJyw2QyQiQoHrz8GN+rkjaMpjARiiQRKjk9AVgwpyLMKhdFKBGJxGyvm1Xu5HEBO9aPu9qhKEJlzylBdBEKQO28koaWdinEL4rQujlB5QsAqwdW07TgTSV7JuL0TFB+UgKlmPTwejwGY9AURQJ+iHAIOF9nzOWnNd7UIxYKdncoHwCHkqSm9NgHSZgOQiBb5Quqgu5RUSTW40V8z+tI1lGw0omEpIjnVBahdTVFdVU4QUoWoRLtBiHmM2ERKqlX8Yw6DWuzWKeDCK0OsueIeybOnpRAKSY9HALf65JM+EyiplrdYj0WdDpMTNt3tcuhJJzrrkDt3IqF1aUAcphMd7WTZPvauRXY9SmmMem8DsyGENbNKyF2fUOzWoQSZlQm1Vugj4hQ0vOpjsSmgiKUwK8328xT3xNanTB7JgtqwlNMBZhMJll7ZkZ+4sz5uPLTZMECUAMFt48LF6GSkAzlMxE9KHrnoonQeJBCSUSEVheFZTIFgTlXPCeO5ivymRqaOsyM22zQFuRIUbgpKUKrF8HEc5WSkp+UQCmmCAz9walExJAfY9RUz2zauTuFN4oitFOdzwRA7RwcjpcselmEzqmorSohItQieEjiJwklESte9pySgyCAZBGqzGciwGckIrQ5TIRyBotBr1FlC0wx9gQjISvXmJVrFHgh2UHpVIFSTBHMnZtfVmJwIN4xBuEjpfwkxnt8+akSoZwYjg/LZ2LC8pmI/CQSUgYCYFtzpyhC8W3tFMzYqBdDSVHC900dOKMJgK3BaJKcz0QMf0zBsgglLlQxicrMuE16rdVsmXoitDoue9pyDFk5mDezcnHEb7BXCu4lxaGUQCkokkZq8jOUz9QcM58J8xqx4nkBQtETWlVav6sDAbRnV7NUvCQl1YeJ0DoxQlVXXbSwqkwSoXOj5DMRESrnAJCTYhGqm2oitDoGe9qCpImnqgSz3mT2TJZDKYFSUIyJ/FSJUDmUpBKhOFFJTqonIlRRhYmT6nG8qAQCiPOZBHOwKUmHUoRKnlPyEi5nEt8ups1LcfkaMfoUnAmKk+oVlZ1mxm016fKz1ROJJymqF0EBtKvYU9ab4T2vYyJBDqUESkGRHJp27nb6kr5xlKEkWYRisGycpHpyt29t7iBmOxahIkKeUDkXiuQz4fcWLgzG4mvnYjWKnQBK1hDEpPogyEmhJUsQKWOy5zNVhwtPzJsRelMFlfxMikNpOzsKioTlZxWWnwgyTMDhjhvnt2QXGPVayMC+Ienm1Cjaixwwp0y6NyEnmthKEcqRRWyViw2ZDpg9A3cOFSNQdXJnPNETipuE4rBSKZDDZhBPjZdDSVAUoXVV+LDY8A9gz4CUgtrSTmx8UYRyZsYtWPQDoUlOk5s9s3KNckv/+IjFngQI4c8wDqgCpZgSSDJ4OtbeT+dQb293R09nu+AJtYsnoSSC+TMVjT6jiVA5n4mBuAmTKEKJk7RD+fvKIlQUrUiZzyTtQDyhIoiMVeUzhUSoOTvHZpu8IlQA7Z37++Wg0OjZMxEdSgmUgiKpsXGp3zJKESpTYTBtM1hlFMw0CuUziaGkhVUlotlORChmB/JUan8ntSMJcaucDUpEqCKfCQFekVSv6GliYV1yj7tMfR+lDZXzfBJvJoxE2HNEDqUESkGREGqqZjY17TbZLOGLlWGPuaHUzqhQilACKft9V0coqb65Q5XPJC7i7FEiqzDzEpYkjBmEJEI53Hq5rrpQ3SdUymfqiCNCzQYtEaEAgKWTR4RWzsM1V7mFYaWr6WLP+BxKCZRiaiD9mumgmdG75JlsVpPFTB77O/v2d/fv7+rHP7sH9nf0mUzGODQqi1BixUv2OKZRkfsUkJuEypWdCHekx9skYwmnhSqT6sXyUEmEil5SyROKcD6TuBEKeOCXwkUogc7EWMUed43B4qXJwp4ESXFosojKoZRAKSiio3mHFPImaGraHatznQqERmWSNVksJht+KPcJy7gUuU8UocVh+UzKKiOEk+rJJhGhcip+yDEaDN/L2U5SxpIyn0lMqiekiRBSdarHItSoEKGlE1qEVs7zKdkzcQ5NVn7G4VBKoBQUI4MM3UwvEALzKlUiNHo+E06qDxIrHosUhCxCiSYNilCx6Ij0aUZSTSfJZ1JWduL4MiJTpKQcAHIlWj3Ua3FQq3H3hE6qr4ygzgSRMntG5VBKoBQUCdnvCcrPOFCJ0DCGCvZnwiIUBbkySIjiNlAQKw4lxRKhBFiEki70wZCUMqkeB8PkrhlaFDLqxfCUxaQn0z4mKXvmxhaho2TPSA6lBEpBMYL9PhbyU8a8yoh8ppbIfCaERxsFoYodERGq8pySynfsBwgXoSSpXhlKkg6iEKGC0SaIWfWNu9snmhVfGc1sT5BD08KeKg6lBEpBkYj8TE+ZY1QRKonHGCJUzGfqIIQouUflfKYgsFtT1KRKzykWoUE6lvOZEMIcLU39VOQzKUWohXVNzHFJlcmY7SoOTSN7KjmUEigFxcgoL8FZQWMDfCPiCLsIKVs+XISS3YgIlXqOKJPqwyEn1csiVB36CE+BCuYzIRLCkjyhGjZX+s5AE0SEVibv9BzToDzhUEqgFBRhOGhmtsp+H733UwmTLawRSePujnmVJSTNE2tAuUkoEpuEhlnl7qAyRUERWqYKJSlEaIhb8VgkEXVzy7ArFWnEEJNKhOLxIbIIFWd2Yt5s3C0105ukIaNckUMTlJ8o+QclUAqKiQJFUn0xST9qCCdEJyAitFOecqwKJUVLqldUdkqtSaQ9QySLgLK9CBGhRkWn+qWlJF4/cZ2ecZCda9BoGeWD1UgPhmUgI36npPotQQmUgiIMYyo/CeZFzFOaV1kiF7krO9XLIE1CsQgNJtXX1ZSgoAiVQ0lyPtPW5nalCJWbK9fOLcO9mpRNQnmBPA1LqkdAa4CWkAgdH1SOjjpxQIzDfVhmzMbTmwnCiRIl24JeBUqgFBQhHDgrL/zjSKx5ZJLY19atfBrGUEyMfCYxlCTvRZLqycXJnlBlPpPc0c7HhL4A6lvaoXLocU2JnKgf7NMcKlLCg1K0GlmETjr2FDiesCfBjNk5IRoV21kFA0GjolBKoBQUIbQ07pC3IULGrEQbJ6dFhEpbKHo+UzCpng8RqyKpXiFCMXXWN7cLuO8J0vudkflMqgCUWCkvadKwpHoD1GpIUn1Hhq34ylGzZ9T1coUUTQsogVJQxOy9NHaIKkIlUoshQlVWuUysshUfFKHYMUpme5D9iQiVcpVapKR6IjNrSTBKttzFWvuwpHqjNi+zneorR+30jMWeRHrKOlRSntSEp6BIu/EOR+kbG6UIjZbPJFvlbtYYGsMZls8kXbMsQkkoKSRCg9M66+aWRYaSQkWlShGqYcm4pMyI0Mo0OT3jAAJQMTunZEbW6B2gVIFSUEQx3gmMVkuiztAE5+yEY19bt5JDw0Qo5EgoSWovEsxnEq1yYApgQqydW4EHJeFQkixC8VPZExqZzxSa1knGW4hmuyhCQaykelYH5CahU4A9ZTAMtOWYRukApQRKQRFDfhqyUhh8NHrAyKT65lA+k2yVuxmjrEwboiXV14qUilBEPlPQE6qEHErCtCuGksKS6lmmOC/NrsO0m+1JsSf+nBko5jONdqYR9YFSTGgcom07RBtztLcSSO6OkQ75iXx2PPjI7iSPpp0t8rYgNTFKA/a19ahWaipLtkUm1QchJ9VjERp0j4bHjvAIT0LE9c0dkOHqVflMJKkegQVzxBr8mCIUJ9WTtzA6YDXrx86Kr0wHdSbFnsRm0Gggy0I0ukQLSqAU0x2q2BFECMU1yb2OAbfTJT9KS0pKS4rJz/zSGaFHsVroRQM6YP7cqPlMcvY7TqoX+3WKVjmuKJUIUdGfSfaE1lWVqrqN1FWVRUmqh9iGlSlSKUKDlfLihnhSAIBew5bkj0l/psrMCk8lWBbn0mPJjyBCKdIoncpJQTEqtOwM67ucLLY3tqhWahShJJzPxCsJAkpJ9QKP1xlWGuQZBrHQkyOaFAHI1Td1QyTUEslJtC0JJRFbHmkA5LAI5cjMTvxUntlJwOiAWfSEYhE6u/SLjrgjSScDe0IAGBZb8RDib0zR3YE3kz0OVaAU0xqR8tNotZBO8uNyPUSEiroScx+JrBOPpJTfLrKbSoTKVrwoGMW5SQrHqOz0VOd+KpLqo4rQ+ub2BbMlN4Jey8qd6iej0zMSkIGMWMcpIwUdShUoxTijxoObBsVCnh//Tdfw8fbBZvUYVAxhDg3dYMHoDgRuOw6CpxEHzJ+r0qFKEdqwq6NuTimRjcRIlwiRDxOhuII+qDElx2hQhBJ3akPT/jo8j17AVrwgsmowoCTb/uJhw0UotvTxq6woQgftaRChlaOmTnyZHD/Kf3NcjgQBzmsQ1ByauBSlBEoxfZFa5rzJaglvSh68kcMFkdsdhfRrqipDT8Q+zTtb9m9rbFFyQePujvmzSxuaO2urSqR8Jp5XiFAu0ioXRWhpyGwXIXlCGVRXXSqni5P8JUyOVWXST0zN+LC4snNne+2cYHH9PEzB8qu4Et+gtZgtTtfovj9ge+tO/P/KmvyUj8GLH7X4dTaaKwG4nwjD8ECtZBM35ymBphlyyPjLQKjfLUUc7DTGG+dtEwdO7AyMMPK7wjs0+g9Zqt0Um7Gn8G7Vc4UTAMqusv3d/fgpx6l2czvV/dZqw6kwXISW1FWVNTTtl6gw6KwM7kbkJH5vUJNKIhQrVg4CDVLmM0VmNSk1KamGqm/BT7Ua1mrUOV340EtLtaP0hLbu7FOtjESp+DfiubBci9FwKIRQo2FYFgZE6yI1DqU+UAqKJO65OC/OmFGayCGirqpcrgGt2S8IUrGmKEJlTyg5CMlnwtb97GIAQ/lMclJ9fXOnmFTfET+pPrQt5TMVR3SqhyJH46dQi8jQ4x1j05+pdWdf5EPRe1PNngQpd6LDITqN2N2OZaL+syQSnacESjGt7XfES2IQIpRVUDCK4yV4Fyd7s0v7B7PlMZfJhKiEKqleFqGhA0lJ9QCLULn3KKZO5V5QWdkpJ9UjJB1fr2XLiySduDQj7UVad/aTx94dfa1N/bF2S4FDIc5xgCaz1mDUMLG/GuNzKCVQimkNyIa8WH6/d6TdFfdpRBHg/k51VvyIKC8vieoxaNnZzGnN1eGd6tX7oSj9mcKT6jtrq4vlp1DsUaISobIVHxSh6ICqIhQhQmFQ5EItshi1qTcJhamPSiZz7lqb+mPRaApSlNUwBpNWb2C1OoZhU+FQSqAUUwZwNPJz7DrXJYhYiVO4a7KoFmEwqV7KZ1ISYtjQYyCLUAihanpSFBEaHPghAjIMsy1chOKWJXganXR8DcsW5o5tZWckVG0/4tNociJUAw0mrdmqNxg1Gm10Wz6OOU+DSOnMuZHTbhLJvIkfPKEYayCeU8rPjCBatCI2iAiV+n4gABVJ9XKQBxNieD4TbhIq8qMIhN2aiqR6gDQNuzqiJ9XjS9MgGFiA34Jw6F88YENLR93cUpmja6tKNaJYa0xrUn0KkDm0sjqslQFK5uuUgVBvYFkWanWMy+l3OwMCH/PfKDKyRBVoerDTaCSPDbqyfjPqNyN5JdYjTWemSEfyvHls0+ZnlMrxn3gwmaL8VZCkemKAg4ik+lgiVE6qhyBChIrTQaIk1YtSFIpvadjVETT2kegJxWGrBXMqakXWNupD45IyYL+P2HUuqiBN/JuKgVCrYw1GjV6v0ekwmSZ+bZRAkwNlw4mK5JpCZFZ+otFUiJK+Sur+TAoGXCASIhGnpPGdslM9AkjpCZV6O+3qIE9DTULFrCbMVEiDAFpQU1wrJk4BsXOKOC4JH6ShuaOhuUOv1eDI9XjPm4tKo0omRclVdjIGk8Zi0xlMWq0uii0PIYpMbKIESjElgEYjP03puooKdqiCHUVGKoPnZyjBSflMnTiwQ/KZUFCEBpuEQnlckpgVGeqLTGJHYgioflcHfkSE7+ubO5TykwxAxk4DJJKseCIUFKENLbinCTbh9cCo01QUFzTu7ti+G4e5xg4pND1W0mjikSUGAp1eY7LorFk6nY6V4/KEN2PlhFICpZh2SER+lpclZHSP0gGKxAbJygWlFa8UobIeapD6LmMhqSJETILBM8hme8ion1Ma3qkeh5JwYWhEUj2mYIX+glL7PkSS6skBjXqN148dCPNnl0wcERqHRhMBA4FGy+j0Gp1BwzCAYUau6aQESjGNQOQnlmRjID/HAphDESAiFD+HAWWTUFmEykn1SHKPkqHHoHZm2YK5WDzW1RTLPe5USfVYhCoC8STKDwFcUFOM95HDVi3SiJEFc/BQZY0eklDSjgRFaEoO0NHP3JDt+gSlKARgsNfl9wa0OkaDCz1pIj0FhRICDliPxUdSgdS1iUajFJtKigZUoSROZ1YVLuKSTTGfKVKEkoFx9SLbNuzqwNZ9S6cqvUlOqsfsJIvQ4BlwYah4uST6Hy2pHpGWzxajbkYxTqqfL8+kSyuU7EmSQEcDmUZjoafTSR4AQkZsFarR4uRQvKF6aKWHVhv0BFNQTHbAMfF+kgniKdZbezzqCveURWgwqT5Y1R4OLEKDoaS6qrK6uVgkykn1tVWlOKkel2nipygY09+2O1plJySuAJxUj/OZyDGlpHq8P8NAEqoymKRw9Y7dHYdMSCs+Eq1N/XvFh3JR4s1wQIib3Wl1rE4f8dBJD62eEijFtEIs+Rm7CCWqfCS7j0ELvegidMceiemwhS42pQ81CcVcJmbay6EkEZLJr0yqD+YzyS8pk+rJ0VVJ9VCUsaHy0GCTUDlfyqjXypWd6UUaBmbGBaHRqNQpAzIQd61nGSbWg1H7sCkopiaS7VzX1t4l/j91+ZkK4ox1QmDBrDJiO4sVqPhrIGjFd6orO4PuUZLPFEyEQgsU+UxhOaSqocfByk4EAMlnUopQ4mklHKo3Mt3dkuMinggdRQXnmMLtGG1nUkqgFFMBicqVoPwknefj75tu4hytpArI9Aql4p/aUFI9CqvsDM9nwtKSJNUr8pmkIxDDn8Ndk+WBxvIZ5XwmctjG5jYIUMPuzm0tbbXBpPqAuOeO3R2N6ctnGmv5mUYOpaWcUx/ToUVp0aGn8u4x6bE2pmjfj1t1RH3JZDa5XW756Y49HQiAhXPL5f7pcpNQnB4F+bqqEixCybgkgcfDlIK71c6WCjeh2LAOitPlgiNDpMpOqSO92Kkep5pWlTU27+Mhqq0qFYtBGbHoc+bu7iGP3YFwqVIrbsahaMhJQklfjl9lZ8pwO3wmK27TlwIogVJMEeisFR739sTnbkb2MI6E0ZoLg3Jv3DF/VmmAF5r2kE71Yj4Tzy+YVcRoNKK6DQAmAJCWECImQRxKCsizPUQGLIUAhTrVE9IMyr365g4NAjxgEIB1VWUCFFgEGpvaeSSO85xTVt/cZrZZjDbcdcXtGGYBCijGauzY3YGrwQz5k0h+ynA7fEYLnpoXTHCTX6FpTBTTBjqr6PgbCfGnFivhcQy47Y7gQ5oL73Y4XU6X2+XGP50ueR2kGyabJTKUdMDMUikQhLC6xKGh5vaGFil1SYa40i51wws6OknkByIkT0/C4fuaioY9bQ0iverFsXHYbsdfHtl6G35gpYlQfbiFbrJmcWIPZhksQM6o7DlqB+joc5gSgcfpJ1OvEBIfAtmQQLbELxs5rig2X8nAlVFQjD1icmIU+Umyo8fsrnS7PXNrqsg2OUns/j5JgxNQExmXBAJ1xGAHoFaa6hFQzI8rDVrxHE6q58RQEisAnCFf0rhzP8Q5SIhwpcma5bMP7u7C3wEmm8Vntzc27yNkSthTn53Fid8QPo4lbfkDzn4IWciwSLwAFqAhA04PmIzyU4bH5TeasQ5VMKT0lxVakC5QWqAESjEVkLCmBBJ7Rp31BpmdTXuCR5TfgH9UVFUjjWV/49cJnoVUYco0CgAoKYmSbd65v01vtPh8Yc4EnU7n9/vJtslmUWrbHXs65s0qPWBmKc7llF2cc0rrW7AntLa6ECc5yRQkpiVByNVWlRG7fkdzGy9ynj4rx+cY3N3lkjva7VZcgEydFQVZPM/tErTO4T4IRTqGUA/xtSn9Gmya2HMiwOPCv53BrCOFt2InwfA95BfEn5RAp3h/0qRalE7eLqV+xz5D1qwEvZ+RBrI46w2jplo6yM7mIJMCoDfgfHsYcFZWVfe24+BJJDiNFSC1rSoXs8dB5PBOt9sTta8dQeOejkVzy8W+nCXiWM2QCK1v3ldXVSIS4ozGpr08o6mrwmHDhh37SDbWvFkVDXs69NYskSWlpsgawO/uVjdAqSjI2tPva+p2E/6wZGW5RB4n7MkreMNhGGkOCipL0IofX/mphNflN5h1+IIgFFuxErWOC+MhGW4dVKSUQCc9RqQ8MtgykdmWUx5R5acS+7vU6aImk5EXQGtzk/h+YDZHD9dqOAcIZuibxMlrKuSXVe4jB0kAUUciE8yfVernpUA81puiq652lpRUj6NG4qI+K9vtcDY2YUvclGVzD9sbW/brs7Lju1l1gLcLBgBAc7ebZRkDEz2AxopTjhOHSa93+9IwCz7THGrSShE28afInMSBjjmV7EYJdHJjkgrGxEU0kc+JaOfuz9/yuJ0ge06sHZTyczTwcJgpTciNtInmvhRXzOF4rmJuNYRBLg53ESQOOZ+pAZdvYrcAZLEjsrG5lWeA6JLU1s4tn12UrbTKWSBEsicAQIs4Bwr9CXkBToaXeDN9ctDtzR9RhE4c+SnD6w7ojVryL6Ww6JX+UUqgFFMaDkNBsjVII8KowSYzAklkDrbt2ePzeYIaFhOWOwHfS6w2eCSfSXrCBICgbdjdYbZl+RyDemuO2+FsaGnDjs6gKxOb3gr29AFsnJJj+oAWAqCPoTRjwelwgGSB4hnyE5A9CXwekUPlmJHCAYqNe0qgFBNcRBP/w4jOh0UjnQVG3KJupxvqJAOWYSRhYUKh3PU0wk/YM+qFMYzRoB/BhA/nUCJCWTGLfVtzp9SFHgC9Fbs1TVYcRleyJ4HTPiwGgfCvqifJrRFy3Cdgk1QfHB+C+6LyeNRoG4tjRKzB6hVfsZgtTlfSaVuT0ZAnHKozaAhfSh9YyKynpZwUUx1ylMNuDIsUm0xGo4YnDz3DG1j8QIYsD2N2w1CXJrd3dPe834sfScJkMsqPqDvMn4UTObdJoaT9qlcJe2oB5+NZ+aE150qvyqUBKAp1KtlThnMYk6+B4cgDpAS3N9+k1ycrPzOTBBoffi9HMkBDCF42rYWnmPpwGApU7Inj5ojhEAM5P+T8oZuZ8+oZzsBKXYQTx4zSGHk8OgN+xEak/IyOcLW4Y09HdUUpi4TdnaEUKC3gvDwrP5w8PrIeBuRHnGPKvKkDvI9jhxyc3cm7vWkmL7d3TFo3ZYhDg6ozyKX4Bw0iUUxTaCB2IyKNmDg9auzv6KlIrE0zDCTs/YyLxj0d1qwcp30YW+UAYNIEODk+ClGK8MW+2b0BPENNCpYwQsA5oMHGPot4zMrphSnckJ+w3s9YHKrVa5RMSgmUgiKjcHMMzsR0DJKnHq8PGLMYBlpNeoaBTrdf61bMldTGVK/Eit8hejaxfR27Zt+HB3BwesiF7QGBj2M8Hh8x5BkG2kzMKIbYJ2PIG/omozOUIODjtDo8uBRPiKcKlGLaguG8giaecR3KA+WURJJoIhSHNLwg6AUPCM92MpqNQAg5DYwWE6nr8XvwIm4ELw4C8fCy9vNEZTSsQK1Wi8Xqcrni8KZoxYd5LX08E3AOzvYj5XgovBuSrtMjvmQ04JlqOS68w2w/goDXA+my95l0adGhaPLITyUCfo7VsiQxlCrQ9Bf/JJi6OAVSOCcWkrwZBUYHBMErsOQuJuxkhl4AUBxiJcXjIvWwTjG+hMPo+HYKobwor627XwM5DQsAm2KfNCMr6cWoaQEMEkw2XFDkdDqICR8Jwpui5R5G+rMqipu2S/o3BTACVzjoK8QH5UWi9yT7J01EqOKA8TzOTETzF16qYh1P8AFe5FBKoJkCpcuJCQMTdvcKQOvhWWWlt9vtseqYqHF0Q36Nt28n3scnviEYut+5r5swsln0l6URkGGQIMjsGQc+pHG7Mb8zkM8ySfxOfq2ONtwfdjeu9gYMg78/EOItYpM6AgeP32g1aQGAgli0uBsbrCxrllKjDNDndDhIdbxdl4oOIBzqcacyM4oNHzU8XnxKOJQq0LEqnaR1k5MURjEEr9R9LmicW1Oya/t28cYNKUrnYDdgxQR1iaCkN/Hiot6aBzzRC+cThyD24lQCMozJHCI7FRx+eX9BwzIM5wYIuJzAbJFHe2K6sdisDrsjWHUKndFao7pcoptSUeppgGl2XBpN5kQ4NH4JmYpP00uprBhpjAoEIeKl3tQUFBQxYWAFjeCtmTe7Zl7MOtFIeJzD0dddYb4dj9PtDTAs52O5KPTExL6BVfAijRdpauaUQt8w9A0zfifDSSdSsOfIIMSqgsU8wviTsU5piqyDiAMWIuVDsS4k+4hJnUFCpwqUYkKPIZkg2NbURpryZJlDeT0afkg2qyPfwqBQ6MZs0rscWGrhOkBjjlfhNjDiVHneyYle11BsaQRoAe8U9AF3yJVpED2eHftH/NxQFJmJs6Ck30trzjVAv8PpNJv15TB1V2mihrwJJGjIp9zKQOTQ9ESrIi+AEijFlMFYhXTdThdOsAwPgkvF5NHAEmLFt5d0f82tmuPwYA8khLCzvT0yNGTUxFSaoiczHEgIANyXU282OyNC8OSyTEYNI07Qc7mC/tuRyMcgtqpLZN9UauFHbciP7x9GLO6mJjzFVEAqCTFpSmiPKj9VqN++p2nnLi3y7dzRAjIN9Z3v5bWy3jSb9WazQcmeAaB1OqPXuXsFLXlYrDGdsBPHkE8XeyoN9khQBUoxTeHRZKluLiMX3WsZCxybTaz4ESEEPAgxDTvbIMQ+UJwKmiHgO58V/C7Wxjl6lbEgZWqo8mPQgkCk5HW4nAxkI9uD2nVSff3EN+STRYJnoQRKMU1hZCOaYrBSsMVosbm7pRlqrDmHd4RKg2SQ24sjUXic/wRjpGxK0DCiUI1oQDGjJE9cxy/s7xpI7Xcxm9VhIo+HAyAgts5DLtYGILDYshz2YSUrSFM6YgC7R9MfOkqDIZ8Mh6YiP5MiaEqgFBRqeJx2eVsPfYQXcekOkmI1AICcvLx2MS4kI4+TbryZJCIEgImX/I8+v9TTXEy9FDoVdNfR5/L4OF7q78mYgHRMpE1apbKCfygYFFKOQVM6NyNBnKTeYHqWgUk4kpU+qFLrxwspaFtKoBQUEvw87qmhExLyjQ4ODChzQlMG73PjusigFkRA7+HFuzKZHsdeKFI2a9DaMF06hoeNRi0JIhH4gU4XLcYvR+EjeVMcXhJFU2sRP+h0TwYRihI/dcpuAUqgFBSSPa7HU38BYkRa9KkNfFy6g4DFJN0yEDJOb1iBktEk3bG7fJICzdJIB3GRwcaSAmUgZl5Xgr6FqFzFCpyLIQa2K1JjRpKBkj09QM87U/EVkNBTQXl5iV67rX4rHFcRmi5n6CgPQgmUYprCE6wigRjAhLDwFNg0dLdTpTGphJDRagLAPxoJ5wEGwACjSJoJ9oX3coycNDpzRkl3e8ApJsyLMhM6olUiDXthebgXgdB0b7u6f3O6kFQ0afTyMy38Swl0rHCItu3LAB4qSzExoUy9xAEg3qjXaxACPj+nTIryxDBXkSKCFESolBPqrKxn/4j3caw+ICNcuUJvSpPO4sLLMQboN5jNDjFptLt9X6w9NQI35JQ8GJqgAzeJDlQTN5oUhjTG8SmBUow/flO5Ud7+Y+uycbkG3IWe4yG2oDErkfR1CPHAojhDhgkQ54GaMLWG/A6RXkcWiF6oYyBkGcgwkOMEQ0DKUS8Jtrhv2hv97Cw+qzHW+A0PztsPqHI8412GoMM0CfUWm4ITxmZC1Ngb8tG/tsYi/4kSKEXSqLCCH5+7nGyvfuyT9H6Cv6ncOF4cmgIIdarYE4P3AtaQSBQjND5EAFoG8Hp8KC/HtnSPQF481OgUOZueAAMBqqwsIwSa0MUj3uXyFs+qdrmaxyX4nhZDPhEdOnapo5RApxQqbfBH56jZZ93Tz5GN1ZdJrBcLf396QyJn8XnB4y99Qjh09WXLx4JD3+uYDSYwiOGMs+ghQGLzNzXYmB1FPQ43YxghwdIgTk4mkHm0ump2c8te5W4OpyRXPQFoFBmwe7+0gyoKH3YBgo53hjJbHX3tZuyWTQgegLvgRWbUpx2jqO8M+9oa66x7SqBTFjIbViTJj8lilByqtN8zCbfbA7VmE1LcpSm1tiTw4sG/EnmFypOCtfBKGI16AAIJ2sZuRxQngBeFGn8EXDiebozNaGazUQt4BzAHXCHSVHb/TBy8xmgUkp4wOl6GfGYKlmgt/FTDEy9LfHTVqhVjeqLHX0qz8EwB3oEB8vhiS/MOV3IBdLNBX1FRkl85N7dCenBQ5+YYD8dq9aZEFA7HZnNsNi8+tIl3Uho1DDAgP+LsVlBWUVBa4XJ5HMBsgH5rsICdaFU9ywC/1ydoyEDjSHiDEz5ksAr2ZOJWMaUFRpM5+TJ5NGL1enpBFSjFyIjDxUTVFmlBd8CYMfnpxUnsaUBfeEaOBhdc4g1O7JxEoDPbymdXNW39Wrw7I5r3BsHD0K1EovPm7II5xeyulsx3D8HweAJ7m5qCzUAlcpdJReRNoNcZ9GE18SHSrCgvxv9rw42jYgKC3938m9vv+uPYidBkDfmM8aYMSqBTEE+8vNHrkWzEq1atkH2ghAdTMOTHyPYfL95MAhD6PU6/L2tmzQEkY7S7TbLTVXcqq+j+SeAa6v3WDq0j2XgzSgqBOBRof3eUivvkkkPJlVgMyjynqJCnwBN4BcknYGAkf25PO+6lHzULbyggJm/ps6+7/pceP/jVDb/Fi334q+gfzzwJxsmQhwzmTiHVKXW0EokiOv7+9Iax9oGmjETk56fdxJrOOHUqsHfP/uqKKG2HiK8zzi3LBgYBGYskCBAISGRdj9tjEucRETS1Yo4wAveMGWVkJRaTothJo4Q9R+RN9buCvBnHharErANPPHDRPJ0O/0Z+n++JJx5BwSOwMN5guNEgEREKmeDnSbpeJ4+UGuVhUAVKMRER5M0xBIs4pd0dHeJt5ff765s7xRsTZltGvmU0/BCx4ufPr2ndvQsX/9TMEwUsfrW7o11jMIP+HrKzUQq463d1DAsCVkIGnQEH9z0xu8FrAOcUwqL8SVFngrzpN9cB50dk+6e/eijLjK/e6/H85e67snLzR+gpnUFDPsSe4wFKoFMKrXZUaRv/oa8p47ZvajN2Lp7VuTkWQKBhGQAgx/FWiFPWlaxKKELHhCrZ3R5kMkoERFgyt6hsuGNbVHHauluKoesFt7K6KeANtlxSvEUHA6SlCOJE+5qMVvZFIQ5byawsAPo7sJUd0ZIvCrCFjgTOM5Sg3vQY5zIMg0eNBFMAHl/zT7kjX05OoQDwhsPuNFtzwBiDYTUCzyXBnqmK0NRACZRi/O33TPKmEiZJ/eGfOg3ggQ5pDB5vQIMHukv1PyOWIfX39kCpplPyOytKPCUGqm/uFP1s2FNntaij2wGB0XKuxFOp9Hpt556RY1NeXg6UCzhzM9r8DxX8JqyUvQN7yC9TEqvblPx9kilEFaExtWcGOZQS6FSD1+M2GGPav90BY6UNyiEmslKkxRyhN5p84rq8EmUfg9HnTc8kjHHkzTiAnFdst8RLIg0JJqNRyaGy/JRNdSiM7EAMaVj8PzUtaRmB8GZAYERtB1iWDXBCVBPZbDLGZ08v7oYnvXHEjHcpIgTATb/6LcfhJgBIQLxvCEIwOGT/5t+/j/ouo8nodiszWfHpAp6BTIrQqOzJMIyQwISVNIIS6BSEkh8xmXpGFd5NBMnmMHkHBm4byBx7mqCORynmLcZSMoVlM7LdzrbObqSYBZ/8waGaSQmQoBevV8lSzU0tcZqPeEk6J0IJFmU6tTV6ne7IRQuq587SaNiuzs5nnvqX2YJNcoSQlsW/VqxeOPJFe4faNRoWGrMM5lwOMcDfDdINpyvbYh5iWI1ShI7s98yUCKUEOpWB9WbGpu9MSMwomunz+f2BAKMlWdlFPq+UqeNiGK1Wo9ObHf3YmWiF0sigBNHV1lpcXjl7jmXXrl1yCzulA3TO3Dmd+6PUEXlcHptCxgK/Z/SlUF5BI3UMSYA0sIsTwhnFebMq8q0WyHOBV//zb9zRLzH4Xb2OQa+taH7l7HL70KA/gH9Hr3fs245AaDSZkzhRRjiUEijFVEbAY2dwww4ABDylw+lyGo220Mu8z97bBfVmjYY15i0ZtjsQQgV5OfbWz5Ph0Dl4u7sPCNK9PWvOHAaCzv2tkkuUD5ONRqM+ZFdDENDZOC4oPBV2KsvixsuR8AbzPYOI0lA5EnYkpmGZcr9/6slk5fXXX+3twIsuh8uWlZ+I5etw2g9eckjTDuDhh7UMcNqHMxmuwf0HMp4nPyIogVJI2GdHRQphNHb4TeXGTNrvI8KI+8Zzjv42Bpfo6IaHBwPm2VCLbVioNWN96B6ZT4uL8kGnNIdOyZ4jQgsC2uh3IQ5tRfKiiiuj9svjbLUajaZqdsXiuprCgjyNht2xq6uoMN/v80EIX37xaZG6Q4yJsNUeLyjk8QySwsqmHTulFbfbYDSmnD6ZghXPMqwg8Aaj2etxTRwRSgmUgiIEPePnvX6PY9hsLcDPBbufZ2FOLQSA97tZvc0DAC/wGlZTWFQyMNDXOigIggAZfB9VBw+SOHuOxc3tMhyQZ81auGDe+++89vWXm6wWqQSeEyQhbNAx6vBUBA0Ou/wFBXmyC9ZozIlG5oBIwswmDiWJMb64EQrNTjzy6LE7N8U0xHj1XkoKZmsoIqRjefIwGvU6xqdjfEYNp4XewZ49kHNokEsHPVrk0CKpsxxG7kEoZ3EiJ0qXfvNZDhCy62bUnbhs5Y8vveDUmrmz83Ozfnjx5UqZqThpBH8qO/B7hiATALzHrZjzMSIDZdmsSw6uA2MMcpUsG0/2MUxGGyRRBUoRE+lNWpo8iE4XiafIeFzY38qaanRajc8fEDg3g+ezS/BqsC3Mw27WvTc1BnXpa3BLOot5cd2Cz7d8UVZatKtxqw+AbYPtB1TNsA8PfPfVxm9hbHdMxEm9dux8MNpKIcsYTVFz4KJc6GD3bmjIMxj1xQX5EIJvv/4OjLEVT7a1Oj2Pp95PCBE6MoGeeOTR7/730zE6PcW0Qubl55DTr+VwTaQ5uyhjJzVpye3NAd6nZwEPdcShSRDgXJzPWVpeOTCYw3Gc/AWl1Rm8YoaAVqMxmCxerw8hxBiB0PeF8uDaoqW1pSUzK8uzbRYI4azKM9f86z5btlSq/84778eSmZEoKZmRn1fQur/d7fEiHEwPAEFxoWEIIyA8vpQJ1B24uHl3t8cx0CNwYiuPMQfLsLyAx66wrIaPXZ6USQ4dgUA7u7ozLIkp0ggx+10tKFozFSwad+RbxJJtUILD0A6HP+BDAHh8UoMfLYt4YNFqDa6BUNM2c67U0SNl7DcujL+DluENZr1jsEuLuTK07nYMGo3EXxng3B4N7jOv5QMuYJxlsuFfhIGworz48EMWFeTn/OXu+3BNZ1Yurm4K2unYEIdhgSHVqb3D7QzL2sqqbTYzz/O9XZ0tTTtI7icuZ40NJEauistmu9yeooI8JOR0dPY2796P5yXLmLBu0LHECAQqIETpM1l8GSg/RNuW+r8JxRjAhMdWhJUAOR3DWWSUhWEWfur2crzgdnk5xIixkQAhIA1nx6PlsjInYGUYtQG332e1mn1+F4SQ1Wl7ezqBcEBvT0+WzTpsx7OP4kfPjebckPiFfkMW/nqwD9u7O1utliyyrNPp/X6fyzlstmTF4kCeA16Pa/myA+rrtzsdjsGhnug5VhkBEq144AcTQYTGI9C5ZTM9bo8FN2SloJgC4SMYJ2xjMRnwFEqXI9emymY3ev1cQQ+uscEsxpPQCr4Vdeaw6qO8AOw3q4/vcjkNyszTlKAnNfs85/d4Xn1trbwuTrKITgqEWL32TnNWocNlj+HZFPeM8ZnYHf0gaKnk5ea2t7s++WQTACA7W11zxWTEfle6QVPEGHAoDSJRUEi3V5wPwoQHGRHo/D6vwYCfOt0+osNYXsz30YE8F3QNYYcAAxGCei3yCKxeK+fJB9GWZUvTFUe5ZtdQl9gh1KhlkCWrVAAwDnsqy0k9QziUZC2q0mg0rMaSZTWADql7NKuw8LHTIEJ+dg4nV8c1SjeoHI6PKkIzWREfj0CRgC9uAmb/UySOVjsq1E4v+TmrUqrVcfQnLkLjsadGo2+1Ksx/K2BJ0bqypkkI6H3Ypu7RY2PfZR8y2wodikwgAUEtbydkZVW+czRQXLV3qLNszuKcbNve3WDx4qqvvxMz3lECNy/ZRWM2WbLtTu9wf4cY4s/SsiHT0+EIJWkpCaHXjoW5I1PsqYRWpw/4o81DzawIHVmBIoTOOP6419//II1npaAYa/bEN7aie5uOswuCz5hTFt+QTw0ul8NgjDLH2Bp0f7lddqvFJgf0+ofkfqDQBLBZasoWZxClhOIZB7AsW1Vdu6d5x/AA/tL4+KNPbbmF+LW4v6vPPSAA1m4fthh1FgP+mjWbzR6XxEr9/f1yMxGlp3VgYABA0GuP2e85k1Z80uH4dHMoNeEppqz3M8ca8mYOihLK4fJnW03DdtHihiaH08lydtmMNSbMYg6Hy2pNPTaQbZPfCwEQ00IF4A4wngAmVoH3mBi/Iat0xOMIyAuR3mnHnDI82E8m4qmABWM4h/oc3TkFhQgBrSnLipAXD5eXgukQQltOsX0QOwGioteR/n5LowklpSJC04roBFpRVHHFJWc/+czLGb8eCooxQY7VSEYSkRR0xSvS0MqBQbtnsA8A6BvwGcNnrhlsOBEqKZhtCXa3C2Ue5dgsWKs6h0y2rIHBYTe+GAxvP+YIU7BJnUajYSxlDHKXlxcNDjsV5KiSmjDU+U30N3iGe2bVLOnq2qe35Xh9AedQnyWbpHlJGLbbrVl5KJrV3zGIedM9MdiTFd2gozpE+kRodAJlIGpu3iPJZoUfh4IicQt69JjPJ3qfNLIjt/uE4n0T69XcHOyXJDtoGXw0p4cLiD2GAy6pZYcgpr2bGA4wFhKrMY/C9A5Cmb0pX4yUYyQy+7Ce5Q02SZBCPJBdcNoHYNwEbcIPPnt3wO/V2Uq8bvtpZ/6QZdmurn2qPfOKSn2KjtE8JzF153Bv+QTwnsdB/FBSZjg0pgn/6Wdf0wxQitHz5gXg3ZQ/xkRoMb0gJIsgA5FgMeJB8TKrOl0uYzB6LhJLnshu/QI04ji8tyuiZCEVmCzqlqC5OVkue8xm7y7ngNminhiqBQGeZzxev9WS67PbdVqdF4APPviY/IbqI7gcGnkMFELdw53jFRdKwQ0ay4rPWCCekiTFyGw4Gl35Ajhx4nzEECAXdvmNgOGhMPogTkSLOYrTMyvLlmMz59jM5hxpevSwgxt2cG4v8Pj8Xp/f7nC5h3Bc2z0c07EYbn0nKotsNly2ZLZalEcYcPjtbt5aOF8DkVXZtnkkdA73upGPsOckAkqgvUjsN6chlhj9xCyDh1hTTD0QKnR3hEy2WZUG8lTeSDteACeORoemDGNOqHu5ZzCOPtRF7dWWuPmvRF5W6MsGAWjQ60iYCACb3RHyhml5sY5IcUxTdvSokcs+YLZFqYOS04m89s7KqhoGopaWVpuoRlkNqxzUFKdaKZFg+peBWKM9JrMblEClUpMvW6dR+KlMjtIiFkDTHTKZGnMYZRtiRz9RcCGog9YKeDx+o1FR/Z0kbMHAvds1bLAWOOwOBCAvCAyEWhhwOPFVQQD0aFBrHblytHd/EzCUQIbhEOrrH1SmZ3pcDjkib8NxobDfyJKD/0IsOWYAPJYcw55WL5hUcEaz4lP0hOIUBcWHk6DVr+DZmAQKkXDpqnOeeFoMxGek7zRFHMQyosv6FC3JxW2fK0xM6fucFpd8a/n1Efvo+5zS02YcY91nTp0gJqAIHRHWvCg93a15Rk+EMotKrC77YFR5mNCpbVb5mC77oJw0ShQry6BBMd0KMZbhYCA3y2LmkaDTagtL52gYYHdgKvSNZPbLt68lF8veWH9ak45JCUbuOpV2KHhWU15YOXNG8aYtUYYWPPH0y0GCpsVIkyl4TZFe89/dK92ieKK7EBaysFlMcSxJsRBeaggfiThSlyDHJrsd8J4Ddo/L450/b25nW7vfE9BZrapAfvW8mvY2dfAHAWDOGTRj3R2FPacSk7LhIjQzcSRqwk9ufmzPV1ugbn/YKHBTviXfL4Uv9gGdSdxfuY8p36J6y6rjlv7jmXfir5xwzJL3Pv5qoolQ06IFqb3RPwCNBi3PI1+AgwBUFOMszn1dQ7yAsq0GYxb0BXiOF3y+AOAxuSCdVa/VGPRap9sHBK8pl4t0BcTHiOypgLRnrs0IAehsk5rvqftMItSJhzJJd7Qhe+RY2ch/rh0T2ornJTco5Hhek/GEDQLyb4COOvjQeHtBcMbxx2XqkiYfZlUaVA/VesauRBj0nXHKEcqVYmPWwad8X37q7hh0d4RZp6qnAAAVV8orl11wvLyy8bNtpGX9ZInIe/uwkHT3It8A9IjbgUEG2TUecVFsY8zmWA16LSv4Pb29/YODg4DVIkYz6OL6nQGnT/ByALFapLNa8/JtVr3BwDqGB4WAG/Jeby+XE+RPhPwI+Y05bmOOu6A8uulmNJuSZU8YYQayCgJVVmxacoejmupT1zxC4uhTPvVw/Cig0TF8R/tE/aKZGJjwf0BheOvj7cqnep3Gag41wigsKT18UWVetmnbru7Pv2wgi+d8/6jPtu7b1zkE/biu8ScXn9TV5/hsa+tgv5R+mF9c0tlrf3H9t/JxPF6fMUn2zDBQj9MDzRajrrIkp7PP4UbYJM/KtZkMOoSQ2xtwOrwAMEgULhwAdg+v1Rk0OgMKAKeAPYsICJD3SMEZMe2aOBPtorbTsEx2bi7PCw6XT0DI7vLKtUcQAu+QNstitJj0IC9KQqWjX+1oZjRRBWw89xmj7IEMgdEadUBnejCxrXs4jufWpForMXUwufhxRGiQn9PbgA9TIQBgW+PubY27ZwZf5Xhhy/a2wxZWzptV+PmX0qLD7Tvl6Hkuj//ZV3Cu9damzrKirCyLYTDYzWjFkTVmo661c+ij/9aTlR+de8yLa/937srDnvnPRxM2mmRELt7nG/ZmBRADWJwUOTzsGR6WshREQhQZivfiKRGQHR7yBTg+qOUQYI16o9VmMbo8PpfHjzUgAwUBsQyTZTXqNKzD7TXotTlZJqNJ19kmZVDqYAAhwPOCe8jpHgJsflgXZwJrnjpryoqz8jEDKl0B8e+5/r5+Sy7mckuuOcZ44+nFpJxoyCcdjlcF4tNLoCtOWL7+3U8mNYNOMX5MBAGOl1Ooz/3+EZ9v3QdaJR20eF7pF/X71368zesclgXM2x9sIRtk5ZOvdpcV2g5bVLm3VTJNnn3r65IC22ELK0IB/0HXpT84mqizH19wvE7DPv1B2OiecUGXGEIpLscTiWGhBfU4Ac/1d+HpADoEAkgDcS05QwgOQoB9aIwBsQYEgA8BDcMYLVqDTosQ0us0ep2mb8jZP+RkGYZloM1mFMShGYxIoz09PQICLodYENgfujs4XtAbjTlmPccJbp/fM8jJt6cpF6WWFRCZxDoaF+fUYFKnwg1qMBq9Hvy9mHlnaIIug4nLntOQH0eEVYfkYZqvf9Cg0bBiazOM9u7hqop8loUmQ+XGzZJJnp2X3z/kBgARn7yWd/Z0Otd2Khw7Pntnm/21ttCcknXvhdI2Hn8BDzIzleLROhPKEwoLsZpD/fgOZ/GfsChMeAA5v06j9TLq+DjHC063D8eFAGB4rxbrGYYR+EAAB3Ptdl6n1ei0rF6jhRoIc2xeP+f387wc6hVDcQKj8fgCHl9YXI7APQAZ3mMoMIwmJWAi34B7JoYslUVoBgLxUQh0TumMKDuOUyYo5cdRgnMPKe2ZnTt3Re5j7++ZOF0jWhq3yvHld959/6prr5df2v7dFtIK/uIfXfHfzf9L8IAwzyDTKAh2ysCyRZBEnA/h3x7huUkmrYb1+jg/xwmsFo+gw58dy2g0Wi2LRDjcPrvTq9OyAEJ/gIOcR1W8wvAjV3ORsJWpYOLqkkkqS0HGReh4pjFRckw7mBy9MDjOHRJHg907G7bWN5zxg/Plp8899dhFl1xGtt9ct/66n/8SQrhrR/1BS48cGk4i1gzzDOLfunSHC30hmjMwvgBiNUar1895fJzNrM+yGoYdnoA3ZE0HFN9CeOCcqNVj8R+CBq3oKMAe1QjIWaWMS6dFfl9y6U+ZQ35HywRnUqcr22iMMnUg9RZNoyHQ2cXlu0VvUSS2btu5cEFNsoem/EiRAmT2xC74lWdueOs1+el1P/8lqez2+/23/ubmX9z8m5Q/YSbfqGJS6B3SiszmHHY741pcQvh0zyivIuQnFIvdI6HGoALP24ccDANL8myH1s6YXZanDUbSH3hbbJU05TBrjJk0alF80iJ0FHGk1BUo5ceJickuQu/9y59uvOnXZLupuXl2TS3ZljfESby6x598Oi2nI0yq7Q+1+dBAHmhsVrNeEJDT45OpMBagmFofC0P9UQZJmgxam8mgHGZ53SnHkA3KpClBbQxwPJ8Z/8gIBCqG4AkQZUwKFfQGo08OV6UDBx5yxDdfbj7zdJz5v2v3nrPPu9BOsi6D2L0T567e+ee7tzfuUK7nFIzKiwvzxDr0PkyjiA9Awe4QnaWcEH38OWINUbbFcAXDQC3LGg3abIthVmluZXGO1azX44BUiDBffe/Lzd+EXf+Zpy4jG9eecTLZeOj19WAqYlZaNem4W/GaKL9bYJIF4ilUYHL0wCPFbaXgeEd/UoHyxGEKehXTgmG7fXZNLcuyZ55x2l/++Ptvv/zfzqbmk79/przD7JpajUbTtO3bk0447twLV42SN1Vg86V2HnyPJEgFBVEyEGo0jNWkryzOKS/M+uSLbQig//vBsSwLSTrX82twgO6miyUt+ep7X7Z3drd3dkeypLwRidfeldIbyk25rfu72Lwx6e0S1oMmBuQeNKQBTQpVxeNr3RMROtaBeFoLP2UxFnQ51siy2Ybtdp7n//PKa/955TWj0bDtW3XFPcdxs2tqd+9sSC97KsEWioPeelx6sX5JpzdcfYG6lPnjz7EW/tfLoToC4uxEOmN8lpQpMj7OXL4QgIXy04fWp9NJmgjZ1YhzkDKAWaNj0ji9QTMgQkcm0JNPXP72J6981yKNuKKYFJiM7HnJxRfdfuuvlb5OjwffURDCoqLCzZ98MLumduxIMxJsoZlIUb/Pe9+T60jZksFgvOqSlQAA8lOJFx55SPn0nY3fkA23d4RuzXt37QYA3PDj05WLX2xtJhtLF1Zh0/7kY8aITGOh3yzFVfqSlJYpI+2aNAOeUKpAx7NmRglSPzOd8dQzz91+66+3bvls4cGHkZX/foxT9BFCPh5X5S897IDmXZhWvty4pae3J5McKsPr9Tzw2GsAgesuP1NFlFGfKilShRBjLq8j//9ia8vShXPJtrwBANq6s1V+19fb9gDclWkqY1YyTBrHDarq+T8WgXhKoKlTXhzkM0m/hQIXccyr27WjnkSKxPHrzvMPnzlDG3CCwuUrjv5kw6dkvb2j7bRzQy2mxhTEnFdCEHPyH1jzmtzKd371LPnV7dvCokMyXl5zy+bN0u8VCzJpfvZdi8kgae1Fi+YuXiSTKVBuEzzwGv6amZKYNQEy85MmUEEbdtu//ckrGbyeSYwD5h2g0+m/3RpFgMydU2U2mb6r/248rmsyASFETPiluerAhdPpPPioA8EEAJNnEPrC7mdCmvKAY01J9tp7b3zv/TDvbSz2hEGiVOLwQ+crnyql0TOv4W8RqPjvZ8EUKBkPTrms0llxC0ZZcQx1VKBAMCaubF6VPlAFmgasffmtspIy5coly2aTDbPZvPGdTU6nU6/Xa7XaCUIBExl3HNjwVqvcPWqCgslX5R6IgaOOPtkwV7FnVJaMhade2QhgyPJcdWpYg9eLTzl8xCPIlDr1mBREk6VOVzYEjnjsiWOCwljQqAbpaXRoVDjlpFPLSsqUzLhl0zf3vrTxxnNxEHbjO5v+fO+fXn7tJQDALTfd+tWnXy85+qDR/7NNPcw/EM+kjNrpbmauvq900jiIH3jyLZwYf+nK+59df8Plp8Xa7cn/fEI4EgJwycojlS9dsjKMMRPEi+s/U3j78GZHVy9bNFELRcfewA9jTxm8kF4OpQp0tFh14SXD4UXZt6z+9S+vCbXAIOyJ07//8oezTjt71CecOnSZOH517iGRi39+KdjQdGLSqIa9b81a4pfIycn60fePUu5w6ffDSDMRvPA26Z+iKGAiEOVqcFH6f0fXNPK/z6o0dPWYvMHc55jsOQZSlBLoaPHgI/c/dO/Dp516+tp1b5CVDe9vaPvw37hAUBvFcCssKMxMBHmyIJwcb2l96c7IfQ5bFoVwX18WlvcTiTOukf5FMgmmCHfJE7pDFiUUwFB/Qn1Pnl8XajEVJXgsLUU05xWDWQiPS5J6WUDttOvxyCrcoPHYMw6NphSIpwQ6Wmz+fPOapx69/derb//1ahLrOOfis4F4w5QWR7ntC/ILKIHGkZaV594C7gZpwet/O328WJUpsvL9YU0873tyHdkozJdSdMMs7kSSbRDobJcmysWCpjwfTEsU5Be7XI5E2TN9Fj0l0DTg748+8vdHHwEAnH/OBVf+6Mr1r70z0NN5ww+O9PkncVOPDCCqYR6Jy6+/4LONHSMK0qRYNQM0yuZJbeRVmaTdvdKkqaKCXJWGhBB0yX2sY4ihaUuRIyI56kyTRU8JNA2AEJIylRdffuHFl1/Q6/WbP/gMQtjVLc0TVqKppSnqQbraemkufYJII5+OSzY+Qbey5384NGWUJTMLQqPK1jGJmfOUQEeLLZu+ef6l5+598B55xefDwrPNzgN1CjZGILXvySkBVQ+LwS2hAhslCsO7vMfaTcb6La05B1eqYkpR7ffxgpyNLzEph6PGlCUnXG8wAeEB0cnUeY1Jcum0wuo/3n7huRcdfUSoc8SG13EuTiAQsAz1fP3t119ulEa2fbxhoyetzd8oYmFCsacSbKFZU2TRlOVT9kw7Sstn6Q1G/eimbeMIXTKRJKpAR4s33147Z9ac+//ygLzi9/uX10p59Vdcc9lH6z/ZsgmXJ3Ecd+gxCXn9pipUTYByDq6MulvPh5sS2S0OzrjmjQnLoZMC1568/IXHpJEe1568/KH1cl/gqS9FUbCEPhEpSgk0Dbj/4fvuf/g+5YqSJ449eXk6TkKRHOQwUSSTJhhBSraVQWoNEMbR8X1twn+ZZM+JT6Ol5bM62vakxZyXpWh8GqUESjHFMXYBd51Wx7Ks1+clIcQJi8SJEtd9vPOFekkvpRNce8bJE79Pfml6OXQkGqUEOirEkg/5jJQ4PYlqEDMPEvZJMJkpkUNlDAfMO+CZNc8pV074/nEgYkro7279/coVKzPTACEpllQS5TmnhfUikZ++8Ei9cv25N3ATk1xo7u0duP7yM+T1hyYepaaXQ0FcGqUEmh5TS8UCa+5vmaQ1iJmH8tNIikzH8WN8Zs1za5589O9rcPIv7kv/tzXvvfmB3EGGQK/Xr1yhbrqcFgjdjusuWRkn975ydrHT7Q92awIPrsE5/BeeFdZUX8WbBE+9KjYfERW1avToRacfHfVc107IIU4jcij58BI0HOTar0gahaecd0Jarni6If6tvub+F/DPV28c8TiZKYwhM777StWtJCcmrK1hqbKOyupxuYxY3syaqprnn3hRpSu3bPrmsbtu3rju5QYhS165ZfWv71z9p/gKNDUfKCkVve6SlS+8/RlZueCs7yX+9mde+0RK3w+nEATAJWcdo2qwf8FPrx3xaHnZ2O2/a3hi1SgTDsWZhaPmUFVxrcyhVIEmjbSYnDISDBaPS1k3RVTsbN4JAFhy0CFffR2SwAcfdWBt0G+D/1lfXPvp5o0OV0Lj2FLGA0+9BVjNdZedpWaC4O397OuhsE+kn3bVmVFEaCRmlNqUT//48JuELmXkZVuadu0Xp/KpZOsksOVlWhyRSZU9CGQpSgl0THD59RcoC2ZGXyrz+t9Opxw6cfDW+jf/+eC/sBIUhBt+df2mzVKrfIJFdYtnlM844/zTlh05VgkYTJFV7lfywGOvijOVYWFBXmgPiQ/wLf/D08PH242UnTO7gszHi86YZNvu8uDa/DbMmzdcdfYpx0pTSVR46PUPwETlUFVTFpy3lLBLVHoLoib8OCnQ1Cg1ZQ6lJnwKiJ+QBCG8bNXlV172E4bB6Ut7W/fefjE2orehbNL1FSF0zNHH3vunv46FCS9D6AuRgqAkBN7PsFpJnMb7NfCPtz+Saj2cbp9MI3zr12Tjgp9eOZorBBOARmVbXmXOx/oqGZFJ5TdSBTr+1dwTvJSbIioQQmueenTNU48CAI464ugH/vLguVfe9NI///LVp18/8Mj9JLEpaj/D9ILJN6poFPChIaBEnGJvqYJJ132s6JaPwKnfWxhVP77wiESg8fHXx6TYkRA7l0sDeDZfyoUaXx2qkqIRnQETdY+Gwko0iDS+PlBlNHlEfyhVoBNBgX716dcDgwMnnna8cvGZNc8dMO+A5bVlnzRE6Th3ylkrunu6M5xIH8aq4sJ1l4V1yL9XjM4T3HjFqaq3v/DIP8lGp74iodMhNeH8ctVJyqcyVT0wToNGourQEauN4qtRqkApKJLD5s/+e+ThR0VmhnZ34H7G8sgWCOFpp57+25tvO2TZwYIgNvvJLCLF6QOPreUAe+PlpxJWuPHyU//6+HqVkJRRksApbrz4xMSvBwHw/Fu4Y3Q+0Hf39md+3EhpOR6eqnKJxhKhCapRSqCTBjSINEHws19eu2XTN1s2ffP+R++vf3edzZZFemmfdxIeZy9XJSGEtBpswo8Le0ajUQa7SfFA5rXkEgWYkIchKZYkqK0LzXne297nHJDiXReuDJuIB8dDjco0mlSafSwapQQ6cSlSadGPEXuOWLhNW5RGxcFHHXjBORf+4rpfHn8sNuQ//+rzn17/E0uEDZsBH2iSNBrWlpRBuLMi4gM/vzRUWSTj+cfWJDWDRIbVlGgyEwoOEM08k8o06vN6RhShcXKeqA90nH2gKZTTJNuuAisCMUVRzvFOHONCoBM8kT4WLEM4jdyZXTjBP2Ghz0Xuf8RjAoUQq6gbLlkZk0BLFqheuvDUkUcrJ4JX3wv7428LuDJfcU84NJkWoCEgqkDHsYJ7TCsRzWZzYUGh3W7vH+gfu7NQTEYw+WGNvvkenO1/31NvEQFdXKhIJhVxYeQkelU+5Eh4JZwolTjr1PAc1YyXhxIpuqdlewocCimBZrKCOzO120WFxW+/GvZn9/JrL711369Uu11+6RVXXf7TzPS5mKRIVhuSDjKTun3Mzy8N6dDnH9umfOnRVyQT+4qzg/VLSPEyjEeUUVkyEq+9+zkYJ8yae4C8vbdle+JvpD7QNGBCNQd5+9X127Y3rPq/i8nT88+54JfX3dT2+bvfbv5Q3uf6q2+4+IJV43eNkxhxvixJB5lJ1z6GLbQodSj2RVjwimoejcybj7wYJSu+KN921inLzjpFQZRwtER55vKw7NSMdSOdqSDTEfmU+kAnH+L45pYcdMg/H/xXZJ8Lr8d95Um1DUIWhHDzB5/pdLrb77ztd7fcMaICnc4+UCVGNDIOW1Z6+Vn3TqgOMimA0ChBESeNRBwyh0LqAICrViXaYuq1RIgyGN7euwdnF93wo7BMVQDAF1ulxmZLF4Za4YxXa+dIMqUKdEphZ9MOAIDNlmW3D0f2ubAM9bhzi7c1Nlx+9WVLD146rlc6rUsnkho3kkm2ZQstSg4l8Ho9DMuqyplUeGcjHlqjgsmgc3v9YQQksqQKEmkeU6ciTcKYSt4k2LqjdfmsmZ/s2QsyDqU4JWRKFejkQ/zo8GcffUGyZ/79yosvvPz8frHdAyHQvQM+OUa8qG7x439/girQMSLQtJfnZli08j1OWYF2oRwcegr2pUcAzK9WaFIU/Qjbt+PvcjzqPgKRMpPgi60tkXRJsHVH2GTWbxpb7YYJMQWAKtCphsOOXbpyxcrf/PLW884+/7yzz8cRxr17Vq8Ka6YrDg0NkwYUYzq5fvSsmuF2XNgxKv4GfaVzCUfIFU0QgMYmSUjODzKpRJfh0JRIXZ2ev+OnAID/bQ6LSkVCyZ6ffYdFqEkfSqT9aPO31//0B2R70eLoPPvga++DzIIS6BTEWxveemsDjgYwDHPDNT+/8NyLnvy45dJjwv7mfH5KoOPDqpO0d0xkYSgK+LZv2xFJl4QxzSb9++9L7UjU1BktC4ow5uGHziNP5Q1511ikKbY9/ZTskwsNAwPDIAdn8pMU/QfHOD+fEuiUwpKDDsnPy9/wXrBBjiDc++A9Tz335DtvvKfa0+/3jccFUkg0GhmXnxRzmAmNBmGUt65dEdb5dESxCQ1q5jl8qciY0auCJGv96VcxUSq9AheLOao/PDlmYj+h0bFjUkqgUwp33Pr7osIimUAJ+vr7iANUueinCpQifXjgqTfJxnWXfD8+UY6Ip15RdKeGmE5XnXIEeYY3UqsZCjLpv9d/3o1GNWZOBUqgUwoX/fiC99/68PnHX7jwx7glPsGXG6V2uUpQAqUYUyZFrFghenkYnyrx5CsbpS2REy899Ujy7JJTJbqMjlAVuvT/bU3NZGNBdZW814vr8aioA2bi8Ffj3kGyd0cXjr5G7QJ1dGUR2fi0NXrXwVigBDqlMDg0eMvvfnPn7X/csiksreSYunLVntQHOtFAYkSxDPkJmzpKwBThuUlCtx2E4741mE8RQDk5WYTFfrRSIspLgxspAoFtzRJ1vrwBt4jm+VDXK0KdL2+QRjefdeISAEB1hS0WP772ntRk+swT8J4yvH5u/Sff+aEeIOw7KKnKV72RpjFNPiTS5CI3N69q9lyvz9ewvZ7nedLnYlK0upiAifRpzwNNvDZpHGdhjWYMjNBtJwqUAAEEBRTZryRBvPC2uufT9mYpu55lQmfheS7q23MsRruP4zncOSUqCqpn9DXhbL8YkLSuD+gggDoYyK8KhQEpgU4+pNCNiRLoZG/BleE/GGUHr9S+RPk+t4pA5aexmPT5dZslr2c4It2eSREoq9XGYc/ECZTAB3Rko6waaxFqwlNQJEF5o2HSiVwRn17+LS4vkIcgKZmUgFTcF+bnRqMoMqo+jEZxj2oIEULdnVJ2rUarP2/FwWT7xXWSnZ4Z6FBYMJYS6ORDIopAdZ+vuf8FsnH59adMjbt6vBD144pk1Un3qS6qW3zicSd5vZ6/r3mE46JLudTAikwaWSHa3TtANooKclVFRUGiHKHW6Lk3sWl/8emSL/WZ1zeldoVx5ecIoCb8FEF8ZSQTKGl1EbVOZuLc9hPNBzrZEV9Czpk156Vn/qNaPO3c7+d0bZObcKtikqTBAkj+O15Jo2H9+zlVVrL0mqZMHbQhGNyDffpcRDVdVDId0YQfKTMq7HWEUIAx4EaiVfh3pAp00iORRkFr7gfKRkGximFeX3b6JIr8UqQFLz3zH7/ff/j3DiVPcb+uDz9b+9KblyybTVbI4Ps//OX3Odk5Odk5uTm5Odk4xj3K1nkq3tSUqRs5xwJhz6h45o3/kg0omvxgDEDYUwYl0MmNsQgQJ1UbQ0l2UqOosBgAsHzF0fIKQujwYw/dsumbukOX13+Ou8bdefsfcaLPWmnE/OjBijQqIhODOW22LAjBQH9ffn6B0aDd39YRK6U0FjgmVHOlE7w4jRrqXG1D5vJsasJPU/ZMY0V22jmUmvAZM+EhhF99+vX7H713829vCvsnGO6dmatHCDUIWVs2feMP+A8/VpKo45v9Vj3rqM8/lKg80oSXAfEAUrh4/iybRc/zQoATAjyPBBDg+Yad+2rmlCOEGAibdo3s/TzjhIPfCBb1y0AAmUwWc3k2VaDTFJFu0JQpdRxzFSlGCYTQF1u+OP7YE8iU5j/dc+fQ8BBZV5rAzzz/tOwGffPttav/ePu4fPJzKg4fHpYmJHN+X9ReeYQ9EQCXnB5W0eT1S27Q2jli0RECnCAkQqCR7Bl2rlPOOyGJ34BiqtvvKdNoGjmUKtAM5yHVVNWsefgxk0ma4tG6v3XVyUfMzMU9jYgCBQCsOOPEQCDw21/dfszRxzTvaj7/knMzr0D3t3i1Rqve354IgcbHxacd4QtwL709cgpUWPOScAXa5bZA5N5y6o/UE8coprkDVBmOT7BLECXQKZDIOWfWnN/fdmdNVQ0A4Mffq+Y5joyBUarRX9/4mx+cec5hxy4NBAKZJND9Ldj5mFtY4nZ7NZ7WURIoAODck5eOnkBxfG3dE39O5BegoKCYYrBZbcVFOI5EsGvPrgt/dD5xd/7p6XfJoiqc/de/4dFPBfnjM3x0oKfT5xNj9zHYM3H4/KNKd+1y4wgY8/6bG99/cyPlUAqKaYi1L7+17hX17HW/GJwpLKvcO+DT6XQLaxcqX+V5HgBgd6j7hmQMKOAe8hmtpYtHeZwN/5PqQVOAXCklBZEIh1JbPjXUf7UTjAfu7EmlxjkRbN83EDLrDvsLAGDZEnU/JyU2ftWWxrNrXVKggCDQOz4f75SB1x29efaM2SW3/O43D9790OWXXrHmyUfldVYcIff8Yw9jX+EFq356xdXKtPmLzvsh7krjVFcWZQycmBXf2bYHAJzkrzPlVM2d2dqQdL9kpM7eTxQOpgRxXgj8DETwlJO+d82qU7Qa/JG5PL5/rY8y9JliYhIobttVESooHiMCjYSSTNNLndEJ1GxN+ymmFeIQKADgP8++OmsmHm20Z++e/e37FtUuzsrKCgQCxx84k7Tv2rLpG57nDzt2qSAIBy0+6NG/Pbb5883X3nh1nDOOnQ9UJtCoyCkoC/RvT9AHqtPp/cQbkIAP1M7gzwrwfoBw0zwGSmfABAoAkDkUf2Qe36OURqcxgcZnzwyAEmgmCRTzTnbOu2vfJxVHAIB7H7xn7b/uU/Y/VJZyrn/37VvvuCX+GYvHiUBjQWvMtlot2dm2npb/xSdQzjzL5/NDCDVajd81KK9DKH04AmAZwIfWCYESDsVnEmmUcuhkIdD00ui4UycBJdAME2ga+x+OdRpTagQ6esgEql6XCVTFofizc/se3UAt+klAoCmT6VgzZqxbNz6sKKzCxAGlDoyJw2DCOYwUU4xAgcihE4pAwyqR/vb021evOiXAYYGq1bAWk/6KFXieOKXRSYQJoiIpJg5ifZ3Eobl8Zlg1hXCCwMbaBzjlZNBxhrqU8+Gn1wEArl51ql+kUYv40V+x4jjKoRRjCAgqyiu0Gm1nd6fbre6/S5H5Qow197eQSqTIBrLj3vYwV+/x+30QAocQmkQ/XoheC//w0+uuXnUqzgjjeB2VohRjBgjhicedKCdFz52LByt2tu7es7NB3mf2rNlVc0MtQQP+wIefUM9Shpw8ytJeuX+Cknn/PH5kamVw2T5B5sm0wBL41U/PitlMRMmhVIpSjBFOPP4kgMA772+QE0+OOuLoksrZ3R373GKqtlavr5pb3dvb8/W3X0uEe/xJ3zvmuA8/phya6WBj1D4Jr4f3kB2vDocymY4pk1qgHJdHAODsunjdmGQOjZCi6NENH47dVVJME+h0ODr0xZbPlWl7mzZ/etIJKxYffszmd9cCABYffgwAgLAnKSvcu3fPTDFvkWKMIHfgTg2vJ9Y8YYyo1soMk7FKUeFMjF4tUKJj0TSKVzMaFoUXEeXcMo2KryN/gKccOu5R+O2f3vvHB15/9j+fqhax0Dgad57f/um9fj+3+LiblTu8+viN86pKyQ5iKbTxk9dW5+Xgql6/n7vypkc/+DRkOxMsO2z+P+6+/Nwr7m/YsT/yXCp8snn7VTc/tvoXPzj39MMj/5pu/dOLz78SmrWg0+mOXf69ay49yGrWHXPGanm9Ij/H43JYTdoP3rn7pNN+F9BZ9uzdI7+6d8sjV/5q7bsfvIOCgx4f/euVp58sGZXfNuw98Zw/xAqbfPrG7/JyozTxlT8x5aLb7fu2ofXyG/+p2vnx+35y/LI6sr11+76VF98lvwQh3Lbxnide+PjuR95UfVYbNzf+5OY1UT805TVkUoHeck0U/2ZmesimTKPDYjt64gONARiHQGUYDCavZ2Rve2wCRUazVWcwjdAP9OqLxY84vJuATsvqtBoaWRpfWEwGOeFMuRi2bQIX/+DoZxQkazLqzUaJXObMLProldv6BhyHr/wtEtC9qy9+4v6r3v7g25/cFKrqAwA8+/A1+9r71j590+yl16rO1dbR/3X9XuXitp2YZPU6rcVkeGPDVzyPyzZk7G/vUz71+3G6ksViMRm1yvXB3m5M7uZss9mw6YM/Fc6/XPmqyYR1q8yePY1rAADX3/rkex9vLci3ffz66p7GNceefceuvfggKphNenJhsT7Sa295gmxDAFccu+iU4w/ct+XhioNDVTf7tuACx2tveWLT5zuKCrI2vPCbfVsePvK02/a394cOctkKFYFaTAaDAf+OH2wMfT+dvmJJrCuZ8j1kU+snS9gzHY1E0gYNyGbBUCixXoWHn3lb4tBw+AOcTstSDp34uPPX5ysJVImPXrlt2O4+6ASpmeEFVz141qlL77/jEuU+Ghanvy07Y/XeL/8WeYQvvtl1/W1PxTr1tbc8MWIe6Dvvbbj5p0ezLHPSCSuIhb57z+6WXc1Z0C8n3n3+zh8PPek3ZLta7LS2tf47JXvKDNvbby+cf/nGtb/76JXblKwXeWGxXlIy2usbcHhk35aHlyya/dV3u2X2lI/cP+isOPjqj1697b9r71CdTkW7UU99+oolca5kclHqYaNj0knak/vGH57AXHv6CsyhsfHwM2/HekmnZW8465QrVqicABQTBcTAb/5fTIeW2xOWsv7qui9+eHUYUT5+/1UIIUGUe/+4K0wJpguDQwMOh2Pb9m0BPy6hmzN7zkknrNDpJCl94LE3zaooLCnCU8xmzZxFqrY7uzoBAOUl2CAtX/gT1QGXnYb7pf/ul+ek6wqXH3EAFuyVuJP57EN/pnr12LPuAADcfM1p8grZ55ofnwSmDT7b2HHGNW9EfYApDfwtnwiHPvDE2qgPP8dbTAbKoRMWtcf8Qq/TXnBG2HgDgutufbKkKHvPlw8dVBeKyWz8rFG5zzFHHPDHB14HANz/6NunHB9vku0o0da+/8NPPnznvQ3/3YxJ/6BlJ5L19q6BgUHndx/fveTgQ6qrava3hfyw1/0fto38gShdHe0Oz4/Ox9GnUeLqH2ESvPfvb4mnO1msI4xirjldXrInAcfxDz/xzk1Xn6bV0pE5ExQ8N6pmoAT3PblOQ/z8156+4qE3NsSx5eMAB+h12hvOOuW+V2NqVYrxgt3heeWtz+/67UUvrf2MF8I8kq+t/7K7z/7C3699/clfkJU3NnylNCotZiwD//nM+7iN7j/WXX/FKQtqyrftDLVfOuvUpWedulR+6vb45x11g/yUGLxKqLyZy45aZjSoq0qcLtdXX3+15KAlsgidd8T1PY1rHv7Tuedc9tC+/fvkPU88dlGs3/qbhr3LD58f61XVhUW6OJX4/X3SCLOjls6LdcAdLR1LFklDgAnu+tvaK1edsOuzB+J4EihSQ9wIUqahQYh4ZNG1p5/00BvvgKEUidnP8VefdtLDa99J9xVSjBY33P702SsPbdp835zDrlO9tPnLnZVLrgEAHLxw1upfnHP6iiWnr1gi3/PbN947OOySdx6yu9Y//2slI6x7/+tb73opdLjwYGPFwVfH94E6nU6j0RS5Tm4Pe/C9J3zvhNX3frj6xu8ZdGFF0E5XzIObjPHK5w88PiwzQYlLr/u72J3IfN/vVjXv7jzunD/IL7ncvvzc6I31jGKASIXqw6/b/cVD119xyv2PTmthccY1bySe2DTpwLy9aTtC0l/+taefBJKZ03ndj9Wfy9WnnUTN+YwhLyfK/ezxRpn1esDRN2q1mrNOWar86maD7ctwv7Kte76/6i8kzq6093OyzPu2PEwe2TazakSMz8f1DzhCj8Hkmux+W/8trszLzYNM6Jh6vf7gA5eIbIztocULFzMs+8Tzr+3d17vprd8r337fP7FlHRWHLJ4T1dYm6B90Kh/Klz7c1PDhpoZX3vq84uCrq2aXKAXpEy/GbNm7oGZGIMKTwPHCmuc+/PlPFCmA0xVnxHWGTmpXKb6F3t7U+PamxhQ49OGn15HaeSV0Ou3Vp00j9/k44qKzj4pcXP8hZiUVnG7v+g+/vf/3lyjpb8+XD82ZKY54DYITs45midGSW64/k6hI5QOz3gehtMdRQuCFjZs+EX0FlpNOWEEexyw7FiH0wdvY8QoAKBIn9iw7cvlv78F7bv/vAwCAk05YodGw/1n7GQDgkvOWqw5rNGD5+YPLpY6WKaPq8OsBAB+/eruSQFeecJBqN7OYc7riwiiDxe746yu8IES6BaZno5kzIoJLk5o6sQQpsmhImijHC2s/2Xba8gUShwKQuDn/8NPr+Ij5fGzBBOqYMiXx4JoNP7t8RU6WWbaySdjklj+9GHX/K3/56L4tD8+bWyrPCEMIffTKbZVLrpFXjliCS87v/xf+Urzy4uMjxeyOlo55c9OZTe3xeAYG+jVMdnd3l9lsdrlcu/futtvt8nf4V18HS60ROOOSttefwqT21ZYvSIbpP59+7+7VF3/13e5twSR/hoGt3zyCEPq6PpR7nxp8/sC32/YuXjBT/rhefvOzR/58WX3jvtY2KaGVZZjGT/+KEGrejRMDIjHrkGsnDoESDh2LJtx/Tr4iflJTpwwNQkg1t5N4RUfpEqUYa9zz9zcvPW/5dx/igUUy/v7ku67YbseKg69W3syVS67Z/cVDrV+F5S3d8/c33R4/se5rjgxFhAhOPO/OfVse/v6JB7/57pYRrzAqcajiSAQ8z3+79dvo5nZ/f8hj+0V/W0d/eWle/4CkpH77p3/rtdqPXpNEIsHgsGvR924C6cBpq+4m7guivm9c/QzLMJ++8TvlPgNDTlW5lwr/ePr9n6w6HkwYpFeH/nlcOzONO+BV//cjgXCoqEK+v3yBTKcQgoeekix0LlrUX6ORhMI0V6Dj21B51TnLqmYXf/K/xvc31qd8hJq5pR9srP/wv9sy31D5lSduzMuxKks58Z9WIDi/QRuqrYqDJYvnnL3y0K+37nl57f8y0FD5kMVzvn/iwV9v3UOS7Scp6pbgqoTEe9yNO2MOj1zHmWgpp1ajC4jDR1Mu5cyvnoH/UFtbW8tmzGDFnRBAAY7XadloMVWKCYqnX9447keIhUSIjGEYyECDSa80LXftbCUbc2pKEpFODU1tDX9ty1gv+i+/3fXlt7vAVMQ0V5TJAkvI9v3YfzSjooIElE46vNpg0E2YRCuKqQmZLs0GrUHLJuKYi9yHtt+nGF+EAu779+EU5dLyGW9v2nHC4dUWk56ZOOmqFFMFkST4XcMeEsiOivzCsETRvh531KNRJqUgSMR+TyPUGUsdbZhGu/qKy4uyjfpQerBGo4nqBo3qAKWgiEQsjfnCy+r8yvxC066dCfGpjGXB9bGYU09BEQuRKZ94Kv27n3x59GGLZ4rNGigoJhGWLSkfcR9KshTpQsyc+U8/+9awDBeEUFBkGH097jk1lfJ2fO05SpKlZDq1AKO2hE87TGZrLAINnf69jZOp2yvF1IbK+5kuSl22pJxy6DQHFHPfU3vvqNptcRwnp4JSUGQY6RKnRJBSGp0+MBhMCbezG4FYKf2NVUIyRbIZ2mnMYUyh/c/UqCykyDAogVJMQchsmCCTUvakSA1MTVVYI9hkcY1yYKcItsA4reo4KSgopi2Ytp6hBfPnKlaSc6be//gb1112BiFNSp0UFBRgUoFlY04zil1IFIq0/z85QXtalX9IQwAAAABJRU5ErkJggg=="}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "Configuration is complete: both ally feeds show the full front glyph, and HOSTILE shows no valid front glyph.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-d66c82f1997e", "created_at_unix_s": 1788842996.628308, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 6, "requested_group_seed": 6, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [6], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "assistant": "Select a central elevated socket likely clear of ally screens; set its socket first.\n{\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSOCKET S4 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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Preserve yaw 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y0 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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z09NTAym451j6P26dmpldKJVv8jxIzy7wCwnx0yDNsTowhcedGKFdBCKgBAKhFbEjniuvNWHzs6rWoGAo5P2EcPvB1OtmDpcOTs8uiIwM8VLC2pq6cp31JHf8A63ily2dojcayrU1khHaVSACSrjGpL2bm/Zu7rW+CkIbEB8dRFGQEbyfPOBPXrgiPRTXKxxCUFGpDQoO8tUw8lX8w09/jpf5QUEBkhHaVT4PIqCEa4kknURDu675GeSD+idV1RpWXD8Jon4goobKVZKCsKKyOjjIH3BmmqKKK2rtBnZiIxR3GGmVEdqxcSQioITOAtHQLke5RT0BAAkJEev+/OvGZRNwrjtDUzzgJZUsKasKCwnA90pLK/wCgyGtkM6DjdDrlk5B/UOwEdpFIAJKuGYQxezq5meQRT2NZu58+lUAwC9bjt+4dAKyQ5GKWq3B6IggVBcPQUFhWd+48EBPikYZ8sBOQyUjFHQRiIASOpF6EkntQnVH5bUmvFFVawiNCHr47rmShl63eByANnPhIYQ0TeXklvTvHUFTkKahgqEVKlF/MQ8//blkhIIuAhFQQueCaGjXQGZ+mlmOgnDvkbTVd84xmZHr87ftJyZOGCw4JEUzE0KQkZU/IEHIT4KApiglQ6P5SLY8/PTn2Ah18cpjnkO2aieBCCih06mkvrJSfuvA6yK43bMOitJRVWuIjQ5WKFQQwv3H0udOGYS6fwBwOCUjMCxUemJqZt6AxEgkqBa3KE3By7nFpTrxPFK4CRmhJApPINghCWKTNubl7+rIu9eZKa+xphlFRQYyNFWjrVarPCCEuw+lzhifxAqz4WqqqyUvKFJPAcnxmX6pwD8krFeIWB0vhZuQESpLFO3kEAuU0JbYGY8tNiSJhnZm81NavHM8r6BpnucZhq7WVgf5+UJhLT99XBJey9sMksMIQplxqWDoyKHBPgxDUx5eYgc86djklI6YB9cmEAEltJZmqaT7ykg0tJOjrTWsXDqRolDAnWU5moL5RaUhAX4Qwn1H02dNHCiopzwUL5KeXTBkxNAGbTkUGi0zuBOdANvOEzjaHCKghI6DaGJ3mtjh66XaevD83PH9KSGkzvE8Q1MFRWVhQf4UBfcdTZszeZBjVntaVv6D917vpUETPgBEipl9pajchHJCIUT59qBLQQSU0HkhgttJmDJAnHApoa01+Pl6AQB2HE6dPaYfskMpaDajcPzV/JLEmDAI4c6DF+zMz4oG/tEHlu89kFJZUoxN06zLhUrfwLhQcRWPY/qg60AElNCp1ZBoaGegtrTAbk9cdBCAwCTUa+4+lj5tZB+KQnYoy3EMTWVcyh+cGCVvWpeWlV9jhIFe9I49f4nGJgUzswt8gkPjwzwBBCpvr65ohBIBJXR2SGZoJzQ/Y+Kj8ORLVjAYk1MyJg1NkOxQmoKnU3M0Htb+8zFxMT5qG2VMz8oPjIgID0BreQpCFE5SoeNh+88ibkOIgBI6AmJIdifzM753eE5ucUhUWI0etY7Henf4dNb4IfHyTiIVlVqxFgkCL8HnKZF2MT88NjrEh4FCKB8CoKKZ3BI0MglVgHYdI5QIKKELqCcxQjuP+Ymzl2gKlpSUD0jqVa0z46xPAMDRM9kj+sdSFKQpysQKLZadKSFWT3+hszI+ggJUxtWK/tE+FiO0eRd5DeWWCCiha0A0tDOgrTMEhATwvFi+mZdfEhcfUatnkckpGJun03KG9YvGGir5QGPDA6UzpF3MT+ofi9UT922ieJiVX9Un0oumoId7Y4Krcw+4ePT/5vcBHQURUEL7Qhbv15xRivxRivwWPHHKgAi79bufFyrZpAVfJ8chO7SirDI0PLjOwErR8zPpVxNjQiEEvgFW3ZSrp3Q3PMgP8rCksiEx3FNYuMNqnVmptukw0gI6Mg+fCCihy6gnMUI7GDv11NYZiqvQ/DgIUfI8L+S9UxTVUFcbHYP8oTgojyQ7KZJhVK7VEwBQXFatrTWE+ymxF7VGxwZ5KxmKwgv51qzfO8wIJQJK6EoQDW0uLbM9nWbOAwBefWRZagZqZYDtUF5YpDM0heo4QwNpmjKbkR26ZtuJ66cluVbPtMw8lEwqrOUpCurNwFdDQxSOpwoLi0EXMUKJgBLaC7J477rMHh5nt0dbZ+jdK/TnLcdffWQZ3gORhlKsYD1SEBoa6tV+fhQFsR26Ydcp+UB4R/WMDA/19RCnHrM85aEQXaYXrxSZVW3QD7RjjFD3fLYEAqFTMtQfpf64IJoWl9Umtokjz1R5S9vleZftHo2NDjaZOQVD/bzl+C0LRv+8/SS2HCGkWI6nachAqqGujvH0BvW1BqNZqbDv9elcPQFgAcVQHOAhD/j0SwXQ0z8+2COnCLSYuWOTth9LE4zQdo/P91ALtMVudcK1NT+THo0hH0FjqtdWOC7eq2oNNEVRFMQJnkhD547AD2F/KCus3CkITfoGI+PBCGt5Px/RlVnLykqSMvOS4sOwemZdKWIBRQMh+gRRgzulb1B8uJdC1l7ETWyUUsl1mBFKLFBC20PUs/NoqEJRLR5marnUPrN64amMwsxLBTwFDUZWpaR/3nJ8xYLRa+zsUCF7iWcNRoWHyqxHgXXLMh4LXKqgnvicWVeKEuPCAVZPAFJRYVJkkDfDshzVlIDanfYaGqFEQAkdTe4ld7tFxPTuoSuka4ij+cnx/NqtKbcsGF1dbygqLuN5YDKzCoZeI9NQwQ4FqKkdTaHhcZypAaok7cKJ8akZeUlxduopkpqVH9ELJYdyHM+5V8opaaj4ArK6+yv5ZXFRwXj7/+b3eWfrRdBukF9QQhtDzM/upJ7VdUZfPx+8bJ8xMl7j6c3QyOJkhVX5mi3HV1jW8ih5nkZ2KFrroyMMco2r0hqQekLn6hkVJ6XWoxseCtIkjdmWceGo+/3csUkdEI4nFiiho9WzBXYlcX1eK2JjgiEAZjPHCOGjFQtGb0xOr6+r4Vj+fHruoP6xa7Ycv3n+6LU7LHYohexTVIYkw4uCvIe4x5l69pICSkCI7GccO93EZdmanHZUVddqG2o6xghlemBcUgpNNhmXbCcnPYHQVczPwCBR2owmVqlAy/ab549+7v3f+ydEJvWJOZ9+dVD/2LVbj980b9S6nadwHAlQlFnoLH8xPQs/t1ZnRP+BTamnYFSmXWxiZFaTGurv6eHv6w2AsQM8oT16CR9POx9BcabKW7p1+EV1YcjivZshLaXxVHeDEZkda7cef+aBRenZBRQF+ydG41nwNA1vmj3ccjCauCnzUIqwgBLVk0e31Kz8AUlxcvVMzbiamnF1QGKUu9fXWNMRFQvM5iv5ZdZw/Lz2Csd3QwvUHdVrk9AkQQ5Jm+9+5mdCXOiCKYO37D+HzEoAaRqlMSkV9G+7Tj28cvb/vtvZr3dE34So8+lXfwbgloWjb5g5dMPuM6hBCKrztD8hzVuDh6nZSD3lj6ZmICF2Rz2hczvU3iIVPKHGuWOStv+VlnyivYzQHm2BEjo/8StRo3LCNXjne4WgwUfe6hnjkix2JYXsUNyF/kjqbcsmZVwqZGjqX/ctRBr653Glgl48ZXCTZ26NegpA13ZocZnWaoSqOBxNaicjlAgoofMqHVHPa2V+CrlKyGD8+c/joYFe44f3keZt0JZ0+lMXLi9fMC7jUuGf+8+tvmN2ambez38e99IoEmLCAC/an1RVPlWVr72SLp05LbvAUT0jIiOas3J3tfPKufNhPoHycDxGMELbHiKghDaD6F33YMzIpMAgP0kof/7zeK9wvwkjRA0VspjEh1Iv5s6eOiwjuzD5r4y/3TQNa+iopEiNxtNhBY/uV5qopKRejuppaa7cBlzJL7c3QpXtaIQSASV0Ug0lcnxNzM8xI5N2JJ8OCQ9GxqZlOMfPW47HhvmNHBSPTT2aRnYoK6S75xeUjh2ZlHGp8Ojp7BsWjEvNzFuz5fjSKf0NZmtDZUwNxwR40m2ini7cmcjqVLFhPoFn0vOsRijfXkYoEVBCN4H0N2gT9dyZfKZ374gpI+P9ggPRrDeaMplFDe0TE5SUGA2EhTxFoaoh3ES5Wls9YmhfCEHqxbybF08AAKzblnLz7EHyM9fxSruhco2pZ7+EaHeuHDYmo8jqLC+rqg4L8sZGKGCV7ecJJQJKaGPaxHJs7klIa5i2IjoWxY42bDuxbMawsMgQYdCmVUOH9AlPjIsQ1/K0EG7n0XZDXZ1CrYEQnkm7OmowMlS3HTgvP62XbXvltlq520fkBeLCI4PDvLAntK6hoaq69mJOcTsZoURACZ1OQ8ni/Rqan7SQBg8A+GbDvgWTh4SEh+C1PI4prdlyfGRSVHxseChVGU5XhTNVwaA8lKoMoyvn9VP4A204U5VzPmVygiqcroz2qI32ZfEtFFZIN1NR5phe6n5e2lCqUn4TnKr5KcLS+9Zh1nx79zUUApCVWwLM5vziqrKqmuIyrZe/yt/To090TDsZoURACe0CEcGuxZiRSFzie4cL8+DEdh4MTc2fNCgqNlxYs4uBozVbjo8eEMWE9i3hAkr5AEiJo+H/Ondl4tAYDj0bpmcXNtYPJL+w3N/fz1PlRHlSL+bHxkd7qxttJIpxmc8JE+OR0zMqOCw4zLMDjFAioE271cjysCMhynutUpd2Jp+RxnVQwriOz9fuViqY2eMGRMUgDaVRLzs0CH7t1pRJw3pdvFwCACjjA0v4wHyjXwkX8OfZmmmzZpTRkaV8YDHrn6vzzqum86rp2rBhJcJhe9Pr6rzi6xRBJVyA3W1vRkNsfLSXM2FtOhNUvkUbJCMUqFh7I3RMGxuhREAJnUgKiXpeE65bgCI/8TFBrzy4CGsollEegM/X7lYw9LTR/cOjQimKYhgar+XXbk15dvUCrKHCGh/i/et3nLhhxgCF2slUOBd+T2x7uque4vXZyKi0mZNfLhmhAAB7IxS0sRFKnf/pobY6F4HQVoKoDgiQbkP9a5u8Sb42dw52p91MTzM/t+9FDZDWbDtho6HCv5+v3R0TEXjD7FFIQ4X+83gtv27biafun3/xcoldgv0v208sm5ak1eGJc+2gnnZeTzs4BaANgGGcGqEZBQVta4R2w1p4QqcifqWXm2XySY/GqAOc7CfNDTrA/IyLDjQYzSolgzX0mQ//GNDPOj3l1Y9+e3r1stsWjlu3/XhuTiGAKNDE0NSGHSefun/+BksvO5pCDUdUSnr99hM3zR604c+j+Ol+SlpSz4xLBf16R7ZYPUc/+5l9u2VL21H8X4ahc/LLe4VHZuWWCKaoQTBC2bqGBhOPXBNAhSzltqqOp157Zw0xQgmEnmx+frlmD26HLIaJbO1QzKsf/f7Wl3/cOHd0VEwYKkWC4pp9w46T181CfZiEPnZoKDE+yd6j6ZI+XanQu6+euC6+eciUMCrQt1d4JKANSD0tRigw0FYj9HLR7PED28oIRZf+2jtrWn+inglZGLqzBnenHTJpmXyt1PObdXsT48P6JsbSFGRoiHOYsIY6Pv3tL/+4ffGE3glRgoYCXIz0265Ti6cPlRXLIztU3uKjWeoZG+NOUbwr4zG/pCKnABV0ZuWWYE8oBntCUWYrY549fmDyiczWa6i4hF8xImjNSfSSPYRoX7dmvbo/ErYnow5wtvC2JenRmLR3G22US9TzWnHdggkHU9I5jrtt2cRf/qTPp10GPKovYmhqzbYTK+aNXLP7rHQwz/MQwjc+3fjkqqU//sHm5hQKI4yQaG7ee2b+lMFbceM7Co04xj2VJdxXT5xy73pmnOxBy4sIC3i0i2GiAn0BEygZoQCogMqAjVBgoP19vTMuX83Jr549fqDRaGozH2iP0tC8atqdfqCkbWgb4lpDCdfI/NzXLzGCo+Bzb699+bEVLMulZeaYWQ7P8LDTUAghy3E0Rb3x6UYksh6eUN/ActyFi1cH9++1df+5eZMHbTtwHhUpCfWe0qv4eSjdUU/Hhkx8IxrqHTURAvw/23Z2PABnz5WV1hl4YOYNvSIDsy6LQXncYSQs2K+qSk9RcO7kftsPXJg9fuD/zevzzraWD/wgQSTCNdZQYn52ACkmJ+vi6xZMOH7mIhIpDhhY7tm31rz4fzd9/xublZ3H8TzP8ZCCdhpKQbTGp2mUJdpQV8vTKg/KlJQYcy49Z3D/XtsOnJ8+NmnvsTQ72dPikR6u1DNXrp4hYxeN/dff8TbyFMjgOJcjXSHQN4DgcC9goFEgHtgYoWE+gRmZRXllNZAyX87VotTXI0hDQSuw+TFWjAhqzbkIhOZC1LOVuJPg1Zj5+fW6vX6env6engHeXpHBAeEBfp9+u6OuuiE0KEBotsThdkprtp0I9/O23HyQhpo5oc8IPWVkfHZ+DU1TT963KC27EOVdBnpNG9u/satF6hlnr5419eYBiVb71B0Ew9P5Q2KOJ8Pkl1RcLagQ8+oB2HUAmZkDksLjwvzmjB++eMYAfPzOIxeeXCJutwBigRI6FLKQ73iifVkTWxvjYSOgDXpzTDBTk3e+qFwbHuQHAMjV2bqzLIfzAJRX1DjVrB07jwd6gMoK7Q+/HQz101SUaz/4bqfjBVSUI3Pv34/cLO3BFU17k9EcujzW+WgyOc1KONI3ALXGIHhCRSN059FUCPhdf6UvmDYsqX8YMLKAUy2aMeCPPakWDR34xia03VoB7VGeUMI14eZHwdp3xQ1CK2kySRb78Sk/r1ydzf7B/eLOZVxB+qjxxg9dzCnq0wu18MAJ8LYxGiu4/VILeP29tXhDUtLpU8U5dHLe/eRXEBDlXgTJOcknMudO7gsYhuNZilUB2jBzQoKa8eR5UFRaFR7iD5QGYASQUS2cnvTn3rTWaCixQAnXACKd15bqOkNlZdWQxIgzFwt5wONpb4mx4SzHQwCyc4vRWA7B5uR5nmJEteLMKAovOCL5nz95/LbVb/FodAePYznur7IlJRUelybCu7pgp9GkYy/e6+x5askIpQw0R7N7DmfPGjsAUAbIqcJD/LGG5hQU9YoMphg1x/Eoux6AO+5fCgBoroYyWw6lLpho4wIgRiihNTRZKBkNrHEACrg6mIyVbidUStFyGjuyr1jYA5EsZuVW+vl6xvaOlaRt975jfeMFs1QWz/nXIyuAkv7x11duu/4ZIIpqsy1TXDgk/IskGBd+Op6gOveA/CneUZOQdL6EpNMdI/SW5dMAAJdyCvvHRwCloVprxhraKy4g50pZr8jgpTNG/L77JE3D225/5ccfnmmuhhILlNDGNKl6HKiSts8BMla6o9EZzZLNqK+pPZKaL1+pFxbje0jQOJ4PCgrAMgcBiI0OOptZUFpZ/+izX4xKigry8xySGBES4g8F8cU9nCgIsCB/vW7voKS4auT8BIF+nuI0eJ6nhB4lVdp6nH80avzIKSN7DxkUC/y9brvhWXwdxtqr1bkHxEu0qLKb4sxyPJpKbzFC73zk/W/f+8fPv+xOv4w01NcPVGvrkYYWWzV07LCov87kU5Sooc0T0L/fPvPzH3YTI5RA6CE4XXE7wgtLdUFLxVU6Sm1yyCI6lZbj9OmDB8hGb1oMWgjg1NGo+IfjeJ3R/NXavQWb9m7ctBfL8chB8TqDmeP5k1cF3W0RuUVVcZEBViMUgDsfeR8AMHv8AKSh/UJLKmt8fTztNHTpLL/NQkAJzQ3d/nTS3FfdfDmq4EqRnXoSSM8eQnflYp59yLt/TJC2Uivdqiu11VXa6qrqWm11Q22tvq4ODe4Q1M9s5nR1dXaRpXOpV/CtT1w4cok20kdZBILklIvJKRcPnMz66+zluLhI+eG7j6YdPJl56KSTtHb3fQO9IgJwNSo2QqXr2XkkNS42ND2jpE98wMWcIgAAiiZxKqShBWXASOPEpttufwVraPOW8N3ME9qkG84EfONp9JtEajQJPRwvjfK2G2aYzCzL8XU6o85g4jie5bjaeiNFATPLa8tLsH7pDCbKLxyUl8bFRRpoFKsBAAxKisPxoszLRXKl43lQnWMvhcJKX4xNmczcpcJaShYfQmInq8wELdVRyrLqxzmhnOA3wBo6e/wAYKSRhl4WUw5EDRXs0MUzBmzek4oX8ufX/GPQCmS6uiGgNFgwZQAQ8227A+43QHNdyjlKOIZA6B6cKoPxgT65haUo+7KozNMDlZ0vmDmysKQCH2AwofRMfx/ksgzwUggzLjmtxY4KDA2Kh9UT5g4+cDIbAL6glo30RoPlMHhj0rihB4+esXtdKb4kdCDBAStgMLG6hgZPtaJXnJhFPyop+nB2ldFo9nCpmH+9JBYpOQW/gBRbnzaq776UTOku1lC1Gxr63f8eeXJJ0hubUJKTC6wlAVsO2btOSWESgdA9OFUGq+sMaAJHRXVocMDKxePvWDTub0snzBiNvIQaD6Wnh1Lq8q6tqdfW1FfVNFTVNNTU6yQR81Az/j4e+09kjx2WOKAPGj6MfaRyJPWUR+QhtOSWWtrI84DHz40T1BPbsx5KZsm0pJEDotzLhhJx9Bkgn4PMCMV3pd4mO4+kogR7rKHCWh6VyZc0iGt5TiUVKd1686wnl6Duy25F4buZEUogEORc0omLbgBAdl4ZACDIFw3eyMkvT820b1CwcNaonLxSuxTMq/nlZjM3cWQfyJknDYllaCrv0pUAL4/KOiSyBVUN2NBDcsmhzkjSWI8ibQPLogCU4BzgY0O863WmmnqDmEo1LDEtK/9CXk1SlLfZaAzwUV0RR2/Y0szsfZbladoayacAquJnaGQybt53avG04cAIJDs0LNi/uATFlIDRDBikoSsffg8A8N3/HhESmxq1Q6k/j1rHgxAjlEDo9iB1k+qNBAb0jRnQN+aZB5cumj16YL/Ygf1iLeoJzqdbg+wQQgWDBDK/sHTN1pR5o2LlliZSTyGTiUb/Uraj3tDAjz5xIQoFTdOUkH6P+OKNe8cP78Pw5iH9Y6aO66/298vOKSkoEv0JLSO/GMU2IATBQT4B/t4B/qKPDvcqZVnxp9YbDOmXC7EdKr4tPLJDgZJFzUcsdujKh99zbYcKFqjlnVwweYAsx5lAIHQrQiidTsqHF/5lOf5ChiiR0kZjpKVfMfNMiK+H3mCODPH5/Ncj8kcZmqJp5OTkkZIClO0pRwjlcxxvZlnA86xgDEIICgsKsY0bHROZJVieNqOUZE93k+mj+1zMLQeUjfRHhqHRcsKoUXEPdobi5FBU2SkWKVUCToULPbEdunlP6sqH33Nhh9on0juG4wkEQvdA11BvJ0YUhPFRoUZKqVSpeJ5XKmiWZRma8vRQKhga8FxpZZ2xvgYfHB8dejG3IjwyojCvYPyQuD8P2giKghECKtCy5pc5QXEiqeCLRHdYYSGvUtAcx6ek5o1Kim5o0CN919D7UHgK+EciB2tjBaKugRDWG1hPD3rUkERfHy+Dia3Vma4WV2sg8hjgnH95QMlWQwOKSivDQwJwoSdgVDj8hZfzTy4Z4KihzH3XjStAMTjo1AjtuplMBAKhSSAEgwbEcTxPU5SZZSkKKmjaZGZVCppFPUF5DxWTlS0KaFwUElAhEAT/PJAWHRuZdwm1I8EYlZbhGSh6jxFdq7xHBA/ApQoINJE8ZdAbtRSEKgVqaj68XyQPeI2H6J+FgDfSUmJMS3qWQAhD/D3r9fpBfaJQRz4hkSA21Kem3nDxUh62Q1nB2wBhoxraKy7g7Pm8IX2jl0wfvmnvKfxdgKJPDpdEffYbnpzHSzfJE0rUk0DoZoT42w+abtDp9XpDQ4POaDDqdYbauoaq6rri8urSiurSypqa2gbpSG0NyqKnKaqqVj8wQZTLihrbLk8CuTk2g+GEGXRIesaOHWEym/E9k5mjKeQblQfaJ44akBiFeus5w61SeygExyAAWVdLLuWVXc4ryy2qqKiqMRgMEcJCHtvdFjesJcFe8IcCClmpSEOL64cMijibmQfM5iUzB0ieU8eeqtR9t0x146oIBEK3wEGE9hxJFbOLpBs+EMKz6bkZlwqkI08LVZvHT6YbjKayqjpjnZAlbTneVJNH6QpBfYGpOjcmmOHrrU8UnK0cy3L7DxwTBiuhVbyJ5ViWr6jRD0wIH9hblGOeY73VzW7QYadqHqhVCky5kHPigugGFUYuQyVDR4YHSQElaTyyVUMByBKLlAIApxI11Egvmz1Q0lC7gJKTa63xCCW2J4HQQ5g2PH73YWHRaWmOpNHguW6WniK2cDxyaKobkNz4cXUMLY7rUHqbKCHfk+U47GRU8mJepBlkmnlOpVTUmvSZRg3n4YEdkWaW0xuQFgMADqWVJgYyDEUJS/tWJVTKp4CkWDRUTqAPStXHyaG484hUpJQYH5B1uSjRkmCPNFRYyy+bPfC3necZmtp5JFXuDKXSswpcV68SCITuDWfQoZtR+Nekr6uuRreamrqamoZaa1U0Z0IrXOw9dMxfp1ErJggptMYXkkFtbF0IoVrFUBTs490QpaqJ1dTGamqrr14IZypV9QVYjgEAB05knkq1+lXdx1HEBveN1NfX2d3wV0J5dQNUKMXEJpkdqmdZUUMtCfZyO/S62YPwt4mgoaIdyvRPjMy/WujGEFECgdA9ufPmWajRHAUNRuSgxDuNJpbluPKqhstXUOwFADBwYN8z6blYJvSeEYJXtLKCRT5QCEC5AckriqIIWfQ8D5S4mR0AJoUP4IGZ8rlUDxt0xigKBcd5Hvj2Glh8pDhcOBUAKPOUM9vPGXZflOyaLg/q12tQv1jcU8pkZg1G1mRmzSzXYOSyciuAqUFKsJfboTjBvjE7dMlMlNhk0VBkh9pMd5LV8hMIhB4BrfYoKKkqLNHmF1WWVtSUlFfjW6W2rrqmgWNRdTzGUyUOA3fk1htm4A0pIiTXMsEyRYmhnNAniRfUmeP5c5kFdgI5YGA//5heLf5Z5PKVX1yZX1xVUFxFU5RaqfDxVAX6aYL9PXuFek8ZFj18QC+oEKpXBTtUauC0ed8pjla4sENl0+iQHUp99nNyiy+XQCB0LYxGs9FkRv/iDZO5MZsJL8Jx7SNGo1Z8+sqdo4f3z88t+GNXyh+7Uly9koPpiIM2OIsIuwEcrcv4sCbaANGy63FEZ3JSCCQoaWVBCRLTolJtYam2vLJWrzf0iw9L7B3lGFMyGvW4WB5raFFplY2Gyorldx5JZe5bPjFf6M5CIHTR5oRoTAgthh3c7E9IhoVI7EHpja6ICBJFbf/xjOsXjv914+7nHr4B79mx7zTeUKmU1jxQCwpLHqhRGebn51tYpWW8lYaKXMADpYJGWaYOr1Vb7yQpys21vMHMFdUaNMKyG80xPnTB8ZhZEwda4/KoNBUqNV7GhjpcpITT5nFA6crVkv7xEYnxKByPGvWXVkaEBAwZFCEVy+O1PBnpQeg+zQmb7E9IcGT8wJhDp7JcvjPe8iDSI3fNE1Lg+Z+2nvZVOpe1vNy86Bihmkigtk5MJuUBMLMsTaFyTwjggBi/pOixJpbLLqoFoJRSoGqoln1GRpYrqjXmaPVSkhG+WmTzKoSkgsZVNTzQSyxSEhIMnBYpRYQEFJZURoQGSIWeeCoyEVACoUcDAbhlyUSGpswsZzZz9TqjmUMp7jqD2WBiSyvr9A24Cz0f2zfRaOLW77kwOClOyTADk+LzssWI+Vc/bWNr6ymvSGHsBx8dEy2tiPG622QyQQp1T45MHEbVXIY8ZDlOq63+41CWWsUM6If8npzJsP/UZd+ISCeX6BKW48vqTVeqdAnDBhhRS1P0hKXzx7McbzCalUpFdZ2BEwrwK7QNVbW68gIxLIYpKDaIxfLCQr6xQs+IUBsNhYxq7uR+REAJhO7p33DTTcGjRp/qr37Zg+/ec+O0mjodJ9bFIwHS1dfi0Hygr4aCYPigBIqCw+L8LuSJJZ4Sppq8PrHBl/MreAAiQ/20lhgM1BUzSsY7NKEoJ61MmxcR4MmyXH5pnYnlkvr1UjDUsHj/vcfRpUhmo/ug1vd6c06VvqLB1Fu2389LLY118rIk54cHol7R3OBoncHE87zRxNI0Va8zamv1dVW4jRMcOyju2PkrrjQ03BM1bTICJaMhAurqt5B41ghd/bca/w5j17DaaNSoUY5RqAIlGHnzyOHo6aXRFWUsmTkSP6usslZvQLlEnKAv9ToDNgBjwgMbzGaaprRlJRCC7fn5x9PyR/WxeBxRwXs4DeGVKkh5ochMYR0v9QONHzCmLOeM2lxKozZ3HCs0BsUWn7a02GRm95SLYZhJU0ZfyGle/40GE3tVqy+tN0oZnQgIKqvrrddmsWKxh+CPncfkZ1ixbKrOYEoRBRQcPXflcl5p75iQRjW0CGno2UyU2MTEhAb0wCDSUP9a+S8WgdBFadLMlI+u8dYb/LzUKEnTC9WbmyGSuM2H8TLcaRNjRESoL/oPD86lXgY3TM4vb1Ap0aKbzkJdmSVoVNYu6wEq2y4sLFYCeCo1LyTQi+Wg0STWRUIICyv1ahVT3SA6STnWPg/U9VreaObyawwFNQajQzbBH3tOAQAWzRgufx6EQK1S3LgYDZfH8Kj1MsvQcMSQxBNnsniA+ozER4fYFSk5aihODmVyC23ehR7CmSpvd2YikdAEodszZmDssTOoiVxjYPXBnY0gBAsn9UUdSSDYbHsYYyjmeT4xNvhyXgUaKO/vJa3wlaYS9Fwx0s0bTKzeKKaX3rloOMvyKhXz9trjlLc3Y25GESfL8aX1plytvgFlLzmPPmEZdZ+wAG/UuBQn2Ft62osJ9mZ0zVk5KMEeeUI5MGRQBFnCuyLFFDVKkd+sD4BA6FpwHD9/5hgzy9XUGyAE/t4etQ0Gg4mtqTfkFGmlwRsoCEN7chyfW2k6k5ZmdFA6KOTQmy0LaWk0JgZ3P+I4tH43s6J9ynH86Sta1N6pFNm/bG3tgbM5fmFWt4ALeB5oketTV603O43dR8f3atAZy/Ns+kI5BTLWBnzFlbWhAd7YmJ48IuHAySyceao3GNQqFS5SupxTEh8TKrwpKiKgBEKPJkJoUKSgoFcg6gIHIPBQqDme99MoFBRfWSWkkQMUW+8fG4BlMSg8gqZgdK+YXEsUXuwRB4FayTBoaAevlmmLVDpvMnMmE6ukKIWgSjQFN29H7TSvWzABXEXONN6hlLMx6gXXZ1k9KmlyfBQCMLRPCJqiPDiK53lcaOTlqeF4Xqc3CZ1O+boGY4PeWFNnYFmuTmesazBAABUKqri4IDTYn6Gpw6cvDesfcyYjj6KgOM5T0ND4Xn6ShqIfY8thJ5PsCQRCzwCVcAut2pF5yLLoX57nFQzlrUHFjhi90eznrU7PKRMT4B3ckf1Gz0HFRbiPCIR2JUNY5oxm1mTmJOP0wx928UYdb9QlRQtuVgCCHXOYnGEwcwXVhsIag9FSgumIgqHVSkaloMNDA6MjgqLCg37989DvWw6rFMBDAT1VdLCvKjrEa2DvoEEJwSP6hY3oFzYgPigm1Du6d5xZqUE/JQTnLuYnxoZZu40YDLhICWlobgmq1Pr5D5uAFIFA6FFcznce9UbOSqN8nQ5LyrUsyw3uF8NyvJ836ggnWaAQgHNnzgJl2IUCAGgUv84uB9J4d9TjTlA6/C8v2J4AgGVLZuC1fFoeCkhwPB8X5pNVWv/Y3+c4XA60dX0ac6ux69M5JjObnWMJiwkbib3Crp8/Vn4MRVlL+5UKWqVkDEZzbb1BSBUA1XWcNzBm5Vdm5VdSli+DT349LD3l0dsmXMopYV55+tZHXvjR1RtMIBC6BZBncVf5NANq4hkojDWuqG4Y3B/N1xSxWRALLUEF9VKrmDq9WcHQ6CY8lltY7miMzpo1edeuA3ava1KFmSmzT5Bfg86gVnlQ5hKlUMyUGBfB83yArwZCGBrk+9eR095eKgCs6UeOmMzmSh3K+tQ24voEAFzMdxIYz5L0tHEOnLIZ76ymzDRDs43FtYw0oE1M2sUeHSQZpchPMaG0NQKh2zOwT2x0iA/KAw1AxYteQuNkvYH9aUujy9DIMH88J06toHPzi9/86FeNWvHfZ+/65oc/NSqFfJ1uNrMMQ+/cuR8N5sRV5Rawzo0dM3xv8tGG+lp/LxSk4TlWrWKkaR8AgCo9a6i1zmdzhGP54pLKq1pdeb1JXunklLMXnPQVHTIwzsVTFByKmNGMTdMpyfy04+21qA0TCSIRCD2F81l5F3PQn3zvSDQMPcgPp7rDO5ZNxC3osH/TaGK19cb80pqC8loADNgEpWmK0gQAPTLu1vyyQzontGxQ+iJAwYTooPxiLc/zIUE+FZahHrChAJq4o8fQ6E2KotC8T4GHnvnio1f/jrePHjrJ87zJxLoQpdp6/YXCS4U1RheuT4kZ4wfvPSz2OsHwkHaqqgCAkYMTzmWXmik1w6GLdB8ioARCDwJPGJbvOX+ldGA/cQmPjUEPFYqk+3kqAn1UlwqqgBn3HobhgZqsSscTAijNhUedN1HbeZ4Xh25KB7EcV1uVq1Ii7WRob51Q7AQAePzlb4b3jdCokFeAYs1XcovEmcZOrhwUldVculKrQyLrAvGniwnxWrlskpgDwDvzSvCo9bOHWnXsTOaBlExUkjRnBGoxdfD87LGJO49ZG6zMmjgIAPD7bicppa6a6xEIhO6GkMoutZLjAZg6sk9RWbXdrbyqtqqmAfJcfIQ/fh4EID7Sv5FTglFDEoVKJLDtEFIiCG0aiaJ+yhT08VQxNOrUoWCQ1OIGo/Lz6AwmnyD7nnjSq7MsW1CsrW9otN8dACD1UlFGjrWuUmo5ikfS4xsFAUXhfyFDQ5PJ2KAX1Xz9jpO6eiGbyta7uuvQeZO+fsn0YY6vSCxQAqFnIXc7BgU610SsPgxN1TYYRH8mBEajsyRNHj10/GxWkJdq6Yxh//5wK94td4/ijsUKhoYs1zc2kGV5p27O3gnR/tW6rBIn1dU8ANW1+qKyap639qZzZNkMpHFnM/PzyrSFpWKHwyY5l40KpTDJJ9Eav6SqHrW2Q7a69UqLiktHJ9lnWREBJRB6BDGefJXepkQd2VaNd1OeNX4ARQmqZ9mjZJwsWHkUPuIUwkMb95we0z985viBG/ac3puSEyebkEzTVO8gFLniOF5nNDkNAJlNJo3K+ZrYbGZzCyura3RC+X6jnMlAYXQ02c5tfjpYbndKHoB6HUq2BygTVMy4Kq1E2QuOEAElELo/w4ORZAVpggb1tZk4NDsIlSFJ7Dx8zlFbw4PRSDgAwOGT9hU3vLCfFgYUcxzqgAcA2H3kwkv/vA0A8NzjL6O2cnF95MfzPBrbaXDmx6QglC/85c8qr6q/nFtuMnPWzH5nCOPswJhhiXm7Ulx8N8gJDOinKxVngmIgSsKnIIDIXyxkILjoR8rECYVcBAKhJ3A+08mcdInwIB8newWV9FBpZo9D8zC2H7KKrMbLW+OF2vEUlJRRkJe6b7zz8brGXgK7R6UhbnbYVdBjjCZzVk5paUWtTTDI+dOR+Xkpr2Jov3gXh0nfE8V6L6DPZ5gQHgBvX39zJXLg4oAYWr0LngbX3ZyZK7lN55cSSLoooSvShq1wJOWNDPYpkdazRbhS3jk4DB8REuz4kJnl1Gqv9GybTh+7BZvR17aZCA9ARVX9lbwK27Kopqmvr7/sLKNe/j1RnI903Gw2KFUe2opiX59oHKIyB4+GWZuQ7dlUM3yyhCcQuj+nymA/b1R9JLr2KGji0Jxhfw0j5bzbJr93hM0bGYyETJLjcCG9X+wMaoFluZz8yppanamhRuHh7sCr+npX5UyOGA02wf2Tx/8CjE0+QCkQk6uCQsKp0r+k/URACYQegTAIEyXo4DgSSiWC0Mjx1Q2m0GBvANCQovJqvZpGIsvzwMQCbzWFGhoBUG/gaIj7gUKjmff0UmkgaxVcHj0FjxJqc6prdVcLKnA30iYpqkBarFaLkfpqA2oi5atGHU7wlwfeaOyrguNxoxPcPcr5j1NeWgRAjHSXCCiB0CPA7eZwXg4PgKeHEkIkmjoj5+mh5DiUVV7TYPJU0SzHQQAbjKxGaEkHIag36DVqBUWhmWsm1uztqSosqw0N8FSpVJWVNUjdeDAwqXd21mUkuII4VenMOOWnpB71IYYUrRAmwwdqxAR7mZyJT3HQNSR/eYVVVdUNTG25iXaVwOTIpbwyjVqBBsk1mIL9NLiPCQdBhVbnoUQ9o3CLUgkvT0+e49FPDoU8UOFxecqXU4iAEgg9AyGuLG6goJBCsEcpjZpjGDSlHQqq6qEQIicQMgrOQ4FsNyw03kLhPMtxJhb09qz10tcFe/FenmwF18Bz6Jy9VJWUr1CzBCDH81WMMYBDXktfpQ4CwAGoomCwSneyJhQCqK03BPqoeWEKJs/zVXVGjQJto4xRKdDOg7oG45X8CpOZZQBQ+rqKC9lxOb8MQvQzouW83uyhYrBMI4+qVie06UNfHrOH+vx1/grHcbW8n0Uq8Y8vxqtwUzu72i05TEIvt/o/EwiELo3U1RiLgbDiRpLh56VGlqBYxKlQMaIlSDOMEumeKCVqFcMjBypt5iBFIWuOoWlPD0UtQ2OB8dKgQk0sPXLTTqVgekWFXsotViuoOujd11fH83wNYwzw5pCCc+i1KoGuEqLBwhBafaAmM5dfVCUE30G9iVe78TPyPB8XGRwaHYoEVKhJFTqbIPcFlkFcYmSxuPkgPw0aZQyAN9SCem2An2eFtp7yjrbYxGKbfRevyGTnFP19+cQeOFeOcK04B2JwdJj0wbqGCKKJNny9VKjYSCA0wEtX34DvBfpoqrV1APADE8KyC6rRoldIUQr205QBLyY4qAzwfr6hFaViGGpM3IDybBS6wU+/WKuNNCOPJO0d3DskUVsMdBD5SXkecIAvNTTwPl5IkIV8oezayugQBSWIb4MwLcloZisqUe6nwYDue6kV2sI0r4ikJn8oPNkOa55CsKxxWpJgSgqJnbL9SoVQU2oJ7+NncbWyqfFCc2hkzNbrcBFCgyZR/opkCU8g9GjUShqLFBJNP01+fQM2vrw0qurqOlz5iVMsgdCnzlujrBaPhwoFRQvaBADw8VSLa2DKpqkow1C+XqrwIO9qbR225xgIPTVKCjtlLalCKgUNIVr7AzNqmVxcWnO1oLK8qg4rnYdaUW9wK44kHwiKe+NL2wCAwYkRmTnlQmkpxQNeo1LI2+KZbLNTpRCZxQwXrqQeVxMIXz9I/wkEQg+jV4S1fEYeJFErGdc57XZQwpNRYw7UEdlH1r3DeoxKwfSODh7YpxdNoZYiSDch9Bca2ls8B8KphHPhuslKbX3GpeKrBZUNOrH6XsnQ/j7uLOLtGZToxEUpTB0R/LwMJR+pJKWaCl8WaENwR/DjB8dyPIposUKoDSczYJwI6M2jAu+8ccWdy1e04HIJBEIXphHN7BPtJ8mM1M3TgqCagoYCZ1AUVCqYuCjk4pQI8JXmfVjOYgEt/3NKC0qqG/RGqWAJQugjRLHcJy7SSQI/mt0UGyC/dLm7Fr+c5ALAljUE0Gy2XIZ1t4j1CydizN8L//pc3I6IAAA89cg/AQCvvfd2s66bQCC0FXGxLTG7rBTKzlMvhGcklXMmdywrFwen0y5RV7pyy6xjG2QnHBAfmporFilBCL75Nfmu66eilvgJYReyi+1MXUcuXS034UEaMrFCT2les+OmQaeXvQRrNouxI5zEJMo60HgoqgX3gwwh7wryTOjIe5HzWNgXvvhplGrvYz00NfXCY3//GwDgrc+/auNrJxC6I62VvHbGZs6Gg4ZyTearQxR4acHrppy3aUSSfPJq3xi/xg6uqdUplYxofqL+oTTP8Y6T6FsPcrnKkaunEBkTiuuFllTO7GsIIJObky191eCzrTmSPH3y+Da/VgKhc9LJJa9tcV2v6WLOUKOWK3RqqoKBCSFVGW712RiUEJ6ZU2FzSuES8cIZQqhSKlpje/aVrdntcGhOgl5Z3Cm6SXEArfE8UDv1dO9lCIROJnnytSrBGVpPa9/LM9WyinO9tfFwdq24P0Gj2ne5OMG2Y3yjQPTP/77Z5uIQeZ3noMTwzFyti4MZ1E1O1B1kACqoxgTUbDIxCvcu0g0LFOmkJdruMn1edqnif63nsStwspyXQGgF9rom6B0Ru05LdgPycmYLsy6yU2V5kQBkZ1hmxQGQfeqStN3HU+3C0DIJ5mRjpljfGLvG+KjBKN7ieV5ZX8VBaDCZ7RTNRZFlpcajsgploWaftWllkn32ivxbAcXUbRFdn+5op1VArZophp0IBKJuhGZxsd5qJWan5QcVZuPtrBpk1WafuYz+PY/612WfzaGDPLIbDAmNBNZRPVJdrUKFTGY8vglVeVKw5PgWXAElihcF+00ec/7AX3ZG6J9nxJd2Cv5WELezi2wes4TdG/NLOBdQskDvHhDJI3RJO/ecjZGoQsIoM+J4cEnM22c9hgzQnbX2mU8YPRT1xOubUJCZ3ZqFvOylZFate3akPJlAWP3bqunnn32+8t6HPvjy49ZfHMERInkEgiNiDlNTiB5bjYfjyr1ltMBXyXz92Qd3/f2hb794HwBwz30rAQCTJw41moWaVirg9rtXcyy3+q5VH33zKehepJiiWtCvm0gegdAmsFoKuTUdUPmL8Xf38Rg2SHf6fOuNUKl0032Yv616eO/uzVg6LfvQZDyIujQ378fonLiSPHdCGSS8SyC0AljL3XczyqLHGE1sxpWyk2n5tcYqBgAzdKJ6xcU1AX60ydCA3aBOUQzsJ7/rMWxQKz+lhOjAKwWVNlfejCW81KxUTCW9lhArj0DoYMKV/leLq5SWjElDOctC+wmYmkC2vtgAKavkUbwwJkR2pCbESXX4p2v2mc2cCShDAryiQn2LK2qr63ScoDkMi9ygjjJaqWUD/FqSsd9i3Kn9d8S2oMpGPZtxOiJ5BEKLuX/BrMYe+mTLLrs9xZZBaRqgHDM4zvEp+85lRnn5JsSEoPkTlvVTPx+/5LQsVGeo1BiN5uFJsQ7P4wf38ps6egDSu92HkU3FG739UbGQVismhzZUIEXjOTGKDSmFXDop3shBZUOpw7LVpDdDBY+0xlhZUVlSUSt/0Owgo1VVSJTlNJRxmuB2b3vUslg646Z6EokkENzk/pkTHHd+IqiSo1Z+tmbX/uMosjxltJNml/cvmCnI6G67/RrQ6ID0BaMHnk3Ly85FHX7lxZJTkxKHDo778ddDSgWTmVM0cUz/shL7bPZk4UpWCdf/0YZD0KRXqxR33zGtvKr+ZHpeVm6ZEaA1tdpYroMauZKiJnVKDfL7mVjIGeTCWm9WezJ6AHijcM0KgPTRZHv9jjJqR0MZ0uV2lVGpr52zFCbeLQuUJIASCK0n+QQaLz51ZF/5zvtnTvhk9+FtB84AAOZNHiLtv2/FzP3H0wAA+N/GZHTO5P5L//WBtKcBGDVA+de5K3Ij1NvH9/k3PpPuPnTfnTXpudLdG+9Ypdc1PPrIcJ2uPuty/oHkXRPH9BcWyw2swd7oAwCsvmEiAGDd7jPrthxFTdoWjJ87vr9Yq16v+2HNH3hbUlIDi/6FlIJ3WPvXm1GYwZPW8Ug6FU3KqFMNxTIKnTlA2wTceLS5IAGdOQd9y8mAX3300QOPPqnxIN1CCV2AVTPHb/hezJ1eNdO+jcOnu49cKxl11FAAwLa/Lm47cFauoVg0XcvojgPpG998SK6hPEAz4iQNpSjq+Tc+O5S8fdP673iej0wa/cFn33739rN7N/2EDx8zcbr8hCU55w/9lW40iQlDgxLxCDjLuS3cNBNd57rdZ9dsObJigfje+nh6rL7nRuxh8JCNhtdBDRDE1C8kBDjUwdezYr6RghaD7y5lVA8AOr6kxGbJLzSdY03tYJDaNxZxDzhy1HgK6O9e9RjqQe1lXxhg1onuD5Zjf92BP4nuAy6WKI9IaPKYJg8jtPk77whdB+69cbLdzk+3HlDXQ69q5OADANxwxx2OT+x4DWUq6hN7Iz+jnYZitv0ltiaSyyjSo283JiX1ke46tUaTj57bdy7VdiHPjxkcP3/Jiulzljz2wM3SkZMW3LxkwdKVk+O1mpj5i6+7d9Xqx1dZH8XsP3YhINBaRollFHtCUV+hbcdXzBstPbp291nAgxULxzt11HKCklIQ1gENBfgYTiwAzWUaHQYnKamjjKKeoT5QodJUFFeYodJHyVIQ4npQOXxAo64MN6nIEitTY8P98y0+DWl4FM6rnzU2IbfIefE+/PAzqyz6eVkV3SxL0WKF6XpEQFv5UXVjivPLpvYX35/kdPErZ9pg8e9Qzr5zqf1CgplydExwv5FoRFIWysYdnBhlc5gQ8ZATFhVMCynSjhr67fqDrgX02mqoaxm101BJRqvrdDize9H04fJHk4+ek7b3nUvVCCtiAMAD96xccedqLKB7jqbNGIfEt+zCcQBAWrV687Zd1dXa/3t4Vf+4MMcrsZPRh26dLX9ULqNrd50FgHfUUCB31BYjrQniyvG9mjD0KWvLbTKE7FDReoaRLduFnkgqjVKh0miLSxlGYW2S1G4CGhPmV1Ba7VxAxyTkCj+UawHl/bxoOwFlWVbyjBIBBZ2VVUKowZFPHYIPEoG0141zbf4y8e+Nj0bxn/V7hoaEjx1kH+H9dA/SoN6efrPGJjkNPljvChrqy3jOnoi8ZuWVOqcCag5KCA+yxjlcK6kkoE41dMOXX7sW0E6ooQvnjVz9ws8mM7d4+jC7hz7Zspuq1IVHRloG+oBF04Y7Cijmr3OiPbv/wFGKojmOO3fq6F333j99XFK5IKAXON+Th05XVlYEBIht4f+1+hazydSYhibERgDAz5tozaxcsw2dRyajyJPb374DCDiaV+C4dCuhRcn29FDpjU4y5yEKHPGMQok1kqLQdA8zy6rQ8GFQX1Vno63tJqBRob5FZTXWM8sEdObo3nkl1p5VcuROBLj+96026slZ1ZPQkayaOWHVzAn3W2545/0LZtrdUOvvkMgBIZHNFVYM73DDPLF8BgDg2Pkrdsdvefsf+Fk7j6XF97GROTuwNWpmua37U7fuT71+xV1vffwjvqUcOJl5yVoAVlSuLSpH3+0Tps3Zue8IVk+spJhpSYnTktAcxOL8MsiKsY4vfjnQ2Et/u+mokx9MuK2aMX7VjA5tdGsO9EzXlmddQh009gmRJTl/bjvx0Qu3AAA27z1t99D9C2bed/uiAl1lYb745/3nvlONvcqYwX0iArzRkn/yuHtuW5qdlTF05ITTp8+9/fFamrFGif/7/n9HDUmaP2vSurVr3vzo57raOvltxIBeFSZdZQV2aPKXc4sryiuk2+xRvZHWSHsqqirEI5uGDvVyUaCJxyLRgBPTMFH3YooSxn82SevV0+Zsjhau5SpcOEdtlvC7dm1fvmw+FlC8bJdDLFDQ/uAMEjs+3X04zgel9c2ZNFi+f9HcwU+8vA5vTxolRkjdsUMDKE8I4XIHCxTILFB819EOBYKAqpWKO2+ehv7SLhY4WqASKRkFX339bWJin8/+90ZWxgWVWv3kC297+/iunIz8Yuer0B9A/8QYAMBPv+0EADz2dxSaaIz0Gi1TbeBoFU40+bvMDpUs0DpfpLZ3Lh7n4jzYlO5ImIr6BIspOs3BFN18OMNDpZg5YSC+aycen33/R2R0JITwytXCScN7n8tAcuzI5bwSM88nRIvT4rLzq44dPa5Sq1dOjr/A+UqHUbUGMw9Pn0v9/rN3Dyej91ziRJ4Y94lQqkyAvnm2k18PzPozmWy5YeKweHcs0NYED9QmqEcppO3F5YtmX1iCtyOCfeQ5qkJjEbGv3YzRvfMbsUCdTCZxqp7dDGyJbPgBfcZ2VolSwfxve6MGTrsyICDk4KmsScPtf+FWzRy/4zi61B0Hz8k19I/t52aOG7T76HkAwMGUdKcyOmdS3zmT+i178kM3r6GmAa3szpQWSRrqyOyxSTuPpn363U4egDmCr60xRvWLTEzsM2/urKR4NDVGr9e/8OTqtz/+8ebVT6396DUoTBZLz8o9cTbt4w/ff+DBfxSVa40mNjbcOoCMd/h2+WLDXp5BGvrFL/vvvXGK3SveuXzSN+sPfLPpyF1LGjU2V80Y38Eaag70zL50NSEeaeiHP+2YOKSX/NEYX3jowtXM7KuOkoSCM4EeeQ2VsLwBAFeVOSmnL0yeNFa+57blc9b/sd/+bN6oUBv9nqsarWDmQnzkr7T+TKYTuzJIJZfLrsilizb+BGfTPaTHGj2JvYAaDIZuY2yumj85+dgFu53J5zKVJlpuMTlaT1KxB/oipex3hkU5n/bXelIrS5MCQg6cyp7soKFzRidgDbXDYDanlhbQlfp+/dAi62BKup2G7jiIfvtXLZjpwh/qFKyhx85fcWqEXm7QKioboqIidhxNnTPOSbBIzpQpU7du+XPUYHSFAIB/PnBb+QWkX1MHD0g+l/ruJ58VFBZodcjBmXkpLy4m4mpRBU5RzBWywe249waUjvP5xkONaehdyyd//cuBrzcd5nnwt6VOLHoAwPKhKNi9/ozNoJ52xRzomVFdzpY47xWkYoBlNrtz+CCNJGrZlU4CMtpqbfL+I6Mmj8B3KU+aCkBfWjmVBuAHTh46PWKijaf14zU/1dejnkGN4VQ3Oxh9e5qfCh655ut5n5jefevqGjjOmjNrh6sl/PybGi0j6zAiNYGLptn70fEf/PSE+D69wuRBgDgPnzmWxY58v53woS9JIzVpJFrQ2eyk4MEUFJcYPziuPPMkksi+4u+cBEPTuy+Ivz0DKdF0l9ZB7SegAICkALRUdxRQDNZQu4U8fq+whlbX6SEACxzeTPmRliU8+q1YPhdFSO1Q0NQHm5Pxdj8/tCScOqKPk1PtOSJoaDjgQVZO4cAENMlV4viFnNEDkZ01dc7iRTfcDgDYvn3bqaO7K8rQigkLaNDA8Sq1+rV3vhwxcdh1i69/+olnRkwcBmv1/RORpTZ4gCjcxUViPFdKr5H4YuNhk9Fw09xh+zaKed3Tli7CGxv3pOl1DTfPbvSt6GABlWBL6uws0KPF4g84Lhq5s11bdna/5HJOHkK+VKPRePL0iXFjkAH+8r8e3L3l9zq/kJOHTnMcN2HmOE+N564/9tTU1ExfYG3wYUeb/IYHdfr8v9xM64Kd41GC1NQZs3ieb2jQZ53cIe94P31UbylA3zwBXbXAPtyJ8VJRb/2G/saceuXx+ijBO3DmaCfZGxI0TX2081BjAuqhZN79ffv0BLSokTQUs+MwsisdZVT+u0UbkekoF1BGofjXc2/7+gXSNP3G66+WXT4jF9Cb71o9YuwkCKk/N/yQvPMPSUM7WECRhvoLGjqiUQ01s/yCqUOcKqOyxhwaFgogWDC1CQ0NpDWAB8vnWQX00zXJq1ZMtRNQUUN5MHWkcw0VvtJ8L+agoutBDhoKABg9sJdCoVz9xIvRvcSfqKS48IkbUZVL0MDxb3/8478fuXvnidNLFy179l/PSVYSrNWzvHUV1TcGvedD+kQ7XsOBC7lOBXTtnyfQx9r5BLSVuBBQ9OuaNOiBv6+Oiog8c/7scy8/46VFJnydH/qNeuW5V+fNRhGODz/74JsfRJexI2316x3UpQRUmO7hxNCcOhNldEEAdFeRUjlCuQ7UOsVL8KI8dt3U4WFO4r8LZX+TrmEtY0/Q2x0ohurswEkbF3OKL+ZYKxvmTBg4Z8JALKMSq2aOnznIqtes0ibndvkt9775/g9qteZUyqHM9LNP/vvpt79YLz36+kc/jhw35YlVK/71wC2Llt/x2PNvmdlr5gVOq0K/9AdO2i/YF84dudCZwYjBn6PRR/TJbE0+4+LIVQtm1lWjjI31205YEvGsvz0m2eeCEB5JPiFqTbmM+ULW9xWd+B1zPruwrr5OuiXFIeu1rr6uSlv5ylMPjR4xdNjQwe/+56XQsIiPtyBb6d8vvpN6/sylyzkDIiLsXFC8t5rycT7vQY6LUbd0iCfoeVxIO//AI6sW37jouZefsXvomZeeHjFx2IiJw1yoZ48ipi9KXbDgPO6fvHtn8u6dF86culSlcXqDm79/U7A4bOJxTZqfyIfy095+cdGjBzi2dQHjxg9c+fLnCd4B992O/qovZbrqW/zpniPBDDLurp8zyqkRSpUht9HUceK61c4atflpBW9mclq24xL+7Y/XfvDWczmXRRU4cjL76F8ndv/+ww/vPh/Sf9R/P1v3z3uX44f8A4KeefOTf967nKapPRcudrwFajFC+ckj7P0PqN7ZQ7l2b6qjBSq3LqM9A4WJrCAywFWr08z8yvLyyrGDUBB87Ii+7/+4f0x/8b39q8DG+ciVoYK0G2eiCQpyGkxgaxp6S4vzy7galGM0eiA6mwSrCv7ym+/vu2UeCvLmFnN1bGJM0JLrV9y68t6Vk+O/O4Dm5Nhh56pDr15jkCzQQ4WlXdTe6RgL1A65BeomPccCtbVDnVugmOAgMbfBEWb2RGS1OQpoqMr52HvIs/V69rtfDzY0Xil/9MiF1bMn7jiKqnrzS6y5qU5ZPXviRzsPYQ11ChfsJZmiU8cNxqaoCxmdmpQgaejBE1mShkrqibIORya89+47hcfRuo/jOEk9xdoBceOa9ZNOqypN8g8+cDLLUUNrdUa1SrHnaFqQZ6O5cnn1FWw5CgZGBohxG6dUcNbZtsdOuooYUMEarKGujvFRYg2Vo+bR14/aQ6MXaoIpL/pSZVVEHLqqnErDyEnWRJmF8xa98NSLjuopnFmVpUW/RbWNqGePolkCFyR8/ZdHtPtXftfFi0K/WnWcj4tYkZNJxRYYvZFVK52kRyybaR+pkCOp5/HUq06NUIYC2bWVn36PDKJZY1y1TsE61WSwTS6jo4f1HzoMfa2dOZ3tQkNZJYc9objVytsfr92y8ee9OzfjlIV1a3/qo7JPBk7oN/D+fz6/848N+O6MgX1K0lLAtSCtqowrrfdVOXljMrOQwzFoqJPguAQdpMIa2ixYjqMp59+LVLBmw9kmPIaUj/JErn0Pidz83Pe/+vVi9sXHnvonTVGf/u+z0JCw2SN62/W/4bnuMP7gWvHkjfarN8yX76E/kI0fLjl2wNIZ1MIbv1ybX+zOiRdVAymKNYuZEHW8D3AzjemAENh9YMHMj2VpLiFK2+fb8snWw9JpDc5qs5pk/a5Ty2dZrY/Vsyeu24uSGe3QOZwcy6iPl8e3a/bduWLa0GEJOr0pM91JavHUJKSwB89cxkboY6tXvPXRmgVLb1mw9Bax4PfBB2rObUdvVp2YWfLJT1twfZuvf4C08xpCNeLFowKVXIWT/mN20EGqI7lNDH2CAWrrat2HOmGJd7chy25eMmPqjP+88tbmX5C9X1NTPWn2RMpgHYHbmmZiPZnGRFNi7OSIL9+zbts9unHyEml76YOb2v76ujJe0GbdzNUbfFEWmbjmq6WsLhEx5iD/9nehnpC38dk3qZ6X6ip7ewbsOpYxa6yNEXrznOFrd1g1lBfy9jfsSLnBwQ3qNIFxZ8bF2f36bNmeMn2qvVfOjklD45NTMutqkZtj1R0LcWrClBnzb7p91Qcffpx++ugb/7hVOvj+Wxfgjc/WbMu9kr176+/gWiMluNhBBSq7UBrznuQ9dmtzx4jhlh1btuxAU78JHc/GD61iagfRViDkPlKWlRlEbapFNzSEskR6uVRdN8sqTArImnjLGp8Hn2wTzU/3ceFBkCg3VwcJbtCz51BFjZwZfRP3ZNr35tmZcZErrdu0By1DJg8V6zcKGveRKZRKkxFZbTzPJ+/ekrx7S79Box994vnGjp+96IbOIKAEQos5dqDwnkdW4I3G7NBmaWv3FlO+cSeSn6dNwRFeLAlV/NDmAVFDZXKngMg2PH0mA98dPthJXLi83ImVVNMgrjEv1VfGe/pXVNgfwwMg7dTWoYVzeVmFo3q6gApBdgxX6mqtjRaGnvTw0RPve+jf990uGpiYHzdvfPSJ53MqDcP7eL375fr7VsyTPwurLaGdQrckuNHe63en2HlCm6unGz9c0v00lIdAqClxhZRRj9VTPn2OOXdeFEfMtMT45BOZkmKOHpZYXmWt98oW8qUl0ipL+HJ9UrSrcBPS0JzCSzmFN85ynnmDmphk2zf+keNofsqhQryk1JZiIVbryOfrfrnvoX9//O3m0VOseZTH9h03CR7PAxnIC/zuVxsmzUbZ3fNmz4cQzr1+PvbKDSRd+dvhrx0HNxz3k8hGR9ICy7T7aWhggpjMXmnpa+cau9md9rXwLMvxvgpLNzxki63fdX5EotjcYVe6K6WTszMrx/qSwR58mbUjJGbGcJuEQYQ/035+vUmzJx7ceQjXuklMGSi+dyMmDjtx8JT06PLbricxjfazj6TghtPIhtxKInraMeD3/I1fUlw4Q7s9ATZDTUTKM1FffWOA85xOJwJ64LJV+ADgU85kDUsI4IWlsNO5yTBI3WR2C9bQa1s219BQP2LiMIVC0a9Pv/r6+qt5V1mWlYcy5DmJhA5eWsqRq6o8UtztfXCETkhQXyfVw3IYp5kufxU0o9qhC2Eymc6nOsmXIlzD4Eaz1o/yu0RP25ylD27qyUZoC3DSD7QH4iIGUg6CcVFaB1Rw9kzka/ZmhTXwn3rPlNE3fklpvbHvFOn9lCvpTxtQ250PfrTvnO8Cxz6QEt3pT6lTtLO7Vkjlutggaszp1rIRkj2TNvmrbm50eOmDm7pK5XXb0oYa6trdvHxqVLPUEzhrY+YmXUtee4QF6rrQ7cvf/onvHjtQ6PRIfBih89ukPQ1J9VqspG6G6ZpUz4fv/8fKW+/E23fff9fZ82fs1NPfz99oMrpu4dwV6c4C6mYsWL7H8TDHImICobNxbdMVTgrpK2kZaaVlJZMnTPn6k28MBsPfZ4mTETw9PQ/sQM00OY6jKIrn+e4UsO2eAurOF7KbyijpLIlgEAiOfPL+Z3ZNCJVK5dG9f8276Z5t675EiT07DlVUVsxeLPYdPnno9PH9J+QZ2V2aHp0mfs8jK+55ZMWxA4X41qznbvxwCYlXdgDHDhS6DhP1zCBS5yGxt311otFo5Hl+4e33S3vmXzdX2r7+1mU07Wo6Xteie1qgHeZ6636FGZ2BnQetFb1bU1DtxtilX4o9tG4b5tQx5yLm282iFp2Nhx978Icvf3r79XefeOYxqZfuyEnDpSCSXR+ZpH5NjCDsWhABdY77KYpEQx2dcW0SHcbSaUdzY8GE9iYtI+3XTRuuX3LD8f1oPEx9ff2sxTPwcF+nvPzsK7n5jc6/7HIQAXXFsQOFJArc+piG+3qKn9ikFUlR1NDBQxfNW1xRWfHLb+tKy5x34YIQKhQKI+kL08689t9XX/vvq/369P/6k288PT2P7DnGcdxdU53kk6UcQPmky27uPrn6REAJXSxGPHfWvFeffw1HdSGEd91+NwDgzim9HdsXnDh4Ki8/b+nNi9vw1QmNkXExffyMsQCAqMioTev++HJ3+j0z+8u/87B6Op3a0nXp0UEkQlfk1edf239o/4iJw0ZNHjFy0vBRk9FU6m+S7XN18Z8roV2BEJ48dNpD7SHfmV+Qn3xwn0JpHao6ZtTYlAMny8rLupl6EgFtmiaj8ySI1JEk9UsCAPzfk49IeziOW/1/D8g7Nr72whsnD50mK/cOgBes/k3CsBY5I4eN4nk+pxJ5QhcvWPLxu5/875P35y5FM9a7Gd3TAm2PvOKlD27CN7s92GfX2K3NL6OHk3ERdaq9Z+W98p3Hjh9dOVmcSgAAmDNzzt8funfCzHHX4gJ7HPc++LfAgMCTh04PGYQa/qpUqt9+/t3Ly+uB25DnhKKo5//9wrc/ffPdT9+C7kh3roV3jF3I82PcZGvKFZyQ0dySXgxJkWkBrr94fvjyJ2yHnj1/9rufvtl/aH9jldcnD512xwdKPqNWfiI+Pr6b1m328bbOUrvj3tuuHt2HupDs+CsqMsrxKS7W8l3r4+jOAurI8HDHaWZN5M20pi1Cl/tt6CQ0abn3SejzzhvvhYeF47scx903Z6BRmPRJBPSafCKOeGlRakSdn3WAZbf8k+lZUfjGJFIiOCj4u89/CA0JxXe11dqZC6fLD4iNjv3159+xx62mpnra/Knteb0E51zMvrjwhvk4iPHEo/+68bqbvtiV5jQQ785AQwKhxXRPH2jLGDNyzPaNO318fL745vNnXnr6l9/W+fn6nTh4SjoAQvjbmo3HT/w1YuKwxcsX+vj47t2SfE0vuccxfOjw6VNmSHd5nn/znTfwenDcrCU4akFoc8Kigt28gR5Gz7JAXfPxe58ajcaJM8fju9t2bn3zHRTPffiVT/73DCrs3fDjbwCABx5F2wVFBbMWzdj1x55rfdU9i1eeezU0JMypB82saomTmtBucwPXCO0m5nfv2VZEQG345bd1dm/Q2fNnKkuL8Hav2F679u6UHqqsqgQA/PfVtx5/+rEO+KgIAIC/PXD3nxu2fvvZ93fed4f0hrzy3Kuopnbtd+Qt6hjcLy0bOzmisTa73UNViYDacNvNt/+2+berudbJenfff5cURAIAnDlnX4s9boxosRI6gKLiorUb1t58w80nD502m80cxymVSpT7+dQ/jI3XXxM6WDrHyprtNlYMLU1g7dJKSgTUythpo4/u/eu3n38XxjuzGZnpD/zf/XV1dfhR7F8rLimxewfVKnVHfmCE/7735jsfvDVt8rQbr7tJrfbY9OfG3zb/6lll/7kAAJ5+8SmdroG8Y52WsYK22s1e7VrFKT0rjcmdbIzYmF5PPfbUyOHiNy3P83dNTcBlFfsvFPzr2cd379tt14u72yS1dfWkmZ6QN3NtaVmfrbFtN5els2krsUDtuZqbc9/Df8fbE8ZN/N9/P/gmOfvOKb3xnqjIJuZEEwgE1/XQrdHTztY9kgioSFBg0D8ffuzfzz8pf3cOHz105313fPvZ92oPDQBoCb9o3uJvf/xGfkxaRlpHfmA9gRaYhEEWP3V5BDEnu4yejm2RknYqDSUCKqLT62bPmPPntj8OHzssf4Nwk+2LRTXIOM27GhMdIz2E0+nfeBu1ViN0hgGrLg7o0pGKnmCZvvFLivszcjqPhhIBFamvrzebzf9768MHHr3/r5RjeKe/n/8PX/4kFAsiGb1r1cq9W5L/76F/vvPB22ha1k40azA1PTXvspjn5EhZMUp1IrSGpJgAafvVD7c6PebSJS3eYH+xljaUlzqPIKXlkg8FMWhk3071m7lU0MSuNWqMCKiVMVNHHd9/4uN3P5G/QWazeeawXni7urr66Rf+/eoLr9960214D+4gS+gY9WyMoBDNpUzn+52fU/iXyGhzaZNJLV2RBxehSLu8KFhvMOMNIqA2jJ4ykmGYxfOXDBk05MrVK9/99C3P816yCuvtu7dv3729gz63Ho876oktzd59Yy9lXh0zydoC3QVYWCfbyuuBE/k9/v3uMixtu/X7akEc7WRRgqFRsbvJzDndX1lRRtKY2iBFxsUSXq2x9uUmtJOAOqUx89NNeoKetmAJ335G6Bu2TmrXC3k3BRSLo6MsSgpoR2VFs/PniIC2o4AS9byGAtp6Me0GGpoQIfYVc8oj91wvbX+2dc811NA3Gg/x2SmpJJ1/mz2tsadI5iFof3qWgLaMoEIxwnvPIyucftjnTzjzwBE6jYD2XBk18gm9wpoU0MpypDXrj59r1rmfdENJaxpcdHhBWSxm1mZ1bHfX5lTaCtD5ID7QZqTINHbYq6Xidx2JS3Rm7ILyrVzjdxWyc4pjwv2dPoR1030eWDhTfrfG8nayrIumq3xjstg5BbG59HQBbdZ6RJ7363TYHLaYiIwSuiL3zbM2WnWKnVCabWWxewhic+m5AtrcvjJf/vZPu/3SdtAvGrmNkxQTQDS0k2A0Kxx3KhkT6I4khIseTx4AluNMJraguNmiJslizxTE5tJzBdRNjh0oxK5Pd5BWhVKKTNf2oHUp0rIrXPj75BjNim6loUYuITbcaidyfGFZdb3OiP68rcOem6Zjoi7tBA8AtPzbkRABdRf5mt39Gt7JI60jCbuTmG5f81RSn0jcreqXzccef+lHx2N8vD2ObX0laZKN5e7I0T9f9vXVOB722X/vnTqu/3W3voonHb3x0l0Dk2Jra3UPPfZJeQWqrHUkO6dY0tA/f3ny7IWcx5626bK8a9NzAIBZS14yGoCxwDR+TN/H/7HIy1Odk1v2/BtfO56wa3xkSir7alFMuBhw43mgYoCB4nvURCgo+7cjITORWsKxA4X41qxnTR4ZJdfTrkvuyY+S+kQuuO3NcQuefeW9329aMu7SsfcdD7uQ/JaXpulmqeMWPuulUZ/b9x/5Ti+Net70oY+99BNWz62/vjh0UPzK+97ZuffUj18+Hm9raZpYpYlFbZUBAAYLGg+lUmFvH2g8VBoPlJnLA/DO63e88uxNr7+z8ea73vfz9fzxs3/TlPXP4cCJ/K6hng6wHNegN9kWzhDaCyKgraK5Gmpnk3ZF/vUgGrMeM2L1+fTcguLKL37ckzDuHwoF0zvWJuXw4KYXKypr3Tzngtve9PPxHDciEQCQGBmWGBmWdvDtmlpdRmqBiVX2TexNUdS0BS8VFes++zr5l9+PfvzuaqNJgW96A5NfrE2/7KShsmuGDup13W3/PXgktbC4fPHNrwMAXnzqbqybXVE6c4tQgT/PA4PRzDaeDERoW8gSvrUcO1Aoz4/p9skxQQE+dnuMRvODT31dVFol7YmJDIqJDJy45PnDm1/CexIjLTajxTDiOKuJ1FBlKCuvWff5I9MXvsRz/KvP3YxSplf8Fz/6+CNIsoXWV4gPPtt647JxBqNlgAcPNCpKr3ccadwEX/+wt6RUbEGCJg7klvWK6fKt8HjA6wymVi7dO96T2HUhFmhb0u3VE1mgr6D2VLknP5o3fShFoT+0xIiw9PMFkf4BiRFhvcNCe4eFHtr84pxlr8UGBeHQcEJ4KM/x4s0ChEgTIQRGo8FoNCxd8SYA4KevHjIYDONG91m+8m2D0YBvUREBx1IuSnc5DplXQYGijvM8X68ztkAyvvrBpvamV0zw12v2gS6KUvxD5jhe/s3UMoh6ug+xQAmuSIxAlqPd3+TcZa/9/vNjn/33Xny3qLhq5aqPzWbU8c9oNBze9er+Q6n19Q0+3sjbaHB71tu9D336xQerkre9VFKqLSy06Th35apYTSvh7aVGoSQeGExmo8lJsTMatTI0/vBONLCzSfb88QIA4IW3NoCuTG5RZWSov2SqE9Y3s7aqBRABRVTV2i9LneLv7Tz42xVJjAhrzFSR/wXq9XrHA4xGw8zFLwIAQoJ977t71tyZw3ZufHrBDa9pq+tvWDoOAPDUiz+34JLSMvJKSrWhIX7X3Sou3iVomrKamMLlSW4+vcHcmMl18szlh5/4Sr7HqZ4mb3lRoWAmzH4adH16mniub3+JdA0RUHfVE+usaw0tL21o21U8NgCbRO7/c+EKNEp+w0aU0U0UCsYkGH2lZdUvv7nh5Tc3HN716m8/PT594QuPrl54LOXi26+tBACoVCiJ/e3XVh75K/PXTWKPatdUVNaGhvjZ7WRZblCSdRAAVtKKilrAo6xvvaFVEedDO17hAZgw++m8Cqs/tEuipICxG8aO1l9riXQNEVDw1fp9f1s+rcK9umB/YVnqgvLSBqkMyS7gXlRodiGLToMgeGdjmigXxA7j8K5Xd+49++Lrv8h3nku9OnhALN4eO6qP/KGxo/oUl7RKm9Iy8gcNsApoYIA3miDQgH52g9Hkov1EkxzeiZJMJ815BnQjmhtMu7as79z62CQ9V0BxUyVc0OmmetpRUy92mjGZlXK9kyrqCgtMzfpFvyaC2Fw2bUlZsmDU59/sKioWw+4URQ0eEPvRF6jP9IRZ1oVweJj/hh8ek+9pGav/+cWB7S9DCPHb+8MXD0vr99bESw7teIXrduqZX1KlVom/jZ2E9V1cIl3TcwUU88YvKUxzUhGqapH5Y4FzvYLuEoLYXP7z3sbxY/pu+OEx+c6KytqffznYTq/IslxeQcWhna8YDCbsFpBEmaagWMHXTCaM6QeFoYB2XtGYEavb7Lp7DOu7tUS6Bm769o3PtrnbS7VLc9+86dK20cTuS8lKv1LCcbyvGiydNrA1Z/5y3S7Q85g6acCIob1zrpb+utkt/2YroSB8ZPXCsxdy9iSfl3Y26I2VNQ3iF5jwT3SYf8uiKl3eB4oxcmqVUm+0LH2E96Sxdnb3uNcLuBvr48xBQzxUCpqmGJqmaUhTFEVBmkLfrRQlZNnh/1tG8Dr+TvUgC/TbX5PvvH4q3mYYOtATeilYIamwB70JbUjywdTkg6kd9nIcz7/z4R/NfhrfuJjy3TNu3eIs+i4hlMNiEj3VCk8PlVrFYOFTMrSCoZHeyYRO/FSbSulq8uEmW6YyFeVl2DRz+q5/vm0v6C4YNFxFeTneNrNcSUUtTskm9BSc/op3qZBL0ygpg8HUpHB0JB6sD0NTDEN5qBTeGpWHWqlUIMnTqJUatUKlQDpIUxRNUwpGVEE7i8+CdZ2x5Bab1s5Wap0PsnaHb7/b3IJnMd3vV8gdqmoayrV11/oqCJ2FvMpusX63rUoCxhb+ZeuqGEqQMIahlApGwdCMoG4eKqWXp0qloJUMUj2FglbQlEatVKsU2AakKSg8gt3Lgn1nr4O84yb+r7ZSNG7suPNBVNfrLt4a9zW0ZYrpREArLXZZD4Hj+ILSaoMRVc4QCN0DsxiwtAoWA5snoMsnjKMoqKAplZLBJiAF0SkaNQYFqirKAWtiWcAC0B49Vr/9cG3zhLURDW0TuXSkZ7n/NqScu2HUYDPL1esMPdLy7o609GPsVlYnADTkVSqV0MiupZjqOQAMABhavg7uOFwJq6ChzVVMnkfmMv7XfXqWgGKa3beHADqxSKG8JtiaFWu3wWAwxIT6lmvrrc0BuuxbwvMWRUMJGG0grE2Kphh9aqb32JWAbkjpAlG5loByBzuRl71T0c3sMkLXbU8HKXTpKM3X9msAtdrneSHLqBk/FzoJThq2bEhy2ZqQW4+zQDeknFs8dEBnilJ2KEQfuyW83Pp2ZnXa7YPXfHgR34RsiY9Cm2FHYuIZei50mrGFhFVmrzoamK2US0d6nIBinPnFW46bCckdg+t1g1+8Td/47gTUoqjg9bOGuXl8d1pgcVVozX7jnOENOuORM1eq63TifiFRb/mc4U6fZdvfqtnyxzfTXSh/LfefKB92ZElxauTaLMJq1dxWG5hN0hMFVP511OXoTn/2hHZxHQKgYGiT0J7V3Wc52+n0T6StVr7tgdXGlP3b3vREAd1+OD00UF7S3okg+thh3DdvRrcsYvb0UOr0aKYxoQPoiQJq0HBtu4RvFkQiJSI8Q4VyFKWftzrQz1NlydkWKpIFP4uzTwkCWF2n23E0I79YKzehvJr5uwwhWDV/xqdbu5WGSov31uM6gN/JrE9wrfh/OWVji+JCM/MAAAAASUVORK5CYII="}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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P3Jhb6+kCNuznuzRdPKJ/B18LBoPBdJ2Ayno/0Q7opUTmZ3JYRpI+MymcIICKIjRKUsHlw6NFOOfMhE5SgoD/qpSKwAAVJ52ctpJEoE4dHREUGABr5AXE+fSBoUFoAxmhaBtHkzAYTHcXUO/mZ4ZgfhYYsorrYdYmQSg4ZXQ5i3CLABRFajRKtUqBJFahIDVqZWiwNjqSV0l3IkJDouKj0DZawmMwGEx3F9Bx8Rov5ifv/UwOy0iE5iegaUKwN13PI75JEoRSQQklmwRBkCShUSv7RoWWlNcKP6EasqSkqqQY/vSPVFcWVQoLeWyEYjCYHiCgyVGBvPmZyJufmUnhDvPTJvZ+ZhXXpyeGeT2ZI9mTW8g7dvIbCooMCwmQeZBIei8bnYI2sBGKwWC6u4COixOZn+d48xOtzTnvJyn2fsKWycUNHek9p1LxrZSlQLOW3xRS7glH32XsCcVgMN1RQJOjAyXeT2R+wvsIm8T7mQ6nHolbfUotSKR8XkYdEQCERIR7u1v0YJQQisAaisFgulceqIv56cj9zIL5mLDBB4vMz5QwbuoRQdNMdnFDRkJYWV2RV5GUgXDtmSzW0CCN0ybdcbzw4pFwCc+ybFlBRTteEQaDwXSRBeo0P0XeT8ByDZPE3k/O/CRIQDBMdmmjIczXYcWyBCmdP3uPnXM/gGFYmpaxc7ERisFguosFKjY/CVbtMD9rWMDlHsmYn43pCWGEmiFsVPlR2EmkVZNTYnj6CBe7J2MSo8sLK9v+aAwGgzn/FqhgfqYlhmbnVznMT5S56eL9PM6bn3ROaSPaA9pFeSWcreQLJJeRL6Q0CWAjFIPBXHgLVGx+UkAt6/3MSAkDYu9nYhhQQfMzu1hGB3ljk/Vtd6sQ8H8hwfxkEQwGg+lGFihvftYb0xNDs/IE81Mpzv3MRuZnEe/9zCmtReanzWbz5SnaFmaSQx8RFhsjnQmCjVAMBnMhBdRpfsKHceYng7yfztzPDEfp0ch+4dnFjWkJYSgbFFDUW8/e7Ks+Eh0SU51WtXHfmXY9FIPBYM6PgHowP128n9D8LITeT4vNRjB0blnbzM9OQUHBF4WNUAwG0118oBLzk1UwhB1klVSzrDIzWZ+dX817P60UQRJKijp0tiaDC74Dzvv51tPzPvvqDx+fC3pAvUbib756QnVllad7hw+O2Vjlsf0dBoPBdLUFKpifaYkhWXlVBKNIiw9FuZ/ZhZUu5mchMj+ZnLJa5A99beENHbrG9rpFaUL66rAnFIPBdLUFOlZkflJAA5QNwAaHbhIAmp+MzZ5dXM8yZGaiXtb8lDmjyMDseNTIEwRBxCfHF591ST7FMz8wGEyXWqD93LyfafGhhMP8hD1ESDsBt2HwHZmf2a7mJ1q/d6SfSDugaYY1GeP7xXft02IwmN6Cok3mJx98tzG55QYAlBlJelYwP5P0BEEoKfLQ2dq0+DBK4zQ/dQGOM6AyI1naVH4kacnsAZLiD4vv52KHYiMUg8F0kQUqmJ9pIvNT8H4CwfwsMGRB89NOMExuOW9+vr/oVl2A5t0Pf5I5r6sGBkaP9HxnO4Fp9QCwJudETwwGg+k6AXX1fvK5nznQ/AQZSfq0vuE5xfWsncxI1A9J5vt+pieEZTpyPwEAMurpWy6ol6N++XMvaAtfrdwaERsj3oOjSRgM5rwv4aXeT0CkxYfmljWxLBd8j42A5idDwqGbFJWeGJZTWJdTVgvsUJetcj1APa/X2Xav60nPrwL5DJITXNSzCxitLAEAHLDFdfHzYjCY7mKByng/AeC8nyAzWT8kPhJ6P+0kq2AE8zMtISzDYX5+/Nqd8ot3Nxyt5H264nas7pUKSk3YsRGKwWC6TkB587POCBgFND9t0PxMT4hG3k+aYZD3k7CTx4v4tvO5juC71WZrXT2JDkaJPDJ8cFJpuYFk6NJyQ0m5ISlSU1paDbocZIdiuuD3jH/VmAuConXzkwCZSeFZhZXARsDcTxYG30mGOV5YBxhofg6Njzx+rha1nYe5n1Yqu6Th41fukAgopXUMkZfBEZ4Xr9ld1+93LpgJIvnJdAu4f195/AN0M1GvLDlXJhtBElNTVi7Zg8PxGAzmvAio4P3kzU9ADIoLoVSqnEJDVmFlRt9wQNoBQwrmZ05hXXZZLWEnWQBsDONuftpNLWiD8Oje9ObyfGXxMm+vwzeLNSI2pqa0vOMaatfzQ0A9Uangtd5ul/aFkqAw5Lf12TEYTLdewou9n5nJ4UDJDooLOVlRl1NoyEjSE3Yyp6RB7P3MKWlkSQoF3wmKQuIrxkbTLNOK8MlpoM8redbxI9o1KD0lOi4mOs4ZQap1M0IvLFg9MX7A6F7sQlF49X42A1aJzE8AQHpCdE6hIbuwMj0xPKe0NjMhAvX9ROYnoWCyzzQ4gu9qyQlhtVIrYugwTAlW3gz1OdN+cHoq2mgy1KGXF9uHz6kCAFhYhcQI/evd+dOfWA46VfiiHX9PRTgQj8H0KgEVeT8J5P0cFB16oqIOeT+z86tzihtYhswqqclIDIcr98I6QJKZySGo9KhfZIDkhDTDcLM2PEsi27pIxsXwIzkJALYdK5o0Ill8rzCYEwBgNJn2Z5eMSokQH7D1YP6UUfAAJSttrPftivWefjuY7oCPDpNWvSXY5Md0xRJe6v20ESwAGVzwPauwMj0hDJB2WLhpJ6+YcQmgYL0k4WhEb+X6fjIsa7HZ7Y48UJVOFx0THhcXJX8JUmHlbVUvBisF58e1E9mhSX+9O7/dJ8RgML0WqQV627hom41G25nJvPl5srwZlR5B87OkATBkdklNemL4e5+shsdRVEZyCAq+94sMsDOM0WRtbLEGapQhgRpSowkMUCkVcAWNvJTu4nfqm9fT7nrBzf5s33ROKN9e7iUIsODa8aVwjhOmZ+CjwwR7SzAXXkCRepbUGdOSQo7nVXEuSZCeqOeD7wnhOWW1GZz3c/JlF+Us/QvOhSdsgvfTTisbjZbqemN9syU8RBuoVQVqlFq10iGaUv10T1vi/uV3+66gNC09cMO+vMvH8s5Qd2Jj9KVcRQDi2xXr2+EJxWD8nlb9J6AXu1BGRLIuS/jbxsF1OoSAfT8JLvh+qrxZHHyHs49KamCh0ZK1ULdIEs1BAlzw3Wy1V9Q1V9YZG4zmphbrmVqLgoIBJF8RjmzzGp0XUB+fbN2uU219AgymF6Iw5As/sgf0U7RzXLkfWqCC+QlYhTP4zpmfMPguMj8fvHv6x0v/So8NRm3nkfnJsKpmk7WwtIZQqAgALDb7qNQotUrRuqhBNypaXrMSI9RHxKt2AoDR6bF/b6v3/hCJEQo9vCsezrz5Q9+fFIPpVchqaHRvTTgZEQlFh5QE31EOkTP3kzM/h/aLAHLmZ05po9j8ZFnWaLYpVWoClp+T+kg9yTWo934d7jEk/r/cpblLL8nNjGsTwZHu8SupOuNYPAaDaZN6uggoCr6X1rfw5icXfE9PhH4NG2tGwXfYJcROPrhgOuy9lBCGvJ/ZhXzwnWbYFoudYVm1klIpKa1GqdOofFlSuxzCivbIPZahPTZ5EqOuL9r295Ztf28Rn1ZAwcm6ZHLn6+/+kLXiYV9OjsFgME4BFXI/0xJDoPmpcHo/h/aLyM1r8GJ+sgoGia/NTttsdpYFCgUJVBqbjW4xWzfsbfOIdhish7ho6LDhg4GbqmbnVfAP6aRpIVhDMRiMj+anU0CF3M/cc0ZoftoJlhXMT5PY/IRHU1SayPy0WflzmW2MxUYjk1NBkc0ma3lNE3SNdkjdXKxQyuP6nfNHEIRDd12Ii+Yr0yWcKqhyHx//+rs/tPtaMRhMr4J0MT8TOPMTBd8rBPOzUWx+wrwlOfNz0/7TCpKgSIIkiLCIMIoiTGZbeXWTzc6YrbbWJbRjJqR3kc7N9hZzd8+rx/3qzx+9uW4a42fmJy+gTvOzgDM/bWLz05wmZ34SbubnlNED1EoqNFCjVFBwLW9nbHa6yWix2ulKg7HJaLFB7yhcazucnHzo3R3xXsGijI4I7vTfhUIBkxAWzB7T6WfGeAdrKMYP1BNqiNj7SQFtVkHFoD586dHQfhHHzlQCkkSlR4BSQvMTgNzSxox+YcBKZZXW9Avjzc9pYwYQSioiNMDQwnAJSXA1bacZgmEKy+sbjZY+4YFy2fQSDXXJXiIciU2u63eiQp2U1QJL3YspJdsSAgCo1jC1uoHlVTXC0ZVhmWgjyxQJAKjSEughiBpdSpYpspIIaDHpAADb85vmpLjUyONWoRgMplVIV+9nJYA95p3ezzTfvJ9TRg9ALsigABVBEAzDwH71HAzD1tS3FFfUs0rYZISzQZ32p4shKmuPOqL4+87AFkoMAwVSreLTV4UNFgCdBg4KbSuZ2upMbXV/bZtbgmIwmF5uftbUNfEClJYQQhEaceW7xPxkKSXBmZ85pY2ZbuYnElCYTs+SOg3VbHNmGrGAtdlpO01X1xmVKFzuPbHJhwz6eHtRZoAdAHC2ztAvAGp9hencpQlDAgwGYclfzjao64s4iYQWaI2pIDPAGYAqM+ZnaskA1gAAfDhBELHJMaXnOqHXMgbjZ24WPBvRC1BTWMA2Gs3I+ykyP128n1DWSBnvJ1JP2JGJQ0mR4SFau80mCZezLDCaeHtVNt5js0NBFOFxkX8+YBhWMJkxGAzGF/MTpmwiV2NVjQUogMT7ySoYggu+syQ0PwkAckoaM1P1wEIK5qdOqxLUEyiUeq1Sp1WV1Rl1Wo2hoYXmVtwIu0OguDCSmypKijcdjZXhTjkF3XW04KJhSdJztDdhSq2CFft9k/qUFfCJpQhshHZu14k2DTvxv94TGL9h7Ki0dRv29U+N45fwDMueKaxJCNWlJ+rrGo3Q+xkfmlsOe4iwLJtT0gi9n7HBrJLOPg1rzNEafU9WwdjMZEH3AtQKnVYVoGF1GlVsVHCLxWZssQpaWVxUGiFUdaJ9rhM4PF9t69aoVu2cQdIOFBQl0/AZ06bfoW9i12tLpzH+ZH6u27CP5aI+pKP/MV3BBK47kFffBM3G3LxGOP/dTjIKOrukEeV+5pY2UiQpzv3kXAD8qVVaNUWRJEEoFbCOMyo8MFCrkr0ap5koiiJ1vJJo79H2N1gSGjTHJjsHKCFwTigGgxEzdlQa2iAIQP5wqMZigwmbaNeGA3nhYcr4cDiCuE+IirCRXM866P0cnKTLPlMvDr5z5iePUkFpVAqSkyKCIDRqJQyRu1p1wtg3mRC8BwUV72YdDoHh/fuATuJUQRW8Hte2fhKwhmL8Cbs+RfxzoS+nh5mfaPEeHhY0aHDS7CtHwyX86qx6oaaTYdkfN50cPTg6PkJbXGEtb4KuT8DAuZuZKYEZiXrB+7knq2B8Ju+FPHSu7rKRQUqFU4ZIAkBz1PVS0hc8m7P0dXep5LyfbKtB+Kp6qPJPPXINAOAO8R1KxZ1uBy+Yf+XtNzwKB8rfPRs2SXl1qbgLfUZieGm5QUMBQClKyw0JerUkBI/B9BI8aaiP3upKx2Ggqg70GtuzytAY0Sc8Sh8mM1QOBosqGEV5/VXDYkml6vjZmvQ4zvvJ9f0UMpQE9UTmp0rh4kaUnSL37Zcf33bvc9BbqpWO7WxqblGEZRIAWA3HxZcintFpaYEBrlff/E64/4Wnb5Wc59VXl7a6p1U27M+7fIxLN3scTcL4DZ3rrY4WDgNxvcf7OXBQos1Of/njZrmxxgRQsLYWO9nQZLLTRgKanw2ZqcEZifpsOfOz0Q4UFFc05Epzo9XY0CSpUkcHMQzLLfZl4jbq8KGc+vKz5bi2TGxQH5jLCcBRycFiMe00KHD5+FTAz4VqJwdscbhaEYPxP/OTBWDQ4CSWZQmSsNOMjIAKines2q5XsQkR2sAQZX1jS0mF1SpnfgZS7OC+gRWVTgM+Wa+tLK4kaTMUVbkVO8uyDAOT9KUaKjv72IdM0H794LefpaXpzoW3ARYwlXWL33EMOBI3dPZdFinpwe0wQivDMltN1sGZOhhMTzE/EdNGpm46lEcQLMESUgGlKAVNw5z2AAXT3NhYZSdDNSRQMnaaAQoGmZ8M7BbvmOjOsoK1KEE+MYg7GCVsSjRU7hSAgFmgbHlFdUyfyNg+sOxdFktLE/R73joL3SQ99K8TcO9iJ1BaZejESVsYDMYPuHbGhNLK+nUb9g1KjY0IDRAUTm4Jz2Gyk1oFE6BgdmYXBoeGJoQFCN5PQT3hORSEB8uOQAF5FxMU7SFgqArFmwQNFauntL8IAXvj+8I9T3wYGwmbNrEAlNdASfVdN9tkhPpiNuJZuxiMn5mfA1Nj0ca8qUNWbobRGo8CqlUwSEOhl7MeVpVfNMDZzYhHbtzRkGED0XiPqHij5nS5TTSB48TJPGPlYegu5f6PAk0EQQT3HeW2XpdOP/aFzupLj8FgMALXzpiAwkf9kvooFdTK9YfmXTnyxkszf9ySJSOgVkZZXWuMDNch9SQACAoOiQmRL/XhlvCwE4ewp7ahkRvUzuQXVRWWVdM+jDBqLj8EggbnLn1dZIvy/02/63mP3gA3Or+YyM0IxXQ9rTpMeu1cckyXmZ+ff/f3wJS+LMtarHa1SoE09PrJGTICGqBkNQGBxoYGmmGDw0KR+SeUHknV07N1KJvJxN3hdh7PryTnq9fQRplWETF6hOTe5Wv2LX5sDrRt89uWxbn8913uO59+/MZXPlh9xbh+nh6FmzNdEFpVPewtwZxvCABuuGrMqj/3KyjSZqcFO1RuCc8Cmwk2GgkOCTFx/ZliwrQyR5HeFsywyMnLPHgvpmKr/e5EzJ81tk3SGZ8Sjzae+fc8YWdxKSxGgk1Ry6rvnHtRaSF3Qsc1/HCoBlUiYfXEYHqh+XntjAmff/f3sMx+P/99aNbUEWs2HwYM5y7k5M1FQBWUAj3UaqdJkqqpb2EBgOU6cjhtT3khldqfdqOz3qnDPepYs4U2W2G2AEkSGpVCpfAq525a6cMzODc37M+rpULbeaWYriX3/aK0xxPwb/18uFB6swPEZoe+vLX/HLtqyrB1W47aaAYZoTIWqIICNAMFLkAJ7HYbAYhThRX9ol0qc0Tz5CHLf96GNl5+7HoknGar/WxJg9HC0rRDipQhFNvi5RLTFjybyxV6Eq0booRaBVuWIInm/vVo7eYdOZo6fBjwgRNnSgEAwSppXTxWz55C7vtFwr9YRrvGGV3JOaDP+osDWtb7OSyzH8tAd6WdpsePTq9vNu/cdwI5Q2UElCRdhgPDxTggtp4wTBns8Xf06r/nim+eyi9lWJaxWXRKOKFO2O9oqYzOKz2JLybkr5uOvnD/VWg7J68MRplSnfGr3UcLQAcY3B/mKJQWlnVJE2fM+QWbop2Fd+GL5ko5/bU5YbWBz4YkSIIigMXGvvjuyhcfm1vXYMo5WWC10VIB1QVoaauZC6PTCq45iIIiG8xES33jb3sarxkPC5A2HCq4fLRLM+NT+dB2E0MAAvpA5VPpPQ/klD1apKzXThuGdNMB67jpwySQNiA0jcL0GJDhKdmD7VBMB3M/6xuNJaI+RACAL7/dgAxNmTzQxqamQA3fx5Oh4aBKtUqho2kTN1QOSudIKJ2sa5/4AaI0ptOOtkZchZKUjmhSC6s6kJXfPymCAKC0siEmMpim2QR9AMON//RFQL93uBruv/VKu50hSajycJY9SXL/EgqKLCks8+7cxXRDlr0tny2HNRTTbqoNTZH6oEh9ELopLMuzTjlXuopW5c1opgFghyUFn6uot9Lgr/15FIXKj4C7aIpPBYPwriaoNkDr0l7eRzV1GJeGeq4Zvo1BIX6GZSlunAhJECrpFclzy+yL0EZTo0ydUrsSATDdVz29a6jZYNDo21KZhull5uecS2HsZNfRfBRyR509JAcpPMWbkOIKOmKy2rlwUBuExWMeqON+6Q4PBp+wODdrk2IHJKHx7cEBgG8BDSqFI0+eq6isgSILAPHMg3M+//Ab369W9lqwjPbElbvsMf1ul0lkxhqK8aSe1YamglJYtz0kNebYmXI4UB1Ft7n/5hdVpiREexJQ/vscDf3gNgmGgW3lkNGo0wVInm35bzvczyNzZkLaBtRXfFOyL//7EIwFDYoHgD15SuqWbQd4DX9hMRtaaexydhlsEYvBdDqBgXzyO0EQ40YOQNMwWJY9XQT/Ji9NSYRzMCXNRGrYwFBQL+ngiahrMttphhT1nBcz/2pYKyqGZdnCioajpytQtibCXa1dbVSPeiUcVHr60IAkmNZeVdscEaaDuzTc6+SM4907DxQY7ClHTsMgmAWGwjB+o5UdVM+zy5pljVAMZoSc+RkUiFouQbbsPcn919GCDoCBiYPRTdJHg89oEfI5fYTTxk5dA2tMnrOUuCeqrDVqWSswGUFLc5XcgAFNgM7KkOhH9jQ+mtKYC047bE9srmJ8QaKeAIAJ6dIkra17T2zde3LL3hMyS3iKUjCMHamtrBMzrk94qxfh6LfU+uXyPoLmEyCInzficBXIkBLPP3VUeCD0SrhyrrS2b0TwuRLouSivlQkTRQapo4Jc3QiuT/TsA7N3H0bfNhg/BNuhGHcOVxOeeicL9I8NO5B9DgAQGubsSsyyHtrZCbOIuA0pJwtrEvRR3p8PBce9tBoRP5k3Wgtc/bj55I1TB/HHsuyh5oiwILior9eYbFz7Z+4O/gvES0ZwptYl1QvTnemILYk1FONFQ4XMeQk6jfLKaWPrm8zNJmttfUt5USFawnvsB8qD2iUz9uCgwMbGZqAJBkZffVVuWUydSZ69L/LVNoaqskwwHAYAqNC29nIwPZ+Or8SxhmK8aCgNgz2qwuLS4nJoVOk0qhnTRpGAtJpNAUowanByi9kGJgxgAZCpREJySXLCSRIESRDifpg6JdMIQCCfaN8KqFtyK8egA50PcWarcrn6Lg83a5NsdlbJpXzGOK6hov5Mppbvd9JshhktfQNhU/pyc5OlDprcAIDUJP6AWC3O++vZdJYfE2soxp3D1QQAUD20ppaoiLArxsGlbWiQtrLJHKCBrr8WsyU3r8Ri5UUxLCRAzgdKcj5QrohTQRF0ezsKC5WcthbYYtnbAt1nQxWp53ni2Fn4hROuk48vYS44nRsFwhqK8U5+MWzeFhGqUwcFEpxWBWjU9U0t0M7jxKuuoUXeAtUoFGabnZ+54UM25He/70Ybzz58HdpgWdDC1lhAdbOdBiqfxqtpOI3v3Jp2CSvW7Hnt2fme7k3LhM1mNmzaf96eH9N+zkcM/eyy5rTH8aIEI4Vl2P7x4UL8pbC0FgBwzWXDGYZ9/dM/ZkwdTRLEueIKOBMOyKEK0KkAMLc02+z8SYxGb2mVt157sXvDTXOLKUgFbCq2c9OYUPkmGi7ven/rxunNN0zOPQObx2A6HV8mlYqHlUomcLBq3pEthig/cb4zkHCxPEZCqL2B4Ep+BEEJoMD+nMLsk4Xo5tqNThvLW9QFdgVlaUA6Oyo31tUTBGw4AkDrUXh3FyhtMXak0NysdWkBpTWL00Jx1eUFxsduj6j7mXsDNHvbE+k7C6yhGCkOOYkIhdU6CooMCwlqbGxyT4ZvJWwtlMPrdBoYhQdAoSCFWk/voExQ8R5KrWvlEV7RaNRms6MCHgCTJslVQ33AS4opprvSBQnwWEMxXlCrqMREmPZDkgRNM0qVymq1q5SkfENlAV0g1Duj0SjeqVUrbXbLxgOFl41O9CWX3vsxrg9w/Oth0a/X67/5ZuXll18i7DFpkgAobIP52Ub1TBnQHxyA7o/zt7ztEW26LyBdVj6ENRTjCYokwwKVMVGhDMPSDEsQwGyxKxWkx0T6xsaG4GBnwj2ldBY2aVRUo4uiyvP96p2e7vJtaIcMZWXlYvXsAvJPn+mCWbuYblJ82Us0tIPe6t753a9QUDWiHPsWMz9dAwroEw88svdQ1um8AppmGEM9RSlo2h4VG19VWswfTltNVjUyDSnUiNmVAf36Sna+8u+5ZTVNq/85UViB+stBAviZde2EbMxXUDA3lb/Z9sV4UEBAU4u3uUy+g2ftnm+6vnS9N6hnpwtxpcNQ8G893bw7BwAwbUK6ZL8iMSrOaCGSY8Mjw3RZOSerzf0oC0xHd6onl9ikZABqxCm7Kj99Vjxmg6fRaCEYq5toEjLGJ3e7VTkMjBtmqTwOOkByXNS5EpfBnEhPv/tlu7BnyijX8XmYCwFWz24b7utVM5EkbOJkFNqCWrXTAv1r7S83z5tbVlHNkmpKHSh0KoYOUKSaAKi0OpUWNNsI19IknpKyqri+0rg8wRUygc6jsbEJaJMlOyeOBL5njzY2NoUHu8y4Dw/WEgTx4kPXCnt27O2QRmM6DlbP7sOB3qGMYsKDZdoWXzIsaev+U8LNZhuvkrwPlCSJ0JDAOVdf3djUfPhov3OFxQ00E8Kc1SmBEeiAqQEeZDMFKqHcUAGhrKX15ZX3IFK7s5k2bNgm8YQK5xk/NOHbNQdr66Cror7ZEu9WclpQ7Gxfj+meYPXEdEMSkmJvT4JTe+2OgR3cuCMbL6DLv/+RBtTUyeMbGpsaaktokwH2n1fCu3RK0GjiDlJC240kQICCMYqsVE/AdqA+aWTbtFRQT11gkLFZ0jqFWPnpk336xwKWrcgr/89jL7d6tuWrd6ENoQPgyDT4a8JgML0Tqw0WsltsdrXSGWMvq4T9heP66MWiplUrFYVVJYlRvJXeYGwEFJg4JvVcYUD2ibMGi0av5guQjFZWp4KPpCgKAJlV/HdurYgXXH+pyQqaLex5mtvmpp6Q/7y1Mj46tNXHDk7pizYWP3EDSnU96ZjMXFLm4iTFdCXY/MR0Hyw2OxJTmArKLeuLK7hyD9b5D5RYmqEozrN58MCRuddea2E1IS2h4fXq+vrGJrs9yJ7H2aGs0Qp0Ko9uzWfuu/rymy6DW0rOcIWraCOr27Ni1QaZESHoyX1qF+pEZ4XRLbuju4nslWQmhEVzY0hr6o3OKJgbJ/Jlol6Y3qae/W4P1OA0M4wPbN6TK7tfMWHkpBNHdgSH8+vWn35Ze8W0ifogEBmiqK0w0ISunkgmwVGkoQAQpIdeRSXlNV+/94N4j8VqP3qm4rxODabDMqm6LGFYye6jsDCpqbmlTdlS3/3mkrI6ZeyAzrk4TLdXzy5+Rv9jtCNS79/AgZo2mPhJKKVxFUV1dVFcUkJIaFRBURXStdra2rSYxtgMYnJGf3TQJ0uPOs7EkITC2NwMCB96vrUahfegpJ4eY1S5pemZmlxi6r5p5aJHr6dZ1mK1UyShoKhFj10vDnZt232sLafE9EiwemLaxHXTxykVlNlia2ixN7VYaYapbzafyzsLl/AkSeSfK6YoPnifFgPbd+blFTrjNhc700dTUxOnup767AlndF8aRCK5hiKiNfy33/992y1XXKja9JceuwFtFBbJmcaYC0e/2wO7zAjF6olpK7/8tQ8AcOcNlwTpaAB0jc3m2HDtoPgRCgIQFEUsmNVPfHRBQYlCQUli9gixsPIoNTCT1mBMTXWpjlcBMCE6+uyqbZ3Vzy40a3195pXQoWuCreaRefvCIzd+/e2aVh7puIL8wvI2PSMBwCN3LdAExb39wavtvmxMt9JQrJ6YdnD1tBEwssKlSMKlr8UOAEvTrOL6yTFIMUGHQdqampqINpAEN9lIr0nucHKH79EknbVICCJFhgTff/v01tWzjUiuRB0IvcNPPfYC1lA/s0MxGN/5Y8vRqy8d5roPev4U3qXTl3iP2PAsKCgRrFdEkAqIGx83W7w9hY8FRYteugf+p66p09XTnfKiU0ouHeyOuTd985NLlAzT4zQUm5+YdoB8kH9scYSCfO8HSrQummwr1mvbZnS0cvRzL9wpbLeqnqroJGslahgqc05V8lVo48effrvvgYeE/UJF6l9//jp+7Fg4EjqZz5O95KIxCx593NeXgulmGorVE9NOCNAvLiK/UCZw4usc4NRURwSc06JOWfILUKSLlSpBHGha8jvMa717tuLr/61wP/Jkcd2geNgbJuGi+5ISoGviu+fhjKbx17/426+rxo8fGxnukmNfVgJdDdfOurS4AOa6Ol8e96RVFVXWFthKKifH2VAK00M1FKsnpv2wIHNgfMaAeKuNDgoMoBnGZmfMVntdk8mjgJZWNVw0Ok24WVDAF+q0FYkNS4kGhLQDqwkWVP2yPSlkOFzFRwS7ZFMt5oo83//oyz7mxjOnnaNAz5w+lZExhLE2l5VwRf0ecY5U7ixGK0t6YTuG7qahWD3bSi9J8PQVFKQhgEpJ0nbYYElJAlJFMAFKhRBnT03hbcyCwtKkxNikxNiCwnaKphg2LK2JmwXCX0lIJ6TUV5ah6XJUQ5XMqDuNSuZbYf++3VMmjfV+pcJ/0RaaXMpddDuuEdNdNBSrJ6aDNJrpxpIawILkuAg77RxoBBPJhRt5+bBQEsV/OiidLSarSkmhU4UFUtYWvp7UEywLvvuj+L57YKJAx4FtUDxAURTtfc69Qz25QlMsnT1eQ7F6YtpKfaMxNFh+etu5khrxTZphfCgo8o2KmiZkzIb3n2G18SKl7jtapoE9GkvvBZ8tvj//3iLzaK+p+VwzlNbV09crwJx/2i2CWD0x7WDIoOT6RuOJsxUnzlbk5JXl5JVlnSyEP6cKpToBgEK2FohlWV/mwTW3WAIDYP3S6YLq4ECYTu97S9DOK4h3IcvA1igjcup4Ba8My4RRoDr6rEWtrxFsb4JhXAaL2hgaADYtFGmrs9cKpqfboRo9bhaCaQN6uiEuIipuYmaUHn5tB3H6xgK2pt607p/DgoYKKGT1zYt6mi02jRr2W4oZOO3MkXV2O61QUA3NZqeAskDt8EKaSw8Qrl5Pr8Cn9bT+rjfbQzVOh8OJBhYAe606OssAH7Bl02bhrsiYhG1bN6LtWK4z1LatG/URkZs2bJCcc9K06dzQZ0rJKaeCdBlNftnUkVY7/NCyZDjaw4jcH5hurqFpjyfgTkuYdrB5/6mUWH1BaTU3Fx62CWZZkD4o8ZbZF0MRYFmKUtQbLScKqvNP57WexnQotyQ9JRqJ5qGckvTUaLSfIAibndFw3UnMFrtO6+hix82OF7a99BPpiBGaoVd8/dn7d917Kxw7wirsg+KHDR8uun/Yzh07Ro0et3/58wCAWfNfEz923bq1V1892/2cmzbvmHb5NO618RfG6Sqxfv3GS6dIGgBgOhmNXm82GLzbjGmP63Pfh576VsHj4TCdGETJLqjOGJTIdfcAFPwPHaJVpCfqk/sMl/eBllc3OqvgRWcbOjCGEWVsCvFuhmGF7s0SWYQdmRy3GyryGypkBloRBKhvlOmO7E5YqCY3ezfB1Nrp+tvuWWBjFTaupSkUcbNN/OPlJCxgLWaZ8L1EPdGlSY55cMH9vlwnph1o9PpWV9y+KCNWT0y7MVAhgAD5pQaxo3PyqP7lNQ3wpxr+oAZMwTp1WJBWXkAZkQZLlvPioJCC4h/OAlbYb8hbJz7eZKZq6yy1debaOrNdHUsExjvucZF5BWH9fMnqVl8ew3XlY0hpVz6CldVfj45MFgCJhm7duqlV9cR0B7zrI1ZPTCclffIf/3DX6hvOvmwoq64vr2moqWvizTeFghI3XhLLSGiwS3SIcogmoQrypRNdSFBgRFgow3LeQ5ZVUExHmtKjk/h8qPy5uRZ7EIvZrNZoWn2sZP2enOjpIZBzhTK2be+ZEt4qaIw4miTe7gG8aY8nyK7lsXpiOsI1w/vklRrKXVu2bdx9QtieNmGw+C6CEOWBitGonfv7xYXbXLQV9rhTKKgWo09KQZIkX+AuK3zt8IN2RQPR9j+FU17LfBLcVum1iuwddw2dh7sUYDrAiEi2sKRcCUBCH2hyDnIUr18eEQGHAe+Cky82icQUoZCVsgCNMyLUwaUsidb2ns1GBdUJgjgwPaOtiUfLl30hbl/Sb0Dako8/Qnc9uhCGniAs9CgHaDg7+QIlNnVQf7tMjj1ZixKiHTWCRR2ucPVkh2IwHSfrFOpDxBMTASetZQ5MEu/csCtL3gKl3CYfIavT05N5SXvi7jrv2vP3unXl5bBMHsCMTsi/Hn1CuDeAS+aCr8IKJ25eM/tim61pwV03wctTqBmarzqYNGkK7EhyMhvYrc7rh6/dWUnVSiFTt6enyHFbNRSbn5iOIqgU0QZVdQqoy6PaaBQqRXlLEhhapo6zqqouOor3hQk0VeWB1txiLTYqLYMvac87kzd0WAbavm7urSiotf7vLePGXQSPbHF2HkXbuTnHRo/lj7ezHj/5gwbxx/BHOtSzp0tnd5Fjz56N9skxlk5Mp2Cy2gkufo5C6CRJakVdNTyZiPIWaFsX1QEoHVSOg3tcqi1JggFAunb78PtcklIRumgCtJLMZLfRBAHdC0oVfEba0iK+F5YTOSxQd4TqI5PJ1w8qVs8eYB0Xyp/hglvHmJ4FsgKFJFClUmGywYYYDMuqtGqY6cOyFjujA7xFhap+fO0H6k5YqAam6nPotB4FdOKlk3NOnLXb7bW1ME1apjQeAH0Il+4PyMLIS+NKnDVFlU3m6CCPHypxrpWAOHtLYP/y58fMf625WbheJ7//tm7W1XMlO1f99ucN11yF1BMbnj0UP3NWYM43qOQHhoIIggVAp1ZSnDraaUbD6RtNsxoN0dzYQjMMw7AkRSkJpv0C2moHOQRB8rVI4eFofc4L6J87amZMinQ5EpZKutRKenGd2qzWjIyhgJHW9rWaFiUxPzMzvbWAwuqJQWA59n8IXj7RpkatJBm4bGVYUqVWsizLMGygTsPYbLCjHQvUaiVjs7VfQM1G53Kb9BxJh4VILv4D1GaTlfUpMAwjhJyqmiyj7nwKAFCy6v88vGBizV+bZk3nyod8w/fFu8XiMrzpl7+/9/1ZMBjvYDnupgjqQ8AVvd3OUiTFcn0/WTi9GISHBDTUN6NeS6OHZx46fKxtAkozLCW3BvdS8O4eoK+qqttxqC4wKNTdyuQiTtKofdwNj5T+zGtoPBDNoLeCuy5PhWH1trN24yphO/fcTundFFZMTI/hgsvxAX+cuUASBMWlsKf1i84pMXCbUFVVSgp1sIsI1amUirYJqNli8+LulGXPzl1B+miXPbkmAGyyktuGIiMAaLsFACJKXSneOW9qP8Cc+/SvbQ/MgJbpfsf+RG1J4qVpANR5SgwAADwwY5Jkz6frtrfhgjAYv5FjH8tAylo5pkf6jglHK07uP6JmHkCrUXIpnjDUlBQXAccat+nMDc1mWQH1pXmoExYk9ks11DhnFkFp19XCJTYD/xX4c/MvUNemXZR4w9zdq35ylU5A6Zw1qjMvh7lHa9cfQTeRerrT3GwMgAG11qUT8e85/P73Vm+/f9oE6euw2z7/50BrrxaD6b0kX2jruK0QXndRfEQHCl6wTk0AD6WcnjA0mPpGBrvv7xMRJC6lFxPShwSgWqK6FbXV3GWFi9UTcVwfm2koqWqyVCamPDANZnQiJtwwN3fNr0g9xdKJ+G3tIeT7/WrlpvtuneF+GdMu6r/676NIPb9auQGEqrzopqgzH6SlyX7/1AmCX+Gf/TlDMxKPZp3ddvLc5MGpsmcAAAi9/ytKqqcMSZfcSxLM5mMnZH6TcS6BNQwG0w3kmHAXU1Sk3jYBbTY5S3R++fv70bG8AraP33cdnTNxmKd7x8Qn2WlGaPgE6zWvnEHbLFSgi3rarOi3QHz1E2ynJKueAIDVfx9VKSmb3f71pp0gVCWRTsFzQHNna7KCIB18q/7vm/V3X+c0Zv/ZnwOLPlP6NplsLANunDqussxlRooscy8dU11jdEuG5ZmS1l/Y3pp7ZlBY1JSRzj2fbtrlfsL7r5LqPm23frlhL7xrqtRMdsLYP9squDQwmN5OskSOjS5J5WZoEHpbWG/8Zw9oax6oJzOz3bj7PFuqGHRRB3JLVAoqLTlq1pUjN20+ZDLbqMBQ2JPE8bKsFjOyOr/+aQsA7H3z5aUToVJSy37ecs8t0x6YMQmdRAzSTYGvV20jAHv3ddPE6okgAJt3uphLPKAk6pk+dNSdD/0Hba//feUPpw6h7eoao1qjWfzeEq4QwPbsY7dDrdydDXRUegwcXi8wKilBrJ4AgDsuHv3NjgNepJM7p4UAhDfphPUFsMoCg8F4g23zsZ2TB9opQOkUYTRZ6+3E0GEpv2/OVSh0ZKDLq7NaoOSxACz9act9869q9eTLft5y9y3T3H8/7tIJALjnOpn+8ySg0TQ8Um4s3YvvfB4Sql/+5f9O5RxJ7Dfw7keeuXL2vNsn9YPazannx/9dVHjuzG33PP72xytGDx/C6DzPtuNwv1R39bTb+FwryvNwQJa2dUn/Ko/cL/LDAAB+/javleMd/uvP1nEdWjGYCwTMtvRihHKmnKJNTUPEjE+M6iyD9L6pEwrK6/YyRRWGZpZhq5osJYGJSpbZf/Rc9qnSCUOT01NiKEeqqY2TTgDA0lWwSNSLejY1OxfOd98CP5YK1mbnKkEl0ol004t0og3W7bfJMjRBwl9XSKj+yXvn0lx8/8TJnIUP3fbOR9+iY15/b0n+mdzCc2cAAAsW3HXgyPHolLjyinJ3oQwMUItvsrRdzZWXyRqeNqsFZqsx3t4shrYJV7zkl21ArwUXVD1d7nIN9JEs/cmfW4fH9fN0gC+Ser+rZ+YznESB8ZkGHayHhJ9lx1qe8Kqk7bFAJ49O8SSa2XmVGY5xSW3QYu4qjuWcZhhGr6FNCqvVTlfqYpTArtaqbTYLYzFn55xqNFQFBqjhSBKSGtQ/4RtOOpkw1YSEuGNZJz2de0Cqs/cUkk47oZQ1OT1LJ8pLBZ/9uOXeGy9zl06kngiNRms0NpFq+DYwDPPEv+aj/Y/fNw9lkG3dnY0Mz8oqPvVq5LCUokKXNCzn+TktJrRqd/VEbl8lq/Cy4mBofqgJC8DXv2yz67VdrJ6OpAXW/UVJmMVlUKz7+9jwuH5jh/IC+s+e45LDCMAM1kdsPS6NvMEuYw7+2ZMtbKsoMCRSv+EITByWi/WxJ5tc8kAwGLGSNjjGAuXlwAaMqUG8vEqSLhWSXvTuSCYAe4ToaN6VJAtUrVTQNMPQ0OzjpjnxfLNqCxPGB/X3ZhWNy/Q44CH/HHRWChw+Cj97gTpeR7bthx8tOhTevH5o/5oal0r5qAi+WRSSTrF6on4lgnSirFKapl/7v2UAgB+WfXbs8H4bN3pEYNfBE7SWXPrd8iEZQ1avXY1+pbdNvwg1mna9atEwFbmMK6d6eoa2O9Nsv/51O1TPrsU95cuTesJY4rrDgABLV23NEMXTxKT3h1/MuWfKadepqJOHDAQAlB6B0TMxKtG39uXDB1q5cICYoZmJx7JgXzLvjmP47m/e7ckcVpLgwzXYydBbyGuCZmne0XPOPbnFqRrHJDgJFEUKf6xGk00ralDviXbM5hDYU+GIxoQ6JUNDKMxWG6D5ASWIfUfzBPWE4hXi0fcHANhxrhD2kuYwivIHkHoi6fTEyfxSaNEcPM1E6Gpr+ctrNjYHaLUEPJsJugiMluhwOLR5x6FTN11zWWJSv5fe+OCm2++/6XY4eG7P9o2fPb9bUM/g4JANm/8OCw2bM3POm+++fv3kUTY7U86NTnWHpWlZ9bRy6qlqRT2tXOcYsPQ3Tjq7k3oSAdIcOJr7pvlm1VZbsFP2xOZn/+QIQT2358DvvPnzbn38oX8LB9x6yUDozRA9i4UGiUn9Hn3m7TEjMy8eMUjyjCmpfVHTnTS9SwTPHYax3ztlzBdb97/9wNyzRS4jr5WOv7tpA/qnJjgzzz7btLt/kH7qWOeTfraJl2AJsm6Zz/7c3qqsC5qOueDkmT3UwlttNEUSaA1eW98SFy2T+ykBmTzoIb7P6fh911FPd1WxFhBIAkA2ADqrxqGwIvVE7C7iO5x7Z+lWPiY+JjZyf2k18KqeiC0FpSCC77Us0MJJp0BlbcPpggo2CIpdYcHZBTddjfbfcvs9s6+7adfYS7L2baO5yX2NjQ0/rFrxw6oVe7bs27t1/7yZlwgnie0bJXmW2uZG4DYCvbbemJzQp7GmHt2M4CYNuEkngl36246uNzzbqZ4/8+oprN/FnDlXwzB2wAKknm+/9t+pk6cu+eStE9lHKIXi0ade+27bqVsuTqVFTa93Hjz93uc/AwDc1RNm/+WX0gy77eTZ5W88nHfSY0N7+KQAfMElfnlST/Ti8oqqUhP4d/D+aRM273PxKd0/bYK7hsqqJ1rKeFdPAvi2HMR0FbyA2uy0WPLqm0zhXIs5LnneJwG9UBMv2gpUTzd+PgYjPO3gVG0dCFJfN/v6Zxc+N/fqS4X93y/7csjw0U++s/SSjFgqnFIqlWbHENCnnl/4wdv/E5+ktMxZ0J+dC7W4j1yCLQvYrFNFieH8e1EjfKk4qG2AQbMdh2GYuzuo54wrYKerTZsOW9Qyfz8S9XRfIyNsVhtFEUg9Jw8ZOHXy1GVfvn8iG5ac0Xb7e68//e4nK595f9lrD9+Cjt958PTOPQeXr/xu/jzYY1sCbAoBwLaTZycPTvWini4vyvXCBPVE6/dNp/OmDXBxsE4dO2jzvpNiI/SuyeO++mdva+ppa109WeaCD4qVvMU/t5ZT0SsElGWBobFFUEz44ax33mz0bXjcBX9rzx+rjp5GGxUl8ivurdu3PLvwuXwDN1PEQXK/1KwjMIVzzap1wcHBEx1/eS8993Jzc3NeqSE1Vr7/PmyWJYfVRh8oqRAE1BPdQToRmzYdhgtqhZv3nYNl2WW/bvOunghG5PEEAIwed0nOkd004Jf888Y7Q4U7D56ed8ftdrutvpB/y2TU88CpyaNkLFNZ89NNPfnFlcT7KTZCkYa6PEpBChoqq5603eaLC6yVrJrOQPgLj3B8T4j/5ienpaJaEvdHTXartaMIdvOx3LiQiLvnuURfxfhBphoUUJudNjQ4TU4oms1trjm9oLmGFxhDnaGmtubQziPHs49//+N3sX3jHrrvYQDAQ7fOgYbYddMP7ji89uc/H37ywS8+XBIaEjpy4nCgU5ypl4kCNyFrtLL1AidZthZA120Xo2igj2VLBUupVKb3jz96hr+eoUNc5sECAL79dZstCH5MJyTEHXdNpSgpgTlecXHQR0lQxM6cU0g9KUC/v/jJx5/779ufrDpzKnvdr8uLClwsIFan/PfDTzxxzw3JqYM8qWdUlNS6Z2kbQTmrdxnaCgjSXT257zBGpaQk6uluhLqj4grqZNXTcRGAdFkEuh9AX9h1XnyQdAaPQHSg1BQYlhF7PKckLiRCNqFi8vghaOPBmdM+XrvJl4w0MToV8e5vfPJMtxDQJqNFopgWqzNwGRji8RcnRilKL09Jjj/riICPT4zaU9i+nnPdjj5xkZ6M0CtmX5aRlrnsi2+HZLwDP1RbN/3nhYWB3F0sy46cOHzhY//53zsffr9y+TffL+3INfx8TMa2urDYQ2SS1ViW/WLVP2OG8YH1Y27pR1ZOPb2AZJTiMmGLimHbn+T46PLSwrlXT37sqUUTJk557Jk3obuptvr+maPRQ/ZvO/jyk/fKSid0aufmA51ioCjmwxcaiIDqyWVauKsnyw3Fko28m6z08BH9jxxujyMIBf0or+rJsvzdn26Rphx0DfFBYSnx4c+89mFEVB+05/SJ45+//yraHtwvhiCI5JQBJpOJoe00TZOKQIZpRewvnzhg867TbVJPncr5W5J1dzizJuTuZQE4W2rYcazAYrGxwbzEGWtd/np14XSbBfTEOakoKLTosw8JD/Qpr14fGSF8P5IkkZqSUFB4AayhC0h2bhY0LT3wzgdvvfPBW8BP2VNRM76PS1BrR0kFCJdfvLcJceqSodGkUSlYlnn/rUWfvL8Y2qdq/Xcrflq2Jff2S9Oe//D72urKslL4zY08zs3NMBJnNFlPF1bROvinPiqhT7MR7gzUBQrqicxPXjoBWPrbzrvmz3S/ErWKEhsWAlemDb5m5hiTI21QFjtNy5fhcnE/hXfb07F6/3zLPnCBYAF494tVLMv+95Uny0sKo2Nin3r5g3e/WIVq7aABMeO6y66C6y2B97jPQlxIRF5hZWpitLt6enouT+rJvVkwhvzxd5tGDpDGG2dOH4n8iJ9u3p0ZIZ9fQQCQEqtPidW3WGzf7oTuNaZRIbHqjbVUgJoiAuH7YhTVRuqiSNk98K/KxCglb2BGSjRwzE6qbLDFS6PEMozJiAcAINHsbdKJcclFE7GnWPqXIE0dkMujqDC4RL0FjCbzkuWbbpjFf8BOVNYCUPv8XbNf++p3+Ec7ClY9ff7DX8Lxn//w18L7bzaaKpF6imk2NtfW1uhDQwz1DWhPTT1M9Nt5JG/UkBR3e1mjgcG9rWfOul+VWkn9+fehGLkuZQJWO9PiSDQOjxCZwD4krKC842Wrd4HgC1N4HRcUNmRQIgDgyfv46WGV5aVP3HvDu1+sGnnx5Yd2bAAAjJs4xdjc9OJTD4ivGQGTEd3YsPP0xaNT7G6Tbr2qJ5ROeMJApbsrY+1ffJoNAKCuqSVlYHz+KZc0cDEBamWgTdusNFEkkZwYVVBU6d6J2FRlQ0skhvO2G6sYFUmrSGB11P4hMYVvSXyf0Ioal3GYZ4pqhqeEiifKy5KawiexFxSW9hLRbFO7uQgSfjhr+uIOdR3lbJ3z7zMkof+RYzA8heifCU0PAMBNE5LPULy7acTQEV98tGT0pJEjYqNO1MjLMTRpHeqJ2FpQAsLa3DDt92PZoKZl4hBnLMud/XnVsyfzbtlah5LWNkDJ3r7/7DWTpA5iMXo9/CSaL5B6ImxWzlJWKu02Xg2HZiR98/Gbp47xnW6CgkPeeOlJd/Usaahxjy8hdhzIJ9qinghzoHJkTPzoNGiuuaPkxO3Ru670op6IW6cP/3TT7ih90DWXpFmtA46eKj2YW2yyOJ9IG6U0VcGbJKCRhiLpVJG0oKG8gCZGaZtcQ+0NTc6bA5Mjm+vrerNo+sLTc3k3nJglH+R5ugsA8OZPuBlzm9m2c9uSj78yGGofe/ox2k6/9uLi5KTkl+6/AfpJ6yxsOCsezsowzMHiCvHDDxY5b5Z4mICNrObx8bESA1HW/GwVlgvSWgPkHYJmi3x1lpi8gvLNrjV1XUlcUFhqfLjNZrVaLG99vIJh6LcXPa7nvN6nsg83NzqVIXPoqJnXwKrlstKiWTfORjsjtLDSxBNWO+u7en6+fg8IhM4Wi83uRT1h+sTyzVNHe3QRuKNSKcZkJo7JTCytrP/nwGkh70gbpVTUmppoBdJQgnNO2lkXlyYU0JSkvs0uI9TAtFGxQn1ntD6wT3hgXj58C5MS+2LRFPCkjIhxk/ou+YDfgFWn28u8PByLqY/8++nHBvYfuPyrFd9+8R1XmmWdOXFIIN35Ve3uzgdvRATsFCXz+s7ugtJWDcsLqJ5innl4/kVTrpxz44KnX4UDyliWXeRYsKOBFGmZw5/413yNNmDxu18c2H5o9CTeKekFlWOab4zGGalWcs5DG8qwFKsnR0ZEzMShyZ5O+OGG3W3KVbjmMpehvLHRofNnjPrkJ+eQNHu4NkikobRbZEyRlAg/3uOGJOSf9fA+EaCgsAy1BSkolKpAr8W7egos+fUJtOFJRjFt5dSZU+jDidDU1gRy8wXEHD562EtA74Kwav8JTzkMqxyZFTcMldpNhkaXyreup6S8Wk3yFt/K5ctWLocNHxKTUl5+5+NX3vnsxbtmFZ7JKSouFapISErx7/tveu+zH/qn9D+Tf6ay2hCilUahj2c53697rrz0y/VbEsIVR46eiIvv2zdc50U9vSCYn7L6SQBm1ebj1091tG9naUBQUSqZRGw4A4mLIEk01MgoScDYWWnqiAKZloiUlPj8/OLExL6FDsWEFYrtFU3fO+P5pXru3V5292M3SXZiGT0fFNe7/MX3XAQl7W588Pny0pKid159Ft3cse/IxZdftHfLvle+WiME4gW2ZcOs3ksmTj6T33pe15k8Z3uOkuKyvuH9m1usuafhA4MCoZjuOnIOBDgN2UGxQUINnqSaGZmfqUHOdCAviB21YmSLWOzhWqaaJgEbFgk1t64aetUJABQU6bKAyOfEtN2KifCbrM9OYe/2MiSaYsZN6isxRZ+eOxqv4tsXqQuinW64JjxRqtMJVOQ3NAFKMWrsRXADoYGKVlFeGp+YXGCwbNu+xdTScvs8l9yv7BOwtSDZWhKkpO7ORjO5pwuFm7uOnDOJ1FOCuJq53pnJzuYVlA5PcSmXiAjXS0oSPtuyz1P9279mTPvErURKG0mZqmlxJRj0btNM54T2sGh2XEMx7TP/P37ntKe78HdSZzHrhhmHdh7ZtWnPzOuvquNCylfPmB2fmHz/TXCOzm333vrtF98VNxmtXH+D226GQ2v27ufX3XluSWnNRVJV3VdaPTYWfmtW15nEgTsv6ilhUz7f2SCvyWWyr8CUYXGorZqh0bz5LDzYPVkNodXKZ2JEhATYzc0RfaLrqmsFE1chXK4kI41l2VaHFWPdbBV36RTfhTW0E93N361ZKPEyYz3tRCZOm7Bz0+5Na2Evc8RTzz95Igt2U8vJzc7LP7Nn6z6T2aTVwFYMM69vfcqOhH1clx/0rwPZvj/t8XLU1HpMZfMdo8rCVBmLm+pDlAqbI4NVIWikRCw9qScWTUx3k07ZbyxPaQ/YLG0fJrPJPS4X6Ni48fa5FEUNyRhqtVpyTsg0HOlSwjVCAyAfEy1Q1hpiy+l8Tw8ho3RMlZEW9Zj3aQmPRfN8gM3PTuHBhXwjO9+VFHM+oGlaXODQs/A9aw1qqOimwp90073Th2xXrvMRyugg2Cw6T75mrKTt/JsvQ0V0qXzvEDkq2v6Z8j+60VhjX+gm79mbPx3wcXUpmD/iz7bEJsLqeb41FNOJCH/5Sz74AejVvbxYWXGgVD5o1XO57877w8PDt27funf/nmwmJIMrSEeQJPnN598mxCe89+G7f6yDHSjajaB6bVVS9zNgukZDcdSurfj4t/10Ly5W7mEWqHcObD9EkiTLsna7/brZ1wMA/vfJB4dXwsozAMCQjCFLP1tWXlG+a++uRc+8tPDRhRdfPrHjT9ob/kp6EN4tUHjvT114Nb2AJY5aO4RsFoQff0ZaaWrbg9i0dgtJkqMnjRx18YhxU8aMnDh8y7bNj/7rMcrR6XnpZ8t+++PXmddf9dxLz4y6eERAgG7MyDEX+qoxnUmr63ccUOpEw/Nut0I79BYIP+ITtiOhokfgPwIaFhr2x7rfxVPsFz4H+2tdMfcudC8A4LW3+R7aLMe7b75/4a4X0/lgfexi7n7spr3byzz92t2V1P/wnyW81Wa9esbsl994Sbxz5MThyAeaNihNcvxPv/5443Ww+xamK2N6bcqLeOy9P68a7Wy9c/nF3vpmInAc6YKw12v4Dt21etLsOQ91KPDQDfEfC/SyWVMBAId2Hjmw/dCfv64fkOrS2CY+TtqBdfc+6bRuTDfkzwPn/jzg7DfhC8gmkphFe7eXzXnodz92xl1w9no2RQVWf8T3CfUb/McCbW5uHjlx+GWXXv7mK29FR0X/8M2PcOR6eemzN14se7zJdIEbhfUGCIIYMWzklZddmZOb/dfGvywW176zDmL7xjU2NjQ1u4xFEIM09HA5HGfkuzfN/+yd7s/e1jLJVn/kV3ao/wgoYuOWDRu3wCEtAIAXnl40Z+acN5dvfHr+ZVm5sDeMmL4x/uya6Q689uLi6ZfzNdHXXn3dC08vYhhmwWTpBGCSJP/4ac1/Xli4aatPU8KxFdmdGefXHk+/XcJTFPX4Q/+W7Hz1zZdzT+bGJKSgfgeSAv9LL+FbwGLOB1GRUdMvv2rR4hdHThyOfqZcNZkkyZe+WC0+jCCIA9vhODCS9M/WsRj/xk8ElKbp+fNu/dc9D0r2o+B7gYFfOQ4f6uyGMGHsRdm5WV17mb2I6+fAOUVr/1oj7GlsbNi+a1vyoCHCnt9/XHNwx+EDh/Zz09t9bVyG6c7s7WWdB/xnCV9UUnTX7XdTCurDT/nM+evnXB/TJ2bx04+gm7v27vryo68mXTHRaDQueuYlhUJx5wMLLugl+zPHso9xEyXDDQZnqdvj/3lMXBgWFxt32ayphjrDoZ1HABbQ84OXbAdP3R7e/OmA+/TptmroOA9reX9ygMIl1FU3XgZ6Dt5zX77/esWgAS6ZLsu+/+brN56DIaZQONv+8//7YtQIGIJgGGb2jbPKysu6STORHor3t+PgjsPIZ7J2/drvfvg2jxvwIAhoNuOc13ho55EXXn3+z7/XeX86/I50+nvkCfQ2ZTMh4jQyH7ncNdtMrKR+pp7+JqCIsNAwu90uhHQD66sEAW0T+OPa8bfjxuvmPfbQ4yolP0RMnBQhEdBFr72wdv1a/I50BwGV/ZIT411VD3PJEr0E/1nCC6CRA5juwI+/rPzxl5UoyvfJ+5+OGjF62fazt0/qB73SoS5HEqSfuON7HLs371WrYVMlxJ59uz9bOF98wOVTr1j46MLQ0LDikqKnXngqL/+M98zcPr1p6eaHAuqdXvXuXkBCQ0JDgkMKi/kBYTRN3/fIvbF94/74aY0uKAQYpK1mSQIL6AXg0M4jDMPcevctuSdzAQCP/uux226+fTz3JYcO+PPX9dFR0Wv/WnPwyMEbrpn747KfPv3ykyXLvrwQF9sdUfix/KGMa9i1EBbtSoe04HTC88rmdVvrG+qnzpgi3llaVgLdKVQIAFIBJUgche9qEhOSAABjLhnFsvyQtP998sHBwwf+778f6YJCjE0NOp0uOir6ljtvPnkazl9b8+cfH7778QP3/AsLaE8VUE+0o9eL+0OwpHYi73/03uMP/fvmubes+Ol7YefKZbCXXOFZmXHhrU4wxHQ6FOc2UavVZrNZXOK8/sev0PbggWkVlRVIPRGPLnz4wPZDWo3WZMaFfD1fQFvVzXGT+i75gN9oNUkNDx3rRJav/G7+vFufeOTJJx55srm5maTIAG0AAOCu6+SDlngJ3/WcLTgL0/s27Vm+8rv3P3oP7WRZ9oePF6Ptg4cPzLhuuvght90ERxZj9ezxAtomk1Po+eqLjGI6iyvnXB4YGHj7zXdMGDeBZWH7qz/W/Y6SIiTcdu+tZWW+TvXCdCJjLhm1f9vB+fNunT/vVgBAVk7Wcy89U3CiKomb1SFBo9E8/MAjjY3OTF5MD0tjap+Auuf0etFQvJA/rykygoDixLLu8x4RBHHLjfNvv+UOfZgejmOvLH/yhoskaUxajXbnpt00TY+5ZJT3s/XpTXHaXhH6dJdLv+/zisH4Dsuyy1d+d9msqSMnDt+w+e/I6JgRE13sqqTEpJ2bdtvt9lbVs7fRU5fwnQKeMtZB2mFrRDiStHvzKMduwqGdRxoaGi6dMVm885lFT18+9YpbHnnx1z/Wolzdexbce/9dD5SVl826YcYFu9buSm8RUO/1uZgu87Es+SDP+wHYhdJlmM3mkBD5WqOiwgK08fpLb14x7Yp3Pnhr5c+wIALTGwVU0E33bq+dYoRmHTzVwTP4B2kJ0IOGWPzRn7LH5OfXow36p39qqlq8nzC3yNCpF9gbMbfIN7EGAFxy5cX7/jlwaOeRN/67+OfVPwMAkpOSVyyFQvn4XXNho+uY2CumXfH+R+9h9ey9QaR2TGpsqxGEBVQsnV6IiArYtwMmFY4V9ZvwLqNYQ8+fgMb3iwnQBmz7ewcpqqNlGGbG+MEtRljPvvHwOZWK72MgZuREZ1vIXh5EUtw3HY4SEkNRraQ0f7LWp87hGIyEmqqWlIGJ+af4+k5ERFSAFxlNS9BjDT1/tJhaRk8aKdwkSZJhmEBOPQEA4y8dKz5Yq9GGh4dHhPcifWwVRW2NNMshJCzc+2PcNZc/F0VyFWFsDxLZjpufmLaSMjCxpqoF6aaAcLPVdT3m/CGeCu6OyWwqKS0pKYX1uBiPPtCGOmcH3HZz/eghrQqxJzQq6VV9vHajZM+bPx3wcRXfpjm3PqrnfVe5fIV8UMb79dzJK6v08al7G+4a6otBimkrmgC1l1U8pvsGkdotxJJCh/CISFmbt851hqOd9vjlueaPRqlAO70ULl3VHpwFPcIfr5HqtZjrRw+prYZme3ikt7VMbZ3Lk2LaRERUANbQzkIToPbRWSnuv4NTzfwkCu/uZGgf4RGRrUqtdx8FSjk21NbwF8bJKADAYvbwDc/3uMHIgyRS1g7FGtoFEb9W13A41cwfBLT7CDHLsgzLoh42PhwN/0mNifZ0f0V1XbPdCnoxYumUXdFjO/SCJEsIeOodIRbWN3t3zKAXCWjHoRm2ockUHqrz6WjOS2CxyBundY3+5uOz2pSy+1VKmy8P92SHYrpYPYUGZpIWPJ46SDzNiWmvlVEsoL7CsqzFaq9tMPoqoD6cEPi7errfZahv6hOp8VExsRu069m7vezux26Svcu7kvZOekUzkU6BZtgWs7Wh2dl6tiOwLGukfTLNuifrf3i26NDHRYc+Ljz40SeL77TIoVQSa3/6j/hRhvomd3H84PV/Lfkfb+yIeeS+a35b8bzQaHnFJw+f3fd/hza8ERPtOk3JA7k73l36wQPuO3N3vCvcjNAHHfz79bP7P/z2wwfb8up7BV5UEvXiGYcLo7GA+g5NM0aT1WjqnIyQHt2AvejQx2kDYmfMf2v8jBde++C3mVeO/OevV9wP+/u3FwIC1GJJlRyANPSxZz/RaFSfvfeY+C6NWjVmxKD/ffpHTiHM5Th34MOJYwdNvHrRz2v27vtzcdqA2FYvMjBAo9VI7eLAAE1ggIZPR02KPrzxzY+Xbrh49qLJE9LO7v+w7b8JP2fv9jLvxua4Xq+k2AL1CRbG7mmzxWa10aB385+HrgYAXDrzFVO9RUsot27OnnzVIqWCSoiPEB/207In6uqNrZ6tpqqlpqrl4Sc/DdRpBw9IEPYv+b8n6hqMn3z/D2z6OzyVIsmEkQ+WVda98eHvXyzfvP6HZzv+Qrb+8uK3q7YvXflPabkhYeSDCoocN7J/x0/rf/iyYF/90ezVH80GvQ8soL7BsjTD2mmG6QzHZY+OIEXog8XBsaYWi81mX/T6j1XVzrzXvjH6vjFh9zz8qewZrDal5CfnZHV1TeNzT9xSVmorK7X9687rYL7t/Pf69+3Tv2+f/70Cx0igbQDAG/9b3VmvZfEHvwnbdjv91vM3d9aZe5spiuiFGooFtG0xnx688O4k/vMaHBK3a+PicaNTLRZLQyOsm9609bjZ7EzJWvXtE1NmvCT7cJ1WJXGVsixgWTD3dhj6XfHVIywLJowdcMvdH7IOYvvq9x/KQ9upMdHJ0TCfd2x6ampMtOQHiawgtVq1WrwH7UQEaGGPDJPomv/+51hyQtR5/uX1bHDsyB0chfcdLJ4QhmEvnfnSulXPvr6It9fKyg033/mBzc47N3ZtXLxtZ47NZvfx12q18sbsPQ9/9uWH92/4/bnKqvqCwgrxMfnnKiQuVLWalOxRqdSSxIbhQ5I2r3nB/RlTY6KTEiLFWboEQbQ0QTFFInumzOXZMQhfokarP5o956Hfe89vTPHzgeOgW3L96CGgO0HAj1knnYtlO+9cF4C88uppV78MVUyrfuieK6+dNeafv16Zcf3r9Q3G6+eMBwA8+/KKdpw292RxZVV9dFTotbe8I7mLoqRLJdqtnEwQYoFDR88+spCf0IvYtRHOm7RYLGaueEwswSyAJ0QDflP6eCx/8AIpGm3fQyW4rgn6Zzzx1zq+SxNi+ozA839F3Z3ua4H6ouxdKrLw00F0SvSc7eHWrIrr9kIzTHFpzWPPffPu//2+a+PiX79feOnMlx5/cObeA6fffR16LdVqGAR/9/Xbd+879cvve305c62hKTpKmqVE00xmWoL0yFrXVghtpLiEr8cVSE6M9qLFbSVeD1+FWq328h3abnX2CHfImdILqd1zepP52a0FtBuKbGdZjQQAPbeIs+jQx6vXH3jn3dViCTieUzgkPRFtjxs9QHz8uNEDKio9dqvyhdyTJZnpTgEN1wcBAIwd6zCEgoEhwQENjoDe6BEpe/efBp2Kpzq0NoFU2KeyC6/Vw1Yu7Vh8mur6QEn/s9oaX5uHFVU2Tp8x1MeD/RhFhCrCZLaaLDZGawL+SOeILGd5dprZ2JPX7yt+3XnztRPf+WQNaWSRPU6S5JD0xI+/XA8AuOiy54QjY/qE/fzdk+I97eNf//5yx9+vEgSBROS7Lx5xX7+3A7PF9uuKp6bOhMEuhYICADz3Kt+FqFvRKSosCLH4T09onW6j6dKqhrXbc26Y7PLl5wWGYf9aJ7MIuG/6pQCAz//aAnoHimunDhGaBrHc74VhGJqBEU87zVisdqPZ2txiaW6xWG201WZvbrEYTdZmQtJzrmfjoyM4PSK6BytfJ/H04h8unZix6w+XzPlaQ9OKn3acp2dkGKaouGbnhtcsFhtyC3RclAEAU2e+tGvj4l0bF6PTZuUUiRMJ/A93ITbU8H4MmmFO55VTdBtMKJIk/KcSuQMQX/9XpooOERbOp0bLqAbBrxc4pYUbNMNYbbTFam8xW40mq81OW2z2ZqPldE0R8C/czdUlP8r0D23mapYCtVIvWF1jS89dv4uZf/VF6WkJJ8+UbtnqaxzSaLLquPyhdkASxCMPzDieU7hlWxboPK6cNnxAaswnS/62O7IIeg93O+ahNRrNe46da26xXDdtmI+PLapsTIj2GHHqtqHpTgQ1vfTmA61zNL70hbDwCKWC0mlV+hBnkwgCgMkgVbgpfGUxDEszjJ1mzBYbLO+x0zYbvTW3Mz8Y3YRiQ4fcf92ZrTuyf16zz2y1xUeHdcHTMSz7wSdrO/206zcdWb/pCOjFsCxbWdvUYu60zgxHT5eDXkOnBZHc1XbBQze24fGNLU8CWCP4+7o9smoLTVqrHVWjbz+Z00lXjWk/pXUNGqJnByExcK6c2VpUUed9GlKboHxsmOsX/YXP4wdg6Uc/tkFMgwMAFw+dPWO8l2ANktdBSdI4I4u6HXPVllab3WyFvtrmFsve/JMdfBUYjH9TWdvU2Ek9xhCn6nrRHLDOFFCGZUkP8WWxmLbZOBUB5dUzv6/bo1CQGrUiGIAoPUzRGDLAUTshCpSxLEvTjM3OmK3QgWCzQ831SwcCBuMdhmWbWywMgwNCHkF5BQiWBWeKqnPPVqBS4E4WUKSeLNIq7h9PaedtM04lj122pqOTkQiCIkmVEui0fLszFoD+ic5fk/AaGIZF8lrbYDxdWF1QZmgx+UP8p7PoyelYGOeUGjy9ywu1jlwFuE4O1ZfVNBSUGWiGGZkafl6W8ARagRMOIWKFvT4Zp91nMlJ4RCRJAQVFadXKwAC1RgXV9lxpJ8x89htMjK8F75huix8NRjgv/HzguJB4U1xSVlZaEUDZAMwbhpzfIIC7bHItdaDl0v07CkuklrZYw7Rsk65tlx2m1YaH8tke6A81v9y/PETKtv1CjCarwXR+u/mhMkqMjwg1Tr7nMHkhK8+//ryl6QqNLa7Jwl0dRYXF5K7GafdXUhFtvtRALWzJDutANOp60UAL/8DeOTUyvSV1rDsqO9dLsBMhetCnuY00t1iKK+sl6QoXLA1FbJzyrTa7/e+eaC0hWUw+164iJc7Zp72wvA4AcKCkGPgFV48evXrr8Raz9Y7pmT4+5FypAQCw/TjfmlcX0WmpM35DFzch49yfnamgJw3+aYGyLFvbYDS6hUC6RR5f95dOpPg9wvPQZRD2lji9qry6zS0UHp8/meFGnFq4bLP6ZlNjs7mm3miy2BqaTGaLjStvc2lZ1Xuk1scank7UWewDbZWfDxy/dlSmrHndLQS0Z8C1s7vQF9HjSY7VG0Q1FwEKEBCq7Buq1A9Nlh4q/mWzsFaYYWEPeYvVXtdkajKa6xpbquuam1osVitto2lkSPUSqe2sPmSoz38nXZQ/QxKEwq0pLRbQttFB6zMxJgyt4jHuiFXVOxQA/bl+8q28GwS0rew0TdNso9HcYrZW1jTVN5tq6431zSabjdbq/bz43ReRnT08Hesn8A3ZL5v/B0JoS+Po+91HAAAAAElFTkSuQmCC"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "assistant": "The hostile’s S4 line runs into the cottage/stone screen at the low tier, while both allies retain a clear view; replace the high setting with H0.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H0 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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+entrIAX3HM/46+1Ts3KKpPJNngcZOYUBYWEBGqQ5NgemcL8LI7SLQASUQCC0InbEcxV1Zmx+VtcZFQyFvJ8Qbj+UdsPM4dLOGTmF0dFhPkpYV1tfobcd5K6/olX8sqVTDCZjhbZWMkK7CkRACQRC25AYG0JRkBG8nzzgT128Kt2V0CsSQlBZpQ0JDfHXMPJV/KPPfoqX+SEhQZIR2lXeEiKgBAKhVeZniB/qn1RdZ1xx4ySI+oGIGipXSQrCyqqa0JBAwFloiiqprHMY2ImNUNxhpFVGaMfGkYiAEgiEFlJhVU8AQFJS1Lrf/rh52QSc687QFA94SSVLy6sjwoLwrbKyyoDgUEgrpONgI/SGpVNQ/xBshHYRiIASCIQWmp8hVvU0WbgLGdcAAD9tPXHz0gnIDkUqarMGY6NCUF08BIVF5X0TIoO9KRplyAMHDZWM0K7ylhABJRAILak7qqgz4yvVdcbwqJBH75sraegNi8cBaDcXHkJI01RuXmn/3lE0BWkaKhhaoRL1F/Pos59KRmhXeUuIgBIIhOYjMz8tLEdBuPdo+qp75pgtyPX5y/aTEycMFhySopkJIcjMLhiQJOQnQUBTlJKh0Xwkex599lNshLp55jEvIFu1k0AElEAgNL9nHRSlo7rOGB8bqlCoIIQHjmfMnTIIdf8A4EhqZnBEuPTAtKz8AcnRSFCtblGaglfySsr04nGkcBMyQkkUnkAgdFcqam1pRjHRwQxN1Wpr1CovCOHuw2kzxqewwmy42poayQuK1FNAcnxmXC4MDIvoFSZWx0vhJmSEyhJFOznEAiUQCM0zP6XFO8fzCprmeZ5h6BptTUiAPxTW8tPHpeC1vN0gOYwglJmXC4eOHBrqxzA05eUjdsCT9t2f2hHz4NoEIqAEAqElaOuMdy+dSFEo4M6yHE3BguKysKAACOG+YxmzJg4U1FMeihfJyCkcMmKoTlsBhUbLDO5EJ8C28wSONocIKIFAaMnEDn8f1e+HLswd358SQuoczzM0VVhcHhESSFFw37H0OZMHOWe1p2cXPHz/jT4aNOEDQKSYOVeLK8woJxRClG/ftd4MIqAEAqEJpgwQJ1xKaOuMAf4+AIAdR9Jmj+mH7FAKWiwoHH+toDQ5LgJCuPPQRQfzs1LHP/7Q8r0HU6tKS7Bpmn2lSOkfnBAuruJxTL8LvR9EQAkEQhPUlRU6bEmIDQEQmIV6zd3HM6aN7ENRyA5lOY6hqczLBYOTY+RN69KzC2pNMNiH3rHnD9HYpGBWTqFfaHhihDeAQOXr0xWNUCKgBAKh2eZnXGIMnnzJCgbj/tTMSUOTJDuUpuCZtFyNl63/fFxCnJ/aThkzsguCo6Iig9BanoIQhZNUaH/Y/rOI2xAioAQCoXnmZ2LvyNy8krCYiFoDah2P9e7ImezxQxLlnUQqq7RiLRIEPoLPUyL9UkFkfGyYHwOFUD4EQEUzeaVoZBKqAO06RigRUAKB0AzzE2cv0RQsLa0YkNKrRm/BWZ8AgGNnc0b0j6coSFOUmRVaLLtSQqyegUJnZbwHBajMa5X9Y/2sRmjz3pHrKLdEQAkEgqdo641BYUE8L5Zv5heUJiRG1RlYZHIKxuaZ9Nxh/WKxhko+0PjIYOkI6ZcKUvrHY/XEfZsoHmYXVPeJ9qEp6OXZmOCavINu7v3b/D4d9o4SASUQujmjFAWjFAUteOCUAVEO6/cAH1SySQu+To5DdmhleVV4ZGi9kZWi52czriXHhUMI/INsuilXT+lmZEgA5GFplS450ltYuMMavUWptusw0gI6Mg+fCCiBQHCNg3pq640l1Wh+HIQoeZ4X8t4pitLV18XGIX8oDsojyU6JZhiVe/UEAJSU12jrjJEBSuxFrdWzIb5KhqLwQr416/cOM0KJgBK6kk1EaMFL3VaZ8wCAVx5blpaZJ7anoyCu1GRoCtVxhgfTNGWxIDt0zbaTN05Lca+e6Vn5KJlUWMtTFDRYgL+GhigcTxUVlYAuYoQSASUQCI7MHp7gsEVbb+zdK/zHrSdeeWwZ3gKRhlKsYD1SEBp1DeqAAIqC2A7dsOu0fCC8s3pGR4b7e4lTj1me8lKILtNLV4stqjboB9oxRqhnPlsCgdApGRqIUn/cEEuLy2oz28SeZ6t9pesV+Vcc7o2PDTVbOAVD/bj1xG0LRv+4/RS2HCGkWI6nachASldfz3j7goY6o8miVDj2+nStngCwgGIoDvCQB3zG5ULoHZgY6pVbDFrM3LEp24+nC0Zou8fne6gFSpaQhO6BXPXaCufFe3WdkaYoioI4wRNp6NwR+C7sD2WFlTsFodmgMzFejLCWD/ATXZl1rKwkKSs/JTECq2f21WIWUDQQok8QNbhT+ockRvooZO1FPMROKZVchxmhxAIlELqzhioUNeJu5pZL7XOrFp7OLMq6XMhT0GhiVUr6x60nViwYvcbBDhWyl3jWaFJ4qSwGFFi3LuOxwKUJ6omPmX21ODkhEmD1BCANFSZFh/gyLMtRTQmow2GvoxFKBJTQ0YtKaV3ZrEUloWNwNj85nl/7e+ptC0bXNBiLS8p5HpgtrIKh18g0VLBDAWpqR1NoeBxn1kGVpF04MT4tMz8lwUE9RdKyC6J6oeRQjuM5z0o5JQ0Vn0BWd3+1oDwhJhRf/9v8Pu/8fgm0Gz10CU8gEDxRz5p6k3+AH162zxiZqPH2ZWhkcbLCqnzN1hMrrGt5lDxPIzsUrfXRHka5xlVrjUg9oWv1jEmQUuvRBQ8FaZLGbMuESNT9fu7YlA4IxxMLlNDGeGI24nVlaxaVhI4hPi4UAmCxcIwQPlqxYPTG/RkN9bUcy1/IyBvUP37N1hO3zh+9dofVDqWQfYrKkGT4UJD3Ere4Us9eUkAJCJH9zONnmjgte5PTgeqaOq2utmOM0G4ooG24hCSrSEIPNz+DQ0RpM5lZpQIt22+dP/qF//7aPyk6pU/chYxrg/rHr/39xC3zRq3beRrHkQBFWYTO8pcysvFj6/Qm9B/YlHoKRmX6JZRn2jSNa2igt1egvy8Apg7whPboJXwiXeVy+9lqX+nS4SdFIHQWpKU0nupuNCGzY+3vJ557aFFGTiFFwf7JsXgWPE3DW2YPt+6MJm7KPJQiLKBE9eTRJS27YEBKglw90zKvpWVeG5Ac4+n5NdZ0RMUCi+VqQbktHD+vvcLxTE9eQpJVJIHgxvxMSghfMGXw1gPnkVkJIE2jNCalgv5l1+lH7579v2929usd1Tcp5kLGtR8BuG3h6JtmDt2w+yxqEILqPB0PSPO2VvNpOUg95femZSIh9kQ9oWs71NEiFTyhprljUrb/kb7/ZHsZoT3aAiUQCI2R2CsMDT7yVc8Yl2K1Kylkh+Iu9EfT7lg2KfNyEUNTTz+wEGnobyeUCnrxlMFNvqStUU8B6N4OLSnX2oxQFYejSe1khBIBJRB6Os7mp5CrhAzGH387ER7sM354H2neBm1Npz998cryBeMyLxf9duD8qrtmp2Xl//jbCR+NIikuAvCi/UlVF1DVBdqrGdKR03MKndUzKjqqOSt3dxuvnr8Q4RcsD8djBCO07SECSiAQ7BgzMiU4JEASyh9/O9ErMmDCCFFDhSwm8a60S3mzpw7LzCna/0fmn26ZhjV0VEq0RuPttIJHt6vMVEpKL2f1tDZXbgOuFlQ4GqHKdjRCiYASCD0aB/NzzMiUHfvPhEWGImPTOpzjx60n4iMCRg5KxKYeTSM7lBXS3QsKy8aOTMm8XHTsTM5NC8alZeWv2Xpi6ZT+RoutoTKmlmOCvOk2UU837kxkdarYCL/gsxn5NiOUby8jlAgooZtA+hu0iXru3H+2d++oKSMTA0KD0aw3mjJbRA3tExeSkhwLhIU8RaGqIdxEuUZbM2JoXwhB2qX8WxdPAACs25Z66+xB8iPX80qHoXKNqWe/pFhPzhw2JqPI6qwor66JCPHFRihgle3nCSUCSugOkO6ibUVsPIodbdh2ctmMYRHRYcKgTZuGDukTmZwQJa7laSHczqPruvp6hVoDITybfm3UYGSobjt4QX5YH/v2ym21cneMyAskREaHRvhgT2i9TlddU3cpt6SdjFAioARCD8Wl+UkLafAAgK827FsweUhYZBhey+OY0pqtJ0amxCTGR4ZTVZF0dSRTHQoqwqmqCLpqXj9FINBGMtW5F1InJ6ki6apYr7pYfxZfwmGldDEXZ43ppe7now2nquQXwalakCosvW8fZsu391xDIQDZeaXAYikoqS6vri0p1/oEqgK9vfrExrWTEUoElEAgIPXE84qFeXBiOw+GpuZPGhQTHyms2cXA0ZqtJ0YPiGHC+5ZyQWV8EKTE0fB/nL86cWgchx4NM3KKGusHUlBUERgY4K1yoTxplwriE2N91Y02EsW4zeeEyYnI6RkTGhEa4d0BRigR0KbdamR5SOgJqUs795+VxnVQwriOT9fuViqY2eMGxMQhDaVRLzs0CH7t76mThvW6dKUUAFDOB5fywQWmgFIu6LdztdNmzSino8v44BI2ME/vm19D59fQdRHDSoXd9mbU1/sk1itCSrkgh8veTF18YqyPK2FtOhNUfo02SkYoULGORuiYNjZCiYASCD2dGxagyE9iXMjLDy/CGopllAfg07W7FQw9bXT/yJhwiqIYhsZr+bW/pz6/agHWUGGND/H29TtO3jRjgELtYiqcG78ntj09VU/x/OxkVLqaW1AhGaEAAEcjFLSxEUpd+OGRtjoWgdAeDA2sa/Ii+do82dmTdjM9zfzcvhc1QFqz7aSdhgr/frp2d1xU8E2zRyENFfrP47X8um0n//Hg/EtXSh0S7H/afnLZtBStHk+cawf1dPB6OsApAG0EDOPSCM0sLGxbI7Qb1sITuhmkuUEHmJ8JscFGk0WlZLCGPvfBlgH94qR9Xvnwl2dXLbtj4bh120/k5RYBiAJNDE1t2HHqHw/O32DtZUdTqOGISkmv337yltmDNvx2DD88QElL6pl5ubBf7+gWq+fo5z9xbLdsbTuK/8swdG5BRa/I6Oy8UsEUNQpGKFuv05l55JoAKmQpt1V1PPXqO2uIEUog9GTz8/M1e3A7ZDFMZG+HYl758Ne3Pt9y89zRMXERqBQJimv2DTtO3TAL9WES+tihocT4IHuPZUj6dLXS4Ll64rr45iFTwphg/16R0YA2IvW0GqHASNuM0CvFs8cPbCsjFJ36q++saf2BeiZkYUjo6ur51bq9yYkRfZPjaQoyNMQ5TFhDnR/+9udb7lw8oXdSjKChABcj/bLr9OLpQ2XF8sgOlbf4aJZ6xsd5UhTvzngsKK3MLUQFndl5pdgTisGeUJTZylhmjx+4/2RW6zVUXMKvGBGy5hR6yh5CrL9Hs149HwlLIHRFblgw4VBqBsdxdyyb+NNv9IX0K4BH9UUMTa3ZdnLFvJFrdp+TduZ5HkL4+uqNz6xc+v0WNi+3SBhhhERz896z86cM/h03vqPQiGPcU1nCc/XEKffuZ8bJ7rQ+ibCAR5sYJibYHzDBkhEKgAqojNgIBUY60N8388q13IKa2eMHmkzmNvOB9igNza8Rc83cT5UgbUMJ3d383NcvOYqj4Atvr33piRUsy6Vn5VpYDs/wcNBQCCHLcTRFvb56IxJZL29o0LEcd/HStcH9e/1+4Py8yYO2HbyAipSEek/pWQK8lJ6op3NDJr4RDfWNmQgB/p99OzsegHPny8vqjTyw8MZe0cHZV8SgPO4wEhEaUF1toCg4d3K/7Qcvzh4/8G/z+ryzreUDP0gQiUDo/qSaXayLb1gw4cTZS0ikOGBkueffWvOvv93y7S9sdk4+x/M8x0MKOmgoBdEan6ZRlqiuvo6nVV6UOSU57nxG7uD+vbYdvDB9bMre4+kOsqfFIz3cqWeeXD3Dxi4a+/Rf8HXkKZDBcbauzC6AwKADoZE+wEijQDywM0Ij/IIzs4rzy2shZbmSp0Wpr0eRhoJWYPdnrBgR0ppjEQiEDsaTBK/GzM8v1+0N8PYO9PYO8vWJDg2KDApY/fWO+hpdeEiQ0GyJw+2U1mw7GRnga734IQ21cEKfEXrKyMScglqapp55YFF6ThHKuwz2mTa2f2Nni9QzwVE9axssA5Jt9qknCIan67vEHE+GKSitvFZYKebVA7DrIDIzB6REJkQEzBk/fPGMAXj/nUcvPrNEvN4CiAVKIHRzYv1ZM1sX52UnoDqDJS6Uqc2/UFyhjQwJAADk6e3dWdbdeQAqKmtdataOnSeCvUBVpfa7Xw6FB2gqK7Tvf7PT+QQqK5C593+P3SptwRVNe/ejOXT5rOvRZHKalXBk0AG1xih4QkUjdOexNAj4XX9kLJg2LKV/BDCxgFMtmjFgy540q4YOfH0Tut5aAe1RnlACodsnyWI/PhXgk6e32z64X8L5zKtIHzW++K5LucV9eqEWHjgB3j5GYwO3X2oBr723Fl+RlHT6VHEOnZx3P/4ZBMV4FkFyzf6TWXMn9wUMw/EsxaoAbZw5IUnNePM8KC6rjgwLBEojMAHIqBZOT/ltb3prNJRYoARCj6Om3lhVVT0kOerspSIe8HjaW3J8JMvxEICcvBI0lkOwOXmepxhRrTgLisILjkj+x4+fvGPVWzwa3cHjWI7nq2xJSYX7pYnw7k7YZTTp+L/ud/U4tWSEUkaao9k9R3JmjR0AKCPkVJFhgVhDcwuLe0WHUoya43iUXQ/AXQ8uBQA0V0OZrYfTFky0cwEQI5TQGjwplMT5YU0mh5Gx0u2ESilaTmNH9hULeyCSxey8qgB/7/je8ZK07d53vG+iYJbK4jlPP7YCKOnvf375jhufA6KoNtsyxYVDwr9IgnHhp/MBavIOyh/iGzMJSee/kXR6YoTetnwaAOByblH/xCigNNZoLVhDeyUE5V4t7xUdunTGiF93n6JpeMedL3//3XPN1VBigRKuW+Wl+xwyQjuhN1kkm9FQW3c0rUC+Ui8qwbeQoHE8HxIShGUOAhAfG3Iuq7CsquHx5z8blRITEuA9JDkqLCwQCuKLezhREGBB/nLd3kEpCTXI+QmCA7zFafA8Twk9Sqq1DTj/aNT4kVNG9h4yKB4E+txx0/P4PEx112ryDoqnaFVlD8WZ5Xg0ld5qhN7z2H+/fu+vP/60O+MK0lD/AFCjbUAaWmLT0LHDYv44W0BRooY2T0D/cufMT7/bTYxQAqGH4HLF7QwvLNUFLRVX6Si1ySmL6HR6rsuHDx4gG71pNWghgFNHo+IfjuP1JssXa/cWbtq7cdNeLMcjByXqjRaO509dE3S3ReQVVydEB9mMUADueey/AIDZ4wcgDe0XXlpV6+/n7aChS2cFbBYCSmhu6PZnU+a+4uHTUYVXix3Uk0B69nQk6e/mpb9rV3ZNaD8u5TuGvPvHhWirtNKlpkpbU62tqa6p09bo6uoM9fVocIegfhYLp6+vd4gsnU+7ii99EiKRS7SRPsoiEOxPvbQ/9dLBU9l/nLuSkBAt3333sfRDp7IOn3KR1u65b6BXVBCuRsVGqHQ+O4+mJcSHZ2SW9kkMupRbDABA0SROhTS0sByYaJzYdMedL2MNbd4Svpt5Qpt0w5mBfyKNPkmkRvP6Ikln+rt5KY/b2v8QOgwfjfKOm2aYLSzL8fV6k95o5jie5bi6BhNFAQvLaytKsX7pjWYqIBJUlCUkRBtpFKsBAAxKScDxoqwrxXKl43lQk+sohcJKX4xNmS3c5aI6ShYfQmInq8wELdVRyrrqxzmhnOA3wBo6e/wAYKKRhl4RUw5EDRXs0MUzBmzek4YX8hfW/HXQCmS6eiCgNFgwZQAQ8227A23lhhsl7ENoJxwMT6Kh7c3pcpgY7JdXVIayL4vLvb1Q2fmCmSOLSivxDkYzSs8M9EMuyyAfhTDjktNa7ajg8JBEWDNh7uCDp3IA4Avr2GhfNFgOg69MGjf00LGzDs8rxZeEDiQ4YAWMZlav03mrFb0SxCz6USmxR3KqTSaLl1vF/OPfYpGSS/ATSLH1aaP67kvNkm5iDVV7oKHf/O+xZ5akvL4JJTm5wVYSsPWwo+uUFCYROhiylm8nTpfDmnojmsBRWRMeGnT34vF3LRr3p6UTZoxGXkKNl9LbSyl1edfWNmhrG6prddW1utoGvSRiXmom0M/rwMmcscOSB/RBw4exj1SOpJ7yiDyE1txSaxt5HvD4sQmCemJ71kvJLJmWMnJAjGfZUCLOPgPkc5AZofim1Ntk59E0lGCPNVRYy6My+VKduJbnVFKR0u23znpmCeq+7FEUvpsZoYROzjf/cVvUTGhrLuvFRTcAICe/HAAQ4o8Gb+QWVKRlOfqgF84alZtf5pCCea2gwmLhJo7sAznLpCHxDE3lX74a5ONVVY9EtrBahw09JJcc6owkjfUo1upYFgWgBOcAHx/m26A31zYYxVSqYcnp2QUX82tTYnwtJlOQn+qqOHrDnmZm77MsT9O2SD4FUBU/QyOTcfO+04unDQcmINmhEaGBJaUopgRMFsAgDb370ffQp/R/jwmJTY3aodRvx2zjQYgRSrjupL+bZ6iqkl+u9xl1N5C6SfVGAgP6xg3oG/fcw0sXzR49sF/8wH7xVvUEFzJsQXYIoYJBAllQVLbm99R5o+LlliZSTyGTiUb/Uvaj3tDAjz4JYQoFTdOUkH6P+Oz1+8cP78PwliH946aO668ODMjJLS0sFv0JLaOgBH1gIAShIX5Bgb5BgaKPDvcqZVnxrzYYjRlXirAdKr4sPLJDgZJFzUesdujdj77n3g4VLFDrK7lg8gBAzALC9TY/r3xTn3i3D3kf2pwwSq+X8uGFf1mOv5gpSqR0pTHSM65aeCbM38tgtESH+X3681H5vQxN0TRycvJISQHK9pQjhPI5jrewLOB5VjAGIQRFhUXYxo2Ni84WLE+7UUqyh3vI9NF9LuVVAMpO+qMj0Gg5YdSouAU7Q3FyKKrsFIuUqgCnwoWe2A7dvCft7kffc2OHOibSO4fjCYRW4mBFXvkGpcK4h2hoe6DXNTiIEQVhYky4iVIqVSqe55UKmmVZhqa8vZQKhgY8V1ZVb2qoxTsnxoZfyquMjI4qyi8cPyTht0N2gqJghIAKtK75ZU5QnEgq+CLRDVZYyKsUNMfxqWn5o1JidToD0ncNvQ+Fp0BgNHKwNlYg6h4IYYOR9faiRw1J9vfzMZrZOr35WkmNBiKPAc75lweU7DU0qLisKjIsCBd6AkaFw194Of/MkgHOGso8cMO4QhSDgy6N0K6byUS4LpAVd9cCQjBoQALH8zRFWViWoqCCps0WVqWgWdQTlPdSMdk5ooAmxCABFQJB8LeD6bHx0fmXUTsSjElpHZ6BovcY0bXKe0XxAFyuhEATzVNGg0lLQahSoKbmw/tF84DXeIn+WQh4Ey0lxrSkZwmEMCzQu8FgGNQnBnXkExIJ4sP9ahuMly7nYzuUFbwNEDaqob0Sgs5dyB/SN3bJ9OGb9p7GvwUo+uR0StQnv+DJebx0kTyhRD0JntAsf6Un5mdz9yR4Tligo29EpzcYDEadTm8ymgx6Y129rrqmvqSipqyypqyqtrZOJ+2prUXvCE1R1XWGgUmiXFbW2nd5EsjLtRsMJ8ygQ9IzduwIs8WCb5ktHE0h36g80D5x1IDkGNRbzxUeldpDITgGAci+Vno5v/xKfnlecWVlda3RaIwSFvLY7ra6Ya0J9oI/FFDISkUaWtIwZFDUuax8YLEsmTlA8pw691SlHrhtqgdnRSBcB4iGtj1OIrTnaJqYXSRd8I4QnsvIy7xcKO15RqjaPHEqw2gyl1fXm+qFLGnr/ubafEpfBBoKzTV5caEM32B7oOBs5ViWO3DwuDBYCa3izSzHsnxlrWFgUuTA3qIc8xzrq252gw4HVfNCrVJg6sXckxdFN6gwchkqGTo6MkQKKEnjkW0aCkC2WKQUBDiVqKEmetnsgZKGOgSUXJxrrVc4sT0JnUQQr3xTn/K4GCcltAfThifuPiIsOq3NkTQaPNfN2lPEHo5HDk21DslNAFfP0OK4DqWvmRLyPVmOw05GJS/mRVpAloXnVEpFndmQZdJwXl7YEWlhOYMRaTEA4HB6WXIww1CUsLRvVUKlfApIqlVD5QT7oVR9nByKO49IRUrJiUHZV4qTrQn2SEOFtfyy2QN/2XmBoamdR9PkzlAqI7vQffUqgdAmEHOy08IZ9ehiEv41G+pratCltra+tlZXZ6uK5sxohYu9h8756zRqxQQhhdb4QjKona0LIVSrGIqCfXx1MaraeE1dvKau5trFSKZK1VCI5RgAcPBk1uk0m1/Vc5xFbHDfaENDvcMF/yRU1OigQikmNsnsUAPLihpqTbCX26E3zB6Ef00EDRXtUKZ/cnTBtSIPhogSCNcHUuLZ3txz6yzUaI6CRhNyUOKNJjPLclxFte7KVRR7AQAMHNj3bEYelgmDd5TgFa2qZJEPFAJQYUTyiqIoQhY9zwMlbmYHgFnhB3hgofwuN0Cd3hRDoeA4zwP/XgNLjpZECocCAGWechbHOcOei5JD0+VB/XoN6hePe0qZLazRxJotrIXldCYuO68SmHVSgr3cDsUJ9o3ZoUtmosQmq4YiO9RuupOslp9A6ETmJynxbD9otVdhaXVRqbaguKqssra0ogZfqrT1NbU6jkXV8RhvlTgM3Jnbb5qBr0gRIbmWCZYpSgzlhD5JvKDOHM+fzyp0EMgBA/sFxvVq8d8il6+CkqqCkurCkmqaotRKhZ+3KjhAExro3Svcd8qw2OEDekGFUL0q2KFSA6fN+05ztMKNHSqbRofsUOqTH/e3+HQJhA5bvBMNbRNMJovJbEH/4itmS2M2E16E49pHjEatWP3yPaOH9y/IK9yyK3XLrlR3z+RkOuKgDc4iwm4AZ+syMaKJNkC07Hyc0ZtdFGgISlpVWIrEtLhMW1SmraiqMxiM/RIjknvHOMeUTCYDLpbHGlpcVm2nobJi+Z1H05gHlk8sELqzEAidk7zLXJNdCvGMEM/7E5JhIRJ7UHqjO6JCRFE7cCLzxoXjf964+4VHb8Jbduw7g6+oVEpbHqgVhTUP1KSMCAjwL6rWMr5KY2Ue4IFSQaMsU6fnqmtwkRTl4VreaOGK64waYdmN5hgfvui8z6yJA21xeVSaCpUaH5OuHhcp4bR5HFC6eq20f2JUciIKx6NG/WVVUWFBQwZFScXyeC1PRnoQOrv5GddbNDp+fKG6sZ6huDkhGRPSAsYPjDt8OtvtLr7yINJj984TUuD5H34/4690LWv5efmxcUI1kUBdvZhMygNgYVmaQuWeEMABcQEpsWPNLJdTXAdAGaVA1VAt+RsAMLFccZ0pV2uQkozw2SKbVyEkFTSuqpHBPmKRkpBg4LJIKSosqKi0Kio8SCr0xFORiYASuhIkoNTmQABuWzKRoSkLy1ksXIPeZOFQirveaDGa2bKqeoMOd6Hn4/smm8zc+j0XB6ckKBlmYEpifo4YMf/ih21sXQPlEy2M/eBj42KlFTFed5vNZkih7snRycOo2iuQhyzHabU1Ww5nq1XMgH7I78mZjQdOX/GPinZxim5hOb68wXy1Wp80bIAJtTRFD1g6fzzL8UaTRalU1NQbOaEAv1Krq67TVxSKYTFMYYlRLJYXFvKNFXpGhdtpKGRUcyf3IwJKaEdI6lJ748at4aGbgkeNPtVf/LQH3/zzzdNq6/WcWBePBEjfUIdD88H+GgqC4YOSKAoOSwi4mC+WeEqYa/P7xIdeKajkAYgOD9BaYzBQX8IoGd/wpOLc9HJtflSQN8tyBWX1ZpZL6ddLwVDDEgP3nkCnIpmNnoNa3xssudWGSp25t2x7gI9aGuvkY03OjwxGvaK5wbF6o5nneZOZpWmqQW/S1hnqq3EbJzh2UMLxC1fdaWikN2raZAJKRkME1N2nkHjWOpt6krEfzf1UyydIq00mjRrlGIUrUIKRL48cjt4+Gn1x5pKZI/GjyqvqDEaUS8QJ+tKgN2IDMC4yWGex0DSlLS+FEGwvKDiRXjCqj9XjiAreI2kIr1ZDygdFZorqeakfaOKAMeW5Z9WWMhq1ueNYoTEotvi0ZSVmC7unQgzDTJoy+mJu8/pv6MzsNa2hrMEkZXQiIKiqabCdm9WKxR6CLTuPy4+wYtlUvdGcKgooOHb+6pX8st5xYY1qaDHS0HNZKLGJiQsP6oFBpKGBdR6OJicQOjNNmpny0TW+BmOAjxolafqgenMLRBK3+QhehrtsYoyICvdH/+HB+bQr4KbJBRU6lRItuuls1JVZgkZl7bIeoLLrRUUlSgBPp+WHBfuwHDSZxbpICGFRlUGtYmp0opOUYx3zQN2v5U0WrqDWWFhrNDllE2zZcxoAsGjGcPnjIARqleLmxWi4PIZHrZdZhoYjhiSfPJvNA9RnJDE2zKFIyVlDcXIok1dk9yr0EM5W+3oyE4mEJloMMT+7CmMGxh8/i5rINQZWH9zZCEKwcFJf1JEEgs32uzHGEp7nk+NDr+RXooHygT7SCl9pLkWPFSPdvNHMGkxieuk9i4azLK9SMW+vPUH5+jKWZhRxshxf1mDO0xp0KHvJdfQJy6jnRAT5osalOMHe2tNeTLC3oHPOzkUJ9sgTyoEhg6LIEt4dqeaYUYqCZr0BBELXguP4+TPHWFiutsEIIQj09arTGY1mtrbBmFuslQZvoCAM7c1xfF6V+Wx6uslJ6aCQQ2+xLqSl0ZgY3P2I49D63cKK9inH8WeualF7pzJk/7J1dQfP5QZE2NwCbuB5oEWuT32NweIydh+b2EunN1Xk2/WFcglkbA34SqrqwoN8sTE9eUTSwVPZOPPUYDSqVSpcpHQltzQxLlx4UVREQAltDzE/uxBRQoMiBQV9glEXOACBl0LN8XyARqGg+KpqIY0coNh6//ggLIshkVE0BWN7xeVZo/BijzgI1EqGQUM7eLVMW6TSebOFM5tZJUUpBFWiKbh5O2qnecOCCeAacqbxTqWcjdEguD7LG1BJk/O9EIChfcLQFOXBMTzP40IjH28Nx/N6g1nodMrX60w6g6m23siyXL3eVK8zQgAVCqqkpDA8NJChqSNnLg/rH3c2M5+ioDjOU9DQxF4BkoaiP2PrEReT7AmElkHUs6uBSriFVu3IPGRZ9C/P8wqG8tWgYkeMwWQJ8FVn5JaLCfBO7sh+o+eg4iLcRwRCh5IhLHMmC2u2cJJx+sF3u3iTnjfpU2IFNysAoc45TK4wWrjCGmNRrdFkLcF0RsHQaiWjUtCR4cGxUSExkSE//3b4161HVArgpYDeKjrUXxUb5jOwd8igpNAR/SJG9IsYkBgSF+4b2zvBotSgvxKC85cKkuMjbN1GjEZcpIQ0NK8UVWr9uMUuIEUgEHoUVwpcR72Rs9IkX6fD0goty3KD+8WxHB/gizrCSRYoBOD82XNAGXGxEAAaxa9zKoA03h31uBOUDv/LC7YnAGDZkhl4LZ+ejwISHM8nRPhllzU88Zc5TqcD7V2fprwa7Pp0jdnC5uRaw2LCleReETfOHyvfh6Jspf1KBa1SMkaTpa7BKKQKgJp6zheYsguqsguqKOuPwcc/H5Ee8vgdEy7nljIvP3v7Yy9+7+4FJhA8hpifnRnIs7irfLoRNfEMFsYaV9boBvdH8zVF7BbEQktQQb3UKqbeYFEwNLoI9+UVVTgbo7NmTd6166DD85pVERbK4hcSoNMb1SovylKqFIqZkhOieJ4P8tdACMND/P84esbXRwWALf3IGbPFUqVHWZ/aRlyfAIBLBS4C49mSnjbOwdN2453VlIVmaLaxuJaJBrSZSb/Uo4MkoxQFqWaUtkZoPSRtvpMzsE98bJgfygMNQsWLPkLjZIOR/WFro8vQ6IhAPCdOraDzCkre+PBnjVrx5vP3fvXdbxqVQr5Ot1hYhqF37jyABnPiqnIrWOfGjhm+d/8xXUNdoA8K0vAcq1Yx0rQPAEC1gTXW2eazOcOxfElp1TWtvqLBLK90csm5iy76ig4ZmODmIQoORcxoxq7plGR+OvD2WtSGiQSRCJ2axLt91KQhfRtxITv/Ui76yveORq9pSABOdYd3LZuIW9Bh/6bJzGobTAVltYUVdQAYsQlK0xSlCQIGZNyt+WmHdExovUIZigEFk2JDCkq0PM+HhfhVWod6QF0hNHPHjqPRmxRFoXmfAo8899mHr/wFXz92+BTP82Yz60aU6hoMF4suF9Wa3Lg+JWaMH7z3iNjrBMND2qWqAgBGDk46n1NmodQMh07Sc4iAEjqv+Ummw7c5eMKwfMuFq2UD+4lLeGwMeqlQJD3AWxHsp7pcWA0suPcwjAzWZDuNDeStGormwqPOm6jtPM+LQzelnViOq6vOUymRdjK0r14odgIAPPnSV8P7RmlUyCtAsZarecXiTGMXZw6Ky2svX63TI5F1g/jXxYX53L1skpgDwLvySvCo9bOXWnX8bNbB1CxUkjRnBGoxdejC7LHJO4/bGqzMmjgIAPDrbhcppe6a6xEInkPErmsgpLJLreR4AKaO7FNcXuNwqaiuq67VQZ5LjArEj4MAJEYHNnJIMGpIslCJBLYdRkoEoV0jUdRPmYJ+3iqGRp06FAySWtxgVH4cvdHsF+LYE096dpZlC0u0DbpG+90BANIuF2fm2uoqpZajeCQ9vlAQUBT+FzI0NJtNOoOo5ut3nNI3CNlU9t7VXYcvmA0NS6YPc35GYoES2ozEu33a0A4litxOyN2OIcGuNRGrD0NTdTqj6M+EwGRylaTJo7tOnMsO8VEtnTHs/z74HW+Wu0dxx2IFQ0OW6xsfzLK8Szdn76TYwBp9dqmL6moegJo6Q3F5Dc/betM5s2wG0rhzWQX55dqiMrHDYZOcz0GFUpj9p9Aav7S6AbW2Q7a67UyLS8pGpzhmWREBJXReDSW0IXHefLXBrkQd2VaNd1OeNX4ARQmqZ92iZFwsWHkUPuIUwl0b95wZ0z9y5viBG/ac2ZuamyCbkEzTVO8QFLniOF5vMrsMAFnMZo3K9ZrYYmHziqpqavVC+X6jnM1EYXQ02c5jfjhU4XBIHoAGPUq2BygTVMy4Kqty/akmAkrojBpKzM+2ZXgokqwQTcigvnYTh2aHoDIkiZ1Hzjtra2QoGgkHADhyyrHihhe208KAYo5DHfAAALuPXvz33+8AALzw5EuorVxCH/n+PI/Gdhpd+TEpCOULf/mjKqobruRVmC2cLbPfFcI4OzBmWHL+rlQ3vw1ygoP66cvEmaAYiJLwKQgg8hcLGQhu+pEyCUIhF4HQeSDq2X5cyHIxJ10iMsTPxVZBJb1Umtnj0DyM7YdtIqvx8dX4oHY8haXlFOSl7hvvfLSusafA7lFpiJsDDhX0GJPZkp1bVlZZZxcMcv1wZH5ezq8c2i/RzW7S70SJwQcYChgmjAfA1z/QUoUcuDgghlbvgqfBfTdn5mpe0/mlBJIu2izIQr6T0IatcCTljQ71K5XWs8W4Ut41OAwfFRbqfJeF5dRqn4wcu04fuwWb0d++mQgPQGV1w9X8SvuyqKZpaGi44iqjXv47UVKAdNxiMSpVXtrKEn+/WByisoSOhtmbkO3ZVDN8soQndC4NJeZne3C6HPbzRdVHomuPgmYOzRkO1DBSzrt98ntH2LzRoUjIJDmOFNL7xc6gVliWyy2oqq3Tm3W1Ci9fz9WzWSdpMtoF90+d+AMwdvkAZUBMrgoJi6TK/pC2EwEldGo7VB1E0ujbBmEQJkrQwXEklEoEoYnja3Tm8FBfANCQoooag5pGIsvzwMwCXzWFGhoB0GDkaIj7gUKThff2UWkgaxNcHj0EjxJqc2rq9NcKK3E30iYprkSfN7VajNTXGFETKX816nCCfzzwlcZ+KjgeNzrB3aNc/zkVZcUA2CYbEgEldCINFeqOiGK2C7jdHM7L4QHw9lJCiERTb+K8vZQch7LKa3VmbxXNchwEUGdiNUJLOghBg9GgUSsoCs1cM7MWX29VUXldeJC3SqWqqqpF6saDgSm9c7KvIMEVxKlab8EpP6UNqA8xpGiFMBk+WCMm2MvkTHyIk64h+csvqq6u0TF1FWbaXQKTM5fzyzVqBRokpzOHBmhwHxMOgkqt3kuJekbhFqUSPt7ePMejvxwKeaDC/fKUL5cQASV0FlIejyNVm+2IEFcWr6CgkEKwRymNmmMYNKUdCqrqpRAiJxAyCs5LgWw3LDS+QuE8y3FmFvT2rvMx1If68D7ebCWn4zl0zF6qKspfqFkCkOP5asYUxCGvpb9SDwHgAFRRMFSlP1UbDgHUNhiD/dS8MAWT5/nqepNGga6jjFEp0M6Dep3pakGl2cIyACj93cWFHLhSUA4h+hvRct5g8VIxWKaRR1WrF9r0oR+P2UP9/rhwleO4Oj7AKpX4zxfjVbipnUPtlhwmqZdH/Z8JhJZBAkqdBKmrMRYDYcWNJCPAR40sQbGIU6FiREuQZhgl0j1RStQqhkcOVNrCQYpC1hxD095eijqGxgLjo0GFmlh65KadSsH0igm/nFeiVlD10Levv57n+VrGFOTLIQXn0HNVAX0VRIOFIbT5QM0WrqC4Wgi+gwYzr/bgb+R5PiE6NDw2HAmoUJMqdDZB7gssg7jEyGpx8yEBGjTKGABfqAUN2qAA70ptA+Uba7WJxTb7bp6Ryckt/svyiT1wrhyhY1AHBaU8HpT+rl2jMGfIuM0ORhBNdMXfR4WKjQTCg3z0DTp8K9hPU6OtB4AfmBSRU1iDFr1CilJogKYc+DChIeWAD/APrywTw1BjEgZU5KDQDX74pTpttAV5b2jf0N5hydoSoIfIT8rzgAN8mVHH+/kgQRbyhXLqqmLDFJQgvjphWpLJwlZWodxPoxHd9lErtEXpPlEpTf5ReLId1jyFYFnjtCTBlBQSO2XblQqhptQa3seP4upkU+OF5tDImG3Q4yIEnSZZ/oxkCU9oY1w6MVMej2tSQwnXBbWSxiKFRDNAU9Cgw8aXj0ZVU1OPKz9xiiUQ+tT5apQ14v5QoaBoQZsAAH7eanENTNk1FWUYyt9HFRniW6Otx/YcA6G3Rklhp6w1VUiloCFEa39gQS2TS8pqrxVWVVTXY6XzUisajB7FkeQDQXFvfOk6AGBwclRWboVQWkrxgNeoFPK2eGb77FQpRGY1w4UzacDVBMLPD9J/AqF1qIOC5JcW2JjE/OxgekXZymfkQRK1knGf0+4AJTwYNeZAHZH9ZN07bPuoFEzv2NCBfXrRFGopgnQTwkChob3VcyAcSjgWrpus0jZkXi65Vlil04vV90qGDvTzZBHvyKBkFy5KYeqI4OdlKPlIJSnVVPixQFcEdwQ/fnA8x6OIFiuE2nAyA8aFgN46Kviem1fcs3xFC06XQGguRD07EY1oZp/YAElmpG6eVgTVFDQUuIKioFLBJMQgF6dEkL8078N6FCto+Z9bVlhaozOYpIIlCKGfEMXynIRoFwn8aHZTfJCzXxiDn05yAWDLGgJosVhPw7ZZxPaDEzXmL0V/fCpej4oCAPzjsb8DAF597+1mnTeB0BhkId9cEuJbYnbZKJIdp0EIz0gq50ruWFYuDi6nXaKudBXWWcd2yA44IDE8LU8sUoIQfPXz/ntvnIpa4idFXMwpcTB1nbl8rcKMB2nIxAo9pHnNjpsGHV72FKzFIsaOcBKTKOtA46WoEdwPMoS8K8gz4SPvR85jYVvk4mdRqr2fbde0tItP/OVPAIC3Pv2ijc+d0CNx0NDuZ362VvLaGbs5G04ayjWZrw5R4KUFz5t6wa4Ryf5T1/rGBTS2c22dXqlkRPMT9Q+leY53nkTfepDLVY5cPYXImFBcL7SkcmVfQwCZvNwc6acGH23N0f3TJ49v83MlEDC3Pg7WvtuJXoxOLnlti/t6TTdzhhq1XKFLUxUMTAqrzvSoz8agpMis3Eq7QwqniBfOEEKVUtEa27OvbM3ugFNzEvTM4kbRTYoDaI3ngTqop2dPQyC0ilsfb2vJk69VCa7Qetv6Xp6tkVWcG2yNh3PqxO1JGtW+KyVJ9h3jGwWif/731TY3u8jrPAclR2blad3szKBucqLuIANQQTUmoBazmVF4dpIeWKBIJ63Rdrfp87JTFf9rO45DgZP1uARCK7DTNVkLHqJ3nZMcHfJy5gizLnLSZHmRAORkWmfFAZBz+rJ0vY+32o2hZRbMycZMsb5xDo3xUYNRfI3neWVDNQeh0WxxUDQ3RZZVGq+qapSFmnPOrpVJzrmr8l8FFFO3R3R9eqKdNgG1aaYYdiIQiLQRmsWlBpuVmJNeEFKUg69n1yKrNufsFfTvBfTjmXMulw7xytEZkxoJrKN6pPo6hQqZzHh8E6rypGDpia24AkoULwr2mzzmwsE/HIzQ386KT+0S/KsgXs8ptrvPGnZvzC/hWkDJAr17QCSP0CXt3PN2RqIKCaPMiOPBZTFvn/UaMkB/ztZnPmn0UNQTr29SYVZOaxbysqeSWbWe2ZHyZAJh9W+vpp9+8und9z/y/ucftf7kCM4QySMQnBFzmJpC9NhqvJxX7i2jBb5K5stP3r/3L498/dl/AQB/fuBuAMDkiUNNFqGmlQq6875VHMutunflh1+tBt2LVHNMC/p1E8kjENoEVksht6YTqkAx/u45XsMG6c9caL0RKpVueg7zp5WP7t29GUundRuajAdRl+bm/RmdE3eSV+SBJpLwLoHQCmAd98CtKIseYzKzmVfLT6UX1JmqGQAs0IXqlZTUBgXQZqMOu0FdohjYT37Ta9igVr5LSbHBVwur7M68GUt4qVmpmEp6PSFWHoHQwUQqA6+VVCutGZPGCpaFjhMwNcFsQ4kRUjbJo3hhTIhsT02Yi+rw1Wv2WSycGSjDgnxiwv1LKutq6vWcoDkMi9ygzjJapWWDAlqSsd9iPKn9d8a+oMpOPZtxOCJ5BEKLeXDBrMbu+njrLoctJdZBaRqgHDM4wfkh+85nxfj4J8WFofkT1vVTP7+A/enZqM5QqTGZLMNT4p0exw/uFTB19ACkd7uPIJuKN/kGomIhrVZMDtVVIkXjOTGKDSmFXDop3sRBpa7MadlqNliggkdaY6qqrCqtrJPfaXGS0epqJMpydOWcJrTd2x61LJbOeKieRCIJBA95cOYE540fC6rkrJWfrNl14ASKLE8Z7aLZ5YMLZgoyutthuwY0OiB9weiB59Lzc/JQh195seTUlOShgxO+//mwUsFk5RZPHNO/vNQxm32/cCYrhfP/cMNhaDaoVYr77ppWUd1wKiM/O6/cBNCaWm2q0EONXElRkzqlBvn9zCzkjHJhbbCovRkDALxJOGcFQPpotj9/Zxl1QFeOdLldZVTqa+cqhYn3yAIlCaAEQuvZfxKNF586sq9844MzJ3y8+8i2g2cBAPMmD5G2P7Bi5oET6QAA/G9jMjpncv+lT78vbdEBkwYo/zh/VW6E+vr5//P1T6SbjzxwT22Gre3AzXetNOh1jz82XK9vyL5ScHD/rolj+guLZR1rdDT6AACrbpoIAFi3++y6rcdQ8diC8XPH9xdr1Rv0363Zgq9LSmpk0b+QUvBOa/8GCwozeNN6HkmnokkZdamhWEahKwdom4AbjzYXJKAz56BfORnwiw8/fOjxZzRepFsooQuwcub4Dd+KudMrZzq2cVi9++j1klFnDQUAbPvj0raD5+QaikXTvYzuOJix8Y1H5BrKAzQjTtJQiqL++fonh/dv37T+G57no1NGv//J19+8/fzeTT/g3cdMnC4/YGnuhcN/ZJjMYsLQoGQ8As56bCu3zETnuW73uTVbj65YIL62ft5eq/58M/YweMlGw+uhBghiGhAWBpzq4BtYMd9IQYvBd7cyagAA7V9aarfkF5rOseZ2MEgdG4t4Bhw5ajwFDPetfAL1oPZxLAyw6EX3B8uxP+/A70T3ARdLVEQlNblPk7sRPMf3mq0xT118H88fSNeD+2+e7LBx9e8H1Q3QpwY5+AAAN911l/MDO15DmcqG5N7Iz+igoZhtf4ivgFxGkR59vTElxfaCuLRG9x87v+98mv1Cnh8zOHH+khXT5yx54qFbpT0nLbh1yYKld09O1Gri5i++4f6Vq55cabsXc+D4xaBgWxklllHsCUV9hbadWDFvtHTv2t3nAA9WLBzv0lHLCUpKQVgPNBTg4zixADSPaXQYnKSkzjKKeob6QYVKU1lSaYFKPyVLQYjrQeXwQY26MjykMlusTI2PDCyw+jSk4VE4r37W2KS8YtfF+/CDT2yyGOBjU3SLLEWLFabrEQFt5VvVjSkpKJ/aX3x99meIPznTBovfQzn7zqclszabJWbQ6PPZKBt3cHKM3W5CxENOREwoLaRIO2vo1+sPuRfQ66uh7mXUQUMlGa2p1+PM7kXTh8vv3X/svHR93/k0jbAiBgA89Oe7V9yzCgvonmPpM8Yh8S2/eAIAkF6j3rxtV02N9m+PruyfEOF8Jg4y+sjts+X3ymV07a5zAPDOGgrkjtoSpDUhXAW+VRsxEkWiKuwyhBxQ0QaGkS3bhZ5IKo1SodJoS8oYRmFrktRuAhoXEVBYVuNaQMck5Ql/lHsB5QN8aAcBZVlW8owSAQWdlZVCqMGZ1U7BB4lg2ufmuXbfTPy58dMo/rN+z9CwyLGDHCO8q/cgDertHTBrbIrL4IPtpqCh/oz37InIa1ZRpXcjoNJ190oqCahLDd3w+ZfuBbQTaujCeSNXvfij2cItnj7M4a6Pt+6mqvSR0dHWgT5g0bThzgKK+eO8aM8eOHiMomiO486fPnbv/Q9OH5dSIQjoRc7/1OEzVVWVQUFiW/inV91mMZsb09Ck+CgA+HkTbZmVa7ah48hkFHly+zt2AAHH8gudl26ltCjZ3l4qg8lF5jxEgSOeUSixRlIUmu5hYVkVGj4MGqrr7bS13QQ0Jty/uLzWdmSZgM4c3Tu/1NazSo7ciQDX//q7nXpyNvUkdCQrZ05YOXPCg9YL3vjggpkOF9T6Oyx6QFh0c4UVwztdME8tnwEAOH7hqsP+W9/+K37UzuPpiX3sZM4BbI1aWO73A2m/H0i7ccW9b330Pb6kHjwF0SBEkeIKbXEF+m2fMG3Ozn1HsXpiJcVMS0meloLmIJYUlENWjHV89tPBxp76603HXPxhwmXljPErZ3Roo1tLsHeGtiL7MuqgsU+ILMn5bdvJD1+8DQCwee8Zh7seXDDzgTsXFeqrigrEr/dv+0439ixjBveJCvJFS/7J4/58x9Kc7MyhIyecOXP+7Y/W0owtSvzmf98cNSRl/qxJ69aueePDH+vr6uWXEQN6VZr1VZX4t42/kldSWVEpXWaP6o20RtpSWV0p7tk0dLiPmwJNPBaJBpyYhom6F1OUMP6zSVqvnnZHc7ZwrWfhxjlqt4TftWv78mXzsYDiZbscYoGC9gdnkDiweveRBD+U1jdn0mD59kVzBz/10jp8fdIoMULqiR0aRHlDCJc7WaBAZoHim852KBAEVK1U3HPrNPRNu1TobIFKpGYWfvHl18nJfT753+vZmRdVavUzL77t6+d/67heeIdL9er+yagp/Q+/7AQAPPEXFJpojIxaLVNj5GgVTjT5i8wOlSzQen+ktvcsHufmONiU7kiYyoYkqyk6zckU3Xwk00ulmDlhIL7pIB6ffLslOjYaQnj1WtGk4b3PZ8q6Acq4kl9q4fmkWHFaXE5B9fFjJ1Rq9d2TEy9y/tJuVJ3RwsMz59O+/eTdI/vRay5xMl+M+0QpVWZA3zrbxccDs/5sFlthnDgs0RMLtDXBA7UZGlAKaXtx5ZLFH5bi61GhfvIcVaGxiNjXbsbo3gWNWKAuJpO4VM9uBrZENnyH3mMHq0SpYP63vVEDp10ZEBR26HT2pOGOH7iVM8fvOIFOdceh83IN3bL9/Mxxg3YfuwAAOJSa4VJG50zqO2dSv2XPfODhOdTq0MrubFmxpKHOzB6bsvNY+upvdvIAzBF8bY0xql90cnKfeXNnpSSiqTEGg+HFZ1a9/dH3dzzy7Pfvv4LEwt8rIzvv5Ln0jz7470MP/7W4Qmsys/GRtgFkvNOvy2cb9vIM0tDPfjpw/81THJ7xnuWTvlp/8KtNR+9d0qixuXLG+A7WUEuwd87la0mJSEM/+GHHxCHi7wcmzh8evngtK+easySh4EywV76uClboAHBXmZN65uLkSWPlW+5YPmf9lgOOR/NFhdroc65qtIKZC/OTP9P6s1ku7MoQlVwuuyKXL9n5E1xN95Dua/QgjgJqNBq7jbG5cv7k/ccvOmzcfz5LaablFpOz9SQVe6AfUspxY0SM62l/rSetqiwlKOzg6ZzJTho6Z3QS1lAHjBZLWlkhXWXo1w8tsg6lZjho6I5D6NO/csFMN/5Ql2ANPX7hqksj9IpOq6jSxcRE7TiWNmeci2CRnClTpv6+9bdRg9EZooxCq/mJeffjTwqLCrV65ODMupyfEBd1rbgSpyjmCdngDtx/E0rH+XTj4cY09N7lk7/86eCXm47wPPjTUhcWPQBg+VAU7F5/1m5QT7tiCfbOrKlgS133ClIxwDqb3TV8iEYStZwqFwEZbY12/4GjoyaPwDcpb5oKQj9auVVGEABOHT4zYqKdp/WjNT80NKCeQY3hUjc7GEN7mp8KHrnmG3i/uN596+t1HGfLmXXA3RJ+/i2NlpF1GNGa4EXTHP3o+As/PSmxT68IeRAgwctvjnWxI9/uIHzoR9JETRqJFnR2Gyl4KBXFJcYPTqjIOoUksq/4mZNgaHr3RfHTM5ASTXdpHdR+AoqGrAWhpbqzgGKwhjos5PFrhTW0pt4AAVjg9GLK97Qu4dGnYvlcFCF1QEFT72/ej6/3C0BLwqkjXCQbrd5zVNDQSMCD7NyigUlokqvEiYu5owcioZw6Z/Gim+4EAGzfvu30sd2V5aWFZw5LuyWOm/nqO5+PmDjshsU3PvvUcyMmDoN1hv7JyFIbPEAU7pJiMZ4rpddIfLbxiNlkvGXusH0bxbzuaUsX4Ssb96Qb9LpbZzf6UnSwgEqwpfUOFuixEvEPHBeL3NnuLTuHD7mcU4eRL9VkMp06c3LcGGSAv/T0w7u3/lofEHbq8BmO4ybMHOet8d61ZU9tbe30BbYGHw60ySc8pNPn/+Vl2RbsHI8SpKbOmMXzvE5nyD61Q97xfvqo3lKAvnkCunKBY7gT46Oi3voFfcdceuXx+ijJN3jmaBfZGxI0TX2483BjAuqlZN79dfv0JLSokTQUs+MIsiudZVT+2aJNyHSUCyijUDz9wtv+AcE0Tb/+2ivlV87KBfTWe1eNGDsJQuq3Dd/t37lF0tAOFlCkoYGCho5oVEMtLL9g6hCXyqistYRHhAMIFkxtQkODaQ3gwfJ5NgFdvWb/yhVTHQRU1FAeTB3pWkOFnzT/S7mo6HqQk4YCAEYP7KVQKFc99a/YXuJfVJib8/cVYnRr7bHc/3vsvp0nzyxdtOz5p1+QrCRYZ2B52yqqbxx6zYf0iXU+h4MX81wK6NrfTqK3tfMJaCtxI6Do45oy6KG/rIqJij574dwLLz3no0UmfH0A+kS9/MIr82ajCMcHn7z/1Xeiy9iZtvp4h3QpARWme7gwNKfORBldEAD9NduvvhzKfaDWJT6CF+WJG6YOj3AR/10o+066h7WOPUEvd7AYqnMAJ21cyi25lGurbJgzYeCcCQOxjEqsnDl+5iCbXrNKu5zb5bfd/8Z/v1OrNadTD2dlnHvm/559+7P10r2vffj9yHFTnlq54umHblu0/K4n/vmWhb1uXuD0avShP3jKccG+cO7Iha4MRgx+H01+ok/m9/1n3ey5csHM+hqUsbF+20lrIp7t02OWvS8I4Z79J0WtqZAxX8j6vqoXf2Mu5BTVN9RLl5QEZL3WN9RXa6te/scjo0cMHTZ08Lv/+Xd0r6Qvdp5D3+SNR9MunL18JXdAVJSDC4r3VVN+ruc9yHEz6pYO8wY9j4vpFx56bOXimxe98NJzDnc99+9nR0wcNmLiMDfq2aOI64tSF6y4jvvv371z/+6dF8+evlytcXmBm799Q7A47OJxTZqfyIfyw95+CbGjBzi3dQHjxg+8+6VPk3yDHrgTfasvZ7nrW7x6z9FQBhl3N84Z5dIIpcqR22jqOHHd6mCN2v21gjdzf3qO8xL+7Y/Wvv/WC7lXRBU4eirn2B8nd//63Xfv/jOs/6g3P1n39/uX47sCg0Kee+Pjv9+/nKapPRcvdbwFajVC+ckjkl2U8Xgp1+5Nc7ZA5dZlrHewMJEVRAe5a3WaVVBVUVE1dhAKgo8d0fe/3x8Y0198bf8otHM+cuWoIO3mmWiCghydGfyejl7SkoJyrhblGI0eaDfnnVWFfv7Vtw/cNg8FefNKuHo2OS5k6dw5tz309K3jeq09ZjfLAePgqkPPXmuULNDDRWVd1N7pGAvUAbkF6iE9xwK1t0NdW6CY0BAxt8EZZvZEZLU5C2i4yvXYe8izDQb2m58P6RqvlD929OKq2RN3HENVvQWlttxUl6yaPfHDnYexhrqEC/WRTNGp4wZjU9SNjE5NSZI09NDJbElDJfVEWYcjk957952iE2jdx3GcpJ5i7YB45br1k06vLksJDD14KttZQ+v0JrVKsedYeoh3o7ly+Q2VbAUKBkYHiXEbl1Ryttm2x0+5ixhQoRqsoe728VNiDZWj5tHPj9pLYxBqgikf+nJVdfIAUYhXjE/IpsVk7IXzFr34j385q6dwZFW2Fn2K6hpRzx5FswQuRPj5r4hq95/8rosPhT5a9Zyfm1iRi0nFVhiDiVUrXaRHLJvpGKmQI6nnibRrLo1QhgI5dVWrv0UG0awx7lqnYJ1qMtgml9HRw/oPHYZ+1s6eyXGjoaySw55Q3Grl7Y/Wbt34496dm3HKwrq1P/RROSYDJ/Ub+ODf/7lzywZ8c8bAPqXpqeB6kF5dzpU1+KtcvDBZ2cjhGDLURXBcgg5RYQ1tFizH0bIsdzlUqGbDuSY8hpSf8mSeYw+JvIK8/37x86WcS0/84+80Ra3+3yfhYRF3TUUfibxqIx8s/oE81x3GH1wvnrnZcfWG+fw99AXZ+MGS4wetnUGtvP7T9flgd058qFpIUaxFzISo5/2Ah2lMB4XA7kMLZn4kS3MJU9o/3p6Pfz8iHdboqjarSdbvOr18li1Nd9Xsiev2omRGB/ROB8cy6ufj9fWaffesmDZ0WJLeYM7KcJFaPDUFKeyhs1ewEfrEqhVvfbhmwdLbFiy9TSz4ffih2vPb0YtVL2aWfPzDVlzf5h8YJG28jlCNePGoYCVX6aL/mAN0iOpoXhNDn2CQ2rZa96NOWuPdbciyW5fMmDrjPy+/tfknZO/X1tbMGtknWGFqk2ZiPZnGRFNi7OSoz9+zXXe4d+PkJdL1pQ9vavvz68r4QLt1M9dg9EdZZOKar46yuUTEmIP819+NekLezmffpHperq/q7R2063jmrLF2Ruitc4av3WHTUF7I29+wI/UmJzeoywTGnZmXZvfrs3V76vSpjl45ByYNTdyfmlVfh9wcK+9aiFMTpsyYf8udK9//4KOMM8de/+vt0s4P3r4AX/lkzba8qzm7f/8VXG+kBBcHqGBlF0pj3rN/j3xtrm6oDw5wLMLbumPr1h1o6jeh49n4gU1MHSDaCoTcR8q6MoOoTbXohoZQlkgvl6obZtmESQFZM29d4/Pg422i+ek5bjwIEhWWmhDBDXruPKqokTOjb/KeLMfePDszL3Fl9Zv2oGXI5KFi/UZh4z4yhVJpNiGrh+f5/bu37t+9td+g0Y8/9c/G9p+96KbOIKDdlXxt0xY0oZUcP1j058dW4CuN2aHN0tbuLaZ8406kAG+7giO8WBKq+KHdHaKGyuROAZFteOZsJr45fLCLuHBFhQsrqVYnfkMuN1QlegdWVjruwwMgbdTWo4VzRXmls3q6gQpDy3muzN1aGy0Mvenhoyc+8Mj/PXCnaGBivt+88fGn/plbZRzex+fdz9c/sGKe/FFYbQntEdnwlXVjqmv/lIaeuX53iYMntLl6uvGDJd1PQ3kIhJoSd0gZ9Vg95dPnmPMXRHHETEtO3H8yS1LM0cOSK6pt9V45Qr60RHpVKV9hSIl1F25CGppbdDm36OZZrjNvUBOTHMfGP3KczU85VJiPlNpSIsRqnfl03U8PPPJ/H329efQUWx7l8X0nzILH82Am8gK/+8WGSbPRDIN5s+dDCOfeOB975QaSrvxt/W3/8M1Ljd1FIhsdSQss0+6nocFJYjJ7lbWvnXscZnc61sKzLMf7K6zd8JAttn7XhRHJYnOHXRnulE7Ozmxbih8M9eLLbR0hMTOG2yUMIgKZ9vPrTZo98dDOw7jWTWLKQPG1GzFx2MlDp6V7l99xI4lpdIB99N2WJx22bJxsFy8metox4Nf89Z9S3ThDuz1BdkNNRCqyUF99U5DrnE4XAnrwijy3mU89mz0sKYgXlsIu5ybDEHWT2S1YQ69v2ZxO1zBi4jCFQtGvT7+GhoZr+ddYlpUXP42c1GjzLkJ7Ly3lyK0heaS42/vgCJ2QkL4uqoflMC4zXf4obEa1QxfCbDZfSHORL0XoSFY9ebvc8GnW+lF+k+hpm7P04U092QhtAS76gfZA3MRAKkAoLkrrgArOnol8zd6ssAb+qvdMGX39p9TWG/sukV5PuZL+sAG13Xn/e8fO+W5w7gMp0Z2+Sp2ind31QirXxdkejTndPBneScC0ybe6udHhpQ9v6iqV121LG2qoe3fz8qkxzVJP4KqNmYd0LXntERao+0K3z3/5O755/GCRyz3xboTOb5P2NCTVa7GSehima1I9H33wr3fffg++ft+D9567cNZBPQMDAk1mk/sWzl2R7iyg7j9V8kI3aYvzbs5FxARCZ+P6piucEtJX0jPTy8pLJ0+Y8uXHXxmNxr/MEicjeHt7H9yBmmlyHEdRFM/z3Slg2z0F1JMfZA+VUdJZEsEgEJz5+L+fODQhVCqVx/b+Me+WP29b9zlK7NlxuLKqcvZise/wqcNnThw4Kc/I7tL06DTxPz+24s+PrTh+sAhfmvXYjR8sIfHKDuD4wSL3YaKeGUTqPCT3dqxONJlMPM8vvPNBacv8G+ZK12+8fRlNu5uO17XonhZoh7neul9hRmdg5yFbRe/vqah2Y+zSz8UeWncMc+mYcxPz7WZRi87Go088/N3nP7z92rtPPfeE1Et35KThUhDJocdrSr8mRhB2LYiAusbzFEWioc7OuDaJDmPpdKC5sWBCe5Oemf7zpg03LrnpxAE0HqahoWHW4hl4uK9LXnr+5byCRudfdjmIgLrj+MEiEgVufUzDcz3FD2zSiqQoaujgoYvmLa6sqvzpl3Vl5a67cEEIFQqFifSFaWdeffOVV998pV+f/l9+/JW3t/fRPcc5jrt3qot8stSDKJ902a3dJ1efCCihi8WI586a98o/X8VRXQjhvXfeBwC4Z0pv5/YFJw+dzi/IX3rr4jZ8dkJjZF7KGD9jLAAgJjpm07otn+/O+PPM/vLfPKyeLqe2dF16dBCJ0BV55Z+vHjh8YMTEYaMmjxg5afioyWgq9Vf7HXN18deV0K5ACE8dPuOl9pJvLCgs2H9on0JpG6o6ZtTY1IOnyivKu5l6EgFtmiaj8ySI1JGk9EsBAPztmcekLRzHrfrbQ/KOja+++Pqpw2fIyr0D4AWrf5MwrEXOyGGjeJ7PrUKe0MULlnz07sf/+/i/c5eiGevdjO5pgbZHXvHShzfhi8MW7LNr7NLmp9HDybyEOtX++e775RuPnzh292RxKgEAYM7MOX955P4JM8ddjxPscdz/8J+Cg4JPHT4zZBBq+KtSqX758VcfH5+H7kCeE4qi/vl/L379w1ff/PA16I5051p459iFPD/GQ35PvYoTMppb0oshKTItwP0Pz3ef/4Dt0HMXzn3zw1cHDh9orPL61OEznvhAyXvUynfEz89/07rNfr62WWp33X/HtWP7UBeSHX/ERMc4P8TNWr5rvR3dWUCdGR4pbwHqUd5Ma9oidLlPQyehScu9T1Kfd15/LzIiEt/kOO6BOQNNRgMR0Ov1jjjjo0WpEfUBtgGW3fIr07Oi8I1JpERoSOg3n34XHhaOb2prtDMXTpfvEB8b//OPv2KPW21tzbT5U9vzfAmuuZRzaeFN83EQ46nHn775hls+25XuMhDvyUBDAqHFdE8faMsYM3LM9o07/fz8Pvvq0+f+/exPv6wL8A84eei0tAOE8Jc1G0+c/GPExGGLly/08/Pfu3X/dT3lHsfwocOnT5kh3eR5/o13XsfrwXGzluCoBaHNiYgJ9fACehg9ywJ1z0fvrTaZTBNnjsc3t+38/Y13UDz30Zc//t9zqLB3w/e/AAAeehxdLywunLVoxq4te673WfcsXn7hlfCwCJceNIuqJU5qQjvFGz5/b43QbmJ+955tRQTUjp9+WefwAp27cLaqrBhf7xXfa9fendJdVdVVAIA3X3nryWef6IC3igAA+NND9/224fevP/n2ngfukl6Ql194BdXUrv2GvEQdg+elZWMnRzXWZrd7qCoRUDvuuPXOXzb/ci3PNlnvvgfvlYJIAICz5x1rsceNES1WQgdQXFK8dsPaW2+69dThMxaLheM4pVKJcj//8VdT4/XXhA6WzrGyZruNFUNLE1i7tJISAbUxdtroY3v/+OXHX4XxzmxmVsZDf3uwvr4e34v9ayWlpQ6voFql7sg3jPDme2+88/5b0yZPu/mGW9Rqr02/bfxl88/e1Y7vCwDg2X/9Q6/XkVes0zJW0FaH2atdqzilZ6UxeZKNER/X6x9P/GPkcPGXluf5e6cm4bKKAxcLn37+yd37djv04u42SW1dPWmmJ+TNXF9a1mdrbNvNZels2kosUEeu5eU+8Ohf8PUJ4yb+7833v9qfc8+U3nhLTHQTc6IJBIL7eujW6Gln6x5JBFQkJDjk748+8X//fEb+6hw5dvieB+76+pNv1V4aANASftG8xV9//5V8n/TM9I58w3oCLTAJQ6x+6oooYk52GT0d2yIl7VQaSgRURG/Qz54x57dtW44cPyJ/gXCT7UvFtcg4zb8WFxsn3YXT6V9/G7VWI3SGAatudujSkYqeYJm+/lOq5zNyOo+GEgEVaWhosFgs/3vrg4cef/CP1ON4Y2BA4Hef/yAUCyIZvXfl3Xu37v/bI39/5/230bSsnWjWYFpGWv4VMc/JmfISlOpEaA0pcUHS9Vc++N3lPpcva/EV9idbaUNFmesIUnoeeVMQg0b27VSfzKWCJnatUWNEQG2MmTrqxIGTH737sfwFslgsM4f1wtdramqeffH/XnnxtdtvuQNvwR1kCR2jno0REqa5nOV6u+tjCv8SGW0ubTKppSvy8CIUaZcXBRuMFnyFCKgdo6eMZBhm8fwlQwYNuXrt6jc/fM3zvI+swnr77u3bd2/voPetx+OJemJLs3ff+MtZ18ZMsrVAdwMW1sn28nrwZEGPf727DEvbbv2+ShBHB1mUYGhU7G62cC63V1WWkzSmNkiRcbOEV2tsfbkJ7SSgLmnM/PSQnqCnLVjCt58R+rq9k9r9Qt5DAcXi6CyLkgI6UFXZ7Pw5IqDtKKBEPa+jgLZeTLuBhiZFiX3FXPLYn2+Urn/y+57rqKGvNx7ic1BSSTr/NHtaYw+RzEPQ/vQsAW0ZIUVihPfPj61w+WZfOOnKA0foNALac2XUxCf1imhSQKsqkNasP3G+Wcd+xgMlrdW56fCCslgsrN3q2OGm3aG0laDzQXygzUiRaWy3V8rE3zoSl+jMOATlW7nG7yrk5JbERQa6vAvrpuc8tHCm/Gat9eVkWTdNV/nGZLFzCmJz6ekC2qz1iDzv1+WwOWwxERkldEUemGdrtOoSB6G02Mti9xDE5tJzBbS5fWU+/+XvDtul6yE/aeQ2TkpcENHQToLJonDeqGTMoDuSFCl6PHkAWI4zm9nCkmaLmiSLPVMQm0vPFVAPOX6wCLs+PUFaFUopMl3bg9alSM+pdOPvk2OyKLqVhpq4pPhIm53I8UXlNQ16E/p624Y9N03HRF3aCR4AaP23IyEC6inyNbvnNbyTR9pGEnYnMd2+5h8pfaJxt6qfNh9/8t/fO+/j5+t1/PeXUybZWe7OHPvtJX9/jfNun7x5/9Rx/W+4/RU86ej1f987MCW+rk7/yBMfV1SiylpncnJLJA397adnzl3MfeJZuy7Luza9AACYteTfJiMwFZoDA7w//d8DAf6aU2ev/O/T9c4H7BpvmZLKuVYcFykG3HgeqBhgpPgeNREKyv7tSMhMpJZw/GARvjTrUZNHxsj1tOuSd+rDlD7RC+54Y9yC519+79dbloy7fPy/zrtd3P+Wj6bpZqnjFj7vo1Gf3/cf+UYfjXre9KFP/PsHrJ6///yvoYMS737gnZ17T3//+ZOJ9pammVWaWdRWGQBgtKLxUioVjvaBxkul8UKZuTwAcTEhP3//9zUbDt9x//ujRyR989HT8j0PnizoGurpBMtxOoPZvnCG0F4QAW0VzdVQB5u0K/L0w2jMetyIVRcy8gpLqj77fk/SuL8qFEzveLuUw0Ob/lVZVefhMRfc8UaAn/e4EckAgOToiOToiPRDb9fW6TPTCs2ssm9yb4qipi34d3GJ/pMv9//067GP3l1lMivwxWBkCkq0GVdcNFR2z9erH/plyx9rNhzKKyibMPtZmqZMvBfWza4onXnFqMCf54HRZGEbTwYitC1EQFvL8YNFFWU66QK6OyFBfg5bTCbLw//4srisWtoSFx0SFx28+J43pS1YFtElSrz0jgiXLrpqY3lF7bpPH0uKDOc5/uXnbkEp0yve5Dme5/gnH0OSLbS+Qrz/CeonYjQZ8cVsNmlUlAKtWJvNh59uk65bLOwbz90Gujg84PVGcyuX7sR29RwioG1JT0gtfPpl1J4q79SH86YPpSikaslRERkXCqMDgyRZPLz5X3OWvRofEoJDw1gWxYsVCJEmQghMJqPJZFy64g0AwA9fPGI0GseN7rP87rcliYyJCjqeekm6yXHIvAoJFnWc5/kGvam5kqFWoyW/wWgLJR08mpEQ1+xu9p0FpfhF5jie41orgB3vSey6kCASwR3JUcjb6PCdnLvs1V9/fOKTN+/HN4tLqu9e+ZHFgjr+mUzGI7teOXA4raFB5+eLvI1Gj2e93f/I6s/eX7l/279Ly7RFRXYd565eE6tpJXx91CiUxAOj2WIyuyh2RqNWhiYe2YkGdjoTGR7gsKW0TGyI13XJK66KDg+UTHXC+mbWVrUAIqCI6jrHZalLAn1dB3+7IslREY2ZKvJvoMFgcN7BZDLOXPwvAEBYqP8D982aO3PYzo3PLrjpVW1Nw01LxwEA/vGvH1twSumZ+aVl2vCwgBtut639MTRN2UxM4fQkN5/BaGnM5Dp19sqjT30h3yLpqfPfjp6i69PTxHN9+0uke4iAeqqeWGfda2hFma5tV/HYAGwSHKq2Xm90N5PJ6F4ZPUShYMyC0VdWXvPSGxteemPDkV2v/PLDk9MXvvj4qoXHUy+9/erdAACVCiWxv/3q3Uf/yPp5k9ij2j2VVXXhYY62Ictyg1JsgwCwklZW1gEeZX0Ly/BmL1rzCyoctkRFtWXd/XVASQFTN4wdrb/eEukeIqDgi/X7/rR8WqVndcGBwrLUDRVlOqkMySHgXlxkcSOLchF02NiYJsoFscM4suuVnXvP/eu1n+Qbz6ddGzwgHl8fO6qP/K6xo/qUlLZqdZyeWTBogE1Ag4N80QQBHfrbjSazm/YTbuCE19TfT1NjreieOKbfviNpoOvj5he0E7K+c+tjk/RcAcVNlXBBp4fq6UBtg9hpxmxRyvVOqqgrKjQ364N+XQSxuWzamrpkwahPv9pVXCKG3SmKGjwg/sPPUJ/pCbOelfaMjAjc8N0T8i0tY9XfPzu4/SUIIX55v/vsUWn93pp4icFo/uWHp2YsehF9DRgaAPDg05+DLk5BabVaJX4aOwnru7hEuqfnCijm9Z9SmeakIlTXIfPHCud+Bd0lBLG5/Oe9jePH9N3w3RPyjZVVdT/+dKidnpFlufzCysM7XzYazdgtIIkyTUGxgq/5zFj04pGdrxzZ+Qo+7KlzV3RC+SOhuazv1hLpHrjp69c/2eZpL9UuzQPzpkvXTWZ2X2p2xtVSjuP91WDptIGtOfLn63aBnsfUSQNGDO2de63s580e+TdbCQXhY6sWnruYu2f/BWmjzmCqqtWJP2DCP7ERgc2KqsydOaxPUuRHn++4WtotemeYOLVKaTBZlz7Ca9JYO7s/e9YLuBvr48xBQ7xUCpqmGJqmaUhTFEVBmoIQQooSsuzw/60jeJ0/Uz3IAv365/333DgVX2cYOtgb+ihYIamwB70Ibcj+Q2n7D3Wc05Dj+Xc+2NLsh/GNi6lw1/bdZ7bvPgO6ES3Oou8SQjksLtlbrfD2UqlVDBY+JUMrGBrpnUzoxLe6qZSuJu9usmUqU1lRjk0zl6/6p9v2gu6CUcNVVoixVwvLlVbW4ZRsQk/B5Ue8S4VcmkZJGY3mJoWjI/Fi/RiaYhjKS6Xw1ai81EqlAkmeRq3UqBUqBdJBmqJomlIwogo6WHxWbOuMJbfZtXa2UdfyUsCvv9ncgkcx3e8j5AnVtboKbf31PgtCZyG/qstn0TtXJQFTC7/Z+mqGEiSMYSilglEwNCOom5dK6eOtUiloJYNUT6GgFTSlUSvVKgW2AWkKCvegJbBo3znqIO98Ff9XW+WYWIa55+Fbm3HqvhrPNbRliulCQKusdlkPgeP4wrIaowlVzhAI3QOLGLC0CRYDmyegyyeMoyiooCmVksEmIAXRIRo1BgWqKysAa2ZZwALQHj1Wv/5gbfOEtRENbRO5dKZnuf82pJ6/adRgC8s16I090vLujrT0bexWVicANORVKpXQyK6lmBs4AIwAGLtCS5yv3QiroKHNVUyeR+Yy/tdzepaAuklZJ7QHHSFSKK8JtmbF2m0wGo1x4f4V2gZbc4Au+5LwvFXRUAJGGwhrk6IpRp+a6T12J6AbUrtAVK4loNzBTuRl71R0M7uMII276HJACp06SvO1/xlArfZ5XsgyasbfhQ6Ck4atVyS5bE3IrcdZoBtSzy8eOqAzRSk7FKKP3RJebn27sjodtsHrPryIb0K2xHuh3bAj/FdA9FjoMmMLCavMXnU2MFspl870OAHFuPKLtxwPE5I7BvfrhoBEu77x3QmoRVHBG2cN83D/7rTA4qrRmv3mOcN1etPRs1dr6vXidiFRb/mc4S4fZd/fqtnyxzfTXSh/Ls8fKB92ZE1xauTcrMJq09xWG5hN0hMFVP5z1OXoTl97Qru4DgFQMLRZaM/q6aNcbXT5FWmrlW97YLMxZf+2Nz1RQLcfyQgPlpe0dyKIPnYYD8yb0S2LmL29lHoDKervIHqigBo1XNsu4ZsFkUiJKO9woRxFGeCrDg7wVllztoWKZMHP4updggDW1Ot3HMssKNHKTSifZn6WIQQr589Y/Xu30lBp8d563AfwO5n1Ca4X/w/gqYEh2IXMPwAAAABJRU5ErkJggg=="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 111\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "The low placement is configured with all three values and keeps the hostile’s line screened.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-bbd868235614", "created_at_unix_s": 1788842942.20384, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 7, "requested_group_seed": 7, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [7], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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pJxuXpHwW8YESCIT2jikw9YyONQFkszNR2OvI1RMLlEAgtHdM3n5PivFWzxiTXrqrQzbcA16UUbKEJxAI7ReTp3pip6dnf0vZ9rRTRsrVW13WULKEJxAI7RSTZ8aSatnuVs84E0CAgZydwq2HGcDJGkosUAKB0B4xqdQTMtrqGRNt5xm7gG1NBnAM4DhF6IgIKIFAaHeYvLoj+1JPbGxCvHK384zUBlPSUGk5709Ai+O7tci5EwgEQttxfudE/zso1VNCBx066LDzDKRxKIkBnECxWEZb/mwJBAIhVDi/c2LukaMNqqdUcaRCBx28gGJjY6W7DOCIgBIIhHZEbgDqGR1tpKITJL+nCkqwlVXVI0oXHx9HfKAtxWA2fzCb32KHJxAIQRsM562e2NgEuILTLjjDR0p00AF5W2llPc0a/VmgcbwZRC6DUtr6DAgEQsi4PpXqKaEDdllGvfdngd1ic/gTUFpMd4pI3vnfH219CoSgQex9QjNdn/78nsDO0BRkDBoPQd8CGsebfYz+jRxa2gglq3gCIVzUMzrWZIiNtdOGzI5qQ5Xirche3+eczNgYZ/hIxmclko7ieQ05jjQGpYDd/rwi2nCJ5/jfoZhJcO7JNZAwwVQcb/TLEwiElrE9dcBxorQe0IZuKVH5RRXKPQ/nnB52fo/a+rhDuWfkjdoWaBxvRgjxQiQrqCB16m9TiHoSIoDBIRwybVA9pX8NrswkSUMlGfU2Rbf/kwMAGD2oV9esjv4sUBpwDl6gWI8RdBEGEn8gAFs+uGP4nA+DK3yprr+n047MZpwjgUBoWfXE7eUV6ikjaSiji+qUaFCaooeO5QEARg3u3aVj/IadRzQENI4383wo2GctC4U9vFhCFzzeOPUktDIBOkwa9JYQk79dMbRXxqF//mmaekrogMPOsyfLLF07JqqW85t2HunaKW3ixX01lvCItyMEaD0LEA0iFwigaITiC8WWD+5o69MhEAhBY0gA6kmZkpjYJEorvC6jAw4W2U+U1nOMMSvN4wp98nTh738dUFug8Y5KfGwx/k4hHkQ0EL/PSLe0w58AHSbEW0KQGNIr43BD6mmIiQHADhCw2nUAsDrK2ejTpymK2BMl9d3SEs8UepiiPnygOhbxDgE6OzBHKtD1z4LHP2yCJ5RAiHga9J+AEHOhmCylh/8pDUA9neig3Y5Yu8BE03YH8pnWqQNYYY+X1J/jqaEeAhpjK4eU6xCIBnQkR+ElJPsz0hNeCYQmopQ8TTHtxnhYZKFcqekL3GYJsXW8Tkzf9Fc9pAOO4yU4yUnHWzUEVMfSPI9oHfZ+YiWNaP2MjzdVVZmV6vnzy7OmPrS0bc+KQAhZNO3H1JBJOGlQPV2uz0Q7crb4lHF2/BRN0QY1lKf0PBXVJdlwprDCbbLG8bUAAan6SOAcgBL0VCQrqPROZfUksXgCIXwxBaCehpgYefFuRxo5mjroSIqLolkjC/1JHy3YaMF2usLmYBTNRBjI49QlhgGIpmgGCFRYl3LGlB6VfnztAAGMjzfJq3gAwNOvLP355VmtdoYEAqE11VPZGlnSUL2Xz7O2ppq31+ui4ziAV/R+gJzVoHM1VI7jawUBURSkAS+ZnzrIW4WITmMSY/AiSJZRoqEEQgSrp4ykobU8qzRFWT2eVAwAqKutSUuO0Rmi/ZuivK3epcCCQxAQT1E80rnNT95faD8COHC8KNpklNbxsrX99CvEDUogRLJ6SshuUFlDHbZ652MAlJaWxccY2Kg4/8lIWEDj+FqEAEVBKHACj81PGnE2xAA6kks5JRiayiuqkm5LSfWkVWi7rZsmhBemwKJGmu5O/8t5isdtQEtKyurNNVmpcQYj9vVpgp+EOFGJGQZBPUVh81MQEF7IRzrXTTyfoqjze7ujh5FfwRoaEA0ltFrM3b+GyqZoPdLrXUIp0DpG5+ysfLawWK9j4uLiNZfzDDY/xVuId1AQIQoiOwckORZCtKFyhbFzg/vUJzh9wFZjmuYOkIHIFp/PYvs8z1HRhy0U1/FYQWFT29wRCIQQUU/XWE1XfhJiValL6lRQgTVb7AZdFG/HC3nObqF1RmSpFmhddVVlQmJiekZ6TZ2tsrJS+URKMj9pHQspA8Kv67ZJBc9ByRFJJ+50J+50XP1J6S6ExAglEMJYPXt1z9IcSizLqK8n6qBDmtLBQ720nOftFoHWsXpsilZWVOSdKaQpmJToUXDlTKRHvIOGWDyRnYOiUQYRR1ENBPLbikQL7ijVAAzOZMKBNga7M7zJYqg+etshV/jIGB9jqaoVE2ERAggCSIxQQntGdrPsbOsM+UapZ25unko9vTW0QVO0FrAsQ0Ox3Mhhs0DGAG01Aq0rKytnDaaOqcnlJSVS3afTcQoBEgAFBDEeLa7ceYGL7GYiV14zVhl8Z2j8UUBihBIIoYopMPU0xCf52keWUT/HkSJLDo53uFJBEWe1UVF6IxZlh9VcVFSqN8UnxMe7LFAIeA7SekEyP3GTN95BUTqBivCIijsTFOAkBGNcjKW6VtooYG8wMUKD3HWiUcNOSPtOQoD06p6FB75LtmesCQg2B9THmvSW2hpfle+ShjZgiiLWDtgY2mETAAs5M3aNMizE9qW5pprRR6d2SGakciOEk0LxKp4GPM/xgBIQJ0Cdz4lJkQFyhYxE7ydkXGUFlNMTGs6VWK1OgI12Qqd0mhAZ5mcvUT3xollSTxEW2BNik212TlCkdjZlOY/YWp4FEOqAnYWcAzEOREdBuwPQnK0uv9jBUBQUBAQgqNElxnOVNAV0esbCAaBDQICpQ6doHrp4xyoQWVAQIoqSPKGSEcoLAk1RxBNKIIS+eubm5kV79pYvKChIT08/VYANRh3SCITooAPRegfPsmKkXhO31AIstZL5WY/w0p6FPAvsTCWTEGevkGwthJCd43CndiygLODsPu0IH8IadvK6P6dwQI+0cUN6FVfVQuj0hLqMUOc+REMJhFBTz949OiFX1nZubp7mZI6CgoIu6ekAgFMFpZoaCnmbUR9ltWMjsuHlvGvVz0LegWgO9w3h8CK9WufpjUKQYpHA2wHTxIbKAcpr8Y5V/vdsYdDmXbmZSabyitryilr3VjEEj3/EhXx4t1QhECI95TM3N4+KTrDzQEdrBL0lO7RLekpeQSnSMkU5Wz0DAK032uyQAXb/GipZo5KG4q20l5dTqumMTU7j7ZbaimrQAvgSTV0MTo+31waQohQUfEfI8oqqOneMhxDKlzhihBIIoaOevXt0ctue0dEA2O1AZ9BHWbU8npKGdk5P8WOK8naLTmd0OHS01qM+Pae83UNAkbh0xV1E7BbA6GFbzAuSZNQPLaGwUhW8OGYOQQp2To8HgnNeUtOM0J2OTFKtSCC0inpidMCeFBcPYFRpSYndazJHQUEBAEAyRXGjkMpqRz1upq5EsFuijNEOnrbbef+mKKINPKJ4XtBBh9oCxR1FRLOrrt4en5amUqsG1a0V8H8OjZJXQVROOQ/B1dcOL+ERBaDgzKqXIEYogRA6K/ecnFOyekqcLSyh9dEWTvRU+ljOd0zPoCBMSYiroql6ryQnm6UOP1dn5Hk95G2+Xhry1oS4eDvH11kor0QligYcjjRF6xBns+h0psDlKeTkVaxEUm5RnX9CfAzlWsrzOGaEl+zmOrwKcOY2AYggksLxrfYWCIR2jsm3ekrmZ07OKdwlBKk1jrfV6fCwTZ2dpzU1tKjgLAAgIzMzPi7O5uB5K1ZMFYLdYoqJFQSdpc4dGlFRU41buKUkJmpleori4UDOlNHACS95jcsaSVPrnW8XAR4IHI/boGKjVMoBxda41CsUT5AnHUYIhBBRTwNu8emwAwMASOclozrRJWrnaRPj8F7OY1s1Hycjp6VnAJBQXFbhnStaV1sTExtrjDZZbBzgnPPjvKmoqPAQUMqZzYT/YSFncUBng+ZgoCmvbaiqykR5QVRLPYs/DewEVvg8xbvYCGWIEUoghMLKPUYezmFPz8oqLa1w1NdqaqjZ93IeAFAoWqNp6Rn5RQLtmrIpU1uDF/isPorSmWxeDlMZDwHFmTsAULroujpHdDRrZFs7iKSZQNpiqU7udwchoMUMUFU7UEQhKPYHkCxTD2UlhEyRaEjNJSe0kHr27tFJqZ4SBWfOpGdmIZSQl1+sMkV1YiDIz3JeorDgbEZ6xpnCUoamoMOietRhq9cbo1lDNMfzyGENSEBxbF4fZamp0bE0iPHZirnV8J+W3xx53XfI4+s08fJRP/+2o1tanLzFpZg4o4kX0L5yIqBtQIOqJ5WHktrQ9qaeEgX5ZwAAnTMz8/JLdMiqaYryiDFSds3lvOQYZQGwcyxehbpUGDJ6xNmckSXaYNAzjCHW7BV3Ui/hpWC0ra6KoWnaNWIplPEjr51SGmjHd/ecaQCALTuPSHeLCksGD+haXlKh3Gd3KY6/IwD2lZM4EoHQZit3nufrap0xH6iPoShoYNx2ZeHZ/M6ZuLuCtilKGw1RcelxxlNnS3y9hE5qvQx1EEAW2RBng4weWGsQowe81VoPDFEmU4xaQz3zQKFzDCdCOA/S5hDCQEGbqrB/P7/raJxGPwtvt0VeUVUZjVtXEQjt2YXSQg4QU2Dt5YGrzFo08Wp5AFQR9NwjzhnmUkmmwJpidC6FdVhqqi31Nkd6h6SCknI/r6XDsSnWDnU6ZMcWKKOnWAOyVCNGb603I1pPs0aGpuyu8L1GFN5ht0u5kbzNCoB7PRumdErRnS7VTotVeTxzTxXh3KZotd1K1JNA8OWMLhYd0CdazgENoUWQ2ndCnXdeEO/wNfuScpjrVNXtdZbq8jL5nsCaaAqyDMV6ekiVGioOLLZyjInlzIjByaF2HuuDIcpkFSNL2g3r2k/9N0LI6fkFoHuXjhCCMs8lPIHQzvEvfKnNc0A3YH6K300dJU1z0yqNVKqnbzHVhHKYEQB2m8+qIxuLI0AxBsQAh52O1jEM4qwMsAsOUMfpWNYoOCxqAY02GqtsdgpClgJ8hPdTxlDO1C0l8ttuN5cRAqEtMAWgnp5bKLn6qHOaT6/a0SMnNJRUHwNsPhPjNaEc2MaUzVhPna2zijasSkCh1VInFjDiyhy7w2cxU3iiviD06NQxzqTnBSQISC/2VaEpuGpduef+REYJhBBQTxEGOfXsbIHz6RxUa2WvPj3l20cPH3OKqb1W+VVmWJazN3dyO+Uwqy1QaT0rIAQFvLatKS6OTU0FkYCGOZ1z8qz0i5Jqj4hSEgithtmY4ktDxagRrf2serspSqcpqTJn8wvl24Y4p3PW6rsuE4+Y7NFL/D/WgDM5zrScQPBKzXGpCEKIwnPiI3kZL/b99LhBIBBaDbMR90ZSQQNB8O7RoaDeyiMOpxlJeZr+YZBD+vF+KNqoM0RHG6KjIUWdyXVG8LFzoNe5Ab8DryCSIDi7twkI4XEf4SQsSq1Xnbb2ZUDA1wd3fRHuokwW7ARCq2uoyWWK0risGtCI53gx4E5rp3JbaFf/eQSMvNfkOIXZB1mDn1enBbewnsk9Jj8JQIqiaSAOeAcARHHYejU7YAMCWg5MSUIVTUFewG8Dd8OU51q0MaoRb8HxTkL39UFq/hlGVwsCIdKW8zQQqo3YYWiwVDpz0nE3H8gIFvlLb0xIEoPxzsQjK0+7xVTCp82nLlKy8UiPHPW8doGMUU+5MkoB0mEV9spvBBZBbJ+hRDI8eUHMGUBI6g0aGihFU9I75RbVeQZ02uIz5Y8b39h3MDcj3edQaQKB0ArLeZYSF4dOEKCwJQgBdIi1mHgEpvMbiwxeRe4IaQioDWl4VBmaYmI6grIShKQ0qUZjpLwaKos1SM5JnXh9GxIC6v8cmn6GYy7sIb1l+Y2OGHjOynW7iDFKIIQgLBQoJAjQaTMqlVTeR+w9qdZTPeTVbULE1b3dijf36t7JHbXPPeO0Lm2CaIQ2gIYFSovZkXg0Jy+EzBI+UMwdz5dvF8c6352Z1mhsesgQv3aPuw9gFnfa42GSwkQgtAUxtvIGAt8ug9EqYLvSIGg0RZZxrk9Zo68denbLUN7t1T3raC4uDQgQr2iXKBwQQoaGDh73GAYhL5RNRnlx0PaAhtnlg0CIcOrrLHIWkzM8xBqdSuq7Z513DZPeoHc6UvHsYcWjvKNX98wGNTQzM12wmgvKajTSBXie0+uYEJcOU9E/De6TyjuDaLZSjXSHvh3SIOogNZyXUkERgBp1SQQCIZSwcHhlbdIBKdYNHBankvrUz4YpKcMTiB2IykjBHfN69eqak3PKahUMBg2FzMxMz8/HU+q0a+FpmhEQClXbM5ixcqmOUw7EV9ZGWOUVgRCZMIYoHFOnnGaeABkdQ0OIdCyNY001DbR3wkaV1aannRJXXVrMUrhaiYV8WUVNcqJnWN8Fy+qqzNjpd/Q4dvdRkBKQ4CWgELA0sNl5RtE8qk3xNoWDZhwPGNCDYfDbxKkS4g2Kgj+v3has4xMIhCbQYOyaEduLcAKeCQlx4w5BigVbrRzOHaJNWiqhrkQyGqMSk3DKjSCqJ9YBxDMQVFWb4+NMPhplKBDXrRoWKIQgJg53sbNVVoX4Qr6Z7M8+1danQCAQGochyqCUUWcltmsUJJSSRxuCpmC9A9YXVWRlKKqhdFHIYaPwQGMsoP7LiJwDkNRbnQN+MSzbiN5QBAKB0ApY662Qt0s/mjswFPL+Ue2D6y0FLiujA24BnOSsl0di+6S4RGyWYi321E+pRFNGuqPRTIRWGK6q57QWrdRQTlooyC1BEQJk8iaBEAY47IDFwXi1hgasV8pYSkV5pTz/jBGLjsRDqZ/C0BRPuxvM45AV7SWgYvm786nRJmfrvVZHKjRq+ZfB1VZiQad3majIY/OvufrJb1t0riSZE0kgNBaLwEDKZNAxoNZzxhGrC1BDcdG9FMSXk5zEfzi7tbIGJcRG4WnmnnF0lqWlnZCjHrLOaUdeAgpxERJnq2fCYaJc81F+2t6f/PPvfOPLCg7irF0CoTVp8E9X+Qfs52+YaZkRSdh6Eu0aP/swBiNAnMPGAV2s9AWlcEc13A6IomB0dDSFG3ogB8dDHMvBo3pUGAx6q1Uj6wYipIPOfCheEFiFk9Nm52get1jmWbcdigU0JbErTdM8j+tPqap9DgHoIEpOyywrbERGfsvQgnboiv2FA0Y2cQ4BmbVLIHCiEMuGQpD11DsQ71ZUyafpymHCVp9zGQkhFBCqqa0VpC4e0n/iZA7g8IjCGw0sa4z1MwTUexUsZ3bSPE4aZRia43imU0p6VHzK2VPZLKujaArG9gDVuXiSWturZ4tjrudNUbiGYd8B/JYlMjo4L7wEQkQSoNJJw44anHeU2ryZSE3AEOVRl0kBQY7CywXYtNx0yIUq3mTjKARsAEQ12XaTnKbYAq2vyqcNsaxObzDoBUHgqnFFvLSTQDUwWj2suWJMX/zJ2rkR53eXncorf98R5Hx9AoEQPOwWq16nnaWOfPQPrTfjpbeM0aCvq8eZ+ElJOKNetXOADUCkZFWnD9QUHWW3O6prrdj5EDuAB1icY/lcSrAD4CyIjLw40vqth1v6JQjNJ4juZhKyayw7W9G0DBCKgnqGseOh6xhEuwM5vhKbVOh1jN3VX8hSWQp0HnaoZg/PzKzM4iKPgJW0l/O1Kb6OARTPW3hewD2YBIGiKNA2aaCtl7yPANp/6ITzDuXsGJjRAXuI5T5ZhDanQdVr/VUkoQ1haIo2RBkNUYKAeEFwcNjhybB6uwP3jzNw6gb1yGtCp46lHTZnJ7Y6OjYjSe0MLSmtbPA0RJ8BYE6XFnRKSZc2QZZhGGCvxe2keAGYaZOJ8jB9I4xB/br2792J4wWGpvdl50kjPcrKK4luEgghi56lU5PjEQCncvGVlWNNCPfuwJ09aQAcdLRnao2UpKiyTJFNgEaArVCTDutgeUV1UmJceUU1Za2hojSCS0gMx8t5oDpTEs8JoO4UtkA5nmLEuno9A2g2ToAxkrgisXw0CmgkAUQGuw+eck/zkJNBidVJIIQwVh4ezxPnborJmDiIpPjOiond8j1ngYz6CHae0kdJvk/E2cvKqxiEFZbmbUBOpPeL3Sw2LaXjGBbEWqrOxiSlSQ9wtnpHfY3RYKAo2mazGWI0ZhpPHzfA13G/W7sPhA/S57zvkDsEj5fwaWSkB4EQkAO6G1NRDFrdAc07KMDLrj6B8ZA8nBWPGvAH2uwcYFlJZylGx+DpnhAboSkdyiuq/U0E9YJJjGfN1aCyqjomxpm+A5FA6eN5HrdxjoqmcDjJSz0/+mq95uFuv/ZSPy8WavLaMTVJr2P69eqk1zGMM/EB/LJ+V1ufF4EQKvhXvdTAUp2Ci4MT9K5cd0DRFKeeFiej0lYZXkA6V3U8J6DEZKybNG/D/wp2oJV6dPbMaVYsHpWQi5EY5xwkRfdPCAHnqEEIde3U1e5wTBvptjfraqvranES6YzLBq74ba/3y2gK63WTB4HQq+MEABQVNzA8gBDKdO3s+noUuO+ezPP5dSJEDJw+zqN3R71XzEeMCfvRVhmDjq6orKEFW3xKh6rSEmUFlLczT0qhF48PWFjv4MShclLOY21NpYBAXFwCQujOK7pJu9XVWutqvU4CocJSr1nMXkaoJLVhhHJGvMykUaP7Dhj+6jvPt9FJEbQUs5H7EFWNJCgIKITjP1CaICEAZIiJMhrFiDzP8XiMHAJIEBBr9ylT0KWtDE1R1rp40QjFtieE8hJe1c4OQncQCZuxSNFMZOENA+UH6mrj/GsfAqCo3OxLNMNIN+VhnPIHpXSfSPQdMBwA8MD8x4iGhqBcNudQRFXDFFrnzExXjl2vq6sTh3S6CjjFTp28DrdDsvMU4NTdlGmK5sXmdfV1lvjkDhWVNVIUXrmEV/WiY2it2ci3X9GrsaonCGj0sL6jh+EyHumJAT49xQhKvaeLqmnNJs5QHILq/Jw0q49279wkbbx02Mj12ze34rm1O4KomE1+OaKqbQ5EPBR4RPmM5eCwuyeCmGPkPTnOjlgDX2MEwHtupx0xAouFODouXkyEtwIQS/N4IV9dhdUManWkdy/hcS08y3EOJnDpjI6Ja76ZGcWb63HD/dbgdKmtU4q/MqqBY28GACz54H8L773f+9E33359/tw7FI2vwYiLB//n9ddb7HzbF60slwFCVLVt0VuqrECHPYw0hI56hSIqk5EMDUqqBAsdPCMWzts9JFTSRQrhSBTLYqWOT8adlbEbtDAfGvFcOW+Meg9N5ziH9lC5oIum4ryhfw1NHTqleMfPoLXYtxcH3C8e3H/zJo3Y1+XjL9n1N5mPFGxtck4zDCe8VZUYqi2EnuKQ2KbTxjGQMjj7nQOo0+FpcZyAa16io6LqqivUz2QN2k0/taBdyZ4IQGzryjF9AOLTMp2VSBC3s6Nxbr4Tk1GjNJPxo5hBCQQt/2W3MgqfV1TbuWOMrKH22jxdTGfQVkhzVHBbZTF8BIGzhlVzXwE1YerxYDY/BKuJ262BGSwi3lAd7MpPan0gRSFB0ENXsbr0nXM4EIC4oTEPKipsLEMnpqRSFEQI2Ry8raqEd/j48DWFFXoIK4dolzsPm7m0q7+yqige92/2ggm6mSnxw7qDRpOpssydJ6TrMm5YFzBMvP3e4k9uv+2OX398D7Q9zjiSgATJQEYCdkbjT1Oc84dxtsYiJUrtVzEDIeJVNUSgKAh1xoqqGpx/KX57KTqa1kGaouJiTQI2hjA1pYV4vJsvYRXJPZrbtWcP6Tb+tivCIN6hGF5Ayig8fl19HBMsMzM6ylhXb/GT9fn31q3ybc5u3b17Z2rmoOL83W2YBypnKrj6sTrvumLzitPBktpqJxUeEMVs8qdEVNUXNfrkWFuZ1iOKQW3GWF5UOx2y2AFLU3gmOU1RCLdSNlMUnqmBf3QmbAIhBGkWODyX/EgAnLM6/vixY+f07CkfngYo52iu5jzOOgt2eioRbI0rW/K3Ntex9IRx/X9Yu7/BJyoN49TMQZXVZW0Whfejia5FfQO7tRuIXLb0h0lUFQBgdHgHzD0wRkcDV886ATKMmIvIOwTZiwlpWrJMpRUkVhPck9MDCkJOZ2J1emArlTRUfkiZCW70dHpWma1iuxIR3gZoHKButID+tuOUcmEuY9CzLON2uHoX8EtvSfIwXjF5RGGpy8cRVMwdz1feLY7Fb9BMa7/WGd4ZbnMgfFYZ0Ow1O6D9qidRzFD4wNuJqposWMjiE5PrIWvjKMquTtuUiDIwgLchUbl8gXispc44huvLq1JQioI0hIhXW5T+wVVPijQmHgQmoO/+kJOgq2mwItNoYFRpUwO7J61XSK20TJY8jDTtDtQkxCV7GaGNFsqmEROfjMepuE47S4ddyw6Oo2latOFtGZm4RYLV4ey3WlgYsY2piFyGJu1KVasqylw+M8WC3RDDcQICiOHq661clIGBvMYwOLHvZ6Ct3x0CxKEjHJRqBKplvXMykv+1+Yo/a5KT4vlanxVRMhZPBwGEuEbKY4v4r+TgPVNUBX1PIwkEU9E/geyWyuPP1Faq/YnH11+kvPvtn78PHjrYdbZuW37n3weGDBkKIguimOFLu0qrEqy1ko5Ia9o6q1jEqY+Ji9ZHGVg7x5vr7TSH146awupHVaGYB9owulggloTionsv3AL65g+HOnfItFZ59HYDAFSWFccGoOx6zxg/Q1M6MUNVyXZXuuWQkZfs3LoONyW5/gYAYOW+RlugwQehwUM01FNVHT9m6OiNO/4E4QaRy8gm3A1VszFFWsUHCLLVVtlqqzw38ozTJqMgNOpZP6ra8PERiopy9V5yFdTXmOs7pKQY9UxevtiNVMRD4/JK8lObOkROuSqX7qos0M5denfu0pvnLCUF+OVHjByDs6rP4HSzAX177jvk9uO2GmL6pxMIHAJCFBBzwVyJABSkJP107hk+kXiimIRwV1UJhmU5R0DOSppzTukQ3W6ewkrraN79KAuRVEfUWOpqKusAuOj8nmdLa0+fLWzYB1pdpXFZSEhO8o4jOVN/XHi3NTp54hTPcUa9qErKJwJQVNiIi08Q6ZX347Eu06Tb2BUqzfRQNKeXcK7ftUZNhQihIJdV+i6B7MYnOGfS1uo1enWriLfhkQGEdq6q0dEec4yB2NSjribQaUNKYZWorqnjmWisPTSjFNYA2Z+NB6md0zkzzmRoShpTx8xBlWVrNJKTlJWrWFE9FIfnOHeipXtXbOzxvJB77Hj3nue0fh6oDI3Tc8WXlxJCXZcAMaPedV4hQygoJiFi0PhzCuGKWxpCHqHoWHc5OM3gfCPeYa+rbSALyv0UTmNPnonCZhTEmaQ1NhSr9+ckLSoqKvJlgcpxpPiEFBrytpoTSvXUfAqejKdspS+3ipO3eN2QU9Vpj+rJVlXPzet/xVm1AIydOHnPlg3SxsnTZ5vr6qMM+ljxl4QQwJ1TGUaS9o14bnxrE/qKGaC1mFzpdLLTxoYbcxHaHNUfXqjbqjG4f50ETTO8A+cv1dVZGmurSnpUx3m4ViVUqtpoCxR5rdad2z0t0MmjepvrPNy3rpx0j9x0Kc8SiUZfh+S0kjK3d7Z1GDLiYiQYcD9ps23Q8MkQgMQEU0VpiYCQvd5cVYE9FcoFvSB2OmhpQl8uCe0T9V9mAQhNMjMyCouKVR4AWlz782KiaODCqvIASKqKg/PRcQ6Ob7SA+oqliBao5xbPDYO71Ow8pTEvFACwbcsmQEdt3fhL934XgLZDMjDLK2p1jNhVRPy45fcqiPhqNdJMiGISwpquYWWoKoUVG6qi6UeJfoDaioYnwkvY68Q8UFVFZiBoOgPFQiOPLd526t6dmyipDwpkxAQmwPE8Q9MzOt+AxHU04rjVrdjOzvvtCILAcTxLQynSJTZqEr254pvRrJBtAkQuCW1Flb5LK4TmurZkqEpcxTbs6EvrmJp/5oxqo2x++kJyrdIMtix5V/Tfj2u10Rbozi04fGRyuxqciArj6fREaNK4QavXutuFzLz+X1arldZF/fj96vy806ojpKd3BK1OXBRbpYjmQQgZht664+/hwwbj3xJWT6zszZwZTxSTEDoEki8RYLIE79qNBpZWU1UYiH56pvoEgsA59AajTSu9SXKtZmZ0LCgqE0S/quwBaEBAz5af6ZScrtxyx4xB1bp+v/7wmfrlsQnquQUhs9AFALeAns4vTUtP9SVEBQVF6ekdr5r3fz8s+g9oJOdddsOB37709WinFP1pH8VIf2zYPWTwEPd9V1sm6SSl3q7N7BxM1JMQYbG+AFPWAkH+dpQeCahCp1kLwYaeaLNaGusBYBpsmXG6rECVXX9WzH5X4Wx+4rmlrEzdOLqwoFjSUNx/U0y8lJvwSadgtTTuikQj1PfymwBAAybNxk3mV38OgoFyXhUAYO12f63piUQS2qcKJ7t0tszYvdVeXVMGpYC7f2hK0Xo+SDDByh3y9oEKArI7fFpw4mUET3Rzl8m7jNCa2M6xNXmBvCiNUD+snmJhq3icy2578LePXwn8tOuRdePfm7y3E8UkEAKkLL311DNA8vPP0LTadGVp2g6aUoDkh6Yk0stKqQxAaSR+IsCy6ikiSDRCr5g8qtQ9n1O+oGANLCgoumn2TYGs4ovjceJ9wbYt6gfE7alVx0GQIAYmgdDSpPTOKj2ijvlEGTXcpk1bwjMMo9fRAaeEBnzYJjzHu0zTuYT38oF6J/04s0DxvGaPt7JGqYPbtoD4c/wr4P33zwEAPPTxupfunqrc/vC7ziD+vx9biBssfbgENB6imARC65PSO0tze/0Zj2ZDTJNSCWma0rPa/etoAKVMJk+8VA5S6Wkd8k/newtoEPoGy7k+MoKA/vx7k9J/um7rdhAM/n3/nN6dcKdOpXpOndwfANC7c9KVD3+qUlX/dO1suGvyaO/tmcm4AuGJz1arthflB1S5n0w1bn+Jjpkpge9MILQHorKSlXexW7CgYY+nCh1Dqxoe+YfScphKI+M1LdBGOEHXbNvi3bRJXMKrtjTLsVqsZYTSCN37wJ3ndkrMLvaINfXqlHjsQL6UStU1SWOs84b1H15yKR7yruKluyeqtnTpaOI4t+t27vjh8u2Nfx8CAFzSNWP5ZtyN9JK+znFU6tc6lBPYWyQQCE0hKj1Bc3v9Wa9xxy50LO1TkbzyomiKomnK213qnUba9JlIDUbhBaFZAvrSbWNff81DQGmElrz1yJESm7d6SjcWf7F6/Jihd83EjfJU5OYUf/D+s0++/nXx0YOqE5f+16WjWnbPFmi0kR4wsHvJadyXfuwF/QSbR2pU3wEX3nL3Q9LtrWtXvv+ff0u3x/TvqzcYnn/9I1w4wDmGjsFZU2P6OMV34+Ec5d0xQ/q+t9bLqwvAXE8beckv7p6kc8ePcG784y/vJxII7YeoDKcUeC//GZrCQzq1wE5VT23V6xheK4VRNXTDr4A2clmPa+Q9z8+rtrMRdDJwi5f+ru/Y2VbkDMe//+bDC175dv4r395z01Rv6Vzy5e8IofFjfPaNRxA++dpXs6aPBGBUtA49+/QSX7r5+XebRg/G3gAVvbMSj+RGF+cVFpXXXdKnh2D1UM8nX34vLj5x6UdvVRedSO90zrTZCy4eN+2mUd3wL0NUz+UfvVJw5sQV187Z/dfe0SOG7Tp8+sI+nWTpXP/30UuH9JIOdde44SoNVakn7n5991UAgKM5JdKbUxjLzt9Z706J9378k59PmEBoV8v/WsVdlVPVW+cYmrI71IPU8vMLYk1RgVugyDl/IzAQzpwEQbRAldx447S3vnA20Fv8mbvW8/opeM783kOnjtVWCeW2I4qHJOQqCgpSVWbz4s9WP3THJCxqGVImsFsEP/8OJzONHtzfWz17Zzlles2Wg/17d1U+1CEhqqQS9xqIi0986eGbJff25u1/b/l79iuLnBmpz7/+0ZlTxwrOnFi1bvfPa+ds3LIzpmNibZHHWkNWT4lFc/4174PvNaWzp3jNcEmnN/gz790pCQDw9m1XLCAaSiA05FTFU5IKPErgeUHwZa42IKDFdkOqTl4gN0IBzXUWdRApGB2I9R0747OqMG84XtPJq34Uq+dhrJ5YIpP0fJnPJPwFi5x1Si9/uPr2GU5VYi+8aMc/uUePO2uGDlfVHliq7nM6e/JgkJX4xufYwi2tql+3HbtB8Yi9SXj+x/LVOwEA1152IR6rF2XKK3ZOGRAE4f67cbE/AOD1p+5GAK1at6sGcNLFrrikmLI4kjvE4xy6EtVgAgxCGtIpqmcCAOhYrs+olKj1UFo9hI563jXO7Uf+9nP3zJi7Jo+Xb7/3yx/KjRu37ZNNgw37nZ+5/3hdo4J1JF5H8ONU5QDQxeh1fp2qQfaBekeNmmOBnra6T6xrZ8Nf+YWaqUVfb/cYLUcnB9QX9qMVbgfi0eMFh6u0Z6jKvPH576c4ddRPkk6JM4XlHMcteGoRXss/8fgfa363ie7R/nHOHZatw+kHn7z3Wf9+/VeuWikIQkaHZI5HVeXqoJ4EhGDR57+pNr79xHX4SvDM0nnXTxB3Ar4s5aff/VFAAjA2buhgS0un+iGFek6d3H/q5AG5h87e89zXyn2kP6Ax/ftKdzdqKemY/r3KDv4t3VA9tHH/0WaeP6GdE+XpVMWres9O+A0I6MJpfd5ceTjAF5NMTjm7XhbQRjV8arkcTDpZbcH+sFv9BaM7qPdZurPht38g5+zQMYN7nNPjw0UfP/vf557973N4oOnXX/2y+DF8hHW4DDQ2Nm7Nut8T4hOmTZn24mvP550tyjurMST5UA7OMps6WsMJu2v/8a9+2YMX+MvUZjIA4KbJg1FWIgTgP+/9VIkbnIa0eqrIOXhWnFKA/2AWLV3Xr5vHCuv8Phmi2V4za9zFS9duveHaG++b92935u+9N6qOlhCj79Axc859z/334Zt+2LL/kr49VTtsEAdwycE3GRKFIzR2+c80QisbMii9E+kjLGuditUeuSddknKO54yZNEraMn/ugptn3XJ61+8HXCM8a2qql3+zbPk3y7at37F9w9+DRgwUanzOC/z1iHsEgEz2SV9+Tye7Dhz/bevh8qaOBWxR9Zx6Ob6C5hw46b1n787JAKAly9Ycra4AgJo3a6y0fpf55/BZ3KwrJWbp2q0v//fVsWPGfvTuS9kH99IMc+9D/33prS9uvbSnNDAmJc7ZHGjOffga9ueOw97qOWDQOZKAUtFGwbMwRSmpfbukzPvwB+yrGXNRXLR7ekTfbh3mv7MUAJAkGGZMwG9qiVbWBKGd0JQlfKKecoAoFrkbNdthFAXBmp35qt6aj9140cdfO+dkhJ1cBs70K69+7MHHB40YKG95Z8nbwwYPe+CVT0b3y6CTaJZlrVanf/ahJx588+W3RDnWmBZNpzq/q3SSUwv4cueXfE9BMYhnQYnPFOIvdx4G6tLZtoEvNi9e+vs9s9w5tjkH8CXhSIn6mnFul2SEwPvL1orqCebNGuv/yGPHjP3swzeyD+4V/3a5159/5LV3v3rglU9fuu8GWT3v/88H365YevWMWQN64SwIJWmJ0SUnseF/Sd+eKvVU0rdLirefQVJP/MudP0vS0BVrds+YMGiu61Lx9vL1C667VN7ZW1jnTtHway9Z9aemOSzTp0vy/A9X+v5ICOEkoDg8EaPneV5QjhrVoXr8iGfi1P6jZ/cfPeu0e02x9eaahVf1/XFP0ErUQ4cNm9Y/9uDjqo29e557YC/2k/78zS+xsbEjxl0sbX/68WfM5kAHCiqV1JeTwa2eIcbipb/Lt2eMH/j9pmyO4+S8CCmbYtaUYRcNOje7CqeVoBpG2lhcqtEVvE93Z1vFrr0uOHoAz6Va/ecBAMCLj96SvcVp5m/dc+zKf810OOxFp6TmaB5roLREfHH6Y9shYKFUhR89MxOO5TtftHsGdl3P+/AHTfXEywtRPasqK+ITEles2X3NhEGS3aBUT+SVjoajgl6pgb27JL89b3r2UefwCY133UUdL25R5EBcgHV0yQGE7yK7uK5hAU3QQ5ZhHVgvpT84NOtKrAWCgD75ZqO0z/hRA2vr7bV11kPZbn28beYl0o16c029WSMpPWKoqKwoKy/b/dfe/Qf3L/36i4z0zHl3zsdfwhvxzOTJ0yft2rxn1be/zn/gng/e+Sg+Ll5pq7YT1mzL9vXQvYuXSzdgLAc88vM8kKZRzbjmqhXf/DBq1OjcowfOlD938OCB46ed6rN1z7FCi/W2OxcM7N934EA8G2bdNnfQ6YbJOEd4zdaDZbxDcNjXbT80ZgiOTfXITIAAqNTTZrep1BOvGGha4HlJPbGVnRRVVY419Ks1ex6ZO+WUK5dDiayh7pwKhaT37pIMEMg+5lM9nfuH0jhYiUtcdR+lR3Y1uLO3vEaSpKoF9ObL+q5e726BDADIStKV1DIAOJb/uue6y/Hf5e+bD5wsrKcoKMcpDDSfV10XG21Q2puRJ5odM1N8XWwnXjm+X5/zPvvg8/79cDO9tRvWPvzkg9JqHCE0aMTABxc+/NYr7yz96stPl34CIh3ZESEjJTrQgFHlRXzlmUfhh2OlWFxzjh0bNPC8F156ZcKEy75Y+pVYnly54ArsizxbV79r856JV6qFT+JEfnHumdIy3oFqnT6Qxa5Y3JsPzZRv3zR1yLodR8+UaSwR3nv2Dlk9lRoak5Bk8922ccnca+Yu+UbKqZg3+zJ5e+8uOFf3iF/17IO9wyB01u8rv/opK9PZ7yMn+8D7bzwr3b6kT4+MDnGHKoVz01MFnsPrU0GoKMfflPSU2M9+wkHU8Rc5UylwvsRFA+Tb/XtmzH3jU82XU2byKUvvQldAU03M2s37u2WlnDjjlokpY7FoKl2ZE0eeBwCIMYA3P3Nu7NAhqUOHpHpzTY8M/KccedIZCAcPH/BjWr7y5kuvvPlSEw67ctGV3hunzfsRhDkaOWeujZQiiU0FFYNjZI/+59FH//MoLkXr2XvZ/5Z/sObgnAn9lrz1fkFRQVl5mWbHs+0nncoNY3SoxsOPvPBld+7Umq3ZxbS2Gq7eoGFt8UlR5cUlT7zwqR9DkTUYpApqZWraTVcOvXBA90UKL4c37z42C4QMu//aKwjCDbOu7xjNpaZlPPjMG69+sEKqtQMAXDz2yvsumaLc/4G7b0hL0R4iqaRTZ+2qdlUetHw3MwXXAj3+6WrZcdy2uRMef6nndutQW2ersngU0S9ZgXMYlaHdz3/eo9cxMyf2lxbpEWlstj7zb8DiO+vqQYGragTIaKPQ6XTb1u9QXqWOHjsye86Nn3/wBZ5QPQg3Gdj9F44vSezdf2j8FeMqxNnU3tkUlJY3uVjlyA8AOjWGL/aXSizEMaBc3ZjCwfELF+NiMz/s2J/zpdi/ps2JjcWejcGjBrEOpuOArsWFZx+YM+PVD1YMGjlh92ZsvPe7cGRVVdV/H5unbJGOPc5bDjDRgKvDrmelEapk8b2z73nLPUhCs4QEAdAtPdahKK+Uw27yjSnTL55818v4A68Q7pnpzIfRRNbcuV6OmiWugo6mCOjFAzrHiGbBm5+5M2bmzhimNDZxcc7UC+prq+trq92fVei5aVqIRrlvkin8EZWlpzRoURICwW7HluPAARfs3YfzYSX698U5s6cqbBeOdM/EvmDABR8s+mjwqEGS5zQo1vGq/T7duHSquqMCX9xAnHDHAfVQRW9CRD2xU9iGc0iiYdSQATgnV+Ljd144ug+HSTsmmaJj4l54+gFV3iIEoMSKPwcmGggW7VZyq/442K97mi/1lOW4Wzo2ZlmWWfz5HwihRKRx8Vv13db37p5x+7PL770Vp38I9T7rEpfMvXrukm/5ao1Tevfe2Xe/9XkijzNkZk68UE6osBVbFypSROQUCw8B/WljtsXmsNnVVfS1VhwRcikmkG94vsd2zSMzcGWnio/exGWLRDGDyJ9//fnR4o8rKsoXPrKQ5/j//t9zXbt0nTf7KlUhnJST3zT1bAkvsCqVAit+Xa13TgVf4nN8bttis9ksVsumzVt4np8+bWpeHp6JtG77wf7xWKSW/bbrgRdAVHzGa0+/KvmpZ1wzfexwt6MT/0YEYd0WZ3rvwWP44jHP1RXoYG6hSj2jdPjXV2d3jkrrKqontlU/X+PghALeMXeCxtetb5dkzmFnkui3Plkz368F6qu1kizlFeWliUkpX/+OnTYzJ14oZ6q9uXSdJKPSliVrt3gI6NTRvWONsNbq9nhGxcR5ymWjY4XLf8EhKakSKcJymDRFU2bYqPSP3mzgCNs3OR1zw0Z5jD6VUS7SiRDjXtqPLOzVo9eXHy+Tlu12u3381LH2kz4mOoYbvnLUQoER4y6+5vLpDz3y6Mqff5UuTpeOGQEATqqVBk+MHDV64IDzTCbT5i3bdu/dN2jggIvOx8N1JKCgNssWKVr/jLuoz4acE4s++9WZK33HJITQYrGX+TUTB3VNj/1y5VYIQJ5YIW2w+qwVmecj4NYrK3HBqz/cM9Mp0Faxldrd17rTziQYluEkL0GH2IoSHAdSyih2l3vmKc8dN9xDQCHE6on1Toy2exmbIKDWTeL/Za2MSotvb9LpSyX90ARvZntzgMoczTk6eJSHp1gd8gdgzz97mpArprpEtdtP2Bff/Prd118tw6m43bp9uWzFxk1b/++2qXk5hwRBGDjgPGdVntk8cMB5e/cd6NGz547jGgV1mpRVeXg86urti5euk+9+uXLr/lK3I7u+tso7+HbTlCEAyDmzSJmDjGscHrlO3vjmw9f6Oo1/ss+4faAdsNkryWhcYnKnc7CJc9orWc1DQAOWS21+3BtRKtlMtm8quH0hbv8RLOTv88pFV5LvdjOZf8PABuN17TNSp8n3y344mXfq/kfvk8rn8srOjpwwfNv6Hf/5+OebRnWD0bT3+vOSCWOPf+YWUCqG8eVi5susB0o9ypQ37/FQXqV6+kE2PyGn5f3knFVw/2Sf+nz7AVThUJrAMldeOtDD2S3KaIfY2GXfbZp+xfDUTqnFrrxjCUY2NpvMj3sDvc4QgkLEf6Ubm2itGayTIH6PoMCyutEj1MHxM/mnz+nW/VSFbffBvXV1daMmelSjHsxWjX7wiayncrufQ6Ul8u3vd6v9M5SYyaSEL6lTBtzoDtGCpzd5wYvOYg0AgMXW6MnGOTVlfHHtP4dyvT2VTWxnR0ST0CaQYF2bMPWaybv/2rtl7bYpV19eWYWrtq6YfOU53brfdd1knJYjZpLpWJ3dgdMkZl9/E16B/Y3z59vKd6yZoybxzV6syDCRpQHr3UHYV66Fr2S1xgko0U1COAbrGmXaq4xW4jCRGDHu4r/Wbl27ar38yTz0xAPZB3Ah2aHDB3OP52zbsMNitRgNON9gytWXg3BAM18t8GS1gASUiCYhLIJ1AYbspIQHP26QafN+VGpoxDtMAsRitXjH5UyuGzNvmkHTdP9+A+x226HsUElfbQW0BZSIJiHsgnWBZDs0VhCJegYOz/PKAocG8eWeDq/PnIkk3fTu9BFgVy5NIqlnDKFRhNd3OIi4/+YLpLhc946+dzYfPNSoMmUZ//kP4ZX8wISXaDZBB1uCF1fsbMLq0s/RgnUoAqGlkf/yPypwzgeU8x/aYc5DMIfKhQh33npXUlLShk0btv+97aAQ10/McZGgKOrT9z/vlNXp9Xde++mXH4Oiek1WUqKbhBAnwL/tle1PNyNTQHdu2k1RFEKI47jpV14NAHjr3Tf3fPW29Gj/fv0/ee+zwqLCLdu3PPXo0w/e++DICT7nKAQO0UECQYW3P1qzWFm1Tg9HIY4cAV27aj1FUcoGPK889+q9dy+89ZvFPI/7MX7y3mc//PT9f1/GXWCfeOaxXZv3DBk05O/deCIugUCQeWTGYKkPjn9uX3jdR28uDzB8t31TQYOmhir/ISzQ7jEVjiTEJ/z0y4/KBjwPPv4Abv884zbpUQCApJ5S5x6E0GsvvtF250sghD23L7yuJeqVwyWCFFEWqN1hv2Lylc+88LRy46ARAyUfaJ/efVT7r/j+65nTfbYVILRQTK9ReRFrNnuUhUwYeW4zfy/E3xL6TAsf9YwoC3T8VNxpavdfe3du2v3r97/17O4xEFye5SKzdcfW1j1BQqP5defJX3dqjJJvGkQ9CUEncixQs9k8aMTA8ZdOePE/L6V2SF3+KR50c7bw7GMzR2rub7H4HAtOCBYQwgvOH3TZ+MsOHT64+o/VNrGfozcZ6Zk1NdW1Zu3BGLKG7ik0Nyo6TBST0NJEjoBK/LF+zR/rnRMWn3zkqWlTpr345R+P3DD+wGF1b5j0NO0exoRg8d//e27SBGdN9L+umP7kI08JgnDLmO6q3SiK+mnFzw8/+eDaDWsDPDJRRkKIECFLeJqm75v3b9XGZ1985vCRw2mdcFvsQ6KAKoc1Xjpa3Y+aEEQ6pHSYNOHyp577v0EjBko/l1w+hqKopz/w6BkOIdy5Cc8soCh5SDaBEDZEiIDyPH/DtTfeM2eearsUfD9V4Vw5DhzgLim7eOjwg4cPtO5ptiOunnYNbg622t2ztqametOWP7v2xjPgJH78+uddm/fsFDPJKK9BxARC6BM5S/i8M3m3zr6Noql3ljgz56+ednVax7TnHlkg3d2yfcuHiz4eNXFEXV3dU48+zTDMrXNvadNTjmT2HcQTxBITk5RThe97eKGyMCwzI3P81LEVlRV4FjER0JbBT7aDr24PL67YKQ/HCBYvRmi9Mrx8pnoycijjP/fly4+XndvLI9Pls6Wf/u+Fx3GIKb4DAOD9tz+48AIcfxAE4cqZUwsK/SUAk2Yizfx17Nq8R/KZrPpt1RfLP889noMHH7oE9KCAR41L7P5r75PPPvHr77/4fznyG2m19hHSr0n6HV0+uGujnjvBK9ssUtUzoixQAMANt12PrZ6ERIfDIYd0lRPH7lwwJygv5F0vEV7Ja63DhSMvmDn92oXz7pty2ZQpl03xnxRBlvChg3KVoMyCkPEvqS9GrlxGuIBKVFRWBPFoAdaWhVcPrlbj6++++vq7r6Qo37tvLLnwgsGfbTpx06hu2CuNp7W6geJoXELrs3Xddr0ej4qT2LZj63sP3qDcYcLYiQ/e+2B8fMKZ/NMPPflQ7vEc/8m5HdtTH8gIFFD/+Prthl0RbogTHxcfFxuXdyZPusvz/J0L5mSkZ/604ufomDhQUeJlgRIBbQN2/7VXEIQbb591+MhhAMC9dy+cff1NF4kXOWmHX7//LbVD6qrVP+/au+uaq2Z8/dmKJR+++9FnH7bFyYYiYSagjbq4SenWUr+D2xde7r8xDCG4rPtlQ1V11djJlyg3ni3Ix/5oOg4AtYBCikThW5vOnboAAIaMvhAhJG156903d+3Z+fari6Jj4upqq6Ojo1M7pM669fojx3BN7c+//vTOa4vn3nE3EdBwFVBfNFiaQkSzlXlj0ev3zfv39TNmLVuxVN741WcrcL7ECRxNUqFM0SW0DrToNtHr9Varezjl1h1bf/v6Y+n2ub36FBUXSeopce+D83du2m00GC1WUsgX/gIaiG4GMrJR2Y8rkMaFvpb8ZIKjzJdffXHDtTfev+CB+xc8YDabKZqKMuJx3rdN1876IEv41ufEKTyNYsvabV9+9cUbi16XNiKEli9+Trq9a8/OydMnKZ8y+zo8spioZ9gLaEvMa5QIMBCk2buQBJGUXDZtgslkuun6my8edjFCuP3VT7/8aKpSL96lweIFBWcD+dgJwWXI6Av//nPXDdfeeMO1NwIADhw68PjTj57KLumS6A4ryRgMhvlzF9TUeMTo2zlhlgcqE8SRRM1MwpBltB2qZxNyDGUBlTJzm+wB9x/0a4e/i+b8jiCEs2becNOsmxMTEgEApcWFD1wzXJWrazQY/1q7lef5IaMv9H+0jiQKTwgc8l1taeSZjv6nOSohvpRGgRD68qsvvvzqCwDAC8+8OGHsxAtGjN/z1x/yDl06d/lu6Q8cxw0d01KGS5gSrkt4QijQBFsj2ZWkLY9yVEKSyVqT3X/tra6uvnTyGOXGR596ZMLYibMW/N/3P62ScnXvuGXOXbfNLSgsmHrN5FY9v3CACCihVX0s8rCdltZKYoQ2iNVqjYtzL9KVnM47Jd14/ukXJ46b+MqbL331LS6IIKggAhoEDuw6GozDhD19OmEPmsRzi37V3Of48Srpxi3zlgXrdTftwumloy7MVG0/IG5v51jrtZtYAwBGXzZyx8adu//a+8Krz3278lsAQNcuXZd9goXyvttm4EbXaRkTx018Y9HrRD19QYJIQajkJQKqUk9fJHeI2uEaczTUs+VEWUm9n2eptFITbwH1s3P7wY+AZnVLizJG/fn7ZkpRRysIwuSLzq2vw83//9hzUqfTeT9x0Ah3W0hvSBCJQAi+ekoqeU6vzseP5p3Tq7MfxQyKDhL1DIR6S/3gUe7QHEVRgiCYRPUEAFx06VDlzkaDMSkpKTmpHZW6NwjDl/u8QPmBTtJIEyMQAuGcXp1b4oOSFXPUhZlEPZuGciq4NxarJf9sfv7ZQK9nK327uSMmdwVOHDeqrV67mSrcEqmgTevERZbwAVqgTePw6WC212qH+FrFZ3VLC0qu7nxXnlmjss0iQ0PbMojUNONXptIcm2CqCcqZhGwHQ2TjVVt6dM2Qb+cWFLX6GRHCD0OUPnBn5SMzBkv9dzRTzUieWeRE4d/9QjvOGzh334hbNFVVa0/TDU1yTja95JGIL0G5YmhwDdeicrly0ZURYISGsYCGjgS/nFcGEDhd5h7+E3biC/VkKGbkE3RPy/ZNBQH2OZO0MvIM2HYtoMGS4Lx8jQYZAdKjK/77s9s5nL0c8hJMiHj19NXAzFcvnu3idqWMRoBdGR5BpIjh0JHjIMxppgVKgkihj/fv6PF57i7jrRZFWBlZzXeIBUrAdE9PFQ1hx+myBkLed9ww9vF7r6LEBvJnzpaP/tczHKeOdDWKFZ8/yjL0Vdf/V7X9iw8fMEUbvLcHyJ4/XjQY2D4j71dufObBa665YphqI6H5PDJjcIAaGhm6KUMG0RAwOScLck4W5J0tRTbez8/mlU8/ed+/Ply6btjlT4y95r9pHRNO7Hg7xmRsTqbRzJteNBr1H7+7ULmxT+9OKclx1936cpOPHGXUm6IMqo16HeO9kUBoMkRACY2gc1bKmMnPLFu2uaC4MudEYbch8zmOP/Tnq1K2pvTT2A8Ut0D/ZmNGWpJy4+sv3LHnwMk9x0ieVnjwYqgmArY0ZAlPaBxni8qsNod896a7F5dX1CrzVQ/l+Ovg2zUL+wogrgt0F708/OI3110z5rcfnu006B4AwF8/PYPXejdjXSYQQhkioIRGYLHac3a/8+eWww899UVBUSUuoNyKx+EGzskzxZrbs/rddebge689ecO7H//eKSO595B7vYsIlBJ8qqSM/OZCJMrXnmFaqKq9mVVGhNCk54ULlrx2x5SJg3asfUHacse97/227p+gHPyZl7556uFrrpl20ZL/ranz3UPIlwR7gUf1nt69OCjnRiBESDu7EJTmCEhjaiwQwuumD3/2sWt1Oib3ZNElU5/W6Zjzzu0k77B734lBA7op7wZy2DMH3+MFoUv/u+UtqoMEfsyjO9+KMuqz+t2l3PjiU7NmXTMyq99dEMIL+ndt8tmGXd2BpgXaJmlMEUb4LeEDN5lDUGrDnYT46MqqOinys+zbv5Z9+9fVVwx74/mbAQApSbErlz4k75nV7y7V3QBfQqopkAnKMb2hKdicI/tyLwRIj67puQUB2tHB4fDpitBcxd91+bj3fl0LwpbwE9DAaZR3gqhtgzz5wPQ5N49Xicu3P21/4/mbIYRnCytUDzVH4HwdJCjHBABwvNBCRw6EnJMBTdj2Q4+u6Q4Hl1daHu4auviLXxr7lHtudI9manPxjWQBbRTEsG2QZ1/9bs7N43N2v9Nj0Hx54871L0oGKQgHwuQ0W0qClQkSPbqmV5pjcScdBLpmojBavC9uvOZK3HPjZBoKi39ZH8STIQIaBKn15RFr5kIvBLlgzEN7Nr585uB7yo1dz8e5R4TwIudkQTOb6dwtdtKZOXwIQmDF1r9ByLP4i1+CHjNvRC387ddO+uSPYIp3xND8hsrhJbUJ8dF33DROzzKLP/69otI5/oEQdvTtfU7bnkDPHuccy2nVAGxbCqgvunfNgq6jQSj+J2ZK51S1lzKSIHakDy8lJYQ1bS6grU/QBTQIS/jck2dUW3qfg4fe9Ijv2KjjtB/BJRAIbYIqVny3Ih71fpPiUa3Xzk5S1QaxOxRZLC7L1ps8cwgVorToTCRikxJaiHZogQbdYm29INKR43mqLb3O6eytkDqWcWso8qmhnU3JjXr1kBLcEESKgxGxJhCU8OU2yUqtr68HAH6xcUsIReGPakkqAIBl8Vk5JBlV5Vf4tkn940dwT+SpB12026HNrVNgQ2SaEEa86zdrKrTSmLwltU1oVFL9/Junzg3GEl5TVgTIUiDSwkpEpgkRQ2gJaDjyzqc/B25SmWJi6uz13tt9HYFC7sZxhEbRauXqxKBuzxABbVXMteE0QpnQakpNVDhMIQIaBNpPNPOKCSPqrfYNh/a19Ym0X0jThpCCTOUktCA9e/bQMXR28UnyKbcJRG1bGmKBElqQY8dyGrX/pEsvMurZH//e0WJn1L4gLXJaGmKBEsKYYReev/NkdlufRbuDGLbAxf8DQOwcbGbX30YAAAAASUVORK5CYII="}]], 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"}], 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"}], 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"}], 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"}], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant": "With Y4, both ally views show the full front glyph while HOSTILE is edge-on. The middle height keeps it well inside both ally frames.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H1 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "The configured pose visibly satisfies the required ally-facing and hostile-edge-on pattern.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-761a043ad378", "created_at_unix_s": 1788843213.793797, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 8, "requested_group_seed": 8, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [8], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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tAjpr+lCJNwmBQCBkzU9h8J6eXaakYB1NP6Mvsi3P9xBrIhPvL4ayCihjoGeMGyy0IiMUgUC4Z9uxjOJaOixEXdXgmIqpjeCVpaRkaQlMNF3C/RsRFlRSXhMd0aFtcgQC4cmyd12wOjYyyET7eQKU9ad6frTyiEcWqJAMWgAZoQgEgmfrhwsll2LbsYyIsODYyKBR/ePa/wLPxp1IgGUYhgUgTKcVe5Na9dwQCERA8OMvmyUt3TvHRIRoosO0CpLosOoJBZRhIYC1lxCJCA0SdiN3PALRwZE1P7PLDdHhWo3IHd98hfSTerK+SKSHw+pKHECpxtVqUqstLq9BRigCgXBlfmp0IQmRmjIDe7WotsPanjx4aHhIaLguPCJUq6ZUSkqlJLslJ8l2RTGhCERHw9n8PJaeTxJ4RKi2e4JuYP9OVwraWOyjd/M44wSBkwSG45hKSaoUJEUSKiUpjqtHIBAInm3HMkw4FRKkigrTKiiiob5h8IDOfjY/A8pmxWGVJ24ET+A4CcUUx3GsT98uMLbJSUOREYpAtDmGUXnDKL4Ue9PY+uFCyfg9ISpEraTio4LDQtS816ROX99MyWtTSUNcgdM04/AGtjr0ASXzCATC/0jUc9uxjPK6hqhwbVxUiEpJtujQraMv3n9XXG8wWyxQQ62GqK1a3oyx/QGGISMUEVA2EaI1C8axgFAGZ2UXhgSpxBmImkw7ss7w2npjXYNJYofCJUoEPmtc/xZdJgQC0do074dq84f3SVq2HcuIjYsc0DV88qgBJOGqFLwHatki9Qw46SUbDGaGYQkCC1ZIQ7p49SytqI0KDxa3D4liT5YiYUUgWp/rwhoJJEoirGstzXQjPU9X2m/zn3/ZKNkbEhoSrFFEhWsjQpSAxgARcFrmDp/JFW6mmQaDuUZv7JIQ7ry7d3KC7MvaujcJDSER7QOx6vlu8L7tWEaQRpkYo9NpVdCsYsyAabomsQFoQbYUkmFYFmPrG8zl1fUMy8IM9U44G6EIBKJNaChFVVu7mZt/C4dGRpbXmqLCtBRpG7zTZgAorqaavwmowS90pbEsMFuYorJaHANq6FxzOEOaYQkc69+32/m0THE7GsgjmjeoFMaVTRpUIvyDs/l5LD0/Jia8S2JiabVRqxJN9NGNa6h9X2DYnqzXA+n5/zEsW1FdX6s3Gs3wL1v8HnMnXefVd0QgEAGK7OA9LFwXHqKJDFZ0iQ+jgWPqELpZY/l2hD2Yq8FgMZkscGl8EKagiKsltd2j4e8/gWN3zr5+5cZjE8cMQkYoolE8MRv5cWVLBpUI/9Cje7xGSUWHaymSALSZICgaiig0s1ZsOHrXnOGcHdpxsQsozTAWmqnRGwAGQrQqhS1TP0w3YvMmOQ/k2/cQEo0iER3c/OyUFNMpRheiVVqdI5yG/rrh6PyZQ++aM9yqof4bv2PNOZYvTWSHeC6GYQ1GS3WtoabOYLZYox8uZpXw57D7wBleQ9uNO74bUSHbfroyWHj4/aQ6BFPjM6fGZ77QeV9rnwiiEWIjgiLDtA6Bn7R5/pyRv24+AQDgNdS/F9EzOcRED18iXY/FsKzBZGHrDBgGzBaaphnCdu14I7RTfESAG6GeDyHRKLJVQLrZVszPhPiovLL6vl2jpb15Dd1wmLdDM3YfAH7FhSi600rMR34kmRUFLMsaTZaqWkNlrQEuUuIy1YuNUAlt2ghFtK56IjENWIJ1IWoVNXpQJwaTyzkvskMbwafyAG1MzPpwMDvFO6T7vIj8kiyWs0MLymoraw31DWazhYaJ6627wMwpw7x6DoiOgqxcIg0NQPPzYk5ZkEYRHxWsJIBGRSlVKlcaWn7E3RC+oKGhwAAfXj5jQSFd6KHvx+5OAkqbHWqTsixbXl1fbzBX1hnKqurNFuhimjJ60E+/7aKtJqkdZIQieAwVFYYK+ZllJJRtBV1UZD2Nx0QExYQHUSRuNBigJBFy1Tvo1nLBu1NIf8ZVucxJVV1nqQYWkm6oravfWF5NEQSGYZk5pRqNymiyzJg8bMvO4348T0SgI9ZNeQ11nXj3hc773soe56MTQzTJ/NRFRf626Qiu1Q2PCtHYwuaNBoNSpTJjgLEEdNAS5vd3xMPjOvEPfW2dvrpWeJB0A0lzhjfL0jRrstAmi0VvMOkbTJt3HLc4ZW9CRijCDa8PPu/++rTcPkX5DbylnqGRYT07R+ZWGMQLu40GA0USBBW4lXqx1nhTMvdyWqOdWJZlOMGkAcuwcKT/y9rdi+ZP3rwDGaEIL6gnj6uxPwBAFS6T6UYMyi7qLcKiwqPDg7rEBj2+8IbNe0/VV9tjpXk7FAMKi9kE2rd0Yp52dJdW2pqXnmV7dotnWbvJmZtfFqxVOvvW0Op4RLPVk+/5yqn+jWpro2KKaIn52bNnUteEsAidZu2WQ3fPG7d+V2ptRbVEQytr6QhLId8SrDLgtox5ElgACIV1VyjVuOaW1ykaUTTWZ9LZ3AO5S4yKYaBn17ie3eKhN620pqC0priivrC8Th2kVWk0P67e2cz3RHQYPFfP5vVHeAtdFBTThKSYxFhdJFcwjm+fNyklJEIn7mk0GCJDtRVUvCko0RSU6NevAAss9XRpgWp1wbC+B8MUldcyNC3WfZYFFgttMMHIpplThrXvUbwwMDxu9u8fCgLh99Cl3zYdUakVUUO6wmXvHL+s33f3vHFzJ6b8tTu1ptzBDo3QaX5Zf+S26dfVGlSVDfKeJRaGMUELNF6lLjd6czGnf6TTk3fBtcFB9ocumH+ID0EQuORADKehEVHhP67ZJTkcciUhWmhOIiPU/5goNQBgysjeU0f2Xb8tVbzrl/XQuTd3YopGJAu8ht49b8Rvf59uBa8O5sPjNDXe3uUQng+cxzCgIAklRTprKE2zLMuWVjSejwPRAWmJDiINbRXzM1KnjgxW3DY1ZdVfR8S7flm/T6HTzr91QlBYiLOGgraIC4FshjI7CChByKzZIglcq6ZUCgcNhX55lh3cD5aPl/DMHW3zmiJahti3gxSwzZmfM8f2VSnghB6JMc4a+t1PWwAAt988PjhcOh8K2hzNWEnvmkaq67EsNELVSlIXpFJS9glTBgaHMjTD9uoeLzFCd+063KwzQbQTvKKeSIL9aX7uOXguWKMUSvC60tDvftpy203jwqPDgN/BvLKS3bXhiQGgjU1xfjTTiQQDmFiWwTBeX3EcD1IQDMuW1xrtriSaaTBapPOjHP+aP/LdXzuEjEpyj6L0d17ETVQTwrvqOTKlR50F04gaeQ11fvl3P21ZvHDG2j/31Yhim9oGGAiKSfGtFz4pIS4rJ09aR4/7jyRxnVapN9L1BhOfVoRmWH2DSasie3dPuHg1X1J1LsA1NEnnUa1Xz0vCInxhOSIN9TUmSt21c2xUqOZfS+f++NtuWpT1g8QYS4OZVFPuNRQDAURQ7BDu/008KXGYURNfSnpyaP74SoqMDNUWllpMFhhUzwK23mBWKwnn8NadOw9NnjwKBDC51dbZXvdVJVDa0CaBxt1t0fwcMqBriFb13cq/F991w49rdnmooTCEPlwnjrH3HQMefFn8FLMLpStakPNTEDPMYwElCJx2WtsuHI+1iSiGgcTokFq9obrOwHBWKJflDu7unZxwMcPBCN2589C/5o8KZCMU4V18lGwJGaFeQTaQ2USpO3eKidBpKIoALPvdiq2L77ph+coduMXYqIYCAGpr9YQaOqC8SL/7X/LSkVqcN5kFb37weYstUP5QNlUOC1GHBqssNFNvMMFAe7epUnfuPBRgBj7CV/g0VR3S0JZUAEsiaH68JWt+9uiWxNJYWXk93/LfT//kNyJ19lWVJMaUFdvfJTLGbidpVIrWk0hfaej55a/bNiOaI6CCM85+Grb/kQSuUpJhwTC7qsVWNImnZ7f4y5kFkplQtDoegWh1knS0ma4NdRxknsssntIrRKuuupJ+qX8yXK5dhdsVNt+qqDLkX6sTP1U7dej/gH3Enf/pC/4VTTGYBxLZUuQtUFv6ec6JxDfYduEYplErWAD0DSaaZnDeDc/C4p2yh0Ia2u7xQ6ZkZIS6otHAD34ev8/ArlWO7ZqEyGpLA0sqE3vG8Lt2HEmfMqJPU7+aIU+/X1RUysc7ihTDo1QgvuP88jf880ZNG8LzwkrgmFatIHDMaLT06hx1Na+cv0DOM6GIdo/f8swjDfUi245lzBrdq7QSI3B7JLignlIlFZbXiMacL7//xBtPfyI+ZuPy2Oh4mg1QlWyCgHbplJidm+/qUwlKSuCYSkmpKZKAOVcx958fGaEdSj1J6nfPj2Ax3+LVM0J4xJwxvfnJutEj+oqzJmcX1/TsGjdl8vVCy3PLfpwyWmqWvvz+EwCAZ59a8N8PV3lSqd1ZOVnP+umLTkJ3f1xKoOmmCwHFMNFg3QrLN1ld8fa9BI5F6NRwwhQDPZMiLueW8yZqaUUtMkI7su3pI01EQfXeQskt2YTp1jCwZdcptVYlmrEDV67mWp9wraOHQanl2Xo8c+ygJNrMvPrkRwAD9S6SMFlxEBKHdeDi53WcRLZdHAQ061pOl66dZHrZhFOirjqttFZfrz5dLl/Mdj4AMkLbH29lj/Pb+B2pZ6DAAlqkAoyTvSV0E1NXfNJpMAvaB1BAXQeBClht0Oy8MiCaNKFI6VL63l2jL6VntXUj1B4a4uBvRLQOSD29SFl1fUKUQ1KlC1cKKyol8fAO8jZphN0IPXomY2i/bkInDMMKikr1RSclEUMu5FE03Pdk5N8WaNyJZIukl9slca9xEwC9+3S51NpGaKPBcWag60bAKhFojWaAG6FIPb2Ls9GDAfDKP24VnrIsy5WMtEYzsix7NNVaNg0D4N9P3r5z+4ne/boDAE6mXhSmKWVv7EO/rXVqEwmG313zreaFtxmgHnXFMKx7j6SrV3Jb0Qj1JKkHH97hfinnMK4PorU0FKmnd1mVWjamszr1wgXuGVtvhAUi//PkzTW11qEWPyIPgVNzIlPRxmtPzgWAJYOst8yQlN68hvI6K40fd4ejjAaqht7QJ2xremWTBVQIArW3OM1+8lzOq+iV6FDhi+9Gcr9yzgN5NBOKQLQKq1LLIugqQISqKTB5eK8uCTBmvqK6/mpeKedWgis1jSYzr2Y1endZPgf2Shid0hMAcDD1ckZ+RQ/3q9LdgQW4KfrBew+DZ75yr6EeDOE9+GTCLCrfFQNYcs+kjMtWdx6ifeMjIxSZn96lnAgFNrnKKoAxM6WV0PDMK6z4Y7tDGQ8YovTgrGu5JZLGa7lQbaePva66vBLDwOiUnr+uO+D8Rk1RwoA2Rd945dtGNdQqoO7yicA4JtbDZaZ8z7hQaHhWh1chI7SD4HUNRerpM1jnZzdzqT97dI1jWZbXTUE9f153aOL1yZJDFBSXAQCucnpqpaWJjvl8RTInGOAa2rgFqhbyBXCiGRcBf8fOX8ri2w6VwWVg5RU1/FN+o7wY/r4hEM0DqaeP6B3qlJMdAys3OGSed8OqdQcjwkI7RWi5YSmrorzrExaN6ANDQ/U0rSUI9xpqFdCouAT4PwwzWEBsQpKwO0it6JYYceTgMZa28NU5y2xa6QlR4cHICO0geMsIRerpO5IipYHb8B7XqBffPr6grK60Us8FOQUrKVKrpmIjgrVqErDg8AmrF95EQ99Gn7490tOuGIyMWiEuoYZ5I0lSgJmiDAUIOC5/45VvwzSErE+JjE20y6XA1TT+kmEkgWWmU0FqT5NW2XMKsACTqVCHaM+8cqo/yqkc2Mgo1wuPzAIAxEYGWWiGplmKxB386aLpu/eemvv+z3v4bbWS6NO3BwD7fZBoTiSjrauhNK0HhJayzm2GaeRqbl5NT3d/EPFHGNi369mL12AjDaMWRCvArJfD+j+bjEIjNDK4tAy54xEegcxPXxMVKi59BCnnXElijCb5NZrF5dbRp0rpNHpnrfd8Y4rHepysk5PPVs0nnBSpyS2r1+OM1laueEFK5KpUOP/beFXO7v0GXD96+IRJY6yh9Czsy6unkDMUroOHD/hMo1WrNSqNVh0UpNEGaxJirKsdXK31QrRLWqKASD1bhY27TrvadfhUxuFTGXyGOhaAvw/Cglfnzl6s0xtq9cZzZy818a0kWiCf+k5EK6djz62uS4rUADOup+15qKb3cShKSmqd1rNbd5BEn67RXeNDQ4M1B/YekPxiMBgRrFWSOMaHiMKpaVtiUAyHYUzWztyuqIjg0nJkhHYUmjeQR+rZWuQXV0s0NEirlO/qE2OI9WA430owFKehQdAOBdChBAAId5wMdemFz754gagpWPr5M6/999nE2HCNVmk02WoaM6yZZliGxUkCWqKceSqeNrEmuOOvDPdvVERw608JIwIVpJ6ty8UMUci204IiYS18+tX8yMgo+UNgjY7fWR/WL/KvhoodSi4FFI7bWfDvF78QTO3EaF1eSbVQNd5MM0rAQq20rZrlXidXDoT7V+KO99u6eITfUIWHN8Mjj9Sz1Xn73wusW3IB3zv2W+3T1x6/+bNV8l4jrNnvbRVPOfUIEBgqp7qukwsNdS2gQiZQPmmV45WlSFjGj2ZYjLNBeS5mwEzMoQmdopRGDDS52hSiPeHPZHcIDzGZ4eJ3CUsWjHf2I8lSp7dXPOZZtHh2SXkjiXs4GrNNBe9zIGlot7iQzELoN8Nc26FyAmpqaGBIEsehfHIf3GJhGBYixBVUlFea1OFJsapuceFhIepgjVKtokaNHsh9euzg8XTplXbMtYzMTwTPW9njBLvVUAHzYyF8isls94eYTDJ66p5nHp4TFxXyyHNf8EJw/8OiNE7uXuciIknsk7caoPIa2ioGamZxebe4CALHr+RX2cfylUY9MAsaahfQBho2qQkaKGCxZ5PJIqzgLK+uUykwS4jSeiUwMHnqiLioEAVFHDp+sdKzZUc0w5IEvAJIPTsIjRqhG7O7AGBP4C0oqYR+VJGwTQVpAZAxeZIIqy54mJ/Qk3xdHQQ3Xnie+Kgga889Z+67M+6Rf3/x1ZuL+Jbjefar3VjUptx++ddINZRtJcO0pMwAQDkMpwegZ2Lo5byq3Oo6QOMsoAQNtQsolE4XUQa4MljfYDKZaeGDZGUX5OUVeZLASnhJIQgFNBAvn0V0ZA3l1NMjLphj7U8q3SUnbDQ/IcKZa/nlJgMcMbqSv/goqxPpUmYBAOClR+cVllSazDS3NLFJM3VyC4xkNDRQRvGXs0syCxQjBsYChrqcV8VrKKDMmBkIGkrGR+kKSqVZLxsMZhzDSFvyeSVuYTDSbKHdz1GcS8+aP28MhmEMy5IEvqukPFCuBCLA8Fw9EX7glUdvZFiWYeDDTNMYV71craIIHDeaLYdOXea7PfHwjSzLGkzm6Pg4DQszEBUWNpIuUw4nyQyQhZtOmFkMmE17U/NGDopX4EpZDSWd1VOA8x1ZPxZNM1yeajvcTkaIqAcA9O/d+cLlPIc+vvpoiDZshCL19E/xBc+nKWrqzR/9sJXffvupm8R3uoqrQMcTHRGMYaBbMvzxy83KTeoiswrcM1wM5wMMAljMLElhzOEzBdER6h4J0Zfzqnokhl4pKAUUw2sovDqMQS+8xkjxK4jgWi7oNrK1cwHzjnX2cD79vP1ziwf0amCfnw4s1xrC7yCPfCtqKz87zE8Nx1FkMLAnSyYVcHvurG6gJuuFh2bwjbV6A8PIWD5/H8kcMqRXZFjYhi2HSivrzBY67NTV7tf1be7ZuTI7scDU0JLyhtKq7E7RcJq+R0LUlcJSfqEnFFBcpRVeoAbwcjdAcWUll5HhA+ZFvx5nMio0KipYowzSKCJ0aq1KwbCsWkkFaxS5OQ7ZlJEpiuB5K3vcQHQtvEejZqa4dI0uRGEB9pu91ASjl37aC6MPAeD/laFvsnUO+pc/Dz65eHZKv84URUTFxX6zcmeLT9+zfCFYQGgoTePZJeV5pTUMy/ZK5DU0yHU+UC6PstjoFP8u5ZcZosK0N07oExWmpUj8UOplg77OoIcx9ZXca7Qw24C1lrzz2gZER+Ot7HGtfQoIeSrrjMYG57BDGQHl1YAgMIZhcrO8VW+i1XMuNQ4BLDSmBCw3s8GYD5zNyS+tTYgKzq2uI/VGc4gGLoenRQvmRYoJPxthaagzAybUvmo+TA2A2XAlMz/LVuQPx2FyJj63CBd2EOgXBYFAQFjw7IOzLTRjphmLhaEZ6O0QHnX1JkO9NQlTdHQ0ACAmIf67ldvMFhojFV6apsPcHKMVjS8zAyhbtiWGoXGCADRDE6yGxKr1xuzi6s4xOtJChFUYQbhS5idIsEDrsCAA6hh79WJ73k/b0VlOQLn+3C8KFF+2zc+CHjcnDqMc3GIIRDvj3/dPhTcohRMEbs9PaWOfre4mAGDWaFhLDgCw+M5p/EbqNWndpBYQcBJB4XYNJYCFIkmTBVcRuFJNDOwa0WBmj6bl4ywhE8nFWkyMxXQtt+jgqSt33T6dDxGVqYzENfJDe05SoUsJBxiOY7h02I4MUgQiEGGt0TashaZpmFMZGqHCQ9xTQREDeiaCjgTFaSiPimB6J4WrVIqEMJ3ebOzfJWLi4M4kZqoGhFryst6DroNevN4Jt08ftPPwJeds8zZYTCqVmOT/1tF8wP26IBAIIFTobBSCwK/llgxP6dOpdych0VqqzQL10f0dILJhZsD86ddxC4lAaGkNzQA1jl8pLO0RF0WSwABoAwAOWUFx2sAy9Nmz6VcuZejr6vlGhmWtYolBDTUazUYuv10xV0Ju0MBecRFBQWqFSkFSFHH6QobDKSADFIFobS5llRQH2XN9VtbCibsruaV3zrq+aQfiZKC+pLLt+YM85gQdFqQDddU1t08btGbbmfSMvLhIGMkQZFVKxmzCaWCQ98KfT4dFN7vEhfZJiM9lrBGd4hF8aY0lMlTTt2tUXEQQzMzEedqPnnEUzYCMLhbg4+OSQHYmLb8E24pHeWoQiDbArVOHBGvsU3a19dACKq2qX7npmKuXjB9mr2m8cuPRY6cyokK1b7yz9OXnvyqtrJs+b4LvzrZ1zc8RvSLyiqtBaHj6ldwBXcPjIoMkvw7RMcGZObXyAkpgcNxPm82FpVVBSlJfW4eThM1DBEkMp5QUU1BYUlTsbnF7a/0cebIGw8PV08Ns3RCIts6xc9ciQ/n5Ongjl1bBFTTZRdXvPHmjpGdFrTXePvW83Sq68YaR58/BZZ0vP/8V6AB0joPVO0iV9uo1GU9yTXWNSuW6JpI1ElSkgbbJTneqyAqIgkbbjVWPaDldOztMFnXtrJK0IHwHdBc5MWFod+fG8GAV/5g03L7WKKV3nOxhWdCuKNbFFOti8kvhKLa6zmAx6DvHhZEEQdpKy4lxHUjPu91F14aA2Vdsz4WCnPwGZ5vym9za+Na2vxEBgOfKKOl5Ldu+3BDhXVibYxcuKeSqRV4/oEtFtcuJqnBdkHDTyyyIYX31/QSmfuiCpf52GAfKbcjGgdqvD6ENhRaoqJAxw7IEV9ADPuczimAwcygAoKxKPzA5xuFQvvg0iIDBu1ak89GQpHoFfhDJ/wOfY2D+jGHuXyLW1gqnxPVsAItd82lK1FBjFqgNDUmzLOcscuyTVUUrFWSQRhEdHhSsVSZ1I/inly5cada5IwKdVhlxIxO15dw7pnP6lRyJPmzYfcZV/zkTB7WW/YP5+f2c395jDXUvoPZLxzBsg9GipDCcgNOmV0qMEWGaAT1iGKKcgC1MfW1NQ51tgACA1ja5KlmzhGhDBOzspMyJwVS/CJcsSIk0Nei7J0Yo1PZkIlFRkZJuWbn26yhoa3w0n6ENbJDJXc82etFH3WYv+xH4VPS9EeQf9rw/SdJ8MJfMrcKtzLSN2RlWbzATuAKqJQZ03Fr47OxCrVrhkNEO/idX2Q9paMATsHLZjE+BhvyuMDXYc1c6Ex+pk2nlluLMHN2Pf1ZX49u4FAy0JsUFufGJw6ura5S16S22QB22WaOJZlT2UCZYZs6WJ5T/F8c47WRhllBYf46jfSjnMCrvuLn9LGJrH1rpBjSLyuPFTA6C8iq4JMIAgIQoObVtGacPnx48Ei6DbBXSQ1PgYCYfZvbTxgyG1ngQsP/g4Moe4GrT5kAd0tlxBimszYlhNM3gmN2pD73u3EJ42xy1w6gdGaCtSLvXSg/p4LOoq1LL5qdECAkmoVdDq2ZY1tTArSYMDArSLgOG/fP33Zpg9fRpI0CgwRizLd0kbbjNCy+DWD0b9PUy0x183JINW/iSo9HZPkzQtgAfUyl5tPZJBSgd9CpxdyeXcxJQJBGh08TFhO07l5fQJTaee+xLK4yICguPCg2NDD2YXhQVqYuMDImICDl2pSwoWKMJUqu16n3n8kJjI9y8Q7OpqTcWVdTVNZgKi6r+3nYEtAXcD+Htc6BqldJkMvHbQjy9x845ZIN6mQ50z/uFDjjkJwm8wWBmWFZBEVwIIoSAYCwL6/XgOEbgGAtgmkoCx0kCZ1k4DMUwTKtWpFYYx6V4uTJgSaW+qs5Ac2twCouqfvhp630Lb5D0Ob/8DeAz+lSl8qP4pgkoZ4TKx4G61EjbqiQU4OlrkFa2Cu1+yK9RUVbbSGndgPV3FQSO4/zMnVZFwVRrLGAxEKRRwGyhLMBhzR5SUZPbVV2tqMkFeLWyzuUkQJIOpptQVV0TWqj60tp4l4lLeneLxUmqpKqetdUDXvPngUVLnwB+ZHBMrdFkSauUGZcrFAp3Xvgu3bpkZcIcIgI2+1OaZZUFIJKPyGdAfZ2xHoC8vAL+R+z+BdO4atHg1Bl7HlaHynMI1yCtDFjasYlKcoGJwjZ3/8L7lSIJ3ObWUFKEUJ6TV16FrRRFkzBrosR6Kqa8Xllcoa+sNcBV4KJj79y+e/LUiX6bC+RNQlX9NbKiUBesEr9vjaJ/k4fwEk+88PRKkV6lIJNidH26RMXAhEz9FFxOJgDAwVQ+fyjSzEZActmmaTcmqqsb1WYFgp3Hr6rV9iWMJIGbQpJiQpJMAGiiY4yk/PIkWPq4OhXaoaFdPTmNisKrxRV1Jou0sFDe5TNg6kS/qQnDsEoFybCshYZec4xb9MpfpZyzu4b2TWhaIL2goLipAaa1t9ElUqlSUhRuLigsKSxqJK0/klKkle2etmuiim0kafSMHyksr9UbYDVgZ3Zu3z2FM0L9A8sCAsfCQ9RQPbk5X14HZVOxuFgLz9AA5/PXWdFTOsxUJz6A2UJTIuMfAHAh/Ro/Abzw9snhIeo1Gw46Rth3CJBWImT/EgJKT/8+lnn/PBlPuoNEuLpnfSCsB/eerq6tt9AwZJ9hGFxkqwlGKPALOA4Fk4BTwbYEH7YU8rLucHkLVK1R1dfp6/Xm4+dgQb6gsHAALAZxKQ8WFOYXDR3aX6UglRRpMtNajXJQ367hIepdhy+cPJfpMO3ZTtUTaaV/SKaqGu0TzPkrYMUFDzoDADLMocC/BKyJ6sndOap/vE/PocFoMZgsbqR5h7+M0Mz8yuSkcKeSbtA2h5LqdLFIGlMQLBefhNnFNKlbDwZgETrNg7eM6Duo064dZ5d9upafB7Glr2PjE2IrKqqVFMHHgFZUgJw8a0Q9dwy2U1xoTqFHf81tAiSXCJ/+RQWInjaOD+whg8niPqDHn0ZokyCt6ukIbtEDi6XGVP32x6tgdJhtPlkXpDKaYIUPEli4KVGWD8qFcOrMsuzFy7m0MrRnjAIolKLLjbVVoURZKlobT6zFyOoyfqNM62/Tsj3paWtFJdLc4N09LTFCT5h1Q31TWkI+mcj5S7kMC1QUMbRPfK+kyMu55blcfmaugjEXGGYLpmcBSLuYZaJ0QRpFWIg6LFg9beL18VHBKiV5/GRam5BNZFoiAhC/Dfk9mfb0Kb/9vpci7bGorsi+eLJ5RugJs074l2FoHGBDlY3XTPMQ+TlQEmMABrRKksKY3MIyNQn0tXVBIUHiMh2ZJYaYcG3PKN3tc8cHaxUUCTPW7zmanptflJtfBOPF7NckIHQUmZaINk0LTdRfU0XliwndexthdSOes2vPCdvp62EWomfm9TlZYJ40xC82LOtpxx3bd8c28djHDEG4UyWOE0ZrGTRZJc3IrfD8+HYvfG6WNMCVzx0iPG2ob8DCVIIazhzdMzpMe+5i1oVa6Ghygb3Q6eBo9lQJ1r6NymSqyv/eCUTHxKcm6nucjAoie0qktrDSnE1w33jUofBEs6Et1tK/7snLOBfbuKlqZ12Nc6QBmai0T1oeM2hxynrEoURzHDbu4kCtiepsEDgmjoTKzSvKL8DEyxhsL7KWlgOkbXJU/L+A0cokz/y2TXXvIhAdeRa1qfz+54Em9T+WXXl9Z5f5jxpTT0ie0XFFpk1O8wDsP8yeb9pjASXpynqLRkPCeVwjUCqBUa2iGgxmPsun0FWjVhlMFtK20ksCnB7lzE1RciapaT5z8uw+Pbu+/+WnICDtSlnjsR14JxAdE3i/FLRanulDv631pFteAyljxpld9DaDxPguAFQ3Wz194kQiFRoAQGznbvmZV5TA6CofKAynbzBrVdxkp62FYbhIfVs+0N7jHxT372TbCFbbD/TCk0+L+7z10futMgbPrbZOjSBlRHQQfG2isty/H6880i+O0xQfkNdAMgWNGKF+U09OQBWabt1hWqr8zCvu50BphqUtFgWFK/jlTRxWWxOAETOXwJoqWdl8e3ScwwJYo22KI9TRQt63b9/UkaMyCk4Cv1MWnxxZkOH/90Ug2r2eXig0DWvKZGWTKAhLBkDkEPOXeg7tkyAjoK7U01q7WDQM5xfYMzQDrU4CGzXzIdKW3F+gS5fO/KzoqVOn9fVwEkBUOx7+LypcXVGBJhMRiPbplfp45REfdfZkJtSftqenJT2E1Z9xfW4YPKh/SIg1AoCkrLGvEj/SwQP7rS/HuO9DpJ6ABWazGa51hTkH7e/j5c+EQCD8bqKyTRfEGA231kaO4npjU41Q/6snVD+zhSnJkVZKMmr7AMNZQfdC+85cMqmzpI/BjNdV5cXGJgqKKcGqmTYB5S9xfT1cUs8H5PODf5STGYHwnKK8Us87R+JNe1VsYpRs+9JZUwEAX27a7ua1CWrtf++f/Oz/dnp+ekoChrgYacKVtrqRUc/d8c0gOszTKVwyq1QN1NZEoQRuDdmJigi7fuqjXTrF4TC3P6TeDI4flZZLTowLzbiyX7LuHhaC57SSZmg+pQnUTgwmhuLTlFhgzJet8JywEhSBQDSXiQN7OTfuPstn5pVnQp9kl7uu7/fVzkNQN2dMmH3jMADAxr9OCzKaEGy/YzPr6vmNeLU2VENU1dP/vX8y3/LbTz+6P+dfN5zmR8BKwtIMGZUYoa1ifkIBZRiraFIkGRMdpyLNPXt275IUr1LCUz90yBqlpQsNd3UIqJgYTELFh83DmsawGSMIUUI8mzVKkSTKT49AuOL5O2ZU1bmK4gFfbtrh3Dgs2To6DA7RvfrO10L7Yw/fV5u2j98e3yf5jnuXGhrqG+r1JAX2pp4z5l1iGGbQoO5c/YhrEjPm4cmj4A1rMq1bewTWTVLaEyrn17I/rPwLAPDiknnWE0iKhu1l7irOu8EoyCgLjIxMovsYDYzcLK43uTJCW0s9oYCqCeu3ReCMUV8enZRYlJdVUZwvifWkaZqLWILbzgposTBcHj1RMiZ+y6agvHzKvrZVXPAIhD9ZOmW0c+OXOw4+NnsSzfKWF7w7+vaIKiisCg2yOrCdlXTprCmXr2Wv2AbNQwk4jr/6ztcH9mxd/9uPLMuOnzzrs69++PH9l3etX8F3GD5mktB5ysw7/vngbSSOp13IohmGxAHNyI8ECQZahkYDtDSVKuvA9r475wIAln29fsFdU/iNF5fMC+NOe9jwvnDGoLzut5+sR9ix++SUiTJrQjU25RGgLZgGN3NpiTAct45ZhfRvvSM1F8usBq/YCG1F9ZRxImEYxojShtrG42xVdY3FwlKUvP3I5WSS7uHNUSGalL8Ym/9aBwCYNu9OjVpFkfDdjRb4B5RbctyXHxOBaDWemD6KlwGzo0jxqkpgNM1aza60K9aZSl5D+X9r6gyMKMFwQVH1xIH9uBH6BaHRZDbdeOu90BZb8wPfsnfnppi4xHuffmPX+hUxfVJGT5rJMPQzS+aLT8DCgF1Hzk0aAY9G4PAMJTLK0iaMgNafArBGFjM06FVq++TgfXfOASyoLy8GLLvs6/VwtXeD8QWAMQx7+HyO0K2+VibfmywEgKNhjFv0qFErcAweimYYM1deQ6KePK2rnnICatvgl2PaxuNAqVCYLS5HFnCKU/Ta44f3Tpt7F45jVWVXAa4ICdbgtoR4s2+aw2WzrzTWa7NL+ERQiI7l2Qhu1gtjE6OWzBwnafx68757RgzSaLW//2wN6V3CjT3tHbi5vNblk78PPTENnhWFsxINpQ1wdo9QwekyQUZ52/PLH9Y//9itNXUGjEvuYxHt5eFllFNPqCz5edaKkBeu5MVEBEeG6z7/8K3Pn7sflsS4kPHES1Nys67iGMuwmL7OXsUopW+XPSeuMhaDIKPVNXVlZfYvJSJMV14JV/78uTdt8si+0qU1AOw8epkJtamqTv3WV38CAHp2SxT3cTZC/9p03Hk9I8ZaWJiVGFMqyCidJkijNFtoC83oG0xmmgFVXkuh5BMB5RZucsakbazNmdLWvUqlssFI80bm8YNWZ1zirbfB1CbnMiPCQ/v07hEaEgK4ydC+Q8bn5eUBAMKCcLWKoAFB05KwJcLC2kIinL8TRDtl4sB+YrvJYVffHvzG7jSZkGQAwPzR11VX1+h0IeLGJTPH1VdU6/UuZ9+WTB4VEBq67RBV0bB0/mRnDR2SknzmQm5dvUmtdogZWnrfPE5GNz6+aA6GAQUOi7Ofu5hZXCrzk5N2taiysvbO+x5774tfGYY5emjvVx+/Je6QmJRUVlb63jfW5ZWPLZxnNtsNQzZYtfPCVazWkNJXGmzDqyfU91DKozs1Fq54vlxfFylqExmhNkMrVCbOngWwkQVAD8BVgx4Y9K20nJ9tpgWqUCoxADTQUQ5jPI8d3iPsGjpmRlBwkEajVSqoxDvug4Pu3LzcgkoAiE4JMRgA5obazHJpZBZN09Y0y47nBq9RrYWmrYqM3EqtztJZUuPuy01W/4MsEkNvzzGpLO5NhybhxIF9Bg3uktx78sixVucswzB3jU1mocvRQTdHT5x+04L7n3nodqFFIqaHUi+NSpHxNWvCdfqK6kZPtdVl1Byu/vLXnbyGpl+1j3AzrsHFez1698zOyTOZnZISRWk/3bgLrzDcNmN4o2+xeP4NIbrQJ599feSYiSPHwNSZiyf1FBIdvf/p+9t2/j00KXbBokc++2n9kgUz+Hba5h9ig1UncouHJkkTLK09Y/0Wtl+UW7mna6y+cYRKZIRi7aMOiqyAcvU7MZhyyUQ3nD2bNn7KbKU62JoZhDNGabMhN7fY4QDcMB+O9FnWbDRSSqV1spOr6MlXiRJ15v+Be/X6eppmKYpC6uktls6CM/pNcuDKSicUxKMwm9k/bhhjMDt8gz16Jz3z+WqYf5sE02bAAJe/NshPXo/vk4wRBMMwk254KCGpy9efvHPl4nmlSvXca++vOpg5fyRc/8ZN81lDqW9aAAebbszS1PPZo1J6VVfX4BgWbFvNwaMNh7ly3RMIpiivoTPGWIfeYr7YtGvWgN6uXsiEq37bctSdhmrwtKKy/nGRNdVVr7/4BMyJXlh2YMeh73ZdvndcNzhUHzOY73git+jE66+kTpuTMHDE+k3QISHhRG5xHtt4fvimUq+w6+xfB2FQVAAu5+/fcwCovdAklbcLaEVlSUVlSW1dmVar1mqsv0olJQ7jhegoa0YicXySSqlkbFfcZDCQlIIXXBxgKpWCJTWCI82Wwx6eYFhYSEWltKI0whPXraxWfrlpR1gQnO+vrJOfs//0qbsf//AXT9STh6gysAAoKdwo0tArF3N5g44FYOe2VDOs4u3yr42loU8gIanL808uMpvgBLrRYPjPc4++98Uv9zzx0s+fvAkA2LQX3kuHTpzds2XthBm39uuTxJV3zZVV0jC1tqqhHsNAfV1dQ4NjHVkb5WXW7FmBiTlc/Vda5tTuDlOEPNsOnJ8w3J2Grj56Bs4aF8rPGqceODXhhnG1dfaJwjFTRqUeOOXqgJIZAz+ww4U7viV40USNCdZFRUWIrp8D5zNL5NfCV9fB+SNdMBmqC7beDJzS9eotM1ziiyMJ/iXeYlUoKIZhMD5DEwYsZpNCaf1UCsfF8tYq85xxqlKqCKKeZUFiYszhkxuBb3Dvo+DXaXjSR9zN1WoNr6MgMZNFOh2zdMpokqvux82yiNptkupKRtMuFy2dNcWVHSrm922nyktL6VDVJ1sPPHHDGAoHjmYoxGgBX67avnT+ZAUhM2GEsTSL2R3HAwdfn3oU6j6PYH7CX1w1+dGyD/Lzco+eSpswA1y7Vti1a1yvXlBfLl2C0+hiLl+1+kkUWpVW7ZjV0QOIKgsd6jYBrr/YfjVPRlt1pPwY2TOqqqt2bd4zbFyK0JLcHf7qZFUYQSiUV8EI5dmwZQPwL/UK3Bca6i0Ttbi2Gpw9BUBwv4GDzRShqDzhyavIcB30oNGM+H5jcRwnCFhu0/rcln6JEaRTFLIEww4IWEiZX1jEYqzZxI3ludg0rogSdxDu/+kX85VKhVJJqdWmoGBteJhOo1FNHXfH9n1wYNhCPHHpjhctwCi/lCpu4aftAgpZDbU0NJBqNYlLNfSdL38HADy39BauKAv0aDofkNdZXkYXTxluNBqV3De1bT+0bqaNHcR3o0Otf4UMNwkjq6H8gPTh+TC6kMRBdY19PJFRWDMoOUavh0bixx998I8n/7ng3qUH9u5Yt3ZlcVEBldgbL4YZzs/XBmuitGPHT0wZM/jmufC0N+85NaTEWlCh3iD9DYiPaGK2W0cCRD19xORZE1MPnEo9cMpkMqWeOjFyOBwovPHvx4QOx/eljp4yUqvRbt+ws7ranefNd9Qr8JaM3/1jol44eyoyIhwA69KhhC5wDgSAzbKdnf6kbINzLi7enrMOODVKEAKVLp09wjDspJk3wzuThTdxRmYWbW4Y0KcHSZFDB/XkYkZBVVU1AJbaqvLaKszIBXN4kRE9uypVUgvl8MVrG1f/FRoeSRDEmp++PrLfwRZbsOjR90aMxTD8o88//OXXn0EA8PHWA/+4YYyzT3BISo/3v948elhfhcLBlbnknjlfbtrBy+hj985W4LCSNZcXG5Jx1V6yZWpvuP6EJzMzR7JdUlIGwqxTk59tP/TY1FG5BcUKUrrSDqswmcPVn247fOugHtU1teKxfHKc3Vf+y08/rt6wZsU3P40ZP2XMeCjfBfk5zy+YwO/dv+3A6Ckjud9mqNBsiCo1rxirMQxxcgeL2XL+6q3D+sjuWnvGofhEhyJlzOD+fQc88tCjnZI6bfp70ytvvBRUVSLsevOVZYd3HYXf6deffv/zd64O4rcBVmvhxkTl8xxJ+udnZUJTJqJ3ehXVuIAKIe9Nco6fOrLz5jtuDg+PranV90ieS1Hktawq6yp3lmVpmrbAdfHQehXU07oTmIxGFrANFUR0ePCIAV06xYVRFIHZrKRl86flldlNm1AtPOEqvYOz8ssdjTsHbrvzwQ/GTK7X1504vFcXFn77wiW3L1zCz69D2+3zXyhK8cySO85fK/l7+57ZM+bMv/d2EKhG6MnUKyzLqpUUzYKrtlEtz7Teydu4YeAnG3Y+OW9KQWFJRbXLieb/rTkwdVQywX1NyQlhhEJNm2QmFj/bfmheP7vgCrDh1p+otWeuJNpyJgjw5ieP0Wh89fkn+F/m60eOffyfL36x6dQjswZv2vp36qkTBgP3FyyO1OBk1PkdK6/Z/1zXHoduLgFx0ExH5nzauUeefFh210uvv/jS6y/6/Yzakp7qy63LnzxHZlDjmEbZgQ1/rgEAjJ06NyQ4iK9EajEUEpQq4aY5JpNZoaDq6o113Mr/BoNZqVTyQ3iKImkLhgnmK+digvmcRIZnz87RA3rEJ0TrhOR43UPjFt8wAIawRQbBTNScjEqkk2fplFGNyujIMZM/ee+VjPNnAcD4NKYffPvbPU/95+cPX8VxXKFQ/vNBGNPar0vUrFtmbPp9CwgMPt56YG7fbgajdDB7KTMvuVOk2TnqRcRH63fcMdw6JJcFC3f4RZVVT571F6T5uiSckMRm2OjTq8/xk1tSxgy+UGDtkPbHWqAOfnwp9BTHxsTGxsSKHR3OU3UIhN/QRpjqK5TigFXeaWOPh3dCblaIe1Hqkf03zLo19fCOe+5fUl9vxjDMbLGAUXAIVl+nb6jT8wZmbCSuDbG65rOy7IPBqurasFBcoaBwHFepVBQBKiproiJgz0o4eLcO2yf2Uuy5BP2zs8fJxHa8+Os2bS3s9sKDc3gZFZTUmaVTRu05dmF3mnUe02QyKRQOo/iszMukRmWpN1jMZpKi1q3+4dA6mDCmtqZGiImDPwnVXp5PaCF/pWVOk3Pabjp3cVpvlwl1eHi/rRt2XLRWEHDANn5vOZnc8Eer1Ypn3Ab0h7+LWRXGoWPt/oTZM+a89sJ/kHo2SpOG2JE4jJAti2/no3IvEkmWsyyIV5gKTLCCMl/mzU1/u4DWlXL3UlAnhTIkIiLu5vmLKJK49c57CRyHysjnXHINr54syzIMy9uaRqORJAkcx2YPUgKg5ISZW7tpDdqzmz8T+ss4B6aN7vflpkJ9MLyZ3/rW6jF8cuGM8GDV0OFw/stkpvfshUlLxUzsCzXFQNM4jNW3hySwLPvBF7/+sfr7PVvW19c3EIRxy7rVhnypfPTuN+jrl9759vtvQCCxzdlpywUn86P1QMZoNObk5ez7+8DljMvPvPA0geNfffJ1THTstBQ4J+CwwMIWWo9oBs/dDmNynVn+UYarve+sQdkn3BFPFYqfXs6t6N8N/g7lWxyCmciySusSfUKVoFWTKiXVq2sMY2koKbDftNBaxCklRTkIMstyVY5FqsrdDjAtE8A0ahXMS8IwNw2GXn5xPPafu07fMmmAkB+hPO88208mQvjq1SymwoRzE228jMJh6U9bnlw489QJmOjwwqWsqjqHUWdRiX0pFKVSKCi73+PhhbO/+mnjzXcsuvmORdZDvfXCqXyHhIlfrthkMUNzOCFeJuAL0Txumj9v8oTJ/33zvb/WwF/BmprqsdPG4EapJxQt6PWWaI4YFy9sL//I2nJkX4GblyMxFWjU75NA5oufkqEhduuPyxVvERd/56morAoOCYXVOG0uKv5doFRym92pkrBwh5H1kdJis6qrzQ3FisObxw2EA1Jxyzd/75nerxcfd+0efbBy2Z87qYr6ueMHuu958VrxwJ72vyReQ+HURFbhU/ctXrDokSdfeCt9xpx3/nGX0GHpXbPgdF56XurpcxcvX1yxWhp2jmgeO/fslIzNrdMxIjb9vWnT35vQFW4eYtFsBryYIhnl8XwdUmG5XjoHCivGMdykqe0wLAvq6upCQsK4CFD541eUS5d/5OQVYZFw/uXjreDWQT0le3/fdY6XUThBA1z6LlxhDtf8fi7jlgHSGcD0MlF6JwqcuGb/1VUqlUajdXLzg+//98H3/5s4btJ7b8GKyidyixyOEgSvycI770UCimh11n0278bHYKa4poomb3I+8OQCYRvhCxwEFCYxhfrJENwyEsES7dere009l/jDcch+7uzp2Eny6wqwSHsK67VnLks0VFBPgb8vyJQf4Mfvrvj9nKczgFMmTvm/N96VGEG79+3iXRkhSSG7t+wV72UYxmgKLFdSgNPU4EHk3PCEdZ/Nk2go3+IKJJRegQWgsKw2LsJ5mCQFx2D2PQf4tUa8315sbhIE9Azx2znFx/gNXQw4dsGjOnytG968YzcMKT2298T144cKjUd2HzNzM541tTV8RPfYaWMAADOmzcRxfN7tMG8pwkezdci50ShirXSvmwgvog+GuqmtrcvgQgeTRSZcml46xxghE0gP3eiMY6Hi9lB1Y+y0Mfu3HZDkVhjf3+osShkz+MT+k8Le2+6+Bfk0fOrfQM6NRrnxsfVN0k1kfnpdRqH02SKw8Rj5onAOAooBEBJpyCk9D9od9fX6lDGDKYrq3bO3Xq/Pzs2maVpso4tjEhHekk7k3PApSDRbl3KlkgwK60B1Ncxm87kL51r7LNo5jYrmkX0FyLnRbCNUmA91ZeYj/El7zk/jIUFVJUH9ZdZB8ZSBqMiCjI6QZMEThPtZ7Br2ZKSJbKXm4coFH8g1r1zlgRRoT7dSuxLQ5vmCYbSH7ccchcJ5iOfTc0g6fcQ7a4570Qj1519+kVs5blvy2q4E1BWyf2cjxsU/cPP7nvTkXcYIRKAhqF6zldRbuvnE0n/cexcslQbzzC5ddOacNenneca6cDssNMxkNrVKElKfQnY00WyNc+lwIKvTz7TuyCmVC19Ju5hWUlo8bvT477783mg0PjTVmrBVq9Xu+/uAkG2TZdn25LAl27d6ulfM5X88/cDN7/MODU+OiQb4rmJrkGejw/Llx1+Li9bBDLYKxeFdR2fc8cCW1csBAPv+PlBeUT5trrXkTOqBU5KI7DYN2Z4WtzXDoeGheiICza2BCBB6cJWXxJhMJpZlZ9+zlBdQAMDMm28Q9t5y102/r/gTtBfItr64DXmB259nY9v+9Ka6gN34fBulbXktAo0nnnns5+Ur3n/7w2dfeoa25QMaOnZIf5uHVrKEum9vlxEvbZE2JqACaJFGR/AObz5ur+OECEzSLqb9vn7tLfNuPbYXlrHU6/VT504WEvc488bLb+bk2TOvt3WwmXdMBW2Kpq4LbrlDY/lHq/jBPpoDbR6e6yl/hRu1InEcv27gdXNmzC2vKF/zx+qS0hLB3hHcvnxRL4qiTCZpQRQJyAJtFE/s+t49+3z35fd8kVeGYRZNSJZ8HXxlUBzH3dcdaFtfR1u1QN2DvMABhXd/eG6YOmPZq2/xdymGYYvuWQwAuG98d+f0BSf2n8zNy71x/lwvvjvCFRcvp4+aPAIWMUtIXL96w/Id6Q9MsVdOxXH8+D5YRbydVW2RFlNso06MGx9bL360xnkh/MSyV9/aewCmHxw2LmXo2CHDxqUAAL7fI43V5W9XhE/BMCz1wCm1yp67ElYty8/bs383pbBX1ho+bMTxfamlZaXtTD3bqgWKJLLD0rd3XwDAP597UmhhGObRfz7y+QdfCC1vvfbO9CnTDQaDSuVQARzhdVjO6l+/ZoMQpcQzdPAwlmWzKowgFMydNe/V51/75MuPf1zxQ/v7CtqeBeoJvpisdHPMorxSVw+vn0YH5+Lli3A++t4HxY1Hjh2+d1w34en0KdMfevzB0VNGtsYJdjgefOz+iPCI1AOnBg2ANbSVSuUfK/8MCgp65G44c4Lj+KvPv/bDiu/bpXq2SSdSS3wXkvgYD73AvINCMh3uIW1rRjxAcP/D8/PyFbwdeubcmR9XfL/3wF7hO5J8TakHTnkyB4q+oxZ+IyEhuvWr/woJDhFaFj54d/bh3QCAFX8fTUyQKcrtZizftr6O9iygzgyJC2pG0IzszekhbeuvIUBo1HLvmdzzg3c+iouN458yDLNken8TV+kTCWirfCPOBFWVwGLpodGgXd8ybXIO1HdxhVGRUT9+83NMdAz/tKq6asrsSeIOnZM6/77yT766SU1N9cSZE3x5vgh5Lmdcnn3rTN6J8exT/7795ju+3Z4m64hn+VrbCIRvaJ9zoM1j+NDhW9dtCwkJ+fb7b156/cU1f6wO1YWe2G8vZ4Jh2B+r1h07cTRlzOC5t80OCdHt2rSnVU+5wzHkuiGTxk8WnrIs+38fvMOPB0dOnQe9FggfEJsY5eEDdDA6lgXqni8++spkMo2ZMop/umXb5v/74J3UA6eeePPLT15aCkvj/fIHAOCRp+B2fmH+1DmTt2/Y2dpn3bF485VlMdGxsjNoFmVzJqkRPqsbuIpbfgIHCu14BQoSUAfW/LFacoHOnDtdUVLIb3fp3GX7rm3CrorKCgDAu8ve+9eLz/jhq0IAAO5/ZPHGtZt/+Pqn+5YsFC7Im68sg0vUfv0RXSL/0NSlus/J9W8fqooE1IG759/zx19/ZOdkCS2Lly4SnEgAgNNnHep6wpHjcKvFivADhUWFv679df6t81MPnLJYLAzDKBSw8uxbL/zD5Hr9NcI/0jlClD3STeFVyaHatJIiAbUzYuL1h3cd/WMlzLVF0/TFS+mP/HNpXV0dv5efXysqLpZcQZUSRWv7lXc/+r8PPn1v4riJt998h0qlXr9x3R9//a6tlH4vAIAX//NCQ0O9f8+uIzKiZXnKeSVtozKKBNSO2WweOnZI505dXnjmhaFDhvXr23/v1v0syy6akCy4d0mSkFxB3iOP8Cc0Te/YvWPH7h3uu23dvsVfZ9ThcCOaRziTs4MUXkUCKiU7J2vJEw/x26NHjvnk3U+/35Nx3/jufEtiQpKfvyEEotULr3qumx0NJKBWIiMin37imedffU58dQ4ePnDfkoU/fP2TSq0BAA7h58yY+8Mv34v7pF1M8+cX1hFoRjSMUGC1LL7DRdL4Oldkowkkj3RI6eRBAmqlwdAwbfL0jVs2HDxyUHyB+CTblwtroHGam90pqZNk8P7O+zC1GqJ1nRhC5VRXHdroFFsbSrzbMUECakWv11sslk/e++yRp5YePX6EbwwLDft5+QpusSCU0UUP37tr055/Pv70B5/Cesj7tsFagxfSL+RmWuOcnCktgqFOiJbQt1O4sL3ss82SvZHRGgDA0atV/NNipw7OpOWgLwUyYGivFv5lHunAtmdHXAvf6JLeY3tPEISDm8hisUwZ3IV3ItWFRt8w5YZlr70t7B01eYTRaHQjoCqNPSsioiXS6SyaEo5ymWKGj+1TVtK45x1pqCsBbWpZRm/RRocIyAJ14PrxQ0mSnDtz3qABg65lX/txxQ8sywaJVlhv3bF1646t/v+eEK50U0z3Xp09UU9empGGNgnep+StaldtkTfvu+n0hauSRiSgUiwWyx9//f7HX7/77YtBtEQ0BcXs3qszupLeEkr3vvh2wJKZk8tLm5Ziylk9kYD6FjR+d8W4ofYckftO5LnZK4uHZiai2bjSTS+WXPXi+P3WYQOb+pKmqqcrOpYF2rxsMUKIzANPzpT9ppGnqOU0KprtWD2T463pEwEARoN9QWpuhdUzFlAIt8BzXlJSyT3VDDVsRTqWgLYwRMZVn2Ul8NcMzan5WjoDmeQ4kQg2cVW+WDTbEO84GRPiu0OljMcw7Fx6ZqPHaVuKKaGjC6ibX1Fh0QWfFsFzlzFSUq8TUFZnUnioc2NTRbMN8do98zyUwoxr4ovQSPLy9kHHFVBZ6XSfFsFNahkxyMPbKPtO5HlieAaUbgbCGF/WVvV8pN+/SyIGQIPJzB/HQ9Pv/MUOIYXNo+MKqOe5ZPi0CChs2D8aKvYpuYoD7SAIpq77Mb6sRSxLdY01tRjCW+Adc/3Zus/mjRgXzz9c9Tmyr4B/ePUE2wMf/OeenNTP+ceWlc+76pa2//1OCZFujrPvRF55LfbVh09VNxD7TuTxD35XZHhw2v73+/a2Lpy9cfbI9b++sv7XV26/eawnZ/jNp0/8ufIl53bZRgSi2ZDtTz3XfTZPNgjDQ21FiumeK4c/UiqoZ99YsfdwWlx02LofnslJ/bxTyqPSy7j5zSCNCscbyfV34VJukEa14adnJUc4uf2dvILytdtO9+0UvuyVe1MGJ9//yEcURX718WNDrkt+7hWHfC7OqJQKtVpmDZjQKJmnHtQNJtnSG0xt16WDaBXalYAK8BrapGwISDc9gaJIpYLqPPQxfm1rYXFVp5RHc1I///mzx+557DOhW1JCRHxMmIdXnj/CrrUvT7r1Db7lzK7/wmWyc17hlS5lcPKgSc/G6aD23XrPW2t/fqGRkyS1/EZtHeO8l2+UDHsrqmo9PFsEoj0P4QVQLhlfoAtWAwAIR7vymf/88sp/14hbDv71urNN6oaFj3+e3DW2SxKM0lUpqTCdtu/Yp/ld998KC0cHEVRtHVNbxxQW6eGK26H9+Keyj4qqWuf6xgiEL2hXFmhTrU7fGZ7t1RFfVgG1KfPYp9//uuejbzZXVkM5W/PXYXGf7BOfrV7v0CIwsGtivdHqAhZz9WJBXZ1h37rXRk998eD2ZTm5ZWEqVZgK1koZPKiL83FGXt9r555zXv1kiA7K2uNnW/LydiWgHoLSIrSEzkMf+/P7ZxbNn7BoPrQNGYZZ/MgXV65a81Hds2A8hmGffLaRHyPH6UJovVl4bWW1Sy/w9JveOLh92W8/QcNzweIPhfaI8GDnzlGRIZ6c6sHtsFonokOxtmWC2NEF1JUR2l5zIniL5PgYz50nTzy9HM7+YNiC28c+8sD0H756bOVv+z//ZiuOYQ8vnjZ66ovNO4eX3lj15ssLJs9+TdzIMDKDcYsZpmdtFOczESS1f197YuzzaTmSp008cUT7FMeOKKABopWtO373PDBQwHP1DNVpq7iRO8OyK1bvW7F636vP337nbWM//2br3q2vV1bpP3j7PqHzC8/cbDCa//n8D54c+ez5bACAwWh2iBLPL3PumZ3b0kwQX3+8RNgePfVFydMWHhzRDpSxzQjokhmThe3ystJ2cNG9qJ4DuiZWuR72+p9fvv1H1y7REon55de90yYNgjYpjoeFaocP7SHsGjRAZgazSazbeOzO26Sxn9t2nWnhYSUfAYmmj1gbqDdpwAlos3WwSZ29ngrBi7m53OjmgC6JAAPNkMKAUk8AwKNPf7v59xffevXOF/6zUmj88evHLRbaWYYObl92x70f5BWUt+Qd8wvgVR0zss+BwzDh/MjrYRL1i5fzW3JMRMtp98robwFtLR30hCUzJn+9ZWejGWU8EVOVEq5ckuRWkA0tFFPVXpbQVdfUf/TFxicfmS32zzAsO34GjNn0EY//63+fvnu/8NTDCQFEs0Hi2Ao1kQI8J5Xs34TnaWbcs3z1dtDB6NYlZsbUwWYLvfyHHYxfgi7vu2siy7I/rtzjh/dqr0jSjrS8qFzH5JNH7+xwAupTOqCAIgKK5qVe7pgC+umjd7bwB3/fsfNeHsIvW3TzqfP2rMMIBMJbBGZe+lbks8fvbrQPyzDy7dyjGULpCqmALlt0MwAgp6i6SdOaSD0R7VWtmhET5qMzaR/c6jhUnTDCJyNXDMclGsq2TChdQUo+D5JCRBuivapMwHLn+LGmemuJMK+w50iLvPlu9FesoWyLhTJw40ARHYe2qHe5FVViI1TytG3RNSaaYVnGYm6238K76tnq+ttykIAi2qfwtb+P3y02mmWBWAGbisUME54iAlFA80r1iVHWPIyINkSAqANCgrOdazYh+Wu/AorUM9BAyuhdusfGsAAmGkV2XFNhAWikMkGbBfvuPWvmWkRbAS0U8Q8dObQZ4SFoDjSAQMoYOCD1RHgCElB/gJQxcLhn4riGOjTti/AOSEBbBFLGNmcYIvVEAO/x/3OVWRMV8GpQAAAAAElFTkSuQmCC"}], 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ym2++9eabb1XwvnzBdyNR3IIoYAfotoYOEi3Pw1xbtmzJL0LJlsWIUFbYssgjQik0mHVwJxGqIZhHhNr4deyEsdkiFP/SOIhQ+iqkW/ZXEerInlR+ugTqos9jzZdrbjpnwsP/eqzIILHOn/jO32S2sGtKSDwPDxxZQ7qE0h53+UUoiyxuyhKhZgK87Qqxs9KUe4QH84hQVYWUdrNZtSQRmr0ylwjNPgROUmAgcP1WfubXngT98d27GGD4fONesjBscG2uJHCa+2mTmf4q3D0IS1AI1u/Ak9Dzg4pQDVv8+jFJZXhGPlOWCGXvM6IT9S0NZ2UOEQq6JkL13wBnETpu/ASQacXnEqE6I9tF6PgDx/djEXpFXvZk5WfUzQN10deBEPpwY/ymG64ncqmjowMAkMuKx4k5MEN+UkCsPY0Ga9kilLW4s9LeixOhuhVfjAhlL6kzIpQeimFPRxFqVE+Zm2zbstVRhGZjy+bN/VWEXlG09ozqTNqP3rqLAYmVGzZgEfr5F3v3NuzduxcilTwcg+8k95MiWGMwZkNHihDruu2FRejWLZuJWMsvQllQnViMCN1Wv4WIUJbAShChxspiRSjiDBFKO9Jni1A4METoFcWxZ7QjTdjTJVAX/QJS4sNluAMItt/1DM2o5jDRSMPBd2f5mY38IrRIaMBIOTKAOilC2Vd7VITSX5eCntD+KkIjedkzxlAnQX953y4GMOKCeOskBSEU1e13Gw6vxZSaXQRE5SfBrmZ850wZXVuqCM1d2ZkB1bzX2HB8XhGKFWL+iFhBEWpsVkiEWpM5c02Ocxah46wX+z6uuGVYMdrTtsYlUBf9BM6TNgGIJ2VNQyXJz5JEaP4cdRpK0guQMsPxGQcpLEK50kUoZWeHywZo6xasInGQiDk1+RSRxhfhCd1COZSDsE8n1RdkT9EndLQZU/lYuATqop/g7h+dSRZY+z0stXbEU4k0XsMSiT8czj7CnrZkSSKULBQUodl0SUcb2bfM3KwYEeqgcHOIUOvsugjVAEQow9eZX4Q65oQi81WPUEytU5nC1mfeBtEniD6haS+m10BItL3qEqiL/oPhw/zRzPARdpDKansslZIU3HtD9OdiT5ZhPQJvE6EENhFKrPjiRaiRz+TEiMTKZkVovZmYmV+E4spOc6WjQGZ43H5i3I5ZX8glQnO+JfOY9aYITUkabibQZ0Woo/wk1Jl/R5dAXfQTZPNSWMJ3MsK3txqJpzlfYNNGhxxyBnBfBzbTJgyv2ry7nX2BWPG5kKeyMyfDGr0884nQbfWGyC25gRztBsKuK0KE2sLx9lASFaEqTzNbNfOwQt8UoY7Gu406ifwksIlQl0Bd9H8ghJK6FY9jR1X2diEUEAKe6SBcUITSUFL+ys6SRGhWe5EJeURorlBSxgWb+UzFiFBbOD6PCKUMunXL5rETcUpTIqXiplZ9SoRms2cxqpPlUJdAXfRPEPlJMW7MAYXkJ27LxPNcdW1l8SI0mzaLFaGZxelEhCIEs9qLZGQL2VB8PlMuEar/ujiwZEERKklOJ+hTvZZZ9iS86UidrPzM5lCXQF30f0wcNzy//CRkwHN4vBFtyJRLhLLMVc+EkvKLUApa2cmGkgj0yXPAUYSiEkWoY1J9tgg1xtWZZ3coqTJ/A2BWtkPQpBsaT1P0w/UJEUrZsxjJ6QjCoS6Buuj/8hNnLznJTybxSc/E5KDowek7tXVVeUSorb3IONOKZ+EQebcl1TuJUGPL3hKh5o6IilDWiqdeWuLVQJyxp6Zp5HOLJy0uJlY8oVcrSF/GiLTGiqFOR/nJwiVQF/0cueQn7v1pMij5L89BT3ElNayKyxahxVR2GrtkilBsxTudzrC1i24vYqwpJEJpKCm/CM2W5PS56LE8oeb7tcbpla0IvfD66mJUZ0H2DIREl0Bd9DdUOspPQYwktEgig7Do3A4SOxI9/O5Go5bJJkJtSfU225yKUGczPldSfZYIJYCZFJmZVG8nWMekerrG1tipSBFqvaCLUNVMlKIvURGq0olRAIwzq+M9PO83ibUMccmNtXz3VZ52xvh34aLPyU9MKxrOk4/rqoKDMKCpwaC3Lkz7NnkOPwjzoFc05hW3NGfY7yw0BXICk7OJ9RcmIAVzE8oel2QNQcoaSIdFqJkFxW7pCATw2I/6LVswwzITk4xDQRVkRoTqN9VTWtVFKJ6YZBQ+6TRqiNBMSiVdV9jrzDWVWVU5OjGpHlfH4+PEU4rfywMZX10g1VpWE5MuudGo3+UFTiUTnTorPwnK94fChYtOYJSYcLjTBXsBiYZQUlKiCSktW6rws7X1n62t/3j1Bv2xXtUt8QnDsRR1FKGseKT5TD4xd/55dj6TTorjzC73eURoHmmbLULZfKZiRahpqCMnEWrlM9FwvClCWUycPJEsCDznFcrOE0rZkwx2t3JZuwCXQF30K2Qb0bmyl1QNJdJyJJ7WJxtbsFehA2ALJRFomaGk7WYzzZSCSq3srN9Yb89nyvn+QP0Wo9mdc+2/uQ6yIrQIT6ieocQkXWXlw9KzWfNKkb2yc9PGjWQhmpCzPaflwp4McnFokfLTJVAX/Q4M90wcN1xnT4c7+cBxBxw47oDxY4YNHVrr9ftVTdMQbjiC9H8BQrj/iGnEFiNCWf+maLrYHCnEsVN9tggFhUSoCjXKhrgEgMSvgOGwVEoUoTiN3xwZh7AvQhXM/W1J9dRzgJysejoauqxmdmawZ6Fk1eLZ0/WBuuhvULOSK4M1mPU0TeXoxE1N3dnQygnY16lpCgBgZB1OnpfTRltlgxkRUHWvJBGhlEZzgfoNJfMSyO5qEZ5QLEK5Yj2h9XhCPQbPccTPoCiQF5BBDYYbtlOeUMQDM5ovCryiKkZSve6lpZ5QUeAk3YEI9TaBWISanlAF9yLAx5RVY/RpeVnuWdA/63zO0PxwTXgX/QrszM1NG7flUIF27G7qaGyLyYjb0xqvqg5X14Srayr3dqQ8FcE8IpRY8dn1mqwyZV0KlFC6LkLH6tn1ElJpAToEZnsR/eAQoJI8oXSJXA8CICHLotlUxSZCJc24flRohF/5iNA8wpM15EuSny6BuuhvsFSPIlP5WQyoDOF5TuA5clMF/GKgyrl1E0XG4I0supZli+MU1XIyZjvfsj2hxcB6u4r1RPeiZuxv84RmX7BqJlSpzEXmrOzEzEiJ2/CEWltbdZ+wXORnIbO9cwElLttV7MJFnwaXa4RFIWhmwxHd9QlJqMQj8F4Pn6ey0xZKotKSreykzkTcSc+M0Wd3LSlJhNLEdRlYnlBKEyojQulxxjIi1GPuw3oSaHSIvIqw0lRohMgmQg2LHVgilH2bZSJCi2RPAp7nWvYluFLgKlAX/QRX3fm9DBbQ5WepkPSUpkRKlhWDB/EMZA7WDHGWsY5J9ZQ3qTs1lVZEM6cnaY6EkFTZq1OLfehx0SKUHcjhkM9kilCrdxQjQq1IulO1vqxYF0T50SZCaWUnMEWowuxVDsDsaYxkLgo7t7an09jnWxLK6z27cNE5XH3zPdkrDzzowGL2pVmPhBBjiXSU5jbhZBxc85ln98kHHcLepIx/k93LWq7wC7nMxvqN9blEaHaYm5aQqpCp7Mx0s9oSoqgITasytcFptErRECVT6gllB8fbRChnewGAAK2PzLTie3/knMGeRWPnViNTrUQOdaipdeGiDwMb4KqsS8dOHiGWlCLxdDwpsWXgtUMysgjZUNLaz9fkEqE0qV7TECUs2kk5IRlyNI8ItY5pLkgSosYytcQd25USEZrZJJTd3iIY0dyCrirGE6oCjbwpmlSfYNqLZKPXrHjHlM/iUTSH4jfrEqiLPo+1z30v26wOVneyglBW1JSkdMQcJojlwuSDDgFOIpQm1SuKSm3heAp5zVJxmjGaS4TS11kRSg1w2VSPrJ+UCkNyGWxca+smY9gnyTeyqJbuooeSbG5fDSLK77bMJI8ZpqdHsAqxGBEKy9hyZ+Vn0RxqncAlUBf9yn7viDinoXBcvgpLFgjhZNJECntRqTMUzz1uTzmLUIBFKMohQkN+T7b4o0mekipbKUfmvZh570NnEWqegO5lsaps0S5JOGVF6Han5ngpWWOp3Chv1QuTzP5MVD5nVHYmFdkvYrNdVVRyJWxZF0ucgbwVrvvLcndkz0IcmnECl0Bd9B/5SVGK/HSQRwgZRBBLpKlyPHyS1ZekICxPqBmtVhXD4NVZhsuO9lBs3Vg/1hSh7JasCKVXRflKgZpgdefLOCDrCaU9mG2VnbQAgYhQNsqEAJCR5jNVM3klO4kA6i8oipXag6xqUdgLVnw3smduDs2eieLCRf+QnyXePJpeZpMLhKAi8XRasjarrQ7nEqG2fCaKhN6UiMCx2Sg1tNmUo1xhK3NYO+bB7JxTGl+SZUOZki7ONk+oY18SWzEO8YSwG1JeJCJUM52kSTPhQVFUY5Mcf4iqgCnGy4M9i0E6reQ3510CddGHsXbp7wnZ0br1rng/s5FMyR04Im8a8o60Zp7ZZsXTmkiaSplMyQGv4NhehFw+tcfrN26hPUbZLfM3e8/QnZlswu6Xu9FyvnFJCVkOmhdPTuQxfw/oVZFfHba9CPWEpmWNiPGeEqGls2d++cnC5FCHc7gE6qKsMcOza4ZnV65Xrz7/h8T8dG5N1GUoqhaJpSLxNCXoXc2xIpPqKdh8JkcGJCLUNhXYrGzPAFu7md1eJKkqJLc0Y3A8WzaaVUeUcRlOHES2Q7bkeV2EyrIph4HZXiSrEUHm8RHxhHY7h15yQ23PsSdBLh3qEqiLvoppg/G/qoY4aPGnPxSiG9h8iyxIJ5GCQAikJaU9kozhDHjTGXrYpOwtNfNVx1ASXYjG0xXMMAlW/SlaRihpaxGVnTT2Tb2crBK3zsJRhctZdZ+OIhTkE6EphqBtIz0oyBmICDXKB0wRmpK07FYvfYI9CdJpK6JI4RKoiz4OnCbTg3kyCGHTu7UjkUwZrLNr1167CKU1lLkrO+lKhYaVsqau23QhDSWxUJjaTQ/PmWWXZggev2r5ScmWKu6QhLeQNCCaDE2DQpkX7AByEJsIJemsEh1uTKd9MM5UycziokAIkNN2lwjtNfbMxaEugbrow/JTUTUO60+wafu+kg9RNO1qCMSTcmskoZhZTflFKItsEZpKydnpn4RqjZSjQiKUtkBKpjQaOKIilO2QRCWqhzci4tRxmUozoXlgJCE5ilCW6JUsjuWzvAHk3RERau9h6vgZ9SJ7dh02DnVnIrnYz5iUxKOKcqFW1ziT1IxtxoQBaMM+NVxwpPPgJvMlKZlWUUrh8KSjjs17Kiu6FPnVFNlsG4oi8bTfZJfdmSLUXgskIFofqTMjb4hQswiJWrLZTUJ5DnIIzzYiG4ybMLZ+s0NHfc5Ui5SPqAhlxyXJssGIMm57ihk4Lml+D5/UMz1pW08z7o8c24nqcpVPmbNPeGhWfyLcJxSPnCPhMg4BDV+DJCt0ypMhWM2XWHRxXFLn2LMr8pMinVa9XrOCoOuHc+Fif4Gk++xr7mBXYV2gpcgjHolaj1h82IiRw0aMjKZgR0yJOg1PyuMbVRStNZoaP6IWN6/PfInNZypGhCLEuEozq9c1TZMR9JjNR+o3bskWoWxCaCpl5cA7jgKxSu85s+GyeUZJsrZXkOGgwCSeFY5PSYwVn9XuCmZJeaJzSY87mwhFCHa9NP70S/wdrQny6GX2tOlQV4F2M2jIeIU8oruP3T+xwe/P82rYg2/XDbK1zbTBoFk33nlsveM19ZHISJ/Rb14XpDnNxM3r19Mv/YTJk/XewDnv5q2G9FPCFcZtkkorbdEUx3GiwO9qjo2oqyhJhFKQmZ3Gxiov8MaASIGHxEkgCrzMBnrygg6XZ0UojyC5gFjcSDDVPaHYrE7Izp+PyENJ30cQOJVJXyUIeoV4Gjs1oSlCOQhEkU9JKi5MMnO2zDdl7K6/I+QoQnnYeRHKvoH8HFpZE+h29mR1qEugLvoeEMKRd7JMvJ++vGOI8pBpvkbl5kKcClwIUqlUa1tUELjKQMakzymjatbtwIERDXfosV8FMc9xk1AmTk7TP3GOuk6CCmNoB31CXA9b+XSSspn8rJ2O53nwBl9T+xr/kJhCOeT3RJNyRsIpNcM1SOM/kn52EpuCAFKbnewVT9mTGjQEUmk5Y9qHyZJ4cDzH4YIuBRv42SNMBA7kHSrcbaD0ilRk9VnpJqTTxgQqFy76UuyIEARrOfrN/heC1z7BuBtAezvpU5IkRU2mlWjCbO3pBLYrkm3ah7FGtfSYKHC0YajPw6PM4ExKr8q3rsWJBKwuSrQ3HdBorya2ot/YLMt3R7Ylu8RSCo1NscRHcpiQbsWTLTlo9FG25h6boL9wooezPKHkas3KTjOy1eMgv1pIRZZd0E1wCdRF3wM2JPV7kAbfg0yvimA4xD78QT9ZcD5WcY3v8CZGXiS+G1VVS6aVvS2ZSfWj8lmjhA/ZTvV0KZZElLBSCmmjByIplVb+sHnvdC4IttOps5JJqqetngQz6zOVNviO5XGL33UmtxHLqIkT6dkp0mbmPE0p1bDGNJtPwwyWVFWVuGxJqpND4SkHVAR7v1N999Koa8K76GPyUw++Z9yO/spKSUkXpMGgOSEOT+g0g0V4mY0J6QZpPBq37Ttx7DDriRlM37qryfFExIpXFSh4EDVUsz2hsqyKokDj4F6BS+vLNDhOg/WYpJjuHaIASYolncrJIm26L5Oq4heFpG7+U8eBZbxngYc4WM9zgqqhtV+spTnzJKzEugiQnlQf9Hrien0Osd+zPaGiwCf1hgMeHhqd90wbX0/eRb0jQh2LCwiHdt2odwm0O3NuaNpNduZNqcETFzmBMMtpGtqys5FUvmu4zNrhJtE0heM68w0PhuxUu1sXmxxzIk1VRZ9/X0QaEracBpMmjd+wwYqb2y6KekIpBXtMrkykgNeM/xNO5CBIqRb1sG5E3TrGTySkegUhrWtALEJ5RLYJePmEHiYmvzSY9VTDT6qaPK5XdmpsBhVdJiw4fsrktV+sJWfEnleD0y2WFEzVbJ97bLJkMiUFfGIiRxEkBDipXtZgqaGk0y7pthun6zTqEmj3gLLhBjCcBOLZwLGLbpOfmtGe196xLU9WfPfVKfEclk1WNQ5bmaODS7YVI0IpG8YTUjAgkmCRinCXT72gU+UgTzZg+FqjIjQlQdFjiFCBh9kERa9KQ8gn8CmdYWl6As9l9V5SrVCSrCmi4JEUbe0Xa61J94pGBTJd2ZFKh/1iRA9P5RKhxFSQFY7nTa1MA00IkgGAveYJ7QkadX2gpWGD3+/46MRH76IT2LR9L25zpGqbd2SUHuXtUlQA48ePK3JLnud8osD2yszGpEkZE9izRaitspMeTVG47AYibLSHfY/0sHGmSRL1hKZlI4KUUhVCwbwerDeOiRzai7AHF82IHPvrQCc4kexOx/YiIMsTmkhKfv3yHAfIewS+eys7i7Tfu8U3KqdV2Y3Cu+hD8nPD1j0TRw/DM95M0vFXVhbT5y0/NtU7lPrYQM4IAfR7PX6vhz1ZQ0TPMtfBte0S9nzBJtVjEcqoFBrGYduLBMyyFvq+MqI9dKoHoxtlhaOHZVWwNeedilB9Qc2sf88WW2yPu1jK6r1Prz2RVrK7ysdlh7JU471Yl0qcsNafiMbE0jJO5lV7fnx8tN36G3WdRgl1kmVXgbroS4AQasjwfrKowPc2zDPMI3/75KJPj0VTVcgneoQRI4Zl154XI0IJWBFKVZKiWPlMjr8IjitTqkJbhCiYr4mBbLyqQI3sxUOr/p0Mjs8jQsmgDlt7EWuZmfah6WfC++qOXZrPhEwRSsP0lEHZgnqPzuk8RD0hQouXnzbk4VBKnQQugbroG9iwdc+BY7D8pGYu2zgZW6xd1jHFdHWCAAR9nqoKr7WKxnbadtk41FGEOrQXSSs0Z9NKwGQ5zkmEKipP92LbxNGmI2bppnGFhBNobJ3ypiV7GREq0x7SDEekJJXmSNm6Q2kM4dD3RRmT/EdWcO8oss6DZA+SRQ+uPvXysBdEaKnIlqKs8KRwCdRFn5KfZvDdBk9XnKAmsrWkw0sQ529WVvhoIicCoDGakesOADhlNq4TdYRjjzuqlRIpXDRJlgu+Jzpmg61PT6cRMdU1tr2IvuDhsH43djEvQ8HF6RmyVy8TUrKr9dlxeKwIJfDgYnt92ocpQmmjZc6omcIhL/JGZV5QOF5RNNns7kcqO0EvhuCLp1FH6iRwCdRFHwD2fo5hMjEB8AeNTCOCQPaIdAPdEOHNVqZk8K/ftLcdmNfeQcOalOncXoSly8z2IvbB8YzYTEvA79QWiF6wbdy8rCF9ZictBLJLRSJCbUWWuKWIuaxoVvY+hQrxSplRxyKPPEgWNPxvMIyr0XmoeYDMccDngR4gQ/0Tw0VNGu7NoifVgzKx37Mhma1gs+GmMbnoGyDRaiP3MxzWGBuTNMLo4vFxYnduBYqLc2JpDw+CsuaPSwLPwTSq09RIUtZwS3wwSOCluHENaM8eZV3ghlmHvr1otZGMKQFarq5DrRdx9ijNCWXbi8STyO/lSQJ8dhW5LZ2eXjJu0WRWhyZTms/LpfAYIutVUjvvwRrQbKZnplWlVaaTiJ5KhbNBkSrwgtEKhPGcWJeDcNKS0V6EA6qqj/PkkKJBSRH0fF0UDBtRPl8FplEppnf04L3AdIzSowkciIjdNsyq2yGlFJEZJUDhEqiLPoCJo7H8zENwRUbhNVXhOttAZ0eFNyDAqpAvHPSKAt+UhJVe1N4SjcTTHIT1qfSooBn4rvYLU0Zhuv8IR+RJbxFewBmsVNzhhp5MErsNtL6Ipo7qItQgRLILyQPNCMEzcR76UVlVQDogTgKzmo8wifoGR+ImUOYIEIHnyEA9IkLJwTUEBKBwSNN4qOi+AQ+SIecVMHUCTUUeKMuZCpywJ471BSuiSQlztO0NIxDx1pSn/EzEpDwc6prwLvpG6xDcP2mHIT+zt3ScktZpaJqzw8sGj8DVhP1+r8eqbzfx7jvLrKOZoSS6BkddnNqLGAEWHkpMlhJdoBmjVFgKHJ7sZhngTJ/QtGTUv1P2JJ5QSd+Xbqc3WsbAVU+Z7wKXQslM9g/zIoc0Uf8riEjGj1AV0LvbGaGhkPEHCurzqXjB5xH9ChJSChdVvUOHDdMvmOF9Te5e9uw5ZNvyLoEOlMGWeWZblj+wdssq+mZxzC/+0UP2u73kKfPVE7QFl1YtvrJuafaO776z7JtfO5tdQ89AiyZt7UUICOXRjRUz711GkCY5kR8MomczZng4qTBKwcY4Yg5q5rgk5+FIRqUQXhSRJCKpIuQVmDiVBLH/wRcMiaEqzJ6kEkkfED9+3PCRtUab1JTMAU6sCvnkVFxAko9TvJwcaWkyZ9shAFBErGn3DQHFoZcjSDoKKFnXhHdR7vKTGKqkc0dFOMx+ozmOjFBDHM9VjT4BgBd64kps3Fq81qU6NLuyU+/K4dxoGdvIOA5uTfugDkqPwEt6o2XaXgQfR89Gd/IDYFOd54DZTVSP9gAo65vShLAUJj7eIGiEJSfvE/T6TL1lstnFioNQ5BBQ8QtEfrIYO3oI0rQtzelNjSktafRP9fEKUJSWFpyWz2tKWqddwAl8lvruXnST/e58EJsh7ypQF+WLDdsabBSWHRAnhi1ZL4ax57HXMD3+Cl2+bvgnJe2bX4Ta4uB6q2OMuKTRsiW/XhdkNEmi2aNmxhIBwja7dSKaesVDq7KTTQ7Fn2FlGAJIbPNAMGBJrYBXDPrEcIhlz1GDwpzoT6nCjjZpWyuOp/l4JWB2vcp4v5wgQE3Q85myO0iVGbA0JkuJmJzfkHcJ1EX5ys8JIwfb3H82/tTLOslCr2Vil3Yi2iSUeEIV2bmyM9sTahuHSTPY6Wz3pNloGacQMZ06bZ8EVpE808kJ4TJ5FWH3JRVS2CWrQ+JwgQDn9YuhSjFkBNAdUVVdjXjf7ohCSBMoKR6l8UIRyHYZl438tKgzPyiHugTqokyxYVsDpUWSvTR46CD27iBF8WaVem9j987dxW88rNqSco5gk+pZWGXjuocRl3WnVaI99bZ1li1Z4fewWZlsXicRoWTkXBoAqEgiknmfR5OxzKzQQ+TYPNc9nnkuUgSaxvlSqpBShYbmKFRTajoBFKtwPj+SCicjbL8zv3b7uQ9TJm+WdjFpnUNdAnVRpvLzwNFDHXrW6bY8WQt1ZUpqHzt2LMpxsKKoNX8GaNbx8P+vG/lpkXtMGVWzZneiNQGaE6g5gRo6QHM0X1I9K0LpGHeS+EkWqL8zkZSoFZ85Dp7p4YRUYo/zEIlIhpomVobFyjDPyEy8qyrjeFFm334RqSnVk1I9CvRWhKsiqldSVB+vkAcoAsOH1EmaoEEP0DQ/p3iQhOVnZ2nzzWcL9NgtHSXzJrtPOoXzXV24KEdgRmDYD/KefY1tHMRZOx6B94l8UAQTxgxXNI30+1l2x/UATOnEicaOGb1967ZuvHIysPPD9YULE9lQEtto2XwVJdOQJtVLuB8K3lLSrGmXJJGThzj9XvQY7ZkFiKjrTtGEijAOXqfiyRzjOIEn4AXAa5EmHwxX+Jrb4prHxwFNhBJAIBZJ+8ymUeQ/aU3wcvloNKlwu/c24sgR66jI7tlXCtavbJo8bVB32O+Ft3R0gNp2cwnURXk1+R8TBqDNyuKBCXy7TtQEVVFxHTrgIAdEFQkKEmWUWlMPIbF+8Wb7PnpNq6xNJvIZlQpWZ7DSeSBxIXFqEvq05Gt5dtneLG1vLsCeZCwl2xCeXSDheNJiQ/QYBMoOs6RXJcs4HG/kesppEQAxVKnEk34BJvVM+4IQkSqLIeJRHVoXamyNabIai7RjutRkkQpkRUpAbOz7eONivAx7ikiJqmJ1yN8aSWppQ2D7negVIZQ95K4bObQ4dJvrwCVQF+UIbCXh3GzaMggHmrG+0Ht5QMjxeja21YKpaCeooGG+jkesNWu/XAeYSfEAgHUbtlY5RJLznecXqw4BoICjs3MitCOmBv3GiGOrpQnSRKDg//Ieoit5CMQKI+zjwWPlseVOourGLsyli0iNIl91ONAWSVTWVifaEj4eb9ze1ooZk8mtgoJfkhVVQz4B+PAZrY9BUvmqgOgROEXRIom0Eu9IcIqPB/nGtOMmUqgrcZ4d9RWjxmbM8qPIddisZqCdP3v2ni6ButjPYPv5TxsMmnWRQiqs9TkL+Eu7S/VAAGVZ5TgoenivR/B7BQ+uSMecws5AD4Sr4m1xQSs2spE9KX7C5MlTJo3NHgSybn19sEa3cyFLmp0EDsfrOaG5RKgxnkgXoZQaVKDykFcRUKBQoQ9uSseTJPIj6mU/BMgrCvhK/Y6xb9FfEYnhmcyJWIeXAx1tbY4s4PVXpJOxEdJOAMBOvs64AOSRFVXWFTFCalNcr20vBaR7COgKIFi7vPGgmUyecLHo0nkdd3YJ1EXZAVde69H1Tdv3TdL1E89xHAc5L2kixwGB81dgdqitCRvSyuS7dDzCq2kAuUOmzWxr74BIbWvYadbP4F3kQvc8oVEbOAgrvQKPNFmSZnne6Qp1FilCNVXD58PtPfGwIeLrxFEgPF3I2tcbLFyck4wl8G+N+RFJyZjpzcyHdNISemmFr60MNLcntBROkucyZbINGXWamRCQouhzocnTiGjwcknwiKIsSaUY8t1gsOc6hBuFd1FewXeEpxbb9Z+moY5okggXnsdBJAUhwcNLsqTilpJMGqSJWNxuSoooKaJkMOB1fHiCVZ4grkrMhW7PlKI5oWxSfcaCpopI8gd9WGWT3COPnwpJKcrE8jOBA0EyDzhvMBhKyTz0hvzh6pKyLwOBipTMIVI7pLs7Y9FIrsh7MOBjR5naTiQgRRWCNYMGK3wgBisEvQ1VV7BlvXGE9SubeqJ7SHYEKc+hXQXqohzlpz4/bp+tyzrOW9Klqejh8TwygSdpoGwzemIeBoP+rWs/p8GWqoBH5c2pwTkgIuwbFXE8OgOTDzs8IPINze176jcDAM4KLu7ud8yKUBzg4oAm6N5GGfDBEI52Cd6AGSQHalrKttkJaUJfuCOGfRcpwNdU+hOxaDyaImJTyTFbmIWKBJ8oAA1JitrUHAVyHPJ2T3Ag6E/EkxByAX9OHsQmgi6SUwATq9/nGTMkvHPXXsGsykd5BWzxIhSgogJKLQ2x2qFFBw1LhEugLsoFKxuxCCXGO4HP78M0QtUfxLa8wOOH6OEFQadTe+9i+2FDoQAb9E2qvN+MIxfEt79xSU3I6xG4V9//ck896HZQT2jGSijkSZXkvSLvNcgrqWB2DPiwNzgST4tpmYrERMzSpxISeCWRS6NpSPCLQtAn4o4tirZnr1E+2wkImpyQNVywr6s3H4eJHsnSzl1x0K3Ysl4cNU6yOk8zb23dSofrb2mw3BGlkml+ZesSqIsygqoiIjaJ/KQl3oRTo+0dfKAaAKWpQwEgEeKwkT5l8hj2W56josdC8ewJAFi+eOk7KzYTGQsh9Prs+rTr0LkS11byOEkLQQ1zj62fJoUIVD5Q2RrBYjng8wwOiU1tcSQnZRnoWUcZMjOl4o/Sy6siVIBH1NI4cEQx1Mzm16AIkfGSgAA74tkbl2qMygErkC2l1XqzHp+FAkQsYUEie8676AtIqfzB+c5j/SdNKsqhr4229znJtCCfFvQLuATqooywugWyTZhwlRHp+yvgOzYUDnGcxon+UMDrFfnayuFBnxjwebyiQL/pRJDGYt0jeWw0dsWgj7rlsKQ03gdk5BHY7E6I0qQ7nBy1bnIRqEno93uFZEruUHgQSfp1mYlkJSons8NBKRWv8vEqTdjEp5P1EiAdAX+wsio8SAg0NuBfKcqe1mcIoWiKXEeMS6sobRGiV/SkJc0bi+4IeNhPTdJ4kdNLAEz2TCS6rZTIsOJtQSuUzXv45WRM9lc4+3Ac+ZQ4QNmjbP7cYRKXS6Auyg4rG0E4uS/ixz0iq2CcfItJz+BoJBoUubBX8ELOzyE1nYjLqYTx/S82hjCsznAd7mULKk1oKeccQxbdFYIX9XEX6WScUyVMVwiIFfYo1qDamobWmCSrvJrmIfDnjp5rnMhpmFAob8rI4/d5Yglc/u4BEk0eSsRjiXhsLwAJXUgSj2TQcP7izzmRSAeCIls3tBPgp4ImR5IKYPYiCAY8caAGCXsyyaqEPXVOx96WUMCbUgWaZt89VrwOnhNUvc+emfxm++ErIXpG+HRXvdGRryBcBeqi7EDYky36JuqpuroyXBmuqwzUVQWHDw4H/Vh+frB8A3u/EJqoCIViEeNGjUYTEMKgR4PeoCNvHjAIE9meJj1Bx5fToPv2pLXd+B59Hi4SSYTDAajKhDeTCp9UAMrkl6amFh6X9BQ+IGFPokA5CENBL0jGFewVxStlp/R1GhFCzKA6R6RUQeC56prqZFtcTnTQyLsj6IzllIrpheMgydJPJ/HFdKMlb4lQB9K0IxmX/cECgcSSoCcguwTqooyhZ8jjBdIxBEKoSqmODiWdSra0tpMZudhCK6Q+K/Sk0Vwg1NnLoPe76qtNMh68gN+fyKxttWBWF2QjrfEp0gMZQ6sMcmn2oOw4YicEg4ZvNx7HQXwIoaDJiieUkhRSleSDEtBAW2vKQ4J6uSEKUDY7jRbZcKRbgEVopgs4G3kMeRZEfhJ+pIKa9RMQrzB51VWgLsoRxgAS8ztLTHgSgtdzmLAkhRDPpaBf9L6FpKR5BRiJJDyBkJ+TO5dmmlL5dJoQGh4CL6iUee3pR9gSz02h0TTHKQxrQxhDfp+c8Nlai3p8mFJxSzwL8USKqNEUybTS5F7rUbdlvTh+MrBEaLeCqW4ze85mbTN4eMglUBd9ACQK39Tc2uoLA5BEAFQK0qQJI8wvOtvJrRvYNEniMIplTd8y2aiX7y54kKTJMIxzOeUC8pJCf90rBiorvIqqRZOSnJIZ0swLXP+T004XEeZFXYpiKkwkJSw5ARjJtbGbaXJKhEY9fHsSeFBGvawP/wwAD1KTORrOi0ht1/vv7TcRCguLUNb7mf+HefAI7Ex3CdRFHwD9Klf5odfDhwKiVwwfMmG4omppWS2hmycASNMSsl4rqakcxwe9HhpW4iDY3RSxUp34zjQHKRowEPRJCDO1B6oFORR6vJqG0rI6rK5qy6Yt5I04bkkrgliwH5CgyTGU4dNAjBWfH4mkIT8pewZNZ2hKFXy8IkNe1TOxKDxIjWr44HXDh1fz3L6d23P6KLoWSuo1DB4Ron8nl0Bd9AHQ3HrSDJTnOY/AfbFhe3Y7eqSHSmK5yxxxxqJgBLtl4APphs27HVrPBZlm790uP/GNV1GFoOopNF4tXFXZ0p5QNW3s4FocAduzZ++ePdmbBQJeCGEikS7mtySG/KLAc7p0JSEdX9a4cxuSwAt1jYnjTrEk+cSDmXEk6vGk0SoSRKoZPqJW4FVNUzXUvHM76G6woaRu9ITmUZ3s980lUBd9APQrS7ox6TWduOox2zNF+WPyYYevW7kccNz4KQc17rGPdJYV5BEgD1KUTLNQOJ+pKzBVJwu9+ydU2Th1MBiMtHd4OLB39x76VgNBfzKeBByOnuvUmTOMhsUmR/IKLNGHzfPcvB2PpyDHpaHvgEEh0GyY8LqXNgOOUXgPUqJpqwsp8QO07tmR41T7w3Wdp9NJoewlVnhSuATqYr/1Ts6DWinju6qBpCphi7U2JvFJ2YM72qWJtxPliI6k0+lxhxzuUePAqbWdxxyPkc2qOPFbQZnzhkvDFLZdkhPW8Xy2y9VYxuXwlk2KedMJjm85EPDG4ylBkyVv1bBBuDVyNIXbrOCSyopAPJLvJyEu8UBmcuOh1NLcksOFgdjy2Y4UJ2h4Rxl6U5ro80mJrDYuvRlK6gkROkQXno5wCdRFH4DlcjLaUTAvZRbD0+X6zVtlPLoX0251KEMuCUo79YHSlYpQRVmVTh96/5PNSM4IOnedPbNJ09a5vRj6gRBSaz0ue4AUoyEgLlwnxdL79CojmkDqwZ1KciIeL+E9akKAV5Nxs+2/AECQzDFGaXNyaD4kUuRcZZc5kUt+5mFPl0Bd7LfeyfkR9mRoLLXaXx/DxMLX4HvVI3AVfpHkNtlAEukFwaNg4uMFnUBjsWT+VFDKqhax8lhb/O3ub32+YSf4sOh3mKUuc8Fx3EUnkMCFRhn0JyUiPobIUqrgFQVZtcvwJCJRI2tfPY6kVyI5xco9SInDoJbo8Asc4PiUpBm8WRzI6fx6Rn23s2cJ+UyoBBGanzoJXAXqoi9h7x6jx91Bk0Y7bsDyrghVm3PwgOHDShpH/I8nFmCDlDv9dH4hKA8ImpxScMtUmxVP5Gc2cGwHT6OzAL3BQXV4Wn3j3t0J/Si2EHx2fF/j/VFZ9cE0MF2fweLaeiZN3vSbCfh5PLbdhXxWfF4faKns6RJoD2KGZ9cKGScquugufPe6s2VFi8RTKUkJB70Bn4c0GWFRjLZRhCoZeb2as3uRDcHPmDj4i817V6zbldGkqLcQDBoST0ByQgjXhP2SokZiKYTrgbBeDga9pI4gDyVBzptIyUi38QmQnGzcW+BXpCOh+fx++mlyapJVtcWC4yhvWtcDcId9slxbNbqlvXvi8p3QtKwIZe13EmrPD/p5uwrURZ/B0pUZZe8sxh08oaRDeWBaE2s1fCfgWwAChZrwLB5+YSnpynzf+ik9kcyUH0ng5ThYWxnA1eWxdKwDx8RxdRAs7CcVRT/Pca2RpJ9L+Dlr+yy726LeQSPGVgT9X366CvOCmvQW7AyYiaRWVFjGM2SWLGNW5XT7oKYSWxKaksLBLkHZ29ECOoXNpYeSHA15x1B7HrgE6qIz+Nm1x5CF3zxaioOwayimZLNI81BSgKh/983KGpAAVWRNL+QwFRFjMeAF6VgHXlOMwUx8l6LHLyVjkhJjg0g5LVf9MyVBpPiG9bny8HNh6NAhja2x2lojFaxJF7aO5B5FQ8gF8Mm43+9PpVIqQj4+LWucT1D2Lw8R+TlkRKiIajB3Lny/Rld4je5b6l69yaE6P+L4s9lmBBx1+ARJtkLM+Uq+9Smf5L8MV4Jca2z424aDurchE4WgKVHOoC3o86KUMXPZr3d0LwiagQRxbiyeGVfMj09S86CkNdy5JN7UeL8kqyOGD1YAqK2tbtprT7PNOJFvPFVqqiqryWbEh734SvFfSk21g4rumbcBS0+qJyKUujvzsGfOMXmduVIX5QpVTvMe7Lb/2bXHPPzUO7Y//hAx37Bfa/vi8LWLjhFEXy9zqDWB0/SlrfhsCwLgsJkl9OjMlcbUM5ec+zI0JQp12oJegKxk9eKzKItPP6LUgHh/VcjXHksNC/nb2zIclJyizzqGAbYTM0VKE0NBr9/LR9rbiUu0OS9v4l0CEzyCUBeuED1Cc3MLkKOAB2an6+4PJOlWfBE/OZlqvCIsNjUUaL9dUZWzd59LoC46D0VKEQ4dIqb2STm/ZN2FZas2H3bopI5oyu8VBIGPJaSOWGrS8CArHchAuk6ATWMCfufC8P/um/3VIcs6d3yH8DT0ZmpMveNUrukUZAvem0hKo0cN27YZT7izl3JmpR95gKr6qtKSwvMyyU9VNK2jvQ0C0N6WtCZ/6GEonz5L3udUpcSLAZ+UkJOSXKgSQg5O9jBaVk5L+/Y1+nnbhfVUGL5UEVoRxq4Rnof52/3F2nMqD5dA+1Vtj7xulwzAB8s3AgBGmStrcdKLDr20bs7sg/McUDyY7ldAmf7n+d6z3AlmHDaOg0qgStBL4LUzj8aqMy0rLe2JIm/KPbt2sWKTZOukFcAUvgNOyhnH6Mb7Pts8T2tYDuu2rT1ynTTrskaPqm3cvbNx9056KY6lnCLQkkLF8EHh9lgqEk1ibSt6Jb3jkocO8MAjNwQNOyLtjCkDUSEdlUyBrkrOsjjpO4h9qqkKkDSvBxfaqqrqgUkPD1ROLeaDgwB0OoJUsghl2LOLcAm0X6WmXzgFJ07tWLMtYyPz9tqhj/l+9PNN+Y6Y/9WygMEmSz5ZT7tXHHPikbYm9sVAURCuChUQy42aWJsniPR6/FjQM/CaAzAyeFMX1Gb+OcDUaQAHPEgaE4UMvQJIIjHIeT18PNKwD+umPN3sxcxkfo8vGNF9An5e8hRRDRADQ1Gq1SMIiqJAMezjUoRwU7FIsKIib1tBy4rmtJQclyJyoCocGBQa1BS1Rr13C4r0hBYUobngEqiLvoGJI3E7IoTpUuM4iIkDIZ7j8M1IHqWY8Cgdg94KQYBAU3BRpOkMpeXwuVASQZcIlNQElLa4mwyPy7M9Hn+SxgQ5YdyIbZs2kZsZKskU2x25EEKVVaThE0jF88xcIkj6cboYbubCc5U+byLariENIeTn8rnXbamq6UiDIHARibSDEaCKqiq7rXNguMYbaS1cldQt8tMlUBd9DBwOvuPydxKFx49MRisQhafuTh5oUqve0glD4QyjPj97GhkA3YqkliFi8o30cEo/Ijtv27SppDC6Pn3eKyu65u1oF6FlreeCf+h0RVGiTdt8XlFTVY33x6Q4HhsHocIMmMuDVMe+FFcHgBAMD41Fm6qrjNi3PpalV9Fd7OkSqIu+hBWf11dU1cmqRnrFHzR5tNlWw9oms7VIqREkfCBN0+K40bKDcZZIg5pA9/OmWRdf+MpFoHUA4rTBtnappIk7UldXN7bGg34PSMWzyrgyoIQOA9H3jOXglGQyhZA6aMiY9o4I4oHIOXtFbb8vcqxFUSWFC6c1T3XlKF+XY30FsWopmnp0hgi1WfGYPVG3WfGuCe/Cjn2Sj014IuF1sqaqqqq9vZ1u89ULjv7vi7hWp3cw+7CxX//quUQDHnLg8Geef1fTgKSoabm0xhw0iCQBn09pKH7HgKDAolLaC2BIXUVJ/USSZNyQ3tTTyHYKFtuWpbqqui2aUFNGh+n2tjYRAjnlYOGm/Di6CCHkOM7v83k4CMy21FCJpxTBx0kpOUpqOgtSTVsEf2Gqw7Wk0CegZfpiTf7sIfkZrvG2NEg9qj0JXAIdcFRI2PCAoYP3NDRmb5MfYmZwAZqRgp7OYaIlIg//9xXHDY489ghjSyKBjCnhuEgQ3zaVYce9BKeGypLqCaBuDmVQDApquanTXghDeRPvAuWSqCYpcx4PP7gq2NqKQ9vZM41tqBpzInYdINC8bzsHYTKqBX0ZuVxqshUEi8p49wV8sVi8Omyp42g0HgiGMt5g5uVE44lu7zCSnc9ksWdmKmjNoEBrU6JzItQl0O7vCkybAU8qNPCr+PZunUY4XAk6mrPXYyOudAR83TlZuydAvv6+ilDNkOH6nYIJq2XvzuIjSyIvK6AqVyC+c6bn7MnVnC+8cdPWglvaHKCYN40TFz5LSuH1Ii0QCnj9WhxoSmtrmu3y53xG7xT8717c5kpTJc4T9AtyIh7FdaRFINWxD3hq5di+hMpXVoYLfT4Gh5LfuXgSZzhpGqoKGwNA93YUyGnvtAjtdu1J4BJoT6EliDbIPc6PxU8TssHfKSoM+HrkW1gQdHYH1TB6SSdZyOi+Rt7u0FHjpLTBHV41OmLYoF177aJSUNpt8hN6scJSVCTgoSHdgNmTq1n1d+DEsY4c6gFqRDXYCooVtHOSxZ7F8CZufgR8urZVUnaFy3OQrqodf7QkyzzPVwT9yVQ6tWMHL/AIBCrDoVh7CQ6Njqh+SK4GqMhfMUQX+YVzMNPRhjSogNAPgBgK4L9cz02lDldbhpEsybh2rRBKFaEugZaMgrLR1gy4N3HXk5/8+Irp7JpcY35DgaL0hQ0e0hB+/zEoZU9shBnTuiHPXJSH5yWgNOyoHz2sOp1MrtmG1Xdt2KEjPbvAOkZ5JQZ4M0CsVzcCvS8R5PmEWtSvzrknTCQLTfss7Y8gBzOj1ZQ3k0A4YPhQ7DfUlXKR1mxaFcikYpolWhDVY4/tiEV37WnEHyF5a7jpNM9p6Xisg1fjuXQuvaC2qOntQTj/SDfMDRSkwWisw+Px+EJDOvPNKzmOhC/H4/XIaePD8YgeWe7++Z0ugfZzCAy7sAWXFYHOaEme5apeBb4fln2+9ZqLTpRkdUdDe0csHRYs8/a3975wkU6AAZ8nnkx3xKRP1xktL0Wo2TrSK0JVdvM6TmohWU0atBTt8uWPvPXq8ideXZFMKzyHGvc2wLy3zMwaLB6PnWoQ6IsLMpwnyGwQl9THVVYPH0F8im17dxLqLAaI86Z0UsD1kYVyj+TQQTzH8QJf6ffXVIW3bd0iaBrkoJRs9/rMHxW2eDR3P6KUJPkCCg/kYEUlWROPRYthz+bWtmAw0NbRVl2ZM2egrOZ7lCRCXQLt5wj6RcckvVS62BAwod2ZR0xYvnqz4xSNXgA566wjJmyqNxpYhL1g1tQpCCFJVqOJ9I5Vhmns07sqeXmN1/LNi2eN92wy1RNF8XGunHsH5lYBV4lzIKf8/PXUL1/bPoY+pSVSxsWbn9mmjVvYIZc8B5t355pYaYekCTiHHyE/nyyY7o4DIxNOURR1z84NHISiV2xPRNpa9uFScb3eaWhHi0/KaBucTktezSqFktIpUVVq8Ug6DCQ142F3kYi+mfGFSqdTopkepG+nbg8bwTpBSza3C7U1VbFYwsMhQRBysWfP2e8EvqAnFZeZaXjdbB26BNrPEa7wOjQKBmB3k9XKrBgIujqjmef7BbZ77aNVuOTfgn5riMUMNiObywnoCbCdQOkvTYjHQlKB/tNmTX7pg7Vtze0pSZFltUpngWOm4HkYG3c0DxtaFwqIpwtvF7jsHCRRkD2twXOYNyVMYQVRO52DkON5TdN4nhP91ZIkp2QsV3nO+iXd4vNVmGRHEI+lgkF9jX6xiXhURp7DgwbdbPcZG8djyYOwSzeDhqC5kOpoTMJav88rVhxQDTRNkQM+Tyy33RyPRysqQg1dLoHfjyLUgUD/+quf3PrL3/XAVbnYD/B7PY4EShpwFA9CnbwZXRk72pfYayU/jR1t2INbtxebEVUayGn1gkDyP+reJb1Bmet0YCtqvycUDgDOAxJIlRSPWX2EEgA45McfO6XmgWcW05AxbmGSeWxcQQrhe+D0OSBjYtK0Qyf4RIHn4fPzu5Qkmz2N3YbhB5+qKEpHJCbLSlqSEdLUBP5d5Diuva2VmPmOcrWyaojq1PapLZr081KwIhQijeudski2h6sJgcZj0UAwJMebkKZp3mqkyaFgnRaJ+EQ/MNpUG5TPOek+UvYe6xn2pG5QVoT2hBvUVaD9HLbMTQqjjE/H8GGDd+/FOaF5QKgT157nBWXSbBTPrT/9zu1v3n8bu+bUy3/BPl254D5Ca0iPJCHabgojn00YELS6A0bt2LEL8iK13BUPdonq7UF1H6im5Wpnl60ljcBWJj988jnpNWetLWinmjMyCYr6oGL8sI3r10AOekWPBn36zwCg+judigWIosyLdKQxBXGTAQ15pGiyOuQHuNKpqNJMWU13RCSe4z2CtwJnxWV71TXKoRTd3i6kNBRtxRcpQp0JdOzQkVsbivVquyhnZI9dy0ZBz+bY0T6yTa6YfjHIxa2UWO/84Y9y7Ssw7+KeP91FFr5+w7UIgA0fPF1SDKJ5z45Ro3DLvh079yAlle0DPfiwI3Zsch5/FGmP1FZg+51gT0NzQwDL2B3aYZiETKiahtttmD82MNMB6kiaZnc7fapw3utP6PVCRN1pqoTltyapUovHV0LOXCrWmkQBBEBN5QFKNIbjQkUnskdiCU2JV1WFq8zyhJQxI94xAmVxcVPUefB6j0JViDe7p+Bw7M+/XG+OS3DRGayQR8zwFGjWXVYEymXG1m3meYYJ3wM+UEKsEw+YtngRtpcd2fDDxUZR9kcff/StW67XIxjYKIMATD7hMkxhrzxe6nlHjR5JFnZs34mU1PhJuLWlzyvmYk/sGw0Fidwk13jSsVMDPk97LBX0iRuXLaKbIcEfT0qy3lIT5hn2W9zEDhzSqZhiUjNHbk1MnWwCU6FYTDIZ5VEa4lNjZ0V1qKZg6ZiNBJIdTRJfA4Do93kDXr+iX0AxSEKU3B/UmTOU1K1wTfh+DqGIzE0SIMqPrivQImHzadoAAVq5YgVZnjZjBllIpeULLvsuWPBbfIXD5nCJbaB9dZ5TNO/ZUTdiLH06ZqwxYj4/e5rN9BBv/iTtaWis8IschKreU47Cg9KVmJ9gM/UhZr6jIqmT8qYBNUXOnEgkQ+Z8duPCMvUj+0SKNaRACGHBiiWqyCsVjhWZeYcjpxIpTRxUEyhIF6hcTPVMN6iDFZ+jmrNUK94l0AGUB5rTPC+i8IZQZ4+mMek1RcjxRtY03HSSqGDcSk1/U5RJly5ZNmv2HDISr662atNmVQ0fAgFQEs3e0AFsLERR9IG6HBfPqm4dOzhlZ0+ne0dWVY+HIy0wIICahrhuKltiwQ86XElGpQT2LYhegytx7zgdKSTh0RvslWaVuleEKjQkxWPJYMVQ1jeZZAbJ2WF+9FK8VeZDqiyPYOghXJg9QYNeNBzf39SZR4TiOJLUnVLUJdCBRaDEXrZZ6MWkx3M9r0ChMVbS4RQ4AA6NFmh6/zo9EK+TWGVlRj7jnh31Xl4BCMWiLaFQLeNRxJKDpCsllIyvvZrEt/3GrcAfNhSfN55Th8oKDjJZkh1CDQH8f7sehHi7Qp9WghmJwZkxdy4AFEUC+nBAPy+l4m3BigLhIFsX1Lb2lspKI929JMQSCQRQVUWNCEBEn95eDKJqOqrPXu4z6L5QUsY36TvXf/Uvj/y3y1fnooyQixwrK3yxaKRIlapnouh5oD2d98wWbLIrmfMa2URYLSGAudScdExexRRf4N4ICAqAwCN4QqGKHdu3TJ8xu662qqaqMhQK8jyvqpqqna2qqi9Y+/UTgKZqHZFoWkorsiIriiwrO1Y+pffBN66WI+2ds99E3s+KpU5N7x2FZ9nxEplbVcxEDUe06tRZPHtK8VYRaLGYJkAUrKisrCiqqxbpPtC0vz2b5eAJdRVofwaNnmdjzPBqmroklIEC3bRn5TEzLnz9+cdmzD7B4WUmwEt5CQIYDtsneiOc00QGHmcjk1uNgUPAy6XjHU3xDmx4Hn0MHnn0xz/fd8st3zU283B+H+4Jcu+99wAAf3j7rdMP+0n2oV9YdzNdnnzc9Ubs6+U/OlxE7fREJnVmDa1k3nchnZSONSZRBYAerz6TriTqjOC2YbgZfXV1KBXLMu1z/6kbImWX+t5zgfj8IjTjwC+9ZtVUjB82Ykuhuc8uyg1jR/v+/dwSdk2uuPnQWsvyLYFAe0yBjh4yc9eOXYfNOElSwIhRI9oyXw0E1A/e0r+cHsaYdcr+LNrD5fxGln6IPz0OqB8ts88cPf5o3KVFoCEkE4s+XH7YERkNXCjW7MPp6AcPMQjz8ut/uGNXw5ZtO9vaIoko5qCgv7Di8wYckvzlRFNUxn+16sohpTY1aG5rDQT8GvRWV1l5VL5CXoI+x5vAKY7kC3qML4meQNz1U7gKtM8jT+56Hs3IkiabZZnzOMRu7tkgkoEXnn/+4Mwv5jvvfHDqGReS5cGDB+u+SKOQJhT0/N911z/28EPHHOrNTsliEYu2VIRq7M1SdegxKhykoneVoii8zpXUEifpAYsXf2A/LvR9tsoIZxF4vZ5///uReDx++3csWXqm/u+guurmpn2VwbpdO6LRaNwWC7J/IMwNno63xLWgpmLnNUIoUFVZXcqo5XSsKalxCKHqqrDo4ZKJaEUl81EgFUD7dwDavJyRPuXl7FaIXr5hp9U5pWaw9ZPmEmg/JE0Wz328ts5p/dPzl9Pl4nygPatAKRAAM2YclVi1JVd6U2Oj4Xl45523cMHlbJzMdPTxcx557b+A430iiMdaUoroR4VHs3k8xvffGPOpR2OMlvaqxnE8bkGHEOvNJAGijKg11IuXGKz65KNUouOEo2es+HjZCSdYHokXX31z147CPZVtiGC3nVwTqvXi+W/RoMmcBIlYe6DC3lSfINnR5OFFFXCRjsbqqkqqNnmPk2bFHCrYxWZnXbH9G62NVsKTS6D9kDRLRTEESjqx9XAU3pBTjl2UkIY7alJd1NTYdNihR3z+xWobwREuq6kKAxBq7bC6ynMQiUoMgAwF6hGs7795EOO/gsDj4Z+ZOepYp+qJVsQpSy4TT0XP/F0hV4/TrTjug/cNxYoAKJ49SVcOTm//UYPLK0tDsqOJD45IwRpcBxSNV+NPozDisY6o0wj1FfII0I/gC9JMpm6w4u0EOnkMnvvsot+TJoviFWj+yHLXYUm77Jcy2ZN+8SVJ9ng8NGqkkvof/Qg1lUbGeCzSUllVA4DxtKU9hnOhtGRT814I4ZqNmwbV1fp84vBhozTApdM4pLNy2Tszjz2Nnt2jd3onjKkf3LpCgTcaG1OYPwBIQxrl5WLevpJsiSq6eQhrqnTeFJgWSo4IhPwAASm6V+d3ReGrw+FKxIdEHokm88Y6GjIM9iz0uXh6F9PpS8oG5fOWolgE+rNv3/Dks6/TQQgu+jdpsigmkZ4QQc8J0O37lp987MUvzHvisGnHO56ErmxqbGKfYsclvjhrp3DVEMJxsiI7hrFrqyqiHS0BvZQbQihikxbLWEVRozEsWr9y3oWaXs/u8YjkwA27tlnZSZnXl6sHMUlcJZ4H7GHNG1BvixKr0E9403zLRemjWDwmiNWaIgdzDH3zBWoGGm/mD8QzIhR0swLN/2d2UW6kObzZPvssHZdOmHngB8txr0yv/mpFnCb+SGRNOm7lzXibY6KnaZS5ZkfQOahrJF9mksd/nreHqjuHicOnkbSii+ZeTdasXmXf5r2FL5CFQ448jl1vhnrMpxwX0GeucDyXSnGpVJEtoDAvqqpR6rNnNxZ0y5cvmzlzNj0PDlhVVq5Zu6Fh99ZTTj1TEPh4R+PSj5bzWmr67FMyDmZMKMI2vpn8n7M+tbUdp/F7BU/A7PReJHsmkqqmaVUV/qoQvosjMYMRkGo3wwVBZKcVYd7k+P1en14esKx452rOQpXQFoGmdMulrHrr92/0vtLMhWI8m2STHjXht2+tz/XSUbNOJAsrViwfMSIkSfK+xrZ1q1cBPmC7eI7jwuGA3k6Y93m9TbKiZhGKHfoBeI57792FR82eY3OqYphEuGf3nlDQf8CRM1tbjISe8y+4hByB7QZImc84jBGeso6ZjjZFZR4BkGpP11RjIRyPJrPZM5mMsnXr6XirCKVIRPbilp2VxdRWDky9WQx8QSEVL3YoQx5Yf4PmFsfGuy7KmjR319mttoSseKaM2LEGm5wB/dU6yRi1uAOKZE1Ctr46gbqKQw8ctmPbnvwnMhWo8fSyow/uLvlJQGxc/SwONjF9fuSR01rasIgeNWLIqBFn+PXGnb5ALUDgMwDOv+VXK1Z+sWbdxqOmHQH0is9UKt3eUUTv/QyvpmWJkf9gc15/56S1lSSlRNFsIL11e/bBhg/zX3DeSbKsIITefG0+XS+ER0Wi+OIF/biEOgmCIX8e2y8db41h3yh+VIUrUnGHfMxcv2wVtUb+TUVNDze9HpCwCLSlzf2B6vNK84oTjnxk3ru2lTtg3ShkNM+4cNZhHAcfe95KZgxqvmVrsMVKEW2Ubekrib3tj7+8jJ3Q+5/nP5RhgDTx7SbguA6hLH0hA9nc0tyGeSG2Cyu/6gr823/4OV+t37S5tqZq6uFTmpv3ybKcSMYgBz0CgjzX3II9p34Q94heMTQ0/3Vk1jtZnT29Xi/PK7IspdNJj+jFW1phJQtpBXj1G+u1196cOetUsnLt2nUAaFUhkkIYiEVyDhBmjffmjpR+6IC5I4YvWAdAYRcepc5SG7P2M6zK0ZbJFKE5Y/EF7fcMAiUNFl30aSNdU6Tr556kKYbDK7HXblU88aLRtrK6bnBbM2afPY26QIOB8cPDO3bhu1rDwWN8u1J+lPWn2Axsi9M11UF+7jknZfN152DGWwr4CHSPZ4blxQo3CIGUins5LVQRwCPV9u458EDcts4jehJG09/KaFtje2sMAFGJ6p2ZUnsiEDaS7uF8RsLQzJmz9FylDIteEARZlnSXprXy05UfZySvIJCS0fxXFxZ809m3rhTdhy16TR9gh7SammA8ojc8tg6uAcjHYx0ZK3GxVgUlzYpaCIAze5b2dS1glrhgCLTnU6T7OTK+f+XxzRs3brRH4DZt2Uk5RxQ9AMCTjz1U4LlX38IEeuU5U3kOchz88BMyiALccMlRybS8fmvjqs8zp7YBUL8LT9oBANx02Swc3JSNnI1po0eu3N71EQbEz5rjNScTuyLoj8bIHHXsAoAQCjyHOE4E0OPBHOT1ihwHNRRKpyWS4YSj8GYDJxWh406by/M8z3E+n1fg+VC4AmlaNJbmOO6pp57klfiRs0/OvAzjImUpjRvNOTacNGnxyOnHFYq/WxwajcXDoaACQDBcXWByOrLGsRD4q40ftp5A/1CsqlNFfNc9ocK4ocPrG3abtxbGomUryYJbDt+HAkEUVH7i2MuhIzkOzjxk5AfPGyFswk/vLPnimguPNaLbyzcrKh4LVlNhfAH++dzH5JbPHuO7dYdh7NuE5+KPvggMc66HcUTDroyWkQ27Ftg2OCTTeNq8aZ1FoDDjG59OS836iHMzYQhMnDjJJwqygt+DxyNAzLOBeCARwaWTdkTbW2i7gBGjxsyb93w8Hp0+/ShDgSIw7IBhmF/1WP9L8/5zpB5wX7PK8P9OPSqDXi2YOfY0TyAXIroQDlcEwxVi8WndxIrvUd4cIMSayxSw2e9DR4bYak4Ki5Mrgg49C1z0CdK0gfYTeeplYwHPANIhSZKNAZv2YZsRANBhiEsgaHbPZrf6OjuD1ogVoK+tou8mAzRhyMwfIrlE+DYQOFBVFUqm0jITPcvutLJrB4686eHvlM/nI4y9e/ceUkSwfPmyq792TSLaIYrVhx58WSye9PvEp59+6SvnXXbA8PPWv/Jn60Dm4FCPmWGafbUaH4rEpOpwIGz+bhUpNvM4N8sEY7tjsGAvdafvsgi1CNRrKlAXfZE0+w1WLFpJgzafLFr4t5/dRF9a+s5HXi+2bm++7caPP/mYro/pKUBDqsfQNRC3+8xozuz3+0IVgbZ254i8UWJkPpVlxeNRSeMlG/0FQpVbtxrz3F95edHpZ563b1+j7daMJbS9DU11wybwPO7fwb4UiVuasTpcmmQpc97sT6K1mPCRcx6oCwqXNHsfK5esWrNuzTVfv+oQDueEPLqo/of3/u/3t11JXlr49sKf3PEjCOEniz+dc9aJkUhG3si+NkM/wt0GSQ0eAbY3fIF75Q09lOe4yspQPG5Lt8zwYVLfJemMT5mPpUDKngRr12+qCARDoYxksqE14WG1Yd7rlyQ5lZYEgQ9VBDfXb4nHE/rc4NIQDPkravo8dZYzsZoitDM1RC6B9ivSnPfhl3OPOQT0ZVzz9avo8k+vPeO3j1ru0Z/cgeceI4QkWfreLbf/8jc/L/KYhEbx1z0Aho4NAoDbjcbbqtnIlNHm3moykqE8EQBXX3sxYU/cUQS3FMGvaypKptJ+e1tPBCHX0riPcLIMYVvTPlVK+ejU9uJQUbM/66rr9hhBxf2IsWWvWIWcg/3yDkfsT+jrpNnP8Ouf/+YXd/6MLO/auvHa48eR5WnHTqXbiB7xyaef6OKJgtWYRrfstZLSJw6fFo9jrYejRpkFTuTJ1JpYTa3RHfCdjakfXH3i3ubosnpNyhwfxHFcW1s7vZ3SucpJEYpEY8GglU5fURutqA0A4Paj6BFizdOanohQUs1ZvP2eQaC01aOqOPit+xn6MWnO+/DLYjYrKWieB9mppl3BnDNPeO+ND84+/WwAwN4dW35940WJzFETK5fgIvm/3P/nDZs2gO7Gpt0rB43QSzZbPs1+VfRNm1oDWluan1qw8ptXnn7ygfgrNH5E7Xi92dv/DI2L0dbeQVynySROgzcqOSnSelGDt64/OTfL/75etTSVK47UFTAEWsRw8D6NfkyahVlyT3M3kqbzKboDkWhk2rFTeZ7/xpknXf+jPzw4f/Wu+g0//Srp5o4x7dipgiB8/P6KOcfPue7m/wO9iE3bV06tmUSWW5rx5/nWCmzkzj0Tp8SymD1a+9/ruHCL4IAJ0wCAt14xqz2SjMRTlaEjQkGvKPAaQqm0LMlqwCc+srB76hFc5L3980l7XYTKJcnP/k+gA5A0e4goewfhUDgSjaiqunj+c4vnPyf6/A+/uca2jaIo046dSqRoL+OZVVj28kOskNGpMyb8/X8LbR2LOV/g6osc8kOXfppzWrKL8kDJEtUiUHZ8Y31DHx4nNzBJsx9g7sVzv3/bD1lfp5TCQXMI4ZDBQ+a/sIB9af/i2dUbLjli0qkzjALOtvaI7Sfr9fc/bY9YybOhoPN38txjreHGf34cT8377tWnPPBWd3ZpcVE8fEGhwzbOsHgC1Zgg0sQRozftcmgzU87oW7xpK8XBuS8jBoGBjXnPzfv+bT9ctHDJ8acbVVJ/eQ5TydaWVEzF2f4Tx0/ctGUTyRVtbGIbyO0HPLvacMIagpS5Xf7239fGjTmAPm1p3NuSKW1uufos9umfH3/7u1ef8t2rjaaiN596zH9eWZr0u515uxntu721o/NZ8Z8v3U1SgoeMdpgO7ViMBM8+fy4p5cSl00NH9C0F2pukmU15eUDSGL/Uih3S3dMESlJSmg/ovXktL91/Hvv0/G+9XMyHSXI86dNkPHrGUZNxanrV4IqKig8WLCbrd+/Zde6lX8l16v3yUxTabvUNaA+ORrKecGrSpjC06rV7bpu/IGN4pw3PvrUylsS397UXZ3SMJnjodaMLTO+nMfXm16anUTU8TQLxslmSl8GeTHMaRw7NJlB8MFoO3yd4s2yV5kGTDxJF7+rPHXxzE8ZPDAYCn33xGRgAsFGnbaWNSW1ACBE7nfwCsYjFYuVjwucHX+cFwOgHcuo4LEomjh/NsqcmOlTHX3T2zIznpgB94uUPAYQVABf1R4Vu6AHsIhsGe1qjVsG+7R2OHGqDQOVnOaNsSZPglWdfGz5sOLuGZi8Gg8FFC5fEYjGv1+vxePoKBfQcXrr/vPwc2i+xaQu28KsGGbGm2jorh+nRF4x+BeTmveYsOkTEwFVZUX6Kh952vaXdyp6Z2Lcd/4rnp9HyHWtc5qRJcdbpZw8fNpxlxpVLVt3zzKLvXXo8bm21cMnd99z17IvP4LqaH/zsk8WfTj/uyP16vfsZhD0LW9l7DAU6pgZLueYD+qqD+K36XUSEssBZUGmV0Oi1Z9sZsyCeXrCclE3VAE8r77bx7ZIbNBd7UuSXouVFoH2FNFlcc8W1HR0Z9uZP7/jx9791G31K2BMA8Ns//ObCcy8CAxuOBn42rr/wHvbpjy6dkb3N3c/kcymWD96qNzxjlwyyYu40mZRVoxRPzv+IKTLV/0cWabN8fUVTcxubU+WiVBRkz4Icuv8JtC+SJov7Hrj3b/f8/dyzz3vldcMyXfD2gl3vPq13M3NocDV40OD9HkHuUZz/rZeLZEmCjxYZ3adnHW9Frm2YdfwBdDNHVu0TZPrsx2sxjR7lQKMLPt6U0VNaH+thsCYCCC8hQqMIoaZ9xjgQ6Onb906ZsqfpBs025wGbLK9vs38ItK+TJoulHy995NGHf/njO3754ztIrOOSqy8CLfjjPmCoAyMMqhvUvwm0JA5laTGbIovHjy6d0Sc41KLRI4yiJoJ2vc9eZWUYQKCPWsm8gZ0ymoQRDtLVRfdqTxug3kWGXdN7BNqfSNOGBx9+4MGHH8Cp4JdcfsPXbnjjxYWtjXu/c/ExaWngdoUoGCl66f7zimHM62+7vIvcWp4gaaQsjSIl3Y4n31lkKQx3KbL7QVqK+APeZKIztyfUEGJqjnqWQPsxadrnSep6ft6zT8179imv17v0nY8ghA2mqcVi42b7oCGChl1NAyqX/vxvvezo2RxQyMzGd72ZvRpH6iSHwgwO7X4CHSCkSbFyyaonn/nfPff9ia5Jp/FfZVdEBUGH7eXM1mcDCsOb8VR0iraVOavdauOw4DYsqqeN7vLVuXCRzw1q41BsNHCwexqIjB3tow8wwHDH7355xaVXHnc0TloiWPDSm4QoK9obP1396YpFxpC+9xcsSurF3S5cuNiPWLsce4QQAj5/gfmnOUGCfRrqvALtl1zpGPrI7857df4r48eOv/cPf6VrJEk64RAjr/7r37ruvTc+IK2DFEU56sQBbbTurqsoUja2LF5acBsXLroOn9+b0itoS4be6rU0Au2XpEmRK3BM1s86/5FcO97797/c+/e/sGtYnphz5gndeJEuyrP7QaizOw4ox3eZYMiQsfv2be0GDi3GB9q/SbN4fPuqqX97Yj/0oOzHuPuZFX06jiR6RJ7nU+mUbRxOn8BNpx6T66UHB0A/vSFDxjY0WBxashvU2CYHgbqk2UX5UGf2wui7NYh9i0N7OQn0oMkHPf7I/9g13zhrWqTNGq9E8Kuf3XnOGefs3wYIeYjShqfm40nRl18w56ZzTsUZAv/c/0PlehRDh45t2LeVVCz4At5Up7KaLAId4KRZMPH7younXXnxtILHIRmLj9y7uR/UIPYO2E+jJDLdjx/j44/875H/PvzgIzj5FwDwz/sf+ef8lXNnW4Pp8dAjr/ecM87p/Ws7fcKocWNH2lYeeeR4AMCnn26hREm4kt3GeEpU18AYKAn1Ai+CznEoPOsy/GvjovgybZYoc1Uf0lJukge+31mg9/uB9oNmlLlcmZMmTnryP/NsunLlklX/+O0P3nsN9z3YxFeTNT+948e/veOu/Aq0232gp08YRRbmf7j2W9ddkHM7xjx9/OVFtIQUAvCb757/m7+/ru0wHFaXfP0muuWDr78F+hf26c5Q+mFkcGgRnpn9XwtfPiDR9oI0mr/6ME9Bd1+vQXRBQAaCTj9yxiefWn84m/x8ad4ri5cuisYz8l57H/X123DD37HWtT324gf4P7QpCSM2rzrPaOS8bnPLRafPevZhB4//TWdjvXX/Y6/xtZ1NACozfOOiC/75/IuddoO6BFo4aakTyrSg8HTRp/HaG6/+475/4tbImvadH922ZOniTXw1HUNw+KFHjBwx8vy55x5/zH5IwFi4eQerQzGNbt02f9EXobA1+e7a80u4sCkT6m759ZN1NRmD8/oZoP4vKt2Qd034ktHtgeNeUKCuCd8J5M9GghBed831N1x3Ixlnu237tl9efRIAYA2qIl1fEUInHjfnnrv+3MsmPIvTD5pIFuYv0ufWq3iOxZknGq788eMyVDPBA0++VRXyI4TEfV+SNYk63D5Kl2Loqxce7XiiB1/XhW0fxHnHHoG92Jki1CrxdBWoCxc9AYTQI48+/MijDwMAjj36uL/+4b5Lb/jBM//4wyeLP/3rA/eSxCbHfoa9iYVrjS55GKrED8lQkVt0A3/hR0ZzhpuvOFH/1xCnzz5sEOi1FxRu+XzT2Sf0aSb9xoUX/PMFi0OLL5N3TXgXLkrDJ4s/bW1rPe1cY4gmAGDJ0sVr1689+8ob/3DXnQCAW2++7dabrY7aK5esOuvCM/Y14sGi+wt8nQ8A5zQbpEhnHT/5lQ/WLVy0GovW47Eis+HJV43CMBuG1vUXuz5X1kEhN6hLoC5clIalH314zGxj8DLFQZMP2rcHd56nI1sghOeefd7Pf/iLGcdP0zStfD7lN9cZsvS0KYaBT3DgyJrXF61p6TDG2XsKsSfFiVOtpnxYxJr4xzsFdiwjIBxNYm35IkWoS6AuBiLYwGCpQ+5u+f63Vy5ZtXLJqrffe/uNN18PhytJL+3LTsfT32hVEkLII2AWKiv2zGZSLjOe3tqKQ2E1NRkTLOaeWqzff/xEIwvl7vtfrgGeppY+NnSEpdFiONQl0AGNglXb/bJS25ZW0QkynXbs1MsvueL2W79/yhxsyH/8ycc333ZjRZatt999oEViwZqtIrbxwdnHH/z6ojVIlVqamoYWwZ4vvfsp+zSexEGqK8/GPySXnG7fqw8JUkqjmEPjqTxbulH4/RyF70QIvqReFQQ0w6bUHfcXgfZcIn3BpDTKoaV+zhXteFJLrGpwSXuV7U8U/RPMOevsRZ/Zu7JecJJ9uGyR7QDKjUZJFJ4ZQZUJBP76xLw8blBXgXaS8rpOoz2avRQMBgcPGhyJRFpa7QXaLvJjYE6uz489Dc1zTz+yttZ5xMhTCz6+/Iyj9O5uDnhy/kfs0xDgrrrw5D5U13TrVXPJwl8ffyr7VZdAe6+Cu3cqjoYMHjr/hTfYNc+++Mxrf/mRbbPrv/r1m66/ef/2uShzlKoNSQeZftk+BukJpC0tzW+uwNX0l59xFPvq5fpTQpQ2Dr3qwpOuuvCk7OOR/9x0tpHJ8ODrb4Oyx61XW9UxlExdAu0GlFUt5vwX3liz9strvnE1eTr3ksu/f+sPdn385uql79Jtbvvmd66+/Jr9d43ljpJqz2wdZPpf+5hnV28QDqghy6fNwE1JWlqa5y38tLrKyGG6UqdI8m8evPquMZrBhh2793E1ItiP9nvpzVMomboE2q8w/Uh891L2JHPuvn/rD2765X03nH4Iya1Z+s5Hoij+8re/+NVPfw0GHkodW1/M8Hqy8pF7c+7e50bYZ+PZ1RsvOeJAdk1be+TME3NaME++ajQVDQXztXn7yjEHAYArnQgefLuPtSJ1CbRfYcPG9QCAcLgyEulgQ8YkiFTR3pioGbpm3ZfXf/O6mdNmgoGKUjm0pPYxs44/IP8QZkKmfZFGn11t1CxdcsSBSMH5PfPf/uisU2ZtqcchpjcWfzluDP4o2iNGJimLbVtx1ROLb191Fvv0L/97+ztXnnLTKcf0LTJ1CbRfIRqLyrL83vz3AQBPPz/vqWef3LlrJ7uBpmnXf/M6PDpUwl6tAYs8YaLO2e/9dYS9YxbEs6s3CsNrs9fXb8PvvaamqrXRGujdkpc3Kb5zpVXZ9eiry/B//KD3kCsKXwgugfY3zJoz85wzzvnJ93922UVzL7sIBxC3btt6xzVG3JNClgc0gZbErX167kjvZOOfRoualBRhT36olTP34t23vPm2swOUxXNvrySZpDilNNJ+40VnZ2/zj/mLQTnBJdB+iNcWvPbagtdwkQnHfedb373i0iv/+/7mr56YISUGuAJ10b14c90mvo5UNFl1TaeMGwEAmDhudC721MSMseoXnlX4h+qGs4578pVlUUEB5QGXQPsVph85o662bsFbRhqTpmn33PenR//334Uv2xPuJKmTYwhduCgSb9fvOmXciE26h5QwaXOkrabOwfbPxuMvEQeo3vVZN66vMnOnrjh9Zvkk57sE2q/w65/dOWTwEEqgBM0tzdiL35rBmJKrQF30LjbVb6+uC7c2G07RmrraR19YwnLkNWfhGlCCq8+0lgvihpOP3l9M6hJomYKGMkqqirny/y5/+7V3n/z3U1f8n5X0u2KRgwHlEqiLXsDb9btYc57FA48tnHvK1JraogRpLjyzcLmem2c+5zt7oM4O0XMJtC+1uijIp23tbT/91U9++8vfrVySMdDmxEPtX1/XB+qi95n0Er25PYvWFlOQ5mbSeW8YY0TZUU50ohOlPtz5aXDFzGE1AKHlDW3FX1vHrj2VI0obZUbhEmh5oWAOzUv3nzfr/EfybLDgrTcWvPVGTU3txHETUun0l2u/UFU1u1FQIhF36zhd9DKeXb4W0+hMO41SJl3wsdGrlE4JZZ6SZCMj4cjMO0II4J7HE0eGJ4wIAQQWLPoCQHDW8Yfajv/RntZcV7Vuy26wZfeU8cPZldForLIyLMlq7aiM9Ta4BNo/0dra8rHbRqSbcPczK/Z7C66uoBPtu/TSfmPfbu8X9axOo3xdIPul9vYIAKCqKqz/4CPMlhgQYZJEbc2435UjOtqxo7+5LU6evvreZ7YNLjt9qkJnwJto27m7euRwi0ZNfLml4YzZkzo6InU1VZE9e3FxygHDHM/rEqgLFyVQXleYtC9WH3WOf4cWx7nPrsYDoi85wmpoT9DeHiGVTgwyTChhuL0v1PRhuFqfyM9ceP0DfbJeJq66Kp/AbG5tX7AM119d/7VzHDdwCbTv4aOXrndcb3OPsrf6I/cazWOuv+2sfnBL70fk+sRsxNrnPtjDDz3itJNPT6WSDz7ygKL0apbls5k0ylKnY72TIyoDQkdCmaP3OgEABIK4Df4r71qRAElWNQ0hgDiIQdd/9H6X/lIugQ4I/2muPhfZeqrP3fllgr77uY0fO/6Zx5+jT7961dcAAOde+hXQYLVTsMUkSYMF0AM0qs//6OQIkNff/+yUo4yCqEQ8hkP/R018Z/lmMmQlLSuahv2lGgAcU7mJECJ5VEgXuY769fIzZzzx1DtXXW4v53MJtOxAVGSnW13k6RhUJPpuqwsXncMzjz8nSdLsk4w0ddyv692PXnnm1WuPH0fWkMH3v/nDndVV1dVV1TXVNdVV1WX1aZ86Hk8BoOzpCMKeWIJiysSLAIILTrXn5K/ZUE8WqqqsUtSn3ljh8zlX5rsKtG+3C3JsXdHP+lm46DkMGYxHH51wxnF0DUJo9pyjVi5ZdehRJ3zxMR7y/ttf/g7Xs7/yQp/+QyBkZDxhAsUCFF500uFPv5HRLZ8BnPdGURrCJdAyRTH588UHNEijIBcubGhswtPq7/z5b3748x+w6796guFMxI1CTj5d6rOtZ8aNwF7UwyaPBgCcdcLh89//DAGoM2iRM5wKwCVQFy4GLhBCy1cuP2XOqWRK811/+m17Rzvu4NWSGlNjtQV5/MnHqBv01fmv3PG7X4KywaDqQwHAPwPZeGf5ZkKgn6836vFHDetm50NGNxQXLlwMNNx06w1XfG1uIhE/Zc4p77z+3solq1546iXbNtdde/0Z55928tlz3l/8/lfOOnfeo8+A8sCBozOmM/UoXlmEU01tcAnUhYuBjg2bNhx32rHTjp166dUXb9i0YfTI0R98uZsXDPN0+nFHTjt2alNzU3tH+/d+/J3nXnx24viJ5TPyvjocWN1S2ihpgqfftKcW5MHhx5wjig5v2SVQFy4GLsKh8NAhOI5EsGXrliu+Nnf2HCzr7nrsTbKSpAFR/Pn+e7DhXFdew0efnF+oYbP+Lqz3knvUuyNkWd5ej4cG2uASqAsXAxevPPva689nND/Uc85xyGjw8NHbWtOiKB52yGHsq6qqAgAiUVxzWSaorgweNOPkPdqoZu/BOTZBGtIzmYwofAnsOeOki6Na1Qfv4zE52RDmzp42b1nhbvsu8uCLT3AOcO/jt42dqXEuBmt35Oy80DvwJqNkIb1n/3y2fQLHTx+x6BOjX1wepBLOzbNHjhv201/95L4//u36r379kf8+TNfzPO4K9+S//o77cl5+zc1f/yabNn/lZVcBAGIxnKleJmhrM3ovScnoHjCKLIv+0OHHHRzdusRMbzVoUy9DQg1tBfIKhJrJbc04NvXu23hmPQQomXQYlgcfu/dHAIBdLaklG9d09/saKNhfBAoAOGiUMbC73xKoP7R/r6Q8cfx0e39CAkc+zUOgeBLREy+MHTOWzM7auXvH4YccUVlZKcvyKVPHYKKsGrxyySpVVWfNmalp2pFHHPnw/f9a+vHSb3/vm3kur9v7j+QPIlECtWHXrnwJ0Xv0LiEl4bbrrIRZAsNPPKLW50pRF2XCni46x570pWJkKcXFV11YXVX95itvjx0zljDpPff96ZV//oVuMO3YqSuXrKJtud94c/7Pfv1T92/kkAfqcmgfJbvu0qEudfZp9mS3KYlD29rbZhw/jV1jK0cv29axG7d/fOBoezlmb8KeSO9yaF+EjfiK59OeZsxcxmNBeGGXjuALWEngLlwAM/7O9mHqqUqkubOn7WpJLtmIm5666ItwhaQLVoTm+jnJ46ms4zpsUwjLFkPUTUPCoKk12iIY3U9sYGPuZiCpuyo5c5Ryjqj133T2qQ++bp+F68JFjwCCUSNGeQTP3n17EwmHWKeLnjD2/2M2rMnuPvPIvZtJKaetgSxBWTbrQlXSZp7DArNFsAr5adjdamBXcg4oCHEdPp//ypLa2bW1trrmvIueBoTwtJNPo5PBJkzAHcm279i2d+PndJtxY8dNnHAgfSpL8rsfvDNg/zSLPtlVjBu0JGT3P8zVQDa7i83d5UGmpD0IablUq+CWdEtWbx1aFwYIqRrSNCT5jfSmjEF0eRHi2guO6yzQTMTlUBc9itNOOR0gsPDtBdSkOvbo40aPGhPZsz0eww19vV7vxAkHNjU1frr6U4NwTzn9pBNPfvf9Ac2hRUaTbGhuzFD3VHjmaSA76/gD8ndH/FHZNJDVOyITvmP6JQOg6vnz3tROwpukfbLX641gfnQG7MZuTK5L1EUPQRRFAMDylR+zDqklSxeffuoZh8464aO3XwEAHD3rGAAAYU/iw9q2besYPdtmgMMWZ++KLO16A9kfXTpj/3IohEbjZ7PvJ5lKZ3RQJrEja5QHBAFfzlp+gRdUlQ41YU1/p42LuTjXJeqCxZ9/dfXF58wiy2s27DzzirsdP5+1i++Zc/6vtu/MVy41fnTNQ3f/5vxr/vDZGqPhGKHLQV519Yp//t83/vj5F6vD4cpvXHPKT267AEL4+7+9/Oqb6/AtwEFcnZcD7zz7s2FDqw867nvsSgjhmkV/sq3sNygmb6n49AzaQLZPNOd+4Z3Pj5s6ljNneDKwT+mgxnseAnU6SE4OFUgd59zZGVlg2XBdoi4INi271yt6fnDn/xYtWzdsaPWL//7ejpV/HzXNXpey7PU7KwI+Lq+3SZKkLdtbgwHvW8/9fPAUa1JeU1PjmnWP7N7d/OmqTR1IfOD318459uCjz/qZKArvv3THxefu+/3fF+dhTwBAMOirCPiy1zuudNFfAO3P9QJOnufMwUfWrPmAt4RuUkTbOr9E/lNkOfzc2dP+8I3Lij+xi34Gj0fwip7R078176Wle/a1rfysnlDno/fdzG42cnjt8KFFiZ2Fby2YexOeGLrq3T+ffuoZp51y+oTxEzcswyGME0/9LtlmzrEHT5p92+atDWs37Drzir8dMmnI51/Yp367cOEIjoP4oRvvhELJgs+b0/jO/mXOowMsZi2SQz/9YvPvv35pMVu66H8Ih/BoLZIsQvH9Xz9xx5+ssY4AgA9f+XW2Js2Fd99/58ob7xs+LFxTKUIIJ086sLqqYuYxt5BXTz0BtwJqa8etK8aOGVtXh8u3Z07t5jC0i34MDmIOzV5ZwiFyb5shTYvk0FVfbrnp7FNKOL2L/oKW1ihCqH753371/UuqK4Nk5dMvL9u6o5Fus/2T+599NdesLme89cHnHdHEX+88d+FbC/59zwV79kUnTTseABCXtDNOPoJsM33ajAMnTtq5a6fOqod269ty0Z+BzAeLUvgTD/OsGFX78oers1+z69iiXaJtN519yoOv40ZPLgYURk//1ov/uf1rc0/82twTcXdITTv7yrvXbtxNXr35q6dBCL93x+PFHOr4Y4/3+/wL314IAJg485bGdY8sX3gXAODki347/cjpotcfl+JDBlVhxjzpVI7n161fu2PnDlxCMxivLIgdK3FDNhcDHchhnUfgCwXYLeTxtzs7AuYtW1mIQ5EeVjpy3jIjv8TFwMEFX/sT8S7dcPUpP77l/AVP/eShx97+3V9f5Dj4o2+fV7zxHovF/P4A7dT49e/+4+E/3zB66s3BijAAoCOewt9dVcPn4vnFHy6iRUqyjHv6FoTtSiCE2z+5nyxPO8xKhFr5+Vbb0yKv30WfAIQAafYqeJsbKj9oMejLH64+7xjDJCIQHvrOV2/8y387xaEYc2cfOfWQCT98uFyGTLnoadRWV7S0YY/ksUcOX7Nm3VU3rPvm9efdeM0pSz5c8diDP2ppjT5+/7fod+6vv/taMild9nWrNxqL1V+sPvWk00475fQPFr+fSqU++mSj/msPp02djhc0zJLRBD7Oog8/SCaSdMdNWxu6+C5e/M/tdHnUtG/annbx4C7KDdkKsqSuInkSPrAC7SKHrvpys2vODxC89cxPJ40/4KobsKFN8cobS2fPOIho0tqa0Amzp9CXZk3DpZm5oKnaoiUfHH/sCScch70BVWGcY3Ti8XMQQu++8TLZ5qNV+y75Cjj+mBPYHfc2cYLAK0pROtQRNpYcIKS5dkdrD3XgLnvo5np+zsxp0We8YBOh3PsffUE41HHXIsNKba2tNKw0d/aRN7ohpn6KS67HCUa33XgRu/J3P79e1Q3tq264i30AAI46/Sdsgmc2ksnkwrcWrFi5fN++hng8jhNIP176pu4VJZj/5ns4+h+If7JyxScrVwQ8eBTPMy8uJGd0USaduu4ug1LOXEBmP5GiNi2xz4jlA33g1mtv/uujRetQ+wWxLtH21tYbzz7lITfE1O/QHok/Ou/Na+ee9sQ/fkxXIoSuvfn3XTlsq469eysBuCwSyZhW1tLScuFX//TCfy0Te+437m1pdXvmlwuH3l3G1GmAJSpTSr70HhaOnQMrQo2ZSCfOOpSMrJu/5MvXVjqoTicOLczVVTU1A4FG9+NMpN4HqbkeccCg42Yfoqrasy8vyjXjcNEnuzrdUFmQcQQJAKB4jNqh7910DgLgzw++VszubkPlYnDo9El52iz1FcY8bkQ1KeW0rV+yeuuwOhyN1P3puCYeFyaZvs89TfrvNGuzm99iXhA0qxZeB+TqJg5nV9gJFN8YMw8hh1mwdO0rKz4pxKHFKt2q6pqH5vdzDh2ABNrtxdo239yWDUZp/PhJo+lKt1F0TxNon8NxI6ppLXweAiUNmSAEOQmU4bNiCJRyqGXCL1r+JeHQM44+iIPcS8uXdy6mZEN7W+uNZ53S7zl04KDT/SiL2cvWb82G7ACIS6kuiget5myJFJhpXLwhn1GJtGj5l4uW4+HGp82efOEsPC/Uhs5NkCcc2oVLdVFeKGlgmQ3NjYk8LFk3OEAeRR7toFE15NHp63HR74FKqzsqDQ5dRgiHnnLUpEuONlqWsXA51EXnOJSlTrLMPrr4qboc6qIbY+s2+HzOTbxe/nC1cyUS4dA5Mw+eM+PAm//6mJMtf2SpF9HehsP0A8ElOsA5tHMGfi4OrRscKJJeDxpV41r0LiioAzQffRZdzZkLVhDJEcfPPBgXOGdxKMn37Nwp+x+HDqggUjmLRJdAB2YQqak1mv3Sll0tdFnVEDSDSMQHmk4baR7ZyA4i+fyBipG1jhvnbBRKsGj5mkXL1zxw6zXZL81b9mnnCuHvf6yoTBQXLly4KHMUIFCCXBxKaLS7L8mFCxcuug15hhg7tpq35zDlhVBkctKi5WtyTeict+zTTpvz/QP9wBTqBRSTpN1FlFuOt4sygenn7P5gPFdSYD0X1ZaqQ6+ec1xJ27tw4cJFZ9G1GHxelDYTqbs4VE7F3YYjLly46AWU1LmuVHAP3HotyXIviUPPmzmjixxKGo4Uv70LFy5cgNKRJw0UctzIIZVd+VT/H/aLbZ6gzTz8AAAAAElFTkSuQmCC"}], 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CdpWTzhd2DfEHDw/JNNsanYnCrHscCcVgMN1iCS/ZTyhhwcZNDUXTBkqvMsGSo6D9hAkiJ8z8NLc9UewiI6pRKa8H/vhkz+7dOg1VVVlZVVlJAK5SZj8lqm3iHvT1bg4v5DEYTDcSUKSeAPALTx2FSpd8Pq80Ndnt8eitarFx0+U2Gw3TB8EMj2IbOCSOyEXyMiWNlNRdZWRrxZdSi6n/IZlmXAiKwWC6yxJesp9S6ZIQ/aSF5HvwJFYDZVSnM+o14tg6gd3F3hsGMZKibQFgTzl1fbYdzaADAJz64BuNJRvef+sb+SuOHT0UyK1osHjKYW+iVdFrpAAATpYyUnBFb3NzBMALeQym13LHihWHmgkGor6etVs2d8gLzZ92etTjP/+5Kd55oBPEHZnE2k/AalD0k1ZrQ/ZTqP10utxurz8VTU2ubchMSwKqgG/nnhiW8JQH3mgq2SC5SwKAt7+E35erLhwu+dcPviviOLaouKypqdGoht6WgtHPsIW8ilYlJxob6hvRyPojFfYkPYmCobgmFIPpTdyxYkV1VWVDQ1PUR+WNkRr7/na+Fps4JupxyrYvriX8HYuGoRtIPatl0U+hQbNBsp/C3BANVE8BpJ5obF2LyBX26guHXy1TT0AQ1VUFrrI9knpGgk426dU5/UWxH5JpNhlEr4pLmjCYXsPy88+rrqrsspfj+WbHa8QloKHckUBaYprX55UaN61GrRj91EHdrLEJpeyC/fR4vaG5Ie0mwHBWfZTcEQL1IGnUNEkSDQ7x909FYwDuqyyANRSD6QUsP/880G0g47OfvJg7Euyn1+vVUmLwVGE/Ac8i+4kW7zpTcMujloiV8wkaUY3B2OwpcBiTKKAEQQzOTUPHB6ToW3xpDAbTU1jendQzriQSmhsi7RYM7ac3wn6yIfuJop9o8Q5zTWa4nVH7EBW0vKaRVgt7KAmwsodJApAkwQtLeI/HBwCRkGhpsMH4iEqlCgQCKhWsxMdloRhMz2XquLHFRUXyIxZrYmHhscgzfWh8u4DGW9fO181OAEVFxZHHSUcdGWf0c5HcftJh9pPxkaL9BECyn5FTkzsceYEUSRBoPw+9Vo2OE0T0hTwGg+mhbNyzV7FgjaqeHU5xcUlzD5Fx2c+g/0xLTIPqKW34DoDeqqZplWg/panJQfspXiW+JFJ0FHWk0uGgL4U3BJCAatU0eoQgwKCcKAt5HAnFYHq6hhIn6uUVoUY+poDesXCYfG4Isp/iYxwHo5xZZsYXuoJUutQF9hMQAEU9CQIOoqdIQpgFClS0JNaE3ISihTy6jTUUg+m5bNyz92BeXkjK5A05YbSpmYaP9hW6Hq84HisGKo1/h5ZTsJ/SnnEAiHl9f4ChaRherKlvROt3efQTqqcwNZnuWPsp7OFBkSQnLN5JkqRpOFMZAJamwp6ycPa4Ldvy2/biGAyme2Jj2Py8vJGj4V6W0I5GkUrxUGF1JQCaZSl2lywq2lqiKshw+75YAnrHwmFIQGHtJ4D206K30ICjNZRkP+HYOl2Y/aysbchISwrZz2Rx045QVWv7sDX5zELxqc8PLEZ1RqYl0awz6zVqFZpGD2ptntrgvnXwbo2NB4BC5hRnkzCY3oKNYQ/m5aVlCnPdIoxWYXWV/K6aIsTZHK1BaiUf3gS10s9yMNciiIkEHY/9rK5tjGo/UeOm3H5C9VQF4Pkh++kxmOGokfbjcrgoAFxN0IGqVZTf5WviPF4nVSl8ouAWnVGe2ODwod4klIvHYDC9ABvD2kqaTe/IkW9g0eYFMFrdKg6SsaOfgv0US5fg3BBZ9FOwn3BuiNx+omelodpPQcsMZhM6p71vXwbPA3+AdfsCDrfP62fQLwrxbNmTxEgFz5v0Id3EkVAMBtMGSECQEUJMxrCfUJiCjZtaWmjcFDbtQIiNmwa9mHyX7GcdrL6E9tMP7adQ5+TXjINbIXUgPA8bk1zeQJMLaijHw0pVjuN5uM0x3OsY/gH/KzpShhVds9yE4mwSBoNpj6kjY9hPNLYOLd5bYT9NiQ1257IVl4iLd4bxBprtv2wbQwdmDh2YOSQ3c9CAjH4ZKQajnlarUY092lUObS5HEISf0nBqLaHVkTrckoTBYNpO1CUx3VzuKGg/m9IS4Tj4oP3kwsbWGfTS2DrJfqaZEhOSdR899zkKfTbanelpyXxHvFmDyUCr1QzDlNeLO8ehgUwUSWhUNE2THh9/6tjsyrom1NZJEMDh42jhdwRJEqyKCriUoWTcm4TBYNpMs3WgQfuZiuynw+kGHFdWXQ/tpzQ3JFg5r7SfN0L7Ce8zjE+wn7a6UGY8Nm0I9nIcXLhrhApQi0lLUyT6okjSbNTotCqNhlarKL1O3RgsC8VgMJgOFtCw0iXBfqLKedpAmTTiTsKhuSHBxk3JfqKOI7n9hJIq2M9Ea8ck4qPCw0FNbECIclIkieKewhIednbSFKkKSurAQRmRT8eRUAwG0zZidSKlJaZqaZph/M6g/czKtsawn7BgyO6UckeAYaqFc2x1toqa0DlRsWRPB+2A43iXFxbKNtg9Xh+0vSh7BHcPFSOjaFEf6k3CYDCYDhPQGPbTKNhPqJ5x2E9UuiSzn3yi1ZSZKQ457lik9T4P5Rq2S+lMFqfH73D7Pb4AzwOdBubchY5P4XwCTJs4KPI62IRiML0Ym9vX1Q4U2U9pbkhZdT06Hmk/PV4vKl2KtJ88AM4mewz7ec1j90R/IOp+crKHmztUWt3ocPtcbn+T0+v1M3qtCq3lpbyURkUnJkSJJ2ANxWAwbRTQ5uynRAz7qTNRKHck2c/jFbXIfgLAGw16aD9JoQqq83F5Av4A6/UzTg/UUBVNKaSYQiVOGAwG00kOtFX2s0awn5Cg/RyQmiDYTx4wTFvsZxxQVLMj8ngehj4DDOd0+10evxgGDSIU2oOkRFhLoACbUAwG02oBVdrPOpn95LjDxZWi/URrc9nYOmQ/U8Ptp8PpEu0nAwuYkP2srlPujMSPGAsH9N/yHOgEeKEryc+wjQ6v2xuQ5pmilqQh/ZMBAE3OLjLFGAymtxLuQNHYugSZ/QTAoBN30dALk5ekxk2IKoDsJ1TPoP00aTWi/QSgoqbB7YZanGZOVLzwtZcvQDfWO7Wd9Nk4jm+wux1Cr6e0kPcHxK1AJp00YPTIgYqnYBOKwWDihwwfWweas58ut2fZinNk9tMG534CgOwnOh01bkr2s6KmITMzRW8kgZ+Maj9ltCIoSQc3FIkHu9PndPvtQkIJaajXL/aV/rmneJMwb78zmKwqm6wq66SLYzCYbgIZp/006HUfvfqdZD8z05Kh/axX2k8owUH7CXjgdnvR4r2hKbQxJz9iLLKfnbJ+J8PE2u0NBBjW7vI1OjyosGnnMTGeCwAYlJ2MTSgGg2kzZKT9lOaGSPYTAKCwn+jJqUal/ayub0T2s7y6IbMftJ9o8Z5gCd+RODUBfnU+jN+PZt/Znb4GO9TQwVnBliqBqqq6ztNQbEK7Buz3MScKMtJ+SmPrkP1saIKTO+K0n6hyHgDQrx/MHdVEtZ8rl0L7ueRBlBNvoeKzw2bfsQ6Xz9bkgTklnleEDCI1FIPBYFqElm3a0WTRm6VNOw4XVw4blgpYTYJFs+ym8z56+RvRftbYMtOTJfsZaj0S7WeSYD9t/frBGU6pkv0MD1wi9US0sShTfBbfqvmhDpcPAN7lMQWYUG1TVVVduvCJ4p/SxCRGaWeSU02L/pphlNkzBbStK/ZlxWAwneZANWxZpQ3Vfka3ny9/E7Kf6bHtJwSqJ+lH50S1nwiG5WKN2u8gaypPOjEs1NBqm8sOlTRMfRfOmdIhL9e694bVE9PzmdyHU6Y0sp8qmvR6vTTgPAGfSWVqzn6W19j6xW8/TQkNTU7Rfso6kZD9ZFkeTu3s6J6gstKK2CcwLF9Z73QJxVUBhqVIkiQIg4aS14q2aEJbFL604M9TSSCrNW8fg8H0JGDCvazSlpWagfaMMwUsgOOg/WQ1DU2OBIvpo5e/kaYm9wvaz1QjnJoM/FSj3XnT/Zd/9OJXaWlJCvuZagpLEyH7idQTmkthYjwBwEyjV5z30VWfubDSSRDAoFUfKqymSEJFkyWV9RoVdfa8k39au72r3gUmXuIMmLQYLcGWH9PhwOUtUk+G8XsDPmPIfoIECxy6UWNrTE3R2eu9DrdPYT8b7U5rovWjF7+qrm9Mjc9+IliOF3rSW/12bY1Shz47dOgA6bifVV6rtOBgcxdBTaAel4ciCYok/AwRYDhOq/L7xRp7CTyvHoPBxIAW7ScaWyfYT7R4R/YTSmEKXKebjQaz2QRUgdp6e4oxAdlPa6J12Ypz4rGfXg8v2U+O40lSzB1NpJ0xtlZW4HI4FQ3wx/KjbnkfHzzPcnDfORQbZTn+m5+3Ljln6o9r/5KfhTX0hBNnwARHSzBdDy3ZT5rUouR7//4mvUbTnP1MMSa0zX7KM+9ozyLYC0rBLNbWf/3fKQ+8EeNdRrWqbVNP5HvF4ime5wAhBBR4H2BILxFglSYUg+nLtBg/AX07hEJ6feLUZAAAWrzrNVaUfJfbTxT9rK23S8l3aD9vOk9Ivictv+FcRfJdXjmP7Ce6zXK8tHQ3NdXQpKSNXRQCHZKbMSQ3A+3rOXRgpsGgMxj1lgSrzqBXabXfrd0d+RTcIN9tuW/AhvsGbJiTWXii30ivhbYdk76injCQjne7s14JraVCY+tg76PbI9lPh9NlstBS8r3WZhftp9ko2s/nv0D288OXvozHfnKCekqhTx1NUZ0mmwaTqODCRp4BSaDL650qgmBZBs21k+Dh7D3WRxI+D2M1ig2smJ7CnMzC7cfbtSsMpkWiamha3y44CXWOR9pPqJ4AlFXXI/sJeCCVLgXtJ49KlyLtp9PlQerp9fC/bz8slXVKjrPheKlBTcWTSEJn3Hn9fIPJaDAZLUmJBotJ+DJbLCaDUS99Wa0WS4IVnRb7mhRJ0OFd80JbFCxsUutg66qCCTktL08wXcx9Azbg7znmxELK7SdCsp/wjo8iAOH2epH9hEeE0iVYXf/859X1TcjFlVcHu+MF+wkAMAoXKSiqkr+YXC5pIQMueyy4kab8qx1k8bXoK+qjFElq1bSaVjpgjocJpSi7H7tCI0gw3ROsp5iuR3BhssZN6QG0eIf2MyNZb6JSDAkR9hOE5Y6i2c/s9DRkPwEAxRX1kiRWNnX9MGNxr2MEQQCNmjLp1WpVuIbyPIwzqKIs4bEJ7Q54bTb0heUS0x0gUeMmQlq8i/YTgKyMFKAK1NlCuSO5/VRcK4b95HmQkwnnhwIA6tzx9mh2THV9M5cgSdKgU1sMWhVNKUbZMywXdeM5DAaDkUOGzw0xKewnupss2E90uzn7WRu0n65o9nP6hEHI/XXB7KW44VUUaTZozIaQ74bbI3NwRFRkZydw1a+85JSufo+9jg7pm/7X+Lyox+8bsAH503ZeH4OJE7Jt9rPBLp7Tr1+q2+2trW9KCdpPQ4T9ZDl+wy4xf1flDEjH/WzYdm8K4vSeOTnZoadE5IWaQ1JHtYqyGrUaNS2t7nnhTbEcn5QQZeM5rKEdQmfPnpBW+lhMMZ0KjSrn0dwQuf0sra7vn5Ei2U8p+gmYQIPdZU20SPaztsmm18I4gLx0Kcx+jh+0aS+s1EO2jqsqAUkZAU6cWw/hW6eeDusQdBYF+CpVgiMhtKsSRdGsoMvVIB0dsasSWKE8ngAEeh1aiIH61LRTQ6tVFFADv5rTVuZ5fYw4SR8Q0IYCvsnpt8hKmtb9tuXMWae26/uNaTfN2c/mkDRUm4hLKfoi12Q460W/1/GQx6sa5KVLkv3s34z9hHcTLXL7qdeqJPuJkNvPovL6jXsE9eT5ape4HxHsQSIJDS0YRpmQxlZPS4oYRY1Kc5Odoh7VqFXoRbP8x7OZklGaWqtJCxNKqBgAEFlpVlgknK2cE7ruty3YhPZQecVuFNOx8FIfeoLF5PF6dVqtaD+r6vtnhtlPVDnfBvu5dMGkzXuLIl+bIoBORbZt5W5qPILOpQGfburnbShBNVI8z9MqNSPUpXIUlcEIOq7t1y8jrbikTDgBHlARhEGnpkneDDRADb8DWg1tNmhZlm9yelmOp2mCRQaZAAoTijS0C0dHYdplPzHtD7Ns75MV8i2CHBg5d+oYZD91Wm3IfmYq7SdUzwj7CUh/dPsZtJRJCebNe4sIAoYUa2XJd0Joh0d9nHL72ayHbEavmDYJGaXRSDkiobgJsCynokmLSWs2arVqmhY69NEZkSYUN3d2c7DIYjobSbXI93/Ysi2v+OdN+3/euM/PeZD9VCTfxcU7tJ9OYXgRtJ/oHKSeSvu5Q7SfIwYko+QMAQOgsjkdBFBRhLyOPpZ6Rn9Y2tODkOxnjA+saHmCiXbZXRQ21QgJJZNBo9XAJlOCAEP7w6BB1GwSbpDHYPomcukgjXp1pc3O8/yESSOShOLHmPZTvCEvXZIjt5+JVjOKfvI8kEc/EVoVGdlMqaTTFspCiigkokywHgBW1xvUBq1ao6JDL47X690DXDyP6W6QGhV13oJTFl84k/I3RdhPRyh3JLOfZTHsZ5poP4vK64dlh3I+NK1S+EE6uIRvlrhla0BOaLKyBJxUJxDw+VEHktxzBrVTPOQLhNyxmqZ0GpVGRQFCzNoDAGobxX2f5GAT2m3Bq3hMJ6FY50IPSFHk3oPHx04Y+8v2/QeLqqD9bIBRUVIDT460n1nIftpC9nP5rRcqku8TRuZs3gdzRxzHVzVJY+SDr0oQdOxBIp1p+rxer2K5LzlQBEURKAwqMWhwvyZn17efYkJg+9lRMImD5F/4hyx+IqOEYhY+0aI7eKRs8skTacAC4N95oJQHpfMnnyRLvqMb0H5CAUWTlYP288OXPikoqpLspzsApvRP+kvITUXIJLxLkYSObH6UHdE6QS05DrPwrYKPEgMNU0xScMfSqzIMlzsw3VajbHHB8+oxPZ3mNDTOrbmrg6eBmgbQq4maYxEFlBLCkYP6J9E0+eZnm7QAnDpmgNx+WiPsJ9zYQ5iavPzWCz986RPpikXl9eNH5GzbX0wQcLhRdYT9TJ+6uLFo25Tr/z1qeK6USCIAsGTDeY5NpZEzysK0rENQOFBYtBSNbzYcHpkLs/Auj1+n0TU6fFZTlGF3mG5oP/81Pu+B3aM75+30bOIcCx/noM806TTQm6udmstQB/cjIuApx0urASBuXDbj/a+3WjTmKPazypaV1YL9zEhPaWhyiL5TmRkP7uRBU3q9VpmGF7D0ny4fZCc/Q206CGqVY+XKy8plMc34vx3ofyKsbJpfVND05YGDM23B+QCxTej2QFaf3Skbg+lNxFYWaDz/3LQX3Rk5FP4OyS8oy8lOq2P5rXnFAPA/b80PU89g9FM+tk6iqNw2cVgGEkCG5artysGaQpsPoGnSoNe2/923l+DV/bIkUlRQkBTa5OSwzfJiZ5OqE05SxJsivzrgU/R22hz9xKkkTJuBG6XFoT/QgY4bPtBRD+OVBwvKRw3LOnC4zKQBHKlKyEz/8Y89A4bkKJ4j2U9hbJ1fbj/Hjxywv6CMJGHPT9ToJ9pOjiQIjUYV/yfp+JRSeP9olNlL4UgfBSWXagxK4WMi9lUOxYYwGMyJo94VMR+94+waFFCXy2UwGODcjXr7gcNlo4dl7T9cSgB+9tRRr7xdtCBTaT9R9NPl8hgSwgKCReW2KycO+X0H1GKW4+pkg5cQ0ph5miIpIYnEd4KG0jTNMHAoXdgVorwSPCTuxpyka+6dDO2fVFBan2EOfVJrSgKoVUaRUimgWMjjvXY7BJx8x3RnxBio0+UuhAFQUWiQEpSV16I+d2hOi6r0Bl1ikkayn4ag/axv8BWWQvs597SR67cXkCRsJCcFq4lUCV1NGNMB8TcV6lWUu3IL3wAvzgfTRyJxKWXopHpHwKonmzxBAfSEfttkwQFSIL1/tpcB6f1gdOLwYVhZRRKEUadWqXUJFpPVqKXRTBOBvMPF6MamukYoghb1lmiN/JEN8pjuCU4lYTpdQA8XVQ3NzUD3p548amh2akFJDapA0qjor37baRT2WXN4HazNKybfZY2b2WkArd8PF1cSBMHxPAGAw6/srSQIqFyUsASmSEKlEufAEwA0lWxIGDADbhD/8P9FvsVTHnpTsoouhwuAsNFUNABOh5QLExk0Yjj8o1RZdTRsWO6x/HyaIkmW9TiZMr+rKrzeM06iqicuaeok+7lH00LWeJwPh5IxJwYaKVNGqgU5UFgPX1g5fFDmoePV0t5BWTkD+qcb/9ywl9YAni+fN0WsDkH2s8kJu3RmTRm2dtthWLrEoTp5UrKfKBhKkAQqUPfb7aRV/dfve06bNSGet7j1oWvRjZrdlUAnVlPF5lj+ISiXwtacxw7mhz0Wf+CgGaTiAb6lyCmmQ8D6iOlGhP+jF/0XScBSUF+Arayoddjd23cfddhgKHPIgHSCILKSdMMHpF+1dE56VjZUT1nj5vjh4kD4oHryJEEcqfZWVte7XR6vx+v3+TkmQPo9BpIzUbyBYPWk32V37skvlFIzXdlrPmDEWHl6jSBDX7C6PvjdEXrlw75CB6U/I8DNnR3Lo+3e6h2XgmLiBP4bj+crHFp+hBICl06ny2g0AEA4bOKQOoIAh46VjxjcLz3JCgAns5/eJqeyTh4u1VlPyHgK0qyiYVCUFGKgoB2ojKIDVfNRmtMjcMMtRoJbHmXkDh01KG1QVkJ+tuXPP7ZIEip/51FvI3Q6HSCgyPp84kfmSembgcH0WqRiuxgV+Nv76sxQGDwclptRUQ1LO1U0lZ6ZUllR63S6C8vgEn7QgDQAwKihWQcKyvKPlh8ra2QzrBkW0X5K0U/JoBEEUVjRiMa6QzkW1JMkYbs9BXeYh6dxPHSpcKAcz8MzWlMDH59uhpFFNJTxsKKo9Mihx25Z0H9Q2smz/maxGHmKVqk1gBfHRPGAd/o5rZq2GMS0EjKkQuUAjOcKf0LV9QppKvgQAI1OvzU8HoojoR3Lo8entzkRj+1nxxK1bLlaKNeL3e7Zqg6ongUdVirUjD08UFA2emhWXkFZulU9dECyP8CsXrPJqNXJ7SfLcZSQelerKY7nWQZ6M4okaQp+wfQRQap0ejRbs7aqWlDXrp0Tx/NPPPthaA/OUBWTsCrnQSDAUSTHsJxaBcVfXKSLJVRStCF4Q/jTalZHmlCsoZjeRGzhSxM67mK3e/ZixBhoZpoFRfZyMxMLiquy0oX8S5BhgzLzCuC3yWrSVlTVVVbbpo0bPn74AHkjOeqmL693UCScUwdnzpNE/6zU9PSkxKQEUqdTmww6vdrP+BnWb01JuP7GC4P1Ta2QUTdLuVmK97Vljyh5BAPu/x5eJUBAA06SJMGwHKohJRQIByI1vxFPaeqWYPuJ6WoHKgENI0m6vWIl/OFjFUMHZhQUVqI+HBVN7j5YKNlPIbIZei4Bn0uQRhgxrGl0qWhKraLgjHe4locPobPRuCPpSXG+XT0l9FxSsOw/fjwsLJmieTZkOXmeYTm4/ZHslVUqSqOiKFIILwC4Zpfe2sHDoYFPacJ+cxKRJjSyNR5zolbxGExsRl/7zzjbEaNCo1hfk8OPDFlRKawALa1yKDRupzBb3mSGtekqjpXUk2VhzTy6XVHvBDzgOI6kSbjWFbJGKppUqygVTdFCHknS2nDRBR2Ih6V0SGeDoLt+NhRu9Xh8JM3Xe1ypqVZkLHmCL3fwei2h01A5SdbBA5KTLHr0jkkCnHbaSVIF1Pc/bArVQgnvXD6lCatndwDbT4xCIlvuZmyTCtFDBqQDDrjcYnJm9JB+m3cVoNsqWqx1r6yoE/LywGH3mMy63YeOh16UDC3BSVgJJK52UU6fFL5UUECFSGjsEfStISPJFDViW1FnV6injLA1u5MywYJ6jqeDg0lH9jdpNTD8QLDeY4VlsQI/4b+rCr3aCSZ4CKsnBtNDaVuBOFzCHymtGpqdgdQl7wicDpeZZikqr5XrndPpMggaard7JPsJqz6D51TZnDRNcsLqFykolE6aokgSpZJIkjh8SGyUfPCBq9v5aSuF6SdtqfMKoqNYgmAzUlJqbE1tT2cFL4ils/us4rH9xLQWedN51IdaiIFmplmOllSFP0/5RJfTVVgGF/gSkgcsq7PDTLtY6MNTJNnkJ4CfA7wfloWSXqtRm5ponDL5pPQko9kQ1yC7zkBR/Q4z7+GbeYi5Jo4nCJB36PjyC2dwQkoJtfajLtXfa5QjQTEYTA+imclCze0LHF1CeWUSCR2gxCM8AKdNGFpV1wh4kJGZXFlRJ5355tO3PvbCqoKiaipoPwWbSTAcB4OeQt2SUcVRJAE3aNOqtGpao6bVNGtvbHI0wYLTnfuPLbl0juLddEXmJeJbwQjlVvIHxX2YCeKkETl7D4aCFfLTcJKo24LtJyY2rV+nxxJWKJfjR4TVxwZnyUs1kmDDtgM8yw/NSUdH3vl0LYp+IhITzTVNbmTvBL9J0DTJ2O0MAD4A0Ep7+NABqLq+88hINgNh6/lKW/Qip6jjUQOsGDCFq3jhI8Mb4e/z0OGQjPIaS4o+dB2spF0GzsVjOgRX1a4OUlioeFBAnS43APrQ0wiAGpMkrlg6990PfslMMx+vqPP4Qzu8nz5l5Ka/DqJuImE5D68BE0cEoUtITTBpGb9XrYK7BMPiIKH7SBBZoUYdxUpbj8sXIEmKE7SSomiWFd/P0XI4e8mTNBwEa1hVajFW6+Bh9DYQYCs0Jgel8aZoSIoKqGEHUZlKw5EMAYhU97FDJXatmtZq6AHp1ux0i9WoVavpAMOOGNhPo6J8AZbnebWKWrdxTxveNqYLwPYTE4mzJcVsazYJFu9AAc3ISK6srJPW48hXyUOgRcJkJvlIeYnTp4zMP1omOTHUd0QQgOYDPi+nVdFq4T6yn/AFgzfarJ7ohidphCDWJCdsZ8TQVpJXzm8GwBvj14ufIxTFX2lmymxQ0xQFGE9llb9SegqakRpsOs3tl1xUHgpoYLoMbEIxreWJv82vrIazfVuFuAqP40QooDv2Hp40dhgaZ5fTP3XrjvwMIae0ccehwTlpUpF5ZY1dkMiQ8CETKXSFo/QRLFpCBfOEEAClhfJPCm4BL4pyqPc93IE2lWywyscqNwMT3CdD1SSmvChCBfiACoRnwEgN4HxpdCm6FxB64T0ud015oAYALQ+MiUnooWRKRwJWUPZgbDMiaIzeZlTHjOfZdR+w/cR0LPFY0VASadyYIVV1MGI5IDMZPjPaU3nB9IVeQFBDuD8HBVUHFn4KUkkShMfe5IF6Cxy8HjZ3UqROo7IYNWaDZuaZ4zNSLa11oNLboXiGFXQ/+ECk8QSAi7ILyrCx46ZPGLh04fiPf9jx4ZfrxWfLU0LSNH5pbJ0kmqL045gnBtOT4Ns5/BddpPkTQuPsDFpVZoq5uLQm4mzxQEaquUgoY9qyPT83OwVu5/nXgWlTRmlUlE6tCjAsTB8Jc0NgEahap6Jh9DNTC1fxKukBwGz9Y8fFS+e2/SOxDnUwBirOcwr7sGhdL6SGwufUkwH35q179u49WN8QqiEVNFJ8Og8LlUIN+mHZJKyc3XsVv/p4zpREsA9ke23KbQgwmHbWhMaQUVFjVtxy4asvfqnTwLsEANffcukflz0kPWFwesLm4CVOO3V0bnb6li1wJ+QrzpteWFFP06RGTVEUnPkmdB4JbUhCMJQSVvSwmF4wp9KbavNUUJppaNsThf2OSiYMTR+RZd3v8xZ6A1phuyd5Pt2oU/Mc7wmGWRFVlbXS7UFDBiaadIDzyraOwsoqEucWzdJmpZ00AE2bKF4WKylGTrjZaqOM8s0J6Ksvfrni5gtefekr6DQzUx64/9VxIwfCpI0HlgQdPV4FhxVR4mT2IlRyz4PC0pqB/VMrquoIofwT5WNQwgh1wauEuUwoPQXXxJxQS8TzLMuhzZFa9WHaCQ1YwDIlFfXb847TNA2AIKCyS3tZakC6Raumkyy6BJNOJW42F2xNFb79W/YcMenFAaBtrSPoncQpdmj6WccOQFt9XLnzNlZSTNvgY8popBWlR0y/suLzZ6CGvvTVitsWv/r853CT3vSkrTvgVkKDBqRGvXoURROGbkjVo9LUpeCDkuuEvwe++OTXJcvngy4n8m3LBVBLBlwOu5ck7E2NxfFKIy6r7+65eMmTYjBtg2/eioaFCV99/vMhuelHiqpIghg8eIBaRfnc4q4egCBKKh1yv4ZuFgrTmwb2T0U3JNFUCaWfAWE4ceh4ZJF6K5Gy8IAEAZaghHEnJAiVpsIBUYQWiFNQjkT7VgRveWEklErQo3054Pw6lDaK8Q4jBRiejjX0RBLVfmIwbSDGSrc5K0pzPD/zor/BdPmRH6HkFFUJGlqZaNbZXWId5R878jViiye/cdO+qaePafGtCDPdBUfa7uV3cxeAIQWZdLI8RRFCdT0fpfwzEq8GVjLRFMGz0RSTh3tuBmtXxU8R3ZXiUiZMn4xW071xi47YRMoo6WVU6EuVey46KmhohoomLUZx6scVF82CmyBlwJFtPAArrhPPjEG7t4+LBcuLDlMOUk94o5md3mRbeEB0FGNUs+GeGg2iF+4QKB2GSv7hbVujo74BfnXwh8G0dbfOR49Pb/+2nZg2wyQOYhIHVSecVJ1wUpyi3PVU1QSr6FvSo/iNkLwpnM7bs5UQSjtHjR2hGnhmoHCdoKGwDQfNn9dr1cWl1fLw6asvwjgpAGDDjkPTJw2P+hqVFWJlO6tPzkg2jRqUmp1u0alV4kZvQ/srzida83kkrYwKC0gxWU/HvibPc8DrZzSUMMQUEMfq/AD4CYIwG7VZaeYkqwEVZhn1ahVFZQ+EI5q0arriOJz4J2TDcBLphIGls5uk+9J6xZ5IbVhGosgdPW/RjHLZpCVBQ3+LvO7IwdnRX1QTtielxOVL5qrVcL8MkiQ27zxcV1tXVytrf9x5CACgGMjUIsvGZ/zvKFRGqRc+HFkdaHwX5Hjg9gZoHYXaq0waXgjdkmYDSfF+t4NF+3E2NoihW6G4HpjVofoBHgCtOQXU4gF3mN5Jr9mvmOiEa8IYaEF+QWN9ldMJt1AfN34qbHyMVvDkUgwckZGZlVYh7IEsZ/cBuAVIZ+OlM+hoDe80F2e5KO/1s0YtKe2AhFr05TvGC6NPpNNDWbK2za/GtIHYy8M6ENqiigFih25z9MGwHaZF2vMPGa5yTz8dOsHq4Jxg85CF9iM/KK6NRoKGBo606TV5Huw/cOzKJbPhvsdt2sJJnoUX3r0XEBTPc4r+dYZOiLPkXjGmXvaA2M0plTCgP0ObzWG6itiqlwxC+/1VgMh1EgbTiUDzdaTgsM3Jq/SJ7oAKfdE550WearKaxR/Z1BZ+z8sRNksWvwAAJ40atOtA0Z6Dxfvyo8wqbhUU3GUTEEBUTw1MyreAENUVT3P74Je0+XtoWH3QW8L4g5A+Qnvhof+2uxALg8H0HmikBxWlRZn9cwsO7kQJJYEBuVpR44ZmpxSU1Bp06nlnTi6rrDfo1PH3SzWX+2pzjp4nNAQPZ4WwhJCL5+BmmwRJ+cKrkQSvGixijYaehvorDMMLXhne5Qg4KbplLRacK3ajGEyPpz3hOLG6EwCw7pdvzjn/0soq+a5H40HVmwCAgqKqobnpBSW1drtTo6aOy3ZGGpbb73ARzEor2FPYqFFRGjWdbNUnmnV6rVqnpQ1aqLwOt2/AkP79M4KL8VaC1FPoi0cFCkLtkbYf7454G5FZ+GBZKsMwAGiiXR0lizAYTB+CEP7bll05DYaUgEecP3+kIH/I0BGShvKEI/u0JeVfv2pOTa5yMUP6pxwprR0zrP+R4tDwTTbgj3rdyxaO1WlUBAG27TnK+Nx2n9suG3JfXVOXs2R2m4eJwE3fZFP1hJl20bfxUBBZ1S8fJpKRnMCz4iQRt9vvBiDBqpefvD/vKAwZTxgiP0ipowkxBtMHmBwsdeqzVpT2ef202gg8oq0jaXu/LG1Zea14qfLfT50y3OOE4lfsBCoNWVxZY05KMCclDBsFdaTG4TNbzGaLGbVySuw5WIzGMkVdqqPYIugIvHSGMJXPC4L5JZbQagLSOPlYu3KGQxSUNZAELPy0mnT908zZ6dZEs85s0EhBz9PHD4YyekDMaaD5fO66MjGlhcFg+piG0jXVZdbENKlYJ/9AfipRogs+/NbqnejG0rMmSs9Beqpg5tTR6IbOaHlr1brEhOBAYkFo0H7xSDRRwoZleRrWibYOvcNmTwgrapGXMfGkhuB8FO8NJuudMRyom4PxBE0wIAAASDfTJr2aJEmdhqY4X3lFdXlFlPdg1olD/0RwTgmD6asaKoYJU4gDKaNoAPLe+hR2IrUWndGCbry1KvR01EcO/y+UTwa3C4ahAUAAOMS+HUid73KI4CB6glTxXMSkepYpKK52e+E0z0Gjx1WXww0/4JZ0YpWSWK/UXHZr34HCMaPgiL/wl8QKisH03ZAoPTS5/q1VYmtmDBwuv8mgblE0w96EoJVyPTqQXxT2aPO0+NZjd3NGUU8ABp809pwZIy86a+JvG/I83sCz/4MCStOU9GIanc5qNfoCTGqSCY6CpkiG5Rwun9GgzU6zzDh5xO9b80PDUUIF9RgMpq/uidSc/Cmwu7ypaXAbD0SLzyqu8SSYtGqaGtgvITXBoNPA0cqnjBkoU87oi+AWZ8PRTENkEilsJ48gtfqMlNDemoD2N/2+ced3q9fzPKfRm2SvKH6XfB5PI8HSFFlTG1DRlPRGbV6Prb5BGGkamsuHpRPT/ckdIM4Dip+i43ENM+sjEC1paESlTzjXXHImuvHWqnV//BU5XrNZXC7HwFTttZcvgMXzQzKl47f98w10Y8ueI0uWzUO3n3rtx5U3LAQdjc3uSJH9/OwvqBiZmzJlZP+S6sayGofXx2iFLUxCXUZCfxRsfw+/Dto7j+X4Q4eLAQCnjB+C1RPTI9Swza/Sy2SUb8e/2NgaSscWzTj9aXO8+f5q0NGgBBELtBombCtjN5lKk2F/64puTjXFp5hUFdU2GoBjJVU0TRMELf/eOBpsjvD+T4IgWF2iXqsy6tRaNT1p0ph+qeaaKmXjfwwmq8p6zTgGTM+SwvbQy+qT2kmsTeWWnjXx42CqHdFO0WwWYR4nmvgueDye4zi0SbKejauKUw5N8YxsioSUjicoDc/6mnkDyunOFCkmuNBdr8qqVlEmvWZAumVw/6T0ZJOwl6iYD/t960GPy3G0SDkPFAdB+zLdXwo76gP2Mk/aBmJtKtchnDVz9Na9JXU2uB/8SbkpYdZZ7DhH845ExUKFouj4Cy989s+nWjEctzmVlI7TTINGZwJ8WCWTQkFdASiPUkdUdpLGZFCraAow3sLjFYVxN+ubjTkUSTTYQykyTI+j10thO+mVS/v2a2i7BDQn1XDalOF/7S89ehxW0es0KpNBUyfflzt8DxHZvsbS6DgR6PTi83LBhbmwxzwdMqFqzi5PIml0pgE5/YEwGVpC4UB1JEORBMeRgCJRZ3uMDUjQQxzMX0X5fCzHJ5hzsYZ2B7AUdsH3ts8qKSH8N/qmcvGw9KyJlXWO7XllaIQoAMCgF3sZN209aEkWt0+pcYEki45lebNRY9RBW8dxvFoNa4Y4nlfT1OTTRjbz3logZ+Cgqqo6r9sua4cXEKIBnqQRKrU2OQXOizpUU5WYcBIMbvIGOOc0wJaorGhzJ2+KhqQoRk2TBOmmgEYNq1JT3cegDnM8SREcLw4KkY9HIQixF0DxTWQ8cBIgA4CeMrrZWBNMMB0vhbKFApbOLkP8VleAPm5F4xLQ/plJGcmmv/bBHLSwyYdKo6IknTAFBRTeNoi3Kb+d4kiKAF6X3xstwllUUoEm0j/92o9S49MpD7255aFrY7+Z2vKS9H7ZdZpxPCAbGxsZv/ibkFZrExITjMGdSGw1YSkmhMJgwvFLPMdygOPI0KjT8PACQRBZY84yWmHDqIJNXz+Jbtx+y/XSwUefeyr2+++zKP7JYbHD9AINjS6gFy+YsK+gUq2i9h2CyTiSIDTq0JlqFa3Vwt2Nzrzwdm/pz1IxECAAmrckXr6V7yZ+astLUvoBHlDJOgAE9SWhaHqA34NOqCk5huo80wi43g/wMM7p8Qb6JWWYKPgOj9Ueo2maSxgPRd+fZ9SpNSoKhRSmnn0DRYZtWldaVqZSRflGnXHxvTTvUKkk/Yfcf/vKR3q7hmLtw0T9kSjqY+t6uCeS/D6tgrLIBAIUSZgNGmTlUIBSmgHK80BFk8h1+lh4UD4WhKaV9e3SXHfUzYnuKuOdQi37Iytvuv+pl+N867XlJcn9cktscG0eSdgMpbA3E7Kgl9/yKKoBAGAJlFFtmDuVPrvww5G9Z+9eh8svPV/8U/hj5vST0WkbNmxAN+acetqaLZtBTwBLIaYzfpyK+oySRnegBAEzQkRQGQnoQEOmjCJJg07cXxM9Kt6Qr3ylVkdxa3UwYd4tu399MdZIdzS9qaV37NaJU0tKa50EcLDR1NKpHiH8LgDADYXs9PlX5GRnGnRaAPiyiuqklH5DxVLXEA0N1SnJ6QCAjRtFHZRjNFmiqifSze6wPSeWQkz3IbebKSnRqQKKSkEXTh/165YC6QGKImCfuADc1oIkszISyyptHM+TJAFrfQCorixOhaIZep+SmM665B+RLzZ+7i1ef2hvzn1ChnzOuZdD2fry1fGzrkDHY/dOsXD+B0JLcw6dOrztnSDUKpXBoK+z2S46b9GuVVBARw3L3bw5ZAkLi0K76Ejs2LHtlMkTmtPB8opqk8mCMkuChxYlk+f5AMNQFCXsWcIHLS3fB6WwUZMTz2lsghjxcGjSWjzZ6hPD7pgeSm5vX9qHHKhCO6DrDAb+hO2ARKfJMJxGLdaXZ/fP9Jbsk0Rz3qV3VOZ9LV43KL5ytmz8Y+ggizEpSmfOzCU3N9Y5/trZclZPT0sCCvzRpzkL75kPHM7PQ7fl6hlZDYre//y5MxsbmkRhDLbHoxQ8z/Mer1dvMKEtkYTiVlE9Ac9TFOqal49NIXqBIGIwvdWQdpKAhv7Z7ykl+hkINrhhEAlX5nBXNRj39DNQQIXbA7LSD5eElFetoqSLbN74e7SXIwoLjzNibabS7JnNxqPHikHKvEm1v7T7c/GTJp3scdZH00ze6/XRNB0UPlElJcVnWA7tHyc6auE3R2KCVbCZsqugbqqwEDAR9hPTxyo84nSLyQ1HxSWOTsz4YfoOub3OkIZSJcrN4EiitsEV5kAFpfD6AtLJeXnQ4kkxzU0bN8YIBcJGSp47fLQkEGDCLWBzOwu3HZ1OL/84HNxCWfSU+w8WkOgXghiNFbWSpsXfJeIhcfaSeAWjQR+mm7L/YjCYVpE7QIu+QO8S0PBVKAFKq8QydSSdyHW6PFBAke36/OPXFWt/giCSk8wy5YKT6NF/0RGtBo58lwRVetBsMgIeCMVRrXn30fUaxiLln0W4F4pC0LDFXXgPMokkmu2Yh6hUqq1//CidIdfQLRvXo69tm9Zn5Qztnzusf+6w1n0MDKZPktvzZTRsCW/QqX1+hgkE1m3Pu2r2wAa7uMgKbowOb9tdwsyOMKkNtrQLORYxER8cQy9KV5DqWme/bNg3KT0t+IfwH/ga7bV1wrXDBNQTIJHB3L3lV5gv2lw+fc65arVGr9dJA5U5AFIzs48VHi8tq05JNAwbPkqthnLOBeMY5158ZXFxSXqaRkWr5BfPPP+cQACGZWvr/IVHxe2SMJjuSaMmp7ul5nJ7coQ0LImUnmxqsHs8Hm+62gsnubHyGKjoQz2+QIDh5NZP1ukYcnlQxSL2iJeeoRimbDYbg1cg4aQ6GR/YBl6WWNiqj+SxHd9fV3noMJ2eJjaWqihodAEAC85bJr1pnvVXVij3nmOZQILFyAQC5WVRxnkdPLgvI/N0BsUDkAEVv0FoLF7zKS0MpkfVS8RZLMEGT6OAp28qaZgDzUqDu3RU18KVO5I+WqViAgEhZCgu4f0BluU4xWJffhF0NzHB2NDgFBSmhSkhW39fI11hxuxzJtf+IlnQj3aV26ypQGyvF7E1wchsaCC90CiKrmDWkQRN0aZka3gZgHPXp/rxSwEAlZWhUZ4ZaeHXFSAJQkXTPq/P7/OpNbBZAH0CpS2Wq2dwnzxWmM6HAhNZyZO8FWK2BIPpZbm+OEvW+kKuSSagACSYddKyXRFDZFkeCajXxwQCbHM1O/JAKtIXQjCjOzb9esHSa0mCyMkVv/UMg6qRiAuXDTHq4ds4duQIEwjsTZ89pmqtdEG9hnzfNujyxNDSOMEK7Srjc2p1yhJ6qS8evXxLsQCxFInn4N6h6G2rVCqSpJgAvI7P66VVaqF9Svxoo0ZkS1cXWqpCc/o2/Lk7NydHpVLRcC+QE11Vj8F0iQonB3W2Tgd3/O5YeoQhDQnoV2v2ZmcmpaeYogpog93NCKtgr59xewOUbEsiaF2FGvuktP6Evx7prNlImI0mmCFCnUuzF9rq5XPuwmC8fH19lK2SxesD4sOGwcsTWmfooqunrGeooqpODNDK9gelKJhfMhgNHAs/F8P41Wqt9CsiwSx8c5B0isOahLp6AiQmWNVqFTqG5RPTp6jL7Hj1PFGGtLWbf4S1chJkMAUUfhWSIIorGj1CARPDsG5vwBycuoRORc+qrKhMTBEFqrgMTgiV0NA6NamJbHisra2FkRSr1uvzqcXuS1H6PtpVnjhuvruylhSI58N4vC690RxLQqPlviJOIQ7lHx46FGXSiYBfXMvDEgKtWlF/hbSU54HJZFAUimIwmB5nSFvrfsIElCQIqSxc4UCr68WtLFiOd7p9FmOo+EA2Y168E/Eq/OGCwhEjRyvak2prBZHlgdPlRkIEhZKXva4w0UMjdD59z044m9qFXqHl1bn4snGcFtHHLt05sGeTdHDm/As0ajUnvHpxSWltnS0jPcVsEkeZcDyfmJRIkiRNU4kJVj1suge79sT1HjEYTM+NkIYLqNyBEnDpjZqRCILw+Zlg0BA4XD5FEgkl6OVlTDC0yHLwuKBQTU218qFNQe+JapeA2WRwu4KxatmVWZYlCGAyGoROIfoPajoAn6rVKr/fT4RPnGuR7ZvWiJ+RABctgyNHGYZhOc5ud+i0Wq0Wesyvv/7q3LNnwsqkjNM1GtWokRdxHF9RbTMbEutsdmkvpOJjh6uqKsza0U4bL44RJQiP2yNf2QeYQHLw/Koy6LLjJD0rtHc0BoPp5hFSpQOV1yQlmHUON8wZBaUPCgQT8Lu90hHhzGCNPSETUxgPEPwjuuDY8SdJ0iyt3CVUKthYqXhnI+Ytq6ispWnaZDIKVagERVFDZl9ha2iyNTQCoHPWFXkI0QPqdUa3l6Upnd0hzhbRaDVawQkirrnhJgDAqk+/HDnypKKi0BBznuedDnE29NkLTjNaxB2S5Oc02R12u8NoNBQeO4w+WkpqOvxcpOi9XW6k/qJ0tu5vAIPB9FgljXCgoSU8yM6wVtY6XC4PSRJMIICmhaI8Utg6PVhjj8QS3RZy1yiBHVRfzktQsGrs91++kq+tR0+cRsO2dHFKx9aN68YMhgHQwXPpapuTY0HB0WO0ivY7a/Rmq1GvU2vUWg3n9vh1iZnSKGOvl02wiMWkaE1t0BIDs1LswUjsoYP5wgekAgGmuUS5VDMvV08AQMHRIp1OD+vqBTetUtEkS7hcHpMxrAxgyuRJ9997J7r94Uer1rwuruFnnDRSp9M98sybwvQT/6lnwvmhM0cOQY+uP3hEfveMKaNeXRsKHUjcsDBsx73XfgzN3Lth1mniwd96xhBSDKbXLO3Dk0iC0RNvAyI10ciwXGllvUJu/AGWCdbYi8ZTcqRBD7trMxwmQpHUabPOV9EQSoAgwKRTJiWlpAAesCyHmiw1WiJVa3I6/Knp/XIGDtxXWHjSQii5Iwf34zjez7AcyxU6G5OFOKOQKIfpGo/XGyPIyTBhBfkSDqfTZIQJH7HgP3gJnudZLuxjog562KBlgNLs8/k5niMJamx/mudpguCrvPD8DK0T6MADT75usSZ++L/nf9ywe/iwYQ/8497lyy65YvpAoTEfqudtt6w4eDDvwYf+s3Pj7kdXwsF9NqFiDEmnUadyCj2yAIAVs09XaKhCPWGRAwfrqy6dPTHstKCSAgC+WLurjugWQSIMprcaUtpuUzpQ+QZqajVl1Asz58MFlGO5BocnMgs/aFAW4apFt8dMnO7x+DIy0h0Op0J/d+w6MmWKTqlrLu/RgoLJE8IUQaNW8zyv08II7Mjhw7zuWo0ukSQJtZrmhQipzx99vUzAoUoKAQ02DQkdVpKAoggmUn40hR7Zz9lDQsv/b8sP+fRQCjmOH9cfxSXg8Qyt86+84ozRsLLVYk2cduokdL1ME3PXzZf/96X30dMfeebNvXt3HzyY11DfcPutN+3Ynbe5uPa0nLBYp6SeiH5qc7nfHlU6P/xuC0GAZXMng+b5fM1OgiBSgLYWYA3F9FFyu6TLPiILLxuuwfMA1Q+FjgrwAFTWwH/ewTNF31paXDwiNbhvJQxZkrU2u1atDktzx8yMS/M3kfUTG3sATwuTSf0eUkXTKNFv1OuYAMM2OaI6TYqi2IBy43j0ykKpfFj8QXYT3h6fCEOi9fViYHTVr7uz0pNtx7ZkjDhjaJLf6RT7NQ8WwyTYyJxkp9N5oAhGCswWy4iB4t5zHMetvHE5uj19+mkaFdVQ38AbxRhIdU31z+XVV18CE1Z5B6JMd44qnUg9AYilnp/+ukP8lcDzdYTyO3CiWDH7dOn25++H6nlXLJot3X71h7Xyg79v2Sc9tH7fgRgXF+uWBXC+DtPFhAkoHPMpW4zzcMUKIh0oz4P6JnFPY+HMkMLKS5pgXoiAm3nInxw2UlN23GjQDhoyRK/XeDzon70w/4mE/fTCDkUgwHBHj9eNHw/X/oCAeSez2cgwrN3hFNbaSjhBheVHCAKMGTXU7mYl+ylnx45tZy84rajo+PiIDk/YAJpmrrTtyiJCma6RMgvpcnsZhvnh598AAG+89NRfWzb4hVHP6DV0GlWdxw6Mqndee2/M6DHfrP6G47g5p57EsiD/UHT1hApep8zd/7gJRksXToZeOCqrftkOa7wIYLcJPQvJ0TeMOlHSqXxIpp7DByU9f+uSx177+brFZ9Q2KjdxnTlmVDxKKpwZNgdr/T6U9MNgOp7nPt668qyhykJ6+RIeDVQCAKzdfEgQRDKYYeZ9fjYyCw+FLLhzOs/D8+GUjXAIkpw0aYqKcAR45eg62RTjYBco9KHio2gTkVBnPYwwqCwWY4Bh3GISXHapKMM64dOOVuxs7tuR2p/Ylgf93dFwTaNSW5YhErAnnzF5yKAhb7/y9nU3r7zu5pUAgN9+Wf3+I7fCPg0PdOtms+XX335JsCacd9Z5jz/9aFlFTUFhlHklR47BJrmyimowrp/iIbvdsWT2BMCz9fWhbVEkVv26OzHBCgje0dDIdgPpjK2eCh58frXUBJxiNXz+E/yLkMhIgy7z0x92WPTGJsa5/JLL/nbz36VHL5sxLOAP89r5x8pzcge+/Np7i+ZNq3I5Z4xQ9sn8kQ9d8PWykDHidZyFw7SGp1YXRC7hJWsGl9Jy76nTqHxe6SdVlKf9lZZRaY1INNHQJvSUYwc3Dh41VbnbevMQcE95bdC0hd6D/JyTJ47zeurLGsKnIaiA3gK/QHi8TwdAYdUeqRgzhnTGz47SKHvNQ8wwpHvk2JFp86ahA7esuPXKZVftXvvl/m1/oCN2e9Mnn3/8yecfb1m3bev6vyZOHT8yWSyZioSA+zW3GltDo5oE3VM9x08YBAB44uVvI8/8+PvNgCAcjY2sFf5OjXSgldWwBletopoY53//89SsM2a99coT+Xm7KZq+7a7/fPDH4WXTBrPCaIVte8WZCS+/9h7s1q2sm3myuAWhRFp6AoAVGeCcc07+7rtt8ofkkvr5ml02Ev5E/fC/u1d/E/rh+fGPvcedcHHw6NL5pTXwjUWtmsD0aFps6Hzu463oRmQSSbxNEOC7dfvnnj4C3bVajAadujFqw3qY7Iat92OP1RALl2RH9h3tkYU4F5570X133j9xKtxlHvHiqy+cMvmUlU++M2N0PyqJUqlUXq+o73f9487n/vs8jKLWwT3rFdiKxL+Rz/8S/pUDsHiK+FdAJem+2J2/eNzQ5t4GlWqIXnnQ5cwdlHWs6Pig3AHSkcdf+gYAYt7MyZ8fCftN9tH38G/cbW9qTj0VzDpj1vv/ezY/bzcAYO/BwmceveepV1bd++x7/7klOKgQgN/Wb/rg/Xcvu/zKSPXce7gUHC6FtWUjBkvqGfkz+tmaXVKcQaGecCcbYwrS0P6pptIah/SrItGksgXLkKMK6/XR4tqvCxVp8goKBV+t21PDyyJmmM4n/obOZrPw4oWCPegwKa+K3vzDwTFuId2VbpfbxD3dej3rN6y77877FQeHDx2xf/d2AMD3n/9gNpunzhb/hTx0/8NOp5ihigdJScW7e0I7p7Y21NDFHJOV0x4prjh/3inHCkOrB3S7oaHBmpDAWODP4T1Lztq5FwaLqmujxChcLhdphKeNmXDqx59+Dr1kgqG2tv6SU0Oj1dwu97LllzNMoLEyymRrqJ4A2Jsc86eN9foC8h/0z37dcfHcSej2ql+2kyRlI73yKK2gnnvQvyyknk+89ePd1yxEGopOkKtnZDlaVPVc9cNfCYTu4lnjm0utfvXb7q4c7iVl4ZLJuPJyyWTL53Tb5rpvN+0xc/TMacrfsq2CbLYTCYDRQ/tJdxsaldVIiMmD+Qa7B5WF7t+5Dm0oBPoYtgZbXX3dzo2733ntvdkzZ1+x7MrtG6Btufmy8wAAiy5coNPqVn/xY25O7prvf7NarDPmiyv9vgPFhwqHFThI0V49/unqFq/zj5XXj5t4ys9rNz/+5AsDBw8HABz2GDQqUqMij3gMpFn795V3/+3ai7xeGBZ3uZzSl6SenEldV98g/Q777NcdCvUUPAGrUE9hlDgLAI/UE/an6cgn3oK7vKRY9fNmj4v6biVzGlU9P1kNLfDFs0ILl0iCUxO7FzNHDkFfIA6qymoVX+BE8+2mPd9ugn0udjK0xW/8SOv3qL3w4u1VP8JVTHaGFd31Ebqo8428LntKguHIcdEysBzn9vbawezpWSnN/fXPO3fO6JEnvffG+2NGPwnTbuvX3v3PO1FrFM/zE6eOv/P2u59/8sWPVn347kfvgN7Or8ci8mNp5u92wxWJFJVGUEmysV4xQfaz9HjhlRfPvfH2+6ecNmPceKh69XV1f79gCjzDqP7rjx3zzp2TY6Cj/JJrdNAE4EzqydkZKMFYVwd/aM+ckLNuVzG6Da9ma1SR/Oxp4+RmGWHUqwubQgO5JQ297bJ5AbhLQXSydYklHptNqKlITA55sY+/3wpgK8SEGB/5izW7AAHq+O6yfek3q77rn9Uf3T6Sv//1Z/+Nbs8cOWRnfumgMSNHZKZxLMPCkeucrR5+5F35xdMmDJH2UkOcceoY6fZ36/eWu6P/m1qxKPRb59UfQq137QRJp/zuuadH//0XD/Sfu4qnTRAXQRv2Vl8wY6BMMUNVkirex/Nir6TOaPE4g9FQlV6jolBTTXJa+mc/weBU3yTv4H55GFTBk8898eRzT3TtO+rW/CqkwhWsOQQPVlU2a1LyKqHSrbjrdnR32NDhH7/9yRu/5l03d/Srz79eUVVRV1+XYxAmFYRDmuEwLThWpqRyUv90+UNnBn/+4Y8zyXPJoZ5gOUcKSyLlvklHHigoOlBQ1CRvLZExNF03zjpcFHFZaVpjY5PVarEFhTsq4sbZ3WNFt3Pjbo7jXnz07uPFhWkZ/e58+Lmn3vgM9doBwF91zf9dfuXV8vNX3rj8r/2FNN3CIMq7r5tz63Mfx1ZP+d31W2Ap2yFbvZT0a1XthEI9xWv+mRf/Ql5uP6GAHq/1SetJYa+O0GMqPjR1KUCEfnTqGl1GlXj8nW92aIK/Ieuqm0lSYzAdgVqt3rJum/y31OGCQ5dfd9n7b3wAZxFMhD5058bQr/DXP/5pzjmzbcI/tshqijI+WsoNqWdrls1f7jt64ZhYE4W/231IygTKcVAtrNVq6+q7SWjbbIZlLpOnT5w3Fv4yqK4sX3nd4qfe+HzitLk7/4QbNZ51zrkul+PBO1eEPY0HLodr9bY9Z508zqBTyU2oHCthbuTtzUmnxKc//pWaYIgsmZBuDB7a785XYXD81nmn+4JrgipvKP+WrtUrNFcK1FQ0xvpNFgM6IOvkSTTrpA2Mli4cd7ioVrp70ZyT9ucXodsEQQSEQnHYbdlt1hddQ6si4skk9Ol1md00iN7jQO0J48dO2L1XzJLDnNIouB4stvkmTQsthyeMnfDGS29Onj5RGhDTKn4VjDCIO9rw5T7l+bNzlWW8baCbqKcwCALWkCyadJK85vqtFx87vBeGjA8cq0hMTHr8IVj+LIcHgBHaKVdv2zN3QnSLl3+kRr4Vbwz1hMJtcyLPfseVZ7s8yl8/RwvKUynT8nnjZ82FPwnffQ+fIpGuDY3+SSWNNZzz1nPmBFjxhTOtyXFq6H+vORPW0ry1Dt2lz54R+t1oNmolB/rtrzsuv2BqXTAfuml3cV2jB9lxNdfbiiq+eenc826OUqIYP/dcHKW98s3njjb3EMyZfAZ/+DCt4o+Nf7z58ls2W/3t99zOMux/HngkNyf35svPj2hyg7fbpp4dwtqi8hilFKgoDR7fG1ZTsXhsszVqJxafz+fxev713Pssy95729VVFTDGXbN+LWGHpXgZqTBTkjt4xD0PPSXEqYvuX7kiNSUs3E0SRHVwiOVRoVtk8CAxcrJkwRTUyCup5y8b4EJ73vRxcvUUYiBQjohkU6R6wordP/erKfDMjxt4ANw+pjn1lAgwnLyTO56F/KhkS4MrIJdR+pdNBcvmiQ1wC6YOa7DZsjKSyirrl55zamWtI++oWKtoq62Ja1cNGZ7KRl0wB9Wd+ealc6X/AgDiV1JJGU+Znhn1hDefEx/duqEixtOxmMbP3++5fdiQYR++9TFatvv9/jlnz/IXweKnXoBCT7sVU2ef9verrr30qhX/fQk2KfA8f9OVF7gEf4UCfeMmnnz54vl6veG1975877Ofrrh4gdPlJA1i7aOtQVlDjmQUwdf7iCRNYWFYFyC6+9PG/OTkhCY4AhhKJwDg9oUz0PZCkZT5UUlZWAgmXav/cu2uJXPGM8HdLhgmAOuPwgPly84+o0UTitQTFsx5A4dL6kZmWOlrLpjsdckmg6h0aP+h97/ssLRXd0bSzU46H9PhHD5yePL0sKldkUmfXXt2xUjoYdrGM++8+csPX8EFb1b2Q/995ZX3vn7gmrOPHznA8/zli+ejc9xu1+WL57//+c/9B+QekO0i3lqGZIVmUjQ1NLLW0Gil46UVtGwjSMSna/Zk9wttYV8fzM6pAKh3uuUH1+0Om/Yrp8WFfF2Te9+RqsMltS6vqKS0XD3f/nQ9aB9o6DITgFfvEfazQ4QyqsEEAFx7+6UxHsVgegpfffx10fHiO+7924EaOKfmQI1tzezTtqzb9q+3vr9i+sC9pVF+wrOGnQS2hhLWf8ncpY0KC63MGT6YiFnNJlfPGATtpzBKIoLqWjGXWFffSKbo5o8efkxmgSW2HiyWFvKKCojDRY7jlQ111WEKG6VirrVECqVyTEgvlU6sjJ1Ea3tXcLKus1Gp1DOmzlAcLC0rGTRwcLHNtzNvt8vlmj5vqvzRvPx4GxFR7RoMfeaHFK0wXzR2pBAslvP1fjiWTM7icUPJlFCU8/M9BReFR5PPGJeteAofbYQbLC9rCCV4FC634EA+x3HBmES7BbRHG0xMD6VVyTqcpusozl68aOfG3ZvWbjnrooUNjTAvcs6icwcNHHzDpYsAAKiSTK1S+wMwt3P5Urjhwta/wkZqdSqR/c1fNB9NJlOgIv9yEI46nDtcWX9GJqmjltZ/u2lP1E52UUCXnjXx49UtzyvCoolSTM0l1jGx+ealc6+94OnWfpcU3+3IlF1zyTrFE7Getoeps0/buHbz2tVi+Y4wFmdl/n6YLj9wMO/osSNb1m/zeD06LZSnsy6Ce/J0f6LWqzUHmwC351AQlwPtxbop5dwVK/d2VjW1lnsuntzr/3m3If/WsSk7pKe9/vvcSXi8nsi8nDF4Y8kVF1MUNWb0WL/fdyC/hbnX3Z/4+zvpPiiaUelixcQoam/bk7XDybruAMuy8gaH3qShURtAowtoj9bNyEkfcU7lOuFjuPqmLYpTNHGyrssI/cxXoCa6wWFTA8Kp6gZzlU44HZCF70q6yd/Z459t78AwaN9UT0wPRfrJf/O5T0Ciphc3K8ezkKe/3nmsF3hPOddffUNSUtL6Deu3/rUlj7OMFhrSESRJvvv6+9n9s5958envfvi2Q1SvbUqKRTMesPc8sf3Kzf1sozzeKdPvQCnBXtysHGPx3iMdaGy2b9iJdkJmGObCcy8CADz/ynO7Vr2AHh0zesw7r71XWVW5aeumB+996M7b7pw2N6xyrW30gp+SrgH9602O+agCXO3Q4f3KrdXQ5tqUFeds7avNyr1HQNeuXkeSpHwAz5OPPHXbjbdf/fnLLAsnTr3z2ntff/fVf/4Lp8D+4+H7dvy5a8rEKX/tDBvZgukycNbuhMSaY2io/DRkLeNRTxDfQqG31pn0HgFNsCZ898O38gE8d96/cufG3fMuvubHT95IsMItMJF6olEIPM8//fiz0+bEu/UuBtM7aENxWOxm5b5M7xFQf8B/zqJzH37sIfnBiVPHoxjoyOEjFed/9tWnSy68pGvfY2+jDTm9VtVF3P7Mjwsn50p3506LMpa4VfRKE9Qi5938bdsqanEMukVaO6Ou+zLn7FloIPn2DTt//OrnoYPDmmGlvVwkNm/rkVso9zV+3C6O8W4/fVM9MZ1K73GgTqdz4tTxc86c+/i/nkhLTfvk3U/h1sqV5fctib4FpsfTt2bpnxAIgpgwbuL8OfMPHMz7ac1PPp8v6mn9MrPs9iaHU5ym05yG7qp0tiq5hBWzteBO5b4roIg1635dsw5u0gIA+Oc9D5531nmPf7jmnuVz9h9UzobJzIgrQI5pM/954JEFc8We6AvOufCf9zzIcdxVZ0SMbyDJ7z77/u5/3rl2PRxLHg9YGdvfsoyTeB1CLxFQiqJuXXHbsy89Iz/478cfHjp4KIp+HhAElCAIaeOHM2fAofyYTiI1JXXB3IUPPvLA6p++R0fMZsv6H39/6I1vHrruPOk0giC2b4BTbEgy6rAbTAeDdbNj6SUxUJZll19y2Y3/d5PiOEq+F9vEleP4saFpCKedfHrewf1d+zb7EBedtxjuJhZUTwCA3d60YdMfucNDe4J/++n3O/7ctV2oJCMjNiLGYLo/vcSBAgBKykquueJaiqZefFWsnL/ovIsy0jMeuedWdHfT1k3/e+mt6fOmulyuB+99iKbpq1dcdULfcm9mb95euM9rYpJ8V+G/3X27vDEsq1/WnLNn2RpscC9iLKCdQ4xqh+amPTz+2fbm+h0wCoiFS+aAnkPs2peP3v54+NCwSpf3Pnr37cfuhykmayrcD/qFNyZNgPkHjuPOXXJ2RWVFNxkm0kOJ/dex489daLux1T+v/uCT948eg4PEJQHN4+BW44idG3f/89//+PGXH2K/HP4b6fC/o+ZAf015nEVeRhYncyOqzXpxzLr3OFC4r97VS6HrSUgMBAJSSle+49j1t153ot5bH2TStAlLLrzk9pv/dtb8s86af1bsogi8hO8+yFcJUcvIYqvq471XLnu5gCJsDXDfK0x34NMvV3365SqU5Xvl2VcnTZj83obCK6YPhFHp8Nk1BNoMFtPlbP5tq0YT2tNty7bNr925XH7C3Fnz7rztTqs1obSs5K5/3nX02JHYxbnpfWnp1gsFNDZ96m/3BGK1WC1my/FScQtZlmWvv/W6fplZ3332vcFkAbYaxfkkgQX0BLBz426O4y67dtnBQwcBALfdePvlS684Vfglh0748auf01LTVv/0/Y7dOxaff/Gn73326v9eefO9/52IN9sdoXux/KFyazi1EDbtLuzLC42u57cf1jc2Nc5aNFN+sLyiDMajKQsASgElSJyF72oGZOcAAKbMmCTV9j3/ynM7dm1/4amXDCaLy9FkMBjSUtOWXb30UEE+AOD7H7978emXV/zfjVhAe6qAtmFwIdpxLJ6nYEntQJ596Zm/3fz3pRcv+/izj6SDq977DABwvFC5LS0qCO3Il8fEASWETTQajdfrlQ5u3rb550/fQrdHDBtZVV2F1BNx2523bN+wU6fVeby4ka8bCGg7t3KTi2Ds0VvNjSyMvBqW0Q7hw1UfLL/ksjtuXXnHrSudTidJkXod3Ln7mgujV33gJXzXU1hcCMv71m75cNUHUhMKz/OfvPwIur1j1/ZFFy6QP+XyS+GWxVg9u4WARk6IiXPga8du1ojpJOafN9doNF6x9MrTTjmN5+H4q+9++NbYqFy8o43FKyrK8V9E1zNlxqS//tix/JLLll9yGQBg/4H99z90b3F+TY6wV4cCrVZ7y4pb7fZQjh5zwupAmxNBhYC2VivbP4ALO9BOrTGUBBRV5rYKnADspL8jgiCWLVl+xbIrExMSAQC11ZUrF5+uqNXVaXUb125mWXbKjEn4r6n7xkDjV0w8rBCD6RB4nv9w1QcfrvoAAPDYw4/PnTVvwtQ5uzaukU7IGZDz5UdfMwxz8hkdtpdi76DbCSimB9EGS5gcLNLurVs59iB2btzd1NR05qIz5AfvffCeubPmLbv1ga++W41qdf/vqutuuGZFRWXF2YsXnbD32l3BAorp0rqIN587GvsEHELpMrxer8USWqTLKTlejG48+tDj82bPe/K5J1Z9ARsiMN2oF75V8U1FbLTz9mtswz/g/TsOg97C9ElZG3bAas02MDIbRtAiSU6F+XfEtj/FmphBwwa0eMGDJbiprL143dGHWAMAcof23/Y7/Gl/7KlHvvjmC3gkJ/fjd1apVeoZo/sBACwjxn/3+epnX3oGre7jJL0v9aqc+GEikTIaTyIeC2hnMH1Slvxuq5QUqadcK6MiCejJ00bU1bhbvCzW0M4T0P4DM/Q6/R+//EnK+mg5jlt06gi3Cw7/X7OrSK1WRz5x4tTQWMg+LqAnfgmPJ7x2T/Vs0Y1Gnh8Pg4YNOHb4+KBhA+JRT6TLWEM7D7fHPXn6ROkuSZIcxxkF9QQAnHrmyfKTdVpdUlJSclIf0scWoRdPCQ24jZ/P/9rXhmdheiht0MoY+hjP4h1zQpDvCh6Jx+spKy8rK29jhKdX0kYH2jbZ7f4qjDMY7ZHOOE0lpivR6jUxVvGYHryEb48KHyv2JSWnWI323q2b2UnhQ98AUMsmjx2tqO7yd4TpeWj1mviDlfdcPBnN38GlZj0jBtpm6utq6+vadYWkZPgD1D/NAkA3FdBI/LKdgSPlNTaxxXfDjrK2hTWx9+xxoIxfi5lYXGrWmwW0/dTXwS63+rraNnthJMFv2ODSdW9hKeix4ltS3xinhkZNKzVXwITpVtVmzf01KQbxNDfALKqwPt5dV29dQw8QUJ7vvhuOIQluaHK0wQ/KXaFBq+4mEoz+WSIZbXNBKKZTQX87rf07iqwziz3ADM8w6yUCGls9eQCI5mW3O4uv3BWi/7ZNgtuPOE1XBpbObotiiRDDirYtILN1Q8W1t18q3W7Te+xD9AABjQ3RjKQi3WyteiIpIZrX5TbT4RfsEMVEnxSWFmWkEQQ0wvG44Kcfumzx2aeg2wcOly1Y+ljU077++B83/u3lyuqGGJcaNDDjqf9cc/cD7xQcDRtnl2A1vv3K7ff/6/2Dh0oAAFdfOvPum88BADz3v59efffXFt/hui/+mZ5mHTntDsXxg38+HXmwF9BmreyE99K36PECGgnRJmkTZTf8Ih2oet1QPeW3oQUmgM/n69+SC163+iGNWnX3vz/6fcvBjNSEb95dWbLz5eyJNyniax/8b6VOp2lxo45jhZU6neaFJ2+Yf/4/5cc/eefu6prGg4dKDpbYPnjp5hmnjphx/sNqFb3ms/tnnDL8khteiH1ZnU5t1Gsjj0c92NfUE1ebdSB4Jy9R2qL+QyfiXvN2Z/jwr/agoimNWnXWRY998s3myurGXfuLsifeBOXypZsPltjQF6yPSUtISY4+pSISJJ1vvHirdOSz9+8FAExecD+62oxTR4w9866ikprDxypOmnnnaZOHgT4Mjq50K3qhA+0Cuo+dBF0r8UYjdHB2l1tuVB99+qt9ecXSEYeL+/m1v58+9/5Nvz7icvMOV1hzi9VsRO+P5UJV95ff8vL7L97k5qni0lqtRmU26aWF9plTRwtpOhe622SHzzp77sTvf93ZOR+x14KNZ2eABbS3wXfm74SGRhfP8xt++vcX32x5+8N1SM5++CVMyzb+8p/V4UfkNNrFPms5jsMVTqd3wzcPIdktKatL0GkTdFCsL5gH558rAgsXzJvclwW0xWozXGrWZdCd1E/ZIb2emK6znHGHe6fO+8cbz99w0XmnXnTeqah7+qobXzlaWIkeveySGQRBPPb0V619/XkX/HvTr498/h40npde/ax0PCnJFHmyxaKPHaulhfFCJTtfjnyof5K1VCh67dHghXwvd6Cd1+eOpfmEx2Gvu+01YR9NYunF01ZcM++9127++PM/X/7fzyRB3HD13NPn3t+2y/7j35/855+Xzjr7IflBjo3yQRmGjeeCke9k069wv8n+SdbRI7Olg3kHSxR3W7xyL5BgTB9dwscvzb1PartD8spqMTQKEUmO5z/8dMOHn254+L4lSxdPe/l/P//x078aGl3PPHqldPJ9Ky/wegN/v+/deK6878BxOL/SF5AfLC2P0q57vLTVm9kpeP2566Xbp8+9X3G3xae3WKsQG41Gg+cYIK5fMOv1n34DPZaeJ6Cd5IK7rdp2B9GU+OCNWwfmpCkk5v1Pfp99BvzukSSZYDWcPGmI9NDY0TntfMVvfvhr6eJpioNr1rd3faP4CG12zW0jnnIxZWVyOBqNRq9V72tN69rBEls37Litr6u9aPKYNvRPI064+PZmAe3pxrZLpTO+AOjNK9/88Yv7H/nn0vv//bF08N3XbkFraoUMbfr1kSVXPlNWUd+e91VeASuZTj9l+Kath2Bz4eShAID8wz2kxzRGZFnezBD1eMxn+bw+n9fXP7F1RtjhDFVEaLSaBqc5G07SAQ5X23uIH+/yXnjUP41olfgmJae88+Uffj3TgW8GC2gHSC2axBFJG1ozT5jfjC+J1GR3P/vK6r/deBYKJiI4np+x8IHOe2u33vXWC/+9Rrp7x/3vtfgU4kQ18CoMYzziyJ+YHwuf11dfW1tf2/ZgSFIKdIKPXHUBAOD+d1qdNux66utqO1Y94d/wO0/H29lG6yyf/PFnx75876D9m8q1M6bWAbSy6WpQbtqCORMYhn3jnTUc3xWyf9XyMwEA73y4DnQ3ulWQpTVce6L3Q0tKSWmPgreBL7bvO2EC2hxnnBLdRd/0woegb9CBu3KeMCXtVr363YRm4o+hh3o4J1xAu54OF9AOWML/vnVfVEl9+dblrbpO3xFczIkk9q8KvndqJSZqzBRFIRCv//jbiXGg7TSqCjgurh/YW176CHQbOnVf+C7ypL3bgUb+TMk/bF+VyD7oQDvcsdLS3Ew0/E0ezurYQHxzRlUBSRLxaOiLNy9r1at3K8HthpTaGrtFKLYDiS+djcHE4KLJY1DVVEYynOHw0AfftsKBymcSizPf+DBtlctuh6itXFLjtKLtZ8Nf+9vZRtULHGhXtta08IkkLxzVFMt/KKRppvL5g5i4wQ60c2Og8pnE4sy38B9o6S5SWPkoYsUPvyS1rXWpJ4RWVXpqDda7OkJAo8oKRatZxg96F10j1q2tkcRgTmQdqKL0TSGVzUlnz428SV7H42qM3yTStLqouibyeHNX6H3q2cVBiS4AK3Vf5gQX0vd09WwtDONvWW2b61HB9F6lxircQ8GdSB2gmz07lhRbr8MfVessFEV8sG5DF703TASt7RzHdCpdV8bUE+H72ufp6FSMSm+mSfKD9VhwTwxYbTsbLKC9XTR71EeidWaaIj/EgtvlYKltG1hAe6jU9J1PEgtaZ161YeOJfhd9Dqy2IMj/AyfcJ9IQLg+sAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], 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"}], 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e9N5777/33vsB1pMr+K4lihvgOewA3VXfTqLlOZhrx44duUUo2bIQEWoWtmbkEKEUCsw4uJ0IVRDMIUIt/Dp63OhMEYp/aWxEKH0V0i37qgi1ZU8qPx0CddDrseHrDbecO+6pfz5bYJBY5U9852/TW9g1xgWWhROGV5IuobTHXW4RakYGN2WIUD0B3nKF2Fmpyz3CgzlEqCxDSruZrFqUCM1cmU2EZh4CJymYwDF9Vn7m1p4EffHdO+hn+GrrfrIwZGBVtiRwmvtpkZnectw9CEtQCDbX4knouUFFqIItfvWYpDI8LZ8pQ4Sa7zOiE9UtNWdlFhEKOidC1d8AexE6Zuw4kG7FZxOhKiNbRejYCWP7sAi9Mid7muVnxMkDddDbgRD6bGvslptuJHKpvb0dAJDNiseJOTBNflJArD21BmuZItRscWekvRcmQlUrvhARar6kjohQeigTe9qKUK16St9k146dtiI0Ezu2b++rIvTKgrVnRGXSPvTWHfRLrN6yBYvQr9bv31+/f/9+iGTysA2+k9xPCn+lxpj17UlCrJt25xehO3dsJ2Ittwg1g+rEQkTorpodRISaCawIEaqtLFSEIkYTobQjfaYIhf1DhF5ZGHtG2lOEPR0CddAnIMQ/W447gGD7Xc3QjCg2E40UHHy3l5+ZyC1CC4QCtJQjDaiDItT8areKUPrrktcT2ldFaDgne0ZN1EnQV963g36MGMffMVFCCEVU+92CI6swpWYWAVH5SVDXhO+cySOrihWh2Ss70yDr95o5HJ9ThGKFmDsilleEapvlE6HGZM5sk+PsRegY48XejytvH1KI9rSscQjUQR+B/aRNAGIJUVFQUfKzKBGaO0edhpLUAqT0cHzaQfKLUKZ4EUrZ2eayAdq5A6tIHCQynZp8ikhhC/CE7qAcykDYq5Pq87In7+HaW7WpfGY4BOqgj+Dhn55FFsz2e0hoaY8l4ym8xkwk3lAo8wj7WhNFiVCykFeEZtIlHW1k3TJ9s0JEqI3CzSJCjbOrIlQBEKE0X2duEWqbE4r0V11cIbVOJQpLn3kLeA/He7jG/ZhefUHe8qpDoA76DoYO8UbSw0fYQSrKbdFkUpBw7w3em409zQzr4liLCCWwiFBixRcuQrV8JjtGJFa2WYTW6ImZuUUoruzUV9oKZBOPW0+M2zGrC9lEaNa3pB+zRhehSUHBzQR6rQi1lZ+EOnPv6BCogz6CTF4KCfhORvj2lsOxFOPxbdtqk0NuAjzQjs20cUPLt+9tM79ArPhsyFHZmZVhtV6euUTorhpN5BbdQI52AzGvK0CEWsLx1lASFaEySzNbFf2wXO8UobbGu4U6ifwksIhQh0Ad9H0ghBKqFY9jR+XWdiEUEALW1EE4rwiloaTclZ1FidCM9iLjcojQbKGktAvW85kKEaGWcHwOEUoZdOeO7aPH45SmeFLGTa16lQjNZM9CVKeZQx0CddA3QeQnxZhRh+STn7gtE8syFVVlhYvQTNosVISmF6cTEYoQzGgvkpYtZEHh+UzZRKj662LDknlFqCDYnaBX9Vo2syfhTVvqNMvPTA51CNRB38f4MUNzy09CBiyDxxvRhkzZRKiZuWpMoaTcIpSCVnaaQ0kE6uQ5YCtCUZEi1DapPlOEauPq9LPblFTpvwEwI9vBr9MNjadJ6uF6hQil7FmI5LQF4VCHQB30ffmJs5fs5Kcp8UnNxGQg78LpO1XV5TlEqKW9yBjdijfDJvJuSaq3E6Halj0lQvUdERWhZiueemmJVwMx2p6KopDPLZYwuJhY8YRejSB9CSPcEi2EOm3lpxkOgTro48gmP3HvT51ByX9ZBroKK6kxq7hMEVpIZae2S7oIxVa83ek0W7vg9iLamnwilIaScovQTElOn/MuwxOqv19jnF7JitCLbqwoRHXmZU9fkHcI1EFfQ5mt/OT4cFwJx9MIi87tILEj3sXubdBqmSwi1JJUb7HNqQi1N+OzJdVniFACmE6R6Un1VoK1TaqnayyNnQoUocYLqgiV9UQp+hIVoTKdGAXAGL063sWyXp1YSxCX3lzFdl3laUeMfwcOep38xLSi4Dz5mKoqGAh9iuz3u6tDtG+T68gpmAfdvDavuLkpzX43Q5Egw5lyNrH+wgQkYW5CmeOSjCFIGQPpsAjVs6DMW9oCATz2o2bHDsywpolJ2qGgDNIjQjXbaiitqiIUT0zSCp9UGtVEaDqlkq4r5uvMNpVZlhk6MakGV8fj48SSktfNAhFfnS/ZUlITky69WavfZTlGJhOdOio/CUr3h8KBgw5gBB+3udM5awGJglBCkCJxISUaqvDLjTVfbqz5fN0W9bFZVi3xcUOxFLUVoWbxSPOZPHz2/PPMfCaVFMfoXe5ziNAc0jZThJrzmQoVobqhjuxEqJHPRMPxugg1Y/yk8WSBYxk3V3KeUMqeZLC7kcvaCTgE6qBPIdOIzpa9JCsonhLDsZQ62diAtQodAEsoiUBJDyXt1ptpJiVUbGVnzdYaaz5T1vcHanZoze7sa//1ddAsQgvwhKoZSqakq4x8WHo2Y14pslZ2btu6lSxE4mKm57RU2NOEbBxaoPx0CNRBn4OJe8aPGaqyp82dPGHMIRPGHDJ21JDBg6vcXq+sKArCDUeQ+i9ACPcf0Y3YQkSo2b/J6y42Wwqx7VSfKUJBPhEqQ4WyIS4BIPEroDkspSJFKE7j10fGIeyLkDl9f0tSPfUcIDurno6GLqmZnWnsmS9ZtXD2dHygDvoa5IzkSn8lZj1FkRk6cVOR99S3MBz2dSqKBAAYXo2T58WU1lZZY0YEZNUrSUQopdFsoH5DQb8EsrtcgCcUi1CmUE9oDZ5Qj8EyDPEzSBJkOaRRg+aG7ZAnFLFAj+bzHCvJkpZUr3ppqSeU5xhBdSBCtU0gFqG6J1TCvQjwMUVZG31aWpZ7BtTPOpczNDccE95Bn4J55ua2rbuyqEAr9ja2N7RGRcTsa4mVV4QqKkMVlWX725OugD+HCCVWfGa9plmZml0KlFA6L0JHq9n1ApJpAToEensR9eAQoKI8oXSJXA8CIC6KvN5UxSJCBUW7fpRvhF/piNAcwtNsyBclPx0CddDXYKgeSaTysxBQGcKyDMcy5KbyeXlfuX3rJoq0wRsZdC2KBsdJsuFkzHS+ZXpCC4HxdiXjiepFTdvf4gnNvGBZT6iSTReZtbITMyMlbs0Tamxt1H3CUpGf+cz2jgWUmExXsQMHvRpMthEW+aDoDUdU1yckoRIXx7pdbI7KTksoiUpLc2UndSbiTnp6jD6za0lRIpQmrovA8IRSmpBNIpQeZ7RJhLr0fcyeBBodIq8irDQlGiGyiFDNYgeGCDW/zRIRoQWyJwHLMs0H4kwxcBSogz6Cq+//YRoLqPKzWAhqSlM8KYqSxoN4BjIDKwfZy1jbpHrKm9SdmkxJvJ7Tk9BHQgiy6FapxTr0uGARah7IYZPPpItQo3eUSYQakXS7an1RMi6I8qNFhNLKTqCLUMm0VykAs6c2krkg7NnZlkphn29RKK337MBBx3DNrY9krpwwZUIh+9KsR0KI0XgqQnObcDIOrvnMsfukKYeZb1KTf9O8l7Ec8HLZzMaarTXZRGhmmJuWkMrQVNmZ7ma1JERREZqSRWqD02iVpCBKptQTah4cbxGhjOUFAHy0PjLdiu/5kXMaexaMPTu1TLUiOdSmptaBg14MbIDLoiodO3iEaEIIx1KxhGAuA68alJZFaA4lbfxqQzYRSpPqFQVRwqKdlOOCJkdziFDjmPqCICBqLFNL3LZdKRGh6U1CzdsbBMPrW9BVhXhCZaCQN0WT6uOm9iKZ6DEr3jbls3AUzKH4zToE6qDXY+OrP8w0q/0VHawgFCU5KUjtUZsJYtkwacphwE6E0qR6SZKpLRxLIrdeKk4zRrOJUPq6WYRSA1zU1aPZT0qFIbkMc1xr5zZt2CfJNzKolu6ihpIsbl8FIsrvlswklx6mp0cwCrFMIhSWsOVulp8Fc6hxAodAHfQp+709bJ+GwjC5KizNQAgnk8aT2ItKnaF47nFb0l6EAixCURYRGvS6MsUfTfIUZNFIOdLvxfR7H9qLUP0EdC+DVUWDdknCqVmE7rZrjpcUFTOVa+WtamGS3p+Jyue0ys6EJHp5bLbLkkyuxFzWZSZOX84K14NluduyZz4OTTuBQ6AO+o78pChGftrII4Q0IojGU1Q5HjnR6EuSF4YnVI9Wy5Jm8Kosw2RGeyh2bq0ZrYtQ85ZmEUqvivKVBBXO6M6XdkCzJ5T2YLZUdtICBCJCzVEmBICIFI+umskrmUkEUH1BkozUHmRUi8IesOK7kD2zc2jmTBQHDvqG/Czy5lHUMptsIAQVjqVSgrFZVUUomwi15DNRxNWmRAS2zUapoW1OOcoWttKHtWMezMw5pfElUdSUKenibPGE2vYlsRTjEE+IeUPKi0SEKrqTNKEnPEiSrG2S5Q9R7tPFeGmwZyFIpaTc5rxDoA56MTYu+z9CdrRuvTPez0wkkmI7jsjrhrwtrelntljxtCaSplImkqLPzdm2FyGXT+3xmq07aI9R85a5m72n6c50NjHvl73Rcq5xSXFR9OsXT07k0n8P6FWRXx1zexHqCU2JChHj3SVCi2fP3PLTDJ1Dbc7hEKiDksYMV90MV122V6+54CfE/LRvTdRpSLISjibDsRQl6LqmaIFJ9RTmfCZbBiQi1DIVWK9sT4O5djOzvUhClkhuadrgeHPZaEYdUdpl2HEQ2Q5ZkudVESqKuhwGenuRjEYE6cdHxBPa5Rx66U1V3ceeBNl0qEOgDnorpg3E/8oKYqDBn95gkG5g8S2aQTqJ5AVCICVIbeFEFGfA687QIyZmbqnor9qGkuhCJJYKmIZJmNWfpKSFknYWUNlJY9/Uy2lW4sZZGKpwGaPu01aEglwiNGkiaMtIDwpyBiJCtfIBXYQmBSWz1UuvYE+CVMqIKFI4BOqglwOnyXRjngxC2PRuaY8nkhrr1NXtt4pQWkOZvbKTrpRoWClj6rpFF9JQkhmSqXbTxTJ62aUegsevGn5SsqWMOyThLQQF8DpD06BQ+gXbgBzEIkJJOqtAhxvTaR8mZ6qgZ3FRIATIabtKhPYYe2bjUIdAHfRi+SnJCoP1J9i2+0DRhyiYdhUEYgmxJRyX9Kym3CLUjEwRmkyKmemfhGq1lKN8IpS2QEokFRo4oiLU3CGJSlQXq0XEqeMymTKF5oGWhGQrQs1EL2VwLJvhDSDvjohQaw9T28+oB9mz87BwqDMTycFBxsQEHlWUDVWqxpkop20zKgRAK/ap4YIjlQe36S8JiZSMkhKDJx21b99XFuhU5FeRRL1tKArHUl6dXfami1BrLRCHaH2kyoysJkL1IiRqyWY2CWUZyCA824hsMGbc6JrtNh31GV0tUj6iItQ8LkkUNUYUcdtTzMAxQfG62ISa6Unbeupxf2TbTlSVq2xSn33CQr36E+E+oXjkHAmXMQgo+BoEUaJTnjTBqr9kRifHJXWMPTsjPylSKdnt1isIOn84Bw4OFki6z4GmdvMqrAuUJHnEwhHjEY0NGTZ8yLDhkSRsj0oRu+FJOXyjkqS0RJJjh1Xh5vXpL5nzmQoRoQiZXKXp1euKoogIuvTmIzVbd2SKUHNCaDJp5MDbjgIxSu8ZveGyfkZBMLaXkOagwCSeEY5PCiYrPqPdFcyQ8kTnkh53FhGKEOx8afycS73tLXHy6GH2tOhQR4F2MWjIeJU4rKuP3TexxevN8WrIhW/XLaKxzbSBoEk13llsveM1NeHwcI/Wb14VpFnNxO2bN9Mv/bhJk9TewFnv5p2a9JNCAe02Saak1kiSYRieY+uaosOqA0WJUAoys1PbWGY5VhsQybGQOAl4jhXNgZ6coMPlzSKURZBcQDSmJZiqnlBsVsdF+8+HZ6Gg7sNxjGxKXyXwu7lYCjs1oS5CGQh4nk0KMi5M0nO29Del7a6+I2QrQlnYcRFqfgO5ObSs0tfl7GnWoQ6BOuh9QAhH3sky8X56co4hykGmuRqV6wsxKnAhSCaTLa0RjmPKfGmTPiePqNxUiwMjCu7QY70KYp7jJqGmODlN/8Q56ioJSiZD2+/hYmrYyqOSlMXkN9vpeJ4Hq/E1ta/xD4kulINeVyQhpiWcUjNcgTT+I6hnJ7EpCCC12clesaQ1qUFBIJkS06Z96CyJB8czDC7okrCBnznChGNAzqHCXQZKr0hGRp+VLkIqpU2gcuCgN8WOCEGYLUev3v+Cc1snGHcBaG8ndUqSIMmJlBSJ66097WDuimSZ9qGtkQ09xnMMbRjqcbEoPTiTVKvyjWuxIwGjixLtTQcU2qvJXNGvbZbhuyPbkl2iSYnGpszER3KYkGrFky0ZqPVRNuYe66C/cLyLMTyh5Gr1yk49stXtIL9aSEaGXdBFcAjUQe8DNiTVe5AG3/2mXhX+UND88Pq9ZMH+WIU1vsObaHmR+G6UZSWRkvY3pyfVj8hljRI+NHeqp0vRBKKElZRIGz0QTsq08sec907ngmA7nTorTUn1tNUTp2d9JlMa35l53OB3lcktxDJi/Hh6doqUnjlPU0oVrDH15tMwjSVlWSYuW5LqZFN4ygAZwZ7vVN+1NOqY8A56mfxUg+9pt6O3rEyQUnlp0K9PiMMTOvVgEV42x4RUgzQWiVn2HT96iPFED6bvrGu0PRGx4mUJci5EDdVMT6goyjzP0Ti4m2NS6jINjtNgPSYpU/cOnoMkxZJO5TQjpbsvE7Lk5bmEav5Tx4FhvGeAhThYzzKcrKCN6zfSnHkSVjK7CJCaVO93u2JqfQ6x3zM9oTzHJtSGAy4Wap33dBtfTd5FPSNCbYsLCId23qh3CLQrc25o2k1m5k2xwRMHWYEwyykK2rGngVS+K7jM2uYmURSJYTryDfcHrVS7VxWbjOlEiizzHu+BsDAoZDgNJk4cu2WLETe3XBT1hFIKdulcGU8Ctx7/J5zIQJCUDeoxuxFV6xg/EZDs5riUqgGxCGUR2cbnZuNqmJj80mDWkzU/qazzuFrZqZgzqOgyYcGxkydtXL+RnBF7XjVON1iS01Wzde6xzpKJpODz8PEsRZAQ4KR6UYHFhpLOuLTLbpzO06hDoF0DyoZbwFASiDcHjh10mfxUtPa81o5tObLiu65OiWWwbDKqccyVOSqYRGshIpSyYSwu+H08CRbJCHf5VAs6ZQayZAMTXytUhCYFyLs0EcqxMJOg6FUpCHk4NqkyLE1PYJmM3kuyEUoSFYnnXIKkbFy/0Zh0LylUINOV7clUyMuH1fBUNhFKTAVRYlhW18o00IQgGQDYY57Q7qBRxwdaHLZ4vbaPDnz0DjqAbbv34zZHsrK9Nq30KGeXojwYO3ZMgVuyLOPhOXOvzExMnJg2gT1ThFoqO+nRJInJbCBijvaY3yM9bMzUJIl6QlOiFkFKyhKhYFYN1mvHRDbtRcwH5/WInPnXgU5wItmdtu1FQIYnNJ4QvOrl2Q6Qd3Fs11Z2Fmi/d4lvVEzJohOFd9CL5OeWnfvGjxyCZ7zppOMtKyukz1tubKuxKfWxgJwRAuh1u7xul/lk9WE1y1wF01rH7VtvTqrHItSkUmgYx9xexKeXtdD3lRbtoVM9TLpRlBh6WLMKNua8UxGqLsjp9e+ZYsvc4y6aNHrv02uPp6TMrvIx0aYsVXsvxqUSJ6zxJ6IxsZSIk3nl7h8fH2kz/kadp1FCnWTZUaAOehMghArSvJ9mBPC9DXMM88jdPrng02PRVB708C5u2LAhmbXnhYhQArMIpSpJkox8JttfBNuVSVmiLUIkzNfEQNZelaBC9mKhUf9OBsfnEKFkUIelvYixbJr2oahnwvuqjl2az4R0EUrD9JRBzQX1LpXTWYi6Q4QWLj8tyMGhlDoJHAJ10DuwZee+CaOw/KRmrrlxMrZYO61jCunqBAHwe1zlAbexisZ2WussHGorQm3ai6QkmrNpJGCaOc5OhEoyS/cyt4mjTUf00k3tCgkn0Ng65U1D9ppEqEh7SJs4IinINEfK0h1KMREOfV+UMcl/RAn3jiLrXEh0IZF34epTNwt7QIQWi0wpahaeFA6BOuhV8lMPvlvg6owTVEemlrR5CeL8zbKAhyZyIgAaImm57gCA047FdaK2sO1xR7VSPImLJsly3vdEx2yY69NTKURMdcXcXkRdcDFYv2u76Jch4eL0NNmrlglJmdX65nF4ZhFK4MLF9uq0D12E0kbLjFYzhUNe5I2KLCcxrCQpot7dj1R2gh4MwRdOo7bUSeAQqINeAOz9HGXKxATA69cyjQh8mSPSNXRBhDdTmZLBv17d3rZhXmsHDWNSpn17ETNdprcXsQ6ON4nNlAC8dm2B6AVbxs2LClJndtJCIKtUJCLUUmSJW4roy5JiZO9TyBCvFE3qmGeRC4mcgv/1h3A1OgsVFxAZBnhc0AVEqH5iuKhJwb1Z1KR6UCL2eyYEvRVsJpw0Jge9AyRareV+hkKKycYkjTA6eXyc2J1dgeLinGjKxQK/qHhjAscyMIWqFTmcEBXcEh8M4Fghpl0D2rdP2uS7adbhHyxepyVjCoCWq6uQa3icPUpzQs3tRWIJ5HWzJAE+s4rckk5PLxm3aNKrQxNJxeNmkngMkfEqqZ13YQ2oN9PT06pSsqmTiJpKhbNBkcyxnNYKxOQ5MS4H4aQlrb0IA2RZHefJIEmBgsSp+brIH9KifJ4AplEhqnb0YN1Ad4zSo3EMCPNdNsyqyyEkJd40SoDCIVAHvQDjR2L5mYPgCozCK7LEdLSBTm3A7eNgedAT8rt5jm1MwDI3amuOhGMpBsKaZGqEXw98V3i5ySMw3a/AEXnSW4TlcAYrFXe4oacpid0CWl9EU0dVEaoRItmF5IGmheBNcR76URlVQCogTgIzmo+YEvU1jsRNoPQRIBzLkIF6RISSgysIcEBikKKwUFJ9Ay4kQsbNYeoEioxcUBTTFThhTxzr8wciCQFztOUNIxB2V5am/IxHhRwc6pjwDnpH6xDcP6lWk5+ZW9pOSeswFMXe4WWBi2MqQ16v22XUt+v46MPlxtH0UBJdg6Mudu1FtAALCwVTlhJdoBmjVFhyDJ7sZhjgpj6hKUGrf6fsSTyhgrov3U5ttIyBq57S3wUuhRJN2T+mFxmk8OpfgUcifgTLgdrdTgsNBbU/kF+dT8VyHhfvlRCXlJiI7B48ZIh6wSbeV8SuZc/uQ6Yt7xBofxlsmWO2ZekDa7eMom8zjv/V37vJfreWPKW/erKy8LLyJVdVL8vc8aMPl3/vW+eY19Az0KJJS3sRAkJ5dGNJz3sXEaRJTuQHg+jZtBkediqMUrA2jpiBij4uyX44klYphBd5JPBICATdnClOJUDsf/D4g3ywHLMnqURSB8SPHTN0eJXWJjUpMoDhy4MeMRnjkOBhJDcjhpsb9dl2CAAU5ivbPINAYejhCJKKPErWMeEdlLr8JIYq6dwRCIXM32iGISPUEMMy5SNPBuD17rgSC7cWrnWpDs2s7FS7ctg3WsY2Mo6DG9M+qIPSxbGC2miZthfBx1Gz0e38ANhUZxmgdxNVoz0AiuqmNCEsiYmP1QgaYcnJeji1PlNtmax3sWIg5BkEZPwCkZ9mjB45CCnKjqbUtoakktD6p3pYCUhSczNOy2cVKaXSLmA4NkN9dy26yH63P4jFkHcUqIPSxZZd9RYKywyIE8OWrOdD2PPYY5gem0+Xbxj6RVH75hahlji42uoYIyYotGzJq9YFaU2SaPaonrFEgLDNbpyIpl6x0KjsNCeH4s+wLAQBJLa5z+8zpJbPzfs9fChoZs8RA0IM703KXG2rsKsFx9M8rOTTu16lvV+G46DCqflMmR2kSgxYGpOleFTMbcg7BOqgdOXnuOEDLe4/C3+qZZ1koccysYs7EW0SSjyhkmhf2ZnpCbWMw6QZ7HS2e0JvtIxTiEydOi2fBFaRrKmTE8Jl8jLC7ksqpLBLVoXA4AIBxu3lg2V8UAug26K8ogKxnr1hiZAmkJIsSuGFApDpMi4Z+WlQZ25QDnUI1EGJYsuuekqLJHtp4OAB5ruDFMXrVeo9jb179ha+8ZAKQ8rZwpxUb4ZRNq56GHFZd0om2lNtW2fYkgGvy5yVac7rJCKUjJxLAQAlgUci63EpIpaZATVEjs1z1eOZ4yJ5oCiMJylzSZmrb4pAOSmn4kAyCudzIyExIsL2u+nX7iD3YUrnzeIuJqVyqEOgDkpUfk4YOdimZ51qy5O1UFWmpPaxvXZxloMVRK25M0Azjof/f8PwNQXuMXlE5Ya98ZY4aIqjpjiqbwdNkVxJ9WYRSse4k8RPskD9nfGEQK349HHwph5OSCb2OAsRj0SoKHxZiC8LsSaZiXeVRRwvSu/bzyM5KbuSskuC7kCoPCy7BUn2sBJ5gAIwdFC1oHAKdAFF8TKSCwlYfnaUNt97JU+P3eJRNG+a90klcb6rAwelCMwIJvaDrOtAQysDcdaOi2M9POvnwbhRQyVFIf1+lt93IwCTO3Ci0aNG7t65qwuvnAzs/Gxz/sJEcyjJ3GhZfxUlUpAm1Qu4HwreUlCMaZckkZOFOP2ed2ntmTmIqOtOUrhACAevk7FElnGcwOVzA+A2SJP1hwKeptaY4vIwQOGhABCIhlMevWkU+U9K4dxMLhpNSMze/Q04cmR2VGT27CsGm1c3Tpo2oCvs9/xb2jpALbs5BOqgtJr8jwoB0Gpk8cA4vl3HK5wsybgOHTCQAbyMOAnxIkpuqIGQWL94swMr3lbKqhLxXEalhNUZLLMfSJxPnOqEPi3xdo5ddjcJu5vysCcZS2luCG9eIOF40mKDd2kEah5mSa9KFHE4Xsv1FFM8AHywTIolvBxMqJn2ecEjWeSDxKM6uDrY0BJVRDkabsN0qYg8FciSEIfY2Pew2sW4TezJIyki8xVBb0s4oaQ0ge21o1eEUOaQuy7k0MLQZa4Dh0AdlCKwlYRzs2nLIBxoxvpC7eUBIcOq2dhGC6aCnaCcgvk6FjbWbPx6EzBNigcAbNqys9wmkpzrPL9aexgAeRydHROh7VHZ79VGHBstTZDCAwn/l3URXclCwAe0sI8Lj5XHljuJqmu7mC6dR3IEeSpCvtZwvKyqIt4a97B447bWFsyYptwqyHkFUZIV5OGAB5/R+BgEmS338S6OkSQlHE9JsfY4I3lYkGtMO24ihToT56mtCYwYnTbLjyLbYTOagXb87Jl7OgTq4CDD3M9/2kDQpIoUUmGtzlnAX9o62QUBFEWZYSDvYt0uzuvmXLgiHXOKeQa6L1Qea41xSqGRjcxJ8eMmTZo8cXTmIJBNm2v8laqdC82k2UHgcLyaE5pNhGrjiVQRSqlBBjILWRkBCXIBdXBTKpYgkR9eLfshQG6ew1fqtY19895AOIpnMsej7W4GtLe22rKA2xtIJaLDhD0AgD1stXYByCVKsqgqYoTkxpha214MSPcQ0BlAsHFlw5SZpjzhQtGp89ru7BCog5IDrrxWo+vbdh+YqOonlmEYBjJu0kSOARzjDWB2qKoMadJK57tULMzKKQCZw6bNbG1rh0hurd+j18/gXcR89zyhUQsYCMvcHIsUURBmuT7sDHUWKEIVWcHnw+098bAh4uvEUSA8XcjY1+3PX5yTiMbxb43+EQmJqO7NzIVUwhB6KYmtKvM1tcWVJE6SZ9JlsgVpdZrp4JAkqXOhydMwr/FyUXDxvCgIxRjyXWCwZzuEE4V3UFrBd4SnFlv1n6Kg9kiCCBeWxUEkCSHOxQqiIOOWkqY0SB3RmNWU5FGCRwm/z237cPnLXX5clZgNXZ4pRXNCzUn1aQuKzCPB6/dglU1yj1xeKiSFiCmWnw4cCBJZwLj9/mBSZKE76A1VFJV96fMFkiKDSO2Q6u6MRsLZIu9+n8c8ytRyIg5JMuevHDBQYn1RGODUNlSdwY7N2hE2r27sju4hmRGkHId2FKiDUpSf6vy4A5Yu6zhvSZWmvIvF88g4lqSBmpvRE/PQ7/fu3PgVDbaU+1wyq08NzgIeYd8oj+PRaZh0xJE+nq1vattXsx0AcLZ/SVe/Y7MIxQEuBiic6m0UAesP4mgX5/bpQXIgp4RMm52QJvSE2qPYd5EEbGWZNx6NxCJJIjalLLOFzZAR5+E5oCBBkhubIkCMQdbqCfb5vfFYAkLG583Kg9hEUEVyEmBi9XpcowaF9tTt5/SqfJRTwBYuQgEqKKDUXB+tGlxw0LBIOATqoFSwugGLUGK8E3i8HkwjVP1BbMtzLH7wLpbjVDq19i62HjYY9JmDvgmZ9epx5Lz4/ncvrQy6XRzz1idf76sBXQ7qCU1bCbkcqZKsm2fdGnklJMyOPg/2BodjKT4lUpEYjxr6VEAcK8WzaTQFcV6e83t43LFFUvbt18pnOwBOEeOiggv2VfXmYTDRI1HYUxcDXYodm/kRYwSj87TprW1abXP9zfWGO6JYMs2tbB0CdVBCkGVExCaRn7TEm3BqpK2d9VUAIDW2SwDEgww20idPGmX+lmep6DFQOHsCAFYuWfbhqu1ExkII3R6rPu08VK7EtZUsTtJCUMHcY+mnScEDmfWVtYSxWPZ5XAODfGNrDIkJUQRq1lGazEzK+KN0szIPJeDilRQOHFEM1rP5FchDpL3EIWAe8eyOCZVa5YARyBZSco1ej2+GBHgsYUE8c8477/EJydzB+Y5j8xeNMsqir7W291nJNC+f5vULOATqoISwrhmamzDhKiPS95fDd2wwFGQYheG9QZ/bzbNVZUP9Ht7ncbl5jn7TiSCNRrtG8lho7MoBK7rksKQ03gNE5OLM2Z0QpUh3ODFi3OQ8kBPQ63VziaTYLrEgnPCqMhOJUkRMZIaDkjJe5WFlmrCJTyeqJUAqfF5/WXloAOdrqMe/UpQ9jc8QQl4XubYYk5JRyiBEN+9KCYo7Gqn1ucyfmqCwPKOWAOjsGY93WSmRZsVbglYok/fwy4mo6A3Y+3Bs+ZQ4QM1H2f6VzSQuh0AdlBxWN4BQ4kDYi3tElsMY+RaTnsGRcMTPMyE354aMl0FyKh4Tk3Ht+19oDGFIteY63G8uqNShJO1zDM3oqhA8r467SCVijCxgukKAD1ijWAOqKutbooIos3KKhcCbPXquMDyjYEKhvCkil9fjisZx+bsLCDR5KB6LxmPR/QDEVSFJPJJ+zfmLP+d4POXz8+a6oT0AP+UUMZyQgGkvAr/PFQOyn7CnKVmVsKfK6djbEvS5kzJH0+y7xopXwTKcrPbZ05PfLD98RUTPCJ/W1Wgd+fLCUaAOSg6EPc1F30Q9VVSUhcpC1WW+6nL/0IEhvxfLz09XbjHfL4QmAsFgNKzdqJFIHELodynQ7bflzUMGYCLb16gm6HiyGnTfn7ixC9+jx8WEw/FQyAdlkfBmQmITEkDp/NLY2Mzikp78ByTsSRQoA2HQ7waJmIS9onilaJe+TiNCyDSozhZJmeNYpqKyItEaE+PtNPJuCzpjOSljemEYSLL0Uwl8MV1oyRsi1IY0rUjERK8/TyCxKKgJyA6BOihhqBnyeIF0DIEQykKyvV1KJRPNLW1kRi620PKpz4CaNJoNhDp7GPR+lz1VCZMHz+f1xtNrWw3o1QWZSClskvRAxlDK/EzKfFDzOGI7+P2abzcWw0F8CCGniJIrmBQkUpXkgQJQQGtL0kWCetnBc1DUO40W2HCkS4BFaLoLOBM5DHkziPwk/EgFtdlPQLzC5FVHgTooRWgDSPTvLDHhSQhezWHCkhRCPJeCftF7FxKC4uZgOBx3+YJeRuxYmmlSZlMpQmh4CDwnU+a1ph9hSzw7hUZSDCOZWBvCKPJ6xLjH0lrU5cGUilviGYjFk0SNJkmmlSL2WI+6HZv5sZOAIUK7FKbqNr3nbMY2A4cGHQJ10AtAovCNTS0tnhAACQRAGSdMHDdM/6KbO7l1AZsmSBxGMqzp2ydp9fJdBRcSFBGGcC6nmEdeUqivu3lfWcAtyUokIYhJ0USaOYHrf7La6TzCvKhKUUyF8YSAJScAw5lW82aKmOShVg/flgAulFYv68E/A8CF5ESWhvM8ktvU/nsHTYTC/CLU7P3M/cM8cBh2pjsE6qAXgH6Vy73Q7WKDPt7Nhw4bN1SSlZQoF9HNEwCkKHFRrZVUZIZh/W4XDSsxEOxtDBupTmxHmoMUDOjzewSEmdoF5bwcCl1uRUEpUR5SXb5j2w7yRmy3pBVBZpg/IE4RoyjNp4FMVnxuxBOa/KTs6dedoUmZ87CSCFlZzcSicCE5ouCDVw8dWsEyB/bszuqj6FwoqccwcFiQ/p0cAnXQC0Bz60kzUJZlXByzfsvuzHb0SA2VRLOXOeKMRU4LdovAA1L12/fatJ7zm5q9d7n8xDdeoBxB2ZVvvFqovKy5LS4ryuiBVTgCtm/f/n37Mjfz+dwQwng8VchvSRR5eY5lVOlKQjqejHHnFiSAG6oaE8edognyifvT40jU40mjVSSIVDl0WBXHyooiK6hpz27Q1TCHkrrQE5pDdZq/bw6BOugFoF9Z0o1JrenEVY+ZninKH5OOOHLT6pWAYcZOntKwzzrSWZSQi4MsSFIyzUD+fKbOQFedZqjdP6FsjlP7/f5wW7uLAfv37qNv1ef3JmIJwODouUqdWcNoWGwyJK/AEH3YPM/O27FYEjJMCnoOGRAETZoJr3pp02AbhXchKZIyupASP0DLvtospzoYruscnU7yZS+ZhSeFQ6AODlrv5ByoEtK+qwpIyAK2WKuiApsQXbijXYp4O1GW6EgqlRpz2JEuOQbsWtu59PEYmayKE78llD5vuDhMNrdLssMmls10uWrLuBzesEkxb9rB9i37fO5YLMkpouAuHzIAt0aOJHGbFVxSGfDFwrl+EmICC0RTbjwUmpuas7gwkLl8tj3JcAreUYTupMJ7PEI8o41LT4aSukOEDlKFpy0cAnXQC2C4nLR2FKaX0ovh6XLN9p0iHt2LabcimCaXOKmN+kDpSokrp6xKpw998sV2JKYFnTvPnpmkaencXgj9QAiptR4TXUCI0hAQE6oWoqkDapURTSB14U4lWRGLFfEeFc7HyomY3vafA8BP5hijlD45NBfiSXKuksucyCY/c7CnQ6AODlrv5NwIudI0llzhrYliYmEr8b3q4piAlye5TRaQRHqOc0mY+FhOJdBoNJE7FZSyqkGsLNYWf3n4tq+27AGfFfwOM9RlNtiOu+gA4rjQKI3+hHjYYyKypMy5eU6UrTI8gUjUyNhXjSOplUh2sXIXkmLQr8TbvRwDGDYpKBpvFgZyOq+aUd/l7FlEPhMqQoTmpk4CR4E66E3Yv0/rcTdl4kjbDcy8y0PZ4hw8ZOiQosYR//35hdggZebMYReB0gCniEkJt0y1WPFEfmYCx3bwNDoD0O0fUI2n1Tfs3xtXj2IJwWfG9xXWGxFlD0wB3fXpL6ytZ0LnTa+egJ/DY9tVyGXF5/SBFsueDoF2I2a46laJOFHRQVfhBzecI0pKOJZMClLI7/Z5XKTJiBmFaBuJKxeR263YuxfNIfgZ4weu375/1aa6tCZFPQW/X5N4HBLjXKgy5BUkORxNIlwPhPWy3+8mdQQ5KAky7nhSRKqNT4DERMP+PL8i7XHF4/XST5ORE2ZVWygYhvKmcT0Ad9gny1XlI5vbuiYu3wFNaxahZvudhNpzg37ejgJ10GuwbHVa2bsZYw4dV9ShXDCl8FUKvhPwLQCBRE14M556fRnpyvzY5sndkcyUGwngZhhYVebD1eXRVLQdx8RxdRDM7yfleS/LMC3hhJeJexlj+wy726DeAcNGB/zer9esxbwgJ9x5OwOmI6EUFJZxDZoliphVGdU+qCzDloQiJXGwi5P2tzeDDmF78aEkW0PeNtSeAw6BOugI7rnueLLwwDPFOAg7h0JKNgs0DwUJ8Op3X6+sAXFQTtb0QA5TATEWDW6QirbjNYUYzMR3ybu8QiIqSFFzECmr5ap+piSIFNuyOVsefjYMHjyooSVaVaWlgjWqwtaW3CNoELkANhHzer3JZFJGyMOmRIXxcNLB5SEiPwcNCxZQDebMhe/T6Ayv0X2L3asnOVTlRxx/1tuMgGOOHCeIRog5V8m3OuWT/NfElSDbGgv+smVK1zZkouAUKcJotAU9bpTUZi571Y7ueUEzkCDOjcUz4wr58UkoLpQwhjsXxZsK6xVEedjQgRIAVVUVjfutabZpJ/KMpUpNlkU50YTYkBtfKf5Lyck2EOiaeRuw+KR6IkKpuzMHe2Ydk9eRK3VQqpDFFOvCbvt7rjv+qRc/tPzxB/G5hv0a2xeGb118PMd7ephDjQmcui9t1Zc7EABHzCyiR2e2NKbuueTsl6FIEajSFnQDZCSrF55FWXj6EaUGxHrLg562aHJI0NvWmuagZCR11jH0mTsxUyQVPuh3e91suK2NuESbcvIm3sU3zsVx1aEA7+KampqBGAEs0Dtdd30gSbXiC/jJSVfjgRDfWJ+n/XagPGvvPodAHXQckpAkHDqITx4Qsn7JugrL124/4vCJ7ZGk181xHBuNC+3R5MShfrN0IAPpOgBzGhPw2heG/+fAsdcPWt6x49uEp6E7XWOqHaeyTacgW7DueEIYOWLIru14wp21lDMj/cgFZNlTnhIklhVJfqqkKO1trRCAttaEMflDDUN51FnyHrsqJZb3eYS4mBDEfJUQon+Sy6RlxZRw4ECDl7VcWHeF4YsVoYEQdo2wLMzd7i/allV5OATap2p7xE11IgCfrtwKABihr6zCSS8q1NK62ccemuOA/KF0vzzK9N+v9ZzlTjDjiDEMlHzlnFoCr5x1HFadKVFqbosXeFPuq6szi02SrZOSgKnwHTBC1jhGF973meZ5SsFyWLVtrZHrhF6XNXJEVcPePQ1799BLsS3l5IGS4AJDB4TaoslwJIG1Le8W1I5LLjrAA4/c4BTsiLQypgh4iXRU0gW6LNjL4oRnivmpIktAUNwuXGgry7ILJlwskBm5kA8OAtDhCFLRItTEnp2EQ6B9KjX9osk4cap2w660jfTbq1Yd8/3MV9tyHTH3qyUBjU2WfrGZdq84/pSjLU3sC4EkIVwVyiEzNyp8VY4g0juxE0D3wK0PwEjjTVVQ6/nnAFOnBhzwIGlMFCJ0cyCBeD/jdrGxcP0BrJtydLPn05P5XR5/WPUJeFnBVUA1QBQMRskWF8dJkgT5kIdJEsJNRsP+QCBnW0HDimaUpBgTwqKvPOQbEBzQGDFGvXcJCvSE5hWh2eAQqIPegfHDcTsihOlSYRiIiQMhlmHwzUgexZjwKBWF7gDHQaBIuChSd4bScvhsKIqgiwRKKBxKGdxNhsfl2B6PP0lhghw3ZtiubdvIzQylRNLcHTkfgmXlpOETSMZyzFwiSHhxuhhu5sIyZR53PNKmIAUh5GVyudctqaqpcD3HMWGBtIPhoIzKy7qsc2Co0h1uyV+V1CXy0yFQB70MDA6+4/J3EoXHj3RGyxOFp+5OFihCi9rSCUNiNKM+N3tqGQBdioSSJmJyjfSwSz8iO+/atq2oMLo6fd4tSqrmbW/joWGtZ4N38HRJkiKNuzxuXpFlhfVGhRgeGwehZBowlwPJ9gNJphoAzh8aHI00VpRrsW91LEuPoqvY0yFQB70Jq76qCZRXi7JCesVPmTRSb6thbJPeWqTYCBI+kKIoMdxo2cY4i6dApa/reVOvi89/5TxQ2gFx2mBbu1jSxB2pKyoaWmJ+rwskYxllXGmQgkeAyMfasn9yIpFESB4waFRbexixgGfsvaKW3xcx2izJgsSEUoqromyEp9OxvrxYuwxNPS5NhFqseMyeqMuseMeEd2DFAcFjTngi4XWypry8vK2tjW5z/YXH/ed/uFanZ3DsEaO/c/15RAMeNmHoy699pChAkOSUWFxjDhpEEoDHI9UXvqOPk2BBKe15MKg6UFQ/kQQZN6Q29dSynfyFtmWpKK9ojcTlpNZhuq21lYdATNpYuEkvji5CCBmG8Xo8LgYCvS01lGJJifMwQlKMkJrOvFTTGsZfmIpQFSn08SnpvlidP7tJfoYq3c31QrdqTwKHQPsdFRI2PGTwwH31DZnb5AafHlyAeqSgu3OYaInIU/+Zb7vB0SccpW1JJJA2JRwXCeLbpixkuxdn11BZkF0+1MWhDIoBfiU7dVoLYShv4l2gWBTVJETG5WIHlvtbWnBoO3OmsQXlo07BrgMEmg7sZiBMRBS/Jy2XS060AH9BGe8enycajVWEDHUcicR8/mDaG0y/nEgs3uUdRjLzmQz2TE8FrRzga2mMd0yEOgTa9V2BaTPgifkGfhXe3q3DCIXKQHtT5npsxBUPn6crJ2t3B8jX3xMIVg4aqt4pmLCa9+8pPLLEs6IEyrMF4jtmeh47qYLxhLZu25l3S4sDFPOmduL8Z0lKrFqkBYI+t1eJAUVqaUmZu/zZn9E9Gf+7H7e5UmSBcfm9nBiPRXAdaQFIth8ArioxeiAus2VloXyfj8ah5HculsAZToqCykPaAND97Xly2jssQrtcexI4BNpdaPajLWK382Ph04Qs8HaICn2ebvkW5gWd3UE1jFrSSRbSuq+Rtzt4xBghpXGHW44MGzKgbr9VVHJSm0V+QjdWWJKMODw0pAtw7KQKs/qbMH60LYe6gByWNbaCfIB2TjLYsxDexM2PgEfVtlLSqnBZBtJVVWOPE0SRZdmA35tIppK1tSzHIuArCwWjbUU4NNoj6iGZSiAjb2CQKvLz52CmIvUpEIDQCwAf9OG/XPdNpQ5VGIaRKIi4di0fihWhDoEWjbyy0dIMuCfx0Atf/OzK6eY12cb8Bn0F6QsLXKQh/MFjUMqe2AjTpnVD1nRRLpYVgFRfWzNySEUqkdiwC6vvqpBNR3rzgtkxykpRwOoBYrW6Eah9iSDLxuWCfnXOO3k8WWg8YGh/BBmYHq2mvJkA3CFDB2O/oaqUC7RmUzJHJhXTLNG8qBh9Qns0UrevAX+E5K3hptMso6Ri0XZWjmXTufSCWiO6twfh/CPVMNeQlwYj0XaXy+UJDurIN6/oOBK+HJfbJaa0D8fFu0Sx6+d3OgTax8GZ2MVccBnwdURLsmau6lHg+2H5VzuvvfgUQZRr69vao6kQZ5i3Dz76+sUqAfo8rlgi1R4V1mzSWl7yULF0pJe48szmdYzQTLKaFGgo2pUrn37/rZXPv7UqkZJYBjXsr4c5b5mZlVg8njBVI9D/LUxzniC9QVxCHVdZMXQY8Sm27t9DqLMQIMadVEkB10fmyz0Sg1NYhmE5tszrrSwP7dq5g1MUyEAh0eb26D8q5uLR7P2IkoLg8UksEP2BMrImFo0Uwp5NLa1+v6+1vbWiLGvOQEnN9yhKhDoE2sfh9/K2SXrJVKEhYEK7M48at3LddtspGj0ActZZR43bVqM1sAi5waypkxFCgihH4qnatZpp7FG7KrlZhVVyzYs3G++ZZKomiuLjXDX3PsytHK4SZ0BW+fmbqV+/vXsUfUpLpLSL1z+zbVt3mIdcsgxs2pttYqUVgsLhHH6EvGwib7o7DoyMO02S5H17tjAQ8m6+LR5ubT6AS8XVeqfB7c0eIa1tcColuBWjFEpIJXlZqsIj6TCQ0ISH3YXD6mbaFyqVSvJ6epC6nbw7pAXrOCXR1MZVVZZHo3EXgziOy8ae3We/E3j8rmRMNE3D62Lr0CHQPo5QwG3TKBiAvY1GK7NCwKnqjGaeHxRY7rUVa3HJvwH11uALGWxGNhfj0OUzdwKlvzRBFgtJCXrPmDXpjU83tja1JQVJFOVylQWOn4znYWytbRoyuDro4+dwH+S57CwkkZc9jcFzmDcFTGF5UTWdgZBhWUVRWJbhvRWCICZFLFdZxvgl3eHxBHSyI4hFk36/uka92HgsIiLXkX6NbnZ7tI1j0cQU7NJNoyGoLyTbGxKwyutx84FDKoCiSKLP44pmt5tjsUggEKzvdAn8QRShNgT651///I57f9sNV+XgIMDrdtkSKGnAUTgIdbJ6dGX0SE98v5H8NHqkZg/u3F1oRlRxIKdVCwLJ/6h7l/QGNV2nDVtR+z0uMQAwLhBHsiC59OojFAfAJj/+hMmVj7+8hIaMcQuT9GPjClIIPwZzZoO0iUnTDh/n4TmWha8t6FSSbOY0dguGHnq6JEnt4agoSilBREiR4/h3kWGYttYWYubbytWy8kGyXdun1kjCywr+QDBIGtfbZZHsDlUQAo1FIz5/UIw1IkVR3BVIEYP+aiUc9vBeoLWp1iifsdN9pOw92j3sSd2gZhHaHW5QR4H2cVgyNym0Mj4VQ4cM3Lsf54TmAKFOXHueE5RJM1E4t/7irrvf++ud5jWnX/Er89PVCx8jtIbUSBKi7aYwctmEPk6pPmREbW0dZHlquUsu7BJV24OqPlBFydbOLlNLaoGtdH744ivSa85Ym9dO1WdkEhT0QUXZIVs3b4AMdPMuBXrUnwFA9XcqGfURRZkTqXBDEuImAwpyCZFERdALcKVTQaWZopxqDwssw7o4dwBnxWV61RXKoRRd3i6kOBRsxRcoQu0JdPTg4TvrC/VqOyhlZI5dy0Rez+bokR6yTbaYfiHIxq2UWO//yU+z7cuZ3sUjf3iILHznpusQAFs+famoGETTvtoRI3DLvto9+5CUzPSBHnrEUbXb7McfhdvCVQFsvxPsq2+q92EZW6scgUlIh6wouN2G/mMD0x2gtqSpd7dTpwrnvP64Wi9E1J0iC1h+K4IsNLs8ReTMJaMtCeRDAFSWHSJFojguVHAiezgaV6RYeXmoXC9PSGoz4m0jUAYXN0bsB693K2SJeLO7CzbH/urrzfq4BAcdwSpx2AxXnmbdJUWgTHps3WKep5nw3eADJcQ6/pBpSxZje9mWDT9bohVlr/h8xW2336hGMLBRBgGYdPLlmMLmP1fseUeMHE4WanfvQVJy7ETc2tLj5rOxJ/aNBv1EbpJr/MYJU30eV1s06ffwW5cvppshzhtLCKLaUhPmGPZb2MQOHNIJTNapmSG3JqZOcwJTvlhMIhFhUQriU2NnRUWwMm/pmIUEEu2NAlsJAO/1uH1ur6ReQCFIQJQ4GNSZNZTUpXBM+D4OroDMTRIgyo3OK9ACYfFpWgABWr1qFVmeNmMGWUimxAsv/wFY+CC+wiGzmfgu0LYuxyma9tVWDxtNn44arY2Yz82eejM9xOo/SfvqGwJenoFQVnvKUbhQqgzzE2yiPsT0d1QgdVLe1CAnyZnj8URQn8+uXVi6fjQ/EaL1SRBEWLBiicqzUsC2IjPncORkPKnwAyp9eekClYqpnu4GtbHis1RzFmvFOwTaj/JAs5rnBRTeEOrs1jQmtaYI2d7IioKbThIVjFupqW+KMumypctnHTubjMSrrirftl2WQ4dBAKR4kzt4iDkWIknqQF2GiWVUt44emLSyp929I8qyy8WQFhgQQEVBTBeVLZnBDjhSSkSEOPYt8G6NK3HvOBVJJODRG+YrzSh1DwQDChJi0YQ/MNjsm0yYBslZoX/0QqxFZIOyKA4z0UMoP3uCerVoOHawqTOHCMVxJKErpahDoP2LQIm9bLHQC0mPZ7pfgUJtrKTNKXAAHGot0NT+dWogXiWxsrK0fMZ9tTVuVgIIRSPNwWCVyaOIJQdJV4pLaV97OYFv+607gTekKT53LKsOFSUcZDIkO4QKAvj/Vj0I8Xb5Pq24aSQGo8fcGR+QJAGowwG9rJCMtfoDecJBli6orW3NZWVauntRiMbjCKDyQCUPQFid3l4IInIqos5e7jXoulBS2jfprhuv/9PT/+n01TkoIWQjx7KAJxoJF6hS1UwUNQ+0u/OezQWb5pWm82rZRFgtIYC5VJ90TF7FFJ/n3vBxEoDAxbmCwUDt7h3TZxxbXVVeWV4WDPpZlpVlRVbOkWXZ46/6zslAkZX2cCQlpCRREiVJFKXa1S+qffC1q2VIe+fMN5HzszJTp6L2jsKz7FiBzK0qZKKGLVpU6iycPYVYCw+UaFThIPIHysoCBXXVIt0HGg+2Z7MUPKGOAu3LoNHzTIwaWkFTl7gSUKDb9q0+fsZF77z27IxjT7Z52RTgpbwEAQyFrBO9Ec5pIgOPM5HOrdrAIeBmUrH2xlg7NjyPOx6PPPr9Hx+7/fYfaJu5GK8H9wR59NFHAIA/ufuO6Uf8PPPQr2+6lS5POvFGLfb15u9tLqJqejydOjOGVpredz6dlIo2JFAAQJdbnUlXFHWGcdsw3Iy+oiKYjGaY9tn/1PXhkkt9775AfG4RmnbgN942airGDhm2I9/cZwelhtEjPf96dal5Tba4+eAqw/ItgkC7TYGOHDSzrrbuiBnfECQwbMSw1vRXfT750/fVL6fLZMzaZX8W7OGyfyPLPsOfHgPkFcutM0dPOg53aeFoCEnH4s9WHnFUWgMXig0HcDr6oYM0wrzixp/U1tXv2LWntTUcj2AO8nvzKz63zybJX4w3RkT8V6soG1RsU4Om1hafz6tAd0W5kUflyecl6HW8CeziSB6/S/uSqAnEnT+Fo0B7PXLkrufQjGbSNGdZZj0OsZu7N4ik4fXXXjs0/Yv54Yefnn7mRWR54MCBqi9SK6QJ+l3fvuHGZ5968vjD3ZkpWWZEI82BYKW1WaoKNUaFg1T0rpIkiVW5klriJD1gyZJPrceFni/XauEsArfb9a9/PR2Lxe6+y5ClZ6n/DqiuaGo8UOavrquNRCIxSyzI+oGYbvBUrDmm+BUZO68RQr7ysopiRi2noo0JhUEIVZSHeBeTiEcCZaaPAskAWr8D0OLlDPcqL2eXgnez9XuMzimVA42fNIdA+yBpmvHq5xur7da/tGAlXS7MB9q9CpQCATBjxjHxtTuypTc1NGiehw8/fB8XXB6Lk5mOO2n202//BzCshwexaHNS4r0o/2g2l0v7/mtjPtVojNbSXlYYhsUt6BAyezNJgCgtag3V4iUT1n6xIhlvP/m4Gas+X37yyYZH4n9vvVdXm7+nsgVh7LYTK4NVbjz/LeLXmZMgHm3zBaxN9QkS7Y0ulpcBE25vqCgvo2qTddlpVsyhnFVsdtQV27fR0mAkPDkE2gdJs1gUQqCkE1s3R+E1OWXbRQkpuKMm1UWNDY1HHH7UV+vXWQiOcFlleQiAYEu70VWegYiXogCkKVAXZ3z/9YNo/+U4Fg//TM9RxzpVTbQiTllymXgqevrvCrl6nG7FMJ9+oilWBEDh7Em6cjBq+49KXF5ZHBLtjax/WBJW4jqgSKwCfxr5EYu2R+xGqK8Sh4E+BI+fZjJ1gRVvJdBJo/DcZwd9njTNKFyB5o4sdx6GtMt8KZ096RdfEESXy0WjRjKp/1GPUFmmZYxHw81l5ZUAaE+b26I4F0pJNDbthxBu2LptQHWVx8MPHTJCAUwqhUM6q5d/OPOEM+jZXWqnd8KY6sGNK+RYrbExhf4DgBSkUF4u5O1LieaIpJqHsLJc5U3O1ELJFr6gFyAgRPar/C5JbEUoVIbYIM8iXmfeaHt9msGegV4XT+9kOn1R2aBszlIUg0Dv+f5NL7zyDh2E4KBvk6YZhSTSEyLoPgG6+8DKU0+45PV5zx8x7STbk9CVjQ2N5qfYcYkvztgpVD6IcJwoibZh7KryQKS92aeWckMIeWzSYhkrSXIkikXrN8+/SFHr2V0unhy4vm6XkZ2Ufn3ZehCTxFXiecAe1pwB9dYIsQq9hDf1t1yQPorGohxfoUiiP8vQN4+vsr/xZu5AvEmEgi5WoLn/zA5KjTSHNllnn6ViwskzJ3y6EvfKdKuvBmI08Ucga1IxI2/G3RTlXY0j9DW1fvugrpZ8mU4e/37NGqruGMYPnUbSii6eew1Zs26tdZuPF71OFg47+kTzej3Uoz9lGJ86c4VhmWSSSSYLbAGFeVGWtVKffXuxoFu5cvnMmcfS8+CAVVnZho1b6vfuPO30sziOjbU3LFuxklWS0489Le1g2oQibOPryf9Z61Nb2nAav5tz+fRO7wWyZzwhK4pSHvCWB/FdHI5qjIBkqxnOcbx5WhHmTYY96PXppQHDirev5sxXCW0QaFK1XEqqt37fRs8rzWwoxLNJNulWE373zppsLx0z6xSysGrVymHDgoIgHmho3bRuLWB9lotnGCYU8qnthFmP290oSnIGoVihHoBlmI8/WnTMsbMtTlUMnQj37d0X9HsPOXpmS7OW0HPBhZeSI5i7AVLm0w6jhaeMY6YijRGRRQAk21KVFVgIxyKJTPZMJCLmuvVUrIWHQjgsunHLzrJCaiv7p94sBB4/l4wVOpQhB4y/QVOzbeNdByVNmnurrVZbXJRck4fVbsAmp099tVrQRi3WQp6siYvGV8dXHTh8wpDaXftyn0hXoNrTy487tKvkJwGxcdWz2NjE9PnRR09rbsUiesSwQSOGnelVG3d6fFUAgS8BuOD2X69avX7Dpq3HTDsKqBWfyWSqrb2A3vtpXk3DEiP/wea8+s5JaytBSPK83kB65+7Mgw0d4r3w/G+IooQQeu/tBXQ9FxoRjuCL59TjEuok8Ae9OWy/VKwlin2j+FEeCiRjNvmY2X7ZAlVa/k2gspubXvdLGATa3Or8QPV6pXnlyUc/Pe8jy8paWD0Cac0zLpp1BMPAZ18zkhn9imf5BmyxUkQaREv6Snx/23NvLjdP6P33a5+J0Eea+HYRcFyHUJa6kIZMbmlqxbwQrcPKryKAf/uPPPf6mm3bqyrLpx45uanpgCiK8UQUMtDFIcgyTc3Yc+oFMRfv5oODc19Her2T0dnT7XazrCSKQiqVcPFuvKURVjKQkoBbvbHefvu9mbNOJys3btwEgFIeJCmEvmg46wBhs/He1J5UD+3Td8Tw+KsByO/Co9RZbGPWPoa1Wdoy6SI0ayw+r/2eRqCkwaKDXm2kK5Jw49xvKJLm8Irvt1oVz/9Pa1tZUT2wtQmzz74GVaBB39ihodo6fFcrOHiMb1fKj6L6FJuBrTG6psLPzj33G5l83THo8ZY8PgLV45lmeZmFG4RASMbcjBIM+PBItf37JkzAbetcvCuuNf0ti7Q2tLVEAeCliNqZKbkvDGED6R7OpiUMzZw5S81VSrPoOY4TRUF1aRor16z+PC15BYGkiBa8tSjvm868dYXIAWzRK+oAO6RUVvpjYbXhsXFwBUA2Fm1PW4mLtQKUNANVEAB79izu65rHLHFgItDuT5Hu40j7/pXGN2/MmJEujtm2Yw/lHJ53AQBPPeFwjmXeeh8T6FXnTmUZyDDwsy/IIApw06XHJFLi5p0Na79Kn9oGQE0dnrQDALjl8lk4uClqORvTRg5fvbvzIwyInzXLa3YmdsDvjUTJHHXsAoAQciyDGIYH0OXCHOR28wwDFRRMpQSS4YSj8HoDJxmhE8+Yy7IsyzAej5tj2WAogBQlEk0xDPPiiy+wUuzoY09NvwztIkUhhRvN2Tac1Gnx6Okn5ou/GxwaicZCQb8EgD9UkWdyOjLGsRB4K7Qftu5A31Cssl1FfOc9odyYwUNr6vfqtxbG4uWryYJTDt+LAkEUVH7i2MvhwxkGzjxs+KevaSFswk8fLl1/7UUnaNHtldslGY8FqwxoX4B/vPo5ueUzx/jurNWMfYvwXLJivW+IfT2MLerr0lpG1tcttGxwWLrxtH3bJoNAYdo3PpUSmtQR53rCEBg/fqKH50QJvweXi4OYZ30xXzyMSyetiLQ103YBw0aMmjfvtVgsMn36MZoCRWDIIUMwv6qx/jfm/ftoNeC+Ya3m/516TBq9GtBz7GmeQDaEVSEcCvhDAb7wtG5ixXcrb/YTYs1mCljs98HDg+ZqTgqDkwN+m54FDnoFaVpA+4m8+Ka2gGcAqRAEwcKAjQewzQgAaNfEJeAUq2ezS32dHUFL2AjQV5XTd5MGmjCk5w+RXCJ8G3AMKC8PJpIp0RQ9y+y0UleLI29q+Dvp8XgIY+/du48UEaxcufyab10bj7TzfMXhh14ejSW8Hv6ll9745vmXHzL0/M3z/2gcSB8c6tIzTDOvVmGD4ahQEfKF9N+tAsVmDudmiWB0VwwW7KHu9J0WoQaBunUF6qA3kmafwarFq2nQ5ovFi/5yzy30pWUfrnC7sXV76503f/7F53R9VE0BGlQxiq6BuN1nWnNmr9cTDPha2+wj8lqJkf5UFCWXSyaNlyz05wuW7dypzXOf/+biOWedf+BAg+XWjMaV/fWN1UPGsSzu32F+KRwzNGNFqDjJUuK82ZdEayHhI/s8UAcUDmn2PFYvXbth04Zrv3P1YQzOCXlmcc1PHv3v/915FXlp0QeLfn7fTyGEXyxZM/vsU8LhtLyRA62afoR7NZIaOAzsrl+Pe+UNPpxlmLKyYCxmSbdM82FS3yXpjE+Zz0yBlD0JNm7eFvD5g8G0ZLLBlaEhVSHW7RUEMZkSOI4NBvzba3bEYnF1bnBx8Ae9gcpeT52lTKy6CO1IDZFDoH2KNOd99vXc4w8DvRnXfudquvyL68588BnDPfrz+/DcY4SQIAo/vP3uex/4ZYHHJDSKv+4+MHi0HwDcbjTWWmGOTGlt7o0mI2nKEwFwzXWXEPbEHUVwSxH8uiKjRDLltbb1RBAyzQ0HCCeLELY2HpCFpIdObS8MgcqDWVddvU8LKh5EjC55xcplHeyXczhiX0JvJ80+ht/88oFf3X8PWa7bufW6k8aQ5WknTKXb8C7+hZee7+SJ/BWYRnfsN5LSxw+dFothrYejRukFTuTJ1MpoZZXWHfDDrckfX3PK/qbI8hpFSB8fxDBMa2sbvZ1S2cpJEQpHon6/kU4fqIoEqnwAOP0ouoVYc7SmJyKUVHMWbr+nESht9ShLNn7rPoY+TJrzPvu6kM2KCprnQGaqaWcw+6yTP37303PmnAMA2F+74zc3XxxPHzWxeikukv/TX/+4ZdsW0NXYtnf1gGFqyWbzmsxXec+0qZWgpbnpxYWrv3fVnFMn4K/Q2GFVY9Vmb//VNC5Ga1s7cZ0mEjgNXqvkpEipRQ3u6r7k3Cz9+3rtsmS2OFJnYCLQAoaD92r0YdLMz5L7mrqQNO1P0RUIR8LTTpjKsux3z/rGjT/93RML1tXVbPnF9aSbO8a0E6ZyHPf5J6tmnzT7hlu/DXoQ23avnlo5kSw3N+HP8/1V2MidexZOiTXj2JHKf9/BhVsEh4ybBgC848pZbeFEOJYsCx4V9Lt5jlUQSqZEQZR9Hv7pRV1Tj+Ag5+2fS9qrIlQsSn72fQLth6TZTUTZMwgFQ+FIWJblJQteXbLgVd7jfeq9DZZtJEmadsJUIkV7GC+vxbKXHWSEjE6fMe5v/11k6VjMeHzXXGyTH7psTdZpyQ5KA0VLVINAzeMba+p78Ti5/kmafQBzL5n7ozt/YvZ1CkkcNIcQDho4aMHrC80vHVy8sm7LpUdNPH2GVsDZ2ha2/GS988matrCRPBv0238nzzvBGG78x+fw1LwfXHPa4+93ZZcWB4XD4+faLeMMCydQxRREGj9s5LY6mzYzpYzexZuWUhyc+zJsAOjfmPfqvB/d+ZPFi5aeNEerkvrTq5hKdjYnozLO9h8/dvy2HdtIrmhDo7mB3EHAK+s0J6wmSE23y1/+8/aYUYfQp80N+5vTpc3t15xtfvrH5z74wTWn/eAaranoracf/+/5yxJepzNvF6Ntr7tqZC4r/qtle0lK8KCRNtOhbYuR4DkXzCWlnLh0evCw3qVAe5I0MykvB0ga49dKoUO6u5tASUpK0yE9N6/ljb+eb356wW1vFvJhkhxP+jQRi5x5zCScml4+MBAIfLpwCVm/d1/deZd9M9upD8pPUXC30TegzT8SiWrCqU6b3ODytx+5c8HCtOGdFrzy/upoAt/e112S1jGa4Ml3tC4wPZ/G1JNfm+5G+dAUCcSLekleGnuamtPYcmgmgeKD0XL4XsGbJas0p0yawvPudV/Z+ObGjR3v9/m+XP8l6AewUKdlpYVJLUAIETud/AKZEY1GS8eEzw222g2A1g/k9DFYlIwfO9LMngpvUx1/8Tkz057rAvT5Nz8DEAYALuqPcF3QA9hBJjT2NEatggO722051AKOys9SRsmSJsH8V94eOmSoeQ3NXvT7/YsXLY1Go2632+Vy9RYK6D688dfzc3Non8S2HdjCLx+gxZqqqo0cpmde1/oVkJv32rPpEBENV2dE+Sme/MDxlnYpe6bjwG78K56bRkt3rHGJkybF2XPOGTpkqJkZVy9d+8jLi3942Um4tdWipQ8/8tAr/3sZ19X8+J4vlqyZfuLRB/V6DzIIe+a3svdpCnRUJZZyTYf0Vgfx+zV1RISagbOgUjKh0evOsTJmXry0cCUpm6oErhbWaePbKTdoNvakyC1FS4tAewtpmnHtlde1t6fZm7+472c/uu1O+pSwJwDgwd89cNF5F4P+DVsDPxM3XvSI+elPL5uRuc3DL+dyKZYO3q/RPGOXDjBi7jSZ1KxGKV5YsMJUZKr+jyzSZvnqisamVnNOlYNikZc983LowSfQ3kiaZjz2+KN/eeRv551z/vx3NMt04QcL6z56Se1mZtPgauCAgQc9gtytuOC2NwtkSYIVi7Xu07NOMiLXFsw66RC6mS2r9goyfeXzjZhGj7Gh0YWfb0vrKa2O9dBYEwGElxChUYRQ4wFtHAh09e57p0TZU3eDZprzwJwsr25zcAi0t5OmGcs+X/b0M0/d+7P77v3ZfSTWcek1F4Nm/HEfMtiGEQZUD+jbBFoUh5ppMZMiC8dPL5vRKzjUoNGjtKImgja1z15ZWQhAoI5aSb+B7TKauGE20tVB12pPC6DaRca8pucItC+RpgVPPPX4E089jlPBL73ipm/d9O7/FrU07L/rkuNTQv/tCpE3UvTGX88vhDFvvPOKTnJraYKkkZppFEmpNjz5ziBLbqhDkV0P0lLE63Mn4h25PaGCkKnmqHsJtA+TpnWepKrn573y4rxXXnS73cs+XAEhrNdNLTO2brcOGiKor2vsV7n0F9z2pq1ns18hPRvf8Wb2aBypgxwK0zi06wm0n5Amxeqla194+b+PPPYHuiaVwn+VurAM/Dbbi+mtz/oVhjbhqegUrauzVrtVxWDebcyomDay01fnwEEuN6iFQ7HRwMCuaSAyeqSHPkA/w32/vffKy6468TictESw8I33CFEG2hrWrFuzarE2pO+ThYsTanG3AwcODiI2rsQeIYSAx5tn/mlWkGCfgjquQPskV9qGPnK7895aMH/s6LGP/u7PdI0gCCcfpuXVf+e2Gz5+91PSOkiSpGNO6ddG697qQIGysXnJsrzbOHDQeXi87qRaQVs01FavxRFonyRNimyBY7J+1gVPZ9vx0b/96dG//cm8xswTs886uQsv0kFpdj8IdnTHfuX4LhEMGjT6wIGdXcChhfhA+zZpFo7vXz31L88fhB6UfRgPv7yqV8eReBfPsmwylbSMw+kVuOX047O99EQ/6Kc3aNDo+nqDQ4t2g2rbZCFQhzQ7KR+q9V4YvbcGsXdxaA8ngU6ZNOW5p/9rXvPds6eFW43xSgS/vuf+c8889+A2QMhBlBa8uABPir7iwtm3nHs6zhD4x8EfKtetGDx4dP2BnaRiweNzJzuU1WQQaD8nzbyJ31ddMu2qS6blPQ7JWHz60e19oAaxZ2D+NIoi04P4MT739H+f/s9TTzyNk38BAP/469P/WLB67rHGYHo89MjtPvfMc3v+2uaMGzFm9HDLyqOPHgsAWLNmByVKwpXmbbSnRHX1j4GSUC3wIugYh8KzL8e/Ng4KL9M2E2W26kNayk3ywA86C/R8P9A+0Iwymytz4viJL/x7nkVXrl669u8P/vjjt3Hfg21sBVnzi/t+9uB9D+VWoF3uA50zbgRZWPDZxttuuDDrdibz9Lk3F9MSUgjAAz+44IG/vaPUag6rS79zC93yiXfeB30LB1RnKP0w0ji0AM/Mwa+FLx2QaHteGs1dfZijoLu31yA6ICADQacfPeOLNcYfziI/35g3f8myxZFYWt5rz6OmZhdu+DvauLZn//cp/g9tSmISm1efrzVy3rS9+eI5s155ysbjf8s5WG/99dm32aqOJgCVGL578YX/eO1/HXaDOgSaP2mpA8o0r/B00Kvx9rtv/f2xf+DWyIpy10/vXLpsyTa2go4hOPLwo4YPG37B3PNOOv4gJGAs2l5r1qGYRnfuWrB4fTBkTL677oIiLmzyuOrbf/NCdWXa4Lw+Bqj+i4o35B0Tvmh0eeC4BxSoY8J3ALmzkSCEN1x740033EzG2e7avevea74BANiAyknXV4TQKSfOfuShP/awCW/GnCnjycKCxercehnPsTjrFM2VP3ZMmmomePyF98uDXoQQf+BrsiZejdtHqVIMXX/RcbYneuIdVdj2Qpx/wlHYi50uQo0ST0eBOnDQHUAIPf3MU08/8xQA4ITjTvzz7x677KYfv/z3332xZM2fH3+UJDbZ9jPsSSzaqHXJw5AFdlCaityhGviLVmjNGW698hT1X02cvvKURqDXXZi/5fMt55zcq5n0uxdd+I/XDQ4tvEzeMeEdOCgOXyxZ09LacsZ52hBNAMDSZUs2bt54zlU3/+6h+wEAd9x65x23Gh21Vy9de/ZFZx5owINFDxbYag8A9mk2SBLOPmnS/E83LVq8DovWk7Ais+CFt7TCMAsGV/cVuz5b1kE+N6hDoA4cFIdlKz47/lht8DLFlElTDuzDnefpyBYI4XnnnP/Ln/xqxknTFEUpnU/5vU2aLD1jsmbgE0wYXvnO4g3N7do4e1c+9qQ4ZarRlA+LWB1//zDPjiUEhKNJZlu+QBHqEKiD/ghzYLDYIXe3/+j7q5euXb107Qcff/Due++EQmWkl/blc/D0N1qVhBBycZiFSoo9M5mUSY+nt7TgUFhlZdoEi7mnF+r3Hztey0J5+K9vVgJXY3MvGzpiptFCONQh0H6NvFXbfbJS25JW0QEynXbC1CsuvfLuO3502mxsyH/+xee33nlzIMPWO+g+0AKxcMNOHtv44JyTDn1n8QYkC82NjYMLYM83PlpjfhpL4CDVVefgH5JL51j36kWClNIo5tBYMseWThT+IEfhOxCCL6pXBQHNsCl2x4NFoN2XSJ83KY1yaLGfc6ANT2qJlg8saq+S/Ymif4LZZ5+z+EtrV9YLv2EdLltgO4BSo1EShTeNoEoHAn9+fl4ON6ijQDtIeZ2n0W7NXvL7/QMHDAyHw80t1gJtB7nRPyfX58a++qa5c46uqrIfMfLiws+vOPMYtbubDV5YsML8NAiYqy86tRfVNd1x9Vyy8OfnXsx81SHQnqvg7pmKo0EDBy94/V3zmlf+9/Lbf/qpZbMbr//OLTfeenD7XJQ4itWGpINMn2wfg9QE0ubmpvdW4Wr6K848xvzqFepTQpQWDr36om9cfdE3Mo9H/nPLOVomwxPvfABKHndcY1THUDJ1CLQLUFK1mAtef3fDxq+v/e415OncS6/40R0/rvv8vXXLPqLb3Pm9u6654tqDd42ljqJqzywdZPpe+5hX1m3hDqkky2fMwE1Jmpub5i1aU1Gu5TBdpVIk+TcH3vpIG81gQe3eA0wlDw6i/V588xRKpg6B9ilMPxrfvZQ9yZy7H93x41vufeymOYeR3JplH67gef7eB3/161/8BvQ/FDu2vpDh9WTl049m3b3XjbDPxCvrtl561ATzmta28FmnZLVgXnhLayoa9Odq8/bN46cAgCudCJ74oJe1InUItE9hy9bNAIBQqCwcbjeHjEkQKdDWEK8cvGHT1zd+74aZ02aC/opiObSo9jGzTjok9xBmQqa9kUZfWafVLF161AQk4fyeBR+sOPu0WTtqcIjp3SVfjxmFP4q2sJZJasaunbjqyYzvX322+emf/vvBXVeddstpx/cuMnUItE8hEo2Iovjxgk8AAC+9Nu/FV17YU7fHvIGiKDd+7wY8OlTAXq1+ixxhoo7Z7311hL1tFsQr67ZyQ6sy19fswu+9srK8pcEY6N2ckzcp7rrKqOx65q3l+D9e0HPIFoXPB4dA+xpmzZ557pnn/vxH91x+8dzLL8YBxJ27dt53rRb3pBDFfk2gRXFrr5470jPZ+GfQoiYpSdiTHWzkzP3v4dvf+8DeAWrGqx+sJpmkOKU03HbzxedkbvP3BUtAKcEh0D6Itxe+/fbCt3GRCcPcddsPrrzsqv98sv36U9KkRD9XoA66Fu9t2sZWk4omo67ptDHDAADjx4zMxp4KnzZW/aKz8/9Q3XT2iS/MXx7hJFAacAi0T2H60TOqq6oXvq+lMSmK8shjf3jmv/9Z9KY14U4QOjiG0IGDAvFBTd1pY4ZtUz2khEmbwq2V1Ta2fyaee4M4QNWuz6pxfbWeO3XlnJmlk5zvEGifwm/uuX/QwEGUQAmampuwF78ljTEFR4E66Flsq9ldUR1qadKcopXVVc+8vtTMkdeejWtACa45y1jOi5tOPe5gMalDoCUKGsooqirmqm9f8cHbH73wrxev/LaR9LtqsY0B5RCogx7ABzV1ZnPejMefXTT3tKmVVQUJ0mx4edFKNTdPf8529EAdHaLnEGhvanWRl09b21p/8eufP3jvb1cvTRtoc8rh1q+v4wN10PNMeqna3N6MlmZdkGZn0nnvamNEzaOc6EQnSn2489PAwMwhlQChlfWthV9be92+smHFjTKjcAi0tJA3h+aNv54/64Knc2yw8P13F77/bmVl1fgx45Kp1Ncb18uynNkoKB6POXWcDnoYr6zciGl0ppVGKZMu/FzrVUqnhJqekmQjLeFIzztCCOCex+OHh8YNCwIEFi5eDyA4+6TDLcdfsa8l21Vt2rEX7Ng7eexQ88pIJFpWFhJEuWpE2noLHALtm2hpaf7caSPSRXj45VUHvQVXZ9CB9l1qab+2b5f3i3pFpVG22pf5UltbGABQXh5Sf/ARZksMiDBJotYm3O/KFu1t2NHf1BojT9/6+EvLBpfPmSrRGfA6WvfsrRg+1KBRHV/vqD/z2Int7eHqyvLwvv24OOWQIbbndQjUgYMiKK8zTNobq486xr+DC+PcV9bhAdGXHmU0tCdoawuTSicT0kwobqi1L9T0Ibhan8jPbHjnU3WyXjquvjqXwGxqaVu4HNdf3fitc203cAi092HFGzfarre4R823+tOPas1jbrzz7D5wSx9EZPvELMTa6z7YIw8/6oxT5ySTiSeeflySejTL8pV0GjVTp229ky3KfFx7XJqt9joBAPj8uA3+/I+MSIAgyoqCEEAMxKDrV3zSqb+UQ6D9wn+arc9Fpp7qdXd+iaD3fm5jR499+blX6dPrr/4WAOC8y74J6o12CpaYJGmwALqBRtX5Hx0cAfLOJ1+edoxWEBWPRXHo/5jxH67cToaspERJUbC/VAGAMVVuIoRIHhVSRa6tfr3irBnPv/jh1VdYy/kcAi05EBXZ4VYXOToGFYje2+rCQcfw8nOvCoJw7De0NHXcr+ujFfNffuu6k8aQNWTw/QO/u7+ivKKivKKyorKivKKkPu3Tx+IpAJQ9bUHYE0tQTJl4EUBw4enWnPwNW2rIQnm5UYr64rurPB77ynxHgfbudkG2rSv6WD8LB92HQQPx6KOTzzyRrkEIHTv7mNVL1x5+zMnrP8dD3h+897e4nn3+6736D4GQlvGECRQLUHjxN4586d20bvkmwHnvFqQhHAItURSSP194QIM0CnLgwIKGRjyt/v5fPvCTX/7YvP76kzVnIm4Ucuocode2nhkzDHtRj5g0EgBw9slHLvjkSwSgyqAFznDKA4dAHTjov0AIrVy98rTZp5MpzQ/94cG29jbcwas5OarSaAvy3AvPUjfoWwvm3/fbe0HJYEDF4QDgn4FMfLhyOyHQrzZr9fgjhnSx8yGtG4oDBw76G26546YrvzU3Ho+dNvu0D9/5ePXSta+/+IZlmxuuu/HMC8449ZzZnyz55JtnnzfvmZdBaWDCyLTpTN2K+YtxqqkFDoE6cNDfsWXblhPPOGHaCVMvu+aSLdu2jBw+8tOv97KcZp5OP/HoaSdMbWxqbGtv++HP7nr1f6+MHzu+dEbeV4R865qLGyVN8NJ71tSCHDjy+HN53uYtOwTqwEH/RSgYGjwIx5EIduzcceW35h47G8u6h559j6wkaUAUf/zrI9hwri6t4aMvLMjXsFl9F8Z7yT7q3RaiKO6uwUMDLXAI1IGD/ov5r7z9zmtpzQ/VnHMcMho4dOSulhTP80ccdoT5VVmWAQDhCK65LBFUlPmnzDh1nzKiyX1olk2QgtRMJi0KXwR7zvjGJRGl/NNP8JicTHBzj502b3n+bvsOcmD9FzgHuOfxYENHapwLwcbarJ0XegbuRIQspPYdnM+2V+Ck6cMWf6H1i8uBZNy+efbwMUN+8eufP/b7v9x4/Xee/s9TdD3L4q5wL/zzb7gv5xXX3vqd75nT5q+6/GoAQDSKM9VLBK2tWu8lIRHZB0aQZd4bPPLEQyM7l+rprRptqmVIqL41T14BVzmptQnHpj76AM+shwAlEjbD8uCzj/4UAFDXnFy6dUNXv6/+goNFoACAKSO0gd19lkC9wYN7JaWJk6Zb+xMS2PJpDgLFk4ief330qNFkdtaevbVHHnZUWVmZKIqnTR2FibJ84Oqla2VZnjV7pqIoRx919FN//eeyz5d9/4ffy3F5Xd5/JHcQiRKoBXV1uRKi96ldQorCnTcYCbMEmp94WJXHkaIOSoQ9HXSMPelLhchSikuuvqiivOK9+R+MHjWaMOkjj/1h/j/+RDeYdsLU1UvX0rbc77634J7f/ML5G9nkgToc2kvJrqt0qEOdvZo9zdsUxaGtba0zTppmXmMpRy/Z1rFbd38+YaS1HLMnYU2kdzi0N8JCfIXzaXczZjbjMS/csFNH8PiMJHAHDoAefzf3YequSqS5x06ra04s3YqbnjrojXCEpAOzCM32c5LDU1nNtFumEJYsBsnbBoVAY0ukmdO6n1hgjrnrgaSuquTMUso5rMp7yzmnP/GOdRauAwfdAghGDBvh4lz7D+yPx21inQ66w9j/t96wJrP7zNOPbielnJYGsgQl2awLlQvbWQYLzGbOKOSnYXejgV3ROaAgyLR7PN6rimpn19rS4pjzDrobEMIzTj2DTgYbNw53JNtdu2v/1q/oNmNGjxk/bgJ9KgriR59+2G//NIu/qCvEDVoUMvsfZmsgm9nF5uHSIFPSHoS0XKqScEu6pet2Dq4OAYRkBSkKErxaelPaILqcCDJtecd15mkm4nCog27FGafNAQgs+mAhNalOOO7EkSNGhfftjkVxQ1+32z1+3ITGxoY169ZohHvanG+ccupHn/RrDi0wmmRBU0OauqfCM0cD2VknHZK7O+JPS6aBrNoRmfCdqV8yALKaP+9O7iG8Sdonu93uMOZHe8Au7MbkuEQddBN4ngcArFz9udkhtXTZkjmnn3n4rJNXfDAfAHDcrOMBAIQ9iQ9r166do9Rsm34OS5y9M7K08w1kf3rZjIPLoRBqjZ/1vp9kKp3WQZnEjoxRHhD4PFlr+TmWk2U61MRs+tttXMjFOS5RBxYsfPHnUybgaVwIoZfnr/jRb57P/IjKQr6Nix8ZM/223J/eJ2/cE/S7zZutWbeGibU+/pc7jpt16Iw59waDIQDA6/+5+5hp41vbotd9/1/4FmAgrs7Ljo1LHvl89fZv3fmEZSXOUjjxh+aVAZ9nzQcPTzjuzl79Vy4kb6nw9AzaQLZXNOd+/cOvTpw6mtFneJpgndJBjfccBGp3kKwcysxbvrqQUk7iEi34lA76MmpX/23KhKFnX/XwrLPveeDR/11+/rE7Vvw5c7P1H/8+4PfkOI4g4HK6Hz/wfsDv2bbyMbq+sbGBZeTTT532s3uebmpu3rlr5/6v/3HCMZOmn/bTl99c/u6Ldw0/pCw3exJa9GbcJAGfJ+CzXhJhVQe9HzD9oUlOlmUYSKlTXYLQ5y6imxTRtvYvkf8UWA4/99hpv/vu5YWf2EHfw09uOw83pp32va8379l3oPWp5z8cd+wdLhc3duQg82ZL5/+6uUUrx8yBRe8vFEXxl7//oCzou+vWa844bc64sTiOtG7VP1pbI+8uWomt+BkTWZYZOPnG/Qda5725fuEn237709O75L2cN2da7Wpc8e2gr4JhIH6oTEoolCx43FmN78xfZiZ71Mlg1gI5dM367f/3ncsK2dJBn0R1JTaozRAE6baf/2t/g1GPPGr4gOGHVJ13/e8LOeBHn3z493/NO9AY/tn3T4YQjh0zdtEr9wMAZh6vVVv/6YHryAKpNXzsKdzcofNwubi//vbb0+f8fMOWPV1yQAelCQZiDs1cWcQhsm+bJk0L5NC1X++45ZzTiji9gz6EnzzwX2LFn33qUaxu2sxftDqeMNrbLH7jvvFFuhQPP+kHAIDf/uSUz5YtmXrYkB/8GvdYiwkK5s0RAz9cvH76tBkTxk/cU7fnizX4WzpogDE0sWMQRWnEtO81NBnDex30SSD9YUYx/ImHeQZGVL352brM16w6lnBoXndna0vrLeec9sQ7XaMFHPQiKAqacNydaz54+MnffYes2bO3+ZSLfyOKWuCydvXfFny4ThBoHDMrTjrhJK/Hu+iDReTpmZc/uPClX2xZ8cd99a2NzTHe7Y0JMfJSQnBVVVZt2ryxdk8tWVMW8h1ozMN9x82Y6FjoDoCdt9zFsfkC7KbvfHZ/u71ztAApitSw0tHOn6cfIpkSp5z4wxHTvnfM2b949e0Vw4dW7Vjx56oK3IDi21fMBgDc/GOjuWQO4J6S2M+vfYvXfLWzbn+Li2NPu/T/AADtsSTdkuPYJZ8tpuyptvXF4jQ3lq3aMmLa98yPDr1dB70bUC3ctFTBk5qlAkGLQTNFKPPkXdfb7lNwWOloxyXa3+BWh8OcNH3Y+OGBN976+OqbHgIAfP7ugydNH3bf3ZcsXrHp+b/d9txfb/vTr6/Fk2eeuuuGq75he5x16/HX8YzT5ng8WmS8oRHnNk+bOl2Vurjz+VFHHCUraPAA3lLimVd+OnBAkakgi+oqkiPhA5vwT951/c1/+k/ma/OWry4kdWnt19sdc77/oHb135av2vi3p9PG1m/dUTdhrJbIfdKsyeaXZp9w6K49DbaHUmRl8dJPTzrh5JNPPIWsKSsrJz/4H72rHX/QoME7d7ccdejIOaefSdaUhzDbJlNiN7y5Po6NtS3d1IG75KGa67k5M6tFn/bCm5+tO//4o+hT7pMV60+ZdXgnObS1pYVy6Nxjjy6vrHzScY/2UTz/6pKrLznxlTc+bWjSKuEYBk4YO+zF1z4GABA1SrBzf+Kz+b8ZOPnGHEdLJBKL3l9YWVk5YtgIv98vipgW3/tgEfXNf7Fm1QXXrf568UOr13xBLKnlC+6VZaUQE95Bj3HowyVQypkNyBjokU91Ft+iyQgiPX7Hdbf++ZmCOdTaEIq4ROctxyV3bS0tN59zmsOhfRIL31961qlH/vHBW8wrW9si77y3osPHbFEBAIjHT7a81NzcDACo2X1g49KHk0nB48HVn7lJ2UFPdjt8uISpU4OZqHQKfePj9R0+nlmEajORTpl1OBlZt2Dp12+vtvF+2nFofrruJ1L0IM5E6nnQmuvpUydMmThy3/7mDz7VCtUtWPxFXYcbKnOiFkGSXJp7lGHg/T+du3Lt9jffzX/HOg2VC8Hh0yfmaLPUWxjzxGEVpJTTsn7pup1DqrWcZUXBNfFqwFLzfe5rVKeKQhs+YzlOMWrhVUCmejwuXKawEii+MWYeRg6zcNnG+au+yMehhYrd8orKJxf0cQ7tnwTatcXaFrtyx5bdZGHsxJF0pdMoursJtNfhxGEVtBY+B4GShkxqDWcWAjXxWSEESjnUMOEXr/yacOiZx01hIPPGSlxF1wF/qAVtrS03n31an+fQ/oMO96MsZC9LvzULMp13DqU6KBy0mrM5nGemceGGfFoe6OKVXy9eiYcbn3HspItm4XmhFnRsgjzh0E5cqoPSQlEDyyxoaojnYMnqgT7yKPBoU0ZUkkeHr8dBnwcqru6oONgk0hMOPe2YiZceNyvzVYdDHXSMQ83USZbNj05+qg6HOsiKzELOIkHzlC1487N19i1JCIfOnnno7BkTbv3zs3a2fNE1SG2tOEzfH1yi/ZxDO2bgZ+PQ6oG+Aul1yohKx6J3QEEdoLnos+Bqzmwwgki2OGnmoQCATA4l+Z4dO2Xf49B+FUQqZZHoEGj/DCI12jVO3FGHE+AIZAVBPYhEfKCplFEobEFmEMnj9QWGV9lunLVRKMHilRsWr9zw+B24Js+CecvXkKzPYvHXZ9/uwF4OHDhwUGrIQ6AE2TiU0GhXX5IDBw4cdBlyDDG2bTVvzWHKCa7A5KTFKzdkm9A5b/maft6WqQ+YQj2AQpK0O4lSy/F2UCLQ/ZxdH4xnigqsZ6PaYnXoNbNPLGp7Bw4cOOgoOheDz4niZiJ1FYeKydjNTk97Bw4cdD+K6lxXLJjH77iOZLkXxaHnz5zRSQ4lDUcK396BAwcOQPHIkQYKGWb4oE7Nhvl/FAVv5hgACFcAAAAASUVORK5CYII="}], 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"}]], 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"}], 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"}], 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"}]], 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CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "Both ally feeds show the full front glyph, while the hostile feed shows only its edge/back; commit this completed configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-0a895268b515", "created_at_unix_s": 1788843025.656042, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 9, "requested_group_seed": 9, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [9], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "assistant": "The default panel is nearly edge-on to ALLY B while broad to the hostile. Rotate it one 45-degree slot toward ALLY B to favor both allies and turn away from the hostile.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y1 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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e8pEQASUQCO0yPwN8vSTz09tbAym490TGk7fPzMopkco3eR5k5BQHhIUFaJDm2ByYwuMujNAeAhFQAoHQjtgRz1U1mLH5WdtgVDAU8n5CuONw2o1zR0sHZ+QUR0eH+ShhQ31jld52krueRKv4lStmGEzGKm29ZIT2FIiAEgiEjiExNoSiICN4P3nAn7l8TXoooV8khKC6RhsSGuKvYeSr+Cde+AQv80NCgiQjtKd8JERACQRCu8zPED/UP6m2wbjmpmkQ9QMRNVSukhSE1TV1oSGBgLPQFFVW3eAwsBMbobjDSLuM0K6NIxEBJRAIbaTKqp4AgKSkqA1bTt6ycgrOdWdoige8pJLllbURYUH4XkVFdUBwKKQV0nmwEXrjihmofwg2QnsIREAJBEIbzc8Qq3qaLNyljHwAwI9bT92yYgqyQ5GK2qzB2KgQVBcPQXFJ5cCEyGBvikYZ8sBBQyUjtKd8JERACQRCW+qOqhrMeKO2wRgeFfLEfTdIGnrjskkA2s2FhxDSNJVXUD64fxRNQZqGCoZWqET9xTzxwieSEdpTPhIioAQCofXIzE8Ly1EQ7juW/ug9C8wW5Pr8ecfpqVOGCw5J0cyEEGRmFw1JEvKTIKApSsnQaD6SPU+88Ak2Qt288oSXka3aTSACSiAQWt+zDorSUdtgjI8NVShUEMKDJzJumDEMdf8A4GhqZnBEuPTEtKzCIcnRSFCtblGaglcLyir04nmkcBMyQkkUnkAg9Faq6m1pRjHRwQxN1Wvr1CovCOGeI2lzJqewwmy4+ro6yQuK1FNAcnxm5BYHhkX0CxOr46VwEzJCZYmi3RxigRIIhNaZn9LineN5BU3zPM8wdJ22LiTAHwpr+dmTUvBa3m6QHEYQyszc4pFjR4b6MQxNefmIHfCkYw+kdsU8uA6BCCiBQGgL2gbj3SumUhQKuLMsR1OwqLQiLCgAQrj/eMa8qUMF9ZSH4kUycopHjBnZpK2CQqNlBneiE2A7eQJHh0MElEAgtGVih7+PatvhSzdMHkwJIXWO5xmaKi6tjAgJpCi4/3j6gunDnLPa07OLHnvgJh8NmvABIFLMnGulVWaUEwohyrfvWR8GEVACgdACM4aIEy4ltA3GAH8fAMDOo2nzJwxCdigFLRYUjs8vKk+Oi4AQ7jp82cH8rG7if//Iqn2HUmvKy7Bpmn21ROkfnBAuruJxTL8HfR5EQAkEQgs0VBQ77EmIDQEQmIV6zT0nMmaNHUBRyA5lOY6hqczcouHJMfKmdenZRfUmGOxD79x7UjQ2KZiVU+wXGp4Y4Q0gUPn69EQjlAgogUBotfkZlxiDJ1+ygsF4IDVz2sgkyQ6lKXguLU/jZes/H5cQ56e2U8aM7KLgqKjIILSWpyBE4SQVOh52/iziDoQIKIFAaJ35mdg/Mq+gLCwmot6AWsdjvTt6LnvyiER5J5HqGq1YiwSBj+DzlEi/UhQZHxvmx0AhlA8BUNFMQTkamYQqQHuOEUoElEAgtML8xNlLNAXLy6uGpPSr01tw1icA4Pj5nDGD4ykK0hRlZoUWy66UEKtnoNBZGR9BASozv3pwrJ/VCG3dJ3Id5ZYIKIFA8BRtozEoLIjnxfLNwqLyhMSoBgOLTE7B2DyXnjdqUCzWUMkHGh8ZLJ0h/UpRyuB4rJ64bxPFw+yi2gHRPjQFvTwbE1xXcMjNo39YNKDLPlEioARCL2ecomicoqgNT5wxJMph/R7gg0o2acHXyXHIDq2urAmPDG00slL0/HxGfnJcOITAP8imm3L1lO5GhgRAHpbXNCVHegsLd1intyjVdh1G2kBX5uETASUQCK5xUE9to7GsFs2PgxAlz/NC3jtFUU2NDbFxyB+Kg/JIslOiGUblXj0BAGWVddoGY2SAEntR6/VsiK+SoSi8kG/P+r3LjFAioISeZBMR2vBWd1TmPADg9adWpmUWiO3pKIgrNRmaQnWc4cE0TVksyA5dt/30TbNS3KtnelYhSiYV1vIUBQ0W4K+hIQrHUyUlZaCHGKFEQAkEgiPzRyc47NE2Gvv3C/9h66nXn1qJ90CkoRQrWI8UhMYmnToggKIgtkM37T4rHwjvrJ7RkeH+XuLUY5anvBSiy/TKtVKLqgP6gXaNEeqZz5ZAIHRLRgai1B83xNListrMtnDk+Vpfabuq8KrDo/GxoWYLp2CoH7aeum3x+B92nMGWI4QUy/E0DRlINTU2Mt6+QNdgNFmUCsden67VEwAWUAzFAR7ygM/ILYbegYmhXnmloM3cMDFlx4l0wQjt9Ph8H7VAyRKS0DuQq15H4bx4r20w0hRFURAneCINvWEMfgj7Q1lh5U5BaDY0mRgvRljLB/iJrswGVlaSlFWYkhiB1TP7WikLKBoI0SeIGtwp/UMSI30UsvYiHmKnlEquy4xQYoESCL1ZQxWKOvEwc9ul9sVHl5zNLMnKLeYpaDSxKiX9w9ZTaxaPX+dghwrZSzxrNCm8VBYDCqxbl/FY4NIE9cTnzL5WmpwQCbB6ApCGCpOiQ3wZluWolgTU4bTX0QglAkro6kWltK5s1aKS0DU4m58cz6/flnrb4vF1OmNpWSXPA7OFVTD0OpmGCnYoQE3taAoNj+PMTVAlaRdOjE/LLExJcFBPkbTsoqh+KDmU43jOs1JOSUPFF5DV3V8rqkyICcXbf1g04F/broBOo48u4QkEgifqWddo8g/ww8v2OWMTNd6+DI0sTlZYla/bemqNdS2PkudpZIeitT46wijXuFqtEakndK2eMQlSaj264aEgLdKcbZkQibrf3zAxpQvC8cQCJXQwnpiNeF3ZnkUloWuIjwuFAFgsHCOEj9YsHv/LgQxdYz3H8pcyCoYNjl+39dTqRePX77TaoRSyT1EZkgwfCvJe4h5X6tlPCigBIbKfeeJcC5dlb3I6UFvXoG2q7xojtBcKaAcuIckqktDHzc/gEFHaTGZWqUDL9tWLxr/8n/8NTopOGRB3KSN/2OD49dtO3bpw3IZdZ3EcCVCURegsfyUjGz+3QW9C/4MtqadgVKZfQXmmLdO8hgZ6ewX6+wJg6gJPaJ9ewifSNS73n6/1lW5dflF9i+fjDz0f766umXAdkZbSeKq70YTMjvXbTr34yNKMnGKKgoOTY/EseJqGt84fbT0YTdyUeShFWECJ6smjW1p20ZCUBLl6pmXmp2XmD0mO8fT6mms6omKBxXKtqNIWjl/YWeF4pi8vIckq8voiSefz8Yf+nj/9Ol9N38al+ZmUEL54xvCtBy8isxJAmkZpTEoF/fPus0/cPf+/X+8a1D9qYFLMpYz8HwC4bcn4m+eO3LTnPGoQguo8HU9I87ZW82k5SD3lj6ZlIiH2RD2hazvU0SIVPKGmGyak7DiZfuB0ZxmhfdoCJVxH5kXZpWoTO7S7kdgvDA0+8lXPmZRitSspZIfiLvTH0u5YOS0zt4ShqWcfWoI0dMsppYJeNmN4i2duj3oKQPd2aFml1maEqjgcTeokI5QIKOH6qyeGaGj3MT+FXCVkMP6w5VR4sM/k0QOkeRu0NZ3+7OWrqxZPyswt2XLw4qN3zU/LKvxhyykfjSIpLgLwov1J1RZRtUXaaxnSmdNzip3VMyo6qjUrd3c7r128FOEXLA/HYwQjtOMhAkroatwIJdHQ7sCEsSnBIQGSUP6w5VS/yIApY0QNFbKYxIfSrhTMnzkqM6fkwMnM3906C2vouJRojcbbaQWP7teYqZSUfs7qaW2u3AFcK6pyNEKVnWiEEgEldCktSiTR0Otrfk4Ym7LzwLmwyFBkbFqHc/yw9VR8RMDYYYnY1KNpZIeyQrp7UXHFxLEpmbklx8/l3Lx4UlpW4bqtp1bMGGy02BoqY+o5Jsib7hD1dOPORFanio3wCz6fUWgzQvnOMkKJgBLagqGmpg03D8Xx+fhDLZ7K+Vmkv0GHqOeuA+f794+aMTYxIDQYzXqjKbNF1NABcSEpybFAWMhTFKoawk2U67R1Y0YOhBCkXSlcvWwKAGDD9tTV84fJz9zIKx2GyjWnnoOSYj25cticjCKrs6qyti4ixBcboYBVdp4nlAgooYv426jLnXRwe7peEhyIjUexo03bT6+cMyoiOkwYtGnT0BEDIpMTosS1PC2E23m03dTYqFBrIITn0/PHDUeG6vZDl+Sn9bFvr9xRK3fHiLxAQmR0aIQP9oQ2NjXV1jVcySvrJCOUCCihK2itILbtKYT2m5+0kAYPAPhy0/7F00eERYbhtTyOKa3bempsSkxifGQ4VRNJ10YytaGgKpyqiaBrFg5SBAJtJFObdyl1epIqkq6J9WqI9WfxLRxWSzdzadaEfupBPtpwqkZ+E5yqRanC0vv2UbZ8e881FAKQXVAOLJaistrK2vqySq1PoCrQ22tAbFwnGaFEQAmdTpulkGholzFhLBKXxP6Rwjw4sZ0HQ1OLpg2LiY8U1uxi4Gjd1lPjh8Qw4QPLuaAKPghS4mj4kxevTR0Zx6Fnw4yckub6gRSVVAUGBnirXChP2pWi+MRYX3WzjUQxbvM5YXIicnrGhEaERnh3gRFKBNQd2K1GloftoZ0iSDS0y1KXdh04L43roIRxHZ+s36NUMPMnDYmJQxpKo152aBD8+m2p00b1u3K1HABQyQeX88FFpoByLmjLhfpZ8+ZU0tEVfHAZG1ig9y2sowvr6IaIUeXCYfsyGht9EhsVIeVckMNtX2ZTfGKsjythbTkTVL5FGyUjFKhYRyN0QgcboURACZ1Ih8gf0dDO5sbFKPKTGBfy2mNLsYZiGeUB+GT9HgVDzxo/ODImnKIohqHxWn79ttSXHl2MNVRY40O8f+PO0zfPGaJQu5gK58bviW1PT9VTvD47GZU284qqJCMUAOBohIIONkKpS98/3lHnIhA6Q/gYxU9/H//XMeEm+W1kYIP8JvnaHPY3d+vjn5Sz+bljH2qAtG77aTsNFf79ZP2euKjgm+ePQxoq9J/Ha/kN208///CiK1fLHRLsf9xxeuWsFK0eT5zrBPV08Ho6wCkAbQQM49IIzSwu7lgjtBfWwhO6Ax1oNlrMNwEAlkfd9PK5odJOtX0rPNLcoJ3mZ0JssNFkUSkZrKEvvvfbkEFx0jGvv//zC4+uvGPJpA07ThXklQCIAk0MTW3aeeb5hxdtsvayoynUcESlpDfuOH3r/GGbthzHTw9Q0pJ6ZuYWD+of3Wb1HP/Sx47tlq1tR/H/GYbOK6rqFxmdXVAumKJGwQhlG5uazDxyTQAVspQ7qjqe+vu/1hEjlNCxdNKim6zlO8n8/GzdXtwOWQwT2duhmNff/99bn/12yw3jY+IiUCkSFNfsm3aeuXEe6sMk9LFDQ4nxSfYdz5D06Vq1wXP1xHXxrUOmhDHB/v0iowFtROppNUKBkbYZoVdL508e2lFGKLr0v/9rXftPRCB0gcwRDe1w9fxyw77kxIiByfE0BRka4hwmrKHOT3/7s9/uXDalf1KMoKEAFyP9vPvsstkjZcXyyA6Vt/holXrGx3lSFO/OeCwqr84rRgWd2QXl2BOKwZ5QlNnKWOZPHnrgdFb7NVS8+jVjQtp5IgKhawSOaGgHcuPiKUmJKOPyjpVTh6UkIlEEQNLQNXNHyA/GTs03P/rllhsmxAq5TTzH456hm/edXyT0YZLsUNxTWcJz9cQp9y7a4XkQRUL/MAwyQiNsRihgVc5G6K5jl7Ed2k5sPwDRUEJPkTaioR1nfu5naBRbf/nt9bcunZwysB8tJDBZLC40FELIcmj/mx/9kldQRnl50zQq6LyYkQcA2Hbw4sLpw8QiJaEGVHpigJfSE/Uckiyqp0RzGuobM9UvZppf7DT/2On+cTPEWzz6F9DGypo6wQitFlOarOAOI7X1jRQFb5g+aNexy0qlop1GKAkiEXqkqJEezK0i1exiXXzj4imnzl/hAYAcMLLcS2+t++sfbv3mZzY7p5DjeZ7jIQWRhi4cu27PBfwUCqI1Pk0jkW1qbOBplRdlTkmOu5iRN3xwv+2HLs2emLLvRLrDCluLR3q4U88CeTu7sIlLJz77IN7GRrEEJyh4s0BgaAKhkT7ASKNAPLAaoUAFVCialJlVWlhZDynL1QItSn1ttx1q92MQI5TQg0xC0rcJTwBzf4v1Z5szP7/YsC/A2zvQ2zvI1yc6NCgyKOCjr3Y21jWFhwQJzZY43E5p3fbTkQG+1psf0lALJ/QZoWeMTcwpqqdp6s8PLU3PKUF5l8E+syYObu4jQ+qZ4Kie9TrLkGSbfeoJsHkvqJjjyTBF5dX5MiN09yE0V25ISmRCRMCCyaOXzRmCj9917PKfl4vbbYBYoIQevKAmdqgnxPqzZrYhzstOQJsMlrhQpr7wUmmVNjIkAABQoLefhWM9nAegqrrepWbt3HUq2AvUVGu//flweICmukr77te7nC+gugqZe889tVragyua9h1Ac+gKWdejyeS0KuHI0ATUGmNMsL9khO46ngYBv/tkxuJZo1IGRwATCzjV0jlDftubZtXQoW/+irbbK6BrxoSsO4MCWASCJxB3ZDefAIYzZKkAnwK93f7hgxIuZl5D+qjxxQ9dySsd0A8FlHACPGzGC4nbL7WBN95ZjzckJZ09U5xDJ+ffH/4EgmJarHl389iB01k3TB8IGIbjWYpVAdo4d0qSmvHmeVBaURsZFgiURmACkFEtmZ2yZV96ezSUWKCEHqyeZBRd26hrNNbU1I5Ijjp/pYQHPJ72lhwfyXI8BCCnoAyN5RBsTp7nKUZUK87CY48kx/E/fPinOx59i0ejO3joSs7crLIlJZUi5/L/uwQ5ap12nvjrA66ep5aMUMpIczS792jOvIlDAGWEnCoyLBBraF5xab/oUIpRcxyPsusBuOvhFSg+1koNZbYeSVs81c4FQIxQQnvUk1H81LE1SM1B1LPNqJSi5TRx7ECxsAciWcwuqAnw947vHy9J2579JwYKeU5IwqyW57NPrQFK+rufXrvjpheBKKqttkxx4ZDwL5JgnCPlfIK6AlsTboji79OQdP4NSacnRuhtq2YBAHLzSgYnRgGlsU5rwRraLyEo71plv+jQFXPG/G/PGZqGd9z52nffvthaDSUWKKGDbU/3qtdREPVsM3qTRbIZDfUNx9KK5Cv1kjJ8Dwkax/MhIUFY5iAA8bEhF7KKK2p0v3/p03EpMSEB3iOSo8LCAqEgvriHEwUBFuQvNuwblpJQh5yfIDjAW0zt5HmUPQpArVaHGyCPmzx2xtj+I4bFg0CfO25+CV+HqSG/ruCQeIlWVfZQnFmOR1PprUboPU/956t3nvzhxz0ZV5GG+geAOq0OaWiZTUMnjoo5eb6IokQNbZ2APnjn3E++3UOMUEIPCn+/fG6oOijo+l5Dz8XlitsZXliqC1oqrtJRapNTFtHZdJQE6szwIbLRm1aDFgI4czzKu+Q4Xm+yfL5+X/Gv+375dR+W47HDEvVGC8fzZ/IF3W0TBaW1CdFBNiMUgHue+g8AYP7kIUhDB4WX19T7+3k7aOiKeQGbhYASmhu644WUG1738OWo4mulDupJIHRz9by+F9CjuVLoGPIeHBeirdFKt7oabV2ttq62rkFb19TQYGhsRIM7BPWzWDh9Y6NDZOli2jV8G5AQiVyi7uuHIDiQeuVA6pVDZ7JPXriakBAtP3zP8fTDZ7KOnLni6nme0i8qCFdSYSNUup5dx9IS4sMzMssHJAZdySsFAKBoEqdCGlpcCUw0Tmy6487XsIa2bgnf1zyhqeYY0ia5J0LUs8Px0SjvuHmO2cKyHN+oN+mNZo7jWY5r0JkoClhYXltVjvVLbzRTAZGgqiIhIdpIo1gNAGBYSgKOF2VdLZUrHc+DujxHKRRW+mJsymzhcksaKFl8CIkd71YxPdNRyrrqxzmhnOA3wBo6f/IQYKKRhl4VUw5EDRXs0GVzhmzem4YX8pfWPTlszX9afi30Dw0WzyBGKKG7Ox+JeraTs5UwLNivoKSioKRif2rm9qNp24+mqXy8S8qrK6u1NbV1DQ0NFpPBX8ME+Sjjw71jQ71jQ2ytkYPDQxKjfB+5Y65wjy9uQCn6SJrwCh2i27TJqKuIA5IZKHQgQf/xPDCaWX1TEwCwX4KYRT8uJdas8m2CXu4V8+TfxCIll+AXkFrezRo3EALb3V3H0gwsK2qoYIcimrFD/7wcdQ51j60kYOsRR9cpKUwidB8NJerZHs5WwrpGI5rAUV0XHhp097LJdy2d9LsVU+aMR15CjZfS20sp9efQ1uu09bra+qba+qZ6nV4SMS81E+jndfB0zsRRyUMGoOHD2Ecq5/Dx83hDHpHHiilXWx6IXUgSBPXE9qyXklk+K2XskBjPsqFEnH0GokgLHDidhe9KvU12HUtDCfb2GlpW3iRqKKeSipRuXz2vRQ21ReGRESoWfREILfDyuaFdmQRK1LP95OrFRTcAIKewEgAQ4o+sy7yiqrQsu9afAIAl88blFVYIm7Yldn5RlcXCTR07AHKWaSPiGZoqzL0W5ONV04hEtri2CedUIrnkeACBZLuWaptYFgWgBOcAHx/mq9Ob63VGMZVqVHJ6dtHlwvqUGF+LyRTkp7omjt6wp5XZ+yzL07Qtkk8BVMWPW5xs3n922azRwASktXxEaGBZOYopAZMFMEhD737iHQDA1/99SkhsQsn2LqG2HLeNByFGKKEbihpRz44FqZt8XQ3AkIFxQwbGvfjYiqXzxw8dFD90ULxVPcElodMSBkKoYJBAFpVUrNuWunBcvNzSROopZDLR6F/KftQbGvgxICFMoaBpmhLS7xGfvvnA5NEDGN4yYnDczEmD1YEBOXnlxaXV7fnpispQlAxCEBriFxToGxQoVmrhXqUsK/7UBqMx42oJtkPFt4VHdihQsqj5iNUOvfuJd9zboYIFan0nF08fAtz2OiEQutgOJerZgYRRer2UDy/8y3L85UxRIqWN5kjPuGbhmTB/L4PREh3m98lPx+SPMjRF08jJySMlBSjbU44Qyuc43sKygOdZwRiEEJQUl2AbNzYuOluwPO1GKcme7iGzxw+4UlAFKDvpj45Ao+WEUaPiHhxQwsmhqLJTLFKqAZwKF3piO3Tz3rS7n3jHjR3qmEjvHI4nEK6XhhL17Fj0TToHMaIgTIwJN1FKpUrF87xSQbMsy9CUt5dSwdCA5ypqGk26enxwYmz4lYLqyOioksLiySMSthy2ExQFIwRUoHXNL3OC4kRSHGUCPFJtluNVCprj+NS0wnEpsU1NBqTvGnr/mRzUwT4aOVibKxB1D4RQZ2S9vehxI5L9/XyMZrZBb84vq9NA5DHAOf+4dtOVhgaVVtREhgXhQk/AqHB5FV7O/3n5EGcNZR66cVJxebV0ZQ5GaC/OZCJ0cw0l6tkFQAiGDUngeJ6mKAvLUhRU0LTZwqoUNIt6gvJeKiY7RxTQhBgkoEIgCG45lB4bH12Yi9qRYExK6/AMhbRPdK3yXlE8ALnVEGiiecpoMGkpCFUKGjXZGxTNA17jJfpnIeBNtNQepS09SyCEYYHeOoNh2IAY3P7ZYuHiw/3qdcYruYXYDmUFbwOEzWpov4SgC5cKRwyMXT579K/7zuLvAhR9crok6uOf8eQ8XrpJnlCinoTrJXZEPTuJsEAfhz1NeoPBYGxq0puMJoPe2NDYVFvXWFZVV1FdV1FTX9/QJB2prUdZ9DRF1TYYhiaJclldb9/lSaAgz24wnDCDDknPxIljzBYLvme2cDSFfKPyQPvUcUOSY1BvPVd4VGoPheAYBCA7vzy3sPJqYWVBaXV1bb3RaIwSFvLY7ra6Ya0J9oI/FFDISkUaWqYbMSzqQlYhsFiWzx0ieU6de6pSD90204OrIhC6TvKIenYiTiK091iamF0k3fCBEF7IKMjMLZaOPCdUbZ46k2E0mStrG02NqFGedLy5vpDSlwBdsbmuIC6U4XW2JwrOVo5luYOHTvA8yqVnOd7McizLV9cbhiZFDu0vyjHPsb7qVjfocFA1L9QqBaZezjt9WXSDCiOXoZKhoyNDpICSPDlU1FAAssUipSDAqUQNNdEr5w+VNNQhoOTiWuu9wontSbhea3minl3MrNGJe44Ki05rcySNBs91s/YUsYfjkUNT3YTkJoBrZGhxXIfS10wJ+Z4sx2Eno5IX8yItIMvCcyqlosFsyDJpOC8v7Ii0sJzBiLQYAHAkvSI5mGEoSljatyuhUj4FJNWqoXKC/VCuPk4OxZ1HpCKl5MSg7KulydYiJaShwlp+5fyhP++6xNDUrmNpcmcolZFd3NL0OwKhi+SPqOd1gTPq0c0k/Gs2NNbVoVt9fWN9fVNDg+0wM1rhYu+hc/46jVoxQUihNb6QDGpn60II1SqGouAA36YYVX28piFe01CXfzmSqVHpirEcAwAOnc46m2bzq3qOs4gNHxht0DU63PBXQlVdE1QoxcQmpyIlpKGyIiXJDr1x/jD8bSJoqGiHMoOTo4vyS/A52tRnmkDoGDuUqOf14p7V81CjOQoaTchBiXeazCzLcVW1TVevodgLAGDo0IHnMwqwTBi8owSvaE01i3ygEIAqI5JXFEURsuh5HihxMzsAzAo/wAML5Zerg016UwyFguM8D/z7DS07VhYpnAoAlHnKWcwO1+a5KDk0XR42qN+wQfG4p5TZwhpNrNnCWliuycRlF1QDc5OUYC+3Q3GCfXN26PK5KLHJqqHIDrWb7uTBLGYCoVOk0JOnqIOCpBv5GDoKWu1VXF5bUq4tKq2pqK4vr6rDtxptY119E8ei4UUYbxWKm7vk9pvn4A0pIiTXMsEyRYmhnNAniRfUmeP5i1nFDgI5ZOigwLh+bf5Z5PJVVFZTVFZbXFZLU5RaqfDzVgUHaEIDvfuF+84YFTt6SD+oEKpXBTtUauC0ef9Zjla4sUNl0+iQHcp8/MOBxVPaNRmZ0AdpUcL+nj/d88Z3f8+fTjSxazCZLAAK/yIVQ/+qbBWeduB1uny8u0at+Oi1e05ezP/5lz1FBShGNH5Y/2Zfycl0xEEbnEWE3QDO1mVihG9JPcoJbQ5adj3O6M1cc7VJDvA8PygxwmLhsnOLpJgSdt2aTAa1SiXZoT4aNW58J9qhQxJwgj3WUOahVVOLSsSyLQKhA/FQQ1tsTTIy0OaGa45YWgw7mNmGDpnF1nfYi9Ib3REVIr5XB09l3rRk8k+/7Hn5iZvxnp37z+ENlUppywO1orDmgZqUEQEB/iW1WsZXaawuADxQKmiUZer0Wg06F0lRHq7ljRautMGoEZbdaI7xERd+pHlTh9ri8qg0FSo1PqamRlykhNPmcUDpWn754MSo5EQUjkeN+itqosKCRgyLkorlsYaSkR6E66mhnjR28kTs8OxJdLCZKGPrmDw07sjZbLeH+MqDSE/du1BIgee/33bOX+la1goLCmPjhGoigYZGMZmUB8DCsjSFyj0hgEPiAlJiJ5pZLqe0AYAKSoGqoUCbMLFcaYMpT2uQkozw1SKbVyEkFTSvqpHBPmKRkpBg4LJIKSosqKS8Jio8SCr0xFORiYASrpuGbsnvB0Ac+QCuLxCA25ZPZWjKwnIWC6fTmywcSnHXGy1GM1tR02howl3o+fiBySYzt3Hv5eEpCUqGGZqSWJgjRsw//34726CjfKKFsR98bFyslGWJ191msxlSqHtydPIoqv4q5CHLcVpt3W9HstUqZsgg5PfkzMaDZ6/6R0W7uES3sBxfqTNfq9UnjRoi+CXQE1YsmsxyvNFkUSoVdY1GTijAr9Y21Tboq4rFsBimuMwoFsvLFvIuNDTcTkMho7ph+iAioITro6GCehLaixv/hoduCh41+lR//uNefPf+W2bVN+o5sS4eCZBe14BD88H+GgqC0cOSKAqOSgi4XCiWeEqY6wsHxIdeLarmAYgOD9BaYzBQX8YoGd/wpNK89EptYVSQN8tyRRWNZpZLGdRPwVCjEgP3nUKXIpmNnoNa3xssebWG6iaz3CMb4KOWxjr5WJPzI4PReDtueKzeaOZ53mRmaZrS6U3aBkNjLW7jBCcOSzhx6Zo7DY30Rk2bTEDJaIiAuvstJJ61TtJQop5d9luNf4exa1htMmnUKMcoXIESjHx55HD09tHoSzOXzx2Ln1VZ02AwolwiTtAXnd6IDcC4yOAmi4WmKW1lOYRgR1HRqfSicQOsHkdU8B5JQ3itFlI+qCNySSMv9QNNHDKhMu+82lJBozZ3HCs0BsUWn7aizGxh91aJYZhpM8Zfzmtd/40mM5uvNVToTFJGJwKCmjqd7dqsViz2EPy264T8DGtWztQbzamigILjF69dLazoHxfWrIaWIg29kIUSm5i48KA+GEQaGdgg/8UidKWGEvXsQFo0M7F3GLuGfQ3GAB81StL0QfXmFogkbvNRvAx32cQYERXuj/7Hg4tpV8HN04uqmlRKtOims1FXZgkalbXLeoDKtktKypQAnk0rDAv2YTloMot1kRDCkhqDWsXUodkeCI51zAN1v5Y3WbiiemNxvdFkcXSe/rb3LABg6ZzR8udBCNQqxS3L0HB5DI9aL7MMDceMSD59PpsHqM9IYmyYQ5GSs4bioDxTUGL3LvQRztf6yn+xmoOEJjoWHDIa3sFnJbSLCUPjT5xHTeSaA6sP7mwEIVgybSDqSALBZvvDGGMZz/PJ8aFXC6vRQPlAH2mFrzSXo+eKkW7eaGYNQh4VyuFfOppleZWKeXv9KcrXl7G0ooiT5fgKnblAa2hC2Uuuo09YRj0nIsgXNS7FCfbWnvZigr0FXXN2HkqwR55QDowYFkWW8O4gwzsJvR6O4xfNnWBhuXqdEUIQ6OvV0GQ0mtl6nTGvVCsN3kBBGNqb4/iCGvP59HSTk9JBIYfeYl1IS6MxMbj7Eceh9buFFe1TjuPPXdOi9k4VyP5lGxoOXcgLiLC5BdzA80CLXJ/6OoPFZew+NrFfk95UVWjXF8olkLE14CuraQgP8sXG9PQxSYfOZOPMU4PRKCWHXs0rT4wLF94UFRFQAqFPEyU0KFJQ0CcYdYEDEHgp1BzPB2gUCoqvqa0VjkKx9cHxQVgWQyKjaArG9osrsEbhxR5xEKiVDIOGdvBqmbZIpfNmC2c2s0qKUgiqRFNw8w7UTvPGxVNAPnKm8U6lnM2hE1yflTpU0uT8KARg5IAwNEV5eAzP87jQyMdbw/G83mAWOp3yjU2mJoOpvtHIslyj3tTYZIQAKhRUWVlxeGggQ1NHz+WOGhx3PrOQoiBeyGMNTewXIGko+jG2HnUxyZ5AIPQNUAm30KodmYcsi/7leV7BUL4aVOyIMZgsAb7qjLxKMQHeyR05aPwCVFyE+4hA6FAyhGXOZGHNFk4yTt/7djdv0vMmfUqs4GYFINQ5h8kVRgtXXGcsqTearCWYzigYWq1kVAo6Mjw4NiokJjLkpy1H/rf1qEoBvBTQW0WH+qtiw3yG9g8ZlhQ6ZlDEmEERQxJD4sJ9Y/snWJQa9FNCcPFKUXJ8hK3biNGIJ3oiDS0ohwAwP/xmF5AiEAh9iqtFrqPeyFlpkq/TYXmVlmW54YPiWI4P8EUd4SQLFAJw8fwFoIy4XAwAjeLXOVVAmu+OetwJSof/5QXbEwCwcvkcvJZPL0QBCY7nEyL8sit0Tz+4wOlyoL3r01RQh12frjFb2Jw8a1hM2EjuF3HToonyYyjKVtqvVNAqJWM0WRp0RiFVANQ1cr7AlF1Uk11UQ1m/DD786aj0lN/fMSU3r5x57YXbn3rlO3dvMIFA6BVAnsVd5dONqIlnsDDWuLquafhgNF9TxG5BLLQEFdRLrWIaDRYFQ6Ob8FhBSZWzMTpv3vTdux1zfs2qCAtl8QsJaNIb1SovylKuFIqZkhOieJ4P8tdACMND/E8eO+frowLAln7kjNliqdGjrE9tM65PAMCVIheB8WxJT5vn0Fm78c5qykIzNNtcXMtEA9rMpF8pAr0Oz6unY0H+VdZtXwz0+0Yg9AaGDoiPDfNDeaBBqHjRR2icbDCy329tdhkaHRGI58SpFXRBUdk/3v9Jo1b886V7v/x2i0alkK/TLRaWYehduw6iwZy4qtwK1rmJE0bvO3C8SdcQ6IOCNDzHqlWMNO0DAFBrYI0NtvlsznAsX1Zek6/VV+nM8konl1y47KKv6IihCW6eouBQxIxm7JpOSeanA2+vP9Bra+E7sHp6nPUwAqGncym78Eoe+pPvH42MhpAAnOoO71o5Fbegw/5Nk5nV6kxFFfXFVQ0AGLEJStMUpQkCBmTcrftxp3ROaN2gDKWAgkmxIUVlWp7nw0L8qq1DPWBTMTRzx0+gNksURaF5nwKPv/jp+68/iLePHznD87zZzLoRpQad4XJJbkm9yY3rU2LO5OH7joq9TjA8pF2qKgBg7PCkizkVFkrNcO56QTnTOwWUQCC4BE8Ylu+5dK1i6CBxCY+NQS8ViqQHeCuC/VS5xbXAgnsPw8hgTbZTZzjeqqFoLjzqvInazvO8OHRTOojluIbaApUSaSdD++qFYicAwJ9e/XL0wCiNCnkFKNZyraBUnGns4spBaWV97rUGPRJZN4g/XVyYz90rp4k5ALwrrwSPWj97qVUnzmcdSs1CJUkLxqAWU4cvzZ+YvOuErcHKvKnDAAD/2+MipdRdcz0CgdDbEFLZpVZyPAAzxw4oraxzuFXVNtTWN0GeS4wKxM+DACRGBzZzSjBuRLJQiQS2H0FKBKFdI1HUT5mCft4qhkadOhQMklrcYFR+Hr3R7Bfi2BNPenWWZYvLtLqmZvvdAQDScksz82x1lVLLUTySHt8oCCgK/wsZGprNpiaDqOYbd57R64RsKnvv6u4jl8wG3fLZo5xfkVigBELfQu52DAl2rYlYfRiaamgyiv5M1IbZVZImjx46dSE7xEe1Ys6o597bhnfL3aO4Y7GCoSHLDYwPZlnepZuzf1JsYJ0+u9xFAIMHoK7BUFpZx/O23nTOrJyDNO5CVlFhpbakwlPn28UcVCiFOXAGrfHLa3WotR2y1W1XWlpWMT7FMcuKCCiB0CeI8+ZrDXYl6si2ar6b8rzJQyhKUD3rHiXjYsHKo/ARpxAe+mXvuQmDI+dOHrpp77l9qXkJsgnJNE31D0GRK47j9SazywCQxWzWqFyviS0WtqCkpq5eL5TvN8v5TBRGR5PtPOb7w1UOp+QB0OlRsj1AmaBixlVFjetoMhFQAqH3MzoUSVaIJmTYQLsugvNDUBmSxK6jF521NTIUjYQDABw941hxwwv7aWFAMcehDngAgD3HLv/tj3egOVd/ehW1lUuwTQzCXd9pijK68mNSEMoX/vJnVdXqrhZUmS2cLbPfFcI4OzBhVHLh7lQ33w1ygoMG6SvEmaAYiJLwKQgg8hcLGQhu+pEyCUIhF4FA6AtcynIxJ10iMsTPxV5BJb1UmvmT0DyMHUdsIqvx8dX4oCSW4vJKCvJS941/fbChuZfA7lFpiJsDDhX0GJPZkp1XUVHdYBcMcv10ZH7mFlaPHJTo5jDpe6LM4AMMRQwTxgPg6x9oqUEOXBwQQ6t3wdPgvpszc62g5fxSwjhFUaoZdTkkEHoQ4xRFHa680aF+5dJ6thRXyrsGh+GjwkKdH7KwnFrtk5Fj1+ljj2Az+ts3E+EBqK7VXSusti+LahmdTnfVVUa9/HuirAjpuMViVKq8tNVl/n6xOERlCR0Ps39FtmdLzfDJEp5A6P2crYSDfFH1kejao6CZQ3OGAzWMlPNun/zeFTZvdCgSMkmOI4X0frEzqBWW5fKKauob9OameoWXpwOvdDp35UzOmIx2wf0zp04Cxi4foAKIyVUhYZFUxUlpPxFQAqFPIAzCRAk6OI6EUokgNHF8XZM5PNQXADSkqKrOoKaRyPI8MLPAV02hhkYA6IwcDXE/UGiy8N4+Kg1kbYLLo6fgUUIdTl2DPr+4GncjbZHSaqTFarUYqa8zoiZS/mrU4QR/eeCN5r4qOB43OsHdo1z/OFUVpfJBXkRACYQ+AW43h/NyeAC8vZQQItHUmzhvLyXHoazy+iazt4pmOQ4C2GRiNUJLOgiBzmjQqBUUhWaumVmLr7eqpLIhPMhbpVLV1NQjdePB0JT+OdlXkeAK4lSrt+CUn3Id6kMMKVohTIYP1ogJ9jI5E5/ipGtI/gpLamvrmpiGKjPtLoHJmdzCSo1agQbJNZlDAzS4jwkHQbVW76VEPaNwi1IJH29vnuPRTw6FPFDhcXnKl0uIgBIIfQMhrixuoKCQQrBHKY2aYxg0pR0KquqlECInEDIKzkuBbDcsNL5C4TzLcWZWTIDXeCmDA30MTXpBNEFooHeJsB/pMsvV6sWe8ygRCkKOh2q0gcZZQgC1OmOwn5oXpmDyPF/baNIo0DbKGJUC7TxobDJdK6o2W1gGAKW/u7iQA1eLKiFEPyNazhssXioGyzTyqGr1Qps+9OUxf6TfyUvXOI5r4AOsUol/fDFehZvaOdRuyWGS+nnU/5lAIPRopK7GWAyEFTeSjAAfNbIExSJOhYoRLUGaYZSAQ51EhKepVQyPHKi0hYOMUMyuUjCBfl6VKoWQcA6CA7y9hG1eWNEDIBaVe3spRw9JOnkx20eNqjyRmHJ8g8Hso0FT4LFoahuMGjUSTghtPlCzhSsqrRWC70Bn5tUe/Iw8zydEh4bHhiMBFWpShc4myH2BZRCXGFktbj4kQINGGQPgC7VApw0K8K7W6ijfWKtNLLbZd/OKTE5e6YOrpvbBuXIEQl9GEE204e+jQsVGAuFBPnpdE74X7Kep0zYCwA9NisgprkPSJ6QohQZoOAhTEsMA4DUqhVqJXKuCBapBfeatgivhq1EF+muC/DUqiPykWF7VCtTqWNBZ0b5DLgLhuU2C5WqysNU1KPfTaET3fdQKbUm6T1RKiz8UnmyHNU8hWNY4LUkwJYXETtl+pUKoKbWG9/GzuAbZ1HihOTQyZnV6XITQpEmWvyJZwhMIfRq1ksYihUQzQFOka8LGl49GVVfXiCs/cYolEPrU+WqUdeLxUKGgaEGbAAB+3mpxDUzZNRVlGMrfRxUZ4lunbcT2HAOht0aJrFGcaCm8nEqBV/o8sKCWyWUV9fnFNVW1jVjpvNQKndGjOJJ8ICjujS9to4GGyVFZeVVCaSnFC18A8rZ4ZvvsVClEZv1WEK5Eh6sJBNnnSTMRAqHv0S/KVj4jD5KolYz7nHYHKOHJqDEH6ojsJ+veYTtGpWD6x4YOHdCPplBLEbyKDxQa2ls9B8KphHPhuskarS4ztyy/uKZJL1bfKxk60M+TRbwjw5JduCiFqSOCn5eh5COVpFRT4csCbQhJX/zk4fGc4G1ghVAbTmbAuLBAV48LVifMBzz4auO6NlwxgXC92mMLRkRDR3WM7Ys0o5kDYgNwlAmvf4HVYhXAzsJmBZeioFLBJMQEnzht2xnk71VbZ5eqKdNx/kpeRXF5ndnCSgVLEEI/jarJUxsUkRAdWmdtmidnUHyQs18Yg19OuhBsWUMALRbrZYi7bddqE9CoCQ+WnPxE3I6KAgA8/9Qf0Szvd95uxVUTCISOIyG+LWaXjRLZeXRCeEb603cldywr1xOX0y5RV7oq66xjO2QnHJIYnlYgFilBCL786cC9N81ELfGTIi7nlDmYus7k5leZ8SANmT8VPaV1zY5bBp1e9hKsxSLGjnASk9i5Cmi8FHWC+0GGkHcFeSZ87APIeSzsi1z2Akq197MdmpZ2+ekHfwcAeOuTzzv42gmELp8v0AMkr5Oxm7PhpKFci/nqEAVe2vC6qZfsGpEcOJM/MC6guYPrG/RKJSOan6h/KM1zvPMk+vaDo1g25Oop2JlCcb3QksqVfQ0BZArycqSvGny2dccOzJ4+ucOvlUDonnRzyetY3Ndrupkz1KzlCl2aqmBoUlhtpkd9NoYlRWblVdudUrhEvHCGEKqUivbYngNla3YHnJqToFcWd4puUhxAaz4P1EE9PXsZAqGbSZ58rUpwhdbb1vfyfJ2s4txgazyc0yDuT9Ko9l8tS7LvGN8sEP3z3y+3uzlEXuc5LDkyq0Dr5mAGdZMTdQcZgAqqOQG1mM2MwrOL9MACRTppjba7TZ+XXar4f9t5HAqcrOcleMDIwAYSnXCJna7JWvAQveue5DQhL2eOMOsiJ02WFwlATqZ1VhwAOWdzpe0B3mo3hpZZMCebM8UGxjk0xkcNRvEWz/NKXS0HodFscVA0N0WWNRqvmlpUF59zwa6VSc6Fa/JvBRRTt0d0fXqinTYBtWmmGHbqC8T6exS3bW14t9dApI3QKq7obFZiTnpRSEkO3s6uR1Ztzvmr6N9L6Msz50IeHeKV02RMEspDnUH1SI0NChUymfH4JlSwRMHyU1uFrHjRCctQcND0CZcOnXQwQrecF1/aJfhbQdzOKbV7zBp2b84v4VpAyQId49J47D7RiRYhkkfokXbuRTsjUYWEUWbE8SBXzJpivUYM0V+w9ZlPGj8S9cQbmFScldOehbzspWRWrWd2pDyZQFj926vpJx9/cvcDj7/72Qeg11FYR193ZSSSRyA4I+YwtYTosdWgnHyHlXvbaIOvkvni43fvffDxrz79DwDg/ofuBgBMnzrSZEE5rjwVdOd9j3Is9+i9a9//8iPQu0g1x7ShXzeRPAKhQ2C1FHJrOqEKFOPvnuM1apj+3KX2G6FS6abnML9b+8S+PZuxdFr3ocl4EHVpbt2P0T1xJ3klHmgiCe8SCO0ANnAPrUZZ9BiTmc28VnkmvajBVMsAYIEuVK+srD4ogDYbm7Ab1CWKoYPkd71GDWvnp5QUG3ytuMbuyluxhJealYqppNcTYuURCF1MpDIwv6xWac2YNFaxLHScgKkJZnVlRkjZJI/ihTEhsiM1YS7Gan60br/FwpmBMizIJybcv6y6oa5Rzwmaw7DIDeosozVaNiigLRn7bcaT2n9n7Auq7NSzFacjkkcgtJmHF89r7qEPt+522FNmHZSmAcoJwxOcn7L/YlaMj39SXBiaP2FdPw3yCziQno3qDJUak8kyOiXe6Xn88H4BM8cPQXq35yiyqXiTbyAqFtJqxeTQpmqkaDwnRrEhpZBLJ8WbOKhsqnBatpoNFqjgkdaYaqpryqvtslksTjJaW4tEWU5TJacJbcWo97bRtlg646F6EokkEDzk4blTnHd+KKiSs1Z+vG73wVMosjxjvItmlw8vnivI6B6H/RrQ7ID0xeOHXkgvzClAHX7lxZIzU5JHDk/47qcjSgWTlVc6dcLgynLHbPYDwpWsFa7//U1HoNmgVinuu2tWVa3uTEZhdkGlCaA1tdpUpYcauZKiJnVKDfL7mVnIGeXCqrOovRkDALxJuGYFQPpotr9+Zxl1oKkS6XKnyqjU185VChPvkQXaNxJACYTO5cBpNF585tiB8p0Pz53y4Z6j2w+dBwAsnD5C2v/QmrkHT6UDAPC/zcnogumDVzz7rrSnCZg0QHny4jW5Eerr5/+XNz+W7j7+0D31GQXS3VvuWmvQN/3+qdF6vS77atGhA7unThgsLJabWKOj0QcAePTmqQCADXvOb9h6HDVpWzz5hsmDxVp1nf7bdb/hbUlJjSz6F1IK3mntr7OgMIM3reeRdCpalFGXGoplFLpygHYIuPFoa0ECOncB+paTAT9///1Hfv9njVenm80EQvtZO3fypm/E3Om1cx3bOHy059h1eZMPnM5y1lAAwPaTV7YfuiDXUCya7mV056GMX/7xuFxDeYBmxEkaSlHUX978+MiBHb9u/Jrn+eiU8e9+/NXXb7+079fv8eETps6Wn7A879KRkxkms5gwNCwZj4CzntvKrXPRdW7Yc2Hd1mNrFovvrZ+316P334I9DF6y0fB6qAGCmAaEhQGnOngdK+YbKWjruCR3MmoAAB1fXu5YwGJhWXMnGKSOjUU8A44dN5kChvvWPo16UPs4FgZY9KL7g+XYn3biT6L3gIslqqKSWjymxcMInuObb2vM0xA/wPMn0o3ggVumO+z8aNshtQ761CEHHwDg5rvucn5i12soU61L7o/8jA4aitl+UnwH5DKK9OirX1JSbG+IS2v0wPGL+y+m2S/k+QnDExctXzN7wfKnH1ktHTlt8erli1fcPT1Rq4lbtOzGB9Y++qe1tkcxB09cDgq2lVFiGcWeUNRXaPupNQvHS4+u33MB8GDNkskuHbWcoKQUhI1AQwE+jhMLQAuYZofBSUrqLKOoZ6gfVKg01WXVFqj0U7IUhLgeVA4f1Kwrw0Oqs8XK1PjIwCKrT0MaHoXz6udNTCoodV28D9/72CaLAT42RbfIUrRYDn1NEQFt50fViykrqpw5WHx/DmSIXzmzhot/h3L2X0xLZm02S8yw8RezUTbu8OQYu8OEiIeciJhQWkiRdtbQrzYedi+g11dD3cuog4ZKMlrXqMeZ3Utnj5Y/euD4RWl7/8U0jbAiBgA8cv/da+55FAvo3uPpcyYh8a28fAoAkF6n3rx9d12d9g9PrB2cEOF8JQ4y+vjt8+WPymV0/e4LAPDOGgrkjtoypDUhXBW+Vx8xFkWiquwyhBxQ0QaGkS3bhZ5IKo1SodJoyyoYRpxV50wHCmhcREBxRZ1rAZ2QVCD8UO4FlA/woR0ElGVZyTNKBBR0V9YKoQZnPnIKPkgE0z633GD3l4l/b/w0iv/buHdkWOTEYY4R3o/2Ig3q7x0wb2KKy+CD7a6gof6M9/ypyGtWVaN3I6DStnsllQTUpYZu+uwL9wLaDTV0ycKxj77yg9nCLZs9yuGhD7fuoWr0kdHR1oE+YOms0c4Cijl5UbRnDx46TlE0x3EXzx6/94GHZ09KqRIE9DLnf+bIuZqa6qCgYHzks4/eZjGbm9PQpPgoAPiFU22Zleu2o/PIZBR5cgc7dgABxwuLnZdu5bQo2d5eKoPJReY8RIEjnlEosUZSFJruYWFZFRo+DHS1jXba2mkCGhPuX1pZbzuzTEDnju9fWG7rWSVH7kSAG/+3zU49OZt6ErqStXOnrJ075WHrDe98ePFchxtq/R0WPSQsurXCiuGdbphnVs0BAJy4dM3h+K1vP4mftetEeuIAO5lzAFujFpbbdjBt28G0m9bc+9YH3+Fb6qEzEA1CFCmt0pZWoe/2KbMW7Np/DKsnVlLMrJTkWSloDmJZUSVkxVjHpz8eau6lv/r1uIsfTLitnTN57ZwubXRrCfbO0FZl56IOGvuFyJKcLdtPv//KbQCAzfvOOTz08OK5D925tFhfU1Ik/nlv2X+2uVeZMHxAVBAqR54xfdL9d6zIyc4cOXbKuXMX3/5gPc3YosT//M8/x41IWTRv2ob16/7x/g+NDY3y25gh/arN+ppq/N3GXy0oq66qlm7zx/VHWiPtqa6tFo9sGTrcx02BJh6LRANOTMNE3YspShj/2SLtV0+7szlbuNJsj+afZbeE3717x6qVi7CA4mW7HGKBgs4HZ5A48NGeowl+KK1vwbTh8v1Lbxj+zKsb8Pa0cWKE1BM7NIjyhhCucrJAgcwCxXed7VAgCKhaqbhn9Sz0l3al2NkClUjNLP78i6+Skwd8/N83szMvq9TqP7/ytq+f/+pJ/fABVxrVg5PjAADf/7wLAPD0gyg00RwZ9VqmzsjRKpxo8qDMDpUs0EZ/pLb3LJvk5jzYlO5KmGpdktUUneVkim4+mumlUsydMhTfdRCPj7/5LTo2GkJ4Lb9k2uj+FzNl3QBlXC0st/B8Uqw4LS6nqPbE8VMqtfru6YmXOX/pMKrBaOHhuYtp33z876MH0HsucbpQjPtEKVVmQK+e7+LXA7PxfBZbZZw6KtETC7Q9wQO1GRpQCmlncfWKxR+W4+2oUD95jqrQWETsazdnfP+iZixQF5NJXKpnLwNbIpu+RZ+xg1WiVDD/3dGsgdOpDAkKO3w2e9pox1+4tXMn7zyFLnXn4YtyDf1tx8W5k4btOX4JAHA4NcOljC6YNnDBtEEr//yeh9dQ34RWducrSiUNdWb+xJRdx9M/+noXD8ACwdfWHOMGRScnD1h4w7yURDQ1xmAwvPLnR9/+4Ls7Hn/hu3dfR2Lh75WRXXD6QvoH7/3nkceeLK3SmsxsfKS40nT48sffLp9u2sczSEM//fHgA7fMcHjFe1ZN+3LjoS9/PXbv8maNzbVzJnexhlqCvXNy85MSkYa+9/3OqSPE7w9MnD88cjk/KyffWZJQcCbYq7CpBlY1AeCuMif13OXp0ybK99yxasHG3w46ns0XFWqj33NVsxXMXJif/JU2ns9yYVeGqORy2RPJvWLnT3A13UN6rNmTOAqo0WjsNcbm2kXTD5y47LDzwMUspZmWW0zO1pNU7IG+SCnHnRExoZ10wWk1FSlBYYfO5kx30tAF45OwhjpgtFjSKorpGsOgQWiRdTg1w0FDdx5Gv/1rF8914w91CdbQE5euuTRCrzZpFTVNMTFRO4+nLZjkIlgkZ8aMmdu2bhk3HF0hyii0mp+Yf3/4cXFJsVaPHJxZuYUJcVH5pdU4RbFAyAZ34IGbUTrOJ78caU5D7101/YsfD33x61GeB79b4cKiBwCsGomC3RvP2w3q6VQswd6ZdVVsueteQSrGftKlE3yIRhK1nBoXARltnfbAwWPjpo/BdylvmgpCX1p5NUYQAM4cOTdmqp2n9YN13+t0dnMxHXCpm12MoTPNTwWPXPM63i+u/8DGxiaOs+XMOuBuCb/o1mbLyLqMaE3w0lmOfnT8Bz87KXFAvwh5ECDBy2+BdbEj3+8gfOhL0kRNG4sWdHY7KXg4FcUlJg9PqMo6gyRyoPg7J8HQ9J7L4m/PUEo03aV1UOcJKAAgJQgt1Z0FFIM11GEhj98rrKF1jQYIwGKnN1N+pHUJj34rVt2AIqQOKGjq3c0H8PagALQknDnGRbLRR3uPCRoaCXiQnVcyNAlNcpU4dTlv/FAklDMXLFt6850AgB07tp89vqe6Eq2Yqi6jjyxk6GSVWv33f302ZuqoG5fd9MIzL46ZOgo2GAYnI0tt+BBRuMtKxXiulF4j8ekvR80m4603jNr/i5jXPWvFUrzxy950g75p9fxm34ouFlAJtrzRwQI9Xib+gJNikTvbvWXn8Esu58wR5Es1mUxnzp2eNAEZ4K8++9ierf9rDAg7c+Qcx3FT5k7y1njv/m1vfX397MW2Bh8OdMhveEi3z/8ryLIt2DkeJUjNnDOP5/mmJkP2mZ3yjvezx/WXAvStE9C1ix3DnRgfFfXWz+hvzKVXHq+PknyD5453kb0hQdPU+7uONCegXkrm3//bMTsJLWokDcXsPIrsSmcZlf9u0SZkOsoFlFEonn35bf+AYJqm33zj9cqr5+UCuvreR8dMnAYhtWXTtwd2/SZpaBcLKNLQQEFDxzSroRaWXzxzhEtlVNZbwiPCAQSLZ7agocG0BvBg1UKbgH607sDaNTMdBFTUUB7MHOtaQ4WvNP8reajoepiThgIAxg/tp1AoH33mr7H9xJ+ovKzkmVtQlUvI0Mlvf/Ddc0/dt+v0uRVLV7707MuSlQQbDKw4hxwxMA695yMGxDpfw6HLBS4FdP0WNIa8GwpoO3EjoOjXNWXYIw8+GhMVff7ShZdffdFHi0z4xgD0G/Xay68vnI8iHO99/O6X34ouY2c66tc7pEcJqDDdw4WhOXMuyuiCAOjzkVI5Q7kP1LrER/CiPH3jzNERLuK/S2R/k+5hrWNP0NsdLIbqHMBJG1fyyq7k2SobFkwZumDKUCyjEmvnTp47zKbXrNIu53bVbQ/84z/fqtWas6lHsjIu/Pm5F97+dKP06Bvvfzd20oxn1q559pHblq666+m/vGVhr5sXOL0W/dIfOuO4YF9yw9glrgxGDP4cTX6iT2bbgfNujly7eG5jHcrY2Lj9tDURz/bbY5Z9LgjhkQOnRa2pkrFIyPq+phe/Yy7llDTqGqVbSgKyXht1jbXamteef3z8mJGjRg7/9//9LTwi6oOtyFZ67q//Srt0Pvdq3pCoKAcXFO+rpvxcz3uQ42bULR3mDfoel9MvPfLU2mW3LH351RcdHnrxby+MmTpqzNRRbtSzTxE3UN5J3XXc/8CeXQf27Lp8/mxurcblDW7+5h+CxWEXj2vR/EQ+lO/3DUqIHT/Eua0LmDR56N2vfpLkG/TQneivOjfLXd/ij/YeC2WQcXfTgnEujVCqErmNZk4S160O1qjdTyt4Mw+k5zgv4d/+YP27b72cd1VUgWNnco6fPL3nf99++++/hA0e98+PN/zxgVX4ocCgkBf/8eEfH1hF09Tey1e63gK1GqH89DHJLsp4vJTr96U5W6By6zLWO1iYyAqig9y1Os0qqqmqqpk4DAXBJ44Z+J/vDk4YLL63J4vtnI9cJSpIu2UumqAgp8kMtqWjt7SsqJKrRzlG44eis0mwqtDPvvzmodsWoiBvQRnXyCbHhSy/ac3tdz9w9/TErw+hOTkOOLjq0KvXGyUL9EhJRQ+1d7rGAnVAboF6SN+xQO3tUNcWKCY0RMxtcIaZPxVZbc4CGq5yPfYe8qzOwH790+Gm5ivljx+7/Oj8qTuPo6reonJbbqpLHp0/9f1dR7CGuoQL9ZFM0ZmThmNT1I2MzkxJkjT08OlsSUMl9URZh2OT3vn3v0pOoXUfx3GSeoq1A+LGdesnnV5bkRIYeuhMtrOGNuhNapVi7/H0EO9mc+UKddVsFQoGRgeJcRuXVHO22bYnzriLGFChGqyh7o7xU2INlaPm0deP2ktjEGqCKR86t6Y2KgFdVV6Ncew0W6LMkoVLX3n+r87qKZxZla1Fv0UNzahnn6JVAhcifP1XRXX6V37PxYdCv1qNnJ+bWJGLScVWGIOJVStdpEesnOsYqZAjqeeptHyXRihDgZyGmo++QQbRvAnuWqdgnWox2CaX0fGjBo8chb7Wzp/LcaOhrJLDnlDcauXtD9Zv/eWHfbs245SFDeu/H6ByTAZOGjT04T/+Zddvm/DdOUMHlKengutBem0lV6HzV7l4Y7KykcMxZKSL4LgEHaLCGtoqWI6jZVnucqhQzaYLLXgMKT/l6QLHHhIFRQX/+fynKzlXnn7+jzRFffTfj8PDIuaP6e/Q/4bnesP4g+vFn29xXL1hPnsH/YH88t7yE4esnUGtvPnj9fnF7p74UPWQoliLmAnRyPsBD9OYDgmB3UcWz/1AluYSprR/vj0fbjsqndboqjarRTbuPrtqns36eHT+1A37UDKjA3qnk2MZ9fPx+mrd/nvWzBo5KklvMGdluEgtnpmCFPbw+avYCH360TVvvb9u8YrbFq+4TSz4feyR+os70JvVKGaWfPj9Vlzf5h8YJO28jlDNePGoYCVX7aL/mAN0iOpYQQtDn2CQ2rZa96NOW+PdHcjK1cvnzJzzf6+9tflHZO/X19dNmz+VMtpG4LanmVhfpjnRlJg4Peqzd2zbDo/+Mn25tL3isV87/vp6Mj7Qbt3M6Yz+KItMXPM1UDaXiBhzkH/7u1FPyNv57FtUz9zGmv7eQbtPZM6baGeErl4wev1Om4byQt7+pp2pNzu5QV0mMO7KvDJ/0ICtO1Jnz3T0yjkwbWTigdSsxgbk5lh71xKcmjBjzqJb71z77nsfZJw7/uaTt0sHP3z7Yrzx8brtBddy9mz7H7jeSAkuDlDByh6Uxrz3wF6HtblzxHDrzq1bd6Kp34Su55f3bGLqANFWIOQ+UtaVGURtqkU3NISyRHq5VN04zyZMCsiaeesanwcfbhfNT89x40GQqLLUhQhu0AsXUUWNnDkDk/dmOfbm2ZV5hato/HUvWoZMHynWbxQ37yNTKJVmE7LaeJ4/sGfrgT1bBw0b//tn/tLc8fOX3twdBJRAaDMnDpXc/9QavNGcHdoqbe3dYso370QK8LYrOMKLJaGKH9o9IGqoTO4UENmG585n4rujh7uIC1dVubCS6pvENWauribRO7C62vEYHgBpp7YRLZyrKqud1dMNVBiyY7gKd2tttDD0pkePn/rQ4889dKdoYGK+2/zL75/5S16NcfQAn39/tvGhNQvlz8JqS+ik0C0JbnT2+t0lDp7Q1urpL+8t730aykMg1JS4Q8qox+opnz7HXLwkiiNmVnLigdNZkmKOH5VcVWur98oR8qUl0mvK+SpDSqy7cBPS0LyS3LySW+a5zrxBTUxyHBv/yHE2P+VQYT5SakuZEKt15pMNPz70+HMffLV5/AxbHuWJ/afMgsfzUCbyAv/7803T5qPs7oXzF0EIb7hpEfbKDSVd+Tvhrx0HN5z3k8hGV9IGy7T3aWhwkpjMXmPta+ceh9mdjrXwLMvx/gprNzxki23cfWlMstjcYXeGO6WTsys7z/aSoV58pa0jJGbOaLuEQUQg03l+vWnzpx7edQTXuknMGCq+d2Omjjp9+Kz06Ko7biIxjc6zj6TghsvIhtxKInraNeD3/M0fU904Q3s9QXZDTUSqslBffVOQ65xOFwJ66KpN+ADgU89nj0oK4oWlsMu5yTBE3WJ2C9bQ61s219SkGzN1lEKhGDRgkE6nyy/MZ1lWHsqQ5yQSunhpKUeuqvJIca/3wRG6ISEDXVQPy2FcZrqcLG5FtUMPwmw2X0pzkS9FuI7BjVatH+V3iZ52OCse+7UvG6FtwEU/0D6ImxhIFQjFRWldUMHZN5Gv2VsV1sB/6n1TRt/8MbX9xr5LpPdTrqTfb0Jtd979zrFzvhuc+0BK9KY/pW7Rzu56IZXrYoOoOaebJ8M7CZgO+atubXR4xWO/9pTK646lAzXUvbt51cyYVqkncNXGzEN6lrz2CQvUfaHbZz//Ed89cajE5ZH4MEL3t0n7GpLqtVlJPQzTtaieTzz85N2334O373v43guXzjuoZ2BAoMlsct/CuSfSmwXUw1iwfI/zYc5FxARCd+P6piucEdJX0jPTKyrLp0+Z8cWHXxqNxgfniZMRvL29D+1EzTQ5jqMoiuf53hSw7Z0C6skXsofKKOksiWAQCM58+J+PHZoQKpXK4/tOLrz1/u0bPkOJPTuPVNdUz18m9h0+c+TcqYOn5RnZPZo+nSZ+/1Nr7n9qzYlDJfjWquf+8t5yEq/sAk4cKnEfJuqbQaTuQ3J/x+pEk8nE8/ySOx+W9iy68QZp+6bbV9K0u+l4PYveaYF2meut9xVmdAd2HbZV9G5LRbUbE1d8JvbQumOUS8ecm5hvL4tadDeeePqxbz/7/u03/v3Mi09LvXTHThstBZEc+sikDGphBGHPggioazxPUSQa6uyM65DoMJZOB1obCyZ0NumZ6T/9uumm5TefOojGw+h0unnL5uDhvi559aXXCoqanX/Z4yAC6o4Th0pIFLj9MQ3P9RQ/sUUrkqKokcNHLl24rLqm+sefN1RUuu7CBSFUKBQm0hemk/n7P1//+z9fHzRg8Bcffunt7X1s7wmO4+6d6SKfLPUQyiddubr35OoTASX0sBjxDfMWvv6Xv+OoLoTw3jvvAwDcM6O/c/uC04fPFhYVrli9rANfndAcmVcyJs+ZCACIiY75dcNvn+3JuH/uYPl3HlZPl1Nbei59OohE6Im8/pe/HzxycMzUUeOmjxk7bfS46Wgq9ZcHHHN18Z8roVOBEJ45cs5L7SXfWVRcdODwfoXSNlR1wriJqYfOVFZV9jL1JALaMi1G50kQqStJGZQCAPjDn5+S9nAc9+gfHpF3bPz7K2+eOXKOrNy7AF6w+n8VhrXIGTtqHM/zeTXIE7ps8fIP/v3hfz/8zw0r0Iz1XkbvtEA7I694xWO/4pvDHuyza+7W4ZfRx8m8gjrV3n/3A/KdJ04dv3u6OJUAALBg7oIHH39gytxJ1+MC+xwPPPa74KDgM0fOjRiGGv6qVKqff/ifj4/PI3cgzwlFUX957pWvvv/y6++/Ar2R3lwL7xy7kOfHeMi21Gs4IaO1Jb0YkiLTBtx/8Xz72ffYDr1w6cLX33958MjB5iqvzxw554kPlHxG7fxE/Pz8f92w2c/XNkvtrgfuyD++H3Uh2XkyJjrG+Slu1vI96+PozQLqzOhI52lmLeTNtKctQo/7begmtGi5D0ga8K8334mMiMR3OY57aMFQkzDpkwjodflEnPHRotSIxgDbAMte+SfTt6LwzUmkRGhI6NeffBseFo7vauu0c5fMlh8QHxv/0w//wx63+vq6WYtmdub1ElxzJefKkpsX4SDGM79/9pYbb/10d7rLQLwnAw0JhDbTO32gbWPC2Ak7ftnl5+f36ZefvPi3F378eUOAf8Dpw2elAyCEP6/75dTpk2Omjlq2aomfn/++rQeu6yX3OUaPHD17xhzpLs/z//jXm3g9OGnechy1IHQ4ETGhHt5AH6NvWaDu+eCdj0wm09S5k/Hd7bu2/eNfKJ77xGsf/vdFVNi76bufAQCP/B5tF5cWz1s6Z/dve6/3VfctXnv59fCwCJceNIuqLU5qQqfNDVwntJtY1LtnWxEBtePHnzc4vEEXLp2vqSjF2/3i++3et0t6qKa2BgDwz9ff+tMLT3fBR0UAAPzukfu2bNr21cff3PPQXdIb8trLr6Oa2vVfk7eoa/C8tGzi9Kjm2uz2DlUlAmrHHavv/Hnzz/kFtsl69z18rxREAgCcv+hYiz1pgmixErqA0rLS9ZvWr7559Zkj5ywWC8dxSqUS5X4+/6Sp+fprQhdL50RZs93miqGlCaw9WkmJgNqYOGv88X0nf/7hf8J4ZzYzK+ORPzzc2NiIH8X+tbLycod3UK1Sd+UHRvjnO//417tvzZo+65Ybb1WrvX7d8svPm3/yrnX8XAAAL/z1eb2+ibxj3ZaJgrY6zF7tWcUpfSuNyZNsjPi4fs8//fzY0eI3Lc/z985MwmUVBy8XP/vSn/bs3+PQi7vXJLX19KSZvpA3c31pW5+tiR03l6W7aSuxQB3JL8h76IkH8faUSVP/+893vzyQc8+M/nhPTHQLc6IJBIL7euj26Gl36x5JBFQkJDjkj088/dxf/ix/d44eP3LPQ3d99fE3ai8NAGgJv3Thsq+++1J+THpmeld+YH2BNpiEIVY/dVUUMSd7jJ5ObJOSdisNJQIqojfo589ZsGX7b0dPHJW/QbjJ9pXSemScFubHxcZJD+F0+jffRq3VCN1hwKqbA3p0pKIvWKZv/pjq+Yyc7qOhREBFdDqdxWL571vvPfL7h0+mnsA7AwMCv/3se6FYEMnovWvv3rf1wB8e/+O/3n0bTcvahWYNpmWkFV4V85ycqSxDqU6E9pASFyRtv/7eNpfH5OZq8Qb7o620oarCdQQpvYB8KIhhYwd2q9/MFYIm9qxRY0RAbUyYOe7UwdMf/PtD+RtksVjmjuqHt+vq6l545bnXX3nj9lvvwHtwB1lC16hnc4SEaXKzXO93fU7hXyKjraVDJrX0RB5biiLt8qJgg9GCN4iA2jF+xliGYZYtWj5i2Ihr+de+/v4rnud9ZBXWO/bs2LFnRxd9bn0eT9QTW5r9B8bnZuVPmGZrge4GLKzT7eX10OmiPv9+9xhWdNz6/VFBHB1kUYKhUbG72cK53F9TXUnSmDogRcbNEl6tsfXlJnSSgLqkOfPTQ/qCnrZhCd95Ruib9k5q9wt5DwUUi6OzLEoK6EBNdavz54iAdqKAEvW8jgLafjHtBRqaFCX2FXPJU/ffJG1/vG3vddTQN5sP8TkoqSSdv5s/q7mnSOYh6Hz6loC2jZASMcJ7/1NrXH7Yl0678sARuo2A9l0ZNfFJ/SJaFNCaKqQ1G09dbNW5/+yBktY3uenwgrJYLKzd6tjhrt2ptNWg+0F8oK1IkWnusNcrxO86EpfozjgE5du5xu8p5OSVxUUGunwI66bnPLJkrvxuvfXtZFk3TVf55mSxewpia+nrAtqq9Yg879flsDlsMREZJfREHlpoa7TqEgehtNjLYu8QxNbSdwW0tX1lPvv5jw77pe2QHzVyGyclLohoaDfBZFE471QyZtAbSYoUPZ48ACzHmc1scVmrRU2Sxb4piK2l7wqoh5w4VIJdn54grQqlFJme7UHrUaTnVLvx98kxWRS9SkNNXFJ8pM1O5PiSyjqd3oT+vG3Dnluma6IunQQPALT+25WQkR6ecuJQiXTz/P2dPjZGuoHewtuv3Flw5n182/a9XfcAOemH346LDnF/qpFD+qUffnvYYFuBLCYkyDf98NtDBtntX/u7RU8/YQscO5OTV4Y3vvzg4S0/ogsz2rPlx2fxRqOOKyk2y28uT9gzvv+UVE5+qclkxDeL2aRiAEPxDNVsQKb3AWX/diVEQNtCG5RUElPQw8k+/s6qpROfffX7SYtfWnnf20MHxRaced/5sONbX/XRqCmqhV/p82l5Phr11u+eddh/dvebWq1u467z0p642LAVS1yPejezSjOL2ipLiqlWKTReSqNTi2WNl5iW6xz1cNDQQ6eLeoZ6OsFyXJPB7OpHJHQ8REDbRWs1FMso6LEoFIxKqYgf+9i6X44Vl9WcuXA1bsyjaFb7e4/JD4uLDomO8DQDCZ9hz48v4rvJ0RGX9v8TAHD32g+SoyPMrNJoUnzy7pOf/Pdx1JSAp0xmhfxmMDJFZdqMqy4aKrvGKixGk9HhhkWzh0pnQSkq8Od5YDRZ2OaTgQgdC/GBtpcTh0rk+TG9OznGz9cLfetCyMrKW//0t+9Sz+fKDzuy+a9xYx6VW6bJ0VbvpPV5HGc7w3OvrHvjlTXTRg0qLatVMLS/n2bJqjd56wH/+OttURGBs5f8bd+Wl7Hw2V0TDzQqymBwHmncDF2/zOtCeMDrjeZ2DnPuek9iz4UIaEfSu9UTAFBd08Dz/LXUd79cf+CdT7bV1unQcjv1Gg2o5CgkkRzH79/68vZd5/tHoIhwXGgIY0GrHEkNJYRegAij0Xjo6OXGRsP3nz0+Zd4L+7a8XFBYVVvXIB35++e/dOguKIfneZ3e1NfnvyspYEJWJ8fx8m+mtkHU03OIgBLcIcmitGf2klfff/u+e1fPvHf1TPzQQ09+kntNXEHfeiNqT/XaPzfiu2aTydkR6ZIFK189uvv1H79GuWJr7vu3p3/TPDCaLSazi2Jn1A97F5rW2XcoKK2JDg+UvpkIG1tZW9UGiIAiahv8PHmzAn1RW+XeQXJURHOmivwv0GAwOB/wwOOo4x8F4W23THv4/gWfvvvQDz8efv/THRSEa383f8q8F9p2SS+9tv7VF1fPWfKKi8eaN6oMRktzJteU+S80J6lDU2zx/cvpBfhuWB2q2Dlz8RrosfQ18dzY+RLpHiKgnqon1ln3GlpV0dSxq3hsALaI3P/nxhVoknkPXSqjJwT4e2uFlTvH899tOPTdhkN/ff7W226Z9v6nOw7u+FutVvf23++WDn7u6RsNBtMfn/doYvuFS2iatMHYUoam7Ae0sJxwfKsXrR+/85C0PWX+C/K7OKjVc1fxvYyN11si3UMEFHy+cf/vVs2q9qwuONC3hfZ0VRVNUhmSQ8C9tMTiRhZdBkHwzuY0US6IXcO3nz6R2C/cwcb8Zt2BubOGI5uUogIDvCeOGyA9NHKY2Iu6kzCazG7aT7jBwTidMv+Fwmqxp31Px9NgWvdgY/fWxxbpuwKKmyrhgk4P1dOBep3YacZsUcr1Tqqoay4923rw9RfE1vLYHz/b9tMLr7982wt/+0Ha+dXHj1ssKLbjIKxHd79+693/KirpxIrAdsdLehtF5bVqlfjb2E3Y2MMl0j19V0Axb/6YyrQmGba2wVd2j3O/gu7+gtha6uqb3vlgy1OPLDm62xaf4Xh+xkKUYNT10BQUK/gI14+NvVoi3QN//erNj7d72ku1R/PQwtnStsnM7k/NzrhWznG8vxqsmDW0PWf+bMNu0MfonxC+cP5oi4X95Ivd3PVbNDYZTDX1TeIXmPBPbERg26IqvWQJb+LUKqXBZF36CO9Jc+3s7vesF3Av1se5w0Z4qRQ0TTE0TdOQpiiKgjQFIYQUBaEweBcP38X/Ov9O9SEL9KufDtxz00zxx2boYG/oo2A5jutTb0JHkXut/L2Pt4PuD9+8mPK9M27d5pTYHiGUo+KSvdUKby+VWsVg4VMytIKhkd7JhE78VFtK6Wrx4RZbpjLVVZXYNHP5rn+yfR/oLRg1XHVVFd62sFx5dYOgnoQ+g8tf8V62/FdSRqO5ReHoSrxYP4amGIbyUil8NSovtVKpQJKnUSs1aoVKgXSQpiiaphSMqIIOFp8V2zpj+W12rZ1tNLgeZO0JX329uQ3PYnrfr5An1NY3VWkbr/dVELoLhTW9Yv2OUVp9+qY2/mXraxlKkDCGoZQKRsHQjKBuXiqlj7dKpaCVDFI9hYJW0JRGrVSrFNgGpCkoPIKWwKJ956iDvPMm/r+2RjRuHLjnsdWtuHRfjeca2jbFdCGgNVa7rI/AcXxxRZ3R5FgUSCD0XCxiwNImWAxsnYCumjKJoqCCplRKBpuAFESnaNYYFKitrgKsmWUBC0Bn9Fj96r31rRPWZjS0Q+TSmb7l/tuUevHmccMtLKfTG/uk5d0baevH2KusTgBoyKtUKqGRXVsx6zgAjAAY274O7jrcCaugoa1VTJ5H5jL+13P6loBiPO7bQ2gvXSFSaGYHbM+KtddgNBrjwv2rtDpbc4Ae+5bwvFXRUNFwBwhri6IpRp9a6T12J6CbUntAVK4toNzBbuRl71b0MruM0HPb00EKXTpK87X/GuAFG0jIMmrFz4VOgpOGrRuSXLYn5NbnLNBNqReXjRzSnaKUXQrRx14JL7e+XVmdDvvgdR9exLcgW+Kj0G7YkZh4hp4LXWZsIWGV2avOBmY75dKZPiegGFd+8bbjYUJy1+B+3RCQKJaZ9j6gFkUFb5o3ysPje9MCi6tFa/ZbFoxu0puOnb9W16gX9wuJeqsWjHb5LEl9YJvkj2+lu1D+Wp4/UT7syJri1My1WYXVprntNjBbpC8KqPzrqMfRm/7sCZ3iOgRAwdBmoTuBp89ytdPln0hHrXw7A5uNKfu3s+mLArrjaEZ4sLykvRtB9LHLeGjhnF5ZxOztpdQb0ExjQhfQFwXUqOE6dgnfKohESkR5hwvlKMoAX3VwgLfKmrMtVCQLfhZXnxIEsK5Rv/N4ZlGZVm5C+bTydxlCsHbRnI+29SoNlRbv7cd9AL+bWZ/gevH/H6AbAFrVnFEAAAAASUVORK5CYII="}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "assistant": "Yaw 1 makes the glyph broad in both ally feeds while the hostile sees it edge-on. Use the central height for clearance and stable framing.\n{\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nHEIGHT H1 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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UKoVapSBJMkCjigwP3JxX7+4CNh3kuzTNGDekk68Fg8Fgek5AZb2faAf0UiLzMzUsMyU8KyWCIICKIjRKUsHlw6NFOOfMhE5SgoD/qpSKwAAVJ52ctpJEoE4dExkUGABr5AXE+fSBoUFoAxmhaBtHkzAYTF8XUM/mZ6Zgfhbps0sbYdYmQSg4ZXQ6i3CLABRFajRKtUqBJFahIDVqZWiwNiaKV0lXIkNDohOj0TZawmMwGExfF9ApiRoP5ifv/UwNy0yG5iegaUKwN53PI75JEoRSQQklmwRBkCShUSvjokPLKuuFn1ANWVZWU1YKf4ZEqatLqoWFPDZCMRhMPxDQ1OhA3vxM5s3PrJQIu/lpFXs/s0sbM5LDPJ7MnuzJLeTtO/kNBUWGhQTIPEgkvRdPTEMb2AjFYDB9XUCnJIjMz/O8+YnW5pz3kxR7P2HL5NKmzvSeU6n4VspSoFnLbwop94S97zL2hGIwmL4ooKkxgRLvJzI/4X2EVeL9zIBTj8StPqUWJFI+D6OOCABCIiM83S16MEoIRWANxWAwfSsP1Mn8tOd+ZsN8TNjgg0XmZ1oYN/WIoGkmp7QpMymsoqHEo0jKQDj3TBZraJDGYZPuOlk8YzxcwrMsW1FU1YFXhMFgMD1kgTrMT5H3E7BcwySx95MzPwkSEAyTU96sD/N1WLEsQUrHz/4T510PYBiWpmXsXGyEYjCYvmKBis1PglXbzc86FnC5RzLmZ3NGUhihZggrVXkcdhLxanJKDE8f4WL3ZGxyTGVxdfsfjcFgMN1vgQrm58jk0JzCGrv5iTI3nbyfJ3nzk84tb0Z7QIeorIazlXyB5DLyhZQmAWyEYjCY3rdAxeYnBdSy3s/MtDAg9n4mhwEVND9zSmV0kDc2Wd92e4WA/wsJ5ieLYDAYTB+yQHnzs9GQkRyaXSCYn0px7mcOMj9LeO9nbnk9Mj+tVqsvT9G+MJMc4ZFh8bHSmSDYCMVgML0poA7zEz6MMz8Z5P105H5m2kuPxg+OyCltHpkUhrJBAUX96+kbfNVHolNiqtOqNh8426GHYjAYTPcIqBvz08n7Cc3PYuj9NFutBEPnVbTP/OwSFBR8UdgIxWAwfcUHKjE/WQVD2EB2WS3LKrNSw3MKa3nvp4UiSEJJUUfO1WVywXfAeT//9eR1H372i4/PBT2gHiPxN1w+rba6xt29Y0fEbq5x2/4Og8FgetoCFczPkckh2QU1BKMYmRiKcj9ziqudzM9iZH4yuRX1yB/60uNLOnWNHXWL0oT01WFPKAaD6WkLdLLI/KSABiibgBUO3SQAND8Zqy2ntJFlyKzkcFnzU+aMIgOz81EjdxAEkZiaWHrOKfkUz/zAYDA9aoEOdvF+jkwMJezmJ+whQtoIuA2D78j8zHE2P9H6vTP9RDoATTOs0ZA4OLFnnxaDwQwUFO0yP/ngu5XJq9QDoMxMCWcF8zMlnCAIJUUeOVc/MjGM0jjMT12A/QyozEiWdpUfSVoyu4Gk+MMSBzvZodgIxWAwPWSBCubnSJH5KXg/gWB+FumzoflpIxgmr5I3P99acZMuQPPGO2tlzuusgYEx493f2UFgWj0ArNEx0RODwWB6TkCdvZ987mcuND9BZkr4yLiI3NJG1kZmJoePSuX7fmYkhWXZcz8BADLq6VsuqIejfvhtP2gPn327PTI+VrwHR5MwGEy3L+Gl3k9AjEwMzatoYVku+B4fCc1PhoRDNykqIzkst7ght6Ie2KAuW+R6gLpfr7MdXteT7l8F8hmkJjmpZw8wUVkGADhkTejh58VgMH3FApXxfgLAeT9BVmr4qMQo6P20kayCEczPkUlhmXbz872XbpNfvLtgbyXv0xV3YHWvVFBqwoaNUAwG03MCypufDQbAKKD5aYXmZ0ZSDPJ+0gyDvJ+EjTxZwredz7MH3y1Wq3f1JDoZJXLL2BEp5ZV6kqHLK/VllfqUKE15eS3ocZAdiumB3zP+VWN6BYV385MAWSkR2cXVwErA3E8WBt9JhjlZ3AAYaH6OTow6eb4etZ2HuZ8WKqes6b0Xb5UIKKW1D5GXwR6eF6/Zndfvty1fCKL4yXTLuX9ffPhtdDM5XFl2vkI2giSmrqJSsgeH4zEYTLcIqOD95M1PQAxPCKFUqtxifXZxdWZcBCBtgCEF8zO3uCGnop6wkSwAVoZxNT9txja0Qbh1b3pyeb748ipPr8M3izUyPrauvLLzGmoL54eAuqNawWu9zSbtCyVBoS9s77NjMJg+vYQXez+zUiOAkh2eEHKqqiG3WJ+ZEk7YyNyyJrH3M7esmSUpFHwnKAqJrxgrTbOMF+GT00CfV/Ks/Ue0a3hGWkxCbEyCI4JU72KE9i5YPTF+wMQB7EJRePR+tgJWicxPAEBGUkxusT6nuDojOSK3vD4rKRL1/UTmJ6Fgcs422YPvaskJYbWSFzG0G6YEK2+G+pxpPyIjHW206BvQy4sfxOdUAQDMrEJihP7+xrL5j64GXSp8Mfa/pxIciMdgBpSAiryfBPJ+Do8Jza9qQN7PnMLa3NImliGzy+oykyPgyr24AZBkVmoIKj0aHBUgOSHNMNysDfeSyHoXyYRYfiQnAcCOEyUzx6WK7xUGcwIADEbjwZyyCWmR4gO2Hy68aAI8QMlKG+t9ueYPd78dTF/AR4eJV28JNvkxPbGEl3o/rQQLQCYXfM8urs5ICgOkDRZu2shLFlwIKFgvSdgb0Vu4vp8My5qtNps9D1Sl08XERiQkRMtfglRYeVvVg8FKwflxHUR2aNLvbyzr8AkxGMyARWqB3jwlxmql0XZWKm9+nqpsRaVH0PwsawIMmVNWl5Ec8eb76+FxFJWZGoKC74OjAmwMYzBamtssgRplSKCG1GgCA1RKBVxBIy+lq/id/uKVkbc/62J/dmw6J5RvD/cSBFh+1dRyOMcJ0z/w0WGCvSWY3hdQpJ5lDYaRKSEnC2o4lyTISA7ng+9JEbkV9Zmc93PWxRfkrvwdzoUnrIL300Yrmw3m2kZDY6s5IkQbqFUFapRatdIumlL9dE1b4v7ld/uuoDQtPXDTgYJ5k3lnqCvxseHlXEUA4ss1f3TAE4rB+D1e/SdgALtQxkWxTkv4m6fAdTqEgH0/CS74frqyVRx8h7OPyupgodGnG6BukSSagwS44LvJYqtqaK1uMDQZTC1tlrP1ZgUFA0i+IhzZ7jU6L6A+PtnGPafb+wSYPs7TyTufTt7Z21fhbyj0hcKP7AGDFR0cV+4fkLLmp+D9hMH3ZPjFkmP3fsIyTRv5f8vnw+L3pDDe+1kMvZ8My7YaLcXldW1mG8sCs9WWlR6tVim8ixp0ozrqj7y5QGUQr9oJACZmxHt9iOvQpOw197frSTF9h4vjzqENrKE9I6bCT0xDNvqRvVfyA/yIcVGsk4Ci4DvKIXLkfnLm5+jBkUDO/MwtbxabnyzLGkxWpUpNwPJzMjwqnOQa1Hu+DtcYEv9fUl5HSW5mXLsIjnKNX0mX/DgW7wfqicAaiukZ9XQSUBR8L29sA6wj+I7MTytrkjU/kfcTmZ9cuhLbZrYxLKtWUiolpdUodRqVL0tqp0NY0R65xzK02yZPYtSNJTv+3Lbjz23i0wooOFmXGKGvvPENNkL7HbJyiTUU0zOQktzPkckh0PxUOLyfowdH5hU0eTA/WQWDxNdqo61WuHhXKEig0litdJvJsml/u0e0w2A9xElDx4wdAVxUNaegin9IF00LwRrav/AglFhDMd1tfjoEVMj9zDtvgOanjWBZwfw0is1PeDRFjRSZn1YLfy6TlTFbaWRyKiiy1WiprGthGM9pRV5xskIpt+t3zh9BEHbddSIhhq9Ml3C6qMbVE/rKG990+FoxncGk13v46YBEYg3FdDekk/mZxJmfKPheJZifzWLzE+YtyZmfWw6eUZAERRIkQYRFhlEUYTRZK2tbrDbGZLF6l9DOmZCeRTovx1PM3TWvHver74N10xI99VEcn07e6U5/MZhOmp+8gDrMzyLO/LSKzU/TSDnzk3AxPy+aOFStpEIDNUoFBdfyNsZqo1sMZouNrtYbWgxmK/SOwrW23cnJh95dEe8VLMqYyGDQ1SgUMA12+eJJXX5mjGc62XvixbE57T3Ys3mLFRbTAfWEGiL2flJAm11UNXwQX3o0enDkibPVgCRR6RGglND8BCCvvDlzcBiwUNnldYPDePNz7qShhJKKDA3QtzFcQhJcTdtohmCY4srGZoN5UESgXDa9REOdcucJeB6HbWlfvxNV6pTsNljqXkop2bYQAECthqnXDausqROOrg7LQhvZxigAQI2WQA9B1OnSso1R1URAm1EHANhZ2HJFmlONPG4V2mdpl3oKD3nuWGb3XA5mQEM6ez+rAewx7/B+jvTN+3nRxKHIBRkUoCIIgmEY2K+eg2HYusa20qpGVgmbjHA2qMP+dDJEZe1RexT/wFnYQolhoECqVXwBlbDBAqDTwEGh7SVLW5ulrR2ibXdLUEx/UU8MppvMz7qGFl6ARiaFUIRGXPkuMT9ZSklw5mdueXOWi/mJBBRWc7KkTkO1Wh2ZRixgrTbaRtO1DQYlCpd7TmzyoX4z0VaSFWADAJxr0A8OgFpfZTw/O2lUgF4vLPkr2SZ1YwknkdACrTMWZQU4AlAVhsIsLRnA6gGADycIIj41tvx8F/RaxvRN9cRGaIfdLHg2ogegprCAbTaYJKVHEu8nlDVSxvuJ1BN2ZOJQUmREiNZmtUrC5SwLDEbeXpWN91htUBBFuF3kdwcMwwomM8ZfbU9svWK61vxkWU5ACUDU1JlR7ucpce4nN6AYmp8kDL4TDOw8nzk4XBx812lVgnoChTI8WJsUE6LVKMOCtJKmcza7QMk3TOY6iDiw91/mKjxlDt9zvEjmHB1NmFKrYMV+XMogyX4cju8jdJX2YQ3FdAmTJ4wELBiSnsDbiQzLni2qbTNZM5LD48LU0PuZGEpwLewy4iOgH5Lzfo5M1eWcaRTMz33ZRZOzHI2NA9QKnVYVFqTRaVTx0cEa3inJ619pSbnj+WUi8B60z8kalTVItWrHDJIOoKAoruVzDxi7mF5WPayhmM6bnxs3HWC5qA9p739MVzGBGw8VNLa00QybV9AM57/bSEZB55Q1o9zPvPJmiiTF5ifnAuBPrdKqKYokCUKpgHWc0RGBgVqV7NU4zERRFKnzlUT7j3e8wZJgK8enOgYoIbAR2rt0h95hDcV01vzkIAhAfnOkzmyFCZto16ZDBRFhysQIOIJ4UIiKsJJczzpofo5I0eWcdZif8EQi81OpoDQqBclJEUEQGrUShsidTTph7JtMCN6Ngop3s1wIHo59HyJda3eY00U18Hqc2/pJwBraW3Sf0g1kDbWFp4l/evty+pn5OXnCyI2bDkSEBQ0fkbL40okwCr8+u1Go6WRY9rstpyaOiEmM1JZWWSpbaChwnPczKy0wMzlcCL7vyy6ampWCHnXkfMPF44OUCocMkQSA5qjzpWQsfzp35SusfOCd9RqEr2mEKv/3B64EANwqvkOpuM3l4OXLLr1lyYNwoPwdi2GTlH+uFHehz0yOKK/UaygAKEV5pT4pXC0JwWN6ne7WOByXR7jTUB9Hc1fbDwM1DWDA2J41+ubIQRHR4WEyQ+UIAHKrGEVl42Vj4kml6uS5uoyEYFZJo7bzQoaSoJ7I/FQpoBvRcRI5j+KXn7x3813PQG+pVjq2s6W1TRGWRQBg0Z8UX4p4Rqe5DeZX/fO1r4T7n33yJsl5/vnPlV73eGXTwYJ5k5y62eOUph6mZyzEgamhPvbl9HGybIxwGPB0mJ95P4cNT7ba6E++2yo31pgACtbaZiObWow22sAF35uy0oMzk8Nz5MzPZhtQUFzRkDOtzRZDU4ukSh0dxDAst9iXCdqoI0Zz6svH4bm2TGzQIJjLCcBxycFiMe0yKDBvajrg50J1kEPWhAE7Kbt/ra8HpoZiOmx+sgAMH5HCsixBEjaakRFQQfFO1NrCVWxSpDYwRNnY3FZWZbHImZ+BFDsiLrCq2mHAp4Zrq0urSdoERVVuxc6yLMPAJH2phsrOPvYhE3TwYPjtZ25rue3xmwELmOqGl1+3DzgSN3T2XRYp6cEdMEKrw7K8DorxszbdnWcgeycxfdz8RMwdn77lSAFBsARLSAWUohQ0DXPaAxRMa3NzjY0M1ZBAydhoBigYZH4ysFu8faI7ywrWogT5rCDuYJSwKdFQuVMAgoUaXFlVGzsoKn4QLHuXxdzWAv2eNy1CN0k3/esEXLvYCZTX6Ltw0ham76snNkIxXrlqwbTy6saNmw4MT4+PDA0QFE5uCc9htJFaBROgYHbnFAeHhiaFBQjeT0E94TkUhBvLjkABeScTFO0hYKgKxZsEDRWrp7S/CAF74/vCnY++Ex8FmzaxAFTWQUn1XTfbZYT6YjbiWbv9yPZ8cWzOK8Uze+vZMf3F/ByWzg9bu27OqG+3wmiNWwHVKhikodDL2Qiryi8Y6uhmxCM37mjUmGFovEd0okFzptIqmsCRf6rAUH0Uuku5/6NAE0EQwXETXNbr0unHvtBVfekxPQ9ufozps1y1YBoKHw1OGaRUUN/+ceS6S8dfOzvru23ZMgJqYZS19YaoCB1STwKAoOCQ2BD5Uh9uCQ87cQh76puauUHtTGFJTXFFLe3DCKPWyiMgaETeyldEtij/34zb/+HWG+BC11cSuRihmJ5Xz+PqTrmJx5i9+1ueO5apCW/P6gQzwMzPj776c1haHMuyZotNrVIgDb1mVqaMgAYoWU1AoKGpiWbY4LBQZP4JpUdS9XRvHbqtjXTZ6cFwzP3sJbRRoVVEThwnuXf1rwdefugKaNsWti+Lc/XPe1x3PvnwtS++vf6SKYPdPQo3Z+oV29MXBewMOAqP8QoBwJLLJq377aCCIq02WrBD5ZbwLLAaYaOR4JAQI9efKTZMK3MU6WnBDIucPMyD92Aqeu13J2LZosntks7EtES08dQj1wk7S8thMRJsilpRe9vSC8qLuRPar+GbI3WoEgmrp1+u3LF6Yjybn1ctmPbRV3+OyRr8/Z9HFs0Z9+vWo4Dh3IWcvDkJqIJSoIdabDRJUnWNbSwAsFxHDoftKS+kUvvTZnDUO3W6Rx1rMtMmC8wWIElCo1KoFB7l3EUrfXgGx+amgwX1VGgHrxTTh8Hq6QtCzslATrmz2qAvb8NfJy67aMzGbcetNIOMUBkLVEEBmoECF6AENpuVAMTp4qrBMU6VOaJ58pDV3+9AGy88dA0STpPFdq6syWBmadouRcoQim3zcIkjlz+dxxV6Et4NUUKtgi1LkERz/7q1dguOHU8fOwb4QP5Z2C8qWCWti8fq2X28Ujyzt4xQrJ7tRTZ7r5pL1zvnLd+5v+ivrPdzTNZgloHuShtNT52Y0dhq2n0gHzlDZQSUJJ2GA8PFOCC25+svGuH2d/TPR5aKb54uLGdYlrGadUo4oU7Yb2+pjM4rPYkvJuSPW44/e89laDu3oAJGmdId8au9ck1CfWfEEJijUF5c0SNNnDG9qaFYPX3Hs/DFcBV3nss9+y+1ej4bkiAJigBmK/vcG98+99DShiZj7qkii5WWCqguQEtbTFwYnVZwzUEUFNlkItoam3/a13zlVFiAtOlI0byJKRLFlJyHAAT0gcqn0rsfyCl7tEhZr5o7BummHdZ+04dJIO1AaBqF8UMNxeqJ8T33s7HZUCbqQwQA+OTLTcjQlMkDbW5pCdTwfTwZGg6qVKsUOpo2ckPloHSOh9LJAlghJDBUlMZ0xt7WiKtQktIZTWpjVYeyC4ekRBIAlFc3xUYF0zSbFB7AcOM/fRHQr+2uhntuutRmY0gSqjycZU+S3L+EgiLLiis8O3cx/VpDsXpifKRW3xIVHhQVHoRuCsvy7NOOla7Cq7wZTDQA7JiU4PNVjRYa/H6wgKJQ+RFwFU3xqWAQ3tkE1QZondrL+6imduNS39gIvblWBoX4GZaluHEiJEGopFckz42LL0AbLc0ydUodSgTAdBnPHcvs7mIkrJ4Y383PK2bD2Mme44Uo5I46e0gOIt3Fm5DiCjpitNgc4SDf8DYjw1cnqHCcSZsSP3S8VZdqCUgNjs8ya1ONGidPwqnzVUfzS47mlxzLL12+dFa7rtb1WrD92fN0q8Bh9cT4rp61+pai8rqi8rpRXJSFYVgY14Fd3SEFxVWoUZxMEMlqH/+Ghn5wmwTDPQ4ZjTpdgOTZVv+0y+2lic9MSNuA+opvBuEn/7kPxoKGJwLAnjotdct2AKyhfmOHYvXEtIvAQD75nSCIKeOHomkYLMueKYHNhmanJcM5mJJmInVsYCholHTwRDS0mGw0Q4p6zotZdjmsFRXDsmxxVdPxM1UoWxPhqtbONqpbvRIOKj9zZGgKTGuvqW+NDNPBXRqtsOreu/tQkd6WduwMDIKZYSgMg8HqiemA+RkUiFouQbbtP8X9196CDoBhySPQTdJHg89gbucCHmljl7oSNUb3WUrcE1XXG7SsBRgNoK21Rm7AgCZAZ2FI9CN7Gh9NaUy30rXWIrY9Me1Cop4AgGkZ0iSt7fvzt+8/tW1/vswSnqIUDGNDaivrxEwYFOH1Iuz9lrxfLu8jaM0HQfy8EburQIa0RP6poyMCGfuMOYHz5fVxkcHny2DHucp6mTBRVJA6OsjZjeD8RE/fu3jvUfRtg/GHhTxWT4xXjtYS7nonCwyJDzuUcx4AEBrm6ErMsm7a2QmziLgNKaeK65LCo71eFpcG6rOCdjQi/t3WU9fOGc4fy7JHWiPDguCivlFjtHLtn7k7+C8QDxnBWVqnVC+MH2goVk9MezVUyJyXoNMoL507ubHF1Gq01De2VZYUoyW8236gPKhdMmMLDgpsbm4FmmBg8N6wnX+ozJykLqPAFod8tc2hqmxjDNpZpfX2cjADRkOxemI6pqE0DPaoikvLSyuhUaXTqBbMnUAC0mIyBijBhBGpbSYrmDaUBUCmEgnJJckJJ0kQJEGI+2HqlEwzAIF8or0XULdkL8egAx0PcWSrcrn6Tg83aVOsNlbJpXzG2q+hqvFslpbvd9JqKgEAxAXCpvSVphZzAzS5AQDpKfwB8Vrc83FAaChWT0wHOFpLAADVQ2tsi44Mu2QKXNqGBmmrW0wBGuj6azOZ8wrKzBZeFMNCAuR8oCTnA+WKOBUUQXe0o7BQyWltgy2WPS3QfTZUkXp2EyfOwS+cCJ18fAnT3bhradzeIiWsnpguobAUNm+LDNWpgwIJTqsCNOrGljZo53Hi1dDUJm+BahQKk9XGz9zwIRvyq5/3oo2n778abbAsaGPrzKC21UYDlU/j1TScxndtTbuENb/ue+npZe7uHZkFm81s2nKw254f00F819BXimd67S1v0kvdULgdPUYMy7BDEiOE+EtxeT0A4MqLxzIM+8oHvyyYM5EkiPOlVXAmHJBDFaBTAWBqa7Xa+JMYDJ7SKm+6aoZrw01TmzFIBawqtmvTmFD5JqoNcL7fu3F6w5JZeWfxuPZuwZdJpeJhpV4HPkv6APmioT4OhsNyifFAqK2J4Ep+BEEJoMDB3OKcU8Xo5obNDhvLU9QFdgVlaUA6Oio3NzQSBGw4AoD3KLyrC5Q2GzpTaG7SOhVuak3itFBcvN7L+NjtEXU/644GaBuKUwBI6tpzYgYoBP/fyFBYraOgyLCQoObmFtdkeC9ha6EcXqfTwCg8AAoFKdR6ersGaSIopdb5dNVu0GjUJpNZuGnUpDhrqA94SDHF9Hk8GKGcemIw3YJaRSUnw7QfkiRomlGqVBaLTaUk5RsqC+gCod4ZDAbxTq1aabWZNx8qvnhisi+59O24TD6l3q0TNDw8/Isvvp0370JhD9dMpLgd5mc71TNt6BBwCLo/um952y/adPdxDcXqielWKJIMC1TGRocyDEszLEEAk9mmVJBuE+mbm5uCgx0J95TSUdikUVHNTooqz9frd7u7y7ehHTJUVFSK1bMHKDxztsMuP+Ts88XTh+mMhmL17C1vtWIgffcrFFSdKMe+zcRP14AC+ui9D+w/kn2moIimGUbfSFEKmrZFxyfWlJfyh9MWo0WNTEMKNWJ2ZujgOMnOFx9ZWlHXsv6v/OIq2MQTEcDPrOsgZHOhgoK5qfzN9i/GgwICWto8zWXyHa9/Pf496qCPaOgrxTNH9fbFDHAhrrYbCv6tp1v35gIA5k7LkOxXJEcnGMxEanxEVJguO/dUrWkwZYbp6A715BKblAywyvRP4jlzTjxmg6fZYCYYi4toEjLGJ3fbqxwGJowxV58EnSA1Ifp8mdNgTqSnX/3gsGgumuA8Pg/TJ/Ex4I7p7nBfzEAyFLZwMgptQa3aYYH+vuGHG65bWlFVy5JqSh0IHHEaYECqCYBKq1NpQauVcC5N4imrqEmIk8blCa6QqQuvvrm5BWhTJTunjwe+Z482N7dEBDvNuI8I1hIE8dx9Vwl7du3vlEZjMP7EoYGhjGIigmXaFl84JmX7wdPCzVYrr5K8D5QkidCQwCsuv7y5pfXo8cHni0ubaCaEOadTAgPQAWMTPMhqDFRCuaECQlkzjMh3JojU4WymTZt2SDyhwnmmjk768tfD9Q3QVdHYak50KTktKq3u0HNiMJgBTVJK/C0pcGqvzT6wgxt3ZOUFdPXX39GAmjNralNzS1N9GW3Uw/7zSniXTgmajdxBSmi7kQQIUDAGkZXqDtgO1CeNbJ+WCuqpCwwytEpapxDffvDYoCHxgGWrCiqfeOgFr2dbvX4P2hA6AI4fCX9NGAxmYGKxwkJ2s9WmVjpi7BXVsL9wwqBwsahp1UpFcU1ZcjRvpTcZmgEFpk9KP18ckJN/Tm/WhKv5AiSDhdWp4CMpigJAZhX/lUsr4uXXzDZaQKuZ7aa5bS7qCXniX98mxoR6feyItDi08fKjS1Cq6yn7ZOayCicnKQaDGZiYrTYkpjAVlFvWl1ZxRcCs4x8osTRDUZxn8/ChY0uvusrMakLaQiMa1Y2NzS02W5CtgLNDWYMF6FRu3ZpP3X35vOsvhltKznCFq2gDq9u3Zt0mmREh6Ml9ahfqQGeB0S2bvbuJ7JVkJYXFcGNI6xoNjiiYC/mFMlEvDAaDkWXrvjzZ/Ypp42fmH9sVHMGvW9f+sOGSudPDg0BUiKK+Sk8TukYilQTHkYYCQJBuehWVVdZ9/uY34j1mi+342apunRpMh2VRDdnCsJK9x2FhUktrW7uypb76ySll9aLJQ7vm4jAYf2eiPVLv38CBmlaY+EkopXEVRW1tSUJKUkhodFFJDdK1+vr6kbHN8ZnErMwh6KD3Vx63n4khCYWhtRUQPvR88xqFd6Ok7h5jULlUOhtbnGLqvmnligevoVnWbLFRJKGgqBUPXSMOdu3Ye6I9p8RgMP7P1fOnKBWUyWxtarO1tFlohmlsNZ0vOAeX8CRJFJ4vpSg+eD8yFrbvLCgodsRtZjjSR9PTk+c4n/pcviO6Lw0ikVxDEdEa/suv/7z5xkt6qzb9+YeWoI3iEjnTGIPBYOT44fcDAIDbllwYpKMB0DW3muIjtMMTxykIQFAUsXzRYPHRRUVlCgUlidkjxMLKo9TATFq9IT3dqTpeBcC0mJhz63Z0VT+70Ow/GrMuhQ5dI2w1j8zbZx+49vMvf/XySPsVFBZXtusZCQAeuH25Jijh32//s8OXjcFg+juXzx0HIytciiRc+pptALA0zSqumRWLFLPzz4G0NT09GW0gCW6xkh6T3OHkDt+jSTpLiRBEigoJvueW+d7Vs51IrkQdCL3Df3/oWayhGMyA5Zdtxy+fPcZ5H/T8KTxLpy/xHrHhWVRUJliviCAVEDc+bjV7egofC4pWPH8n/E9DS5erpyuVJaeVXDrYrUuv/2KtU5QMg8EMBJAP8pdt9lCQ7/1ACe+iyXqxXts3o8PL0c88e5uw7VU9VTEplmrUMFTmnKrUy9DGd2t/uvve+4T9QkXq77/9OHXyZDgSOpXPk73wgknLH3zY15eCwWD8AwIMTogsLJYJnPg6Bzg93R4B57SoS5b8AhTpZKVKEAeaPv0Z5rXesVjx+X/XuB55qrRheCLsDZN0wd0pSdA18dU/4Iymqdc899OP66ZOnRwV4ZRjX1EGXQ1XLZpdWgRzXR0vj3vSmqoaSxtsJZWb62gohcFgBhwsyBqWmDk00WKlgwIDaIax2hiTxdbQYnQroOU1TRdMHCncLCriC3Xai8SGpUQDQjqAxQgLqn7YmRIyFq7iI4Odsqle5oo833r3k0Gm5rNnHKNAz545nZk5irG0VpRxRf1ucYxU7iomKssGYDsGTH9ngCR4+goK0hBApSRpG2ywpCQBqSKYAKVCiLOnp/E2ZlFxeUpyfEpyfFFxB0VTDBs2soWbBcJfSUgXpNRXV6DpclRTjcyoO41K5lvh4IG9F82c7PlKhf+iLTS5lLvoDlwjBoPxE5pNdHNZHWBBakKkjXYMNIKJ5MKNgkJYKIniP52UzjajRaWk0KnCAilLG19P6g6WBV/9Unr3nTBRoPPANihuoCiK9jzn3q6eXKEplk4MZsDR2GwIDZaf3na+rE58k2YYHwqKfKOqrgUZsxFDFlisvEip4ybKNLBHY+k94LPF99uf22Qe7TE1n2uG4l09fb0CDAbjX4wantrYbMg/V5V/riq3oCK3oCL7VDH8OV0s1QkAFLK1QCzL+jIPrrXNHBgA65fOFNUGB8J0et9bgnZdQbwT2Xq2ThmZ28AreHVYFowCNdDnzOrwOsH2JhjGabColaEBYEeGIm119FrBYDADinC6KSEyOmF6VnR4IDcECOobC9i6RuPGv44KGiqgkNU3D+ppMls1athvKXbY3LPHNtpstEJBNbWaHALKArXdC2kqP0Q4ez09Ap/W3fq70WQL1TgcDvlNLAC2enVMth4+YNuWrcJdUbFJO7ZvRtvxXGeoHds3h0dGbdm0SXLOmXPnc0OfKSWnnAqS61Vl5+I54y026L1lyQi0hxG5PzAYjF+y9eDptPjwovJabi48bBPMsiBjePKNi2dAEWBZilI0Gsz5RbWFZwq8pzEdySvLSItBonkktywjPQbtJwjCamM0XHcSk9mm09q72HGz44VtD/1EOmOEZoYrPv/wrdvvugmOHWEVtuGJY8aOFd0/ZveuXRMmTjm4+h8AgEXLXhI/duPGDZdfvtj1nFu27po7by732vgL43SV+OOPzbMvkjQAwGAw/olrECWnqDZzeDLX3QNQ8D90iFaRkRyeOmisvA+0srbZUQUvOtvoYbGMKGNTiHczDCt0b5bIIuzIZL/dVFXYVCUz0IogQGOzTHdkV8JCNXk5ewmm3kY33nznciursHItTaGIm6ziHw8nYQFrNsmE7yXqiS5Ncsz/Lb/Hl+vEYDD9ET0VAghQWK4XOzpnTRhSWdcEf2rhD2rAFKxThwVp5S1QRqTBkuW8OCikoHj9ZQEr7NcXbBQfbzRR9Q1mXtTV8UqlINlOSqsgLB99un6Kt5fHcF35GFLalY9gZfXXrSOTBcBsMqk1Dr/t9u1b5syd6lk9MRjMgEn6JNDNCOfqG86+5NPJGcZuvikUlLjxklhGQoOdokOUXTQJVZAvnehCggIjw0IZlvMesqyCYjrTlB6dxOdD5c/NtdiDSDTU3WMl6/fUZHcPgZwvlrFtB86UcK+gMeJokrjXg/171Dimr3Hl2EEF5fpK55Ztm/fmC9tzp40Q30UQojxQMRq1Y//ghAirk7bCHncKBdVm8EkpSJLkC9xlha8DftCeaCDa8adwyGuFT4LrlQGryBhMTzIuii0uq1QCkDQImpzD7cXr8yIj4TDgPXDyxRaRmCIUslIWoHFEhDq5lCXR2t692aigukAQh2VktjfxaPWqj8XtSwYPHfnpe++iux58HIaeICz0KAdoODu5lxKbOqm/PSbHPlqLMfYawRJc4Yrpw2SfRn2IeGIj4aS1rGEp4p2b9mTLW6CUy+QjZHW6ezIPaU/cXd2uPX9u3FhZCcvkAczohPztwUeFewO4ZC74Kixw4uaVi2dYrS3Lb78eXp5CzdB81cHMmRfBjiSncoDN4rh++NodlVReCpn6PP1FjjGYnkZQKaIdquoQUKdHtdMoVIryliQwtEwdZ01NQ0w07wsTaKkpAN7cYm1WamQmX9JecLZg9JhMtH310ptQUOuPP7dNmXIBPLLN0XkUbeflnpg4mT/exrr95A8fzh/DH2lXz/4unX1Fjt17NrAcY3oRo8VGcPFzFEInSVIr6qrhzkSUt0Dbu6gOQOmgchze51RtSRIMANLuRO98nUdSKkIXQwAvyUw2K00Q0L2gVMFnpM1t4nthOZHdAnVFqD4yGn21m7B69gPruFj+DFiOZbGFp+HQnAcrUEgCVSoVRitsiMGwrEqrhpk+LGu2MTrAW1So6sfXfqCuhIVqYKo+h07rVkCnz56Vm3/OZrPV18M6H5nSeADCQ7h0f0AWR81OKHPUFFW3mGKC3H6oxLlWAuLsLYGDq/8xadlLra3C9Tr4+aeNiy5fKtm57qffllx5GVJPbHj2U7CzojP5Ej4mS1TbDwM1nAOtP4NKfmAoiCBYAHRqJcWpo41mNJy+0TSr0RCtzW00wzAMS1KUkmA6LqBeO8ghCJKvRYqIQO8EL6C/7apbMDPK6UhYKulUK+nBdWq1WDIzRwPG0SjPfhIv1rPE/MzK8tQCCqsnxs/kuEtifT6mrPUzULCGUysCZiIpSQYuWxmWVKmVLMsyDBuo0zBWK+xoxwK1WslYrR0XUJPBsdwm3UfSYSGSk/8AtdlkZX0KDMMIIaeaFvOE2/4OAChb9z83L5j49fcti+Zz5UO+4fvi3Wx2Gt70w59f+/4sGIx/y7GgwjGCzrr45folgvoQcEVvs7EUSbFc308WTi8GESEBTY2tqNfSxLFZR46eaJ+A0gxLya3BPRS8uwboa2oadh1pCAwKdbUyuYiTNGqfsOSB8u95DU0Eohn0FnD7vHQYVm8/GzavE7bzzu+W3k1hxcT0G3pdjg/5Y0YaSRAUl8I+cnBMbpme24SqqlJSqINdZKhOpVS0T0BNZqsHd6cs+3bvCQqPcdqTZwTAKiu57SgyAoC2mQEgotXV4p3XzRkMmPMf/L7j3gXQMj1o35+sLUuePRKABneJAQCAexfMlOz5YOPOdlwQBuM3cuxjGUiFl2P6ZSiPsLfi5P4jauYBtBoll+IJQ00pCZFwrHG7ztzUapIVUF+ahzpgQfLgdH2dY2YRlHZdPVxiM/Bfgd+2/gB1be4FyUuW7l231lk6AaVz1KgunAdzjzb8cQzdROrpSmurIQAG1LxLJ+KRK/j9b67fec/cadLXYbN+9Nchb68Wgxm4pPa2ddxeCI+7KD6iAwUvWKcmgJtSTnfom4xxUcGu+wdFBolL6cWEDCIBqJWoblV9LXdZEWL1RJwMj8/Sl9W0mKuT0+6dCzM6EdOWLM379UeknmLpRPy04Qjy/X727Za7b1rgehlzLxiy/s/jSD0/+3YTCFV50E1RZz5IW4vtnjnTBL/CXwdzR2cmH88+t+PU+Vkj0mXPAAAQev9XldVeNCpDci9JMFtP5Mv8JhOcAmsYDKYPyDHhKqaoSL19AtpqdJTo/PDn1xPjeQXsGD/vOX7F9DHu7p2UmGKjGaHhE6zXvHQBbTVTgU7qabWg3wLx2dotAABZ9QQArP/zuEpJWW22z7fsBqEqiXQKngOaO1uLBQTp4Fv1vy/+uONqhzH718FcWPSZFtditLIMuHbOlOoKpxkpsiydPam2zuCSDMtz0cghwvb2vLPDw6IvGu/Y88GWPa4nvOcyqe7TNssnm/bDu+ZIzWQHjO3D7YJLA4MZ6KRK5NjglFRuggahp4X15r/2gfbmgbozMzuMq8+zrYZBF3Uor0yloEamRi+6dPyWrUeMJisVGAp7kthflsVsQlbn52u3AcDevUxeOhEqJbXq+2133jj33gUz0UnEIN0U+HzdDgKwd1w9V6yeCAKwBWdKucQDSqKeGaMn3HbfE2j7j5+//eb0EbRdW2dQazQvv/kpVwhgffqhW6BW7s0BOiojFg6vF5iQkiRWTwDArTMmfrHrkAfp5M5pJgDhSTphfQGsssBgMJ5g231s1+SBdglQOkUYjJZGGzF6TNrPW/MUCh0Z6PTqLGYoeSwAK9duu3vZZV5Pvur7bXfcONf19+MqnQCAO6+W6T9PAhpNwyPlxtI99/pHIaHhqz/57+ncY8mDh93xwFOXLr7ulpmDoXZz6vnef1YUnz97850P//u9NRPHjmJ07mfbcbheqqt62qx8rhXlfjggS1t7pH+VW+4R+WEAAN9/WeDleLv/+sONcEmBwfQWMNvSgxHKmXKKdjUNETM1ObqrDNK750wrqmzYz5RU6VtZhq1pMZcFJitZ5uDx8zmny6eNTs1Ii6XsqaZWTjoBACvXwSJRD+rZ0upYON9xI/xYKlirjasElUgn0k0P0ok2WJffJsvQBAl/XSGh4Y/dtZTm4vv5p3Ifv+/m19/9Eh3zypufFp7NKz5/FgCwfPnth46djElLqKyqdBXKwAC1+CZL29RceZms4Wm1mGG2GuPpzWJoq3DFn/6wA4RrQa+qp9NdzoE+kqXf/2372ITB7g7wRVLvcfbMfIiTKDA+06SD9ZDws2xfyxMelbQjFuisiWnuRDOnoDrTPi6pHVrMXcWJ3DMMw4RraKPCYrHR1bpYJbCptWqr1cyYTTm5p5v1NYEBajiShKSGD0n6gpNOJkw1LSnhRPYpd+cemu7oPYWk00YoZU1O99KJ8lLBh99tu+vai12lE6knQqPRGgwtpBq+DQzDPPq3ZWj/w3dfhzLItu/NQYZndQ2fejV+TFpJsVMaluP8nBYTWrWreiK3r5JVeFhxMDQ/1IQF4PMfdtjCtT2snvakBdb1RUlYxGVQbPzzxNiEwZNH8wL6176TksMIwIwIj9x+Uhp5g13G7Py1L0fYVlFgVFT4pmMwcVgu1seeanHKA8FgxEraZB8LVJALiwXSg3h5lSRdKiS96F2RTAB2C9HZvCtJFqhaqaBphqGh2cdNc+L5Yt02JowP6u/PLpmSxfc9daXwPHRWChw9Dj97gTpeR3YchB8tOhTevGb0kLo6p0r56Ei+yBdJp1g9Ub8SQTpRVilN0y/9bxUA4JtVH544etDKjR4R2HM4n9aSK79aPSpz1PoN69Gv9Ob5F6BG085XLRqmIpdx5VBP99A2R5rt5z/uhOrZs7imfLlTTxhL3HgUEGDluu2ZoniamIwh8Is572wl7TwVddaoYQCA8mMweiZGJfrWnjd2mIULB4gZnZV8Ihv2JfPsOIbv/ta97sxhJQne+RU7GQYKBS3QLC04ft6xJ680XWOfBCeBokjhj9VgtGpFDerd0YHZHAL7quzRmFCHZGgIhcliBTQ/oARx4HiBoJ5QvELc+v4AALvOF8Ne0hwGUf4AUk8kne44VVgOLZrDZ5hIXX09f3mthtYArZaAZzNCF4HBHBMBhzbvOnL6+isvTk4Z/Pyrb19/yz3X3wIHz+3bufnDf+wV1DM4OGTT1j/DQsOuWHjFa2+8cs2sCVYbU8mNTnWFpWlZ9bRw6qnyop4WrnMMWPkTJ519ST2JAGkOHM1903yxbrs12CF7YvNzSGqkoJ47c+F33rLrbnr4vkeEA266cBj0ZoiexUyD5JTBDz7170njs2aMGy55xrT0ONR0Z2S4UwTPFYax3XXRpI+3H/z3vUvPlTiNvBYme80dOiQ9yZF59uGWvUOCwudMdjzph1t4CZYg65b58LedXmVd0HRMr1NgclMLb7HSFEmgNXh9Y1tCjEzupwRk8qCH+D6n4+c9x93dVcOaQSAJANkE6Ow6u8KK1BOxt4SvxvXMyu18THxSfNTB8lrgUT0R24rKQSTfa1mgjZNOger6pjNFVWwQFLvionPLr78c7b/xljsXX339nskXZh/YQWvhp625uembdWu+Wbdm37YD+7cfvG7hhcJJ4uOiJc9S39oMpG1SQH2jITVpUHNdI7oZyU0acJFOBLvyp109b3h2UD2/59VTWL+LOXu+jmFsgAVIPf/90n/mzJrz6fv/ys85RikUD/79pa92nL5xRjotanq9+/CZNz/6HgDgqp4w+6+wnGbYHafOrX71/oJTJe5eCHxSAD7mEr/cqSd6cQUlNelJ/Dt4z9xpWw84+ZTumTvNVUNl1RMtZTyrJwF8Ww5iegpeQK02Wix5jS3GCK7FHJc875OA9tbEi/YC1dOF70/ACE8HOF3fAILUVy++5unHn1l6+Wxh/9erPhk1duJjr6+8MDOeiqCUSqXJPkj57/94/O1//1d8kvIKR0F/Th7U4kFyCbYsYLNPlyRH8O9FnfClYqe+CQbNdh2FYe6+oJ4LLoGdrrZsOWpWy/z9SNTTdY2MsFqsFEUg9Zw1aticWXNWffJWfg4sOaNttjdfefKN97996q1VL91/Izp+9+Ezu/cdXv3tV8uugz22JcCmEADsOHVu1oh0D+rp9KKcL0xQT7R+33KmYO5QJwfrnMnDtx44JTZCb5815bO/9ntTT6t39WSZXh8UK3mLv/eWUzEgBJRlgb65TVBM+OFsdNxs9m14XK+/td3HuuNn0EZVmfyKe/vObU8//kyh3qklYurg9OxjMIXz13Ubg4ODp9v/8p5/5oXW1taCcn16vHynRdgsSw6LlT5UViUIqDv6gnQitmw5ChfUChfvOwfLsqt+3OFZPRGMyOMJAJg45cLcY3tpwC/5r5vqCBXuPnzmultvsdmsjcX8WyajnodOz5ogY5nKmp8u6skvriTeT7ERijTU6VEKUtBQWfWkbVZfXGBesmq6AuEvPNL+PSH+m581Mh3Vkrg+apZLrR1FsFtP5CWERN5xnVP0VYwfZKpBAbXaaH2Tw+SEotna7prTXs017GX0Dfq6+roju4+dzDn59Xdfxccl3Hf3/QCA+266AhpiV88/vOvohu9/u/+x//v4nU9DQ0LHTx8LdIqzjTJR4BZkjVZ7L3CSZXsRdN32MIom+kSOVLCUSmXGkMTjZ/nrGT3KaR4sAODLH3dYg+DHdFpSwknnVIqyMpjjlZAAfZQERezOPY3UkwL0Wy8/9vAz//n3++vOns7Z+OPqkqKCuFET6vOhUucwIaSOfOT+Rx+9c0lq+nB36hkdLbXuWdpKUI7qXYa2AIJ0VU/uO4xRKSmJeroaoa6ouII6WfW0XwQgnRaBrgfQvbvOSwySzuARiAmUmgJjMuNP5pYlhETKJlTMmjoKbfzfwrnvbdjiS0aaGJ2KeOMnPnmmTwhoi8EsUUyzxRG4DAxx+4sToxSll6elJp6zR8CnJkfvK+5Yz7k+x6CEKHdG6CWLL84cmbXq4y9HZb4OP1Tbtzzx7OOB3F0sy46fPvbxh5747+vvfP3t6i++XtmZa/j+hIxt1bvYQmSS1ViW/XjdX5PG8IH1Ey7pRxZOPT2AZJTiMmFLSmHbn9TEmMry4qWXz3ro7yumTb/ooadeg46L+rpHrpyEHnJwx+EXHrtLVjqhUzuvEOgUw0QxH77QQARUTy7TwlU9WW4olmzk3Wihx44bcuxoRxxBKOhHeVRPluXv/mCbNOWgZ0gMCktLjHjqpXciowehPWfyT3701j/R9ojBsQRBpKYNNRqNDG2jaZpUBDKMF7GfN33o1j1n2qWeOpXjtyTr7nBkTcjdywJwrly/60SR2Wxlg3mJM9Q7/fXqIuh2C2j+eakoKLTosw+JCPQprz48KlL4fiRJIj0tqai4F6yhXiQnLxualm54/e1/vf72v4Cfsq+qbuogp6DWrrIqECG/eG8X4tQlfbNRo1KwLPPWv1a8/9bL0D5Vh3+1Zu3Hm3Lumpf5wX8/qq+triiH39zI49zaCiNxBqPlTHENrYN/6hOSBrUa4M5AXaCgnsj85KUTgJU/7b592ULXK1GrKLFhIXDpyBFXLpxktKcNymKjafkyXC7up/Bse9pX7x9tOwB6CRaANz5ex7Lsf158rLKsOCY2/u8vvP3Gx+tQrR00IBZcffFlcL0l8Cb3WUgIiSwork5PjnFVT3fP5U49uTcLxpDf+2rL+KHSeOPC+eORH/GDrXuzIuXzKwgA0uLD0+LD28zWL3dD9xrTrJBY9YZ6KkBNEYHwfTGIaiN10aTsHvhXZWSUkjcwMy0G2GcnVTdZE6VRYhkmZSYCAJBoDjTpxDjloonYVyr9S5CmDsjlUVTpnaLeAgaj6dPVW5Ys4j9g+dX1ANTffNdNX378FfwLHA/t0I+++V04/qNvfn/8nhsMxmqknmJaDa319XXhoSH6xia0p64RJvrtPlYwYVSaq72s0cDg3vaz51yvSq2kfvvzSKxclzIBi41psycaR0SKTGAfElZQ3vGq9XtAcO8UXicEhY0angwAeOxufnpYdWX5o3cteePjdeNnzDuyaxMAYMr0iwytLc/9/V7xNSNgMqILm3afmTExzeYy6dajekLphCcMVLq6Mjb8zqfZAAAaWtrShiUWnnZKAxcToFYGWrWtSiNFEqnJ0UUl1a6diI01VrREYjhvu6GGUZG0igQWe+0fElP4liQOCq2qcxqHebakbmxaqHiivCzpaXwSe1Fx+QARzXa1m4sk4YezLg53qOss5xocf58hSUOOnYBOT8SoDOhQK9KbJ8xwjLcaN3rcx+9+OnHm+HHx0fl18nIMTVq7eiK2F5WBsHY3TPv5RA6oa5s+yhHLcuVgQe3iWbxbtt6upPVNULJ3Hjx35Uypg1hMeDj8JJp6ST0RVgtnKSuVNiuvhqMzU75477XTJ/hON0HBIa8+/5irepY11bnGlxC7DhUS7VFPhClQOT42ceJIaK65ouTE7cHbL/Wgnoib5o/9YMve6PCgKy8cabEMPX66/HBeqdHseCJttNJYA2+SgEYaiqRTRdKChvICmhytbXEOtTe1OG4OS41qbWwYyKLpC08unei689O3C9zdBQB4bS1uxtxuduze8el7n+n19Q89+RBto1967uXUlNT7br5SPJBWGM7KMMzh0irxww+XOG6WuZmAjazmqYnxEgNR1vz0CssFaS0B8g5Bk1m+OktMQVHlVueaup4kISgsPTHCarVYzOZ/vbeGYeh/r3g4nPN6n8452trsUIas0RMWXgmrlivKSxZduxjtjNTCShN3WGys7+r50R/7QCB0tpitNg/qCdMnVm+dM9Gti8AVlUoxKSt5UlZyeXXjX4fOCHlH2milot7YQiuQhhKcc9LGOrk0oYCmpcS1Oo1QA3MnxAv1nTHhgYMiAgsK4VuYkhyHRVPAnTIipsyM+/RtfgNWne6s8PBwLKY+8siTDw0bMmz1Z2vQst1isVy8aI7lvNt+CB3G1fngiciA3aJkXt/ZW1Tu1bDsRfUU89T9yy646NIrrl3+5D/hgDKWZVfYF+xoIMXIrLGP/m2ZRhvw8hsfH9p5ZOJM3inpAZV9mm+sxhGpVnLOQyvKsBSrJ0dmZOz00anuTvjOpr3tylW48mKnobzxMaHLFkx4f61jSJotQhsk0lDaJTKmSEmGH+8po5IKz7l5nwhQVFyB2oIUFUtVYMDiWT0FPv3xUbThTkYx7eX02dPowyngiHjaOXr8qIeAXq+w7mC+uxyGdfbMiiWjpXaTvtmp8q3nKausVZO8xfft6lXfroYNH5JT0l54/b0XX//wudsXFZ/NLSktF6pISErxyD3Xv/nhN0PShpwtPFtdqw/RSqPQJ7Md9YR3Xjr7kz+2JUUojh3PT0iMi4vQeVBPDwjmp6x+EoBZt/XkNXPs7dtZGhBUtEomERvOQOIiSBINNTBKEjA2Vpo6okCmJSItLbGwsDQ5Oa7YrpiwQrGjoul7Zzy/VM/9OyvueOh6yU4soxgPCEra13j7o9XlZSWv//NpdHPXgWMz5l2wf9uBFz/7VQjEC+zIgQuCC6fPOlvoPa/rbIGjPUdZaUVcxJDWNkveGfjAoEAopnuOnQcBDkN2eHyQUIMnqWZG5md6kCMdyANiR60Y2SIWW4SWqaVJwIZFQc1tqIVedQIABUU6LSAKOTHtsGIi/Cbrs0vYv7MCiaaYKTPjJKbok0sn4lV8hwdD4WBdNxKoKGxqAZRiwuQL4AZCAxWtqrI8MTm1SG/esXObsa3tluuccr9y8mFrQdJbEqSk7s5KM3lnioWbe46dN4rUU4K4mrnRkcnOFhSVj01zKpeIjAiXlCR8uO2Au/q3vy2Y+75LiZQ2ijLW0uJKMOjdppmuCe1h0ey8hmI6bP67C9bh76SuYtGSBUd2H9uzZd/Cay5r4ELKly9YnJices/1cI4OyiQrbTFYuP4GN98Ah9bsP8ivuwtcktJaS6SqeqC8dnI8/NasbTCKA3ce1FPClkK+s0FBi9NkX4GLxiSgtmr6ZtPWc/Bg12Q1hFYrn4kRGRJgM7VGDoppqK0XTFyFcLmSjDSWZb0OK8a66RVX6RTfhTW0W4N1kgdiPe0M0+dO271l75YNsJc54u//eCw/G3ZTy83LKSg8u2/7AaPJqNXAVgwLr/E+ZUfCAa7LD/rXjmzfn454Oerq3aay+Y5BZWZqDKUtjSFKhdWewaoQNFIilu7UE4smpq9F6lyDdR7SHrCMdgyjyegalwu0b1x7y1KKokZljrZYzLn5Mg1HepQIjdAAyMdEC5S1hth2ptDdQ8hoHVNjoEU95n1awmPR7A6w+dlVv8Y7Hrre1U/iQUkx3QFN0+ICh/6F71lrUENFNxX+pJuunT5ku3J1UyijM2CzqJt8zVhJO/g3X4GK6NL53iFyVLX/M+V/9KGxxr7QR96z19Ye8nF1KZg/4s+2xCbC6tndGorpQoS//E/f/gaEqwd4sbLiULl80Kr/cvdt90RERGzfuX3/wX05TEgmV5COIEnyi4++TEpMevOdN37Z+HNnnkVQvfYqqesZMD2joThq1158/Nt+cgAXK/czC9Qzh3YeIUmSZVmbzXb14msAAP99/+2j3/6P7zqROWrlh6sqqyr37N+z4qnnH3/w8Rnzpnf+SQfCX0k/wrMFCu9d24NXMwD41B6+Q8hmQfjxZ8RLU9t+xJYN20iSnDhz/IQZ46ZcNGn89LHbdmx98G8PUfZOzys/XPXTLz8uvOayZ55/asKMcQEBOtQADeM3eF2/44BSFxqed7gU2qG3QPgRn9D3hIr+hf8IaFho2C8bfxZPsX/8Gdhf65Klt6N7AQAv/Zvvoc1yvPHaW713vZiuB+tjD3MHl//g7tfuqqT+h/8s4S1Wy+ULFr/w6vPineOnj0U+0JHDR0qOX/vjd9deDbtvYXoypteuvIiH3vztsomO1jvzZnjqm4nAcaReYb/H8B26a/3MxVfc16nAQx/EfyzQixfNAQAc2X3s0M4jv/34x9B0p8Y2iQnSDqx7D0indWP6IL8dOv/bIUe/CV9ANpHELNq/s+KK+372Y2dcr7PfvSkqsP5dvk+o3+A/Fmhra+v46WMvnj3vtRf/FRMd880X38GR65XlT187Q/Z4o7GXG4UNBAiCGDdm/KUXX5qbl/P75t/NZue+s3bi4xKam5taWp3GIohBGnq0Eo4z8t2b5n/2Tt9nv7dMsvXv+pUd6j8Citi8bdPmbXBICwDg2SdXXLHwitdWb35y2cXZebA3jJi4WH92zfQFXnru5fnz+Jroqy6/+tknVzAMs3yWdAIwSZK/rP31iWcf37Ldpynh2Irsy0zxa4+n3y7hKYp6+L5HJDv/+doLeafyYpPSUL8DSYH/7Av5FrCY7iA6Knr+vMtWvPzc+Olj0c9Fl80iSfL5j9eLDyMI4tBOOA6MJP2zdSzGv/ETAaVpetl1N/3tzv+T7EfB9yI9v3IcO9rRDWHa5Aty8rJ79jIHENdcsYSblfirsKe5uWnnnh2pw+EMOMTP3/16eNfRQ0cOctPbfW1chunL7B9gnQf8ZwlfUlZy+y13UArqnQ/4zPlrrrgmdlDsy08+gG7u2b/nk3c/m3nJdIPBsOKp5xUKxW33Lu/VS/ZnTuSc4CZKRuj1jlK3h594SFwYlhCfcPGiOfoG/ZHdxwAW0O7BQ7aDu24Pr6095Dp9ur0aOsXNWt6fHKBwCXXZtReD/oPn3JevP18zfKhTpsuqr7/4/NVnYIgpFM62/+h/H08YB0MQDMMsvnZRRWVFH2km0k/x/HYc3nUU+Uw2/LHhq2++LOAGPAgCmsM45jUe2X3s2X/+47c/N3p+OvyOdPl75A70NuUwIeI0Mh+Z55xtJlZSP1NPfxNQRFhomM1mE0K6gY01goC2C/xx7fzbce3V1z1038MqJT9ETJwUIRHQFS89u+GPDfgd6QsCKvslJ8azqh7lkiUGCP6zhBdAIwcwfYHvfvj2ux++RVG+99/6YMK4iat2nrtl5mDolQ51OpIg/cQd3+/Yu3W/Wg2bKiH2Hdj74ePLxAfMm3PJ4w8+HhoaVlpW8vdn/15QeNZzZu6ggbR080MB9cyAend7kdCQ0JDgkOJSfkAYTdN3P3BXfFzCL2t/1QWFAL201SxJYAHtBY7sPsYwzE133Jh3Kg8A8ODfHrr5hlumcl9y6IDffvwjJjpmw++/Hj52eMmVS79btfaDT97/dNUnvXGxfRGFH8sfyriGXQth0a50SAtOJ+xWtm7c3tjUOGfBReKd5RVl0J1ChQAgFVCCxFH4niY5KQUAMOnCCSzLD0n77/tvHz566H//eVcXFGJoadLpdDHRMTfedsOpM3D+2q+//fLOG+/de+ffsID2VwF1Rwd6veAhjt3KW++++fB9j9yw9MY1a78Wdn67CvaSKz4nMy7c6wRDTJdDcW4TtVptMpnEJc5/fPcZ2h4xbGRVdRVST8SDj99/aOcRrUZrNOFCvv4voF5108PIRndnw5Zpl7D626+WXXfTow889ugDj7W2tpIUGaANAADcfrV80BIv4Xuec0XnYHrfln2rv/3qrXffRDtZlv3mvZfR9uGjhxZcPV/8kJuvhyOLsXr2ewFtl8kpGdk40HJ9e4tLr5gXGBh4yw23TpsyjWVh+6tfNv6MkiIk3HzXTRUVvk71wnQhky6ccHDH4WXX3bTsupsAANm52c88/1RRfk0KN6tDgkajuf/eB5qbHZm8mH6WxtQxAXXN6fWgodgC7dYUGUFAcWJZ33mPCIK48dplt9x4a3hYOBzHXl352JILJGlMWo1295a9NE1PunCC57MNGkhx2gER+nSVS7/v84rB+A7Lsqu//eriRXPGTx+7aeufUTGx46Y72VUpySm7t+y12Wxe1XOg0V+X8F0CnjLWSTpga0Tak7QH8ijHPsKR3ceamppmL5gl3vnUiifnzbnkxgee+/GXDShX987ld91z+70VlRWLlizotWvtqwwUAfVcn4vpMR/Lp28XeD4Au1B6DJPJFBIiX2tUUlyENl55/rVL5l7y+tv/+vZ7WBCBGYgCKuima7fXLjFCsw+f7uQZ/IORSdCDhnj53d9kjyksbEQb9Nq/6mraPJ8wr0TfpRc4EDG1yTexBgBceOmMA38dOrL72Kv/efn79d8DAFJTUteshEL58O1LYaPr2PhL5l7y1rtvYvUcuEGkDkxqbK8RhAVULJ0eiIwOOLALJhVOFvWb8CyjWEO7T0ATB8cGaAN2/LmLFNXRMgyzYOqINgOsZ9989LxKxfcxEDN+uqMt5AAPIinung9HCYmhKC8pze9v8KlzOAYjoa6mLW1YcuFpvr4TERkd4EFGRyaFYw3tPtqMbRNnjhdukiTJMEwgp54AgKmzJ4sP1mq0ERERkREDSB+9oqivk2Y5hIRFeH6Mq+by56JIriKM7Uci23nzE9Ne0oYl19W0Id0UEG56Xddjug/xVHBXjCZjWXlZWTmsx8W49YE2NTg64HaYayaO8irE7tCopFf13obNkj2vrT3k4yq+XXNufVTPuy9z+gp5u4L367lSUFHt41MPNFw11BeDFNNeNAFqD6t4TN8NInVYiCWFDhGRUbI2b4PzDEcb7fbL89dfmqUC7fBSOHVV+79F0CP83q9SvRZzzcRR9bXQbI+I8rSWqW9welJMu4iMDsAa2lVoAtQ+OivF/XdwqpmfROFdnQwdIyIyyqvUevZRoJRjfX0df2GcjAIAzCY33/B8jxuMPEgiZe1QrKE9EPHzuobDqWb+IKB9R4hZlmVYFvWw8eFo+E96bIy7+6tqG1ptFjCAEUun7Ioe26G9kiwh4K53hFhYXxvYMYMBJKCdh2bYphZjRKjOp6M5L4HZLG+cNjT7m4/PYlXK7lcprb483J0diulh9RQamEla8LjrIPHkwO5hhgXUV1iWNVts9U0GXwXUhxMCf1dP17v0jS2DojQ+KiZ2g/Y8+3dW3PHQ9bJ3eVbSgcmAaCbSJdAM22ayNLU6Ws92BpZlDbRPplnf5I9vni458l7JkfeKD7/7/su3meVQKokNa58QP0rf2OIqjm+/8rdP/8sbO2IeuPvKn9b8Q9JouejQuz5eYd6uN1a+fa/rzrxdb0h2BgZozuzljC6MCA8qiXrxTMGF0VhAfYemGYPRYjB2TUZIv27AXnLkvZFD4xcs+9fUBc++9PZPCy8d/9fvL7oe9udPzwYEqMWSKjkAaehDT7+v0ag+fPMh8V0atWrSuOH//eCX3GJHLsfWdf8gfZ78ERig0WqkdnFggCYwANq/YlwlFYPYv7PCs7E5ZcArKbZAfYKFsXvaZLZarPQA/3Q9cd/lAIDZC180Npq1hHL71pxZl61QKqikxEjxYWtXPdrQaPB6trqatrqatvsf+yBQpx0xNEnY/+n/Hm1oMrz/9V/CnuLD7w4ZHNu1r2XRvPElR97r2nP6H74s2Ne/u3j9u4vBwAMLqG+wLM2wNpphusJx2a8jSJHhweLgWEub2Wq1rXjlu5paR95rXGx4XGzYnfd/IHsGi1Up+ck9VVtb1/zMozdWlFsryq1/u+1qmG+77M0hcYPQT8mR9xoaDXMW/bMLX4hCQb336m0TL30651RpF552YJqiiAGooTiI1L6YTz9eeHcRT7z09bWLp+7Z/PLTL6zZsTu3qbk1Kixwy/aT4mPWffnorMtWREYEuT5cp1VJ1vIqFUzzXnrL29s3Prfmsweuv+1/0yYPvfGOd8RBttkLHS4CD5lhEseIVq0eEjfI3cE2G500/v98eMWYjhT1DRCwgPoOFk8Iw7CzFz6/cd3Tr6y4Af1eKir1N9z2ttXGOzf2bH55x+5cq9Xm46/VYuH19M77P/zknXs2/fxMdU1jUXGVu+PdZYapVGpJYsPYUSlbf33W9UiJBKuVCoIgxFJ7tsLtsw9kfFHP9e8uvuK+n8GAQfH9ISfboe9wzcRRoC9BQAOni87Fsl13rl6goLJ27uUvQOnRqu+789KrFk366/cXF1zzSmOT4ZorpgIAnn5hTQdOm3eqtLqmMSY69KobX+/AwwUhFjhy/NwDj/MTehF7Nr/sKsEMwwKWFY/2TRvk1sj1gDjA1U8luKEF+mfc8ftGvksTYv6CwO6/or5O37VAfVH2HhVZ+OkguiR6zvZza1bFdXuhGaa0vO6hZ754438/79n88o9fPz574fMP/9/C/YfOvPEKHH6rVsMg+Buv3LL3wOkfft7vy5nr9S0x0U7dCXoFVy1uL4nh8FWo1WoP36Htwqf0A+6Qs+W9qd1XDCTzs08LaB8U2a6yGgkA+m8RZ8mR99b/cej1N9aLJeBkbvGojGS0PWXiUPHxUyYOrap2263Kv3HnbWgXSIV9KrvwWD1s4dKOxaepbQyU9D+rr/O1eVhJdfP8BaN9PNiPUUSqIo0mi9FsZbRG4I90jchylmeXmY39ef2+5sfdN1w1/fX3fyUNLLLHSZIclZH83id/AAAuuPgZ4cjYQWHff/WYeA+mt1RYEGLxn57QOt1K0+U1TRt25i6Z5fTl5wGGYX/f6NwPjePu+bMBAB/9vg0MDBRXzRklNA1iud8LwzA0w7IszNoxW2wGk6W1zdzaZrZYaYvV1tpmNhgtrYSk51z/xkdHcEZkTD9Wvi7iyZe/mT09c88vTpnz9fqWNWt39d5FYToixPo6vq8YzTBnCiopuh0mFEkS/lOJ3AmIz/8jU0WHCIvgU6NlVIPg1wuc0sINmmEsVtpssbWZLAajxWqjzVZbq8F8pq4E+Beu5uqn38n0D23lapYCtVIvWENzW/9dv4tZdvkFGSOTTp0t3+acw+QBg9Gi08rM2MH0CnfY56E1G0z7TpxvbTNfPXeMj48tqW5OinEbceqzoekuBDW99OQDbbA3vvSFsIhIpYLSaVXhIY4mEQQAs0C6cFP4ymIYlmYYG82YzFZY3mOjrVZ6e1428DtK9X7r/tu+K+f7Xw+YLNbEmLDevhZMx2FZtrq+pc3UZZ0Zjp+pHDjvR5cFkVzVdvl917bj8c1tjwFYI/jzxn2yagtNWosNVaPvPJXbRVeN6TjlDU0aon8HITFwrpzJUlLV4HkaUrugfGyY6xf9hbvxA7Dy3e/aIabBAYArcFy8YKqHYA2S1+Ep0jgji7odc9WWFqvNZIG+2tY28/7CU518FRiMf1Nd39LcRT3GEKcbBtAcsK4UUIZlSTfxZbGYtts4FQHl1T0/b9ynUJAatSIYgOhwmKIxaqi9dkIUKGNZlqYZq40xWaADwWqDmuuXDgQMxjMMy7a2mWEpAcYNKK8AwbLgbElt3rmqALsrvysFFKkni7SK+8dd2nn7jFPJY1f92tnJSARBkaRKCXRavt0ZC8CQZMevSXgNDMMiea1vMpwpri2q0LcZ/SH+01X053QsjGNKDZ7e5YF6e64CXCeHhlfUNRVV6GmGGZ8e0S1LeAKtwAm7ELHCXp+M074zGSkiMoqkgIKitGplYIBao4Jqe768C2Y++w1GxteCd0yfxY8GI3QL3x86KSTelJZVVJRXBVBWQPH3dm8QwFU2YdYTVwje9zsKS6SWNlvCtGyLrn2XHabVRoTy2R7oD7Ww0r88RMr2/UIMRove2L3d/FAZJcZHhBon33OYPJBd4F9/3tJ0heY2k9MatKejqLCY3Nk47ftKKqLdlxqohS3ZYR2IRt0oGmjhH9i6pkZmoKSO9UVlh31UuvJ8RD/6NLeT1jZzaXWjJF2h19JQxMYp32qzz//uCW8JyWIKy6DrJC3B0ae9uLIBAHCozE/a914+ceL67SfbTJZb52f5+JDz5XoAwM6TfGteXWSXpc74DT3chIxzf3algp7S+6cFyrJsfZPB4BIC6RN5fH1fOpHi9wvPQ49B2NoSwlWVte1uofDwslkMN+LUzGWbNbYam1tNdY0Go9na1GI0ma1ceZtTy6qBI7U+1vB0oc5iH6hXvj908qoJWbLmdZ8Q0P4B186uty+i35MaH64X1VwEKEBAqDIuVBk+OlV6qPiXzcJaYYYFRpPFbLE1tBhbDKaG5rbahtaWNrPFQltpGhlSA0Rqu6oPGfdVhaNI3iEJQkHJFAhgAW0HnbQ+k2PD0Coe44pYVT1DATAkKcr7txkBbSsbTdM022wwtZks1XUtja3G+kZDY6vRaqW14X4+H9AXkV08NgPrJ/AN2S+b/wejgNTqUS1Z3wAAAABJRU5ErkJggg=="}], 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"}], 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erGWbNxL2goyk8mNtQhEq1KQMLSnMikVakr3LilDe/QoRE5MCGUuSgLDNzUwurWJFaH4UkrHsQ5nJk1qErvBrtvtnT75riqhARYxvdLAdY5NTnHOqT5xoGvnTUMqlBAE1SpkduIhQPC6p7DDqbfzovz+WScnTi6ZYLLZsxxil/bWNiBMZcLAajdZg5WR8eX0L7j635sYL8WJhbgJ+iE3B50RrYW7C7Jx4hgEFOYlYps7NS+Ivbl6EAgaU1iIROjcvsbySFaFSlFU6mbFCIDwDiba7qE7BRycSqIhxjH179yUSTa00Yk+CcH6vTz3JGUsZAfl5xqlzNSqZXCaRkASJXZmsCOX3oTH3AfDMA9fUtvXHRagoimYYZMUjsz03EVvls7MTZmcnlleiXvGFOcgq37ljF4CgKD+xrA7Ra/nhDt5PWpCTUHa4Y39Ne3ltY/mxVkyIpVXN7Osh3VTegGTpU/dfBRgpepQBpUfQT0uBw0tQ14xiULkpUZPNil8xmNnuS356tusTCVTE+MbWkprE5LTRPQe5TBKhUchlEgghAWFCfKTwUSxCcZrn/U+jnorz8pPaOvUEATFXVtQgETonO35/Vcv+mlahCC091LHmxgtLKhE/siIUFuTGF01LYhhQXofIFCvdgtxEJEKBQ4SybIqUaXb8g099iNcLcpBHtTAvsbyKHeZBTlIRuiIk9nQTnjxEAhUxjrHzwMH8wmUE7qPBilDefh8mEeopP+fPm2a12VUKqcShPSm2Szya2yW45kqOdPAitKQGWfSFuSiChEUo47DKZ2cnzMpOxLQ4OzOeE6HsHg4R2llytAVHjQpzEiAbbS+vaeR8pkiEsq4AWiJ4cQbQ0vJ6tM52sEcsjNmzrmVyidAVgbGnG/z0iRYJVMT4xpHKY60trfytTYCROQG1UhYTqebZEzkYJYQugsu43FJWz4tQWiBCkWP0KOJ6LEIhYYcQGdf7q1oO1LRWsIsHTqC3UHqo418P3oT4DoK5Dk8oEq18gSbrQi3MTXCIUIYXoeX1rcgTSnE/MIU5iRWVzfyLokSoCThyeBD2DMTpyctPX8KTh0igIsY3UpMTDx+pxNvNzS08kR2uPz4C8jMlNUEhl8plJHupcesEhAoZEnSQYVYUZfL7lx4SNIdkz5MXofOwVV7LDllgw+5zcuKxYgUAHG9m+Y4BeABS+eFONiKUXH68szAnAdjRXI8yJEJbZ2cn8CL0P4/cKjDSGeRIPY7YsxCpV1aEskH7SeIJXeFgz0H3xOw5KHViiAQqYnxj14GDDV2DD3cJO2YXTGVpkIiNVEklLF0KHhUK0t+ONHMilACzc9h6GJxC79Ck5ZXd+460QAIF8FGEhx00W1HVzSUqAfDyG99jEVpe38KL0NJjqP0oDtazDURRLH4/6xxgGCRC737kPT6UBCi65EgXZs+yyiZevT7z0LWTwRO6ImD2xAh8tpNIoCImAo4cPdbczKUBjYz8RCvnL0IJTAqp5zA1CGFKKuJKhqYXzEjl18uPOLl+bi4yqIWeULQDK0K50FBOPB8x5+AQoc4Dsp5TVHcEQXltIwBMYW6S2+nMy0/yFKEVVT2FufH3P/M+7wmdqEn1Ky7SBZ6rpFAKfceDQyRQERMBC4k6r+pvmDC7YOrTL39tstilEjKoUZQ0YOY4QkmltZxa5J2SRXnJKFCekwBZEVqORShbsvmXR5GcLMxPwLEgZMU78pmQFe8QoUhgsvmkfCiJf+mn7r8ai9C5nAiFmIqRCGWBHbUTkj3BYGCzJwiVRkYE+eURCVTEBMEZS9Middb9tbX7a2uHVX5i4/2CM+fp1HJh8qkneBEKCcIZSmLJEYeSilBmO5u5yaKkWmCVsyjIRZ5QvPKvB28qq2pzSapnUcbmMxXmc3WfC6ansdEkLpTEMGBfTdO8/CScz1SUn1hax4lQzNr3P82K0OYJKEJX+GVPTJr4hiwJNfdjExSHigQqYiIAD8lIiHE20xxWPP3y1yqFVC5lR266IpntjSSkRTeUHe2c45rPhKPneBGFhrBVzobOyyuRCMVJ9Z98uc4tn8klqZ6NUJXVNO45fKL8WCvDcEn1UDh5l2GwCEUHYUUozmfiPaETSYSu8MGeQtLkwbNnsBwqEqiIiQII4gVN2odVfl51wWKl3Ivr048IdYaS0ARgpwjl2baCzW8vZUWo0PWJk+rd8pncRChOqsfKtTA3ccH0NHxqc/OSCvOSGIBE6NzcRByeQmTNBuudIvQZFxE63rHiIt3Kq9Xu84e98aYfBMihIoGKmCCAAETpUL/34cbTL3/d3WPmSzYDhEsoqbJzTm6c13wmvFbGhpLmZHPFRQtmJM/KQvt/8pUXETo3D3VRwflMvAjFTyytai6rap03NRFAQakrLa1oYJPqkfsVFmI5/NB1vAgd11b8ClfhGQhvusnPoDhU7AcqYiLginhj2sqVOo3k4Zc3D5/8TJmSFhOp3l9+1LHg2reOW0P/KDU6Xp8a9X1uWrW6qWf+zISySjbVn5Fwie74csUZToykIDMOFRcJmoQyVoiOwyY2Id4UNgnFTUVd+zMxDHA2CYU29By+SShhmzslufRYK5qhjI7PPTrem4SucLBn4ErTF3vyoB1lCF4hKlAREwdBBcRDw9Mvf+3ygp5ZlOyCydBv1Pfhm/MRNpSEsfegoFDKmwh1LXWPXzAjZVYW6yTN52QpfsghQpM4z6kN5TPhJqH4zHA4nnHNZ5qbl1B6vBlH7dmXRkf+v4evH9eVnSuvVitUTFB2+qDsOagOFQlUhIggcj9XLpnutgI9adSDVHGuEuNwQWJPaNFUTF6Y1ACFU5dYpyQbQEehIS6fqbJ7z6Gmg41sZedBFHdCpjrrGHUk1Z/wzGfiT2NuXhJkJE6xTNGlR9lXcSTVl7E97u57+t3x+1VYda0ucN4MnD0H5VCRQEWICAgpU9Le+GijPMJl7O3M2Xn4Nmt2/qzZ+RGxDsL1JYUFItRiQ8ZmGct9RXmJ+6ta5jpEaGn1CWepOwscL2JXGE6EMg7GdADnM+GkeixCZ6J2JOgghYiRGS6UxKJoamKps7LTGY/CIpQBoGj8iNBV1+qCtT8CZ0//HCoSqIgJhLC2xvj1lZs8FzUqeaPempIah2/f/LivR6/fUVbVo9d/uWmfVsqkJsVwNOr1cnaIUKQrj3ZgEYoyQHEJprfRHXw+EzqkFIV9sAj9yx2Xlh13bxIqpNTC3MSDde18KAkyUr5ramFuYslhR1I929OEI2uSO7288ZPPtIplzxHw4XjlUJFARYjwjtff/l54942PNl524cmo6aeP/fmLVytlOvstqanxqWnodrS5LzUrOSnDZWQ8tuJtOEDBBo6K8jguw1xZxopQt3wm3OcT58P/67UvWQZETUKxE5MXoX+/5yr+WdhFMDcvqYDdwCIUvxDXqZ6r7OxlzwQI85mKEsa0CF11rU7InoFzaLDy0w+HigQqQsTgwEM3KTSa2PfV5bcqyfNBBoCSw+1z8znN6NIk1Jng6SpCGQBYZybWqv/++y2lbD5TGRtKYlgiLsxJePzZz1jnALLiHZWdzeXVLYygspOtq5dgxsR8yr2ohGu0jEToGO4zssqDOgNEyOzplUNFAhUhIiD7fcXyQgVKng/VlwBhyhQXEYrNZLwn5j5nk1DcNJ5ddBGheVwj5NKD7UX5iX964i2+SWhhbjw+N1z3iUQoO4lTIEKT57JJ9dj1iUgZP4GttXeOS2LBtxcZl+wJ4TCxpyeHigQqQsQg9juWn3jqke/wkAtgwBf2bwdQbB0OllT/8B8v45+Cy5A4MACLUNylia8E5URojYsILatuQV5XBrk+8bOL8rlgPTcuyZHPhB/NS4kaa6GkVd7Mdh5sAoLPjzos7OnGoSKBihAxuPyMSYjTquUSv0b6oGAYJiWTS1rCwGYyroLnm4QKPaEAcp3qn/jXZ7wVj2NHJWwmKTs8BInQiiOdhY4moWj8EQCP/PFqPsiOMTcvWU6gYi2HCKX4/kxcMinOZ/qHIJ9pLLWsXxUIdfJw5dAwsqeQQ0UCFSFicCw9aSaKTgxiwPvQnfwGhOxIIndY7c6yH8xlbiIUc8Mjf7qcW2QHeAAAi/IT0RkJ2zdxSfUolPTYPz/lRSgPNAuUPaBDhDJ8f6bSI2hP57ikMRZKWuWXPb1jmAsrCFKQlSZChAgsP93s95SMVI1KrpCGw30GQW23UbiSlxK1v7ILh5IKc5Nwk1C+Uz1nlbP5TI89+6kzRs+a7SVsPtO/Hr4Za9Kyw1iE4vC9Sz4T3iitbLbZKZxUz3tCkQhlR8Y7RGi3SygpNWossyczqERmOTRQ+QmDvokEKkLEIIjQyLNSouHQ7HfHJQplEi8Xs81O80n18/KThJ3quf5M2UiNPvpnhwh1aNWi/MS/PPm2u2OU789k49qLOAkCgIUz0nE7qDlsbagwn4nVss6kKD6UNIoidJUPp+fg1OmARIoqCwK6BQ+RQEWIcIGb/MzNz9SqFfHR6nB9TAQB07PT3RZLj3Tw+Uz7HAOKnd1KGIBF6KOsVY5FIpaQSISyUznLBPlMgLDPyUl0S6rnXqiqefehBgkhc9IPRQFGikVoGRtK4l6Ujc6Prghd5Vt4BggJm7Zrs/hrCDIUiAQqQoQTJe/f5fZxKOSSmAilUo7KeMIFymgQ3sUMZcOeUNdQEm4S6uia7JrPxLEIUzQ1EZGEIJ8JPfFYKzdF+S4kWjHYgk6u/R3NMBUNrbMyncX+WISifKYqNp+JPf4oNgldNUThKYGYPTFsFno4aFQkUBEinPjXi5/z2+uKD6VOSZVJyOgIVbDdPweFNxHaOVh/JvfKTlYkQn4wpzCpHtUdsb6CZ1740pnPxHZmwp3qZ2Qh6kQDkbE+dSTV43ymOdlx6K7AEzrCVvwqD/Zkgheengg7h4oEKkKEz95LaqUsJSEi8Pz5wFFbw/U8ForQwjx/+UxzchLdRSiL0oOOcfNOEZpQXnOi4ljrHNZzikUotuKL8pH8nJuXfKQWScuKE22OF4KIn9hmo3PzkiqqR02ErvLm9AwqmcoXe4afQxmRQEWI8Ga84+R5tVIWoVH4r9EMDRB68YT+5mhV5zWfCVnlbBbU/b+/mN8NR9u5piSOfKYytr0I4kHWcyoUoSXs+JDSquZFM9OxjT+H7XXPjaVjweYzsaEk/L5HUISuGprwHJQ9w27OiwpUhAh34x1DGxkZrVPKZIg1pN5C5zwidUGHmCBEnlAhh2IRyulKaMf5TJwIZdt3FuQmQamUJ0SsTMu5pHqumVPZ8RZehLrnMzEuItRitRNAyotQQFFljiahmHzLsPJ19GcaAaxyZc9gqTNA9uQRFg4VCVTEhAEMo/w8bfGcmEhdbJQa50ozI3W2Zgs7vYPdLmIbyCMRCpF1X34MWeU4n+mBP1zins+EQ0muItQlnwm6iFAMhk2sd4SS2KR6NriPRaizSegwW/GrXM32EKgzWPYMF4eKBCpiTGO+tHG+1KWWxheGUhTiKT+ljHV6iqa/o7uxrrmxrvnjz3/EG411zVkJ0SBM+Hn7freVA9XdM7NiIasBS4T5TOy/fFL9P57/wpnPxNYRYRFamJ+A85lYj2oCIOzzp6Y4PacOEcqHkgCD3LsHGtudSfUCEWq30yMQSlrlITyDhVvAfSQ5NMz1oSJEjPfY0briQ+eczM7t8HFJmo36RqMeX+g5CdqbrjgDr6ONeJeUyXee+8T/Sy8vyrJL1W0NXEApLzWqqpFricQ2pZcwgCk72lQ4NaWssqUwP760qgEASUF2fDnb+w6L0PKq9rm5iaXVrUVTE0uOtuKhx2XVbfgM9+LWn4gqIJAyrAjlrvq5eUloYhK726zM2AMoyo8ULCtC0c/RwZq+wvz4smr0WnXNfZnJaORceLHKVXiGgJCp041DpfIgf4HZ0xUJVIQIfDU4r8OgrsjH/4GGqvtBfnJEI5tiiY+bpJE0tXQRADS1sE5GgP91ouxoh1TGnQIkSW5mp2AEKBahaJ0kkAiFwC5odO8YekwgK16Kckjn52XYLFY04xOP98hNLKtu5efNobGghJ0VoQR7TAmA9rl5iciKJ0nuNSU0nujJOmp7Stqo8LInE+oRhs6ePGwWOmgOFU14EZMcVxbFMgxjsVF4ptu64kOXnLNAHalLSnFPaXLBsPUowqGkmWySpq98pnlTU7AnFKMgL35/PSLEkoPtXDhekM+ERShuITI3z1mb5MhncnSHcnpCUT4Tdgvw/ZnC7gldJXB6jgX2DNmcFxWoiFFGvsnk59EYK7pI8il/+wwFNMMYzbauflOEWh6hkSNTTkrIpCRJEEcae89cys3g3F/VPCsvueJo45ypqZycHKrMdSIhPY234j1FKDbPC3OTyqpbCvPiy6ra9/FWuUCEcma744VKBSIUP7cgJ770UDuQ4lCSxJHPhEQomnJc1YaT6lFlp6sInZMTX3GsHYlQW3hE6KohU+dwsGdo5rxIoCImL64sijWYrM2d+s4+I01rItSIQC02Sq2UuV3eZouN/T+6aNuMdizfUOumfTXLF+XzO/H77zvSmBih9HzFaTNy+O3ePtSWqbWxpe2EW1J9dFVj98ysmIO1XYJ8JsRlKLUItb2wz8/PsJktQk8oW2VEoFASvqZZEUozDJQzZVWsO9VRVs9b8UX5ySWVzcgTerQFECifaVZmPOsJ5Ss7iaK8pJIq9Cg+5tA9oaecJ9X3op9DTaSXz2d02TMIc97x5RAJNMzgQ8Z7banhPvbERKXS34WkQ3MoQaVtkIutELj0iAsQDMP06E0t3Qazxa5VWdfvOHzWiiK5lOS0WCBHcGz8sqf61DnOpE4IYGoaZ2X/vKd6xZJpeFtvNO4ur1s4FfEvjmenpKI89qZGRzWRA/sruwlH/b2nCN17tGl2RgxOcsIN57HBXlbdhndAE48YUH6MZViHCEXbKPuTZscluYhQN3XMqU4ASlgRWpAbX17dzjAMdOxUlEAO0RNqYGlUiEAodbipM1iXqJjGJGLygqKZHr3ZZLFBCCxWRAe9epOC7xviYWFGat2vcOHVXFA4zdkj2ca5L93390HNmEZ5PHXf1RRDz8yK8Uyqx+VDhblJ++u7CrLjXQZ5Cmrh5+TGl/NJ9XkJZWzlKJfP5BCh/MshTyiDtBSfz1TBtSJlM0wdnlDIzrMbvv5Mhl6T521U2DPwgiWRQEVMUlxZFGun6Ka2LgJCmZRs6+xJSk+Li1JLQ+obcuqCXOFdhdy7bceNX/ey7mWRErSv5ys7K6p6kMZkq4wwmWJCFCbVF+TFV7C5R46GTYxLOxI2no6T6nFnJsdJCEJJKHU/gcswZTvVc+1FRrxTvcHBpMZ+8wgTKIZ/DhUJVMTkhcVmV6nVUgkpkyAukJBApUDeT1/ISHGm0FcI6nkwnn9vk+BeQJe6Ljbulz1VnrMn3v94w1P3Xb2/qguH45GbkhWhQjAM2F+P8t4Bmz8gTKovZ0UoEp6OTvXOnng4fM++XGFuIk6nL61q4USoIKm+TJBUj95vNTs4XjqkTvWnnBd6V8DoeNUQc+aHg0NFAhUxeZPn9xw4brNTJAF7evtjkxIZhukbMHOyzn3UUHAI4ZluVjwGJlY2qR6h7Ggj8mayXAmFhMjCOUMJMGxYqY3PZEJFmYxAhNrcRagzxCQQoSg6z1Z2FrK2/Jwc9gyZUWgSSrj2cxl5GnUx5wV/XZFARUwEhHAx/bTn6Jx8Z6BPrZRSFN3c1m9iq9GHAl+jxoLtiff+82tKjwiCS8i3wB2Ca3wnTKp3bRKKRagjqZ7VpGz4HkAB5zJg3tQULEK5EJOrCC09yolQvlP97KxY3PJuVJqEugHT6EgyqacUFQk0zBCD76MEJoTaTZVcqlHI9HqDWqu12mmLjeroGejoGeCcj6EKUFpYF8RiRm5GIE9MSfJSZT8jkwslsfdQZSfiO0dSPeud9CpCndt8KAnTL+bcAjxDiUuwT+Zb5/EitCA7Hj3IVXYiN8L+6t797JD60JLqTwnVfh+0neBI0ihNuXwtRAIVMRnx056jZyyYqpRL4yKRZw1CyDCM3U6bLLbGtr6uPqNw1DCP0K7RFa7xpQDx/scb8MYBPNWDxexsbniniwj13qneCa5T/SEkZudks1Gm7Pjywx0Agn3sxDqvIrS8ph2lu7LReS6pHp1A3OysWN7LAcbM0OMREKSkBJLswWma4cdTiwQqYvK2DiFJeLSuJTMjGaICHJoNkjOdvcZjJ7q6eo1Okgj4kty6acvWTVtCPret5fVuK+8/v0aYz1TBciVKqneI0FlZ8VBolQMwbxoX9uGseFZTzs1NmJMdPzcvoaIOPZf38hbkxs+bmsJXdnIi1FHZWXqkEVOlY1wSJ0IJNjm3rqUvLzVqhmCq0nAghG7W4aVRzJuYOoXANCoSqIhJKj9xujsAIJYVoah1GwuLzd7aZaAlKpRy5DHt1s916f+ShdBLrlJqYrTwaQzDCK34pxzNnPZjXQn4/E3oEKEJB9iwOz4wmoPkAJ/PNCMzBjf9rKhpL+VEaALuVA/sRHl1+74qxNo4qR6L0Dk5jl702G9L8eOSOBHKlstzMbZDdZ1jR4SGkUZ98aYbRAIVMenkp1qJSjZx65ALT58bE6EiCMiOjWNbEjPAaqM6ewfsFB2iGzR8RiTmUDvNiVBI2MvYfCacT1rGBnnm5CRWHO9EVjnrHt3HCk8+n+lQXRdBQL5TPUpIqsNj4+LdRCgGm2qK+PrAiXYUjqe4vCUczS/KRyLULal+RgAiNDQH6NCHqYRg1wfCmzxEAhUxubBzf93i2Zn8XbmUjItUyWWS+BgNSTovG7ONttkFiewBALYec1s51tSPNyI0PosU4WChpPefX4PmfzjOhUFNkRPKK5u5TvX5CRVsNhIvb+dkJ2ArXihC2ax79CjfZN5FhFY6XQdYhDIelZ04pbTkMJtUn8OJ0Do2lHTIUT4fXgjZEyeBDgWD0miAktMNIoGKmDCAwTZOvua8hXKZRCGTyKXklOQolcKpkux22k6h21BOiGBwC5IhnTYWoRWVXFL9vPzkslpnbhOWokiE1nOhpIqaNk8Riis7nY5RYfie5Uo3EQpdKzvLBZWd7LR698rOojFpxQcoSEPgTR4igYqYvPKzb8BMUTSEUC6VxEaqNCq5kJgomh4wWnkrnqpssbO3iLZ+qqpVx/4b2zVAVbbgW8wAxDd7dVtEu56qalU29VBVrVRVa+uuo/aj3G5R7XpKMKvDK9xEaF5qNJtDik6lpLoBi01hFTwWobMd8zWFoSScVF9R3U4zTCGfz+QqQvlnFXmr7MQRKr6yk6VR6CZCw47hmIQqhEQCCXJI1IkhEqiISYEri2I8EztVcqkStw6BABV0Sp0yqrGxmaaZ1i49brSMdEp+koS99SXoyLzEfvbfzhg1mZ+Eb11qBt8kuQl98VoyL9GUEkXmJZJ5iYmLpkqmcrv1xGtJflKmK5YXTvHzFg5UonFJ3B22YJ8LJTnGEXOVnaxqxiJUmFQP+W4jh1ACk1sOKbbiSxz5TOjIgnwmSNPY34r6QgmS6qGUqWI9qv7zmYZSwTms8NWaIHCIBCpiEmHx7ClYWa0rPrRkwQylXOpP6TCgqb2/f8CCiXeIimjIlyqwMyjRiivixNlFOJ+JpTbPpPp50xL51yzIi4cQYhFawOYzuYhQGzHHNZ+JC7GzInQmFqEMqDrW3NrVFxetzkzQ8kn1iwuzAWDCm8803PIzjBwqEuhkGWwZ4GzLcYpZqx8N9ikqhVQq8ff9ZwDo7Te1dOiD94QGev0fPljp66HKph43K35qesx+zJXsK8zJjodsPtPs7ARehHKE6DhhNN1T0LaOgLCsrqUc5zO5itCKY1woCeczlVe3VtU0psaqAAQHWRFakJeUl5W+61hnd98AACA+isxO0lUda+4xmOmxnc/kFcJagKFwqNhQWcREAARQnVAI9O5TggX2uxPrig+fNj9PQlmbW7n8yvyUyGOVJ+rqWoxml7CP3sREaPoTY7X+X122978AgDNY/+FPzVk+zjA4eO5/tAGx5+ysWNyp3pFU3wRIAmWsEoCBdshI5mTHV7Cd6pEVD3EoiSg/3FEwHc3vZAAoyk2gKIZUMqVVaHInJkScz/TFhv2nnjQ1MVZXUtkcH6/dsgflFZyzcDqEsPxo0+H6VrVWW9sxUNsxMDVVXVxSS0iVKFcBvTzMS42SSsmStkHcu2NKfvJAvaKDbVXAQlSgIiYIIATalAWB7HnmwqkSr9rTQ4hIgeXEibaKipp4paSxrgXfEtXSpsaOWPbf3ERdk2MOu/NMQHiQkpnkJkIRYx5BvDk7J45x5GYW5ibNyUmYm5+AXxdXGRbkJs3KcuQzsWEfXFYPAejoMVTUtvPh+xM9JittSY1Tlx9rl+si+gbMXb0GFGEzE3JtlFwbZbFR63ccPszOreNxtHHAzsov/s1uKD70we624XCADj2HKRCEpkNFBSpiIoD12cEA5efKZdPZkuaAjmyn6I5eY0yESqeSS1CiqPurZHb+z23lRHPXy/+4lbvDdhoFAOwqOwrCAoh4cP+xDkiA0poOyL73/TUsc0FQkJNYfqx1Dk6qx52hITfdCIWS2Br5hl59dV1LWjJKTe3uQ1zZ0ms/fqJarkUtPg+d6LEcOo4yZHXc7KMte6vkEZF2vR6lx1JkhEZuMtssAyj4brXZ+o0Wq436de+RXqX3yNgYlJ9C+32IOlQkUBETAYGrh3OXcYM2hZhdMNVgtFpl2j37DnFL/HXEAIPJOnfh/Pi46F1bfnW0q0cPv/sT6oX8eKGXV3nimQ8BAA/ffy2/cuPVZzk2nLu98/b3M7KdCZgYtc29WcmR+HpOzEhorW9zmzdXcbRLwiZcQTyM82hz4dQUiqb31zaVsxM3+TOfk5PQO9BX32YoykssqWqtqmleMCdjT0W9PCLy512IMWPVmoIcpFKFRfg8dabH6yiKqqElJiNXEQABsAwYhOq9R2/+dW9VWNhzLADr0MBpVCRQERMBhtYSVfzcQOQnT6Apmc6cx66+fovV3t7VPW0aNzXzyFFnWREpkW35rWZ6tuHG68//+cdt/k30M5Jr68DvMMNiGh0UTY2sF9IBubMOHXo6Gqamxxyq7SzIijtY21WUl4wTjxgGidDC/ISyqjYsQqvqGlcsnF7BKlO1RIodnXkZqTsqmtRaxM5y9l+9yfLFpn08Y/JIj9dVtRqrWrlRfUqVaoBVoHKIfMQUdPJGK60BSo2fd7f1B1uAVvyoeD+HKEVFAhUxibBy2XQsVX1dHCea3b14GrXKaqMqDxw4Xnl49/Zd55ycL7zMHy886HmQd3+qfujaeW6LN/3luif+8nKA59nU0p2XEuXpXcWhJFICsaOTTapHieDllc2AJMoquwAEWITKIyLNVntVTaNch4awx+iUVXVN8gh3oqxq6ReypxTYaHnEgNmKqVPBNr33BMkE13N670/N83GIbQzAl/0eGoeKQSQREwSH3n6i7IW/+tlhXfFh7ML0eVn4vbLsFOq4TDNMfmZSfmbSxz/85mfnJz/c9+SH+/i7N11/IbLon/v9Q391GPWk4xYkcD5T6ZFOnFSPrnHWrVBV06hTg6qaplgN0oMZcbp9Ne2YPQEAm3+r9GRPxJiMzcxI0I1G/zKKCJphFNCuINANhAlGs5dpJWNWfgYVVhIVqIiJjE9Lutys+JBB04zVRtnsVCUrDK8+7yTZwfe87vl44cG/l83ENIpXSo60xeqQ27K5s39RAcpzamp3dqgbBCQArs2djzZ0ESTk+t5DO0NLjtS3qnW6fQfq5dqovgFzZmLEph2HePZEpreAPc0Mqr/SquVmix2luJqtCnbUB/qRMAdEmiazOdicn61+DfkxyJ4B6lCRQEVMFPiMwyOsKz6MOxLxl+rrH//6779dLtwnSqt585MfHffcj8WwrUJ79GYJqSIJwsfcI+/YsA9Nhbv2tBxPUbN2V+Xqixa5LTa1dPvh0LzUaIKA3f1oZvqc3Liyo9zOOIx+rK3/UDVnuQthMjpnrDNWY7+V828qBARhYSRaldxqcvpkU2mUUtpIoGR7UqE1s6ehlMusNpuXlv1j3pAP0H4PnENFAhUxWURovzIBdTV2rK++bEllTZPQQt+5v8EqcfCOHcVMUDqkXq9UoDxEkoB2iu7sNdI0o1PLdSp/A5C94sMtKDC15UDHQ9ctDLyZSFObF616tKFLoyKyknUV1R1Hj7tPPMbsKWFsBlqhUsjkUrJXb4ZyDWNBSUsKNhDk9stjpiUKwi6HdqvJiwg1Gf25RAOE0RznlUP9y8+RSQINmUNFH6iIiY9PS7r6PfJs8jIT8zIT12/dv34rql/C0kQpYfDN8yAkQZAE0dVvGjBZe/Qm2SHv9ruf4JJXeMpPEMCVmpcabTDSG7cfqW83ChyadoaQmykS3wy0Amkui9Fk0MuhDdnpPsHw5CgFNgtF9A3Yew22Xr3Pdnz8UKCwO0PHlPwc1B8qKlARkxRVdai0ZuUps51Lfq8viqYJiIRqQ1u/XEbmDuHS+ei7HacumjZE38TU9JjmXpPNYsF3rbREqdFYzT6J0sy2+PT+kJ0NZuEXgSRjNqhJwmL118w0ZCra6ypCx6z3M8AUUZFARYhwXCF+PwmKZmjAmK2UxWq/SrV7KJ/aE/deuaW4HG+//r9dT957mfDRZ57/nN+++vzFglNzuXSPNnQlRyprmjnPpgzYTAabVzayMBI5tCug3ekEZWGlSJMJ8S/D2GRSUiWDTsIOqTB8KIb8eIGbOS8SqIhJik++23nVBZie/CEiIsJqYbUYg2xAM5vKNMSXFvpeV1+1/ESTixPz6stOBQCkpXCN6ZraHB2YCAbQbOkmi/y06F8qGpUKxcCASx4+DzMjwWpU7qpJLRRBmwcyzZRKIR0wcwIWWIBczvWTtlopACjUHRWCqAFEFnYbxdC0HHCatEE11PL2vSyHjqT8HIr97odDRQINGvkmtx9yd8RY0YebTw2yW6XS55wcESEg2OvjqlULAEXlCxp2RGhUb32OhhITSu/tlxhWh16X6C8D1DOZKTS4sargFBDWFx9S6VApkcls9nUEzJ4WhmQAJFmqIgkLTYHMtMSa6hoAUVjM13P9CFBI2xP70JEZNo/KJPieB/iVNprjVArnVJKybShFwY/3YuYCl6YqCWnuCQYjD55DRQIdIYh0OawIWcngpE58SZRXtxJytckOgdXJxn19fVoZYQ3jpM0hYO2uqlWL8tYXH1Ky7OkHFoY0GpG6VMiAUsoRL00SNhtobkT6t1ElTUvQ1p6wotCZQkZInKLSRNAyKUkSBICAhuwIeDNJU4BUcb8rCmg1mc00m8fUL1OGbMjLlL4bunAtnb38MLad4ArzR5dPMYeKBBp+KtRJ0d+80iYKzBHF0BmOD8Sj4npELBysMl1+ftqR8vIhxE5CRGZaXFM9qnbnsW5XFdaeXqG3ADnb/8lqp6QkCe1G2mIxWoFKrRbuplQqzCZzr8GiVsmR2vRwSkhJQj/AaltBmbsCCj6UcMBqIqQKSvinQ1sQ/SFQwRheZsDBPS1uItQPn3ql1JDt9+pyn+1N41PZXLHQjitCxOSBgmSgbWD6jFyaZk5n1gb+RK9WfJza5WJ+/ZNfn/7Txf945VvkVbhoqdvOdSc6vBbve/5amGl0LednJ9RW1yTFaBrbnY5Rnj3deETiaC1FEJCXgiYT4k0376RSLhsQ5OGHBUbOkPcSr3JZYbf9c2iAEtUPGwYFTJ0YIoGKGIsIegBJOCxsPyrlUCXy0zEMfXruUF/l3/dfRhJEUwvnBFx96ZLjJ9oeu+cSxyhQxm6nrXbKarNbbZTVUe/jlsokZWx6WsHYnCyJEzmbG5sYxwh4/2AQhdkGWDNfp1Zg/kUfpFyjgFaFTGJXKVIJl7EiYYfRHKcCHf4MeQGC4lAh2k7093Y6s2V9AX1mws/NGa5j77ElGAkC6sQQCVSECP4a8k48Br0B9f0g4epM7yNDgsLRmmZPzSWMy/uCYIgPbQOkgrABucxk8WJW9+jNyDZnr/sBbImzcMsfMFvtznXoYqETftOY/ESuQoDVRATIoaGht9Po5k7lC9Lcla5vnS9UnUKIlUgiJgLYr3pwfq6Pvt8V1P68wTtE8NGREBCgzqZomnR0wvcsHzJTEjOFlBNF0WqVQq1W2CiaZ08rIzGZrQOus6EwuL5NjESpQDVOYUHgtUkM6wwN9vhYe3JduCB341cCQXyqzhd7igQqYvLib2suSktP4m/pGclHqurtJj2+ee4fWm65Z03nW1/uACMB59kSlMUE5bxsVBBWBYHokvFWlyljk5/chuthnYva3LE3fr1fFoZOV0ZznNU0+I8T/vhD4NDQgHnTD3ViiCa8iEkKt0RLo9kWl5jQo0csM2CDwMJxqEQTzRjR/J+b0kqH4zQUKhTh6TegEI2MCMiS9SRytWuQHQLQ34/co2o1kooDbIcUpUopbMiEeJOiWAaAngntNMMMGM3CEPxwwxqwIR+4MzQQ16cnBiVNIUQCFTGGahDcihGcTxysKiEs4GWYWsoYHBU6aimNWhgBYOv8zv/TpbEXBPIqWVYXmohn+4TifwEAT7/2A964+sKTgzp5rDQH2AxWtyCS0m/60cAAcpiaAXcCHr2aRgJG19T60UJQ1IkhEqgIERxkcoW1z0raMGGGDf5LkhqaXIjj6vMWpqcI3YKwsdlrSZILTJClP4kcSBBdmgdMOp2asjttbQsjxeOM3ICj8BKCUDjKNLlXBUCtUgDg5XdLwtj0lmB7gYZThGJn6KAiNCj5GQJ1YogEKmIslmPhYgTnE0ekKsFqMSMjWs42oPSg0QAFpifc2LNWRqRnpvBpTINSqleQlNVA4rogq6fG9IxSCdnTxMiBRS/06KK+9K4hMq8OXxMbespIT4+GoP4Ymkg6KiKUd4aGltUULurEEAlUxCRFXlYyRxMMCls/8eLX6OJUeK+CBwC83VB4c3rZ0F/3tquXNzQOKaMbMSAhV7Kc6Nu14cKiJhsBHEmjU9IT2xptdtQyhMKVSFYrJXMN2kMI+swg1bWXsZINPbU3oqnxw1HZakQR+UDTQocuP4dInRhiGtOYSQUXMbKoqm2urGlCt9qmY8db/nbX7/Km5ZnsEN/43fp6ep0yLdxYW1wZwrOUhE1J2AJPaTXZCPQUOddCv63xhEBjon+58UqseY4rkTRKmYT2l+w5fI2UrAFE5BFCymrCCCS8HiBEBSpCBEJVbXN/n57vRY8NeIqiIrRaw4Ax5B7sgzZk0sTFpMRHkCTHRw899THemDEjG2/EqL0TCsHYzMCLZ8M0gFSpXm/ko/Be2Vb4e8AgVStldadMqUQy00oDtUbtT+COejQJ+jTkfcnPcJGmECKBigga15yanZHGTch46oOdY+MTDF0QsTV8DM0g+N/z3ROFN6aFwYoXwtCvr9Q7/a1XXXoKAGB6Xiq/8uNP3rvn0VAqA3YL5MrYkZEOmCnpKcePVQfyujTDaNUK/YD59lsufu2tr9VBBt/D2F5ziIZ8IM7Q4aBODJFAJxSuPS0nPZVrxMvjx0+/wBsPXuevfzADQP3xgNwODGWrP9GWznLoA9ct/sdY4dAQwdbwQVS8GG6r1Kv8fOPjX5/6w/lNDQ7z01u45nCVlz9EVm4O2OESujGZzbinJzbSWfO8Hj+k1arYNE8vMNFSnOUqZauVPvxsIys2A3a/spJ22PhzqPWdbvJz+KgTQyTQiQb+i+3JhscD48dgMUQO9Z4c6uhOGcN2RB+OPNA3vix+Ys35T7/O5V0CAJJTwxDVHRS3XbyoXhBn9z81t9G1lx2Gs/GHTAPsiC/8uETVaqWEseuhmi8NwO3s2Nb02E4PFCZGPqjvdeTTQn1lNQ03dWKIBDrRcKKxPY0VoRlTUo/VNEjDVMHtiQaHCB2nuO2iRWdcccYZV5wBpGwE2mS7fc3LdS1hSwINsB19CKpXOF7Y+xQkFgnpWQQBjx0+TKgjUcheocDVnJgEZSSwmk0WidZz5geGmZG5nZyQPX3J25EXoejXx8GhWH6ODHViiAQ6vuEm36R1KOna1sfFT6n6FqVKjkUcumbqO/nGaHw3Gre7loxYfpFHxpQUH6/P1B9HbYSuXpb+8baGMCaHzndsdLHdM4cpD/Sdf3/Cb/cZzG3t3WpH/qnBwRUGOzF11uwjZftQ/zc7OBRx/oy+78N5EsMTzdbrjfqjR5ACRRrTXTZaGInFDuRSpRzY3UmTRVoq+9PY5EX/ChEdkdHdxzkNhkOEBmvIjyR1YogEOr7hxj4nZSLt2QoAbWe7P0xPSclIOdzCOc7mLpuDTPt6f53TnLktAmCWnATw4tjTyICdsufOLiAIIjVOS/WjYchhxMycjPpmNDZObzSEwKbQ1TuJoOBSRP3s7CY5TQ6fAM70BAB0NKFUJ2ckSwADTNRqNStWFMTGZzhX7cg/8OYnH4BRisjLldJjBzoT0kKs3o9JDPGJIoFOcAjp0j91ivCF6qPH8qfEAQhOHO9OiXbwVKj2+xtf7VDLieUL8jGj9fbpYyOUT/z3mzuuORvvwDKpF9i8JqJCB3tC4Is3fUHAm95HybvBqMhKTU04Z96sqCidxWz+/LNPJKSKTV5gCFclG0YEIkIV7JRQhgFdrQMxiUF4dXl0tYbouhEJVMQkxWfrfrvi3JMC2dNutx881kwSRHykKjVGbp15g+zgeyG/7m2XL0tNjttTchjfbWxGIuua8xbFRnDGBN544uVvAADL53HZoO7V6DTXkRP7KYOK7QTIm0bFNGDejrdvuvv/1m/YEBsTlT0lGZg7P3iPj7wxDAqDsTOMRsmQx+zpf5Lo8EEk0AmFD7ccu/a0HD9GqZtz0xcC3G3cQfimrjh/0ZS0ROGjG7buR29d5i5hpGyqJQR2m9Wyec+x7It4D22I4SNMmqikXRDiq2twcQ5cc/4idk8vnUQSs/JTSKK+GntmBv9DmWkJw9DAZsQUM6jeNCqzSJJMSYgENdzKxx++i9zEnU01lc72ppnpmQ2NrcM9Z4+QSGhBVxRf7MkjZBEaGkQCneAYdBhZIM8aBwjpjI+fcHKWyWKLio9r7DID1/ByX18fCb10dx8qBLwzLSflwee+xNvXXOAvVxcjLSFi164SvC2Xks65SK4wUTw704g05TKzdZCxmhYV61iwdBQWTE1NjjvsIFCvsNmw7B2JSaVeRagne448h4oEOtFA2a2khIuleuLjbQ3XnJrNUE6L7+NtDVcvS0ezehp6p6ZHClc896EhSTBOgqlv6c1IGmRA+fiCMwrP9gMlSVKjUhkMXG42BODi0wZRl2vrp7jMAg4AR441Yd6clsNlO/Bp6p4KVK1S8uzJJ8MLYaJIntTcxaYH1xlgUkpywoL5hbroFAchAjnhaIYqfKrAKoEAMoAhWPmMbXgAgc2IQmEjJkK9sqfdxkikI/rrLxJo+LsC882AB03/Dry9W+CA6Gtk4YeC7TzQHOu2A4ReNUMg3zu3Yp2OngFMoCHnMI0MLjzj7CmZmahQp5wVet7ePxOYmMKf6xDdoM4XdMWRY/6ifCdqa72dDxTwJjpsgP7QAflU7o8ujVBo4u02W2tj5c7d5egLHBPd293hKwrPgySIge4GCUnapeqIyAQ7TQBbeOYGC2EwRGk0PYREYjXZeRHqS3uOvAgVCXS40KVmRqaLpZ+L6uNtDZlRgf6JQ5j5YzQNYhKOOt79z/N4o7oOVWFlLr4+KsYlp7WlrePLz1HyzXlzlSNijILXvyiOUEqWFmXhux996xyRdE3wXejx38xEkYHHkVJnnTq3YJpKpTxeV4c8qom5X33xGYRw8cICvAPlN0neYmi3Wi3a+Kl2RqKMTGWARQKAxRTK8IzgQBBWE9DFBOpOGRkOFQk0aAwqG92aAY88QrNhoh1RYD8gCCiw4AE13BXRQ8ahgy4z3bZs37l48XLhDNtdu3bcfuf1mzbv/Py3Gps9Dl8QOjvKggzhk2Xtd1C7799Z8/7ka59bf7eQIGBTG2fwPsJOhMdgGIam0Y1hGKmUhN7a0ZvY2RskASUkwX78iDcHZU+jJAVCmJGdnJKUpFDIO1s6NEpr8XbUpmTRfGwdM3Y796e1+nCVIhcqIVNo4qPUGr3B1HjCx6c0fIBjrv2mSKATHJ4Dvn21ZdtU3urm+hwcY4Y/NYnzAHBWtQeFqqNHmhvqpdAqlQKjFRIEoU2by/TbLGaLjWL0iDbLPHW6Pyu+x/DQtfMAAE9+uG/QTpo1x4NrasklezIAZ15CbwEum3a68KdNIiHnz5g5bWrugMFASsi136HmMnlZcUK9ybgIT/fvjN1mICSo352CJwyGHjE646x4gqBp2tgPVAFXG42ACBUJdIJDLnP/E5ssNuUgHqTxCCbAtnU4R8vxH/cAP6tdJWOkEtjZdEymjJdAWiIBdkYC4uZCCO3mnt8/+LDEEbRpaGwBB73IT1Qh+gFX6+mHRsMFGS7kF0CRcvKCmdN6+/rbmqoyM1LKyg8CGzhUvrOidDfeQUKC9L4eZZ05g53cSR49mtHfAyDQHK/L6O9GxzQP6A0GkiRjKI5JpRpgNfbRFINjVnKLyWSyYH8wA+wKhdxsQofqGP74DUUFl2I33Bw6CIGeefLSH3dwybQixiMitQqba3S0u8+YFKP0VKaegKRUGK8f78Bt6/i7Av503y8rN7+xsQffk7BVjyRJLF665HhtldD853LZXdkTEVDu75BErUaZ8ACAc+alxWhlEILmTi/j5ocItQJlXERkLF5QNCspMQ7LWwqq9P390/OzmxsO4Yi527MaIqJgSmQ9hXojyJNS6gfsAIL4KZn1Pab+/k6tQmKDOqlMITEf5/K95CqgkDIUkEok6f09nkYMxdAymXTYypGcYBhg1FMqrU9P6AgH4kUFOsERpVO6edG6+0yRaola6TPVaYLDRXziFTeGgV5bK9fW1qSl+mqq4oK0FLYTR8rt+O5P1e9deOVq/tHjJ5DNvmPPR4unBN2jD5+VLGHB0pNPSk5Cr9LT2zRPIYM25LXs7Wnr6UXBnG3bilFMHIAlC2exb8iLQOfHeAAILP3olFq7UpUaNQO85DBhyOQyhqIbdFGQlNjllKm/BchjFAq5SqXUpWta29pAn2lYrXjHewkuLXdYRejgBCqK0HENlcLdXDearZ29RplMMvROd8M2Fydo9DY4Y9lCQLnu9Zf+hQoob7sNrzi408mhrFXPhJCN0L/iE93PV3mu11S7TDpatvSUY9VH+bs5uVOfe+7ZP/4B0euC8527dXb1RsYk4+3mFi4fKCkFtOx9y3luAJx75V+io1HqmJlNp1PKowe6j67dtAvvQAAXcunoRCa5O9i3PjBgzpyS3ds3YKOIk5adVXesUt/T6d+1I5NKLRSiV1NfG5BrM3Lz2toMKgUpl8DOtvAnMHkCu0EpmjHqaT8idCQ5dBACbWsf/Wn3IkLG1cvSKY+5gce6bASg1EppbOSQvlIMG50Yy3+du2683Thguvamu5Di+23fwvm5pywtAKAPP/rT5u1nn3PJ8uWnvfLi0/xTdPFTIGQzwz3ALrrL1/4VjoZ47wR6Vseqj151ybnNJzjrmMfn//vhggsudmpFBqRn5n733ddm85QzT0eZA4ABa3CZEmNqaWlmAFi3YQs7/yMWn5JKrTQPuM43dn0f5r4mgiQs8jidTt3da+rurU9LS62rRclMEVq5i1J1r2BDnhyDiaEokiAJUqUjSbKzqxcTyPANmPMKhgauPxOjiTGXFjABsNfmPwF5ROFVTxnNttbOoXYOpmjaM0I1ZjF37jwrTVgp543N17FZrbbzLro+OnkGJU+yS5P0vf29fWaalpgtpv4Bfb9Br+86bug+MVKdKpzsiSas1FW3tzYvX1yQm5mKblno1tLa2tLasnHTT3293LjQ4/XIAGdQk2N33q+qcWTmQwCgVRGRItMkyaUSrZL7wxEEgd8aGgFC0wMDA14rJqwWKjoyJj8vS4rKWhmdTif0e8ARbuPBWvFGfXAdnbtafZW8Dgn+LoD/PvVw3fETP/3KBe9EjEd4/WrTNNNrGKq7ymC0ShyzJMc+GIaRy2XCDEc+EkKziZdREYgU9H2WmAi1QkpolDKVHF8dKqPR1Nunz8/Llsllr7/MZuazGTw33nLHoK/7zQ8bV64UGOoOkCTpM1/dI+hz6JAz3r9x4yb+HTk3kAT1MtPJ3N8ijUrs6e3R6lzn3XtQHp8E6tgB9Ou7+MEqKpUKAlBVycbQIMH2QOHq2SiK7u3tB8MPpxuU/dsRwYvQ4TDkx42CEBEifFCc3T7UKecd3YbR6SAWEjxVEm94onAKW9HtO6wE4mOjU5ISaTt96+13b/lly3nnIJvaxhBYgz//7D+zBNezza4/dLhqTuGSN195IS5Z0HU4EA51eWV/uVl4gLvnvgN9yB0Zn5qhUWk7ZDIGAHf29PaxMAzobEa9QygFik0pFIpYpQLoj3s9lf5+Pf+9stlsBCRa+kbI14fdoDSDbGdf4fiRDMT7I1CaGv6sBBHDjE9+rZ3vLTQw9Bqirl6j0iNCNRaQmSHML3KBTOYiQl2dmwK4sq1MoYF4bCeL0049DQD08yOFNM2Gv++++54fXr6f39/OKjez2X7rnffwiwQpfeXF5zHv/P5OZ1De/WS8bnqDG/maeppiY+emJOvqasCMGZmHj9YZjWYIldDbcfi3x8vYtq5+VWSyyWKPVMtystIrq+pcHI20d7HcoR+J2JF3MCSA1KiL0IAUaHZyWk3ziJdtiRgphNb902y1jwqB+uFHt0ejohQ8e/znuWdpynrb79d4cihE9jj0//MCWQZ1RlmEHxkDvvlhg9cLyWzmJMjevbvnz194w81cYhNNclWzVsr27uvIJ7B69R0h9TZB6DfLIyN0GVPyG+trerrYsVd2TpwygPE6rBkf2DLQ1dxKqFQKq6OSXSqVQQhlUonn2DgB59IQgLbe4eq9FKgVz6AkEP85oSPAoaIJPxnBBpqdd01mWwhUOEx18BwDNgdBmr5gNjgT1+/+8714g7ZZaAodbcXp55EEeP2VFx0cCjQxPitZCYLA+2D8sPFXbMWHVsw6MMA5oEmSvOFWpFItNLjs8ivxot3S9vYbH1x/8+/9H5sBgJTYIVDICGi3mltbWtwMczQwGXjpFW/RtzZZ1FqtOiE1vbO7Ty5xceaYLTbvnhl2cTQlpyskUoK2gxBEaHjhnUA3ffrWPQ/8c8RPRsQIgSSgXRCx7egZSI4foXGGoVHh0GEe0MtkXO2A3c69dzugbrjlruMNlWUVRzs6Oi3dqL9yc1trRwvK7OGhjs4QmvBO+Ga4/OkzfD/K9s9kj2Yxm+UK7gOxWlCq0M8//3jGmSfffNt1ON1q5dkLbXYUPn7nrY/wbiQJoSpZKaf0hi7KTkuRsw8dymw2SQgvowH5szD1ttPyBLWSkeui2RI1WXs3egn2TNBeUrnCbrJCQuZJny19HSPm5QzQDepZXjUqItQ7gfbr9RYzV5BwxvKF36z7OSwvJmKMQKmQGYzOgpOOngGdJqBZaUPkx5ROAwgsfUrjGMWMIQ8t6Yof1szCarXyHOqJmBhEKwBApVIRHx3V1NLe3W/ERzAM6I0mY2tbk0YZCQB87+2PM3Jnv/jSywCAP6xZE/xpOSM3mPgUCmcfLM4pSTr/HBBYPv/8u+tu5l7oi69QhahaQdb57kLiMNuhsbfZbrPIdUlJibG26GS5Qt7SzP022Kyce8FK0QS7P0ppQlmWFBabLX0dqWPRxc0BAqjQSkwGKgQrPowc6tOEn1c4fehHFzE2oVHJhK08UW1ST9D9HIWCLEBd2RQbaKv2WKvLgKDOgJ8YgovXNYjEEATR3YMcbdE6leMAhFwm7ehonzIFdTC65ubrAQBLlxWyH103lMafvfK8za9uDf7McFW9y8lpNR6kRcp9NoAGICpC1ad3z0jbs3OXRJsqk8mV2pjomJj2jh6tRn2sFnVE5V/NiIL4iDFpmiIgzodHdzv0bZuKx4qd7t8N6ieUNGKBeDGRfjJCJkX1JPxdimZ6+o1+2NArP3oOkxi7cDCQ1Wr9+ddi//sSBNHX62KuQkATBDE9P9dzZytN2qw2m9Wl5YpWHa1VR0dEyCMi5Eql/L23kFZ9/53XfL2ixexMSFp08gKXx6B3h6SEJPED7V0daIiRhTvCgBVERMcro7LlEgLSNl7uSiTuUslsdomktfR11DbXjR0X56DAJrxSi35vgk2qD2NqvXcF2tTk8vsvYsIAUyEKiDguy8wMhaXdYLLYMzPUgcs4mmEiNAqKb0gxJvH1jx/z2xefeTX6nzcyio1JZRhnrTrprToAAq6TWyCwm7lfI4phrJTshltQLen8+QuUSjS9HePVF59j/+/8Gbt59e89XxUC8NnHX1x8yXVuj8jlMuDo+tHXXgdVkTIpqdFE2fuNXZ2dhCDLHOtKz4RTm90uk4KOfoGaGw+Vdc6YXjgmHzbXcdVcGMmZQQ/4EqPwEwdedSJadI1oe4VbgzKKRqnkwlaTdgolr/ArJos9NkrV3jXUetBRIdP1W6r57ZWn3RBIegGESMQ1HG9Jn+IyCTko8Knvhw/tv/PuO+wWg83G8drnX3yHNSJFKd97/d833nGHu23vdo6AsfY1A2WyVCqNTYw3mGlhUieyyh3M3NKKEpvMjpAGcuBEsSZ/FAmASROlqKt3Tcgf8zB4s+JD84QqNTKTweqLT31ByLM+CTQ6UvvKu1+fe/rgQ1ZFjAB8ORlRWIYHuy1zdcukdBr4gIysvhPvH0mSUG+W1aNLK6XTYDOgS4gYsMgAYc1AM+j0RgtgQFSkM5TRbzBDCGJk3EpX70BMpKq9a1jqi0cS67egrvIKgYvVQvepY4BtwD3IbrV66fO2Z+fORSefHeyLYqKTyDV2xkleFgtHptfc/EebFURqCMBOV73p5ttQOidKPQO9feasrLTa440Gk1QdnWFj40BO963vzDITS6CaaK6Ritev1jhjUsj9X6mVmgyhmPBEqIXIQp6VrPvoja3Fe//52pue+52yqCAjLamebV8oYkIl93j75iDR6UBbl4GAICrSWQLY3j3AMExMVAS+29E9kBCjHVPz4+vau6clhC0ZS6p2JorfcTqe9dadjz4PF+9WxslZfipMfv51+9KlKzzXsVvTLHB9eoedE0cUy5QShSoyQqHVqCXAbjT0Qd5Od/zhBswmr39aTXSfmenT4CwD3xhnTMoIttlELDcROjJxJNGEH9/86BnXzmAlpHMHQPMR7ZiM2Ca2BhHIZM0A4j2bAG11FILnsCtL87OK9xwWxi7yYuOrjtULD9uuRxb9WMNWRXxoT9TaVUsLMtMTIwb1rNmtiFwk2ih+JQ2ceHXdZp/V5j7SFQdjT+ez7Fhnsq5ViqIOHTzkvqtjXwqND+DytBSR+jB8XQNw/owKDIYordZpxccmKTubTSMjP70T6F9vv9WrCOUhVnMGx4/No5M0viQ7NS0lrr7eOV6cNJM1RFo2zemkhelxvXpTfUuvxXFZLs6I/01vMZpt/HW4eft+t8MeOIwmkptpZ4ZNhBI5CmQOi37sw9juHu+CEGanRC+ZMyUmgktXElKkG0vykMiVq86Ygbd/2FiOme72s5d9/DrqVIQRJ217bd1mPycz76SZgxEodGNPP6OG2VCekw682ulDQWbGWPSTMq4/TLHJiEND84QOBZKamrqa2uNC803EGCmeCQu6e43xMRrgSMtp6LRW1/dAAk7LdpYtUqTSwsCYCC4Kb4PymEjVvOmpgOJMSKU2oiA/KSs1uqWVM2xTEyL6jfYx1Yxp+Q2Xf/FLCX93UVJcmwEcOd5BUU7qjIyJBBDERKjOXpyvQGMmvUAid6a1Wy0uugbr07U/cRoQkugIXq+c2889HW+8tu59z0dXnXGp28qCBcs8d8Ps6X9KO+6HBKCMCw2BYRmqMc6s+5GSn5wCZTsOTF6Ma370xImmjoyMFF6Err6o0GC07l5/BN+dnp1wyrysrj7jvoONIIqzP4qmp+w71DTgSAy88YKiqoauXfsbFs3gIs5xUertpcePneialck50pq6zdlJ0o5+Wz4Yo4AA9nZ3RChBt0lKksT0rMSFs6do1XLPr7oFuQ6FT+QgUyhlAjIVwo00T1+U8/ErzruUDUVs/rcJTV336hJY+9OXnouHare7ZQX4p06GpjTRfAhxuIYRjSMmjQ1JhLoF4oMFuoQggDR2jXngpTc/O//sU8B4xgTjx2Dx5ZbK+TOceXxrtx7RqeXzZ6YV5sWajNxVJyeoS1fkMwzT3IpG6LT3DOSkxTiHjgGwdO6U3PSYvYfYUhYWsZHquNiogukZzc2tGRkpGRkpn25zDk8fFK2NgxRWxxLB7b9r71F+t8RUVC+0s6U9TSHv1fdpJNIZ2YkaNZARlMXRdoifrClXqOSCSko/WDAnDW9oVMhx8c2mUpWS82Bs2ubil5RIUbPhS1e5pMRDAF5dty3ArAA3cEmsAigiRzmBbNSZ1GCIIslehRpx5UA/o9ZBnkNHTH46faAj3ZQ/fJjk/OgVJ5qcdJOeGBmpdRLEDRcU8X9rXqWmpyY4AlBo6mR9fVOLUa+VAsahgJrZwoqinCh+ynFukhLYjc3NiI+ELtfRwt6jx718DpQFRCsketpotmlViqM1nCN4x94jS+dPmz3TqZ49zfC+Ppcu6yqFdHcF6jfMQ6vz+cXzatQzyK5fppKCf387OI36SWJVBJ3rPXIXYN3YkKW8CB2BQLyXKPwLj9w3qNtlxCDy4xCRFEH29zhHMzY0eAmsjgUG5LF3WwkungEA7Nu26aWHnI0yd/68Wy5Hou/Oe27fs29PgAe0a4kaQ29CNIqT79jLuTIAAPsPcrMz09OQp0JCkmqNs9VIRERAP8yUI+LktqhV+3v67StQevVrP+8EEwuZoy1LAxShw57G1NHVa7XZcV/V4YNIjmFHcU3jkuzUcdGP2StKissOHTl03a3XzCRQKPn9bbX3vfDx/92DDNiS4rJNmzc98Oj9EMJ920tPXbm8vz+IcPPOBiQ/iXgvTUnq6hviYiJT8DD3wcgxEJC0xKi3+2dPvEHbLW9sdQa+xhRim4+NcSbt64tUqL1na/nyhIbXfheY8ABcdObp//uRy72orDnB0PbYGM5a+N25K0LoaCfy4xgBMzRCHJQfw/uVvO7Wa/jtB68/+6n3N/J3H3gUjc1gGMZqs/75D3955MmHQ36VHfWcOX9yBufZLN9fiX2a2RmDFGuSdBDCYt48ZwuS+Gj1+h/LhY8SEvlEVaMjw6TmAQq7QYciQocSR+K/CoKR0AA0NreTBKNgzSVfEPlxvIjQYAnOfSQ4GFE8/vCTf3/iIbzdWFd1/bIsvF20BLWPw5BJZZ98zjUYHiIwky4UrNTUt+alBaHiIePSiskN+/Y56+79gJelIpOGBbHJSqPeOtzy00mgwkwmmmGMJjND27t6+k5eMIffVWRMEW7YeaBp8SwUdwoXTj3nlF82bD33rHNRF4yGmsdvv9hocAnmlBSjWP/zL/+7sprzYIYFu0+gNm6n5aUzDPPL7iMxSk43xcZwpatuYKDU63aAsFnNbmaBjO0S75pA6i8Vf/wiM6yaNAQrfrhMeAAgfm82m42macpO9/UboiK0MV7KMUSMaRTXNC7O4Ioad9a3IyIQbIcTOml4j9mv7y9aUkiS5G3nnHbL/f98dX15Y23lgzecw+9QtKRQIpHs+XXvqctOvfnOm8L40gCALVUN6H8xmkDIESd7klKfVprNw4UqdbCkVOFS+yQEwXDO0ztXLv/gm+0G+VgJ54476z6WFaF8IH445KebCc/BarXjuiS7ncJNXESMR4SfLocfOq2uX99PUdT29V9tX/+VTKF880f30m+73V60pBBL0WHClxWoLvPSOXldXajyKlKn8SRK3NDXbvN28bP86IsleYr0D9pqveZcZybpa5snppMUDJlJvbpBR0yEenGHO6c1QWTOf79xq41oYSddixg3GI/secUlV9x7z31CX6eVrRGCECbEJ6z/30bhQyOALyuqLi1AuaK9/QYADO3dyFTMnZIo0SIhKfGtIoUnj+hUPsjF89K73wMA7rrapS1eE9vHE/UbTET2w+2CxpITlUwzw61JeU/oMMlPFwLleZOvHUbTBMZtgv0Yh2dpDa6fmcz47KvP7r3nvm2bipedtQSvPP/VDnQ5dZkNFPJR5mbnVtegmMzebSXtHSPxC/FleSXmUB7Vx1ttbGeB2bO4dRJQvqjTjSLdwDMmv9HU2onpkudNjH69s+7o47V7YFxApVOThEn7fLtBUQWEVmY1+4vyDTEQ70WBstOmEKCPeSyTE4NWE3pWIgb1FBEo6Wfp3H3bS3nz3DSgP2UmF6Q65eylWzdu54imufH8y84bmU/sy3L3aNXi9DQAmP0HjspknLSckT9FuMObH693eQJ7Fb37wj372HpTP+BJ02axmBxzlrQxEVpBLOv26890e9br67mPZeIhMxyadPjkp3cC/WrjJ+zLgq4B0FVTLc6dCxDTp06XyeTl+7345nKyc9UqVcWBiiH+tSY8GIbBdjpOpBfCYDCMsAnvCzsbTmAO5VdefPtbYYElmRjx8p+vP1jhksDkiz0ZmRcnnUSm8tVg1OaYH8exAgSrHSlQPF6fcFmlmX4LRs0DlFrnPTm37hArYhiQlj8s0XCxoXIY8P2Xa1OSXLJ5+OxFtVq9bVOxwWCQy+VSqXSMUICIIQLXNfEgEyNAvXOe5e2nL3ZjT68s6Quc5xffgVDqGn9wu+u1TIKn1InHpMCbLO3ri4xJMvhkTxYnKlED5rDTqOT5d54P7xEnG1aedW5KUoqQGUuKy577YtufL0MdHrdtKn7muae//OYLVFfz14f2bS+dt3TuqJ7vmMDgDt9mToFOiUbh787k8eQgPnikZua07K7+nuhYl+kAvvykMuiswfe8GyBou8Wtb+lXP+1z9KefjAZ+nYA9+TqhE5U94eVQUYEOFddeeV1fn4u9+eCjf7t3zT38XcyeAICn/vnkRedfPOQXnMi4/7L5eOOtF455XRfimS/2grGKg0dqklKiuztRJL2zx5CRmiAnhsqSOPPUvTbM1b/HjyRB7DlpkJmhGOjX4452XtjTFeGVoiKBDhUvvfqfl5777/nnXvD9uu/wysbNGxu3fI6sLamX/JX4uPiRiSCPF3glRzcsXJa8e1uznyeOKTItbmpbImhNEqtWDvT0y2N8qlHv7Ut4H2fAeO3TH/FGXGwMmGQwC9yg3tnTpVjdC40GHYhnm0SIBDpU7Nyz86333nzkb48+8rdHcazj0msvBl1IkyYnJnvuHxcbJxKof/ZcuCz5rRe47bf+9+dbLnpu0L/C/ZfNH2scemmKyxjM7i4ur1On9dIRigd0b+Li0svl9c84ivQFMhlRp7N94aRBdGQiAJ3+tacnhmTRs38WkUDDgFffeuXVt9BIhysuvXL1jas3fLOpu73lj5ecbPE2SVyEL/ZcuMzL783ubc233HOlp/wc+/jyN6736KUFecL1WrbuHgCQlebeQA9R5OdeS+AZN4oU4YnBqdNVhIbFohcJNAwgCAIPwPjsy08/+/JTuVy+8+fdEMLWNpcZ4hhVx5zjG4VobeyYtLn0XqlzwuDL8qpL5+R52uJveOdKBDJ5sCHuIsIKTKOxKWqnT8AYUMWtSKBDRUlx2adffvKv/zzLr1gsSHg29lPA+edwnaE4KfHtyxfgjQvXfOefN8ej3gykuJ5Xo5t3HhBZMuyQyxUWR5JsUCKUR2fTAARAHRXEsG6RQIeKR558+LGHnvht32/bdmzFKxu/RY4qm82m6W0vLS/du61k/rIiAMCvG7eZPIr8Jht7um1PNnxZjphUFJjDgaSUzJamOlbBhF62xAAw0GMJnENFAh0q1m5cm5WZ/fz/vSBoZ2XlCxBvXXPzLxu24tpEu92+YPngEecJiQvXfBcIb0487TlOcfuKxV99yGWS3b5i8fhq8yz3L0X9ilDMoYYeS4BSVCTQMODFV//z4qv/Ea4I46ynnjO+50KPAMYmbwbVykAb6hNH0fHNt8EPcM+xT6NJDhEakDkfgBQFg9GoSKAiRlOEjk3qDBAyqYwkSbPFzOAGumMVgRMl2+HUI3VEzjXuu33l6a+t3zypOBQORqMigQ4JvuRDrKMXxviqQRxu8OGjYBPpB8UIJ4FOnzr9w7ecs9oBALetLOrvQd2XhXjsoSdWnb1qZBogBMWSQqKUaFwyeCQOunQHjZLMbz972Vcb9lxy7lJ++bWxR6mDc+hgVjwPvJcfGhUJNDymlhsL8JWI464GceQh/DSCItNR/Bg/fOvjt95/89U3X+ESkl5+6431JVcscmlqJ5fLV529ajhefVFS3JzpzmGfnli8aGqkztkz9OfNpSi0IlEPSpQ2K9/HxCPrylGKKmRPwMrSMcikYdShwmIGTxoVCTRE+LnUhYU0gz5XJFMh+E8jdgx/Pvm5qJUyz55Ifq65paS47NTzLv/lB1TCi7Hz590PPvq3px59OuwnsKuF87HOmZ7LUFz1IVQ5O+rtLGWHO/FwpU4U57SYfBXUS2VBd2vW6/tVSvmtZy5988ft44ZDAxahGMKCMGGYXiTQsFGnMKsxwALEMViDKGJQ4IGg8+bO31fq/MO5yc9vP/t++85t+gEvPdbCiIrD1UAqnzNzqq8drBajr4J6BgCZPCCuXLE4R3j3202lKqWLJatSyv+36TcfrzMBdahQiooEOlT4KUAc8rFFjFGs3fDD6y++gRyDNP2n+/+4fee2ajIKN4E+SEfMmVWQlpp24RXnLzt5uBIwdrV0LErifEoVB3GrZjg1J81tN3zByxTBicqFBeg4X/lgTLxtt1MMAG9/sgGNJLlp1aXnnuT1UK+Ndrd8fxwapAgVukQxBnosIoGGiMlTRSPCE4889fdH//HIzdfdsvrm21/4J8pgO15//JFrT8Mj8N559V3c9ZUg2NEuwwNsyC9KT3UsMEePOds879h3ZOn8abNmuAx08oW+/n68oVLIAGC27XGpNtZqvQSgSQlqEX3XTYM4eW9fuXTUaVTIoUNMs/eUoiKBBlp36LbuBpE3JxsYhnnr/Tffev9NAMCSxUv/888XL7/j/s9ffWbf9tL/vPICTmzy2s8wvNjV0OhKo4g6+e0Dh7iBTkIm7et3aV8boVNE6IQUCUOL5uvUzqkbbohhyC5IjQUOdZeiwYtQNw4VCXQQfPvyBZhDJ3MBoggh9m0v7e7pPvP8053N63ZuP3Tk0Morb/u/px4DANx95z133+nsqF1SXLbyorPb2p0zP4aJRjGIeC/t8g4cqqQoa8FsVIkfoeOYTsqRGvrXxpBBZIYGhgE91/bhktNdDPzXf94Fxg6HhgpMvCKBDo5BqVOUn5MKO3fvOHkRN3iZx4xpM9pamvBgUbwCITz/3Asevu/v85cV4WZdI4wd9ciiPznD6Rgt318VFxuZkhyP79oYUkCOATUfcgNJk5506Qe0HeUM3HpK0dc/lXSP+LiRpJRMAIC7SzRUEYohEmjoEHlzcuIP9/6+pLispLhs8y+bN/y4TqeLwL20Lz9zATbt8W4Mw0glyIQfFfb0pFGSlFFszlP5/iqVCtnsWRmJwbJkgJg3z9kCNT5avf5HblQtIeFY89JzFo2WGuVpNCw6VCTQoJtfTCTeHLRqe9K2KPWPoiWFV1561V/uvvf0U5Ehv2ffnjvvuV3jUdA5Aj7QoGh0YapLC+faetSvdsuuw6svO8Pfk30LNILxolvNNvu+vd6b3npi9Wksk8KRZlKeRhGHDkGEwpWX+/3sJjeEHMpHk8JSesgjhCTQoHpVYPAZNsE+cbQINLaZK+XqTHZJQhxJBPs5a3rRqCtDJGcgj9lPeGFqAlagW3YdRg7K05DPISbGvdH91x99iDcuvdo9IY+G4RFeNteRDe8Uo4qp21eueG39z2CkwHFoqAQqKtCga7cx5Q2dRoc1f16tVsfHxff393d1uxdoi5jk2N3oCGfFOMNNXV1d3X0DqM4qMznsdGnzPdtGpnNW4t++coXbxggwKZaix2uPhMahIoGOXAX3yFQcJcQnrv8fSm/m8eU3X6x9/n633W654dY7brlzZPpcjFMEqw1xB5lx3T4mNyud9hv/EfoxAydKIUv6AcHmA4wKpmRN47eP1zjzwAaFSKBhwJiqxVz/vw2HDh+87rZr8d0rLr3y3rv/2rjnx/KdW/h97rnrj9deed3oneM4hp8fS9xBZty1j/mKHTdyyRwU9ulm7ZVInbC7KXDjTX48vRA0g1hSpvDRySkkoqQtVmGLqRHrRjol20mmg/KpSKATCvPmoquXZ0885+7eu/96xyMvrj5rJs6t2fnzbplM9shTf3/swcdH9WTHEwY1Mvx3kBn77WMwjeKRTb39euFDbowp0QY6wJIIRlG+9O73qLTpqrPd1pvauHHQo0KmbnzqSaYigU4oVFahsmidLqJfUG1StKQQB5E0ve3G6MRDRw7ectfNJxV5L14WEWznLc8V/3ka+Ghjk0bRAFHXIcwAgOr6VitANalzZvosDLX6yAdSyKWeLOkGTJpC6sSkmZIQy/8rRL/BcNWC2Z/s2Q9GHJ5kKhLohILeoLfZbL+s/xUA8PnXn3365ScnGp310Tgn8Za7bkYVwVbOpSUiNHhSJ24fM96z3PDYOyGNbtlzCJWrLphVcRAVhspl0hl5ztZTJov3L9KbH69H/4NeCkM9ZSZGU1unH9IU3v1k3W8g1mfl6EiSqUigEw0LTz1p1dmrHrj3ocsvvuLyi68AANQdr3v0Oi6sycNmEwk0bNQ53knTE1+WV2EO60zOcUujt1hth6qO4+0ZDibl6NIVZCKXNvfm/Teikta9XGG+Lwh5E8ejTFZnddNH322748bz8Pbqa70nX76+YaS7logEOgGxduPatRvX4m5Af1zzp6suu/q9X4/dsNwloVJUoCFgMlBn4F1Lfi4u/7m43JMuMWOaLdSh/Vwy76DUyTOmRMOFoSQytCEMZvHs6e25Zix0b14x/60vt8NoBZ+i//qW4c3PFwl0QmHe3PmxMbEbf+LSmGiafu7Ff73/8XubvvvJbU+r79S8yQyvpRN+uhdONgi7lpACxnSbyDQoYzIy9/JQzJj+YbO6e1qlDGIwqYDHbr/kVC+VTsPGpCKBTig8/tATCfEJPIFidHYhl/zxbhfGtIo+0MAacYlduALBwaOc2Jw5Ncc/UQ4KqwXNZRJCBrhIlJAogwVmUoayvbF1HwgfRAKdULj6pis3r93yyTufXnWTs/xu77YSzz1FAg0Ek6EfwjAxaWIyqg2NiXUPB/kiSp4l+Y0Acai6GtcQzczNdUn7h6CyDs2Gys9MZ6vt4Vc/sdTprQ6guQqV4QIAkvOCq8QVCXRCoae358HHHnjqkX+UFHP9bzCWz3K6rjBEH6hXDCo5Rer0iuImREBLUtzZp6sTWT9dvYYMRx8TOQyRKD1xqLoatzr54ReU0kR6RPxLD6HOdaWH6+bNzAIAzM6L//U4R5RuUEpoNyZ1PkTSJorV0RAk57qXmYkEOtGw8acNG3/aEB0dk5uVY7ZYDh4+QFGUZ6Mgo3FArOMMikNF6gyQRoE3JjV093v2KwkQXCa/kx7h4Squ29Pa4ko3unTZk93o67c2t/folIiv58UgT6vVTnX2GZva9X0DFppmorMTPZnUCQYoSVQRYKKI5qp2ObTH5Dod4iKBTkx0d3ftEduIhIlDL1zz3ai34BoKQmjfxZb2c88NoV9UcVP7JawJL0RXF9fXxheTera+9zFdxHvbD6udkpAEAd2FaGSkGrOn87AQoN1gcNNLlCTiVhMlwSoVG/sigYoQMXgjLp7yhsKkY7P6aDj4NzE17qu9XNXjJfOneWXSCJ2X0SN+xrcBBrz++Wa8OT03MTpSBwA495TZ67ZyJUlWG0UShJAUsd1lNFntNC0RDPgjIJSShIQkguVQVFvFqlEeIoGOPwSiCNyu87de+BRv3HLPyolxVY8WvH5cnqw67j7VObMKzlxxltlsevWtV+z2UMZ7+AJmUtysRIjaE1xXvaw0lzbPAIA3HETpHySDikHOPWU2vltcUkkzDAkg5k2G4cqg5DKJG0sSEEokhExKkgRB0VRPLWosHRpEAp0gCFkZjf0+F2Mf4/pzy87M/uJDfgg8uOEaVDV0/mXngVZnOwW3mCRusABC7fnkhtoTbZt3HvD6LDI52tcBu3v74yIUPI0CAE6Zm0lBLsR+4OhxpC5Z4ozSKAjClUIhqjFB9r7bemAwU07aFAl0glMnzgD30yjI81Djmg5EBIsvPvzKarUuOg0NdOL6dW3Z/f0XP1y/DEWu+en2T/7ziajIqKjIqOio6KjIQBsy+adRIW/64Uo35Kt9NiTlybSr3xQbwWXmqxRST0sdBju+2Rt7igQ6YdnTrXLmrf/9+ZaLngv8mCKHThIkxKMA9ClnL+VXGIZZdOqCkuKyWQtOObBnKwDgib8/BQD45vv/hetFv2JpNCjSHAqS4hHdmwYGlGo13sDe1cA70JvZeicMGUSRLisj0zd0adNjRAU60djTa9Hh7m3NuFeQCBFCtHcgR+QTDz9538N/Fa4vn5U6JRrNTQIAnH362dYx03omLbEI6L3b+244aWa6lXYGjjB7ohyAKHXvADXQb+80UCbL4KOYLYxUyrinB7DZTujgIoFOHPiiztE4FxHjAwzD/Fby2+mnnoGnND/9r6d6+3rxOj+fGXVC+vRD3g36w/rvH/3HI6NytqmJRfw5mc1mwPpAPbFlb825J08dsLkM25SQnMlutAI7RVuslM1OEwSk6UGUqCd7CiES6LiHrz4XInWKCAR33L06Pzf/rf++ffqpp+MpzfUn6q87x6U5yE3X3XzO786yWq0P3//381aePzV/2hXXXzbyH+9Af69aF+m4N4gPUy11Y0bnXVIBo7TKxvZ+QJIWOvSUg5oqG3HF8tNCfr6IkcG3L1+Ab27rC5cli+wpYuiorK5ceuaSoiWFl117SWV1ZUZaxtaDTaSEU1fzls4tWlLY3tHe29f757/96ctvvsjNzh35kfdRqmxEgQwY0MwanD79giSIOJ3EZLEr5JLQAvE8kBkvcuhYhpA38bYvPhUhIljotLrEBGchY01dzVU3XrHoVBSRf/qDH/Gi0JYHADz/8r8BAHGxozN81KDv5cz5BGczvWABAZBJSKmEUCukUjLoflEY7WaUyU+oZLRKRoscOl4waKsLfBvBMxIxjvH9l2vXfe3S/JAt6UEho/iUjOPdFplMNmsGUnw8KAqV4vTr+8EowaDvtUXOrLVlD+UgBAFT43UalSw0EUpRnOEvwZ4BlRRx6Ge/OiffiggcB/YN3nA7ZGQv/Oeyee69lDzR2W4M44sebugGowq5iRsMaWkexs92ksBs9B4GSctKevCxB1589qVbbrj1rffe5NdJVpR98vZ/AQDXXHHtXbetEabNX3XZ1YjFXIcUjSSio5D2VMhgM0C9R81WRGFZ0pqgDgIBkJOMTCJVK6Rmiz0oT2g/SIC0nQAUhDQxoO+XStDnpZLSa87zPmlExFhGZ7sxvOwpYvJgx67iuuN1d9xyZ0lx2Vcf/e/5/3thy7pff9u6z2azvfHC0wCAdz54GwDw29Z9OJ1+bsHcu++8Z+eekZsqPCgUMojIFOY0w5ygZKlMSsqlJBKhMtSDxP/O/SAB3egYPR0DaTuENISotwh8//n7UMOSqCibnSuSVymkL//gPgFCxGgpUAxPEbptHxquMD09/KnIoy4/XRSoUjgXR0SYFSjeiIqM+vH7zZgiAQDPvfiv7994Hm8bIuPdSjk3/Lj+occf9P+KicE3cAo0iORQoH7AvxEeJgvXpG6aqp5frG7sUcoIo9nW3jPQ1Wey2JAI7aOdjfgI4MwSZRjuCDSDcp/4dY5AMYeiqSMsjYocOtYIFGPZvFTMm24IF42OBerEEAl0JAnUE5rediGBBovEYSBQxFGq7NAI1Bd6enopijYYBrq6evSGAeze9QqeQH3mgfb29ERGRUklpM1OGc22m85YrlLIXv6Bi8SJGAvwyp5uxBcUmQ43Y/q6dAeFHA7pCAoVV0UjYiKh11iDOTSMIAhCoZArVQqzxULTtFvKwaBwSaTnORRLUaPZynpFoUij4whjR0WKGCPw9XPiRyfGEn1uUwjHCNSmGhA1N4wHhBBIJBKVUmkymm02ux8RGlAlUm9PDzbnMY0azcgLsOa8M0UOFTGMgCA9NV0qkba0tRiNYkBs9Pt4vfXCMVwL79lAdnT7ddE0PXBiX7/BmJsabdDODKsIVYYgQr2XcmIpiupAOYtelKIihgUQwjNXnMk1bgQgJwcNVqxvON5SxbUZRxkqmVm5Oc4+kjarbcvWn8W/R8gYusd8VHvIQoah7Xaaohmt4RDO6IQQ9CqnB+sAdRehKoXJJA9chMYqTH9efaHPWnghh4pSVMQw4czTzwIM2LR5I1+pvGTx0oz0Kf3N9QMG1NBXLpfn5uR1dLSXlpdyhHv6WactX7HlV5FDh4s62RJhrv/hwmXJ/usy7h/ZHrIkav8BVQoJRdNoIgdLfwwDIo2HGbbVfJ96RogiVC5XKZVmsz8RqoU93P4oVRYVs/pja2zOYzho1LrmvDPXnHdmCKcoQoQbZDLUGfe3kj3C1ozFO7cDAGYtPAXfXbzwZDRwkWVPXFZ4/HjdyBdiTxL46a7gH/eHdeieH0AISBKqFFKphKQZhmIYmmZ4wmMAc3DXhmjz4SjT4WjzkRjLkWgzusVYuJufw0okEqVKoVDICYLQwm580xE9GtClAV1a2MOzZxDdmHiXKM+hNjsllRCiV3SSY+OnD0zPS8GM9sX3u+99/CPPfSJ0qsPbnsuat8b/oX795kGtRiHcrbS8lBjoeemF3y89edb8sx7RalHRMUbroTfnn/UE+sYTkPHbiOzw9uf2lBy78Z5X3RaREFv6Z+GiRqUo3fxM3uJ7wCSGJ2/iBrJjsCyYICBJoEp2hgGoGR0ENMXYGdSbDjLoC0njdcDgoXFIpzKcj4jn0J9+O16QGye32tFkOQhJAlI0I6XsFDQSpMXGsLJ2MFA28yD+AraHv3v7fpudxlI01E9AxPhGQ8l/p+elrLz6mYUrH3ryhW8uv2BRze7/eO524JdnNWrvHRsxrFZUc/3XpzZr1IpKwRE6OtpJgjr7zPkPPfJOZ1dX3XFu3nfx2icIAk7Nn4qIe7A2jhqVQqlwF6oalUKjcj8lzKqTGW7sOcbbKUAAYiPVEP2GQglJSAhCKkE3lj+Rk4emGYqmOWXK9jVFdx3biBi5qXMIZitltVF2Ci1x7UXQuM5AT4b4fDdnHA1qxQuBg0ti6eckxH1rzgcApBfddfDoiea2njc/+jln0d1SqSQ7w2W24vbvHuvq5qqJ/GDTTxttNtvfn/05KkJ9z53Xnnn6WTnZKI5UvveN3l7D2vW7+T3bDr+Zl40SvwmC2H+gIizv5YKz5zWUoIrvSQtPm30sU2eaD72HZ7wjPmXHxEklhFwqkUjQ3pg4IUD2CsueyOSnWHVpRULQbrNT7CR5tBsBoUImUcrQuDk+sOkHBC0h5k3JDY1DsRS97rRlIo1OKsRGOw1qDKvVvuaBd1rand+TjNTY9JSY8294NpADbvn159fe+bSto/+B358CIczOyt70JTLS5y++c8DKlX+0H3mrvbN/wTloOE9HR3tLa8vQ34hUKnnpqRvnnfXAocoTYIJi2bxUX51oYuNVnmb7WGbPQOGILJEElLKziyUkQZJojjEiVzSPExnsuEe9WilVyJB2xaoUQoiq45UymYwMgD8RiPwpCadOnzUoh+4uOej1NmCy9A9YRA6dPLjvyY+xFb9yRQH6oWbx/aYSo8k5Nmf7d4/lBulSnLXsTwCAp/66fMfO7YUzk/78mEuPtfhpt9z4x8/ycvOFAaUhwmazpxfd1d7pHN47wcBTpyeHxsZzEysnFSBEYXrMjPHRagmJmonIZaSUJKQkiW4s4aoUUpUcuQQGFaHdXZ1IrybH6c6YNefz3aWXLww6xf/AsZYpydEMo73xjFPe/QnN8BMxsUHTTN7ie0o3P/PaP2/FKyeaupZf/LiN7cWAuXXDlnKrdfD+YMuWLFMqlJs2b8J3z778Hxs/f6By97+bW3vauwZkcuWAFQ1QBACccdoZBEkeOXo4qFNdPD9/klvonl0U/FDneJGfEiK4akuvwCKUIAiCjeBztMqSrFxGqpVSk8VOmW2DJtUTkIBKhSw5PmJVUZF/HeoVPf3Grt4Bo9lKQLj6HHE6yKSA2WKbvvTP6UV3LVj54Fdrd6elxNTs/k9MlAYAcOMVywEAq+91Npf0A9RTEikC7rtbur+2qbVbKiFPv/T/UF+cAbOQPbfv2NZwoiGo89y5ryq96C7hDUxiLJuX6os9x3jUaPiApsM7voF4UjwEKDClUkg1SqlUQgw6OYS48MrTIUQN7uOjtRcvWhA4hzIMWFg046LTZs+bnhYbqZaQyJpbfc5pojk/4SGXcdHtlrbePz3yISamXeueBAA8du+lv+48/MFLd33w0l3PPXItAODzN/9489Xef1nLD5TjXHqFgouMt7Ujg7qocB4rdVFBSMHsAoIkt+3YKpZ4hqv1jBsmJ3WSgkA7pk7nXeQJlWhUMpVCyjupfAHlgV54JRrF9+2nm6N0qquWnfzJth2+bHkUxkIpVSjdCgLIZ4kKZa7VTt12zmlvbBCb209MNJT897uN+37/4LvCxX0VtfPmZOHt5Yu5ojqMU5fMaGjs8HoomqK3FW9dtuSUU5Yu51JHI1CnMoZhtmz4Dq8ksBN7lp3M5dUDAM4642wAwM+//GR3dLAV4Qvb9jX6GWcwQamTCWQnkhV8yHj3eAgiLwFUyaVWFWW1U7xvyhNfVVQ7EukHzBeev+Tb74uVCukVSxefaO3ZUX3Ek0YdUhcJXX6xt6dnV3l1wG9PxPjGR19tv+aSpc++8kNDUydeIQli3pysf/znG5zexO+ZlhKz4/vH46fd4udoJpNp008bo6Oj01PT1Wq13Yaa1/y4eRNf4LGvdK/rdXHJvpLf2KE03vszCsE7ByYzvHJo2EcYPDN67UXcEZiDFJeB+jwAhFIpqVbIrDZ6wGjx4wh1r0QiCKhVybPTYjWqAq9hJfErOcnxwNOfnbZ0ZvH3jwkXW9t7X/tgc8jH7GaBfseNnBTl0dXV5b7C7ikiNA4N0K4fl9QZVhAQymWkTiNv64Z+QkmQMf8GHN56ZMh/X4xNdbPF1trZH6PhGPZobZPnk6dmcXlk7gqUAQ2WUZs5NYE70o8pnH3qnEXz8o7VtX74FapeD29DZYmN+07apf5qmXxBbKgcCGbNQ2lhwVa1jyJjprEOSQnBROt85hLsOtCwtHDKoIeqbe7TqaReFSFPlrhyqaK61UZRbsKWIMmoLORcknzw6tfXXXcu/wDn34RAIZfGx2gpiymYN8i9ZLBdnUWMR2z8pWLjLxVhJDJhr6CaSm52TXa+c+yE2Ct6BDBRFaUbAhllzKY0obpOioa0D05DAvODD9YBADCNXnD+yd99vwNzqFoh6w+YQBmu8tSXZ1aEiKF2pfR8ikipIkKDMIojhCtNopQmEqV6QtqHb9XpA+VplOfQoGgQQog4Hdfqi/wpwgfCO0aUP5rIpCIwArHfWQUayF5s5RJb+ukr48M9iIRpNCIyEtvy/PrUrBSvblCnAxSzp6OyX4SI0KgTJ3vXBOlVnp4eLXLoZAATpuMESFJc9SdbAerVM+mdh/t6e/2oXJ8vBrgmeiJ/iggBsfEqvlRmwdJp/OKoaFsRYxNwOA/O+OBZApvX3uBTyGIODfYERPUpYojUidHZbszOz8jOzwh7xqKICYxdB4Ir9g1UhBKQnbzkJFF+JoLXjvScFzNoDmXb6uGtoZyxiMkDT4EpMqaIEYCQoSQkxA2V/ezsy4j3qkADpb+jtV5KwUTuFBGa6hTZU8TI4Kffjge4597DTTiq4yFCYUAzkYID15UUTDZ4TUgW4StD2+vYsrDUZU+SHEYR4QAMKjyF5iZBSHuI0LASKJtIL0bhJzO+ffkCfvvCNVxDEB6hTXwUIWLUwYpQgqAZxpVDJRK50h5kuZEQn24+PCWW86ey6ffwhIVrgitChFdiFSFifIERhJJoVwaVaFSKAQht5hBjnYVZkTsOtWvixNpNEe749uULLlzz3aDUOUGbqokA4wK/HW5aPCvVK3/p1FwhMg6O4zlLFI0aNfH7/D9qfbspCobFZgAAAABJRU5ErkJggg=="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "The configured glyph is fully framed and unobstructed for both allies, while the hostile view remains edge-on. Commit this pose.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-57778649b690", "created_at_unix_s": 1788843019.039482, "source": {"kind": "ark_eval", "case_id": "MutualVisibilitySignal3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "MutualVisibilitySignal3D", "requested_task_name": "MutualVisibilitySignal3D", "group_seed": 10, "requested_group_seed": 10, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 6, "rollout_step_budget": 6, "attempt_group_seed_history": [10], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt] Place one village signal so its FRONT glyph is validly visible to both ALLY observers while the HOSTILE observer does not see a valid front glyph. The three semantic roles may arrive in different fixed CAM delivery slots each seed; use the role chips, not delivery letters, to interpret them. A visible front glyph must face the observer, fit inside the view, and remain unobstructed by that observer's socket screens. The neutral back is never a valid glyph. Set socketId 0 through 5, yawSlot 0 through 7 in 45-degree steps relative to the authored socket forward, and heightSlot 0 through 2 for center heights 1.2, 1.7, and 2.2 metres. Setters replace prior values, earn 0, and return a fresh decision; rejected out-of-range setters leave the plan unchanged. Commit only after all three values are set. Commit ends this game round: both allies visible and hostile invalid earns +1, every other committed visibility pattern earns -1. Camera role order, village rotation, open height tiers, private ally screens, glyph appearance, and the correct pose vary deterministically with the seed. This is one game round with no shared prior-round history. Each synchronous edit returns immediately; the configured decision interval is only a maximum fallback. Available tools/functions are provided separately by the environment.\r\n\r\n\r\nAvailable tools:\r\n- CommitSignal\n kind: method\n docstring: Commit the selected socket, yaw, and height. The round ends with +1 only for ALLY A visible, ALLY B visible, HOSTILE invalid.\n signature: CommitSignal() : bool\n return_type: bool\n access: call\n example: {\"name\":\"CommitSignal\",\"arguments\":{}}\n- SetSignalHeight\n kind: method\n docstring: Set or replace signal center height: 0=1.2m, 1=1.7m, 2=2.2m.\n signature: SetSignalHeight(int heightSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - heightSlot (int) [required]: Height slot.\n constraints: range=[0, 2]\n example: 1\n example: {\"name\":\"SetSignalHeight\",\"arguments\":{\"heightSlot\":1}}\n- SetSignalSocket\n kind: method\n docstring: Set or replace the signal socket. socketId is 0 through 5.\n signature: SetSignalSocket(int socketId) : bool\n return_type: bool\n access: call\n arguments:\r\n - socketId (int) [required]: Socket ID.\n constraints: range=[0, 5]\n example: 2\n example: {\"name\":\"SetSignalSocket\",\"arguments\":{\"socketId\":2}}\n- SetSignalYaw\n kind: method\n docstring: Set or replace yaw in 45-degree slots relative to the authored socket forward.\n signature: SetSignalYaw(int yawSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - yawSlot (int) [required]: Yaw slot.\n constraints: range=[0, 7]\n example: 1\n example: {\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":1}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 6 operation step(s).\n- Across the whole task, you can take at most 6 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Combine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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rJZmf3t4aSME9xzP+/IfpWTnFUvkmz4OMnKKAsLAADdIcmwNTuN+FEdpNIAJKIBDaEDviucp6MzY/a+qNCoZC3k8Itx9Ku2H2SGnnjJyi6OgwHyWsr2uo1NsOcsef0Sp+xfJpBpOxUlsnGaHdBSKgBAKhfUiMDaEoyAjeTx7wpy9dle5K6BMJIaiq1oaEhvhrGPkq/tHnPsHL/JCQIMkI7S5vCRFQAoHQJvMzxA/1T6qpN66+cQpE/UBEDZWrJAVhVXVtaEgg4Cw0RZVW1TsM7MRGKO4w0iYjtHPjSERACQRCK6m0qicAICkpav2WEzevmIRz3Rma4gEvqWRZRU1EWBC+VV5eFRAcCmmFdBxshN6wfBrqH4KN0G4CEVACgdBK8zPEqp4mC3cx4xoA4MetJ29ePgnZoUhFbdZgbFQIqouHoKi4on9CZLA3RaMMeeCgoZIR2l3eEiKgBAKhNXVHlfVmfKWm3hgeFfLoPfMlDb1h6QQA7ebCQwhpmsrLLxvYN4qmIE1DBUMrVKL+Yh597hPJCO0ubwkRUAKB0HJk5qeF5SgI9x5Nf+iueWYLcn1u2n5q8qShgkNSNDMhBJnZhYOShPwkCGiKUjI0mo9kz6PPfYKNUDfPPO5FZKt2EYiAEgiElvesg6J01NQb42NDFQoVhPDA8Yz504ag7h8AHEnNDI4Ilx6YllUwKDkaCarVLUpT8Ep+ablePI4UbkJGKInCEwiEnkplnS3NKCY6mKGpOm2tWuUFIdx9OG3WxBRWmA1XV1sreUGRegpIjs+M3KLAsIg+YWJ1vBRuQkaoLFG0i0MsUAKB0DLzU1q8czyvoGme5xmGrtXWhgT4Q2EtP3NCCl7L2w2SwwhCmZlbNHz08FA/hqEpLx+xA5607/7UzpgH1y4QASUQCK1BW2+8c/lkikIBd5blaAoWlpSHBQVACPcdy5gzebCgnvJQvEhGTtGwUcN12kooNFpmcCc6AbaDJ3C0O0RACQRCayZ2+Puofj90cf7EgZQQUud4nqGpopKKiJBAioL7jqXPmzrEOas9Pbvw4Xtv9NGgCR8AIsXMuVpSaUY5oRCifPvu9WYQASUQCM0wbZA44VJCW28M8PcBAOw4kjZ33ABkh1LQYkHh+GuFZclxERDCnYcuOZifVTr+8QdX7j2YWl1Wik3T7CvFSv/ghHBxFY9j+t3o/SACSiAQmqG+vMhhS0JsCIDALNRr7j6eMWN0P4pCdijLcQxNZeYWDk2OkTetS88urDPBYB96x54TorFJwaycIr/Q8MQIbwCBytenOxqhREAJBEKLzc+4xBg8+ZIVDMb9qZlThidJdihNwbNpeRovW//5uIQ4P7WdMmZkFwZHRUUGobU8BSEKJ6nQ/rDjZxG3I0RACQRCy8zPxL6RefmlYTERdQbUOh7r3ZGz2ROHJco7iVRVa8VaJAh8BJ+nRPrlwsj42DA/BgqhfAiAimbyy9DIJFQB2n2MUCKgBAKhBeYnzl6iKVhWVjkopU+t3oKzPgEAx87ljBoYT1GQpigzK7RYdqWEWD0Dhc7KeA8KUJnXqgbG+lmN0Ja9I9dRbomAEggET9E2GIPCgnheLN8sKCxLSIyqN7DI5BSMzbPpeSMGxGINlXyg8ZHB0hHSLxemDIzH6on7NlE8zC6s6RftQ1PQy7MxwbX5B93c+38L+3XaO0oElEDo4YxRFI5RFLbigdMGRTms3wN8UMkmLfg6OQ7ZoVUV1eGRoQ1GVoqen8u4lhwXDiHwD7Lpplw9pZuRIQGQh2XVuuRIb2HhDmv1FqXarsNIK+jMPHwioAQCwTUO6qltMJbWoPlxEKLkeV7Ie6coStdQHxuH/KE4KI8kOyWaYVTu1RMAUFpRq603RgYosRe1Ts+G+CoZisIL+bas3zvNCCUCSuhONhGhFS91e2XOAwBefWxFWma+2J6OgrhSk6EpVMcZHkzTlMWC7NC1207dOCPFvXqmZxWgZFJhLU9R0GAB/hoaonA8VVxcCrqJEUoElEAgODJ3ZILDFm2DsW+f8B+2nnz1sRV4C0QaSrGC9UhBaNQ1qgMCKApiO3TjrjPygfDO6hkdGe7vJU49ZnnKSyG6TC9fLbGo2qEfaOcYoZ75bAkEQpdkeCBK/XFDLC0uq81sM3ueq/GVrlcWXHG4Nz421GzhFAz1w9aTty4a+8P209hyhJBiOZ6mIQMpXUMD4+0LGuuNJotS4djr07V6AsACiqE4wEMe8Bm5RdA7MDHUK68EtJr541O2H08XjNAOj8/3UguULCEJPQO56rUXzov3mnojTVEUBXGCJ9LQ+aPwXdgfygordwpCs0FnYrwYYS0f4Ce6MutZWUlSVkFKYgRWz+yrJSygaCBEnyBqcKf0D0mM9FHI2ot4iJ1SKrlOM0KJBUog9GQNVShqxd3MrZfa5x9afCazOCu3iKeg0cSqlPQPW0+uXjR2rYMdKmQv8azRpPBSWQwosG5dxmOBSxPUEx8z+2pJckIkwOoJQBoqTIoO8WVYlqOaE1CHw15HI7SXWqAEAsFD85Pj+XW/p44eGB0Rgca+Iy+n0Otz7daTqx3sUJbD8zgZ3qyDKkm7kI5CQT0THNRTJC27MKpPfESAEpeHelLKaaeL9pn3VwsrOs0IJQJKIBCaVM/aBpN/gB9ets8anajx9mVoZHGywqp8rUxDUfI8jexQtNZHexglOxE5AbRGpJ7QtXrGJEip9eiCh4I0S1O2ZUIk6n4/f3xKJ4TjiYASCIQmiY8LxQ5NrKErZqRofHyhkMB0UZjBuXbryVXzZHaoYJ86dFH2oWCAlyg1rtSzj7/1XiBE9nNSzzfzlrgt9qypre80I5TphXFJKTTZbFyyg5z0BEJ3MT+DQ0SpMplZpYJGcrlw7Iv//XlgUnRKv7iLGdeGDIxf9/vJWxaMWb/zDI4jAYqyCJ3lL2dk48fW603of7Ap9bSpIUQZoyjPtHkglLfLkxPo7RXo7wuAqRM8ob3aAk2kq11uP1fjK106/aR6F+lv56e/7dkXhtDpSEtpPNXdaEJmx7rfTz7/4JKMnCKKggOTY7EdStPwlrkjrTujiZuyHFARFlCievLokpZdOCglQa6eaZnX0jKvDUqO8fT8mrJDVSywWOyM0AUdZYT2QAvUE9Vrl9AkoY1I0pn+dn7K43Hk9exq5mdSQviiaUO3HriAzEoAaRqlMSkV9KZdZx69c+7/vt45oG9U/6SYixnXfgDg1sVjb5o9fOPuc6hBCKrzdDwgzdtazaflIPWU35uWiYTYE/V0DB+JKu9okQqeUNP8cSnbT6TvP9VRRmivtkAJ15Gv/203uYHYoV2NxD5haPCRr3rWhBSrXUkhOxR3oT+adtuKKZm5xQxNPXX/YqShW04qFfTSaUObPXJb1FMAurdDSyu0NiNUxeFoUgcZoURACddfPTFEQ7uO+cnzwIwDR1tOhgf7TBzZT5q3QVvT6c9curJy0YTM3OItBy48dMfctKyCH7ac9NEokuIiAC/an1RNIVVTqL2aIR05PafIWT2joqNasnJ3t/HqhYsRfsHycDxGMELbHyKghM7GjVASDe0KjBudEhwSIAnlD1tO9okMmDRK1FAhi0m8K+1y/tzpIzJzivefyPzjLTOwho5JidZovJ1W8Oh2tZlKSenjrJ7W5srtwNXCSkcjVNmBRigRUEKn0qxEEg29vubnuNEpO/afDYsMRcamdTjHD1tPxkcEjB6SiE09mkZ2KCukuxcWlY8fnZKZW3zsbM5NiyakZRWs3Xpy+bSBRoutoTKmjmOCvOl2UU837kxkdarYCL/gcxkFNiOU7ygjlAgooTUYqqtbcfFQHNPfzm/2UM6PIv0N2kU9d+4/17dv1LTRiQGhwWjWG03huqMftp7sFxeSkhwLhIU8RVEcx+MmyrXa2lHD+0MI0i4XrFo6CQCwflvqqrlD5Edu4JUOQ+WaUs8BSbGenLlQ3OQKZHVWVtTURoT4YiMUsMqO84QSASV0Ele+buigndvS9ZLgQGw8ih1t3HZqxawREdFhQu2mTUOH9YtMTogS1/K0EG7n0XVdQ4NCrYEQnku/NmYoMlS3HbwoP6yPfXvl9lq5uyzoTIiMDo3wwZ7QBp2uprb+cl5pBxmhREAJnUFLBbF1DyG03fykhTR4AMCXG/ctmjosLDIMr+VxTGnt1pOjU2IS4yPDqepIuiaSqQkFleFUdQRdvWCAIhBoI5mavIupU5NUkXR1rFd9rD+LL+GwSrqYS7LG9VEP8NGGU9Xyi+BULUwVlt5/GGHLt/dcQyEA2fllwGIpLK2pqKkrrdD6BKoCvb36xcZ1kBFKBJTQ4bRaComGdhrjRiNxSewbKcyDE9t5MDS1cMqQmPhIYc0uBo7Wbj05dlAME96/jAsq54MgJY6GP3Hh6uThcRx6NMzIKW6qH0hhcWVgYIC3yoXypF0ujE+M9VU32UgU4zafEyYnIqdnTGhEaIR3JxihREDdgd1qZHnYFtoogkRDOy11aef+c9K4Dkqodv9k3W6lgpk7YVBMHNJQGvWyQ4Pg1/2eOmVEn8tXygAAFXxwGR9caAoo44K2nK+bMWdWBR1dzgeXsoH5et+CWrqglq6PGFEm7LY3o6HBJ7FBEVLGBTlc9mbq4hNjfVwJa/OZoPJrtFEyQoGKdTRCx7WzEUoElNCBtIv8EQ3taG5YhCI/iXEhrzy8BGsollEegE/W7VYw9IyxAyNjwimKYhgar+XX/Z76wkOLsIYKa3yIt2/YceqmWYMUahdT4dz4PbHt6al6iudnJ6PS1bzCSskIBQA4GqGgnY1Q6uL3j7TXsQiEjhC+/Fxu/4t1o8JN8svwwHr5RfK1OWxv6tLL3yln83P73rN4GJydhgr/frJud1xU8E1zxyANFfp+4rX8+m2nnn1g4eUrZQ4J9j9uP7ViRopWjyfOdYB6Ong9HeAUgDYChnFphGYWFbWvEdoDa+EJXYF2NBvj+qKv1oZ/mxLv9JE2qu2bGJDmBm00PxNig40mi0rJYA19/r3fBg2wdSd49f1Nzz204rbFE9ZvP5mfVwwgCjQxNLVxx+lnH1i4ccdpacYc7le/YfupW+YO2bjlGH54gJKW1DMzt2hA3+hWq+fYFz52bLdsbTuK/88wdF5hZZ/I6Oz8MsEUNQpGKNug05l55JoAKmQpt1d1PPXPt9YSI5TQvnTQopus5TvI/Pxs7R7cDlkME9nboZhX3//5jc9+u3n+2Ji4CNw33iys2TfuOH3DHNSHSehjh4YS44PsPZYh6dPVKoPn6onr4luGTAljgv37REYD2ojU02qEAiNtM0KvlMydOLi9jFB06v98a23bD9Q7IQvDTpY5oqHtrp5frt+bnBjRPzmepiBDQ5zDhDXU+eFvfvbb7Usn9U2KETQU4GKkTbvOLJ05XFYsj+xQeYuPFqlnfJwnRfHujMfCsqq8IlTQmZ1fhj2hGOwJRZmtjGXuxMH7T2W1XUPFJfzqUSFrT6On7CXE+ns069XzkbCEThO4K183yNfyhLZww6JJh1IzOI67bcXkH7fQF9OvAB7VFzE0tXbbqdULRq/dbWsOz/M8hPD1j355es3y735j8/OKOY7nBNHcvPfcwmlDf8eN7yg04hj3VJbwXD1xyr37mXGyO61PIizg0SaGiQn2B0ywZIQCoAIqIzZCgZEO9PfNvHItr7B27sTBJpO53XygvUpDC2ppT/qBEs9a1zQPr3zdkPJ4UOc8V083P/cNSI7iKPjim+tefmI1y3LpWXkWlrNYOIZx1FAIIctxNEW9/tEvSGS9vKFBx3LcpcvXhg7s8/uBCwumDtl28CIqUhLqPaVnCfBSeqKezg2Z+CY01DdmMpoagieHyG1dHoDzFyrKG4w8sPDGPtHB2VfEoDzuMBIRGlBTY6AoOH/qgO0HL82dOPj/FvR7a9vl1r6iJIhE6J6La9KDuUWkml2si29YNOnkuctIpDhgZLkX3lj79/+75ZtNbHZOAcfzPMdDCjpoKAXRGp+mUZaorqGep1VelDklOe5CRt7QgX22Hbw4c3zK3uPpDrKnxSM93Klnvlw9w8YvGf/Uffg68hTI4DgXjRBtQGDQgdBIH2CkUSAe2BmhEX7BmVklBRV1kLJcydei1NejSENBG7D7M1aPCmnLsQi9luvimiR9m9pofn6xfm+At3egt3eQr090aFBkUMBHX+1oqNWFhwQJzZY43E5p7bZTkQG+1osf0lALJ/QZoaeNTswprKNp6un7l6TnFKO8y2CfGeMHNnUOSD0THNWzrtEyKNlmn3qCYHi6vkvM8WSYwrKqa0VVYl49ALsOIjNzUEpkQkTAvIkjl84ahPffefTS08vE662ApDERunFgh9ihraa2wRjgo7p0Oe9yXkm/Pq4Kz616ywNQWVXnUrN27DwZ7AWqq7TfbjoUHqCpqtS++/VO5yNVVSJz75nHVklbcEXT3v1oDl0B63o0mZwWJRwZdECtMQqeUNEI3XksDQJ+14mMRTNGpAyMACYWcKolswb9tifNqqGDX/8VXW+rgPYqTyih7ZCweDc1P6eMHnAh8yoAIDleVE9JSXECvH2MxgZuv9QKXntnHb4iKenM6eIcOjlvf/gTCIrxLILkmv2nsuZP7Q8YhuNZilUB2jh7UpKa8eZ5UFJeExkWCJRGYAKQUS2embJlb3pbNJRYoIRurJ5kFF3rqG0wVlfXDEuOOne5mAc8nvaWHB/JcjwEICe/FI3lEGxOnucpRlQrzoKi8IIjkv/hw7/e9tAbPBrdwTtMgW92lS0pqXC/dSe3eukymnT87/e6epxaMkIpI83R7J4jOXPGDwKUEXKqyLBArKF5RSV9okMpRs1xPMquB+COB5YDAFqqoczWw2mLJtu5AIgRSmiLeubnunXzt7wGqSmIerYalVK0nMaP7i8W9kAki9n51QH+3vF94yVp273veP9EwSyVxXOeemw1UNLf/fTKbTc+D0RRbbFliguHhH+RBOPCT+cD1OYflD/EN2YKks5/IOn0xAi9deUMAEBuXvHAxCigNNZqLVhD+yQE5V2t6BMdunzWqJ93n6ZpeNvtr3z37fMt1VBigRLa2fZ0r3rtBVHPVqM3WSSb0VBXfzStUL5SLy7Ft5CgcTwfEhKEZQ4CEB8bcj6rqLy68fEXPh2TEhMS4D0sOSosLBAK4ot7OFEQYEH+Yv3eISkJtcj5CYIDvMVp8DxPCT1KarSNOP9ozMTR00b3HTYkHgT63HbTC/g8TPXXavMPiqdoVWUPxZnleDSV3mqE3vXYf796588//Lg74wrSUP8AUKttRBpaatPQ8SNiTpwrpChRQ1smoPfdPvuTb3cTI5TQjcLfiXf6qEkaaGtxueJ2hheW6oKWiqt0lNrklEV0Jj3P5cOHDpKN3rQatBDA6WNR8Q/H8XqT5fN1e4t+3fvLr3uxHI8ekqg3WjieP31N0N1WkV9SkxAdZDNCAbjrsf8CAOZOHIQ0dEB4WXWdv5+3g4YunxOwWQgooY/39udS5r/q4dNRRVdLHNSTQHr2dHH1vL4n0K25XOAY8h4YF6Kt1kqX2mptbY22tqa2Xlurq683NDSgwR2C+lksnL6hwSGydCHtKr70S4hELtEm+iiLQLA/9fL+1MsHT2efOH8lISFavvvuY+mHTmcdPu0ird1z30CfqCBcjYqNUOl8dh5NS4gPz8gs65cYdDmvBACAokmcCmloUQUw0Tix6bbbX8Ea2rIlfA/zhDbbr8wM/BNp9EkiNZrdC6Ke7Y6PRnnbTbPMFpbl+Aa9SW80cxzPclx9o4migIXltZVlWL/0RjMVEAkqyxMSoo00itUAAIakJOB4UdaVErnS8TyozXOUQmGlL8amzBYut7ieksWHkNjJKjNBa3WUsq76cU4oJ/gNsIbOnTgImGikoVesyVtYQwU7dOmsQZv3pOGF/MW1fx6yGpmuHggoDRZNGwTEsu+ewLkadwWa8jJN96WcY4R9CHJSHo+7XkYoUc82cqYCJgb75ReXo+zLkgpvL1R2vmj26OKyKryD0YzSMwP9kMsyyEchzLjktFY7Kjg8JBHWTpo/9ODpHAD4ono22hcNlsPgK1MmDD907JzD80rxJaEDCQ5YAaOZ1et03mpFnwQxi35MSuyRnBqTyeLlVjFP/EMsUnIJfgIptj5jTP99qVnSTayhag809Ov/Pfb0spTXf0VJTm6w+fu3HnZ0nZLCJELXCeAQ9WwLZypgbYMRTeCoqg0PDbpz6cQ7lkz44/JJs8YiL6HGS+ntpZS6vGvrGrV1jTV1upo6XV2jXhIxLzUT6Od14FTO+BHJg/qh4cPYRypHUk95RB5Ca26ptY08D3j82ARBPbE966Vkls1IGT0oxrNsKBFnnwHyOciMUHxT6m2y82gaSrDHGiqs5VGZfJlOXMtzKqlI6Q+r5jy9DHVf9igK38OMUEKHkninT2cmgRL1bDu5enHRDQDIKagAAIT4o8EbeYWVaVmOS4rFc8bkFZQ7pGBeK6y0WLjJo/tBzjJlWDxDUwW5V4N8vKobkMgW1eiwoYfkkkOdkaSxHiVaHcuiAJTgHODjw3wb9ea6RqOYSjUiOT278FJBXUqMr8VkCvJTXRVHb9jTwux9luVp2hbJpwCq4mdoZDJu3ndm6YyRwAQkOzQiNLC0DMWUgMkCGKShdz76DgDg6/89JiQ2NWmHUluO2caDECOU0AVFjahn+4LUTao3EhjUP25Q/7jnH16+ZO7YwQPiBw+It6onuJhhC7JDCBUMEsjC4vK1v6cuGBMvtzSRegqZTDT6l7If9YYGfvRLCFMoaJqmhPR7xKev3ztxZD+GtwwbGDd9wkB1YEBOXllRiehPaB2FpSi2ASEIDfELCvQNChR9dLhXKcuKf7XBaMy4UoztUPFl4ZEdCpQsaj5itUPvfPQd93aoYIFaX8lFUweBdkuCJvR8OsEOJerZjoRRer2UDy/8y3L8pUxRIqUrTZGecdXCM2H+XgajJTrM75OfjsrvZWiKppGTk0dKClC2pxwhlM9xvIVlAc+zgjEIISguKsY2bmxcdLZgedqNUpI93ENmju13Ob8SUHbSHx2BRssJo0bFLdgZipNDUWWnWKRUDTgVLvTEdujmPWl3PvqOGzvUMZHeORxPIFwvDSXq2b7odY0OYkRBmBgTbqKUSpWK53mlgmZZlqEpby+lgqEBz5VXN5ga68S3Izb8cn5VZHRUcUHRxGEJWw7ZCYqCEQIq0LrmlzlBcSKp4ItEN1hhIa9S0BzHp6YVjEmJ1ekMSN819D4UngKB0cjB2lSBqHsghI1G1tuLHjMs2d/Px2hm6/Xma6W1Gog8BjjnXx5QstfQoJLy6siwIFzoCRgVDn/h5fzTywY5ayhz/w0TilAMDro0QrtvJhOhu2soUc9OAEIwZFACx/M0RVlYlqKggqbNFlaloFnUE5T3UjHZOaKAJsQgARUCQXDLwfTY+OiCXNSOBGNSWodnoOg9RnSt8l5RPAC5VRBoonnKaDBpKQhVCtTUfOSAaB7wGi/RPwsBb6KlxJjW9CyBEIYFejcaDEP6xaCOfEIiQXy4X12j8XJuAbZDWcHbAGGTGtonIej8xYJh/WOXzRz5694z+LcARZ+cTon6eBOenMdLF8kTStSTcL3EjqhnBxEW6PhO6fQGg8Go0+lNRpNBb6xv0NXUNpRW1pZX1ZZX19XV66Q9tXXoZ5KmqJp6w+AkUS6r6vTOz5KfZzcYTphBh6Rn/PhRZosF3zJbOJpCvlF5oH3ymEHJMQFNnLtHpfZQCI5BALKvleUWVFwpqMgvqaqqqTMajVHCQh7b3VY3rDXBXvCHAgpZqUhDSxuHDYk6n1UALJZlswdJnlPnplbU/bdO9+CsCITOkzyinh2IkwjtOZomZhdJF7wjhOcz8jNzi6Q9zwpVmydPZxhN5oqaBlODkCVt3d9cV0Dpi0Fjkbk2Py6U4RttDxScrRzLcgcOHhcGK6FVvJnlWJavqjMMTooc3FeUY55jfdUtbtDhoGpeqFUKTL2Ud+qS6AYVRi5DJUNHR4ZIASVpPLJNQwHIFouUggCnEjXURK+YO1jSUIeAkotzrfMKJ7Yn4Xqt5Yl6djIzRibuPiIsOq3NkTQaPNfN2lPEHo5HDk21DslNANfA0OK4DqWvmRLyPVmOw05GJS/mRVpAloXnVEpFvdmQZdJwXl7YEWlhOYMRaTEA4HB6eXIww1CUsLRvU0KlfApIqlVD5QT7oVR9nByKO49IRUrJiUHZV0qSrQn2SEOFtfyKuYM37bzI0NTOo2lyZyiVkV3kvnqVQOg0+SPqeV3gjHp0MQn/mg0NtbXoUlfXUFenq7dVRXNmtMLF3kPn/HUatWKCkEJrfCEZ1M7WhRCqVQxFwX6+uhhVXbymPl5TX3vtUiRTrWoswnIMADh4KutMms2v6jnOIja0f7ShscHhgn8SKmt1UKEUE5tkdqiBZUUNtSbYy+3QG+YOwb8mgoaKdigzMDm68FqxB0NECYSOtUOJel4v7lo1BzWao6DRhByUeKPJzLIcV1mju3IVxV4AAIMH9z+XkY9lwuAdJXhFq6tY5AOFAFQakbyiKIqQRc/zQImb2QFgVvgBHlgov9xGqNObYigUHOd54N9ncOnR0kjhUACgzFPO4jhn2HNRcmi6PGRAnyED4nFPKbOFNZpYs4W1sJzOxGXnVwGzTkqwl9uhOMG+KTt02WyU2GTVUGSH2rVulNXyEwitpxVS6MlD1EFB0qW1p0ZwhFZ7FZXVFJdpC0uqy6vqyipr8aVa21Bbp+NYVB2P8VaJw8Cd+cNNs/AVKSIk1zLBMkWJoZzQJ4kX1Jnj+QtZRQ4COWjwgMC4Pq1+k+TyVVhaXVhaU1RaQ1OUWqnw81YFB2hCA737hPtOGxE7clAfqBCqVwU7VGrgtHnfGY5WuLFDZdPokB3KfPzD/kWTUIc+AsFzmpWwlMeDPO85kvJ4HOnv2TmYTBYAhX+RiqF/VbYKTzvwOh3XPmI0asVHr9x14sK1Tb/sLsxHMaKxQ/o2+UxOpiMO2uAsIuwGcLYuEyN8i+tQTmhT0LLzcUZv5pqqTXKA5/kBiREWC5edWyjFlLDr1mQyqFUqyQ710ahx4zvRDh2UgBPssYYy96+cXCh0ZyEQrkvfpmZbkzTbnBAAEEuLYQcP+xN60q+rl7AHpTe6IypEfK0OnMy8cfHEn37Z/eKjN+EtO/adxVdUKqUtD9SKwpoHalJGBAT4F9doGV+lsSof8ECpoFGWqdNz1Te6SIrycC1vtHAl9UaNdcDorsOXnPeZM3mwLS6PSlOhUuNj0jXgIiWcNo8DSlevlQ1MjEpOROF41Ki/vDoqLGjYkCipWB5rKBnpQbieGupJYyfPmxM225+Q4MzEwXGHz2S7fWV85UGkx+5eIKTA89//ftZf6VrWCvILYuOEaiKB+gYxmZQHwMKyNIXKPSGAg+ICUmLHm1kup6QegHJKgaqhWvcemViupN6UpzVISUb4bJHNqxCSCppW1chgH7FISUgwcFmkFBUWVFxWHRUeJBV64qnIREAJ101DVz1OXvzrDwTg1mWTGZqysJzFwjXqTRYOpbjrjRajmS2vbjDocBd6Pr5/ssnMbdhzaWhKgpJhBqckFuSIEfPPv9/G1jdSPtHC2A8+Ni5WyrLE626z2Qwp1D05OnkEVXcF8pDlOK229rfD2WoVM2gA8ntyZuOBM1f8o6JdnKJbWI6vaDRfrdEnjRgk+CXQA5YvnMhyvNFkUSoVtQ1GTijAr9Lqaur1lUViWAxTVGoUi+VlC3kXGhpup6GQUc2fOoAIKOH6aChRz3bBjX/DQzcFjxp9qj//cQ+++aebZ9Q16DmxLh4JkL6xHofmg/01FAQjhyRRFByREHCpQCzxlDDXFfSLD71SWMUDEB0eoLXGYKC+lFEyvuFJJXnpFdqCqCBvluUKyxvMLJcyoI+CoUYkBu49iU5FMhs9B7W+N1jyagxVOrPcIxvgo5bGOvlYk/Mjg1GvaG5orN5o5nneZGZpmmrUm7T1hoYa3MYJjh+ScPziVXcaGumNmjaZgJLREAF19ykknrUO0lCinp32qcafYewaVptMGjXKMQpXoAQjXx45HL19NPqSzGWzR+NHVVTXG4wol4gT9KVRb8QGYFxksM5ioWlKW1EGIdheWHgyvXBMP6vHERW8R9IQXq2BlA/qiFzcwEv9QBMHjavIO6e2lNOozR3HCo1BscWnLS81W9g9lWIYZsq0sZfyWtZ/Q2dmr2kN5Y0mKaMTAUF1baPt3KxWLPYQ/LbzuPwIq1dM1xvNqaKAgmMXrl4pKO8bF9akhpYgDT2fhRKbmLjwoF4YRBoeWC//YBE6U0OJerYjzZqZ8tE1vgZjgI8aJWn6oHpzC0QSt/kIXoa7bGKMiAr3R//jwYW0K+CmqYWVOpUSLbrpbNSVWYJGZe2yHqCy68XFpUoAz6QVhAX7sBw0mcW6SAhhcbVBrWJqdaKTlGMd80Ddr+VNFq6wzlhUZzRZHJ2nv+05AwBYMmuk/HEQArVKcfNSNFwew6PWyyxDw1HDkk+dy+YB6jOSGBvmUKTkrKE4KM/kF9u9Cr2EczW+nsxEIqGJ9gWHjIZag7OErsC4wfHHz6Emck2B1Qd3NoIQLJ7SH3UkgWCz/W6MsZTn+eT40CsFVWigfKCPtMJXmsvQY8VIN280swYhjwrl8C8ZybK8SsW8ue4k5evLWFpQxMlyfHmjOV9r0KHsJdfRJyyjnhMR5Isal+IEe2tPezHB3oLOOTsPJdgjTygHhg2JIkt4d6SaY8YoClv0BhAI3QuO4xfOHmdhubpGI4Qg0NerXmc0mtm6RmNeiVYavIGCMLQ3x/H51eZz6ekmJ6WDQg69xbqQlkZjYnD3I45D63cLK9qnHMefvapF7Z3Kkf3L1tcfPJ8XEGFzC7iB54EWuT71tQaLy9h9bGIfnd5UWWDXF8olkLE14Cutrg8P8sXG9NRRSQdPZ+PMU4PRKCWHXskrS4wLF14UFRFQQjvTbNpmrKzjLAXc7UyyNTuBKKFBkYKCPsGoCxyAwEuh5ng+QKNQUHx1TY2wF4qtD4wPwrIYEhlFUzC2T1y+NQov9oiDQK1kGDS0g1fLtEUqnTdbOLOZVVKUQlAlmoKbt6N2mjcsmgSuoU8C71TK2RSNguuzohGVNDnfCwEY3i8MTVEeGsPzPC408vHWcDyvN5iFTqd8g86kM5jqGowsyzXoTQ06IwRQoaBKS4vCQwMZmjpyNnfEwLhzmQUUBcVxnoKGJvYJkDQU/ZFbj1wmxUiE9qJZ1eMA/k4iLlhzDAnXD97aqt2ujEfBUL4aZbX1vTKYLAF+fhl5FUh7HFuFIAaMnZd5cofQUAQdz6FkCMucycKaLRylFh/83re78JWUWH8UhQcgNCraVjraNEYLV1RrLK4zmqwlmM4oGHGkXGCgP+4q9fkPOwEAy+aPBagSivJSqDg/JQz35TgULmvQGY0m1mAy13kn6Awm2qKHEF64XJgcH5FbgFwQzhraNy6c+eE3u4AUgUDoVVwpdB31Rs5Kk3ydDssqtSzLDR0Qx3J8gC/qCCdZoBCAC+fOA2XEpSIAaBS/zqkE0nh31ONOUDr8Ly/YngCAFctmYblNL0ABCY7nEyL8sssbn7hvntPpQHvXpym/Frs+XWO2sDl51rCYcCW5T8SNC8fL96EoW2m/UkGrlIzRZKlvNAqpAqC2gfMFpuzC6uzCasr6Y/DhT0ekhzx+26TcvDLmlef+8NhL37l7gQkEQo8A8izuKp9uRE08g4WxxlW1uqED0XxNEbsFsdASVFAvtYppMFgUDI0uwn35xZXY+Sg3RufMmbpr10GH5zWrIiyUxS8kQKc3qlVelKVMKRQzJSdE8Twf5K+BEIaH+J84etbXRwWALf3IGbPFUq1HWZ/aJlyfAIDLhS4C49mSnjbNwTN24U01ZaEZmm0qrmWiAW1m0i/36iDJGEVhqhmlrREIPZ7B/eJjw/xQHmgQKl70ERonG4zs91ubXIZGRwTiOXFqBZ1fWPqv93/SqBX/eeHuL7/dolEp5Ot0i4VlGHrnzgNoMCeuKreCdW78uJF79x/TNdYH+qAgDc+xahUjTfsAANQYWGO9bT6bMxzLl5ZVX9PqKxvN8konl5y/5KKv6LDBCW4eouBQxIxm7JpOSeanA2+u209q4QmEXsTF7ILLeSjs0TcaNdMKCcCp7vCOFZNxCzrc4MNkZrWNpsLyuqLKegCM2ASlaYrSBAEDMu7W/rhDOia0XqEMJYCCSbEhhaVanufDQvyqrEM9oK4Imrljx1GbJYqi0LxPgUee//T9V+/D148dPs3zvNnMumnQUd9ouFScW1xncuP6lJg1cejeI2KvEwwPaZeqCgAYPTTpQk65hVIznLteUM6QKDyB0IvAE4blWy5eLR88QFzCY2PQS4Ui6QHeimA/VW5RDbDg3sMwMliT7dQZjrdqKJoLjzpvorbzPC8O3ZR2YjmuviZfpUTaydC+eqHYCQDw15e/HNk/SqNCXgGKtVzNLxFnGrs4c1BSUZd7tV6PRNYN4l8XF+Zz54opYg4A78orwaPWz15q1fFzWQdTs1BJ0rxRqMXUoYtzxyfvPG5rsDJn8hAAwM+7XaSUumuuRyAQehpCKrvUSo4HYProfiUVtQ6Xypr6mjod5LnEqED8OAhAYnRgE4cEY4YlC5VIYNthpEQQ2jUSRTFvCvp5qxgadepQMEhqcYNR+XH0RrNfiGNPPOnZWZYtKtU26prsdwcASMstycyz1VVKLUfxSHp8oSCgKPwvZGhoNpt0BlHNN+w4rW8Usqnsvau7Dl80GxqXzRzh/IzEAiUQehdyt2NIsGtNxOrD0FS9zij6M1EbZldJmjy66+T57BAf1fJZI55573e8We4exR2LUV4Ry/WPD2ZZ3qWbs29SbGCtPrvMRWowD0BtvaGkopbnbb3pnFkxC2nc+azCggptcbnY4bBZLuSgLCXM/tNojV9W04ha2yFb3XamJaXlY1McO0URASUQegVx3nyNwa5EHdlWTXdTnjNxEEUJqmfdomRcLFh5FD7iFMJdv+w5O25g5OyJgzfuObs3NS9BNiGZpqm+IShyxXG83mR2GQCymM0ales1scXC5hdX19bphfL9JjmXicLoaLKdx3x/qNLhkDwAjXqUbA9Q3xUx46q82vWYLyKgBELPZ2QokqwQTciQ/nYTh+aGoDIkiZ1HLjhra2QoGgkHADhy+rLDYXlhOy0MKOY41AEPALD76KV//OU2AMCLf30ZtZVLsE0Mwl3faYoyuvJjUhDKF/7yR1XWNF7JrzRbODTGqGmEcXZg3Ijkgl2pbn4b5AQHDdCXizNBMVAoIoAAIn+xkIHgph8pkyAUchEIhN7AxSwXc9IlIkP8XGwVVNJLpZk7Ac3D2H7YJrIaH1+ND6olKyqroCAvdd9464P1TT0Fdo9KQ9wccKigx5jMluy88vKqertgkOuHI/Mzt6Bq+IBEN7tJvxOlBh9gKGSYMB4AX/9ASzVy4OKAGFq9C54G992cmav5zeeXEki6KKE70o6tcCTljQ71K5PWsyW2qlxncBg+KizU+S4Ly6nVPhk5dp0+dgs2o799MxEegKqaxqsFVfZlUc3T2Nh4xVVGvfx3orQQ6bjFYlSqvLRVpf5+sThEZQkdC7N/RbZnc83wyRKeQOj5nKmAA3xR9ZHo2qOgmUNzhgM1jJTzbp/83hk2b3QoEjJJjiOF9H6xM6gVluXyCqvr6vVmXZ3Cy9POCY2N7sqZnDEZ7YL7p0+eAIxdPkA5EJOrQsIiqfIT0nYioARCr0AYhIkSdHAcCaUSQWji+FqdOTzUFwA0pKiy1qCmkcjyPDCzwFdNoYZGADQaORrifqDQZOG9fVQayNoEl0cPwaOE2p3aev21oircjbRZSqqQFqvVYqS+1oiaSPmrUYcT/OOBrzT1U8HxnLAD7h7l+s+pLC8BwDYJkQgogdArwO3mcF4OD4C3lxJCJJp6E+ftpeQ4lFVepzN7q2iW4yCAOhOrEVrSQQgajQaNWkFRaOaambX4equKK+rDg7xVKlV1dR1SNx4MTumbk30FCa4gTjV6C075KWtE/ZUgRSuEyfDBGjHBXiZn4kOcdA3JX0FxTU2tjqmvNNPuEpicyS2o0KgVaJCczhwaoMF9TDgIqrR6LyXqGYVblEr4eHvzHI/+cijkgQr3y1O+XEIElEDoHQhxZfEKCgopBHuU0qg5hkFT2qGgql4KIXICIaPgvBTIdsNC4ysUzrMcZ2ZBX+96H0NDqA/v481WcTqeQ8fso6qm/IWaJQA5nq9hTEEc8lr6K/UQ9TCEKgqGqvSn68IhgNpGY7CfmhemYPI8X9Ng0ijQdZQxKgXaedCgM10trDJbWAYApb+7uJADVworIER/I1rOGyxeKgbLNPKoavW+GiW2uOcO9ztx8SrHcfV8gFUq8Z8vxqsEJbXLBnWASerjUf9nAoHQrZG6GmMxEFbcSDICfNTIEhSLOBUqRrQEaYZRIt0TpUStYnjkQKUtHKQoZM0xNO3tpahnaCwwPhpUqImlR27aqRRMn5jw3PxStYJqgL79/fU8z9cxpiBfDik4h56rGuirIRosDKHNB2q2cIUlNULwHTSaebUHfyPP8wnRoeGx4UhAhZpUobMJcl9gGcQlRlaLmw8J0KBRxgD4Qi1o1AYFeFdpGynfWKtNLLbZd/OMTE5eyX0rJ/fCuXIEQm9GEE10xd9HhYqNBMKDfPSNOnwr2E9Tq20AgB+cFJFTVIsWvUKKUmiApgL4MKEhFYAP8A+vKhfDUOMSBlXmoNANfvjlem20BXkkad/QvmHJ2lKgh8hPyvOAA3y5Ucf7+SBBFvKFcuqrY8MUlCC+OqGjssnCVlWj3E+jEd32USu0xek+USnN/lG4oTPWPIVgWeO0JMGUFBI7ZduVCqGm1Brex4/i6mVT49EvCzqiV6MeFyHoNMnyZyRLeAKhV6NW0likkGgGaAobddj48tGoamsbcOUnTrEEQp86X42yVtwfKhQULWgTAMDPWy2ugSm7pqIMQ/n7qCJDfGu1DdieYyD01igp7JS1pgqpFDSEaO0PLKhlcml53bWi6sqaBqx0XmpFo9GjOJJ8IKhgWNuuo4GGyVFZeZVCaSnFA16jUsjb4pnts1OlEJnVDBfOpBFXEwg/P0j/CQRCL6NPlK18Rh4kUSsZ9zntDlDCg1FjDtQR2U/WvcO2j0rB9I0NHdyvD02hliJINyEMFBraWz0HwqGEY+G6yWptY2Zu6bWiap1erL5XMnSgnyeLeEeGJLtwUaJnErSVYSj5SCUp1VT4sUBXBHcEP3FoPMejiBYrhNpwMgPGhYCuGhN8182r71q5uhWnSyAQujFNaGa/2ABJZqRunlYE1RQ0FLiCoqBSwSTEIBenRJC/NO/DehQraPmfV15UVqszmKSCJQihnxDF8pyEaBcJ/Gh2U3yQ/NTl7lr8dJILAFvWEECLxXoats0ith+cqHH3FZ/4RLweFQUAePaxvwAA/vnOmy06bwKB0F4kxLfG7LJRLDtOoxCekVTOldyxrFwcXE67RF3pKq2zju2QHXBQYnhavlikBCH48qf9d984HbXET4q4lFPqYOo6k3ut0owHacjECj2kZc2OmwcdXvYUrMUixo5wEpMo60DjpagV3A8yhLwryDPho+9FzmNhW+TS51CqvZ9t17S0S0/c90cAwBuffN7O504g9ETaKnkdjN2cDScNdRjM6fLxuGNIS0m9aNeIZP/pa/3jApraua5er1QyovmJ+ofSPMc7T6JvO8jlKkeunkJkTCiuF1pSubKvIYBMfl6O9FODj7b26P6ZUye2+7kSCF2TLi557Yv7ek03c4aatFyhS1MVDE4Kq8n0qM/GkKTIrLwqu0MKp4gXzhBClVLRFtuzv2zN7oBTcxL0zOJG0U2KA2hN54E6qKdnT0MgdDHJk69VCa7Qetv6Xp6rlVWcG2yNh3Pqxe1JGtW+K6VJ9h3jmwSif/735TY3u8jrPIckR2bla93szKBucqLuIANQQTUloBazmVF4dpIeWKBIJ63Rdrfp87JTFf9vO45DgZP1uAQPGB5Yf67G034HvQo7XZO14CF61zXJ0SEvZ44w6yInTZYXCUBOpnVWHAA5Z3Kl6/281W4MLbNgTjZlivWPc2iMjxqM4ms8zysbazgIjWaLg6K5KbKs1nhV16As1Jzzdq1Mcs5flf8qoJi6PaLr0xPttAmoTTPFsFNvINZfdKmY2Xp3u9Ee7dbzINJGaBGXG21WYk56YUhxDr6eXYes2pxzV9C/F9GPZ875PDrEK0dnTGoisI7qkRrqFSpkMuPxTajKk4JlJ7fiCii8G0PBAVPHXTx4wsEI3XJOfGqX4F8F8XpOid191rB7U34J1wJKFugYl8ajQiEucM6Zu7ppSSSP0C3t3At2RqIKCaPMiONBrpi3z3oNG6Q/b+sznzR2OOqJ1z+pKCunLQt52VPJrFrP7Eh5MoGw+rdX008+/uTOex9597MPQI+joJa+7spIJI9AcEbMYWoO0WOr8XJeubeOVvgqmS8+fvfu+x756tP/AgD+dP+dAICpk4ebLEJNKxV0+z0PcSz30N1r3v/yI9CzSDXHtKJfN5E8AqFdYLUUcms6oQoU4++e4zViiP7sxbYboVLppucwf1zz6N7dm7F0WrehyXgQdWlu2Z/RNXEnecUeaCIJ7xIIbQDWc/evQln0GJOZzbxacTq9sN5UwwBggS5Ur7S0LiiANht12A3qEsXgAfKbXiOGtPFdSooNvlpUbXfmLVjCS81KxVTS6wmx8giETiZSGXittEZpzZg0VrIsdJyAqQlmG0uNkLJJHsULY0Jke2rCXFSHf7R2n8XCmYEyLMgnJty/tKq+tkHPCZrDsMgN6iyj1Vo2KKA1GfutxpPaf2fsC6rs1LMFhyOSRyC0mgcWzWnqrg+37nLYUmodlKYBynFDE5wfsu9CVoyPf1JcGJo/YV0/DfAL2J+ejeoMlRqTyTIyJd7pcfzQPgHTxw5Cerf7CLKpeJNvICoW0mrF5FBdFVI0nhOj2JBSyKWT4k0cVOrKnZatZoMFKnikNabqquqyKrtsFouTjNbUIFGWo6vgNKEd3vaodbF0xkP1JBJJIHjIA7MnOW/8UFAlZ638eO2uAydRZHnaWBfNLh9YNFuQ0d0O2zWgyQHpi8YOPp9ekJOPOvzKiyWnpyQPH5rw3U+HlQomK69k8riBFWWO2ez7hTNZI5z/+xsPQ7NBrVLcc8eMyprG0xkF2fkVJoDW1GpTpR5q5EqKmtQpNcjvZ2YhZ5QLa6NF7c0YAOBNwjkrANJHs/35O8uoA7oKpMsdKqNSXztXKUy8RxZo70gAJRA6lv2n0Hjx6aP7yzc+MHvSh7uPbDt4DgCwYOowafv9q2cfOJkOAMD/NiWj86YOXP7Uu9IWHTBpgPLEhatyI9TXz/9vr38s3Xzk/rvqMvKlmzffscag1z3+2Ei9vjH7SuHB/bsmjxsoLJZ1rNHR6AMAPHTTZADA+t3n1m89hpq0LZo4f+JAsVa9Uf/t2t/wdUlJjSz6F1IK3mnt32hBYQZvWs8j6VQ0K6MuNRTLKHTlAG0XcOPRloIEdPY89CsnA37+/vsPPv60xot0CyV0A9bMnrjxGzF3es1sxzYOH+0+er1k1FlDAQDbTlzedvC8XEOxaLqX0R0HM3751yNyDeUBmhEnaShFUX97/ePD+7f/uuFrnuejU8a++/FXX7/5wt5fv8e7j5s8U37AsryLh09kmMxiwtCQZDwCznpsK7fMRue5fvf5tVuPrl4kvrZ+3l4P/elm7GHwko2G10MNEMQ0ICwMONXBN7JivpGCFoPvbmXUAADav6zMsYDFwrLmDjBIHRuLeAYcPWYiBQz3rHkC9aD2cSwMsOhF9wfLsT/twO9EzwEXS1RGJTW7T7O7ETzH95qtMU99fD/PH0g3gHtvnuqw8aPfD6oboU8tcvABAG664w7nB3a+hjJVjcl9kZ/RQUMx206Ir4BcRpEeffVLSortBXFpje4/dmHfhTT7hTw/bmjiwmWrZ85b9sSDq6Q9pyxatWzR8junJmo1cQuX3nDvmof+usZ2L+bA8UtBwbYySiyj2BOK+gptO7l6wVjp3nW7zwMerF480aWjlhOUlIKwAWgowMdxYgFoPtPkMDhJSZ1lFPUM9YMKlaaqtMoClX5KloIQ14PK4YOadGV4SFW2WJkaHxlYaPVpSMOjcF79nPFJ+SWui/fhex/bZDHAx6boFlmKFitM1yMC2sa3qgdTWlgxfaD4+uzPEH9yZgwVv4dy9l1IS2ZtNkvMkLEXslE27tDkGLvdhIiHnIiYUFpIkXbW0K82HHIvoNdXQ93LqIOGSjJa26DHmd1LZo6U37v/2AXp+r4LaRphRQwAePBPd66+6yEsoHuOpc+agMS34tJJAEB6rXrztl21tdr/e3TNwIQI5zNxkNFH/jBXfq9cRtftOg8A76yhQO6oLUVaE8JV4lt1EaNRJKrSLkPIARVtYBjZsl3oiaTSKBUqjba0nGEUtiZJHSagcREBReW1rgV0XFK+8Ee5F1A+wId2EFCWZSXPKBFQ0FVZI4QanPnIKfggEUz73Dzf7puJPzd+GsW/N+wZHhY5fohjhPejPUiD+noHzBmf4jL4YLspaKg/4z13MvKaVVbr3QiodN29kkoC6lJDN372hXsB7YIaunjB6Ide+sFs4ZbOHOFw14dbd1PV+sjoaOtAH7BkxkhnAcWcuCDaswcOHqMomuO4C2eO3X3vAzMnpFQKAnqJ8z99+Gx1dVVQkNgW/qmHbrWYzU1paFJ8FAD8gsm2zMq129BxZDKKPLkDHTuAgGMFRc5LtzJalGxvL5XB5CJzHqLAEc8olFgjKQpN97CwrAoNHwaNNQ122tphAhoT7l9SUWc7skxAZ4/tW1Bm61klR+5EgBt+/t1OPTmbehI6kzWzJ62ZPekB6wVvfGDRbIcLav0dFj0oLLqlworhnS6YJ1fOAgAcv3jVYf+tb/4ZP2rn8fTEfnYy5wC2Ri0s9/uBtN8PpN24+u43PvgOX1IPnoZoEKJISaW2pBL9tk+aMW/nvqNYPbGSYmakJM9IQXMQSwsrICvGOj798WBTT/3Vr8dc/GHCZc2siWtmdWqjW0uwd4a2MjsXddDYJ0SW5GzZdur9l24FAGzee9bhrgcWzb7/9iVF+uriQvHrvWXfmaaeZdzQflFBqBx52tQJf7pteU525vDRk86evfDmB+toxhYl/s9//zNmWMrCOVPWr1v7r/d/aKhvkF9GDepTZdZXV+HfNv5KfmlVZZV0mTumL9IaaUtVTZW4Z/PQ4T5uCjTxWCQacGIaJupeTFHC+M9mabt62h3N2cK1noUb56jdEn7Xru0rVyzEAoqX7XKIBQo6HpxB4sBHu48k+KG0vnlThsq3L5k/9MmX1+PrU8aIEVJP7NAgyhtCuNLJAgUyCxTfdLZDgSCgaqXirlUz0DftcpGzBSqRmln0+RdfJSf3+/h/r2dnXlKp1U+/9Kavn/+qCX3wDpcb1AOT4wAA32/aCQB44j4UmmiKjDotU2vkaBVONLlPZodKFmiDP1Lbu5ZOcHMcbEp3JkxVY5LVFJ3hZIpuPpLppVLMnjQY33QQj4+/+S06NhpCePVa8ZSRfS9kyroByrhSUGbh+aRYcVpcTmHN8WMnVWr1nVMTL3H+0m5UvdHCw7MX0r75+O0j+9FrLnGqQIz7RClVZkCvmuvi44HZcC6LrTROHpHoiQXaluCB2gwNKIW0o7hy2eIPy/D1qFA/eY6q0FhE7Gs3a2zfwiYsUBeTSVyqZw8DWyIbv0XvsYNVolQw/9vepIHToQwKCjt0JnvKSMcP3JrZE3ecRKe649AFuYb+tv3C7AlDdh+7CAA4lJrhUkbnTek/b8qAFU+/5+E51OnQyu5ceYmkoc7MHZ+y81j6R1/v5AGYJ/jammLMgOjk5H4L5s9JSURTYwwGw0tPP/TmB9/d9shz3737KhILf6+M7PxT59M/eO+/Dz7855JKrcnMxkfaBpDxTr8un27cyzNIQz/98cC9N09zeMa7Vk75csPBL389eveyJo3NNbMmdrKGWoK9c3KvJSUiDX3v+x2Th4m/H5g4f3j40rWsnGvOkoSCM8FeBbpqWKkDwF1lTurZS1OnjJdvuW3lvA2/HXA8mi8q1Eafc1WTFcxcmJ/8mTacy3JhV4ao5HLZHcm9bOdPcDXdQ7qvyYM4CqjRaOwxxuaahVP3H7/ksHH/hSylmZZbTM7Wk1TsgX5IKceNETGup/21nbTq8pSgsINncqY6aei8sUlYQx0wWixp5UV0tWHAALTIOpSa4aChOw6hT/+aRbPd+ENdgjX0+MWrLo3QKzqtoloXExO141javAkugkVypk2b/vvWLWOGojNEGYVW8xPz9ocfFxUXafXIwZmVW5AQF3WtpAqnKOYL2eAO3HsTSsf55JfDTWno3SunfvHjwS9+PcLz4I/LXVj0AICVw1Gwe8M5u0E9HYol2DuztpItc90rSMUA62x21/AhGknUcqpdBGS0tdr9B46OmToK36S8aSoI/WjlVRtBADh9+OyoyXae1g/Wft/YiHoGNYVL3exkDB1pfip45Jpv5P3i+vZvaNBxnC1n1gF3S/iFtzRZRtZpRGuCl8xw9KPjL/zMpMR+fSLkQYAEL7951sWOfLuD8KEfSRM1ZTRa0NltpOChVBSXmDg0oTLrNJLI/uJnToKh6d2XxE/PYEo03aV1UMcJKAAgJQgt1Z0FFIM11GEhj18rrKG1DQYIwCKnF1O+p3UJjz4VK+ejCKkDCpp6d/N+fH1AAFoSTh/lItnooz1HBQ2NBDzIzisenIQmuUqcvJQ3djASyunzli656XYAwPbt284c211VgVZMlZfQWxYyeKJKrf7nW5+NmjzihqU3Pvfk86Mmj4D1hoHJyFIbOkgU7tISMZ4rpddIfPrLEbPJeMv8Eft+EfO6Zyxfgq/8sifdoNetmtvkS9HJAirBljU4WKDHSsU/cEIscme7t+wcPuRyTh9GvlSTyXT67KkJ45AB/vJTD+/e+nNDQNjpw2c5jps0e4K3xnvXb3vq6upmLrI1+HCgXT7hIV0+/y8/y7Zg53iUIDV91hye53U6Q/bpHfKO9zPH9JUC9C0T0DWLHMOdGB8V9cYm9B1z6ZXH66Mk3+DZY11kb0jQNPX+zsNNCaiXknn75+0zk9CiRtJQzI4jyK50llH5Z4s2IdNRLqCMQvHUi2/6BwTTNP36a69WXDknF9BVdz80avwUCKktG7/dv/M3SUM7WUCRhgYKGjqqSQ21sPyi6cNcKqOyzhIeEQ4gWDS9GQ0NpjWABysX2AT0o7X716ye7iCgoobyYPpo1xoq/KT5X85DRddDnDQUADB2cB+FQvnQk3+P7SP+RWWlxU/ejKpcQgZPfPOD75557J6dp84uX7LihadelKwkWG9gedsqqn8ces2H9Yt1PoeDl/JdCui6LafQ29r1BLSNuBFQ9HFNGfLgfQ/FREWfu3j+xZef99EiE74hAH2iXnnx1QVzUYTjvY/f/fJb0WXsTHt9vEO6lYAK0z1cGJrTZ6OMLgiA/hpSKmco94Fal/gIXpQnbpg+MsJF/Hex7DvpHtY69gS93MFiqM4BnLRxOa/0cp6tsmHepMHzJg3GMiqxZvbE2UNses0q7XJuV95677/++61arTmTejgr4/zTzzz35qcbpHtfe/+70ROmPblm9VMP3rpk5R1P/O0NC3vdvMDpNehDf/C044J98fzRi10ZjBj8Ppr8RJ/M7/vPudlzzaLZDbUoY2PDtlPWRDzbp8cse18Qwj37T4laUyljoZD1fVUv/sZczCluaGyQLikJyHptaGyo0Va/8uwjY0cNHzF86Nv//kd4RNQHW5Gt9Mzf30q7eC73St6gqCgHFxTvq6b8XM97kONm1C0d5g16H5fSLz742JqlNy958eXnHe56/h/PjZo8YtTkEW7Us1cR11/eSd113H//7p37d++8dO5Mbo3G5QVu/uZfgsVhF49r1vxEPpTv9w5IiB07yLmtC5gwcfCdL3+S5Bt0/+3oW52b5a5v8Ud7joYyyLi7cd4Yl0YoVYHcRtMniOtWB2vU7q8VvJn703Ocl/BvfrDu3TdezLsiqsDR0znHTpza/fO33779t7CBY/7z8fq/3LsS3xUYFPL8vz78y70raZrac+ly51ugViOUnzoq2UUZj5dy3d40ZwtUbl3GegcLE1lBdJC7VqdZhdWVldXjh6Ag+PhR/f/73YFxA8XX9kSRnfORq0AFaTfPRhMU5OjM4Pd09JKWFlZwdSjHaOxgdDQJVhX62Zff3H/rAhTkzS/lGtjkuJBlN67+w5333jk18euDaE6OAw6uOvTsdUbJAj1cXN5N7Z3OsUAdkFugHtJ7LFB7O9S1BYoJDRFzG5xh5k5GVpuzgIarXI+9hzzbaGC//umQrulK+WNHLz00d/KOY6iqt7DMlpvqkofmTn5/52GsoS7hQn0kU3T6hKHYFHUjo9NTkiQNPXQqW9JQST1R1uHopHfefqv4JFr3cRwnqadYOyBeuW79pNNrylMCQw+eznbW0Hq9Sa1S7DmWHuLdZK5cQWMVW4mCgdFBYtzGJVWcbbbt8dPuIgZUqAZrqLt9/JRYQ+WoefTzo/bSGISaYMqHzq2uiUpAZ5VXbRw9xZYos3jBkpee/buzegpHVmVr0aeovgn17FW0SOBChJ//yqgO/8nvvvhQ6KPVwPm5iRW5mFRshTGYWLXSRXrEitmOkQo5knqeTLvm0ghlKJBTX/3RN8ggmjPOXesUrFPNBtvkMjp2xMDhI9DP2rmzOW40lFVy2BOKW628+cG6rb/8sHfnZpyysH7d9/1UjsnASQMGP/CXv+38bSO+OWtwv7L0VHA9SK+p4Mob/VUuXpisbORwDBnuIjguQYeosIa2CJbjaFmWuxwqVLPxfDMeQ8pPeSrfsYdEfmH+fz//6XLO5See/QtNUR/97+PwsIi5o/o69L/huZ4w/uB68fTNjqs3zGfv5DR17+s/Xp8PdtfEh6qDFMVaxEyIBt4PeJjGdFAI7D64aPYHsjSXMKX94+358Pcj0mGNrmqzmmXDrjMr59isj4fmTl6/FyUzOqB3OjiWUT8fr6/W7rtr9YzhI5L0BnNWhovU4ukpSGEPnbuCjdAnHlr9xvtrFy2/ddHyW8WC34cfrLuwHb1YDWJmyYffb8X1bf6BQdLG6wjVhBePClZyVS76jzlAh6iO5jcz9AkGqW2rdT/qlDXe3Y6sWLVs1vRZ/37ljc0/Inu/rq52ytzJlNE2ArctzcR6M02JpsT4qVGfvSNeOX6w2M3DiZg64APt1s1co9EfZZGJa756yuYSEWMO8l9/N+oJeTuffbPqmdtQ3dc7aNfxzDnj7YzQVfNGrtth01BeyNvfuCP1Jic3qMsExp2Zl+cO6Ld1e+rM6Y5eOQemDE/cn5rVUI/cHGvuWIxTE6bNWnjL7Wvefe+DjLPHXv/zH6SdH/jDInzl47Xb8q/m7P79Z3C9kRJcHKCCld0ojXnP/j0Oa3PniOHWHVu37kBTvwnty2eb/tKUhko8ffMYoqFuoClIWVdmELWpFt3QEMoS6eVSdcMcmzApIGvmrWt8Hny4TTQ/PceNB0Gi0lIbIrhBz19AFTVyZvVP3pPl2JtnZ+Zlrrzh1z1oGTJ1uFi/UdS0j0yhVJpNyGrjeX7/7q37d28dMGTs40/+ran95y65qSsIKIHQao4fLMbq6QlEQ/mmnUgB3nYFR3ixJFTxQ7s7RA2VyZ0CItvw7LlMfHPkUBdx4cpKF1ZSnU5cY+Y2Vid6B1ZVOe7DAyBt1DaghXNlRZWzerqBCkN2DFfubq2NFobe9Mixk+9/5Jn7bxcNTMx3m395/Mm/5VUbR/bzefuzDfevXiB/FFZbQgeFbklwo6PX7864N0J7LTwEQk2JO6SMeqye8ulzzIWLojhiZiQn7j+VJSnm2BHJlTW2eq8cIV9aIr26jK80pMS6CzchDc0rzs0rvnmO68wb1MQkx7Hxjxxn81MOFeYjpbaUCrFaZz5Z/+P9jzzzwVebx06z5VEe33fSLHg8D2YiL/Dbn2+cMhdldy+YuxBCOP/GhdgrN5h05e+Ab3tTwQ2ykGxfjh8sHj/VrrqB4EBwkpjMXm3ta+ceh9mdjrXwLMvx/gprNzxki23YdXFUstjcYVeGO6WTszM7z/aUoV58ha0jJGbWSLuEQUQg03F+vSlzJx/aeRjXuklMGyy+dqMmjzh16Ix078rbbiQxjY6zj9wENxweSPS0FTgoJtFQDwmyG2oiUpmF+uqbglzndLoQ0INXbMIHAJ96LntEUhAvLIVdzk2GIepms1uwhl7fsjmdrnHU5BEKhWJAvwGNjY3XCq6xLCsPZchzEgmdubRs9mhERttLT8kqvqWE9HdRPSyHcZnpcqKoBdUO3Qiz2XwxzUW+FKGT8Ty4QWhHiA+03XHRD7QX4iYGUglCcVFaJ1RwEgge8vqPqU0Z+02t2duini2qH22qD6RET/oq9SgBbV0sGIDaP92MutSQ1WLnQxaVrca9hrb05e1MP0mpWznuXvLaowS0dYVuze7pvBuhRWCDSPomk7BGeyGpXrNK2uwR2sijD/z5zj/cha/f88Dd5y+ec2jBExgQaDKb3Ldw7o70ZAH1MBZM6FCk5STJp+k4rm+c7bSQvpKemV5eUTZ10rQvPvzSaDTeN0ecjODt7X1wB2qmyXEcRVE8z/ekgC3Tm2PBOJTh4RqHlGq0AiKaPZ4P//uxQxNCpVJ5bO+JBbf8adv6z1Biz47DVdVVc5eKfYdPHz578sApeUZ2t4akiZMveQfS7I8TiQt3d5L7OlYnmkwmnucX3/6AtGXhDfOl6zf+YQVNu5uO173omRZosxDLqNPAEtmiuPDOQ7aK3t9Tr7Yx5tss3Stq0dV49ImHv/3s+zdfe/vJ55+QeumOnjJS6kTu0EcmZUAzIwi7F71UQOUQI6jdnXHOLpRWvMgeSifh+pKemf7TrxtvXHbTyQNoPExjY+OcpbPwcF+XvPzCK/mFTc6/7Hb0cAGVGz5EKK9XTMPz8iT8wGatSIqihg8dvmTB0qrqqh83rS+vcN2FC0KoUChMpC9MB/PP/7z6z/+8OqDfwC8+/NLb2/vonuMcx9093cUkj9SDaIzjilXLQE+hhwuoHLJs7xkx4vlzFrz6t3/iqC6E8O7b7wEA3DWtr3P7glOHzhQUFixftbQdn53QFJmXMybOGg8AiImO+XX9b5/tzvjT7IHy3zysni6ntnRfen4QiRiePYxX//bPA4cPjJo8YszUUaOnjBwzFU2l/nK/Y64u/roSOhQI4enDZ73UXvKNhUWF+w/tUyhtQ1XHjRmfevB0RWVFD1PPXiGg7g1PIq/di5QBKQCA/3v6MWkLx3EP/d+D8o6N/3zp9dOHz5KVeyfAC1b/r8KwFjmjR4zheT6vGnlCly5a9sHbH/7vw//OX45mrPcwqJ69Zjx+sNiNRLZIPd2sQ0sLK5q6tPDECc2QeRl1qv3TnffKNx4/eezOqeJUAgDAvNnz7nvk3kmzJ5BXsxO49+E/BgcFnz58dtgQ1PBXpVJt+uFnHx+fB29DnhOKov72zEtfff/l199/1SPfDrjwljmgh+Icu9h5KKNvf7G1X26W1pOD/J56FSdkSEVpLYKkyLQC9z883372PbZDz188//X3Xx44fEDoey0mzcjfptOHz3riAyXvURvfET8//1/Xb/bztc1Su+Pe264d2wcA+H7HiZjoGOeHuFnLd6+3oycLqDMjI52nmTWfN+Pyy+kh3evT0EVo1nLvl9TvrdffiYyIxDc5jrt/3mCTMOmTCOh1eUec8dGi1IiGANsAyx75lelFUXhPUgtDQ0K//uTb8LBwfFNbq529eKZ8h/jY+J9++Bl73OrqamcsnN6R50twzeWcy4tvQg20IIRPPv7UzTfc8umudJeBeE8GGhIIraZn+kBbx7jR47b/stPPz+/TLz95/h/P/bhpfYB/wKlDZ6QdIISb1v5y8tSJUZNHLF252M/Pf+/W/df1lHsdI4ePnDltlnST5/l/vfU6Xg9OmLMMRy0I7U5ETKiHF9DL6F0WqHs+eOcjk8k0efZEfHPbzt//9RaK5z76yof/ex4V9m78bhMA4MHH0fWikqI5S2bt+m3P9T7r3sUrL74aHhbh0oNmUbXGSU3osLmBa1G477GFPXsoCxFQO37ctN7hBTp/8Vx1eQm+3ie+z669O6W7qmuqAQD/efWNvz73RCe8VQQAwB8fvGfLxt+/+vibu+6/Q3pBXnnxVQDAL+u+Ji9R15x89bSr/XuGqhIBteO2Vbdv2rzpWr5tst49D9wtBZEAAOcu2M31RCvHcaLFSugESkpL1m1ct+qmVacPn7VYLBzHKZVKlPv57J9NTddfEzpZOsc3PXjV+VDdWkmJgNoYP2Pssb0nNv3wszDemc3Mynjw/x5oaGjA92L/WmlZmcMrqFapO/MNI/znnX+99e4bM6bOuPmGW9Rqr1+3/LJp80/eNY7vCwDgub8/q9fryCt2vfhM6Lfb7FiRbj17lQioDbPZPHrKyPi4Ps8+8ezokWMGpQw+sP0Qz/N3T0+SwrsM49jKUF4DQ+gcWJbdvW/37n273e+2fdc28o5cF44fLJYPXu3Bk69IFN6Ra/l59z9636jJI0ZNHvHoXx+BEMrrrGOim5kTTSAQeg/EAhUJCQ75y6NPPPO3p+WvzpFjh++6/46vPv5G7aUBAC3hlyxY+tV3X8r3Sc9M78w3rDfQimwY64BVUBnV6zJpugXje6gRSgRURG/Qz501b8u2344cPyJ/gXCT7csldcg4LbgWFxvnsHh//U3UWo3QRQasNrVDN3WxdV+ONzGevofRu0o53VekndifyjDMg48/cCL1ON4SGBC4e8teAMC0wdEAADo+ee/W/d+v/+6td98EABzadUTjpRk1eUTBFTHPyRm1xtbUi9A6UuKCmt0nN+savtK3f3yzO6fno/wzwpDR/ds9Cj/eKpouR7m4MUK76S8csUBtjJs+5uSBUx+8/aH8BbJYLLNH9MHXa2trn3vpmVdfeu0Pt9yGt+AOsoTrq54hYZrcLNv1ynKdJ8ckMtpx6gl6Vv/yh5cgK1NeFGwwWvAVIqB2jJ02mmGYpQuXDRsy7Oq1q19//xXP8z6yCuvtu7dv3729k963Xo8n6on8nuW6cVNszc8JXZPjXdIH+pAgjg6yKMHQKMxutnAut1dXVRABdcRisWza/NOmzT910BtGIHRTXv8xtaU1SF1BHJ1lUVJA4ftuE8c6bVVLn4IIaAdCHKDXBU9W8b2EpCixr5hL7l9oa8vy8e972q6hx1s+wlo6LGghf5w7o6m7JPOw3Knspd3pXUGk1hFSLEZ4//TYapdv9sVTVg8c4Xos4bFoym96IqC9wgdq4pP6RDR152N/uhFfqa5EwdUNJy+06NhPe2CN1uncdHhBWSwW1m517HDT7lAtNw87AWKBtiBFpqndXi2v6EXfSUK3IievNC4y0OVdWDc958HFs+U366w/Uizrpukq35Qsdk1BbCm9XUDb16dDwrudg9zexMZmZbnOwQgltJT7F9hW9C5xEEqLvSz2DEFsKb1XQFvaV8bzaoqUuCBiinYcDkLpXjdNFoXzRiVjBj2RpEjR48kDwHKc2cwWlbZY1CRZ7J2C2FJ6r4B6jtQWoWvmYRAwB48WyPx9LnRTwmRR9CgNNXFJ8ZE2O5HjiytqG/Um9PVuSaOb6qpuPESWBwBa/+1MSDMRQot586Xb80+/jy+/f2/XPUBO+qE346JD3B9q+KA+6YfeHDLQViCLCQnyTT/05qAB4valC8f9svaFX9e9eNPyyW6OlpNXiq98+cEDW35EJ2a0Z8uPT+ErDY1cUmS4/AK6L0oq51qJyWTEF4vZpGIAQ/EM1WRApucBZf92JsQCdUdPqqZoL7KPvaNSKp56+fuDxzMjwgN+/uIv+affjxv1kMNux7a+7KNRU1QzH+lzaXk+GvXW755yOMKZXa8XFldt2HkuJS7o5RduHzOy332P/E+hYN5/68FRw5Oeeekr+cq9pMSW6GcU2iqrVQqNlxJfl6PxEitrnaMeSZHhOSUdnvXSCbAcpzOYXf2JhPaHWKCEFqBQMCqlIn70w2t/OVpUWn36/BUsfN++97B8t7jokOgITzOQ8BF2//g8vpkcHXFx338AAHeu+SA5OsLMKseM7Ld89RvZV7QZlyuXrfr3iGF9TWZFcZEZXwoLTIWl2owrHmufVViMJqPDBXRn8ktQBgjPA6PJwjadDERoX4gFSmgBfr5e6FcXQlZW3vrXf3yXei5XvtvhzX+PG/VQ/un3pS3J0VbvpPVxHGc7wjMvrX3tpdVTRgwoKa1RMLS/n2bxytd5YYdxo5NRQKNeh/tW1zeg4e+TJvTbe+CidECNijIYnEcaN0GP7n/NA15vNLdxmHPnexK7L8QCRYyfGoUv1/vt6OpUVdfzPH819d2//3VloL833ngm9SptppKjIpKjIvpGhOeffn/bznN9I5BXMS40BHsYeY4XL1YgRInUEAKTyXjwyKWGBsP3nz1iNBq3b3o2v6CyprYeW4UTxiY5WIuoh8tYWxshnucb9abePv9dKX6ROY6X/zK1DqKenkMsUNctZJy7GfbOEHxyFLIc5d/JmYtffv/Ne+5eNf3uVdPxXff/+ZPcq+IK+pYbUHuqV/6zAd80m0zOjkiXzFvx8pFdr/74NUp4WH3P29L24CBf551Dgv3Eazwwmi0ms4tiZ9QPeyea1tl7yC+pjg4PJCNmJFpaW9UKiIAitm0VJ8fJ8HPa6Bfoi9oq9wySoyKaMlXk30CDAS2ZHbj3EdTxj4Lw1punPPCneZ++e/8PPx56/9PtFIRr/jh30pznWndKL7yy7uXnV81a/JJ8o3iS9meKu1yLZ2i0NGVyTZr7XFOSOjjFFve/lJ6Pb4bVooqd0xeugm5LbzMeN3S8RLqHCKhL9XRNTX1nayg2AJtF7v9z4wo0yeIkLpXREwL8vbW1jUjaeP679Qe/W3/w78/ecuvNU97/dPuB7f+o0Ta++c87pZ2feeIGg8H0l2c9mth+/iKaJm0w2mVoFhRVOu95Lb8CS6qF5YT9W7xo/fid+6Xrk+Y+J7/pnFHQPVBSwNQDY0cbrrdEuocIKPh8w74/rpxR5VldcKBv8x3mmypDcq+GLoMgeGNTmigXxM7h208fTewT7mBjfrN2/+wZQ5FNSlGBAd7jx/ST7ho+ROxF3Wp+2XLy1pVTHDbu2id+qYwms5v2E25wME4nzX2uoEoLegSeBtO6Bhu6tj42S+8VUNxUCRd0eqieDtQ1ip1mzBalXO+aysp2kEjnD3rnC2JLefgvn/3+03Ovvnjrc//4Qdr41cePWCxoTe0grEd2vXrLnW8VFrepIrCoGP0aTRo/4MjxTOSkHovUOSOrEN/b5nhJT6OwrEatEj+NXYQN3Vwi3dN7BRTz+o+pTEtSEWrq5TENzv0KuusLYkuprdO988GWxx5cfGSXLT7D8fy0BS923JM++tfP//efP0o35Q4BmoJiBR/h+rGhR0uke+CvX73+8TaPeql2d+5fMFO6bjKz+1KzM66WcRzvrwbLZwxuy5E/W78L9DL6JoQvmDvSYmE/+WIX1ymLxrtvQ2/fl9+hGX8SOoOpuk4n/oAJ/8RGBLYuqtJDlvAmTq1SGkxWP7LwmjTVzu5PnvUC7sH6OHvIMC+VgqYphqZpGtIURVGQpiCEkKKELDv8n3UEr/NnqhdZoF/9tP+uG6eLfzZDB3tDHwXLcVyvehHai9yrZe99vK0zn9FBOj2Fb1pM+Z4Zt251Smy3EMoRccneaoW3l0qtYrDwKRlawdBI72RCJ76rzaV0NXt3sy1TmarKCmyauXzVP9nWqk9tl8So4aoqxZCuheXKquoF9ST0Glx+xHvY8l9JGY3mZoWjM/Fi/RiaYhjKS6Xw1ai81EqlAkmeRq3UqBUqBdJBmqJomlIwogo6WHxWbOuMZbfatXa2Ud/6US5ffb25FY9iet5HyBNq6nSVWk+zlwg9noLqHrF+t69KAqZWfrP1NQwlSBjDUEoFo2BoRlA3L5XSx1ulUtBKBqmeQkEraEqjVqpVCmwD0hQU7kFLYNG+c9RB3vkq/r+22kW+GgDgrodXteDUfTWea2jrFNOFgFZb7bJeAsfxReW1RpMtGZtA6O5YxIClTbAY2DIBXTlpAkVBBU2plAw2ASmIDtGkMShQU1UJWDPLAhaAjuix+tV761omrE1oaLvIpTO9y/23MfXCTWOGWliuUW/slZZ3T6S1b2OPsjoBoCGvUqmERnatxdzIAWAEwNgdRpp+5UZYBQ1tqWLyPDKX8b+e07sEFONx3x5CW+kMkUK952FbVqw9BqPRGBfuX6lttDUH6LYvCc9bFQ0VDbeDsDYrmmL0qYXeY3cCujG1G0TlWgPKHexCXvYuRQ+zywjdtz0dpNCpozRf+58BXrCBhCyjFvxd6CA4adh6RZLLtoTcep0FujH1wtLhg7pSlLJTIfrYI+Hl1rcrq9NhG7zuw4v4ZmRLvBfaDTsSE8/QY6HLjC0krDJ71dnAbKNcOtPrBBTjyi/eejxMSO4c3K8bAhK78/Aft0AtigreOGeEh/v3pAUWV4PW7DfPG6nTm46eu1rboBe3C4l6K+eNdPkoSX1gq+SPb6G7UP5cnj9QPuzImuLUxLlZhdWmuW02MJulNwqo/Oeo29GTvvaEDnEdAqBgaLPQncDTR7na6PIr0l4r347AZmPK/u1oeqOAbj+SER7sok1vV4DoY6dx/4JZPbKI2dtLqTegmcaETqA3CqhRw7XvEr5FEImUiPIOF8pRlAG+6uAAb5U1Z1uoSBb8LK7eJQhgbYN+x7HMwlKt3ITyaeFnGUKwZuGsj37vURoqLd7bjvsAfhezPsH14v8B4/Pw/gu/V40AAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "assistant": "The selected signal already presents its broad front to both ally cameras while the hostile camera sees only its narrow edge. Preserve that authored-forward orientation with yaw slot 0.\n{\"name\":\"SetSignalYaw\",\"arguments\":{\"yawSlot\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nYAW Y0 SET", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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KSqdVB+k0KpVCrVKQJBmgUUWGB27OrfN2AZsO8l2apo8d0s7XgsFgMF0noLLeT7QDeimR+ZkclpEUnpkUQRBARREaJang8uHRIpxzZkInKUHA7yqlIjBAxUknp60kEahTx0QGBQbAGnkBcT59YGgQ2kBGKNrG0SQMBtPTBdS3+ZkhmJ+F+qySBpi1SRAKThndziLcIgBFkRqNUq1SIIlVKEiNWhkarI2J4lXSk8jQkOj4aLSNlvAYDAbT0wV0crzGh/nJez+TwzISofkJaJoQ7E3384hvkgShVFBCySZBECRJaNTKQdGhpRV1wleohiwtrS4tgV9DotRVxVXCQh4boRgMphcIaHJ0IG9+JvLmZ2ZShNP8tIu9n1klDemJYT5P5kz25Bbyzp38hoIiw0ICZB4kkt65E1LQBjZCMRhMTxfQyXEi8/M8b36itTnn/STF3k/YMrmksT2951QqvpWyFGjW8ptCyj3h7LuMPaEYDKYnCmhyTKDE+4nMT3gfYZd4P9Ph1CNxq0+pBYmUz8eoIwKAkMgIX3eLHowSQhFYQzEYTM/KA3UzP525n1kwHxM2+GCR+ZkSxk09ImiayS5pzEgIK68v9imSMhDuPZPFGhqkcdmku04WTR8Hl/Asy5YXVrbhFWEwGEwXWaAu81Pk/QQs1zBJ7P3kzE+CBATDZJcZ9GH+DiuWJUjp+tp/4rznAQzD0rSMnYuNUAwG01MsULH5SbBqp/lZywIu90jG/DSkJ4QRaoawUxXHYSeRFk1OieHpJ1zsnhyYGFNRVNX6R2MwGEznW6CC+ZmWGJpdUO00P1Hmppv38yRvftI5ZQa0B7SJiio4W8kfSC4jX0hpEsBGKAaD6X4LVGx+UkAt6/3MSAkDYu9nYhhQQfMzu0RGB3ljk/Vvd4sQ8L+QYH6yCAaDwfQgC5Q3PxtM6YmhWfmC+akU535mI/OzmPd+5pTVIfPTbrf78xStCzPJER4ZFjtQOhMEG6EYDKY7BdRlfsKHceYng7yfrtzPDGfp0bjBEdklhrSEMJQNCijqlSeu8VcfiXaJqU6r2nzgbJseisFgMJ0joF7MTzfvJzQ/i6D302q3EwydW94687NDUFDwRWEjFIPB9BQfqMT8ZBUM4QBZpTUsq8xMDs8uqOG9nzaKIAklRR05V5vBBd8B5/185bGrPvjkVz+fC3pAfUbir7l0ak1Vtbd7x4wYuLnaa/s7DAaD6WoLVDA/0xJDsvKrCUaRFh+Kcj+zi6rczM8iZH4yOeV1yB/6wsPL23WNbXWL0oT01WFPKAaD6WoLdJLI/KSABigbgR0O3SQAND8ZuyO7pIFlyMzEcFnzU+aMIgOz/VEjbxAEEZ8cX3LOLfkUz/zAYDBdaoEO9vB+psWHEk7zE/YQIR0E3IbBd2R+Zrubn2j93p5+Im2AphnWbIofHN+1T4vBYPoLilaZn3zw3c7kVugBUGYkhbOC+ZkUThCEkiKPnKtLiw+jNC7zUxfgPAMqM5KlVeVHkpbMXiAp/rD4wW52KDZCMRhMF1mggvmZJjI/Be8nEMzPQn0WND8dBMPkVvDm55vPXKcL0Lz+9ncy53XXwMCYcd7vbCMwrR4A1uya6InBYDBdJ6Du3k8+9zMHmp8gIyk8bVBETkkD6yAzEsNHJvN9P9MTwjKduZ8AABn19C8X1MdRP/y+H7SGT77ZHhk7ULwHR5MwGEynL+Gl3k9ApMWH5pY3sSwXfI+NhOYnQ8KhmxSVnhiWU1SfU14HHFCXbXI9QL2v19k2r+tJ768C+QySE9zUswuYoCwFAByyx3Xx82IwmJ5igcp4PwHgvJ8gMzl8ZHwU9H46SFbBCOZnWkJYhtP8fPeFm+QX7x44W8n7dcVtWN0rFZSacGAjFIPBdJ2A8uZnvQkwCmh+2qH5mZ4Qg7yfNMMg7yfhIE8W823nc53Bd5vd3rJ6Eu2MEnllzIiksgo9ydBlFfrSCn1SlKasrAZ0OcgOxXTBzxn/qDHdgqJl85MAmUkRWUVVwE7A3E8WBt9JhjlZVA8YaH6Oio86eb4OtZ2HuZ82Kru08d3nb5QIKKV1DpGXwRmeF6/Z3dfvN61aBKL4yXSruO/P3/8WupkYriw9Xy4bQRJTW14h2YPD8RgMplMEVPB+8uYnIIbHhVAqVU6RPquoKmNQBCAdgCEF8zOnqD67vI5wkCwAdobxND8d5ma0QXh1b/pyeT7/4hpfr8M/izUydmBtWUX7NdQRzg8B9UaVgtd6h0PaF0qCQl/Q2mfHYDA9egkv9n5mJkcAJTs8LuRUZX1OkT4jKZxwkDmljWLvZ06pgSUpFHwnKAqJrxg7TbNMC8Inp4F+r+RZ55do1/D0lJi4gTFxrghSnYcR2r1g9cT0ASb0YxeKwqf30whYJTI/AQDpCTE5Rfrsoqr0xIicsrrMhEjU9xOZn4SCyT7b6Ay+qyUnhNVKLYih0zAlWHkz1O9M+xHpqWijSV+PXl7sAD6nCgBgZRUSI/SP11cueHAt6FDhi3H+PRXjQDwG068EVOT9JJD3c3hMaF5lPfJ+ZhfU5JQ0sgyZVVqbkRgBV+5F9YAkM5NDUOnR4KgAyQlphuFmbXiXRLZlkYwbyI/kJADYcaJ4xthk8b3CYE4AgMlsPphdOj4lUnzA9sMFF42HByhZaWO9L9b96e2ng+kJ+OkwadFbgk1+TFcs4aXeTzvBApDBBd+ziqrSE8IA6YCFmw7y4oUXAgrWSxLORvQ2ru8nw7JWu8PhzANV6XQxAyPi4qLlL0EqrLyt6sNgpeD8uDYiOzTpj9dXtvmEGAym3yK1QK+fHGO302g7M5k3P09VGFHpETQ/SxsBQ2aX1qYnRrzx3s/wOIrKSA5BwffBUQEOhjGZbYZmW6BGGRKoITWawACVUgFX0MhL6Sl+pz9/Ke3mpzzsz7ZN54Ty7eNeggCrLp9SBuc4YXoHfjpMsLcE0/0CitSztN6UlhRyMr+ac0mC9MRwPvieEJFTXpfBeT9nzr0g57M/4Fx4wi54Px200mCy1jSYGozWiBBtoFYVqFFq1UqnaEr10zNtifvO7/ZfQWlaeuCmA/nzJvHOUE9iB4aXcRUBiC/W/dkGTygG0+dp0X8C+rELZWwU67aEv34yXKdDCNj3k+CC76crjOLgO5x9VFoLC40+3gB1iyTRHCTABd8tNkdlvbGq3tRosjQ1287WWRUUDCD5i3Bkq9fovID6+WQb95xu7RNgeji5bxbnvlnc3VfR11DoC4Qv2QMGK9o4rrxvQMqan4L3EwbfE+EHS7bT+wnLNB3kP1ctgMXvCWG897MIej8ZljWabUVltc1WB8sCq92RmRqtVilaFjXoRnXVH7XkApVBvGonAJiQHtviQzyHJmWtu7tVT4rpOax5lXe4Yw3tGjEVvmLqs9CX7L2SL9CHGBvFugkoCr6jHCJX7idnfo4aHAnkzM+cMoPY/GRZ1mSxK1VqApafk+FR4STXoN73dXjGkPh/SXkdJbmZca0iOMozfiVd8uNYfB9QTwTWUEzXqKebgKLge1lDM2BdwXdkftpZi6z5ibyfyPzk0pXYZquDYVm1klIpKa1GqdOo/FlSux3CivbIPZahvTZ5EqNuKN7x17Ydf20Tn1ZAwcm6xAh96fWvsRHa65CVS6yhmK6BlOR+piWGQPNT4fJ+jhocmZvf6MP8ZBUMEl+7g7bb4eJdoSCBSmO3080W26b9rR7RDoP1EDcNHT1mBPBQ1ez8Sv4hHTQtBGto78KHUGINxXS2+ekSUCH3M/e8CZqfDoJlBfPTLDY/4dEUlSYyP+02/lwWO2O108jkVFCk0WyrqG1iGN9pRS3iZoVSXtfvnD+CIJy660ZcDF+ZLuF0YbWnJ/Sl179u87Vi2oNFr/fx1QaJxBqK6WxIN/MzgTM/UfC9UjA/DWLzE+YtyZmfWw6eUZAERRIkQYRFhlEUYbbYK2qa7A7GYrO3LKHtMyF9i3Rutq+Yu2dePe5X3wPrpiV66qc45r5Z7E1/MZh2mp+8gLrMz0LO/LSLzU9Lmpz5SXiYnxdNGKpWUqGBGqWCgmt5B2N30E0mq81BV+lNTSarHXpH4Vrb6eTkQ++eiPcKFmVMZDDoaBQKmAa7asnEDj8zxjft7D1xbo2xtQf7Nm+xwmLaoJ5QQ8TeTwposworhw/gS49GDY48cbYKkCQqPQKUEpqfAOSWGTIGhwEblVVWOziMNz/nTBxKKKnI0AB9M8MlJMHVtINmCIYpqmgwmKwDIgLlsuklGuqWO0/A87hsS+f6nahUJ2U1w1L3EkrJNocAAGo0TJ1uWEV1rXB0VVgm2sgyRwEAqrUEegiiVpeSZY6qIgKazToAwM6CpqUpbjXyuFVoj6VV6ik8ZPAN0g5hGEz7Id29n1UA9ph3eT/T/PN+XjRhKHJBBgWoCIJgGAb2q+dgGLa2obmksoFVwiYjnA3qsj/dDFFZe9QZxT9wFrZQYhgokGoVX0AlbLAA6DRwUGhrydTWZGprhmhb3RIU01vUE4PpJPOztr6JF6C0hBCK0Igr3yXmJ0spCc78zCkzZHqYn0hAYTUnS+o0lNHuyjRiAWt30A6arqk3KVG43Hdikx/1m/GO4swAB3w71esHB0CtrzSfn5UwMkCvF5b8FWyjugG6yTK10AKtNRdmBrgCUOWmgkwtGcDqAYAPJwgiNnlg2fkO6LWM6ZnqiY3QNrtZ8GxEH0BNYQFrMFkkpUcS7yeUNVLG+4nUE3Zk4lBSZESI1mG3S8LlLAtMZt5elY332B1QEEV4XeR3BgzDCiYzpq/anth6xXSs+QlTNpGrsbrWChRA4v1kFQzBBd9ZEpqfBAA5pYbM1HBgJQXzU6dVCeoJFMpwrVKnVZXXm3Rajb6xmeZW3AiHU6C4MJKHKgodRIBbY2W4U05B9xwvvGB0kvQcbU2YUqtgxf6gpAHlhXxiKQIboR3bdcL3sBNW7WzF4ISoyOtY7cN2KKZDmDQ+beOmA0NS4/glPMOyZ4tqE0J16Ynh9QYT9H7Gh+ZWwB4iLMvmlBqg9zM2mFXS2WcaoMHIqeG+rMJJmcmC7gWoFTqtKkDD6jSq2OjgZqvd1GwTtLKkuCxSqOpE+9wncHi/2patUa3aNYOkDSgoSqbhM6ZVP0P/Kp199Op3eEk26ljLEWsopv3m58ZNB1gu6kM6+x/TlUzgxkP5DU3QbMzNN8D57w6SUdDZpQaU+5lbZqBIUpz7ybkA+FOrtGqKIkmCUCpgHWd0RGCgViV7NS4zURRFan8l0f7jbW+wJDRojk12DVBC4JzQ7qUz1t14LY9pD5PGp6ENggDk10dqrXaYsIl2bTqUHxGmjI+AI4gHhKgIO8n1rIPezxFJuuyzDeLgO2d+8igVlEalIDkpIghCo1bCELm7VSeMfZMJwXtRUPFu1ukQGDNkAOggThdWw+txb+snAWtod9F5StefNdQRniL+6u7L6WXmJ1q8R4QFDR+RtGT+BLiE/zmrQajpZFj22y2nJoyIiY/UllTaKpqg6xMwcO5mZkpgRmK44P3cl1U4JZP3Qh45Xz93XJBS4ZIhkgDQHHW/lPRVT+R89pKnVHLeT7bFIHx1A1T5R+65DABwo/gOpeImj4NXrZx/w/J74UD5W5bAJin//kzchT4jMaKsQq+hAKAUZRX6hHC1JASP6XY6W+PwWh7hTUP9HM1d5TwMVNeDfmN7VusNkQMiosPDZIbKwWBRJaOoaLhkdCypVJ08V5sex3k/ubbzQoaSoJ7I/FQp3NyIslPkvvjo3etvexJ6S7XSsZ1NxmZFWCYBgE1/Unwp4hmd1mb4jvr3y18K9z/12HWS8/z735+1uKdFNh3MnzfRrZs9jiZ1MV1jIfZPDW2/t1r+MODrsD7m/Rw2PNHuoD/6dqvcWGMCKFh7s4NsbDI7aBMBzc/GzNTgjMTwbDnz0+AACoorGnLHaLCZGpskVeroIIZhucW+TNxGHTGKU19+thzXlokNGgBzOQE4LjlYLKYdBgXmTUkF/FyoNnLIHtdvJ2X3rvV1/9RQTJvNTxaA4SOSWJYlSMJBMzICKijeiRpHuIpNiNQGhigbDM2llTabnPkZSLEjBgVWVrkM+ORwbVVJFUlboKjKrdhZlmUYmKQv1VDZ2cd+ZIIOHgw//azNTTc9fD1gAVNV/+JrzgFH4obO/ssiJT24DUZoVVhmi4Ni+lib7vbTn72TmB5ufiLmjEvdciSfIFiCJaQCSlEKmoY57QEKxmgwVDvIUA0JlIyDZoCCQeYnA7vFOye6s6xgLUqQTwziDkYJmxINlTsFIGAWKFtRWTNwQFTsAFj2Lou1uQn6Pa9bjG6SXvrXCXh2sRMoq9Z34KQtTM9XT2yEYlrk8oVTy6oaNm46MDw1NjI0QFA4uSU8h9lBahVMgILZnV0UHBqaEBYgeD8F9YTnUBBeLDsCBeTdTFC0h4ChKhRvEjRUrJ7S/iIE7I3vD7c++HZsFGzaxAJQUQsl1X/dbJUR6o/ZiGft9iLb89waY9r9rfnbwPRL83NYKj9s7arZI7/ZCqM1XgVUq2CQhkIvZwOsKr9gqKubEY/cuKORo4eh8R7R8SbNmQq7aAJH3ql8U9VR6C7l/keBJoIgggeN91ivS6cf+0NH9aXHdD24+TGmx3L5wqkofDQ4aYBSQX3z55Gr5o+7clbmt9uyZATUxihr6kxRETqkngQAQcEhA0PkS324JTzsxCHsqWs0cIPamYLi6qLyGtqPEUbGiiMgaETuZy+JbFH+3/Sb/+XVG+BBxxcTeRihmK5Xz5/Oj2zPyS9LFuV1eGHwDYEabIBivJufq7/8a1jKIJZlrTaHWqVAGrpsZoaMgAYoWU1AoKmxkWbY4LBQZP4JpUdS9fRuHcpmMnF3eJzH2ykAyPnkBbRRrlVEThgruXftbwdevG8ptG0LWpfFufaXPZ47H7v/yuff+vniyYO9PQo3Z+oW29MfBWwPOAqPaRECgOWXTFz/+0EFRdodtGCHyi3hWWA3w0YjwSEhZq4/08AwrcxRpK8FMyxy8jEP3oep2GK/OxErF09qlXTGp8SjjccfuErYWVIGi5FgU9TymptWXFBWxJ3QeQ1fH6lFlUhYPfvkyh2rJ8a3+Xn5wqmrv/xrdObg7/86snj22N+2HgUM5y7k5M1NQBWUAj3U5qBJkqptaGYBgOU6crhsT3khldqfDpOr3qndPepYi5W22GC2AEkSGpVCpfAp5x5a6cczuDY3Hcyvo0LbeKWYHgxWT38Qck76c8qd3QF9eRv+PnHJRaM3bjtupxlkhMpYoAoK0AwUuAAlcDjsBCBOF1UOjnGrzBHNk4es/X4H2njuvmVIOC02x7nSRpOVpWmnFClDKLbZxyWmrXoilyv0JFo2RAm1CrYsQRLNffdq7eYfO546ZjTwg7yzZQCAYJW0Lh6rZ+eRdn9CdxmhWD1bi2z2XhWXrneupXzn3qK/st7P0ZmDWQa6Kx00PWVCeoPRsvtAHnKGyggoSboNB4aLcUBsz9NfNMLrz+jfD6wQ3zxdUMawLGO36pRwQp2w39lSGZ1XehJ/TMgftxx/6o5L0HZOfjmMMqW64ld7jxeCdjBiCMxRKCsq75Imzpju1FCsnv7jW/hiuIo73+WevZcaPZ8NSZAERQCrnX369W+evm9FfaM551ShzU5LBVQXoKVtFi6MTiu45iAKimy0EM0Nhp/2GS6bAguQNh0pnDfBrZnx6QJou4khAAF9oPKp9N4HcsoeLVLWy+eMRrrphHXe9GMSSCsQmkZh+qCGYvXE+J/72WAwlYr6EAEAPvpiEzI0ZfJADU1NgRq+jydDw0GVapVCR9NmbqgclM5xUDpZ9z7xQ0VpTGecbY24CiUp7dGkZlZ1KKtgSFIkAUBZVePAqGCaZhPCAxhu/Kc/AvqV09Vwx3XzHQ6GJKHKw1n2JMl9JxQUWVpU7tu5i+nVGorVE+MnNfqmqPCgqPAgdFNYlmeddq10FS3Km8lCA8COTgo+X9lgo8EfB/MpCpUfAU/RFJ8KBuHdTVBtgNatvbyfauo0LvUNXDN8O4NC/AzLUtw4EZIgVNIrkufaJRegjSaDTJ1SmxIBMB3G4BsCO7sYCasnxn/zc+ksGDvZc7wAhdxRZw/JQaS3eBNSXEFHzDaHKxzkH17zQJ33S3ewLRxn0SbFDh1n1yXbApKDYzOt2mSzxs2TcOp85dG84qN5xcfySlatmNmqq/W8Fmx/dj2dKnBYPTH+q2eNvqmwrLawrHYkF2VhGBbGdWBXd0h+USVqFCcTRLI7x7+hoR/cJsFwj0NGo04XIHm2tT/t8npp4jMT0jag/uKfQfjRf+6CsaDh8QCwp05L3bJtAGton7FDsXpiWkVgIJ/8ThDE5HFD0TQMlmXPFMNmQ7NSEuEcTEkzkVo2MBQ0SDp4IuqbLA6aIUU958WsvBTWiophWbaosvH4mUqUrYnwVGt3G9WrXgkHlZ05MjQJprVX1xkjw3Rwl0YrrLr37j5UqHekHDsDg2BWGArDYLB6YtpgfgYFopZLkG37T3H/OlvQATAscQS6Sfpp8JmsrVzAI23sUFeixuw9S4l7oqo6k5a1AbMJNBur5QYMaAJ0NoZEX7Kn8dOUxnQqHWstYtsT0yok6gkAmJouTdLavj9v+/5T2/bnySzhKUrBMA6ktrJOzLgBES1ehLPfUsuXy/sIjHkgiJ834nQVyJASzz91dEQgIxo6jzhfVjcoMvh8Kew4V1EnEyaKClJHB7m7Edyf6Ik7l+w9ij5tMH1hIY/VE9MiR2sIb72TBYbEhh3KPg8ACA1zdSVmWS/t7IRZRNyGlFNFtQnh0S1eFpcG6reCtjUi/u3WU1fOHs4fy7JHjJFhQXBR36Ax27n2z9wd/AeIj4zgTK1bqhemD2goVk9MazVUyJyXoNMo58+Z1NBkMZptdQ3NFcVFaAnvtR8oD2qXzDiCgwINBiPQBANTyw3b+YfKzEnqMPIdg5Cv1hCqyjLHoJ2V2pZeDqbfaChWT0zbNJSGwR5VUUlZSQU0qnQa1cI540lA2izmACUYPyK52WIHU4eyAMhUIiG5JDnhJAmCJAhxP0ydkjEAEMgn2rcA6pbcwjHoQNdDXNmqXK6+28Mt2iS7g1VyKZ8DnddQ2XA2U8v3OzFaYCb2oEDYlL7C0mSthyY3ACA1iT8gVoubPvYLDcXqiWkDR2sIAKB6aM3N0ZFhF0+GS9vQIG1VkyVAA11/zRZrbn6p1caLYlhIgJwPlOR8oFwRp4Ii6LZ2FBYqOe3NsMWyrwW634YqUs9O4sQ5+IEToZOPL2E6G024/Mdb2v3hrSpSwuqJ6RAKSmDztshQnTookOC0KkCjbmhqhnYeJ171jc3yFqhGobDYHfzMDT+yIb/8ZS/aeOLuK9AGy4JmttYKaowOGqj8Gq+m4TS+Y2vaJaz7bd8LT6z0dm9aJmw2s2nLwU57fkynF3qm3Z/QYm95i17vp3Zj+icsww6JjxDiL0VldbCx99wxDMO+9P6vC2dPIAnifEklnAkn+3hVgE4FgKXZaHfwJzGZfKVVXnf5dM+Gm5Zmc5AK2FVsx6YxofJNVBvgfn/Lxuk1y2fmnsXj2jsFfyaVioeVtjjwWdIHyB8NTbs/wZ9rwHKJ8UGoo5HgSn4EQQmgwMGcouxTRejmhs0uG8tX1AV2BWVpQLo6KhvqGwgCNhwBoOUovKcLlLaa2lNobtG6FW5qLeK0UFy83s342e0RdT/rjAZoV93fsefD9GMI/t/IUFito6DIsJAgg6HJMxm+hbC1UA6v02lgFB4AhYIUaj1bugZpIiil1vl11V7QaNQWi1W4adYkuWuoH/hIMcX0eHwYoVg9MZ2HWkUlJsK0H5IkaJpRqlQ2m0OlJNUqL6WZCF2gLjAYxqTEaNVKAMDmQ7w122IufSsuk0+p93p/eHj4pk18PzqEs5mI38/SSvVMGTqkVcdjOhvZRTpWT0ynQpFkWKAyIyV6eGLk8KSo1NiQwYNCkgeGDAzXyVugBkNjcLAr4Z5SugqbNCrKYGr5Kb/6ebe3u/wb2iFDeXnFvHkXgi6k4MzZrnw6TBvsUKyemC5AoaBqRTn2zRZ+ugYU0AfvvGf/kawz+YU0zTD6BopS0LQjOja+uqyEP5y2mW1qFB+nUCNmd4YOHiTZ+fwDK8prm37+O6+oEjbxRATwM+vaCGkoUFAwN5W/2frFeFBAQFOzr7lMmN6loWn3J4wE3TzXE9Mf2Lo3BwAwZ2q6ZL8iMTrOZCWSYyOiwnRZOadqLIMpK/yLdKknl9ikZIBdpn8Sz5lz4jEbPAaTlWBsHqJJyBif3O0W5TAwbrS1ql0jwpPjos+Xug3mRHr65Q87hT0XjXcfn4fpkfgZcMdgOpAtnIxCW1Crdlmgf2z44ZqrVpRX1rCkmlIHAlecBpiQagKg0upUWmC0E+6lSTyl5dVxg6RxeYIrZOrAqzcYmoA2WbJz2jjgf/aowdAUEew24z4iWEsQxNN3XS7s2bW/XRqNwfQlDvXRaXE+iAiWaVt84eik7QdPCzeNdl4leR8oSRKhIYFLL73U0GQ8enzw+aKSRpoJYc7plMAEdMDcCA+ymwOVUG6ogFDWamxnEKnN2UybNu2QeEKF80wZlfDFb4fr6qGrosFojfcoOS0sqWrTc2J6boZpLQh1HQki+sBkXUwPJCEp9oYkOLXX4RzYwY07svMCuvarb2lAzZ45pdHQ1FhXSpv1sP88jLcDnRIYzNxBSmi7kQQIUDAmkZXqDdgO1C+NbJ2WCuqpCwwyGSWtU4hv3n9owJBYwLKV+RWP3vdci2db+/MetCF0AByXBn9MmJ6Db9WLFPlAywFe12Pai80OC9mtdoda6Yqxl1fB/sJxA8LFoqZVKxVF1aWJ0byV3mgyAApMm5h6viggO++c3qoJV/MFSCYbq1PBR1IUBYDMKv5Lj1bEq5bNMtuA0cp20tw2D/WEPPrKN/ExLpPEGyNSBqGNFx9cjlJdTzknM5eWuzlJMRhM/8RqdyAxhamg3LK+pJIrAmZd36DE0gxFcZ7Nw4eOrbj8ciurCWkOjWhQNzQYmhyOIEc+Z4eyJhvQqby6NR+//dJ5V8+FW0rOcIWraBOr27du/SaZESHoyf1qF+pCZ4O2hsPZ3UT2SjITwmK4MaS1DSZXFMyDvAKZqBcGg8HIsnVfrux+xdRxM/KO7QqO4Net3/2w4eI508KDQFSIoq5STxO6BiKZBMeRhgJAkF5S70sraj9942vxHqvNcfxsZadODabDMqn6LGFYyd7jsDCpydjcqmypL39yS1m9aNLQjrk4DKavO6AHK/RVIKwNnQ16F3Cgph0mfhJKaVxFUVNTHJeUEBIaXVhcjXStrq4ubaAhNoOYmcHX4bz32XHnmRiSUJiMRkD40fOtxSi8FyX19hiTysPDZW5yi6n7p5XP3LuMZlmrzUGRhIKinrlvmTjYtWPvidacEoPpy/hWvZhO62zQ07hiwWSlgrJY7Y3NjqZmG80wDUbL+fxzcAlPkkTB+RKK4oP3aQNh+878fFex5rzprvTR1NTE2e6nPpfniu5Lg0gk11BEtIb/4qu/rr/24u6qTX/2vuVoo6hYzjTGYDAYOX744wAA4KblFwbpaAB0BqMlNkI7PH6sggAERRGrFg8WH11YWKpQUJKYPUIsrDxKDfwI0ptSUxPFu1UATI2JObd+R0f1swvN+rMhcz506Jphq3lk3j51z5WffvFbC490XkFBUUWrnpEA4J6bV2mC4l59699tvmwMBtPbuXTOWBhZ4VIk4dLX6gCApWlWsWzmQKSY7X8OpK2pqYloA0lwk530meQOJ3f4H03S2YqFIFJUSPAdNyxoWT1bieRK1IHQO/zIfU9hDcVg+i2/bjt+6azR7vug50/hWzr9ifeIDc/CwlLBekUEqYC48bHR6usp/CwoeubZW+E/9U0drp6eVBSfVnLpYDeuuPrz79yiZBgMpj+AfJC/bnOGgvzvB0q0LJpsC9Zr62Z0tHD0k0/dJGy3qJ6qmCRbFWoYKnNOVfIlaOPb7366/c67hP1CReofv/84ZdIkOBI6mXeQX3jBxFX34ra9GEw/gwCD4yILimQCJ/7OAU5NdUbAOS3qkCW/AEW6WakSxIGmj3+Bea23LFF8+t91nkeeKqkfHg+TKhIuuD0pAbomvvwXnNE0ZdnTP/24fsqUSVERbjn25aXQ1XD54lklhTDX1fXyuCetrqy2NcNWUjk5roZSGAym38GCzGHxGUPjbXY6KDCAZhi7g7HYHPVNZq8CWlbdeMGENOFmYSFfqNNaJDYsJRoQ0gZsZlhQ9cPOpJAxcBUfGeyWTfUiV+T55jsfDbAYzp5xjQI9e+Z0RsZIxmYsL+WK+r3iGqncUUxQlvbDdgyY3s4EZ34SBoKCNARQKUnaARssKUlAqggmQKkQ4uypKbyNWVhUlpQYm5QYW1jURtEUw4alNXGzQBBESAek1FeVo+lyVGO1zKg7jUrmU+Hggb0XzZjk+0qFf9EWmlzKXXQbrhGDwfQRDBbaUFoLWJAcF+mgXQONYCK5cCO/ABZKovhPO6Wz2WxTKSl0qrBAytbM15N6g2XBl7+W3H4rTBRoP7ANihcoiqJ9z7l3qidXaIqlE4PpdzQYTKHB8tPbzpfWim/SDONHQZF/VNY2IWM2YshCm50XKfWgCTIN7NFYeh/4bfH9/tc2mUf7TM3nmqG0rJ7+XgEGg+lbjBye3GAw5Z2rzDtXmZNfnpNfnnWqCH6dLpLqBAAK2VoglmX9mQdnbLYGBsD6pTOFNcGBMJ3e/5agHVcQ7xcNjQ0t2qFu6oklFIPpf4TTjXGR0XHTMqPDA7khQFDfWMDWNpg3/n1U0FABhay++VBPi9Wu4QZzDhw25+yxjQ4HrVBQjUaLS0BZoHZ6IS1lhwh3r6dP4NN6W383WByhGoU3Q3L1++8L28nDRn360btoO40rT/30o3fjEpPffft/whOhf2668wGue5RCzZ3YYXVrtzx39jibA3pvWZJv08uI3B8YDKZPsvXg6ZTY8MKyGm4uPGwTzLIgfXjitUumQxFgWYpSNJiseYU1BWfyW05jOpJbmp4Sg0TzSE5pemoM2k8QhN3BaLjuJBarQ6d1drHjZscL2z76ibTTCP30gzdvvu06B220s4qJF8waPWaMcNfcuXN279o1fsLkg2v/BXPgb76T2w3LQAEAGzduuPTSJVxrFKjWNis/t2Tb1l1z5s3hXht/YZxIE3/+uXnWRZIGABgMpm/iGUTJLqzJGJ7IdfcAFPyHDtEq0hPDkweMkfeBVtQYXFXworONGjaQEWVsCvFuhmGF7s0SWYQdmZy3GysLGitlmrsQBGgwyHRH9iQsVJObvZdg6hx0w/W3rrKzCjvX0hSKuMUu/vJxEhawVotM+F6inujSJMf8c9Ud/lwnBoPpjeipEECAgjK92NE5c/yQitpG+FUDv1ADpmCdOixIK2+BMiINliznxUEhBcXrLwtYYb8+f6P4eLOFqqu38qKujlUqBcl2U1oFYVv98c+TW3p5DNeVjyGlXfkIVlZ/vToyWQCsFota4/Lbbt++ZfacKb7VE4PB9JukTwLdjHCvvuHsSz6dnGGc5ptCQYkbL4llJDTYLTpEOUWTUAX504kuJCgwMiyUYTnvIcsqKKY9TenRSfw+VP7cXIs9iERDvT1Wsn5PTvT2EMj5Ihnbtj/gu/OuQJUClor1+Ra8mN7IZWMG5JfpK9xbtm3emydsz5k6QnwXQYjyQMVoUFSFY3BchN1NW2GPO4WCajb5pRQkSfIF7rLC1wY/aFc0EG37U7jktdwvwW2RfqvIGExXMjaKLSqtUAKQMACanMOdxevzIiPhMOA9cPLFFpGYIhSyUhagcUWE2rmUJdHa3rvZqKA6QBCHpWe0NvFo7ZoPxe1LBg9N+/jdd9Bd9z4MQ08QFnqUAzScndxNiU3t1N8uk2M/rcX+08Mc06vJOo36EPEMjIST1jKHJYl3btqTJW+BUh6Tj5DV6e3JfKQ9cXd1uvb8tXFjRQUskwcMbyz/494HhXsDuGQu+CpscOLmZUum2+1Nq26+Gl6eQs3QfNXBjBkXwY4kp7KBw+a6fvjaXZVULRQy9Xh6ixxjMF2NoFJEK1TVJaBuj2qlUagU5S1JYGiZOs7q6vqYaN4XJtBUnQ9acos126m0DL6kPf9s/qjRGWj7ihXXoaDWn39tmzz5Anhks6vzKNrOzTkxYRJ/vIP1+s4fPpw/hj/SqZ69XTp7ihx792xgOcZ0I2abg+Di5yiETpKkVtRVw5uJKG+BtnZRHYDSQeU4vM+t2pIkGACka7e3v8olKRWhiyFAC8lMDjtNENC9oFTBZ6StzeJ7aZja6VXmGIaPX5nN/tpNWD17gXVcJH8GLMeyOMJTcGjOhxUoJIEqlQqzHTbEYFhWpVXDTB+WtToYHeAtKlT1428/UE/CQjUwVZ9Dp/UqoNNmzczJO+dwOOrq4Ex6mdJ4AMJDuHR/QBZFzYor3Srsr2qyxAR5fVOJc60ExNlbAgfX/mviyheMRuF6Xfzy08bFl66Q7Fz/0+/LL7sEqSc2PHsp2FnRnnwJP5MlqpyHgWrOgdabQSU/MBREECwAOrWSQgWKNKPh9I2mWY2GMBqaaYZhGJakKCXBtF1AW+wghyBIvhYpIgL9JngB/X1X7cIZUW5HwlJJt1pJH65Tu82WkTEKMK5Gec6TtGA9S8zPzExfLaCwemL6mBx3SKzPz5S1XgYK1nBqRcBMJCXJwGUrw5IqtZJlWYZhA3Uaxm6HHe1YoFYrGbu97QJqMbmW26T3SDosRHLzH6A2m6ysT4FhGCHkVN1kHX/TIwCA0vX/8/KCid/+2LJ4AVc+5B/+L96tVrfhTT/89ZX/z4LB9G05FlQ4RtBZD79cr0RQHwKu6B0OliIpluv7ycLpxSAiJKCxwYh6LU0Yk3nk6InWCSjNsJTcGtxHwbtngL66un7XkfrAoFBPK5OLOEmj9nHL7yn7ntfQeCCaQW8DN89LhWH11rNh83phO/f8bundFFZMTK+h2+X4UF/MSCMJguJS2NMGx+SU6rlNqKoqJYU62EWG6lRKResE1GK1+3B3yrJv956g8Bi3PblmAOyyktuKIiMAaIcVACJa7dZC6arZgwFz/v0/dty5EFqmB537E7WlibPSAKj3lhgAALhz4QzJnvc37mzFBWEwfUaO/SwDKW/hmF4ZyiOcrTi5f0TNPIBWo+RSPGGoKSkuEo41btWZG40WWQH1p3moCxYkDk7V17pmFkFp19XBJTYDvwv8vvUHqGtzLkhcvmLv+u/cpRNQOleN6qJ5MPdow5/H0E2knp4YjaYAGFBrWToRDyzl97/x88475kyVvg6HffXfh1p6tRhM/yW5u63j1kL43EXxER0oeME6NQG8lHJ6Q99oHhQV7Ll/QGSQuJReTMgAEoAaiepW1tVwlxUhVk/EyfDYTH1pdZO1KjHlzjkwoxMxdfmK3N9+ROoplk7ETxuOIN/vJ99suf26hZ6XMeeCIT//dRyp5yffbAKhKh+6KerMB2luctwxe6rgV/j7YM6ojMTjWed2nDo/c0Sq7BkAAELv/8rSmotGpkvuJQlm64k8mZ9knFtgDYPB9AA5JjzFFBWpt05AjWZXic4Pf301IZZXwLbxy57jS6eN9nbvxPgkB80IDZ9gveb8hbTdSgW6qafdhn4KxCffbQEAyKonAODnv46rlJTd4fh0y24QqpJIp+A5oLmzNdlAkA7+qv73+Z+3XOEyZv8+mAOLPlMGNZntLAOunD25qtxtRoosK2ZNrKk1eSTD8lyUNkTY3p57dnhY9EXjXHve37LH84R3XCLVfdph+2jTfnjXbKmZ7IJxfLBdcGlgMP2dZIkcm9ySyi3QIPS1sN789z7Q2jxQb2Zmm/H0eTZXM+iiDuWWqhRUWnL04vnjtmw9YrbYqcBQ2JPE+bJsVguyOj/9bhsA7O0r5aUToVJSa77fduu1c+5cOAOdRAzSTYFP1+8gAHvLFXPE6okgAJt/poRLPKAk6pk+avxNdz2Ktv/85ZuvTx9B2zW1JrVG8+IbH3OFAPYn7rsBauXebKCj0gfC4fUC45MSxOoJW0FPn/D5rkM+pJM7p5UAhC/phPUFsMoCg8H4gm31sR2TB9ohQOkUYTLbGhzEqNEpv2zNVSh0ZKDbq7NZoeSxAHz23bbbV17S4snXfL/tlmvneP58PKUTAHDrFTL950lAo2l4pNxYuqdfWx0SGr72o/+ezjmWOHjYLfc8Pn/JVTfMGAy1m1PPd//zTNH5s9ffev+r766bMGYko/M+247D81I91dNh53OtKO/DAVna3iX9q7xyh8gPAwD4/ov8Fo53+q8/2AiXFBhMdwGzLX0YoZwpp2hV0xAxUxKjO8ogvX321MKK+v1McaXeyDJsdZO1NDBRyTIHj5/PPl02dVRyespAyplqauekEwDw2XpYJOpDPZuMroXzLdfCt6WCtTu4SlCJdCLd9CGdaIP1+GmyDE2Q8McVEhr+0G0raC6+n3cq5+G7rn/tnS/QMS+98XHB2dyi82cBAKtW3Xzo2MmYlLiKygpPoQwMUItvsrRDzZWXyRqedpsVZqsxvn5ZDG0XrvjjH3aAcC3oVvV0u8s90Eey9Hu/bx8TN9jbAf5I6h3unpkPcBIFxm8adbAeEr6XnWt5wqeStsUCnTkhxZtoZudXZTjHJbVCi7mrOJFzhmGYcA1tVthsDrpKN1AJHGqt2m63MlZLds5pg746MEANR5KQ1PAhCZ9z0smEqaYmxJ3IOuXt3ENTXb2nkHQ6CKWsyeldOlFeKvjg2223XTnXUzqReiI0Gq3J1ESq4a+BYZgH/7ES7b//9qtQBtn2vdnI8Kyq5lOvxo1OKS5yS8NynZ/TYkKr9lRP5PZVsgofKw6G5oeasAB8+sMOR7i2i9XTmbTAer4oCYu5DIqNf50YEzd40iheQP/ed1JyGAGYEeGR209KI2+wy5iTv/dlC9sqCoyMCt90DCYOy8X62FNNbnkgGIxYSRudY4Hyc2CxQGoQL6+SpEuFpBe9jx4cLUC0N+9KkgWqVipommFoaPZx05x4Pl+/jQnjg/r7s4onZ/J9Tz0pOA+dlQJHj8P3XqCO15EdB+Fbiw6FN5eNGlJb61YpHx3JF/ki6RSrJ+pXIkgnyiqlafqF/60BAHy95oMTRw/audEjAnsO59Fa8rMv147MGPnzhp/Rj/T6BRegRtPuVy0apiKXceVST+/QDlea7ac/7oTq2bV4pnx5U08YS9x4FBDgs/XbM0TxNDHpQ+AHc+7ZCtp9KurMkcMAAGXHYPRMjEr0qT1vzDAbFw4QMyoz8UQW7Evm23EMf/tb93ozh5UkePs37GToL+Q3QbM0//h5157cklSNcxKcBIoihT9Wk9muFTWo90YbZnMI7Kt0RmNCXZKhIRQWmx3Q/IASxIHj+YJ6QvEK8er7AwDsOl8Ee0lzmET5A0g9kXR641RBGbRoDp9hInV1dfzlGU3GAK2WgGczQxeByRoTAYc27zpy+urL5iYmDX72/966+oY7rr4BDp7bt3PzB//aK6hncHDIpq1/hYWGLV209OXXX1o2c7zdwVRwo1M9YWlaVj1tnHqqWlBPG9c5Bnz2EyedPUk9iQBpDhzNfdJ8vn67Pdgle2Lzc0hypKCeO3PgZ97Kq667/y44jxpx3YXDoDdD9CxWGiQmDb738VcnjsucPna45BlTUgehpjtp4W4RPE8YxnHbRRM/3H7w1TtXnCuGrXAEhMlec4YOSU1wZZ59sGXvkKDw2ZNcT/rBFl6CJci6ZT74fWeLsi5oOqbbybd4qYW32WmKJNAavK6hOS5GJvdTAjJ50EP8n9Pxy57j3u6qZq0gkASAbAR0Vq1TYUXqidhbzFfj+uaz7XxMfGJs1MGyGuBTPRHbCstAJN9rWaCZk06BqrrGM4WVbBAUu6LCc6uuvhTtv/aGW5dccfWeSRdmHdhBa+G7zWBo/Hr9uq/Xr9u37cD+7QevWnShcJLYQdGSZ6kzGoC0TQqoazAlJwww1Dagm5HcpAEP6USwn/20q+sNzzaq5/e8egrrdzFnz9cyjAOwAKnnqy/8Z/bM2R+/90pe9jFKobj3kRe+3HH62umptKjp9e7DZ95Y/T0AwFM9YfZfQRnNsDtOnVv7f3fnnyr29kLgkwLwIZf45U090YvLL65OTeB/g3fMmbr1gJtP6Y45Uz01VFY90VLGt3oSwL/lIKar4AXU7qDFktfQZI7gWsxxyfN+CWh3TbxoLVA9Pfj+BIzwtIHTdfUgSH3FkmVPPPzkiktnCfu/WvPRyDETHnrtswszYqkISqlUWpyDlB/518Nvvfpf8UnKyl0F/dm5UIsHyCXYsoDNOl2cGMH/LmqFDxUndY0waLbrKAxz9wT1XHgx7HS1ZctRq1rm70einp5rZITdZqcoAqnnzJHDZs+cveajN/OyYckZ7XC88dJjr7/3zeNvrnnh7mvR8bsPn9m97/Dab75ceRXssS0BNoUAYMepczNHpPpQT7cX5X5hgnqi9fuWM/lzhro5WGdPGr71wCmxEXrzzMmf/L2/JfW0t6yeLNPtg2Ilv+LvW8qp6BcCyrJAb2gWFBO+ORtcNw3+DY/r9l9t57H++Bm0UVkqv+LevnPbEw8/WaB3a4mYPDg16xhM4fxt/cbg4OBpzr+8Z598zmg05pfpU2PlOy3CZlly2Oz0odJKQUC90ROkE7Fly1G4oFZ4eN85WJZd8+MO3+qJYEQeTwDAhMkX5hzbSwN+yX/VFFeocPfhM1fdeIPDYW8o4n9lMup56PTM8TKWqaz56aGe/OJK4v0UG6FIQ90epSAFDZVVT9ph98cF1kJWTUcg/IVHOj8nxH/zM9NSUS2J56NmetTaUQS79URuXEjkLVe5RV/F9IFMNSigdgetb3SZnFA0ja2uOe3WXMNuRl+vr62rPbL72Mnsk199+2XsoLi7br8bAHDXdUuhIXbFgsO7jm74/ve7H/rnh29/HBoSOm7aGKBTnG2QiQI3IWu0quUCJ1m2F0LXbRejaKRPZEsFS6lUpg+JP36Wv55RI93mwQIAvvhxhz0Ivk2nJsSddE+lKC2FOV5xcdBHSVDE7pzTSD0pQL/54kP3P/mfV99bf/Z09sYf1xYX5g8aOb4uDyp1NhNC6sgH7n7wwVuXJ6cO96ae0dFS656l7QTlqt5laBsgSE/15D7DGJWSkqinpxHqiYorqJNVT+dFANJtEeh5AN2967z4IOkMHoGYQKkpMDoj9mROaVxIpGxCxcwpI9HGPxfNeXfDFn8y0sToVMTrP/HJMz1CQJtMVoliWm2uwGVgiNcfnBilKL08JTn+nDMCPiUxel9R23rO9TgGxEV5M0IvXjI3Iy1zzYdfjMx4Db6ptm959KmHA7m7WJYdN23Mw/c9+t/X3v7qm7Wff/VZe67h+xMytlX34giRSVZjWfbD9X9PHM0H1k94pB/ZOPX0AZJRisuELS6BbX+S42MqyopWXDrzvkeemTrtovsefxk6LupqH7hsInrIwR2Hn3voNlnphE7t3AKgUwwTxXz4QgMRUD25TAtP9WS5oViykXezjR4zdsixo21xBKGgH+VTPVmWv/v9bdKUg64hPigsJT7i8RfejowegPacyTu5+s1/o+0RgwcSBJGcMtRsNjO0g6ZpUhHIMC2I/bxpQ7fuOdMq9dSpXD8lWXeHK2tC7l4WgHNl+l0nCq1WOxvMS5ypzu2vVxdBt1pA885LRUGhRe99SESgX3n14VGRwucjSRKpKQmFRd1gDXUj2blZ0LT0wmtvvfLaW6+APsq+ytopA9yCWrtKK0GE/OK9VYhTl/QGs0alYFnmzVeeee/NF6F9qg7/ct13H27Kvm1exvv/XV1XU1VeBj+5kcfZaISROJPZdqaomtbBP/XxCQOMJrgzUBcoqCcyP3npBOCzn3bfvHKR55WoVZTYsBCYnzbiskUTzc60QVkcNC1fhsvF/RS+bU/n6n31tgOgm2ABeP3D9SzL/uf5hypKi2IGxj7y3Fuvf7ge1dpBA2LhFXMvgestgTe490JcSGR+UVVqYoynenp7Lm/qyf2yYAz53S+3jBsqjTcuWjAO+RHf37o3M1I+v4IAICU2PCU2vNlq/2I3dK8xBoXEqjfVUQFqigiEvxeTqDZSF03K7oF/VWZGKfkFZqTEAOfspKpGe7w0SizDxIx4AAASzf4mnRi3XDQR+0qkfwnS1AG5PIpKvVvUW8Bktny8dsvyxfwbLK+qDoC662+77osPv4R/geOgHbr66z+E41d//cfDd1xjMlch9RRjNBnr6mrDQ0P0DY1oT20DTPTbfSx//MgUT3tZo4HBve1nz3lelVpJ/f7XkYFyXcoEbA6m2ZloHBEpMoH9SFhBecdrft4Dgrun8DouKGzk8EQAwEO389PDqirKHrxt+esfrh83fd6RXZsAAJOnXWQyNj39yJ3ia0bAZEQPNu0+M31CisNj0q1P9YTSCU8YqPR0ZWz4g0+zAQDUNzWnDIsvOO2WBi4mQK0MtGuNSjNFEsmJ0YXFVZ6diM3VdrREYjhvu6maUZG0igQ2Z+0fElP4K4kfEFpZ6zYO82xx7ZiUUPFEeVlSU/gk9sKisn4imq1qNxdJwjdn7SDcoa69nKt3/X2GJAw5dgI6PREj06FDrVBvHT/dNd5q7KixH77z8YQZ48bGRufVyssxNGmd6onYXlgKwlrdMO2XE9mgtnnaSFcsy5OD+TVLZvJu2TqnktY1QsneefDcZTOkDmIx4eHwnWjpJvVE2G2cpaxUOuy8Go7KSPr83ZdPn+A73QQFh/zfsw95qmdpY61nfAmx61AB0Rr1RFgCleMGxk9Ig+aaJ0pO3O69eb4P9URct2DM+1v2RocHXXZhms029PjpssO5JWar64m00UpzNbxJAhppKJJOFUkLGsoLaGK0tsk91N7Y5Lo5LDnK2FDfn0XTHx5bMcFz58dv5Xu7CwDw8ne4GXOr2bF7x8fvfqLX19332H20g37h6ReTk5Lvuv4y8UBaYTgrwzCHSyrFDz9c7LpZ6mUCNrKap8THSgxEWfOzRVguSGsLkHcIWqzy1Vli8gsrtrrX1HUlcUFhqfERdrvNZrW+8u46hqFffeb+cM7rfTr7qNHgUobMUeMXXQarlsvLihdfuQTtjNTCShNv2Bys/+q5+s99IBA6W6x2hw/1hOkTa7fOnuDVReCJSqWYmJk4MTOxrKrh70NnhLwjbbRSUWduohVIQwnOOelg3VyaUEBTkgYZ3UaogTnjY4X6zpjwwAERgfkF8FeYlDgIi6aAN2WEK5oZsNzl47f47f07nRMSvDwci6mfPPDYfcOGDFv7yTq0bLfZbHMXz7ad99oPoc14Oh98ERmwW5TM6z97C8taNCy7UT3FPH73ygsumr/0ylWP/RsOKGNZ9hnngh0NpEjLHPPgP1ZqtAEvvv7hoZ1HJszgnZI+UDmn+Q7UuCLVSs55aEcZlmL15MiIHDhtVLK3E769aW+rchUum+s2lDc2JnTlwvHvfecakuaI0AaJNJT2iIwpkhLhW33yyISCc15+TwQoLCpHbUEKi2SEoH8iq55INzGdyumzp9GbU8AV8XRy9PhRHwG9bmH9wTxvOQzrnZkVy0dJ7Sa9wa3yresprahRk7zF983aNd+shQ0fEpNSnnvt3edf++DpmxcXnc0pLikTqkhISvHAHVe/8cHXQ1KGnC04W1WjD9FKo9Ans1z1hLfOn/XRn9sSIhTHjufFxQ8aFKHzoZ4+EMxPWf0kALN+68lls53t21kaEFS0SiYRG85A4iJIEg01MUoSMA5WmjqiQKYlIiUlvqCgJDFxUJFTMWGFYltF0//OeH1APT2lc//O8lvuuxptdOGlYXoxgpL2NN5avbastPi1fz+Bbu46cGz6vAv2bzvw/Ce/CYF4gR3ZcEFw4bSZZwtazus6m+9qz1FaUj4oYoix2ZZ7Bj4wKBCK6Z5j50GAy5AdHhsk1OBJqpmR+Zka5EoH8oHYUStGtojFEaFlamgSsGFRUHPra6BXnQBAQZFuC4gCTkzbrJiIPpP16Sey0tnakzy2YgJexbd5MBQO1nUigYqCxiZAKcZPugBuIDRQ0SoryuITkwv11h07t5mbm2+4yi33KzsPthYkW0qClNTd2Wkm90yRcHPPsfNmkXpKEFczN7gy2dn8wrIxKW7lEpER4ZKShA+2HfBW//aPhXPe8yiR0kZR5hpaXAkGvds00zGhvb4tmj+/w3vEl971S4urdWxvdr357y1Yhz+TOorFyxce2X1sz5Z9i5ZdUs+FlC9duCQ+MfmOq+EcHZRJVtJksnH9Da6/Bg6t2X+QX3fneySlGYulqnqgrGZSLPzUrKk3iwN3PtRTwpYCvrNBfpPbZF+Bi0bHobZqeoNl6zl4sGeyGkKrlc/EiAwJcFiMkQNi6mvqBBNXIVyuJCONZdkWhxX3bd305Od3liy96xdBTzHdHqxDn2TegnWSB2I9bQ/T5kzdvWXvlg2wlznikX89lJcFu6nl5GbnF5zdt/2A2WLWamArhkXLWp6yI+EA1+UHfXci2/enLV6O2jqvqWz+Y1JZmWpTSVNDiFJhd2awKgSNlIilN/Xsb6IpwYd6Ytuz66WzDWfDMto2zBazZ1wu0Llx5Q0rKIoamTHKZrPm5Mk0HOlSIjRCAyA/Ey1Q1hpi25kCbw8ho3VMtYkW9Zj3awnfz0XTN1g3ux6JdOJgXU+ApmlxgUPvwv+sNaihopuKvqSbnp0+ZLtytTaU4W3Z3oHSic2irgzZYVoO35WjIrpUvneIHJWtf0/1PXrQWGN/6K7fGQofiaNJvleXrQKrp6yvuUX1xNLZLQh/+R+/9TUIV/fzYmXFoTL5oFXv5fab7oiIiNi+c/v+g/uymZAMriAdQZLk56u/SIhPeOPt13/dKPMW9Y34XS2oXpuVFOumLOhTCn0XJBJXKHQLfv5tP9aPi5V7mQXqm0M7j5AkybKsw+G4YskyAMB/33vr6Df/47tOZIz87IM1FZUVe/bveebxZx++9+Hp86a1/0n7w19Jd+FDN7H52b1MnjFo8owHb7n8dX+Klfvwe6SFpra9iC0btpEkOWHGuPHTx06+aOK4aWO27dh67z/uo5ydnj/7YM1Pv/64aNklTz77+PjpYwMCdKgBGqbXgdWzew1P4YPt4x8f9OfX8diKCR3o8upR9B0BDQsN+3XjL+Ip9g8/CftrXbziZnQvAOCFV/ke2izH6y+/2X3Xi5FH1vspsH9nOVbPnrMs2O+sV+639J0lvM1uu3Thkuf+71nxznHTxiAfaNrwNMnx3/347ZVXwO5bmK6M6fmTFzF56cd3r5SmHBacbpg33VffTExng4N4fVlA5y6evePPXUd2H2MYpqa25r5H7jmT70qmjY+TdmDde2AvFtAey9tr4eBigUsmeO1g5j992BPXgaDwHSruEoPVs48LqNFoHDdtzNxZ815+/pWY6JivP/8WjlyvKHviyumyx5vN3dworD9AEMTY0ePmz52fk5v9x+Y/rFb3vrNOYgfFGQyNTUa3sQhifj8Ee/YcrYDjjPz3pmHFbC2SfGchOuQZzfMWOOpv9B0BRWzetmnzNjikBQDw1GPPLF209OW1mx9bOTcrF/aGETNoIO7d2bm88PSLC+bxNdGXX3rFU489wzDMqpnSCcAkSf763W+PPvXwlu1+TQnHstjtiRBYOvtaEImiqPvvekCy898vP5d7KndgQgrqdyAp8J91Id8CFtMZREdFL5h3yTMvPj1u2hj0ddElM0mSfPbDn8WHEQRxaCccB0aSfbN1bF+K4CGwevZBAaVpeuVV1/3j1n9K9qPge6GeXzmOGeUKTUyddEF2blbXXmY/YtnS5dysxN+EPQZD4849O5KHwxlwiF++/e3wrqOHjhzkprf727gM071ZEPjn3zeX8MWlxTffcAuloN5+n8+cX7Z02cABA1987B50c8/+PR+988mMi6eZTKZnHn9WoVDcdOeqbr3kvsyJ7BPcRMkIvd5V6nb/o/eJC8PiYuPmLp6tr9cf2X0MYAHtHHxkO3hrXL30rl8iO1o9X+6jEby+I6CXXbXkq0/X3Xjtqhuvdcnimq8+37ThB7R9z0N3rf7fhzv/ghOjGIZZvHwh7TGWGtNR7Nm3m2XZzb9Ct+aGPzd8+fUX+R4DHsTt0UiyjyyG+oa2omyzyUs/bkP+wzyPbLO+qp59SkABANfedA1atjscDiGkK544dvs9t3XXtfVDxk8fe+UVV9131/2L5i9aNH+R76QIvITvOYhXCSj/QYJvVX2578plHxdQBBo5gOkJfPvDN9/+8A2K8r335vvjx05Ys/PcDTMGQ690qNuRBLZAu4m9W/er1bCpEmLfgb0fPLxSfMC82Rc/fO/DoaFhJaXFjzz1SH7BWVlVbfNIq15NHxRQ3/Sr3243EhoSGhIcUlTCDwijafr2e26LHRT363e/6YJCgF7aapYk8BK+G0CFJ9fdcm3uqVwAwL3/uO/6a26Ywn3IoQN+//HPmOiYDX/8dvjY4eWXrfh2zXfvf/Tex2s+6o6L7Yko+rD8oYxr2LUQNi2/pD8vNLqerRu3NzQ2zF54kXhnWXkpLHmgQgCQCihB4ih8V5OYkAQAmHjheJblh6T99723Dh899L//vKMLCjE1Nep0upjomGtvuubUGTh/7bfff3379XfvvPUfWEB7q4B6ow29XvAQx07lzXfeuP+uB65Zce26774Sdn6z5js4N/uczLjwFicYYjocinObqNVqi8UiLnH+89tP0PaIYWmVVZVIPRH3Pnz3oZ1HtBqt2YIL+Xq/gPozdMzbyEZvZ8OWaYew9psvV1513YP3PPTgPQ8ZjUaSIgO0AQCAm6+YK3s8XsJ3PecKz8F8iS371n7z5ZvvvIF2siz79bsvou3DRw8tvGKB+CHXXw1HFmP17PUC6kM6JcVnH//It33FdDHzl84LDAy84Zobp06eyrKw/dWvG38JbJAZtHX9bdeVl/s71QvTgUy8cPzBHYdXXnXdyquuAwBk5WQ9+ezjhXnVSdysDgkajebuO+8xGFwxegxxyZXyFkFvFNAOqdvFFmintrMTBNQYGt3ax+IAYCf9jgiCuPbKlTdce2N4WDgcx15V8dDyCwAA2UyIcIxWo929ZS9N0xMvHI9/TQJkHxowIDOvEVeeYTAtwrLs2m++nLt49rhpYzZt/SsqZuDYaW52VVJi0u4tex0OR4vq2d/orUt4Adwtphtpg0kY6UzS7s+jHHsIR3Yfa2xsnLVwpnjn4888Nm/2xdfe8/SPv25Aubq3rrrtjpvvLK8oX7x8Ybdda0+lpwuoeJKweD8eN9ZLndQfv5Xv+wDsQukyLBZLSIhrkS6muKgQbbz07MsXz7n4tbde+eZ7WBCB6WU+UHGHV8lwdm+0c9nehjdw1uHT7XnGPkNaAvSg+abgNJ9anzIsscWDc4v1HXFd/RpLs3wTawBA8tD4A3/Dv/b/+8+L3//8PdyTlLzus29UStWFGbEAgJARY35dv+HNd95Y+82X/j/jgP5Uq9KbBLRFOsrj2VoNxQLqj3RGRsM0JgDAgV0wr3DS9BG11c0tPgpraOcJaPzggQHagB1/7RJ3cmEYZuGUEc0m2Px/89HzKpXK84HiLjD9XEAVty+YLdlFUS2kNL+3wa/O4V0JDhb1ZATpFEgZluiPeiKrFmto59Fsbp4wY5xwkyRJhmECOfUEAEyZNUl8sFajjYiIiIzoR/rYIoq6WmmWQ0hYhO/HeGoufy6K5CrC2A4U2aV3/eLDCO0M3cQ+uE6VztrqZn8W75huQTwV3BOzxVxaVlpaButxMV6DSI31rg64bWbZhJEtCrE3NCq3q9r8Z/Pc+dL34dK7fmlD+WZHqeftl7h9hLxV3uDtyPzyKtBfkainnyYnpmPRBKh9rOIxPTcK32YhlhQ6RERG/bGRX1MI3L5gdr37DEcH7evDUyrQXrwU/1wMPcLv/rbZx8OXTRhZVwPN9ogoX2uZunoD6K/IGp7ddC0YqKF+OivF/XdwqllfSGOCSuThZGgbEZFRfkqtNx8FSjnW19XyF8bJKADAavHyCc/3uOmDzBgfhzZ2Hi71LZ2YHhvxa3ENh1PN+oKA9hwhZlmWYVnUw8aPo+G31IEx3u6vrKk3OmygN6sn2kYa6kM6sfnZi5Il/EEsrC/377aQ/UhA2w/NsI1N5ohQnV9Hc14Cq1XeOK039J0lLdLT8jK77L0qpfx+TA9UT1SfMnkG338H9zDrL7XwXQDLslabo67R1IEnBL0TybLdh3oCAGx2pfirssbVehLToxBX98EeZvddjbMDWwQLqL/QDNtssTUaO+b9z7Ksie7FptkTD16/dvXja1c//uUHj1912TyrHEolseG7R8WP0je4B/44vvjwwZ/W/ctz/5MPX/XTun8JjZbXvXf3uQP/O7Lp/wbGuE9T8kLurtc/e+tOz525u6S9DQMDNGf2cl1j+zGS2mgsnX6CBdRfaJoxmW0mc8dkhPTqBuzFR95NGxq7cOUr9z7+7rrvty6aP+7vP573POyvn54KCFCLJVX2bNff9rpWq17/5RPinVqNavrU9P++/2tOEczlOH/o7WmThk+79Jnvf9t/4PcX04bCQkPfBAZotBql587AAI1kp6ek9jewerYZLKB+wcLYPW2x2m32/j5K/tG7LgUAzFr0vLnBmpVV98maPTMveUapoBLiI8WHfbfmwfoGf90ddz/0flCgdmSGa1juT18/Vd9oeu+rv2HT3zGpFEkmjPtneVX9/739y4drt/75tZvatpnF88YVH3kX9G+werYHLKD+wbI0wzpohukIx2WvjiBFhgeLg2NNzVa73fHMS99W17jyXgcNDB80MOzWu9/3xytqsytzTtXU1Bpe/fdN6ObTj8Hu6MtWvjFk0IAhgwb893k4RgJtw84X//25Q16IQkG9+383TZj/RPapEtB3mTE+Dn3Jpkn46GqG8QcchW9dzKcXL7w7iEdf+OrKJVP2bH7xiefW7did02gwRoUFbtl+UnzM+i8enHnJM5ERQZ4P12lVkrW8SgXTvFfc8Nb2jU+v++Seq2/639RJQ6+95W0hyBY7KPzgkXx0U0gLm5SeWqdv8u0Y0arVSHNlcTjohHH/BP0JpKEoqywyOgCrZ/vBAuo/WDwhDMPOWvTsxvVPvPTMNejnUl6hv+amt+wO3rmxZ/OLO3bn2O0OP3+sNhuvp7fe/cFHb9+x6Zcnq6obCosqxccUnK+UyK5aTXoKsSSxYczIpK2/PeX5jJLkXLVSQRCEWGrPlrs9ex/DW8YuDhy1AcX3h9xsh57DsgkjQU+CgAZOB52LZTvuXN1AfkXNnEufg9KjVd916/zLF0/8+4/nFy57qaHRtGzpFBijf25dG06be6qkqrohJjr08mtfk9xFUVJfE+1RTiYIscCR4+fueZif0IvYsxnOm5QoL8OwgGXFo31TBngtf/ABKRpt3+skGKlnfRP0z/hJWFD/LVbuBRaoP8repSIL3x1Eh0TP2V5uzaq4bi80w5SU1d735Oev/++XPZtf/PGrh2ctevb+fy7af+jM6y9Br6VaDYPgr790w94Dp3/4Zb8/Z67TN8VES7OUaJrJTEuQHlknkxHVUXhqcWuJD4evQq1W+/gMbbM6e4U75GyZm3bvPFwqrhyTBduefVBAe6DIdpTVSADQS4s4UQ7Tz38eeu31n8UScDKnaGQ636Ru8oSh4uMnTxhaWeW1W5U/5J4qzUx3CWhEOHStmnpDhyFvmVutAqmwX2UXXqqHxWUO4tP89itvQmpU/IdWXa2/zcOKqwyzJuPGoEARqYo0W2xmq53RmkFfpGNElrM8O8xs7M3r93U/7r7m8mmvvfcbaWKRPU6S5Mj0xHc/+hMAcMHcJ4UjBw4I+/7Lh8R72sY/Hvho11//JggCiciXH97juX7vw3SICgtCLP7TE1qn22m6rLpxw86c5TPdPvx8wDBsfZMrSHj7glnie1f/sQ30DxSXzx4pNA1iuZ8LwzA0w7IszNqx2hwmi83YbDU2W2122mZ3GJutJrPNSEh6zvVu/HQEp0fG9GLl6yAee/HrWdMy9vzqljlfp29a992uTnpGhmGKS2p3b3rBarUjt0D7Rbkf4inE+lq+rxjNMGfyKyi6FSYUSRK9tRK5QyE+/c+D3u4Li+BTo2VUg+DXC5zSwg2aYWx22mpzNFtsJrPN7qCtdofRZD1TWwz6Fp7m6sffyvQPNXI1S4FaqRes3tDce9fvYlZeekF6WsKps2Xb3HOYfGAy23RamRk7/kASxD13LjyZU7RtR1bbzoCRcItzHprBZNl34ryx2XrFnNF+/pSKqwwJMV4jTj02NN2BoKaXvnyg9c7Gl/4QFhGpVFA6rSo8xJUkQQAwE6QKN4WPLIZhaYZx0IzFaoflPQ7abqe35/bBN0aJvl3uv57M9l3Z3/92wGKzx8eEdcHTMSz71nsbuuCJ+hssy1bVNTVbOqwzw/EzFaDf0GFBJE+1XXXXla14vKH5IQBrBH/ZuE9WbaFJa3OgavSdp3I66KoxbaesvlFD9O4gJAbOlbPYiivrfU9DahWUnw1z+0R/4U58A3z2zretENPgAMAVOC5ZOMVHsAbJ6/AkaZyRRd2OuWpLm91hsUFfrbHZur/gVDtfBQbTt6mqazJ0UI8xxOn6fjQHrCMFlGFZ0kt8WSymrTZORUB59c4vG/cpFKRGrQgGIDo8EAAwcqiz1FcUKGNZlqYZu4Ox2KADwe6AmtsnHQgYjG8YljU2W2EpAcYL4gQDlgVni2tyz1UGOF35HSmgSD1ZpFXcN29p560zTiWPXfNbeycjEQRFkiol0Gn5dmcsAEMSRXkYztfAMCyS17pG05mimsJyfbO5L8R/OorenI6FcU2p6cvTu9pNnTNXAa6TQ8PLaxsLy/U0w4xLjeiUJTyBVuCEU4hYYa9fxmnPmYwUERlFUkBBUVq1MjBArVFBtT1f1gEzn/sMZsbfgndMj6XXDkboIr4/dFJIvCkpLS8vqwyg7IDi7+3cIICnbMKsJ64QvOd3FJZILW21hWnZJl3rLjtMq40I5bM90B9qQUXf8hApW/cDMZltenPndvNDZZQYPxFqnPzPYfJBVn7f+vOWpisYmi1ua9CujqLCYnJ347TnK6mIVl9qoBa2ZId1IBp1g9xAi16No0eWU/bY1LGeqOywj0pHno/oRe/mVmJstpZUNUjSFbotDUVsnPKtNnv8z55oKSFZTEEpdJ2kxLn6tBdV1AMADpX2kfa9l06Y8PP2k80W240LMv18yPkyPWxvcbKc7w0a2Y8qMntmEzLO/dmRCnpK3zctUJZl6xpNJo8QSI/I4+v50okUv1d4HroMwtEcF66qqGl1C4X7V85kuBGnVi7brMFoNhgttQ0ms9Xe2GS2WO1ceZtby6r+I7V+1vB0oM5iH2iLfH/o5OXjM2XN6x4hoL0Drp1dd19Eryc5NlwvqrkIUICAUOWgUGX4KNdAJB7xD5uFtcIMC8wWm9XmqG8yN5ks9YbmmnpjU7PVZqPtNI0MqX4itR3Vh4z7qMJRpJYhCULh0ZQWC2jraKf1mTgwDK3iMZ6IVdU3FABDEmAjtRZ+GwS0rRw0TdOswWRpttiqapsajOa6BlOD0Wy309rwPj4f0B+RXTImHesn8A/ZD5v/B/slLN/TNfHNAAAAAElFTkSuQmCC"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nUNSAFE VISIBILITY PATTERN | VIS 000\n\n\r\n\r\nReset context:\nCombine ALLY A, ALLY B, and HOSTILE feeds. CAM A/B/C identify permanent delivery slots; semantic roles can move between them with the seed.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [448, 252], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"CommitSignal\",\"arguments\":{}}", "assistant": "S4/Y0/H1 shows the marked broad face unobstructed to both role-labeled ally feeds, while the hostile feed has only the unmarked edge. Commit this configuration.\n{\"name\":\"CommitSignal\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} diff --git a/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_results.jsonl b/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_results.jsonl deleted file mode 100644 index 110e4b7e97ee8ef32dfc2742bcce06efb5fdaa0b..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_results.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 1, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 1, "actual_rollout_group_seed": 1, "initial_attempt_group_seed": 1, "final_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 442.7574, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32546 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7520, "output_tokens": 719, "reasoning_output_tokens": 675, "total_tokens": 8239}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7520, "output_tokens": 719, "reasoning_output_tokens": 675, "total_tokens": 8239}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32546 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14846 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32962 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 11397, "output_tokens": 711, "reasoning_output_tokens": 658, "total_tokens": 12108}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 18917, "output_tokens": 1430, "reasoning_output_tokens": 1333, "total_tokens": 20347}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32546 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14846 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32962 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38934 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14866 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33114 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 11264, "input_tokens": 15974, "output_tokens": 841, "reasoning_output_tokens": 782, "total_tokens": 16815}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 18944, "input_tokens": 34891, "output_tokens": 2271, "reasoning_output_tokens": 2115, "total_tokens": 37162}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The forward arrow clears the rack once hook 1 is released; no other item intersects that path.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32546 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14846 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32962 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38934 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14866 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33114 chars]"}}]}, {"role": "assistant", "content": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39502 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15618 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33746 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The forward arrow clears the rack once hook 1 is released; no other item intersects that path.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15616, "input_tokens": 21393, "output_tokens": 412, "reasoning_output_tokens": 366, "total_tokens": 21805}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34560, "input_tokens": 56284, "output_tokens": 2683, "reasoning_output_tokens": 2481, "total_tokens": 58967}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The forward arrow clears the rack once hook 1 is released; no other item intersects that path.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-2847da4d779f", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0001::codex-gpt-5.6-terra-medium", "job_index": 0, "case_index": 0, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 2, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 2, "actual_rollout_group_seed": 2, "initial_attempt_group_seed": 2, "final_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 407.3744, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "assistant_preview": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37794 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 312, "reasoning_output_tokens": 263, "total_tokens": 6798}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 312, "reasoning_output_tokens": 263, "total_tokens": 6798}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 0 Sword\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 0 Sword\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "assistant_preview": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 0 Sword\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 0 Sword\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37794 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 0 Sword\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13606 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31122 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 10329, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 10383}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 16815, "output_tokens": 366, "reasoning_output_tokens": 263, "total_tokens": 17181}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "assistant_preview": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 0\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37794 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 0 Sword\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13606 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31122 chars]"}}]}, {"role": "assistant", "content": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38674 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30710 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 14257, "output_tokens": 1266, "reasoning_output_tokens": 1200, "total_tokens": 15523}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 31072, "output_tokens": 1632, "reasoning_output_tokens": 1463, "total_tokens": 32704}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 3 Left\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The configured leftward path is clear after releasing the retaining hook.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 3 Left\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37794 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13570 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30570 chars]"}}]}, {"role": "assistant", "content": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 0 Sword\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38130 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13606 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31122 chars]"}}]}, {"role": "assistant", "content": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 0\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38674 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30710 chars]"}}]}, {"role": "assistant", "content": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39254 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14302 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31682 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The configured leftward path is clear after releasing the retaining hook.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 20116, "output_tokens": 52, "reasoning_output_tokens": 13, "total_tokens": 20168}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 33408, "input_tokens": 51188, "output_tokens": 1684, "reasoning_output_tokens": 1476, "total_tokens": 52872}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - RackGuard\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releasing a...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The configured leftward path is clear after releasing the retaining hook.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - RackGuard\n\n\r\n\r\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-1292eae75edb", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0002::codex-gpt-5.6-terra-medium", "job_index": 1, "case_index": 1, "model_index": 0, "worker_index": 1, "parallel_slot_index": 1, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 3, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 3, "actual_rollout_group_seed": 3, "initial_attempt_group_seed": 3, "final_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 392.2006, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37914 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 378, "reasoning_output_tokens": 330, "total_tokens": 6864}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 378, "reasoning_output_tokens": 330, "total_tokens": 6864}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37914 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37954 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13866 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31534 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10026, "output_tokens": 433, "reasoning_output_tokens": 386, "total_tokens": 10459}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 16512, "output_tokens": 811, "reasoning_output_tokens": 716, "total_tokens": 17323}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "assistant_preview": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37914 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37954 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13866 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31534 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31686 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 14323, "output_tokens": 977, "reasoning_output_tokens": 921, "total_tokens": 15300}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 21120, "input_tokens": 30835, "output_tokens": 1788, "reasoning_output_tokens": 1637, "total_tokens": 32623}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 3 Left\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The direction is set; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 3 Left\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37914 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=37954 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13866 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31534 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=38366 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31686 chars]"}}]}, {"role": "assistant", "content": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 3 Left\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39002 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32598 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The direction is set; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 19873, "output_tokens": 48, "reasoning_output_tokens": 13, "total_tokens": 19921}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34560, "input_tokens": 50708, "output_tokens": 1836, "reasoning_output_tokens": 1650, "total_tokens": 52544}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The direction is set; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-730c4ca2457f", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0003::codex-gpt-5.6-terra-medium", "job_index": 2, "case_index": 2, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 4, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 4, "actual_rollout_group_seed": 4, "initial_attempt_group_seed": 4, "final_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 407.4901, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40338 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30554 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 165, "reasoning_output_tokens": 123, "total_tokens": 6651}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 165, "reasoning_output_tokens": 123, "total_tokens": 6651}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "assistant_preview": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40338 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30554 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "action_extracted": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 10629, "output_tokens": 56, "reasoning_output_tokens": 0, "total_tokens": 10685}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 17115, "output_tokens": 221, "reasoning_output_tokens": 123, "total_tokens": 17336}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 2 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 2 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 2 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 2 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40338 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30554 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}, {"role": "assistant", "content": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 2 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 14556, "output_tokens": 364, "reasoning_output_tokens": 317, "total_tokens": 14920}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 21120, "input_tokens": 31671, "output_tokens": 585, "reasoning_output_tokens": 440, "total_tokens": 32256}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40338 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30554 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}, {"role": "assistant", "content": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 2 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}, {"role": "assistant", "content": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40938 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14522 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31386 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 19493, "output_tokens": 691, "reasoning_output_tokens": 637, "total_tokens": 20184}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34560, "input_tokens": 51164, "output_tokens": 1276, "reasoning_output_tokens": 1077, "total_tokens": 52440}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The extraction direction is configured and the required clearance actions are complete; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40338 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14234 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30554 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}, {"role": "assistant", "content": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 2 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14506 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31226 chars]"}}]}, {"role": "assistant", "content": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40938 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14522 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31386 chars]"}}]}, {"role": "assistant", "content": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32022 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The extraction direction is configured and the required clearance actions are complete; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 18944, "input_tokens": 25464, "output_tokens": 142, "reasoning_output_tokens": 99, "total_tokens": 25606}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 53504, "input_tokens": 76628, "output_tokens": 1418, "reasoning_output_tokens": 1176, "total_tokens": 78046}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The extraction direction is configured and the required clearance actions are complete; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-218cdf46232b", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0004::codex-gpt-5.6-terra-medium", "job_index": 3, "case_index": 3, "model_index": 0, "worker_index": 3, "parallel_slot_index": 3, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 5, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 5, "actual_rollout_group_seed": 5, "initial_attempt_group_seed": 5, "final_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 153.0483, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40342 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14534 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31426 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6492, "output_tokens": 874, "reasoning_output_tokens": 815, "total_tokens": 7366}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6492, "output_tokens": 874, "reasoning_output_tokens": 815, "total_tokens": 7366}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40342 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14534 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31426 chars]"}}]}, {"role": "assistant", "content": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32114 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10539, "output_tokens": 470, "reasoning_output_tokens": 415, "total_tokens": 11009}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 17031, "output_tokens": 1344, "reasoning_output_tokens": 1230, "total_tokens": 18375}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40342 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14534 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31426 chars]"}}]}, {"role": "assistant", "content": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32114 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40958 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14666 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32270 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 14892, "output_tokens": 775, "reasoning_output_tokens": 712, "total_tokens": 15667}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 31923, "output_tokens": 2119, "reasoning_output_tokens": 1942, "total_tokens": 34042}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The configured forward trajectory is the cleared route after releasing the hook. Attempt extraction now.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40342 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14534 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31426 chars]"}}]}, {"role": "assistant", "content": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40582 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32114 chars]"}}]}, {"role": "assistant", "content": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40958 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14666 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32270 chars]"}}]}, {"role": "assistant", "content": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41370 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15430 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The configured forward trajectory is the cleared route after releasing the hook. Attempt extraction now.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14592, "input_tokens": 20266, "output_tokens": 187, "reasoning_output_tokens": 144, "total_tokens": 20453}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 32512, "input_tokens": 52189, "output_tokens": 2306, "reasoning_output_tokens": 2086, "total_tokens": 54495}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The configured forward trajectory is the cleared route after releasing the hook. Attempt extraction now.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-551cfd0150e9", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0005::codex-gpt-5.6-terra-medium", "job_index": 4, "case_index": 4, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 6, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 6, "actual_rollout_group_seed": 6, "initial_attempt_group_seed": 6, "final_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 459.0086, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39578 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29854 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7322, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 7368}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7322, "output_tokens": 46, "reasoning_output_tokens": 0, "total_tokens": 7368}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39578 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29854 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39782 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13994 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30442 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 7040, "input_tokens": 10530, "output_tokens": 1664, "reasoning_output_tokens": 1611, "total_tokens": 12194}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 12928, "input_tokens": 17852, "output_tokens": 1710, "reasoning_output_tokens": 1611, "total_tokens": 19562}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "assistant_preview": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39578 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29854 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39782 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13994 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30442 chars]"}}]}, {"role": "assistant", "content": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "action_extracted": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16064, "output_tokens": 258, "reasoning_output_tokens": 197, "total_tokens": 16322}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 23168, "input_tokens": 33916, "output_tokens": 1968, "reasoning_output_tokens": 1808, "total_tokens": 35884}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 0 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 0 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39578 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29854 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39782 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13994 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30442 chars]"}}]}, {"role": "assistant", "content": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}]}, {"role": "assistant", "content": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15616, "input_tokens": 20910, "output_tokens": 934, "reasoning_output_tokens": 877, "total_tokens": 21844}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 38784, "input_tokens": 54826, "output_tokens": 2902, "reasoning_output_tokens": 2685, "total_tokens": 57728}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The forward direction indicator projects into the open space in both SIDE and TOP views after the clearance edits. Extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39578 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13686 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=29854 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39782 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13994 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30442 chars]"}}]}, {"role": "assistant", "content": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}]}, {"role": "assistant", "content": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14018 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30642 chars]"}}]}, {"role": "assistant", "content": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40566 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14778 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31266 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The forward direction indicator projects into the open space in both SIDE and TOP views after the clearance edits. Extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 27142, "output_tokens": 270, "reasoning_output_tokens": 220, "total_tokens": 27412}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 58752, "input_tokens": 81968, "output_tokens": 3172, "reasoning_output_tokens": 2905, "total_tokens": 85140}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The forward direction indicator projects into the open space in both SIDE and TOP views after the clearance edits. Extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-bfe146465260", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0006::codex-gpt-5.6-terra-medium", "job_index": 5, "case_index": 5, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 7, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 7, "actual_rollout_group_seed": 7, "initial_attempt_group_seed": 7, "final_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 437.879, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39694 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13846 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30402 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7524, "output_tokens": 262, "reasoning_output_tokens": 224, "total_tokens": 7786}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 7524, "output_tokens": 262, "reasoning_output_tokens": 224, "total_tokens": 7786}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39694 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13846 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30402 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14138 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30998 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 7040, "input_tokens": 10938, "output_tokens": 1209, "reasoning_output_tokens": 1161, "total_tokens": 12147}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 12928, "input_tokens": 18462, "output_tokens": 1471, "reasoning_output_tokens": 1385, "total_tokens": 19933}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39694 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13846 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30402 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14138 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30998 chars]"}}]}, {"role": "assistant", "content": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10112, "input_tokens": 16002, "output_tokens": 534, "reasoning_output_tokens": 477, "total_tokens": 16536}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 23040, "input_tokens": 34464, "output_tokens": 2005, "reasoning_output_tokens": 1862, "total_tokens": 36469}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The selected forward vector leads out through the opened depth corridor.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39694 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13846 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30402 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=39890 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14138 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30998 chars]"}}]}, {"role": "assistant", "content": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40270 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14158 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31150 chars]"}}]}, {"role": "assistant", "content": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40670 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14922 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31774 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected forward vector leads out through the opened depth corridor.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15616, "input_tokens": 21101, "output_tokens": 377, "reasoning_output_tokens": 339, "total_tokens": 21478}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 38656, "input_tokens": 55565, "output_tokens": 2382, "reasoning_output_tokens": 2201, "total_tokens": 57947}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - WrongSelection\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releas...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The selected forward vector leads out through the opened depth corridor.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-a5feed00e148", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0007::codex-gpt-5.6-terra-medium", "job_index": 6, "case_index": 6, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 8, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 8, "actual_rollout_group_seed": 8, "initial_attempt_group_seed": 8, "final_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 378.2394, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41142 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31274 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 285, "reasoning_output_tokens": 243, "total_tokens": 6771}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6486, "output_tokens": 285, "reasoning_output_tokens": 243, "total_tokens": 6771}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41142 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31274 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41498 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31958 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 10445, "output_tokens": 63, "reasoning_output_tokens": 0, "total_tokens": 10508}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 16931, "output_tokens": 348, "reasoning_output_tokens": 243, "total_tokens": 17279}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41142 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31274 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41498 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31958 chars]"}}]}, {"role": "assistant", "content": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15618 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32578 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 14382, "output_tokens": 341, "reasoning_output_tokens": 287, "total_tokens": 14723}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 21120, "input_tokens": 31313, "output_tokens": 689, "reasoning_output_tokens": 530, "total_tokens": 32002}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The hook is clear and the configured forward path remains open.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41142 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14538 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31274 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41498 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14882 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31958 chars]"}}]}, {"role": "assistant", "content": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15618 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32578 chars]"}}]}, {"role": "assistant", "content": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41702 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15986 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32638 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The hook is clear and the configured forward path remains open.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 13440, "input_tokens": 19306, "output_tokens": 62, "reasoning_output_tokens": 24, "total_tokens": 19368}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34560, "input_tokens": 50619, "output_tokens": 751, "reasoning_output_tokens": 554, "total_tokens": 51370}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - ExtraHookRelease\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Rele...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The hook is clear and the configured forward path remains open.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - ExtraHookRelease\n\n\r\n\r\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-70a50da27c70", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0008::codex-gpt-5.6-terra-medium", "job_index": 7, "case_index": 7, "model_index": 0, "worker_index": 7, "parallel_slot_index": 7, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 9, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 9, "actual_rollout_group_seed": 9, "initial_attempt_group_seed": 9, "final_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 110.117, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31442 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6484, "output_tokens": 316, "reasoning_output_tokens": 274, "total_tokens": 6800}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6484, "output_tokens": 316, "reasoning_output_tokens": 274, "total_tokens": 6800}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31442 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41430 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15118 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32182 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 9956, "output_tokens": 944, "reasoning_output_tokens": 896, "total_tokens": 10900}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 16440, "output_tokens": 1260, "reasoning_output_tokens": 1170, "total_tokens": 17700}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant_preview": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31442 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41430 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15118 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32182 chars]"}}]}, {"role": "assistant", "content": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41774 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15574 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32210 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 9088, "input_tokens": 14759, "output_tokens": 1293, "reasoning_output_tokens": 1238, "total_tokens": 16052}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 10880, "input_tokens": 31199, "output_tokens": 2553, "reasoning_output_tokens": 2408, "total_tokens": 33752}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "assistant_preview": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 1 Forward\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31442 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41430 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15118 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32182 chars]"}}]}, {"role": "assistant", "content": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41774 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15574 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32210 chars]"}}]}, {"role": "assistant", "content": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41834 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=16350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32834 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "action_extracted": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14592, "input_tokens": 20619, "output_tokens": 453, "reasoning_output_tokens": 392, "total_tokens": 21072}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 25472, "input_tokens": 51818, "output_tokens": 3006, "reasoning_output_tokens": 2800, "total_tokens": 54824}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 0 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The hook and corridor blocker are cleared and the forward direction is set; extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nROTATED ITEM 0 TO SLOT 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41054 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14810 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31442 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41430 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15118 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32182 chars]"}}]}, {"role": "assistant", "content": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41774 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15574 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32210 chars]"}}]}, {"role": "assistant", "content": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 1 Forward\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41834 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=16350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32834 chars]"}}]}, {"role": "assistant", "content": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nROTATED ITEM 0 TO SLOT 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41834 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=16350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32834 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The hook and corridor blocker are cleared and the forward direction is set; extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 19968, "input_tokens": 26357, "output_tokens": 104, "reasoning_output_tokens": 60, "total_tokens": 26461}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 45440, "input_tokens": 78175, "output_tokens": 3110, "reasoning_output_tokens": 2860, "total_tokens": 81285}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - ItemInterlock\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releasi...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The hook and corridor blocker are cleared and the forward direction is set; extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - ItemInterlock\n\n\r\n\r\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-8af6b336c020", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0009::codex-gpt-5.6-terra-medium", "job_index": 8, "case_index": 8, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "ThreeViewWeaponRackExtraction3D", "requested_group_seed": 10, "requested_env_id": 0, "actual_task_name": "ThreeViewWeaponRackExtraction3D", "actual_group_seed": 10, "actual_rollout_group_seed": 10, "initial_attempt_group_seed": 10, "final_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": -1.0, "last_attempt_index": 1, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": -1.0, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 9, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}, "duration_s": 117.3414, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40302 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13902 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6484, "output_tokens": 1021, "reasoning_output_tokens": 973, "total_tokens": 7505}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6484, "output_tokens": 1021, "reasoning_output_tokens": 973, "total_tokens": 7505}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "assistant_preview": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nSELECTED 1 Axe\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40302 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13902 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13914 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30934 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "action_extracted": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10667, "output_tokens": 1064, "reasoning_output_tokens": 997, "total_tokens": 11731}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 17151, "output_tokens": 2085, "reasoning_output_tokens": 1970, "total_tokens": 19236}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 4 Down\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 4 Down\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant_preview": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 9, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nDIRECTION 4 Down\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 4 Down\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40302 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13902 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13914 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30934 chars]"}}]}, {"role": "assistant", "content": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 4 Down\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41182 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14606 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31498 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "action_extracted": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 15613, "output_tokens": 863, "reasoning_output_tokens": 807, "total_tokens": 16476}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 32764, "output_tokens": 2948, "reasoning_output_tokens": 2777, "total_tokens": 35712}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant_preview": "The released axe has settled free of its hook, and the selected downward path clears the surrounding silhouettes. I will extract it.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward_total": -1.0, "reward_by_agent": {"0": -1.0}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 9, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 9, "max_rollout_steps": 9}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nRELEASED HOOK 1\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40302 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13902 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30278 chars]"}}]}, {"role": "assistant", "content": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nSELECTED 1 Axe\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=40586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=13914 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=30934 chars]"}}]}, {"role": "assistant", "content": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nDIRECTION 4 Down\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41182 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=14606 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31498 chars]"}}]}, {"role": "assistant", "content": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nRELEASED HOOK 1\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=41982 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=15126 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31914 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The released axe has settled free of its hook, and the selected downward path clears the surrounding silhouettes. I will extract it.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "action_extracted": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 21069, "output_tokens": 206, "reasoning_output_tokens": 155, "total_tokens": 21275}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19712, "input_tokens": 53833, "output_tokens": 3154, "reasoning_output_tokens": 2932, "total_tokens": 56987}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nDANGEROUS EXTRACTION - RackGuard\\n', '\\r\\n[task prompt]\\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\\n\\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target\\'s retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\\n\\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\\r\\n\\r\\n\\r\\nAvailable tools:\\r\\n- ExtractTarget\\n kind: method\\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\\n signature: ExtractTarget() : bool\\n return_type: bool\\n access: call\\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\\n- ReleaseHook\\n kind: method\\n docstring: Release one rack hook by item ID 0-3. Releasing a...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n retu...[truncated]"}, {"role": "assistant", "text_preview": "The released axe has settled free of its hook, and the selected downward path clears the surrounding silhouettes. I will extract it.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=-1.0, done=True\n\nEnvironment response (step_msg):\nStep context:\nDANGEROUS EXTRACTION - RackGuard\n\n\r\n\r\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-0ea2e7c02ebb", "output_path": "ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "ThreeViewWeaponRackExtraction3D-seed-0010::codex-gpt-5.6-terra-medium", "job_index": 9, "case_index": 9, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} diff --git a/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl b/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl deleted file mode 100644 index 01d4db9cd57ee4d52c97c68f1496f7dc3bf2694a..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0165-ThreeViewWeaponRackExtraction3D/codex_gpt-5.6-terra_medium/ThreeViewWeaponRackExtraction3D_seeds1_10_trajectories.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-2847da4d779f", "created_at_unix_s": 1788846048.245261, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 1, "requested_group_seed": 1, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [1], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass band is on the axe handle, so select item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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J4yrZJGBEK4DMxp8RogNw70AjNgmIF/0x3vrDtwAAvxTFo3u3KVIEG1jSjbbWU1vWvvn333/2v79579BfOSMPoX/o4CEAgKKoAIDzL1iSWRkwG9HIgUr/eNhUYEQrgJFv+qfEjzFmTalJKf1j/N2q2p8q5zcePyAFfQCrmoq7us/u2PTup7/56999/3NDcLWMdGb9bOR+DAwwBGDnjl30ZTpNwGxEw8viGePSSX9KY3NHlbcvBYwxakxAI20SQMks/SlfWT3x358Ld7SdC3S3Q6xiHYdCvr3b1t3xme9OmTXnJ1+7q9hXy8hg1s8k9DG+oRJBU+NjYNENrBn4rXY1XhNkMy1gNqKhJFn0t3f5qyrcQ3oRYw40cob/jz/6eTDa+Od7Zh0/vLe8ZjyAGGgqUGRZjZw5duDIvr3f/eP64b66sc9n/+GxdJsOHTwU+6GqfZUXrvLCGyrRTVUcghBCACFAEAocsgrcbeOsN9dabqoRsAkAeOeOXbGfdO/yj9/8ftH+OEYf6Qb+jBE9Axgtxp8YMM4TkCUv/fIfbv/cjxZcfHXbmQaAMVQUzOPW00fef833nSc++M4jV4LRwM0nWn63TafLn7mYe21qLRgl0l+SZKvVknKkHxvsIzjAR0o2Q8CT3eBt461UBSi68VabSg9NOTMw9QTRAf/5/W+CUuK6Oz6PMWg/so5DQNM0UeAxxpoBidrE2C5CSQVWHiMIdANIio4xsIgkeUvVsaQBAeGISixvGACOHEQKLgLIYQzqF6+EELzz0vAbA0qE4haDy0MBDKMh6PY776EmIAjAigtnptynsc1/+zXnJ6+/9eHvnX/V6qaTB8lDjbEgCIgTnOU1l1977Xe+cOvIzAS+6UTLE71CPwM1944HI3vsf/LEifhNGOPrvYgM8gvxRgbGBgaaYbzZqgJqNuodIZx3/uLYbiWiAyomLpzh1RVZJ/cAAp4DqqaJHK/pOoZcTAGoOpHshoE5BGSV5PCKPNIMAwCoYwBBrwLAGAIMITQwUQCGAbBB1AJvQZpj0rFd78fe9zvf/ErK64k3AVEfQHwxuBisFlA60Egb/o9GQxAA4C+/f+ytZ348ecp8xPMAkWddN3Cgp3Pz2ncf/cn/gRHGTcdaWp89l430BwC0Pnuu9dlzYKRK/472NpfLCQC4vgLe4EXUwsMXSPqTbwipBQutPHdbnfUWYiMSb6jmMcAIod279paaLQgDbOg4efJEDGoJK8303YSDE0ItyArTEJe4o0E6tOZ6be2dPbkewiiWAhh1xp+C8NqzP6qfutBitWOEdKDrutbT3bVt3ftf+rcnwIiBiP6P9DyOGiFqIEH6AwAus4RvrDSH/NERelGAEPCIOAxEUxlQ+VRqOgBiDXNQBYAM5iFAVH7oZLwDscoBjYM6QBjoMgA6EeSYbAVk7E+mBwBrwFAgwjzUEdAh0IGBgW5AUt6HnMmgrnwEoUFKvDGKzUisNTbEk4BVt9276o77V91xfzZDjofvXFFWRsab6Xj5T/9WVTfJ5nBiAAxoGIYuSf79H2/5zDd/AUYAgxTiw64DkqX/5ZbwEFeGMY0fYHWtUIo6IOlLQk1tnJmoRacBWB/AyEl9J1Rb08OSJ2x0n1yZO2NiWZkr+Wf/5pf2b37p4NaX8zjn2AbqMnl8iYaOY+Hcawc88qe/+lH0eTA/qZVXnbdgzrUFHP4PmTNg1W33AmQWhIHRQUcGH8C1l6cODUzc7YHvTZm9MOjv4SAQeQ4hJFhsk2cumDhx8tQd0Y7V3PJH9M1PDKUPoFDie7i8AimlPxg+MAavtpLgURKovmRhKfgDKsbPmVqFJFk3H2wybDc0A2HiKYlousfJS4ohcFBXNcRzhg44hGSFTDcFwfQBYMMwDMBxmmYIPAqENZHjSKVPSHJ9dQOoGpkSWCzIsNZMnzE19r5LZ1elvJ6EMNAVFy/IcPG0hujcS24v3P0Y9fAROckagMH23W/TxQsXR2sb/Nf//jimk02h3yv5e/X0u+9//JNf/mjlVef94sd/HEXGn1W3FyVx970nH7vy3u8sOf+Cjo5OTdcFCHRVbjp+iFdC5VetLX//mub6T7gDqmOg8xRwzOI9ubRQp2p99tzQ64CRJv3pGHZ1rfBqM4kU2r1rb0wHjNK4oP2bXxpwn8vveYyYa+jIHQMeAR2RBYSJcYxHiCcjFowBJP9DsiOK2okggkgnQgRCTBQDhyD9jTHmiAIAJBSo98zR2QSjyMB3330q1fpeWd83D4vK/5jkp8u9s7mo1wcAcHTnvkIpgGJPAv72i18irb4wbusJK6pewBkA5fK7H7tixfIzTR1m9AgvCGKZ215RUTOz6ggv8C+8fvwLM7dlPsMlf/cXMMKkP6Wz/sP8Dsxv/FVw6d86bcnkSamrSwo8H3jv1ZE5D8hGRheVy+95bFo51EgRLIAgsAhAM/Rely+HTCGOkKFoGocQNohlSDOIkCCqgsRTaToJDeIMEvsDDMOAZrAQJmdDihY1RXA8xK66adOmFGMGwCYBAyqAeKnf+y8GHrfN54/0G/r37mgYGCIiNwso/YutA371sx9QH9XOvcdEgW/tCsSCEQqlAMgTedc3rr3mqhNnW0nEGxScdstl88Mcx7301skvzh5YhhZEARRc+g9SBwwN7/427eSpdRr5EDMoAABAbjoAgFdbojrg59+8G4xdLr/nsake0pCLMz3APAcMbCBIRvEGJiN90jobGrpBVgKMouYC84tlBnkSExCEyOhNGiATgN5hpGa6hA3DlNSecdOm1Q9eAbzx/o7YcoXHsfzCuXSZGYLSJYIlS//oApH+8TvgBNWKCy79i8dvfv7D7XsbyBKZkZLoBFNAD3zga+99lNMbffWztwEAIqiiu/UkxupFMwMG5s4cba2vnwzAiBago52Vn0k91Xjq/WhT2cy4rl29dFIODpiBnWZjBV7gsKrT778Z4GMQKW5+lczpNMDItPST7xXx7tKEL4yBjoFB9AWxD1EFQDLFsAGIC4FYEeiuCEGOJ+kCIsg5EChe3Kdk8/aDdOHg1peZDsiYCRynCACIDv/7hH8+LvoRVCMoKv0J5C9RtWzDIslYhfwX7wohS31hDOZoBpkDG2AmuUAAl07WPgL1Uxx7IC+++PLeT942KxyRnj92693lfymsvknmoelfAsXBe3LpCJ8EMApCvFTFAFl5KEBkTp5Jm14Iey0rGBB/LgaIQyTZCwBDp9+CaLw/UafRZDFg6NHvDrEBmQGiNCiI48i/CMEQJHEZudJrp+7LLYjZpqMRSmScB+M1QYnDZ5L6dCGl8SeRIg7/i6EDfvvbJz7zmUfog0mCk02bZowM2o2Gmsengybv0runuRytLAHn1x4Mh+Avf7upwmN784OTM6d6p1aKb4bvucHyXIbr1HWDfL2iKif6gPfqG3JqcyN9V/KHxPljhoJ0+umWay8asmtgZDPyLRDkkQsruqSZwyYS5EOCPjlIDD4GhooOBWL1icp9kgpAtpOkXyKRMaRmIvNMWNEBjzBChqZjHUOIOB0D3rQMCRzE1nwe4+gkIvrdi1vfbweyy2e+nLaKVMkqgJTGn3hhmML4Q18X2/hTRB1gph3G95suYMfRjj1PS4GOCZf+w7HTu0Qrv/XD1k8/cPec9v/++rvjJFWdNKFi/kznxs4HL1P7xU3FE+2D2pfflOKp7nu4qb7pnZkMgSZ4aPqX/nDs58WYuGTJKNI0QyWjKbEhQ/Rf+kXGxEBPBgp0uECferomKhzN+W1MSvb+2zeOpiMbg5RwAAJHXLuKga0iJyAoq1Dk+nazCEDXoaJBQGr8kLUiT/K8FDVqEeI58g4iz8mqLmvkbKpOdiBzCDq8yZOBH3sm/dOagESBV1Qt2fjTpxLA2IHogE8/bBBPVIFlpaCeaTm0SQp0AF1pWv8DN8Q8h++55bGmdh8A4Ecrm//pbaxoRkRSJRWCAkZUxk8/ANhS/yAoMg3WqVhRZuiNcZdAA/lShxKYNThh/zXEhUhf0+kOrclHLWnmXmm/1HlomgOHOqKu4ObUbZwdDjtd6DhGesUUhNc2Hvd6vd09PTct64tsMcF/fu9gRYV35XmkJFe/eV6vsDYFcXTBvLkwg4xOxNw72lGL7tQ7g+0dVMQGy9FfyYONvmmseYGKhhGAFh4oOnEFW3gk8oiDUNGi9gE6CO89Be6vOmIz1V6bD4ICj8w/kqyxIChrWM2hXFbOfObv2dg/hQKIPnuKqsX7fj1uW++2YRv+F9UZ8Nvf/f5Tn/qUOeQoGC7UcvrI1kigHegKAhpvSn+RA9qO70+1QGBWrvzx9S3fWIvOtkqzsbqpecalF8R8EoXkt1zR/fCKGsAQbDwrXz7RGr8+vihDr3rNsCZOjPWKjmg8eJxMpEPUvlXRf6PPZ5/sSrKYmSYIIowQiUyP7pGQaxoX30wjAnq1UNSHSUYJdCFu+ExOSJepsH7xvUO6pkAANCmsawqtbWZxl9981917du4MdHcBPDn65/Za7sLBwOw5c9ftbmo8cVKw2OyusltWTO03q0u+n7EE/lQTw0T63+OBdhroFAaJ19R0Q4cGxDrGHCbpXZwZ0m8Qt4CmI5JWSSxC5i3U6fza9PFyZFdkRgL16i6ys2YQpzC5g4ZuIOIWIJXkijLUZNI/pQKI3mtR4GWl3/CfBv+k9/0OaeRPkXTAn/70J7pw2533DLjzpt2nzDhlgcRozk89dD+zf2PE1wJ0GQGdSH+EBQQsPHRYQJUTzqnhrRdZt74cePyaJgDAP71UjgGuOjC/oWbuTZXPkzCVE8ttsy7RVFWRw9rOsxwvIosjfHR7Vbmt2i0Qg6k5UTbTbGgAE5mJc5hMKUSO5Nq0+GS1Zg4RS9NBsVFDPZ7Kqm5krHl3U02F+6IL+gLhcyZZCmWnRBJdMqktZvEewahA6retz25sbjK9/bHDYr4f86jokX9Z26BrJHadRxAhEj9aYbMIvJPjOAEZuq4pqrKroeGypctPHD8ZCofLJ4ENzYe720O6ZDWgyomaFIjoOt/d3T1r3oKujjaLoWqB9ldf71ABRwKrEYdE6x1XzwIjBgywTqo7Y03HHBH7kOi4+M3JcjuVPQcbGBKXcWwf05VsAPPkkISV52WI3bz3jKlUEsO3KtyO3/+WlZjOaAJKMP70Df/T+34TpH+psXV/v8oKrz5PMioevXdmuLsZ6wrCOgcNIv05U/qLoNKBZlRx1iUWsLjyksUPbf32f5OpwB3dX3upQjnTeh2C64R7t7YHA6f3za7za6pMJZCmKdgwOgIhRVPbusnAysKZnmEiphCtm4VV2SJYJElRNBVj3Nwdrq+a4W+P2jqKyr4N25yVXiUYCnT6gt2djS2dgqv6lhXzwMglO8lC+8Wk4sW3DwqGYXfYeWxomipJEVmOKEpYUiRNI7mugsBzEPG8wHGcaLUfP34iHAoEurtDchfikd1ltdmtiIeypEohWfZznMAbuiY6XDAY8Hpd5juLBsaiKCIOrdtyMiIpmgF4q3XVpeYEYlhBCCBSvS1a/wf0n6Ql3F1zEmDqzoSb2RuT02eCMh9l3YwNpfO0V1/qi49YkqYcdJb0BIY5aXzEwlPBTqV/svGHDP/TG38SXg9B4P9I6BzJIWQVBZWkNEbtALFN10/Xdn9EwstmTObipD9wWIj0n17FORdawIVeAK4B4LFLvvvY1m9XAwD+446ub7zifa7h7LWKNt1adbZ2gjlEAlYnuZ+qIol2p8XiNBCizgqZhFP0pWFjzAFgD5FPzwI5C/nsyh281cHbh6KJki8YimhGqLsbRuTK+mm3Xr0IjFFeeb9BCfhtooA0td3X1Xy4lcSriIJA/hd5QXDYHLxgwRj7utv9wYBhGEgQRYvF19k+Ycq0v39wVboz//SJ19qaGp1ud3vzOSsvkAM5qOsGRMgiik6Hw+nwuBwem922e39PIBgMyuqqFX1JUgXh6996PJvdyurmkNE6D3WdhPjQB5VYcIzkQT0NRTALR0SdCH3flFhQpmGYCcKYnI3MJUh+Gd0154K1GUiI8mCkcgLjjOFAIJHhSvsaXh3Acai6wrl04fROX6jbHw6EpFBEkRRN143rp/f1pG04HX18L5kD7CL02tHUSs4zzwKWVQCwAoBozchLvrsBgEe3fnvL47d1fuO1mtfPdJxXp1d6PfdbfwOsgyoGd2Cv6R29/z9AkUG6poYC3grvwa0ft5w5um39WwMe8sPvPgpGFX96ebud50QehUM9J061qJo6eca8b31y4Nyvp9/Y+Q/TaVmt4+DIu+l2+8Gl0YVvbV0EDCiIos3hfug2kr/9xIubwkF/c1vzaeWkpumIE2qqausnTN6yrbHDH9y8sTCVQnKAtAMgY3SMzaKfpnA304B7LUFRR7A58ielgHoNQGSyasr43ooyUScL7b5GcsRIWgGVM2aUUSEVACOTAogO/0Hi8L8n/fD/6M6+ErhjjwyjhZoKl9tpnTO1RjewquqyooYiSiAsP/+Tf0y5/1azH+03buYq5ojg8jIAlgEQE8otADwGwPFLVti3rg8/fksrAOD/rXesXOQ+UX77u7vDn58cLck3kjmzL+c4nEe//cOU6+//3FdjywLPLYwQH8lgkKaQudRgeOblj6wWC1YiJ5uaVVWePHP+N+69Ot3OlpN9oVCUR+ZW55TN2nS8b1D19Y/7+mHFUzepfsuOjW6nZ9L4SXes/lR7t2/DhqFWA2YnAGi6uAFvln9LJJac0hvxRc1Ase9WzNlPXyHOTBkwrUBmmGk0eCnnC6NXFxdXxsgMLwp8QuA/lf7mi9jK4Tf+jJBJgMtBgnggIF2ieAuyWgSPyz5gYvTjaxSwRvnpVdcBEIualwH4BwCOgnVBrVGbWMU3tpMJxL+uOPXou+qy8ybcfGHdi/uvBeCvg7namnvHD3sF/zwQ+HyyQJOxnjqbvLK9W68qH7AMK/hg3UYb5rq6WgOhwL/echkAvSaXJCk/xBzdvR4AEPa1yKp0+NihyeMn37jq3g6f31ZWvu7NlIUdC0k0TMr84U3RYfrLzVAgs8ynSYJvoP8p+rcA00l/sajLl+OQrvUrOpwr5W67zSp6nLZjjR2kz3AvolnfiZEMuS8Jgf+UHl90+D9yjD9DpgMyeAlTtr7LsnTtV1a+9NN3f9376tMAHAIbA8ZptTVgRNS+26zrYMf+5vau8MRq97WXXfjexu35/A2MNFxy4bzWtq5gKNLZE2xv63jgflIySPrgzb32WrpDOKLs2voRlNVDjUe/d9uVI/ZGdredJi6iSVPXb11XU1VXx/OXrbh14/pizwY4YqbnoKYioTfsxyD2fogxkf9fOP9Ilif6r+0zSRlpMo8gB5I2mwBqmMSAkrpxgJw+Vy6aP9lmFeqqy8s99hONHd2BiKJqVlGYP2McCbBjJBGt3p1g/KGvEv4tuPHnjY37wWgj3dTy2z9/NpvDv7Ky0vz3YQD2gy0BcFJrMaW/boByV3Qq/eNV58Jh/cSZ7o8PtAQCATCCGZn94jPwVsDKc5zNZnE57bpKrDPhUITMFa684aKl5x081LBjy8d7tmw/eerkw198eCRL/xiHd28I9jQdb9ix//Cek6eOXXf9nStuLm45UoSgrmOeuKnNfo5xcaB5Z9MQHwJxJpNaQGZxuVgqWW5UljsdNovDKl40f/J1y+YsW1Q/sbb8/LkTL5o//NFTIxMUH/gfk/7m8N9cNdDxgxz+D0YHDE/7+EGbFk0dsBt85AMn1Ga/HlYwKUSEgUZKo0T3+Y8bzkUkvb07tG33YVAaxDsASE+6QTsAEmifvsRYetWiedOIsV4UPG6HJMk330QM+uFQhP7IPX6fr7uxrflzX/pca0sbXnoVGD10t508e3r/5g+38SHlkituLNK7kPG+maLVG8ZDguL6NucitOOnzZDWE+1dGW0hPAgEjquqcC2ePeGapbOWzJ5ot4iDOt3YJfbhJeZFJvybcvg/GOkfE/2D0QEH970ChpaCFNj5ysozoEFt8RkhFVDpr+s4Ihv+sNHSGdXHP72p+Sc3tYzkQXqRzlwoB0A87dNJA4APNkSNaXabNRgIWOKEwu+feOq3//u/x04dGzdl0kOffoCuHI09qYLdZ7fv3tnZ3LZ8Wdqo08FBqzaYhfvNnz4RkfvtSkjsJtqFVgalbzQ4IAA8Ql6PwyoyB8BACiDe+NPjiyZN4KJJ/40HW9xeagwZFK8+8w4oDuu3H029If1TnqUViPKVP0VCCkmnNKuom9I/ZARChqQUPnph1Blq0jH/ruvylv60NDdpFPPOhi2bP4YQXnpptJDc/z3zh5amU5/5/Of/9fvfiT+wpaUNXDyaJgEUf1fj0SPbDzYcWbXyjoKf3AzkIcb+ZFNozAWcy+mSRp1mmTmAMaJlEBlDUwoizvQfZSijqN7YuP/Gy+bnd+zBfa/MXUCargwNsXrig+eXbymfvVbQDSyZY/9g2IgoWFbxvhPKgqkjfcZaVKUS8ndvBYkP5Er869PPPLns4ad+8odN2Z9q+pzZ5r94z/7jc2dNAhg0nj4+cUL1qeP+jvaTZ8+1nm1srvDW/cu3vkX3v+vOm1548fUrVlwSO8NWNfFKcmIlGB66209s2K7OX7zc6anYtn5NMd4iuYBWTgV8EmwONDCU+hNI3hiL4xwS+sWAxg//i2f82Xgwatlweyv9nR2D1AHFmwSkIZP0f/sYH58ONiC/eU+971LkDxuBsBGWsawCRQNyDicYnnjQgkv/mAMgpf1nJY6FToF9v78fwM/lKv37+sFC8MjnHmpvbQcAzJw9w/QHZNK1LS1tl1x2ydaNW+nLj3Yfaj57WpZlp6vc6610OhzzZo9c72Kou3F/d+PE6XMuXnHltvUfFOScpIG7afSno6CEUXpO0ffxO5N8YRr+TyvEDS6Qv6m9p66qLO/DSwrUL/C/l4R7XwzpTymIIajgnoAMj96Ao/+3j+VmcHxmE7H8RGSsEOmPFQ0o0SbbBaZQUnsoDUor8a/jpT/lqw/1Js5mxFXhjX2Y+w6emD1jIl0fL/0TwGZAy1133rRufVTiU8678Dy6cNHiOatvuv7uO1bfsPLypefPLbj0Hz/jgnkXXFPYczYeO3TgwJErV1173e19E+WLr7jtylX35XtKYqcnKcAG6QGpG6QDsKbrZnP3HOo40xxgXSe1QQ0zCoikB9NK2ORcxSwJzegFxUt/OvxPHfxffPL2Br/6zDtD7w0uoA7oHfsT6S/rUNHRe/ug76onfVc9WdgLG7zsHjLp/5l7L08W/X2TgOzw+fzZv2M64dXS0maxmiW8i8+5hh0HdqwtyKlmX7jSXTMN2atslZOcFd5Ov7Tjw131cxcvu+bqZSuuVsL+D956Jo/TYgAcIhY4bCE/wMoDu0WwioJF4DmOy8lvDiHkSR08ziIKVpETOSAg0hZGQNgmFK4rEyMjqURVOuOPWdEp8/BfNbCQvrb+xoMtZrPcftO7ghiCCkumZy99c5K8+egUN6dGU3Qy9ld1pGJO4ZziO/ebN4kbObagIkn/i1f+TfzLe29acPqZ+04/k1r6U7760KWZPQG9w3/g85FEitjwn1I3oY4uyJKsa5rdHu0AM5Y4vD1aeigSBpEO0GzGNHSeAycH1woXIaQZpBaoQf3ApAMAKeKAEInfz8lsYw7zISkBRIrBQUy7RZrfL530V2BO4KEApRj+pzP+DFr6iwLntIvJdt54Q1B+84ChmwTAokwCTNM/VAxT+iPnldP9NgHahWLZgnIV5Xkckj0z6qPi+FO3Ll7u++HpZwY2TWQ/CSDVis4Rm0+8/YeKflmS6XI4HA6FQvGH3HTjNQlWoEsu63MLlzKGYTZ24ZDAkfa/tLgPx5ESPiSYPxepbfazMI+BiJaF5iHpE4nMkqK6UfhoYEYySR9YvsafzNLfLDnHeT328VVuj9OaUEm8UM6AEcXbx/js1cDxTp5If4NTkMNROcFW/CCgLGV6UUV/jE/duviK0I+zEf0x0hmIYlh77TbJw3/DMGQlsUhbOEySHyMRKRJJLKNN4kEZJoGWAz4ZhnQ+oMCgAiUNyConqUhSkawhWc9BASg6UnRyiLnAyRoIqyCsoojOK8h5+kBhvNaMzPD9hv/5Gn/e3nr46qWZ+hZtPtxaWWafWONxOy0chyIyKaKZbsKYnyGITgIKEhJaQDNP9nFBpvS32SrqllUeyq8dUh4kCHdqHRpKN+/at54yDT75HDtX60m3qRFETUBnzrUnuH/D4QjVDbKi0BCgmOXC5/OLvUFBN914zetvrI2PB73kskt8Hwxc7DqRMTeQPdMFptWQrHXaGk0jycCA40gNq5xMQIKpLBACqkbORmpFY+Jc1gDQ+fLiXT8jnj6Nnbfxh0r/zMYfm4Uf53XVVDjLnNaacofXY+eLYAgqVERQYaVvlvMABVotZXWLy44Iwycyhma8H+OJTxo5jfoTmMD1dYyKp7Ga1HtIOfx3uMhjLEmy1Gv/oagmGd6LTQJiBFoOmnWbScw+/dKTxgB5fWvoITR5AELiD6D6+PTBDbmfjJEPfF/sf17GnwHH/ut2kOqA/jD4uLPj4z1xG4oQaPTqM++svi+fZNFik808QPTULi5r8FjN5rNjnSc+WdwgPzrGTx7+R8f7mNirqQ4w0tuakycBDMrJlsjMauKhMus46+ZcgKQHYFLbOVtI7D+iZzALQECqTbCKWAj/0MEP7PvNGPh/9UVE+me2/lNkaxZWfqk5tph3RNDgDUFFiPSJzgMyqIFKdEzkiTcNjGkKK/oncM/xZZ9NWPlxeydpXljmmtN/+K9jYuRBCAkCL/A8hDDgD9jttpi3gE4O0r1XS0tb7ZWryvduyekK05upRjEQ8WQSwEXFN/EA076P0Uze7E4CDdL8l/SWIR0liU5G5vFozFnNRoEJKE4DHN2RrfSXVFIJPIP0f2d/p2KrRGXj3HVTJo0rmzm5cuK4MquFTydf+fJxo71edGbePsZPnZDiSzKvHjst0D7SC0AMirVvPVXsgT/l9gryNHf7+3y5bebwX1FUfyDU0emLRIgJSJJkTdPD4UgwGA6HI1H7dX8z9uWXLU0IB2IQZ0nzoWi8jzmXIgogn8RdUgmITMeiPbyIRwFCcO7oh+wmD1spiAIaf476xRn1E+qq3Ncvn+WpnwXM6i473ntB0/S2rtCxMx1tXSGNmA/7v31Vlb+9L24vPytQkQoENbb5B2+8MjBYOBVPKke23ihPVcftQeCyQm6MDv97Pb35m/szoPX8xi9G63fGYUsY/nd0B3RdD4YiRNhA4HTYNI3Mxnie1/0hu90qySQuyO1yIoTIPAym8QQsXIa3pW7WmBI3GJs0tKizxvG6Ek2Op3GceRZQpc2BDcAJoFtO3aztO9//6Xe++ZXBXjQjCTKVi4cM/+HAw/+3tx6+aiDjT2vTWaddrLDKO3Yr7oajsRpqHIfK3LYarysUUfwh2Sz/1+8q3HE6YFgMQQM8xYOT0hgDScNBBVt4UvOEfAYc8DqgOBYnvkUV/cS3lEL0E7DZrip++O8PhHRN1XUDIRiJIFXVdLNlIO0fGwpHBEGAAAeDIY+H9BXkeTMcfYyq5AIAOZ7jI0DlDc7M6aWV3HLK3jJvMOCwQZLINAhExLedHsvNxkcgKA/jz9tbD1923ozMX42Nh9sEHnnL7LWVLqdNjK+gCSG0inxVhaOy3GG3CmaVrqS0gKqqvA1BQ14eLmdUHYTkvsqJEEALsUjDsWfwKaroT5b+OikpQ34Shv9Hjp9VFUU3o1UMA8uyHAgEIpIckeRwRIpIss/n7+nx+QOhnp5gU1PruabWYIh0E8QYX37pRfFWoJaWNjgKa0QXHF/L4QNNms1CTAi8ABFHkrlyzAQmX3zEQY4j8y2byDWHB1V4lZEHSRp7wGJnWw9fOL9eNIN40w3/Nx5u4zlU5rLVVrpo4H/iuyLoslvGVbmrK5w2i1Ak0VfY3OCo/cc0Ab28dudgToUBkEjRNzAmKbroFx7wC4miH2PcFZKbfeGWQGS95kkY/mNDjy9ToOukaoGha+aPbpBCZlhVdVlWQ+FIIBj2+YONZ5sbzzZTNZDnhQ5HQa2hxNd86NA5mXSI1MifGteDPVtIFQnym1QYPevjOs/EhwnmTJWXhQ/lDEox/Mdph/9vbz08f8YEp5momsH4Q2ysdnF8jbuq3MGnaezA88jrsU+o8VSU2cycgKJMAg6eeA8U/Js7aHWFMZA1sxPk2KLYov+s/olG/RMBWQ0pakTRJFVXNF3VDU032oPSkTbfia7AiU5/wvD/cANpnp75EzZLUBKtoOuGqmrhsBQIhP3+0NmzLQ0NJ88/b2GCKxhefJWqG4qm02tQNJ1eT1BWw4oWUbSApJrXWYTS3iOJQFsDqeMDMfEC5/g807KfphkIn+nRfc2HBnMlTPrnB99P+sNMaV9vbyX9ab1uW3wT0GQ2Hm4TBa6qwjmu0mW3CuksGxBCi4X3ltklRQuFlZ6ARHuNFtgZEAqCvMBFjhI1MJBU7LIQ+w8YQ+b+Ip18Q/uNHISKTiZhHIQcMn/IAqJlRZr9YZ9EMrms0+YmDP9z1e+YdOgkJY51XZdlWRB4sWFfYjxobXVjT0jVSQljDiGys1lEV9UNzrRpKrqBIPHxjBtrJU4S2XXw8IULZppRoBiQMnHZgg1yCA8MGRqhjpP5vXt7l7+qYqw62oeCbIvVUOl/6ZLpPD+A8Qch6HZaar1Op92CMuoKBKEo8pXljkBIlmQtTL7Amb6cOemAWFLYwRPvzZ16LciFRK908uZB8OsNYy3Yc+1bTwEAiif9XzhxFQQRs0kUiTWhjV3MkEESi0hHGLIWNUAkDP8PHTkBs44rx9joK2dGWnVqGMKyYOdJCGoAWLd+a3xS2LQbV7/3p6eo60Y3R7JkBGzGMvaObc0rHesKAACwfd/R5efN5lBuPgBoln8gn1FDntI/Bhv+D0oB9DP+pK/6cMG8KRYzVCWz8cdhE2q8rnK3jXYOygyC0GEVa7xOf0hWNF2jLdLTTALyJlcdgIeqK+Rop9ii/7njVyV+LNHPxgwfN//nzIE2XZ08/HeHe/z2sjgdgLPUARgTjeIIdPZlEwiJkwC1X0/05HcY606AODZ/fHjZ4tk5uQEEjhSX3rG/AQwJm97446U3PghGM1/+5o8AAD/7/tcLeE6+X+RPeuPP/BkTXAPlKW083Gaz8DVe17hKl9UiZDlS5jhI3MVeJ4kKDUrJg4i8DUGDqQyRsbQ5k/5DLPozIXCcHq1MD+bMmBQvds/t+jheB9CIz2zOSaW/K9iVsD5hEsCIZ8vuwysuyMFIa7FwH+4hdgVGlnK/sKI/yQSURqy9vfXwzPpxlR477aqa3vjTihAJ8B9f7fE4rdEWrFlAnAEiX1Xu8JuGIJn4zQpmCMpjEkAzEmm8YMq/ghiIaP4iKEWKLfp/ceYOMqkH3dnsDAGw8lxAjrf+k4+lrbVfAWeqA8jonhvY5mnoCkRcsvR3h31+e198UUtL2w0PPfDmHwrctW1Us37H/gdoG+YsYNJ/GOV+jF4rjSnMkof/b289PHl8dW2Fk8TqZj4RQi67pdbrqvDYUrb2zngsdDosNV5nmctqvlGmiKCcw4F6/cBZRwTRZqXEExhbZaql6DbNMIJhSVJUqqZkVYuYAmjMs/atp7JJ7FrwMNEQefDzU6t/fmo1NnKInEFm8QADx4b/qYEAeCUf+fh0LfYTvwM2tNhPyrE/Y8TSl1AzVnjgC48Ogein9Lm8Ukp/AECt1xkL5czg+7VZhdoqV7XXKQpcHmNjnkMVbnu112m3imqwSw12J/zE75x/sejsdIA5wMdqnEUzLJE6AZpuhCW1ozvY6QvvPnxWUTXdMHYfPnvgRDO1SY9hss/pzaldF+WRp9EjT/fzGLULWVWE50iHQiL++1v/22L2HwqCgEfIHfHTH7oyqgl6hb477Iv9pHs7d9iXkBR2w0OpU5EZQ0bI/G6OGR74wqNP/uqHQ/Z2A8yIF8+e5CChnJmk//vbiZ7oCYCe1pb9KRO5C2Ersdn4SETLwxBEPAGfJVaFbKBjf/qfJGt2K3F7GBg3tvTYbJZgWA5LKhnvY7zr8FmLyNd43YdOts6pr6GGoy1vPbls1RiUCLlG9y94+Kks1UCC3M8VHkGNVJLMNPyn3lg5Tp3HdABjtIMxON3UVT+xJmF9U3tPXdXoywt7YGilf98MYEpN4vB/7fbj86ZPKHeR+vSDIuvDVbEixY/Q98O7q8GgGXAS0Cv/yaif+gxJ8QBV6/KHewIRuhIDEAzLh060bvz4uNtpnVVfM+bLOOdENtI/edQfI3srkKobsqpnHv7TrItC2QnYJGBEYRhGt99sZzJWLD9DDJ9e+teVOy0xL2i64f87e9uQ1VvpsY8jNX8Eu1WwirzVIlgEziJwAs8JPCKpMWa9WIwxTSMwO0FT4sZoitbUETjT6m/uCLR0BWXFTDCnO/R+e3l3teZvy3kS8JuX4icB6RzCdPgfjTAEQJKJiUfgubauQDAs67hfDInLYQ2EJQjhquVzPU4rGNM88jTKtZJzhklAOrm/e9OaxZfeHHvZLpRXqQO4gjUDIwgzD/+LjdU6xj/9kYyuY7Oi8BiR/k8O7fA/kwloQpVLUgbwbb67t81ttyyZWTtvarXLLsaPglvbOrAKFBXkZJ9z8GDOeMec8aQkrKLqTR2BY2e7uvyRfknC5RX+7q5i6AAq/GPvFJYUqgCoBzhe+vMcEgWuxx+5/Pzp46s9Yyabt4DkKv1jmA8RrTBvRIvFZNgZQWHq/MTh/87toJi4g13r1m2+4orl9OWpU2du/9vPvvzL/ynqmzJSopk522Pj5jw55NKfjMVTDv+vumhGfCRPuuG/24LryvhyG4gE/e3tna1tHbGfwV8ZKSbqsVdXOEjFwf7v7y6vyOOEr/7mpQH2iEaTR/+JyJpZDxJISRVdOA51+8MGNhSN+iDHPnkY6xPCgTLYfGIghDiOE0XB5XRko1a54R7+x7j97/52uC+hFCEFvkd/FNADQ276j5H6C2kTyczAZbdmkP5bD7dUuKzjKl0ue79qz4UCQmi38DXljgq3TUiqKBfTATlFBMXrgGRnAELRhFITrOl6OKIahiH1j/LkOaRqutNG+gjub2g+cqqN+iGpuzj7iymdSUA2op/isNvcLkeZm9QRmTatPlg+ieNFUbQIgsgLIscLiBcgR34AxwOOH7f0soQznN21CyN+kD8gbiH6w0UXSCYBx7sjwXXrNsfe9NSpM1QH0GuLXScvjLWyHyMNVR31NXUfGD7pn8IEtHb78WsvnjmgQN90oMntsI6vcleV2dPV+xw8HIc8TmtNhTMYUX3BFNXi8kkPjrMFJRiCEsQ3CfqUlbCk0hhQCqk/g6CFF1Yum/3hvtONLd0f7j0lCty0CZUcQv5QX+GAsUd+noCLrvpU9vtvfPvPt977OV3XIIRKJHjJhQtkZU7NuQO6QdIvNIMYfDUD6+ayOn2Bt8wZO/bgwcPSmeTCn/2AANhF0nuBMz9H8kSRnqbIwNjCcwbGuoEtPCltr2NMWlVAsoAgFEi8qQEhEDmOdB0A2MZzCYagKVMmza0tIztzZGdSoq58CojkU4+WkQ0Yg/yLdZew4zeTAli2qF4cyPiz8UCTReCqyuw1FU47qeZfLAv4U3/ZlOWeBdEBvfI/WgvOMLAkqd2BsEoKjUVXCjzSDTzB66qv80IIVU1v6fBv3XPS7bDWeN1N7WlDyEsQMup/OgfpT8HYIDUEzbpvx44dnzd3dnjWBVUnPsaYo0XW6O8G77QE6W9+cgPMwAQOjXNZnRZBMIuI6hgTTWA2gKRqwMBYMHubGBjzpJgVSTOiBUcNUxMgCEkBZJKCAOvc4NU4HQAAmHXrnYG/vk5DHhrKpzqhWcWOURwwxsnm2dFFU1Pz2r/8aaQogLXbj1+/bHZMoEuSnPIYgecq3PZar9Nho/288mHyhMTQ3WT++XO3HjnTuXnvmdauULJ1hbcCzZ9nnbgEnzAlzs0bFfdhWcWkCGVvM0uEDANXuO1mshucPK5CWaBv2nW8rSt47Ey7w2bZ39AExjRZTgIGE93/6v/9dvXffCbWi/HAwcPz5s7umHpe9cld5gqyfr9nSrL0l8+cggCIPHkiDRxTFWa72b7ma8SCV+mwiBxHo716q4uam+luseWEhRQr+kEnAa6rb5LWvXnIUw8BCPjZgKCIqKpeIkn4Q6EA1m4/vuL8abQTagY2HWz2um0Tq90VHltyq6/sOX22NZvd3BZw/QUTI4p6rs1/rj3QFYgoqg4wCOtkmsK7q2I6INcaQTEdkCYqlFSkUFRd1aJWHQihIHAA43K33TCwPxhxO23TJ1Yqqravoeno6TZM0k9YhtFgc7v6dECvoKU6oK1+Sa8OSAGV/l6HpdJhxWZvSFKj38A6xkFZ9UvRuh2aYXSH5SqHVSSGy6RHHaZazrjXJ6Z4/pxkCDrkqQelKv3P/9yz2e769CcH+V5hiSTjj96iXNfcmvP8uIgKYNGs8Q6rELubKYf/G/c3WS18dYWjuoJ8h4bmxkMIrAJfXe4EAOqG0emPGDq2c/pgokITSJcZoJm9A+my0yZGZLWuykO6D/rC2/aeWr5kqsMmzq6vqa5wbdlzcpdZHAKMdTJMAgYv+hN1QC/xOiB5+C+fOUUcyBZ+ZpXHIfLURkQnATrGTb5wyIzWohlhAUntjsgOogEK8/wm64AlS+ZsWL8NlCQ7f31v1vsO6mnBGIQldbTXZl87rPafvs9g7fbjVWWOWP+WdMYfUrrZaa0pJ6b/ocx9JdXibEJNhaO63GkTE3sI5xcVOmBQEHUDxJuF7DaRJ3lhwW5/+PBJkgbc0hlAEFaVO8+fM9GsEloSUUDJgj77OJ/sefX/fhv/8oBp50mW/vSeIwAmehweqyhwSOSQhedsAmcXeYcoeGxkZewQWdd9ESUWuFUkLl9xcVHPzzAMQ1a1sV6Cq+hEvxjLF9dbBC6z9N98sNms9+n0xGUIDxkchxxWogO8HtpDOAGYZ/fgAZMDeolImtNmaesK+IIRg3if1APHm9duPfzhvlOdvpDHaSUtEEqPYoj+dDqgras7WforjSTyx2Hha9325DEJgsAu8C4yXomuMTAIKVpQIRkeheITUzzJUaEAgGef+O+CvQcjOQe4tw0cI2/IV3ft9uNlTiudSaWT/hsPNNksQl2lq7bCrPc5HHAccjssNRUOknXcXwO5y8uLrQPsVqHTFwqG++6PrhtNHf4te042nGkXBZ5WjispuV880Z+sA6pqaxIdvxhQ6U9aVTus1jRFyG0C53VYxbhJQEjRukKyWtBJwKJFixN0AJsEFBVN10d1EYhrRoADgCiAtduPX7dsVmbpT7OfvB5bXaXL2b/kwxAjClylx1FV5rCIyenBfTogV7LRAZKixroC9IGxrhtnmrqCEdlpJ6lhjGLPA2IY56LSX0CwxmlL91TyCJXbRJdFjG1XdaMrLIfkgk0CqNc3mf99MmuPKCNHVG3sFIEYRvgrLpgucAOM6Lccai5zWmsriPHnuTeK793K62sZkul8kJji8/AGD6gDSPRRGs61+XYcODOafVEjneThf0SGnXx5lUL8/5VOa5lNzNSnWuQ9NrGLVPSL1voIKmpXRHZa+OQk81yJxfxMra9ft27ziiuWw7iIoEGenJEOUqhrlBeBWDvcHmCiAJy935x0w/91Zrn/tqCvraW1rxeGiWzLpVEXBA6r6LCLtNlWRFYlMgQr2EfI24HW3TIYHZA3mq4fPtlqIzFUjMLz6X98zEtiwKIcPEAeyM7ODgyFNqFigtEzqcyZOaSHR8hp4a0CF+qdwyma0RWSvXZLBs2RDQkRn1Pr69ev23zhReeLIomSOHHi9KzBnJ2RIQdYKZUyXEMRBprB+JNCykOSEiXwXJnI26yC0y66HRZNN7r9EYyBIHAij0SBt4icKPCKqnX5IgACkedEgbOIvEXgRZGLSGqXL4IQjK4UOYvACwIXDMk9AYmU2xSjO1tEjueQLyipqqHpRktn4FybnwRcJn36fHltvA4AQ4iikspxQ/mOpSz92zv8ZuMeBXIimDCzzBrIfBIIgdsieKxCRKXhoOThCMhqQFbd1vzj2ZLj/anXt7Zugt1u5XnOZmVWwaJgGKQOIwsBKowCyCD93znQ6XZaZtdXL5o5rrLc0dqaX+ZtytY8VjAxsRApwcMDQMpBJ1But9GFWROc3YGy442d51p9sqInCvqqSn87rUUK39h44MbL5uV1wYyRKP0pkqQrvYXKOaBPnjiu0z0jQ44YxSbwFXZrT0QJ95ryFF0PyKqqG5YcW1hnlv6kVWTTWYvDgxCyWsSLSigyYOjQDaxqo9gBsGjZqva2aF+T4QVlkP4bD7e5LXiC1zK+QlSkYL7Sv8AgBGn3eY/TaqZzJneQr4wtv7HxwJBfIKNYHDxweM1zf3zv1afo3J9UdrNbzzaehQi1Tzs/9pPyWIRguV10WYTY44Ix8EWUgKxmb0g4O2FB7Ced9AcAbHt/jRzyKYrqD4QG8ecy0kJqAo7mGNA9W94CIwM0QPqVXaz1koLPXG+O2EhA4FGFx1Zb6fK4rKYTL9MUnumAMWP8WfPcH+ny7k2vm2HBnMNuAxDs23dg374+TZ9SGUBzEuC00CpwoNcVrHWFFSVjNGGC0KecOH4ifgSXHO+/7f01uhz0uFPMZRmDR1W1UR0DOnJI2xFs05E2h41If2+ZfTA1f4oBhNBmEWq8Lpr03xNIrBTt7jME5f8WmXfQNZXjmdd3CE3//Qfquze9vnzlXYLAGzqp0gwgjNcBCxZETX8xHVB1fCcHoccq2gQuKJvdRs2BZFdY9pLacJb4Dzxe1sc4cfxEwhpJkq1WS4ZsrymTxn1kfMvQDRL1oBiyrLz8x5/leCcYSWAQklQ97iv/4tq9d16zkN2pgimAjYfbLCJX43XWVbvt1iIWfM4bDiG3k3xpFUWPyFpEUhOcAfE6IFdnQFyv4j54BKMORMYwSH+w5vnEmLnN775g1g2lXl2iH2g3SdKLJkkZUE1gqJpx/Aw6vr8vHlRWu8OyyyKIHEqW+8lCP4EM0n/b+2sAACtWLKcXhzH2BdOaWxnZo+mGmY/Z92VUpFHZF37RslXDbgvi00ZNOKzjqtxlLluhamYVGAgEjiNV+CudgbCkqjQtcPA6gMh+0rDebFo/2gONRyOf/sfHkktwrnk+avzJkCN2yyc+Tf6hkcWknUBUj8eUwYIF8wSBnzplwhFVbWluj4Ugd6u2Htv4msoKmLXcf/mZX2fzt1AdcMUVy2mj4wqP7Yvf+LdfPP6tbI5lpENWNEUZOASoqb2nripl+AkjowLYeLjNZuGrvU6vx06aw4xI+U+ApDhductW43WFIqo/KA06LDgq/RGCHEI6E/7DRGXc8P9AnOk/M6/9+Xd9moBO4aLKwPxcTVeBuRLoqurxOHt6SPAoxjgYCjeebelqb7MOFLWZpdzPwLzZU7/+b//xo299DYxRhqA6iKSoYyAHeM+WtxYtWzXiFMDGw22iwHnLHDVeZ3K5hZEGhNAi8lXlDn9QlmTNLNWQ/ySASn+OQxyHdFIIushXz0ji0//4WIL0z+MmxTRB37TAlPvmf2b1YAjlcABihegGc1Mk5LcK7pS1hQcp9GOTAPry9OnG+ikT//Unv/h/X/3iYE5bsmAMZJUa8BiFVgAbD7dyCJW7beNr3B6ndehLfuYfFVrpDIbljp6QTmLD8tQBCEKeQ4hDHIKyokM4PDXvSpZk6Y8xfj3J9D84ZRDNEOSIZRPSpmBmXLmhm49OoUb6GXTAqVONrERE3hgGVtRCVhAocfrPACC0WYVxVe7q4Sv5mQc8jyrc9mqvHJKUUFjJ+9ngOMQTDQBVc4gBSUfYAl8qI6ex/yClfzplcONdD5GPGcKYHMEYr33tmeJ9OtveXxPfOpjWifv8A9n3TmFEMQzD7AKWD5ve+OOlNz44om7louH2A/cZ7DYeabOKfGU5Mf6MzMifdEAIrVa+uoI4LQSS1ZlnapjAcxxHvL+KppG8h7yk/+q778/nsNKGOn4TyNL0nwdvvPCH9W89t3PDa7s2vU5/Pt5ABulFZd26zbpumOCTJ0kdU1YrNN8UsF4HAAn/BaOXPcMdAtSnADYebjULPtvHV7uddnEUSX8KB0mrgNpKl9tpEXiUnw4wR/9IGc1dRkvN8TuK2Pb+mo0bt7Z19HR2+bp7Arv3HGR2jDzQzKSK0S76R5wJCEKS9Du+xl1Z5iD1FUYVTz3/QUHOgxCkVQZ5DqmajtCoMYKNPdM/GItse3+Nw10BEfmP41AgrPzXb//4D58ZWUaJkYvpsVE1fUy5gHVl0dKr93z41+FUAO+bBZ/b/aD9XBMYw8RVCE3pDYYQymaVMWyS35vE+ioz8jT9D9rxO5IJ+bvc5ZX0EWtv73jz3S3/9K8//vH/+6fhvq5Rg6rpeAxl5+z58K+Lll49jDqAp9J/ZKLZJ9AFnkM2q1jmstVUuCbUlJW5opVBKRMroxVXMCD9uXqCkX0NTfsamnoCkXg5zpmFG/RAazodgDGWVQ0hmF+ZEYTQuEr3pHEVr/w5j6NLkQTpT0nO+B1LbHt/zcVX3bxw8UIEoa4Tp8DeA8eG+6JGDaQFtDrW8nP2mDpguN6d163VE6rLFs0aP31ildNuIQkzoxYIIM9xlR7n5edPnzu1dtfhswdPtATCUrw7l3PVpNMB5NmiseK5D/8hhDMnV1923jSP01qYP6b0SGf6//aPfvbdr38ZjBW2vb9mxRXLzWeSPGiLF82tqix7+lc/Gu7rGi0xoEkV4MeKDtgzHJMAVOayzZ9RN6e+1jXKpX88HEQ1Fe5rLp5198oli2dNsPfv+sS5alIeJSsagqTUeM7vB+G4Svdl502tcNtHVNnUEc7v/vN7kYgU+7nttht//+cXEvb59o9I9bQvff3bYFTxtW9/72vf/t4TSX8OAODz//SdiEQmmogAm841L1/KCpllhaYb1Eg79tgzTPMA/qL5U6ZPrCJJv0WT/oePN4JhAgM8wWs3VE9jS3dYIikCNPdH8FRKvo6ESYBhkOiCPIb/Hqd1xQUzyt32IvwFY5yfPf6dL3/jO3R5z54DixbN+5fv/zi2leeJ5u7u6gSjDbMWETh08MgTf37h+PFT8ZuCIdlhi9aRhRCue3/91g/eHKbLHE1gAMKSkncSwMhnz3DMA3geaM2t7S2tY2TsnwAE0GoRqsqdoYhiYCwpGugt8WBNrQNyOz9CyOO0Lls8dXy1BwIoq2NzeDIEOoCWYdiydWd1tTe2KV70f+nr3/75j74LRgOXXHE9hFEdcPDAEUv/EkMx6c9Ef05gTBTAGHIAp2aIdQCykh5Jwy/9zYF57LPt9yHTmBximk9YT7f0OzBuU+9KDiGXw1pb6aosIzkC/TqI9f+7e5vFZgWEcPK48qULJl+zdObU8RWarjPpnzc/e/w7H+/YZRhGt1mgLZmf/+i7o0X6AwC2rnv7P7772JaNm0jhGllJuQ+T/rmiG0aKFrBjiz2m6B9KWxCfd0fswtJfCfW7pHRZaeSQNNeesEHkOa/HASFUNa29m8ReUPVgdVdK/g6Ao5OA7I0/CMGaCtc1F8+yWy0cNyJu4Ghn09rXSL6etzp+JcbgF/8+auR+AlvXvb113dt/+7XEJOd1768nW5nZJ0d03dDi6jWNbR1w/iVX79w6FPOAkvBYkloRolDhdlSVu+w2MVGhmC/3N3ZneTaEUI3XXVPpdjmsTPoXkE1rX/N39uuUPXqlf4z/+Y/vJazZ+sGbTPrngVmwbywP/+OJSf/zLynubKAkFECscHRlmYPUCxL42BTB5qkyN4PTp7JKguMQqq1011S4RsjMaUzOAxiMZDTNGANtAAajCYqhDNL2BB57IAgdNkt1uSsYVrr0kBoOEiuQzRqhm7OQ5wLP1XjdVeXEmlT0y2UA8Ms/PWsRk2Zs2aGqWpZ7KoqKAbCIiR2eMcaqppl9xcj/rW1dre3dPn8wEAh3dPbQcBQIACI2QFJYdBD544yBUUu7R8fO4liESkgB0MLRZW57VVhubWmh0p/YncvK/T1Z2X/G15SVu2y0jDDTAcXg1vs+e/BgQ+xl/HKMZcsvznAGu22Arl4pgPDj3Uf2Hzw+oHSJ/9Bhb8JHdbV31TWXxGaExxqOJhwlRaJjDMZgIEXax3wM0JBTWgoAAvjWB9vJwyR4RA5EDAwBsKLUcRrJUOlf5Gssaf7yzG9uve+zmffZsnlbhq3j6/q5kbNECvqsvJ7fAB4roRPHUigqRuGrALF7WmhKSAG8+NZmMn5cXD99+vSWDv/Z1p6eQKStyw9wtpOAmPTPoAY+3HemoFddcsRJYUxNK713u+9lXCMXEtZrGDj6G8FzTa3j62ryeNO8rQux8n/RmGQ4QIgaIz9U0gaAqYDRoAA2bE/bdAUA0NreddcNl4HhkP5f++zt9GWF21E/vjIYkfcePdtgJmq6y8qH+JIYKYlTrmTBfAWTtpJ/qfQ3W7kRlcBxprk+L7mraondpLMn1jmVtp5nFIkxnAM8jPAvvLnprhsuLci5Tp+LxvBNriOhNaeb2uO3trb3DbFfeHNjwrHFUwlU9N9y/YqZk/pSTHkeuXiLy2HZdySfpyomehgFx9fdE46EB9xtXF0dhOacABA7Xpy2AOea2nIyBGGMtUEIl5jGOrD/0IA7v/OX/8v7jUqcPBRAU3tPXVVZcS5nDM0AXnhzkymCLx286E8n8Slf/NSNZJAG4X//ITHaL1klDF4rpBT98ZxsbHH0rxOXJRmkfwnHKRSGD958/sob7h5QBzQ3ZQrblbNQITEMjEOhCM4uySghHR0AEPB1H9jvz/7tGPlBGoExCg08s/NVAMAPf/VCHjrghTc3p5s4f+nBm0xRT4prYowNA5tduKMhddTkapCoLowgacObUivQK4x/kf0VvvjW5syiny50dIc+3HNEk0kFAouzr22kaVhItI8tnBXtT0C5cuns5DN/uO8MDrR+44f/m+V1MlKSjQ7IQEV5DuM+VdPD4X6tI7JXACS22J5daAAE77Lh/yBYfcfdqe8rJD6Ye29KHRuWMAMYaU3hh52ojHv0C3f98FcvZDMVMIV+Yp+tv3/45pSJUZj0cklcT/x4pt3UgFg3jJiN9+8fvoWGUut6VFv84o+vJ707ucIEEi44wdyfTvRHrx4CKv0Ly+OPfp7pgMFg8LaFi6b1huBHPcMQQgNTg090pkXWE/evgcgmoKoqxthiEXmOq6nxxneBi19IoPFc25GGM9koAOpkiKfM47p82eKYGyAD//zFz2f5tzMYQ0bfIPfRL9xFpwIJaiBJ4kf5wgM3mGI6xbciRsr1sd3NkujRvrv064cQ0nTDtO2SY//+oZuJr48kAZIIMAjgr558I/mE8VoBQjhh4oRPrLpoQNEf21+2RGcJuQeQM4pIfHPNvmemLxAr+i9Nzya/zc5xfZFasWOTFhLgTfdxfsY78mSSA5lHiDEq4Y+dapo+pS72mk4FAADPv74eokQbyJcfvkkzbTr0G0jSIKlZZ9DETkJa0ptd6WMjsmjYh/mOX3zwJmSqHMPApm2JfPeiWgHjLz9yi27gV9/fs+/ImQWzJmUW/YMkpf0nHjYJGAyzp/eztuX6FNWaw/8s4agCyILkBEDS1lE3OPOJzcy//+J/2SSAMdIgIv5YbxkcqgnuvvnKcVXO//rdX+jKv/vUDTBqpicvBT46Zi828d80UeAT47Uhif8wSDQO/uKDUd8y3WHe1NqcRL/LLobCSjEcTEwHjAo4jnTmAnnFAdG+voW/JkaOvLh2753XsMZqOdNvjE81AeJEjMGXHl6tE2tM/0n0cAc/xuYbHIQARa/eIAniUdPwax/spQpg/UcHJ42ryOacM2rsZ1pUibSaS6wGwxgWVtzyqaFWAPk+2LqB9RKoUTzyUaT8QwZKmRRT146uHoQgzyGLyIsCL/AcHSINu/RPB22vSicB47zOLl+gy0cKvWVJjdfp9dispClmAcC9Hecpjz/KXH8jHfp853csNozB5BAwCscIlU4jnNTP/d7Dw9bFtxBQh162Pr0yl7Wuyu0tcxTpapgOGOHQAUR+xxoYZ1929N9/wYKDi0W+H2CpM0S1gH7yh8SAzgqb+NAnVhbr/XKZrFhFvrrCASFo7mCzyFHsAc4bhBDKwoubjuwVAKN48EPlmxxjFEUB/OeTb6mKwnGZPpKuiBKvFSrMjNwiqoT0IAjtVqGqvFgzAOYNHskOAKoAaCBpfiiqWtDLYeSDM48y4IxCKYCEAT7KKPpT0hVR4s9TZole2CP3Xj8EHxOC0GYZwAOckAacKywiaMSCIMwmjjMdMgkfYAwPNA0YAOBxkt4eCUGDJFEjzkXP5y6Xxjx8YQ07/YjWRuxNFjNf6mb8fnwyZ0p6ZM0wo+ueePbtoVEDXBbJnIwxCUQw82w1M4rMFMDw47Rb4uV+Skqhp/wQKYAfP/FqLIk3HTaLQOr8EA8b5MgP0dWSopHoUgR13ZDkgY2nPeY+VA0M2YSgSLBJwAh0ANAZAM8P0QyApYMVB9Lxu0inHtvwhRr19w7zyUgfQWQgWFVu5xDiOcRx0d8Y42BY0Q0DISjJWnNHIFqjBWelBoZyQlAkmA4YiUBIqkHk2xBAVTVWHnx4QbCf2YeRPXwBDD5E4hMNbBFIPDXHQVK5B2vjKt107E8mAWYyMcbAYRNpPr0ka/6grBs4VuabFAc1a3sR8MATgtGrBhgjxwNMA8h5PtsvQnI1CDMKiJUDGk4QQjPr++rZMLKHz1X0Q0jK9ciKLvBErtNizjyPPE6r0y5yHAmoCIZVXdPcTgv9qvT9j7HQO9e2WvjaSpei6WHT/YsBkEy7P4RQ0w1NNwBNsE81MKNq4HcvbYJycPBq4OlXoq0ILG7Sx6bYsElAscmpEBABkiDC+E6TOaFqmmGQfmR5HMvIkrs/+bAcSZvdSQqIMQqrAMKSgiD85bPvxNZQaz7PIadd7Ooxyt02Ku45DooC53Fa7VaRDvYBiPT4SXR14klNbWEuYRFxddVuRdUikoYQcQ4HQjJpGwBhRFbDsurzRziOeA5IuUXabTUJbHEqqqaoumFgRdOfeW3LhfOngBEP0wEjCnMGwKUcamSDWQ0oq3pwFOYGKDiDceGUOGkVAK2+GV2GZJJls/BWi2ARuHKPTZLVumo3lf4IIR4RExDPk0qdNCljoOIqZKvLLuqG4LSTikOGgd0OCx2BRWQ1FFEDQclmEawWUVY0RSVFSM2aP2n6BJmF4PK7BbrNq5tnzhBIzGKExqoHOL4ckJHXI6Trhm4YrIxUUckw/E+I+4ynzGUr2hWNeRMQ7a9t2vetFt4q8h6Xze2wCDzntIud3aEKj51WZqY9O/KoFASJ8w2i6vO8ZXYAwNnGs3S9CIAHgLrpZJl2E1NVTVbUSEQ+fniXphlkOhD7qpIrwIaO4xtyHzrVFvudTCxN4dqLZpAK2A9eHowokqw9+8bH6S614DEGbBIwQhwA/VoC5AVRAKxb4bCSdyGvm2+Ldhn79J2Zus+uvu9LoARnAJKi2q2CwHPlLpvDLrrsFrtVIH5dDlpFXsyYe33Blasjga7k9RMnjo9/SX2+MdGf4jJIkg5ESBQF3mYVy5Zetv/jLYqqEyeBiUBajnE2C1TyzchvbukYcB+xCEFmTAeMuIrQeWEMrqc8Y/A4HSwNOE/SyjWLyGOMq8udFpEvc1ttFlIWVHVMNQDQAJg8t15Kf1KXDaSU/qT9XuO52PLEieMziP54yAyDQyIJJAXVFc5QRIn0Zt8MjXFGYDmEY74gaG9OaV4FQVk5oGEDQljuIiYERh5keugjkjKuylXtdbgdlq1HJNUxNXmfYJjE8ORHvDKIR7Sm/TghBLWVrnFVrsqiFe9MibW3NEXmBOZcYYVCRwgkYiHvckCQZDXmdAQrC1pAOERqeRXyjKUE3+ELpdyw5wipCG23l5FJLgCXzrODUIruWh4AQOoT5I8eIv1d02GzeWw24C0H9XHXSVlx0aycdNI7Ww97nLZgxGO3ZXqAIAQeZ5w3qaBTDmYIioGw3tY2sDluQGKhxtkjyYqhqxBnZ8npNwKANpslEg63trCCEMMDx3GsyE/eDFP4FE76nbyVLuLEQXfqNb2n0nXc3OHL62rSwvOcy9FXaorFe489sFmiKp8jIaiocItibiPQn3zv2/m8FyMVAp+//4bRTwEkfAVI8H1sDRWy8TvgVD9piE+xoTH90cj+3mWyQN8utrX33WO/Y4I+xZreU3X6QoGQnOvn2u8vTULgkChkVSzsw31ncn1rZggaCRhmGnoeB/IcV1HmZvJnGMnyuwkAuOKWR4p8LaMPngbXx2LgEl7SHKxYv/Xe8W80t5fU9Yzbs68AaOxlX2gdNIzozgnhdjD9Qkp636Bvv9iaiKy2dgZy/yab15X+IDPfrbhDDGYIAgB8sObp2z75OTAcmFVIclcAELpdDquVtLJgDBejsQrQe2uewQBMmHdpw9bXhvdK+PhWv0So9g+H7vcqKVIapduaIOfNFo2xnQtTOav/GTAGsqKda+vJOxg0g9oheW3ZeQgxBvsbu+dPLM/zGhjDhJnMm7MCQBBWV5bl7z1mFALrQJ08isR7a56JLY+ftyJpe6I/KV7Wh/0kSHLYpT9VAOllYHyFq4GkdsLGxGF+hncZPJgUrmjrDPiDGWJT80ckCiCHq85PB7BJAKny5AuVeYY0vqvPCWz2n8gJnuecTjszPw8vNoswLtfqTzny6jM/T15JhTilYesrYHSScfbUby4ARiySojW3+/2hokh/AEmvifi5TobCj4Oc2TAdMCzohhEM5tMOmudI/kCuRzEP8HClAd98652fvqtvqN7eHXz1r7sKdRnJZWJHBSNg9hqLBcorCsMwcHt30B+S8nPiDQiHYEJFEQjAlUtnZz5qf2N3MS5mzPPBmqeH/k0lSQlH8hk9mK6h0fedH0tACK05hmAVCTgKpX+xmsJnT1RokyAcsjRQCbkUyIrWE4gUSfrTUV5OaSanTzdNnpx/aXI2CXjl6V8vXTGoEt9NjafBQO3q8ifuSfPJwc2bU9ebSseH694swjWVLqLADZcPYGwwnDMAjLEkq7KikfrPkpKHFw5j4A9JsZYyxYD2Msty59ffen/wkwCWHvzh+ret1sHVcTRGYnEeJv0Ljs0iCFmHgTKKPgNAMKssXAPjk+e6WjqCCXYfnXeVuWzLFk+aNK4sKkYPZIqs13WjqMN/Wmwu13YTg5wEMAAA698hXrUV190mSZE8b4ihF3EekCNM9BcJqyjkZDT43Qvr490AjAEUwPx5k1KsNeWtquk9ASkQlqESX6q7LyY6IqVVBpsOmA503kkOwOQbbrMKc6dV7zxwLuyPrN1AttZVu2dNrsx0cRgEwlLIbChWPBx2kbazz0MH5B0SygxBlPXvvDKydEBeQw0m/YuHJa5IVwKsGcBgFQCCysGDx0DhsFnFD7afAABcdyGpK0eXKRFJlZV+0/awNEBxFZ24f0NFHf5DCMpd9uzdO+4Kr7+rM/t5wOsb9t10+YKUm5gOiNcBpqs2MurmAUz0FxsbcwAMDr4yKey6y0dC4ibUlp9tTVH9bZBwCNL2W/EYhl5d4Tx0ok3XSRoXx/EepzhtQhkGQOT6hWcourX/8D/nqg+pSRPaiRCym3meTR2BbE6jBhPt/pknAemkPyPZHAQAyNM5PEw6gEn/4jNSQoDG5gyAQ4asklbAAIBjjd3tXX4AwPIlU4+eavU4rcT3gkFdTdn2/WfmT6ux2Synm7pIuxgEK9xp6zkrqlpXU7Z+x3GHjQhWXdc4jlxDhdvW6euT9RyHdNLkK5GYPuj2S8fO+D99+/LoBghDEeV3L6ynPYHT9QJLS5ohPodgri4mm4giSjSliE0CCsuH698u8BkZoxlIOjWNFDfPKGUA67aZA0uw9drazrb57FbR47LZLILDToS422GhNYDcDgvtGhaRVeI5hQb9wUTu91VoCIRkgefimyySeRyMb/NIU/OzyMw021bmHYp93SWzZ06qaukONnUGugIpLAykB3IuvcBUW5Vqq+I9NbE1p083ZQ4HuunyBa9v2JduK4sIYjDSASEU+dFXCGg0KQAqkqHpC6VrmtsDVRVRqxGp7wZIhxaaDmMRSWk5CCFCMD5yBpq2lNhLSdbGVbk8rr4Cy3ar0O3vJ3913cjO8j44DWBy7/ULb79y7g3LZyZvcjusQsbOlwnYOY3+uCv6ctNPn24azOUxGIyUEDmTde+HWCnQ372wnt3PGGn1Z21VWXN7U0yyxjpw6bphEXjalp2qB0tvNT4Eoc3K01pvCSld8frAMHBlmSM+eickKaqqx2sjnkM5Sd7BMPH8XjvSsxuTi0DkpFsQIkVPkx3Cb2w6cOOl8zJPApg3mMHICQ6hXEO0xzCr8+pcz+dSdBsKPKLyjQz5U2XBw+wyovn+FfYjkgYAjs+3cthEjsul+FyRLIw5OgBEgZPkPmNXvA7IzB3XLX3pnQ+ZDmAwsv+CWi2kF0yxK8GNCjmeN9kqAEhG+pwo8mEirAtAf+dqYlxQXtl9ecaD7njzLwCAC264NWE9QjA2uckSj9OqaiFdT3ElmScBciSfYmQMRslC7NK23Cboa/7y4s233gnGohwvfE/gj/adAMAan9mLuJybX2eAG2G1k6gaiEfkeVuOzaZrKpxhSQ2G5VhyQvaGIDYJYDCyh+NQLDKl2KweJdI8D3K4g4qiGxijrEOqG9p42mh1Rk2KEbEZWzSydEACDruYa5RxdbnDH5JkRVOISyM3QxCdBGRwBjAYjBgch/LoBb/mLy/GfgMAbr7t7hK/pUQB0OEqlcaJy3EiWk8flxkfwRnDGGi6kEH+kyBS8737Mr9MJFlrONNGxSuE8ImXN8dvdTrtOWcApMduFXNtNu12WKrLnd1+KV4BxJPNJCCdDmC5wQxGDB7lUKWRkQ7U22CdlFSILWdokc1xSFIS3QCBUIqCPDarSEz5/UVofHRQBg9+LMM7oS98dyCcTrbmwTtbD28/eObFDxpe3nDilY0n+29M7QDIXHhC4Llyt83lSNQc8VGhb2w6kLcngKUFMBgUni96p+5SwGwJ2dv2nTpS+5r3pjrAbhWa2nxTJ/RJNFXXz7b6qr2uhD0D3W2SrGq6AWr6EqNEkVe16IktIh+RUtdyiIX3xjfi0jTdFyhO26/eLvYxIATWlBbGzKFJENgtQoXb3u2XQpISfwezNATRkFBmCGIwMgJ5joM5KoB77nuA5P3ccOGYt+xnjynj0jTshak0AIKwrTsUrwBCYSVgGr7nzp0eXzxuTn20pHM8Hqe1oyea8yWkT+JQVT06ADcviKqksKRK8gAV4goFNJMAUqwf6ECeR5Vl9u5ARFE1VTPyMARRmCGIwUj79TQDBfPoHxXP737xeJYtIZN7Ao8Z5ZGxGijqM+xEJGX5YlJmJ4GIpJS5bEsXTMKGES/9k2tBz507/eSJMwtmjDME5/oteyfUeBbNHPfBdqIMOI5fMKN2X0MLrQtEaqgdb7/8/Pr4ww0Dd/lCGfwQhYXrLQOX+4HQ7bDUVjhDYaUnIJle8NwigjIXh2DOAAYDIihmTBRNrgV95yc+qetGodwGr6ZqE58lI0p5DF0ljYMHj9moSFVIZc1OX3gG6JtGKMldvfpr94ik+orU9j0JCCGXxsWUoSN8DIvAV5Y5/CE5IpNmZ/GbsjQEXb50wYYPmSGIwUgNgqQIRE7j/5ETc/jqIJRHwfXHAAoAAxxL+E23HLdz36oMO9NPIpYxyyHi7z12uocuxDh0vKu+zkuPwxh3+cMFzELIDC1nlPKJyfwQPfNqv6ikZEKRPn2QYRLg7jU+MUMQg5EmBhTVVHqyvzkQEivCUJQQGAH6I3slkUkBGJgM2OMdAdg0xXQHIi3tJNSRpsua4UNY4Dm3w1rhsdusgqKSZmHd/rCcFC9EobJ+w87GBKEfT7xTVlH1wFAN/+mzQhRAjkd9uO+MzKd4IqkioU5m3gW0QEc2OuDGK89/44OdGd6ORYUySoS7P/mwHIlvO5hzrbArbnnk7nvvH/3CvwBKIkE35GYCwpjIYl8gIpvlnTkOcQhpmln6oHeoLitaOEKavA+yV7vdKkb1NQbBsJwce1pAEEQGjk0vIJlg5mUr/OKdF+kGDoTlxlb//hNt3f4IiaftH1/EuyrjdUA6oB51orCIIAYjAYvI5/ENLRn5n4NuyO0mGgYOhEgfLkxSgpFVFMjH0HtfMQCyqvuCkj8kRSQ1VhczD+xW0VvmoINw3cCdvnDxWj8KPFdX3Tdyh5AotjwshhiDcDAgh4MiUKdUWZfPqZo93llmBXZet3O9PwK2C9hdVp5NWsBtKy+iC+l8wiwtgFGCQAhJmV7zG5pjJTgYiwFl5KwAMAayovqCkqqRYv1WC2+3Cn0ZT5hUig6GZX9IGuTwHyFYW+kWqIYvbOvHVO/lLXPMn1YbWwPN4X8GBXDl0tkp1/cdwXGcwLvd9tpqj9th6TMm0aYISYFM6XSAGtcFl+kARsmSYP8Rec5mEfIYzo8YN/DoVACGYfhDckhSMMY8h1x2i9g/V1ZWtWBElmRtkMafMqfN7bTSiYWBMTGkDGIykRmrKHicVpfDGgv65BCMb1+TM731SQSe8zrF6nIHKVll9sRRg93kJ+xTwz6bmNVb3HHd0vyvhMEYi4hCPvYfs08J0wCJ8DnYNyTVF4xomg4hdNhEp90SX5XBMLBukEaOYBDwHLKIQk2lO1YrVFY02fQ2FwmBRwiSh2nu1NoP9xwjAaD55pf89a/r8r6MdN7g+OIQLCKIwTC7knC5DtHuufeBlPXKGHylJ9rqK0aXjwidhbMnAgAONByNra9wgwq3GwBwrj3S4/NZBE9UAZg3NtnbmSsIQofdUl3hoi2Co0YnVeMGMx4fGLPl/ZnW+glVH+4xA0DNnzxUwHe/+gmqCOld6AlEth88t+tIcyAkp7wvvBVo/vYBdQCtEEeXmQ5glDi0TVOuVRrNQEXSrbZo1zVayScRjDZrMfWBZXJtinoJzR3BxqbOJXMnZDjJ8TPtUydWptpiABC1fZ8811leVpVrxY/8oGqGSP98367hWKN5Ig7o0YnR9ZfMuOaiaY2tvk27Tx8725Vcxo53V8XrgJTIkXBZZXVPR8GqnDIYoxeIoDV3BwBMXdeGMYSZwPnhdWOv2wmAM2F9KKLuaeiuH19x9cVz6dMQjMi/e2HDhfNJvYosi0Jfd8nsLl842NudmHYoQGYOWP5X3Cv9YypB1YyJ5YIcEtq61RS5bGXl/p7uzJOAq8+vf+md6F/EJgGMUoZ4gK05KwCz0vFYyAIrOGjNttOvf3Tuje3nCnziIt/qWCG5v247OMi3nDfRXl8p6OEuGhSUXGO2qYPUrsgbnkPlLltNhdNhEwbULCkjguRIePq0vkJMmSsFMRhjF2izCjmlgPXBpgCp4H/67zQxDJs/GrHAGLra0oUxFgV+xsxpQDd2Hzm7effJQFjyOG0rzp9+64w6RVLe3nL448NnaYKupib6aXmhctMhCUKwfHa/ji7Z0+XrC4JMhkNoSl15YfUNhIDUmC20oZBUlhb5KrM6EPFpq3rCs+iOmwSkY9H0mmPHT2Xeh+UGM8Y2EAKbRcwjSOP5Z5+KlYNmpDQBQepfIYsICM5ItLSPYTS1dO852hQMyxZRmDW5eup4LxA4TkVej73cZZP8JKlV6O+mJZm0SNd1Q9eND/enHT53HOjoi5gsc3g9Doto9icwuWnl8iOn2175664EccnxwuVzzSDR2JSuQBIbQVJgJPM+Oz7et+PjFAPwf/rbuzKdGUGXXaypcAbCilnUKHE04h7IECRHwldesvCDrXvpS2YIYpQgHEIi9UDmxXPPPPncM0/Gr7k5u5aQKUvrDLKm2wiBf+EP/0eX7nrob/pvMjsyhuR9x5o7ekgihk3k9+w/dObMaavIhyJKc4c/GM7UziW+EvIAF8H1L71pSvYNWz/WdGNqlaW1M9CvCrQubd3XT6n87qXN+bWE/LCh59arFlpFgSgeAPgtpzIM/1PK/Rg//p8XVDnTlCUnUuoAh9DvfrISEYxSg0OQflVHAqsLV5XztltuIb1S3IkBmelwufp8otdcurAwTuCYJiDK4I5rAQB/fv6djp5Qa2eAJnZF/EKZm1TZ1g0ckpR0xXnMIF2kqnqWUaHR0pux15hoDk0zFFX3ByOdvnDxegDokZ5dew7ERv3pMgDqKl2vvbsFjABuW3nRK+9+lFkHMEMQY4zx6kvPE4F7x90WkaczgMz+3IRmAFfc8ggY2bzy2msZtt52yy1UNzhNuZ/wh6/dtHcwyiB1FNALL71HM796AhEq/cnQuPedFVULhZWUxZlJHQWeM5PCspXaGGPdTPSFkCyrpuEoLKs9/kgwImuDSyrOAIdQhceOIKlmCiHQdWxWgE79XN2ychkAILMamDJrNm1UDSHwBeWwpJhBpaRzKYQwGJH9ITlDSjNvA5qvNfMkIL44RAaYDmCMVTWQwA3XX5du/3/+QlbmnZHPgOoBADBhfE1+yoCPDdPN+m59hX00XfcFIxGpv3cXk0SnUESJyKQgROrEWgRz6ttudl8hDVgQgppuSLIaltRQRPYFpeIVgKPNjT1OW1TkY2KmnzW1dnx1+cadfblvKdVA/YQUrS6zB2OSI7anoeXAibZgmNzG2B/Je2oG1AHxeWHMEMRgpOPff5VCYYw9XkmjHn7+P9GF22655cF7iEUnJcSgFp1P0bGwOcfAJNBeCYRkOpAnw+TeAyRF9YdIPbjkcyGEBJ7X9NwKQkDa3oFHqkakfyiihCJKukYCBcTttJlF3/oug3LZ+TNFSAfaqUtQtLYOkLqVDTPrHDXucS0dgbPtftI5MjYzqCDVDTN0DZMjYZvHG/H17cAMQQxGSt58+x12Z1557bUMcwhSpywaSkNFkKkQVI1UdaYtXziSdw2pwUfTDTmkhc16cMnnIinaEGhpOqGnAyJS3MMwsD9Iqn5GZFXV9OJVf4thEYbTm8Qh6HJYIAQKUXtaOGGmlXEScPWiutc3mAogYzwcMwQxxjxMxA+SuNgbGghqRu8EwnIgRIalHCn6T+qlURuRpKjBsJzS+s9zSOQ5YsHP0Wdr1vWAgbDcE4gEI4qiDoX0jwpPOJx5IoJZUbXW6yx322jcVAy3OQ/IkBr2iZsujZf+r2/cX+SLZTAYYxCUqui/1uMPKyqp+mmzChZRoIYSw8DpSj1DSMKziNEkF+t/77GkzUAgJEVkhTQXK6bdP+N1DMN7chzyOK21XqfTJiaM5uN1QDJBvy+b87OOMQwGI2sFgIFuGL6gFIwQI48ocE67hRZdwKTIjaHFFbqJh+cRz3OyabsBuQAhNAwckdUhG/hH39csKttv1XDoHUia23GVZY6qMoelf3OFeFJOAm66bH78y3STAKYDGAxGVgoAAyDJ0aL/CEKnzWK3irHIyHSJXQhCi8DTyH2QO6ZeIcWkwRDC86iqon+BuWFyCJj3WazxOstcVo6DORmCstQBDAaDkZJ++be6Tob/EZk4JC0W3uOyZVN3ibTngVBWtTyEOMY4+4ThAuL1OCrcdjAy4HlU7rZVVzhNdZuDIYik7K0ikamZYZMABoMxgAIgoZ+S4g9Kum6Ytmmbwyb2VcZMI6URghaB08zsLVAE8uzOlREOocnjyovcZyYHIOlMyVdXOCo99gwaN+UkIBL0x79khiAGg5E9vUIQk/zenkBEUlQIod0qesww+ehGUksbpw2mJJlf+Qz/M0Obs4sCn9B5ePC4HBZvWbZlN4YGDkG33VLjdXqcloSONANOAhIMQQwGgwGy4/8DkRRMydeeKs8AAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The selected axe is held by its visible retaining hook at its brass-banded handle, so release only hook 1.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "In the top view, the axe has clear space toward the FRONT side, whereas the back path intersects the bow and shield; set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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6CRjWCiCz8WeY6ADcM9CITQLiRX+Mt//4XQDAL3n+4M7NihTBBpZ0o7Xl+MbVb/3DD5/7n+/cMfhXTslD6O/buw8AoCgqAODMsxZlVgbURjR8sKR/PHQqMKwVwPA3/VvEjzFmTKpJKf1jfHlF7c+UMxuO7JGCPoBVTcWdXac+Xf/eg9/5ze9/+NAgXC0lnVk/G7kfAwMMAdj66TbrZTpNQG1EQ8vCaWPSSX+Lhqb2Km9vChhlxJiAhtskwCKz9Lf4+srx//F8uL31dKCrDWIV6zgU8u3cvOamz39/0oxZP/3mLcW+WkoGs34moY/xVZUImhofA0E3sGbgt9vUeE2QzbSA2ogGk2TR39bpr6pwD+pFjDrQ8Bn+P/bwF8FI419um3Fk/87ymrEAYqCpQJFlNXLy8J4Du3Z+/09rh/rqRj9f+MdH0m3at3df7MdS7Su8cIUXXlWJrqliEIQQAggBgpBjkMgxN4wRr60VrqnhsAkAeOun22I/6d7ln77zw6L9cZRe0g38KcN6BjBSjD8xYJwnIEte/uU/3vjQT+adc2nryUMAY6gomMUtJw588Lrve09++L0HLgYjgWuPNv9+s24tf/4c5vXJtWCESH9JkkVRSDnSjw32EeznIyWbIWDJbvCGsaKlAhTdeLtVtQ5NOTMw9QTRAf/1w++AUuKKm76IMWg7sIZBQNM0nmMxxpoBidrE2M5DSQUiixEEugEkRccYCDxJ3lJ1LGmAQziiEssbBoAhB5GCiwAyGIP6hcshBO++PPTGgBKhuMXg8lAAQ2gIuvHm2ywTEARg2dnTU+7T0Oq/8bIzk9dff/8PzrxkZeOxveShxpjjOMRwzvKaCy+//Htfun54ZgJfc7T5yR6hn4GaO8aC4T32P3b0aPwmjPGVXkQG+YV4IwNjAwPNMN5qUYFlNuoZIZxx5sLYbiWiAyrGz5/m1RVZJ/cAApYBqqbxDKvpOoZMTAGoOpHshoEZBGSV5PDyLNIMAwCoYwBBjwLAGAIMITQwUQCGAbBB1AIrIM0x4fC2D2Lv+73vfD3l9cSbgCwfQHwxuBi0FlA60HAb/o9EQxAA4K9/eOTtZx+fOGkuYlmAyLOuGzjQ3bFh9XsP//T/wDDjmsPNLc+dzkb6AwBanjvd8txpMFylf3tbq8vlBABcWQGv8iLLwsMWSPqTbwipBQtFlrmhTryO2Ij4q6pZDDBCaPu2naVmC8IAGzpOnjwRg1rCSjN9N+HghFALssI0xCXuaJAOrbleW1tHd66HUIqlAEac8acgvP7cT+onzxdEO0ZIB7qua91dnZvXfPDVf38SDBuI6P9Ez+OoYaIGEqQ/AOACIXx1pTnkj47QiwKEgEXEYcCbysCST6WmAyDWMANVAMhgHgJkyQ+djHcgVhmgMVAHCANdBkAnghyTrYCM/cn0AGANGApEmIU6AjoEOjAw0A1IyvuQMxmWKx9BaJASb5RiMxxrjQ3yJGDFDXesuOmuFTfdlc2Q4/6bl5WVkfFmOl75879X1U2wOZwYAAMahqFLkn/3Zxs//51fgGHAAIX4kOuAZOl/oRAe5MowpvEDrKzlSlEHJH1JLFMbYyZqWdMArPdj5LR8J5a2tg5LnrBZ++TK7Gnjy8pcyT+7N7y8e8PLeze9ksc5RzdQl8njSzR0HPNnX97vkT/71U+iz4P5SS2/5Ix5sy4v4PB/0JwBK264AyCzIAyMDjoy+AAuvzB1aGDibnf/YNLM+UF/NwMBzzIIIU6wTZw+b/z4iZM/jXasZpY+oG94cjB9AIUS30PlFUgp/cHQgTF4rYUEj5JA9UXzS8EfUDF21uQqJMm6+WCTYbuhGQgTT0lE0z1OVlIMjoG6qiGWMXTAICQrZLrJcaYPABuGYQCG0TSDY1EgrPEMQyp9QpLrqxtA1ciUQBCQIdZMnTY59r5LZlalvJ6EMNBl58zLcPFWDdHZ595YuPsx4mEjcpI1AIMt29+xFs9eGK1t8N+/fjymk02h3yP5e/T0ex989tNf/mT5JWf84vE/jSDjz4obi5K4+/5Tj1x8x/cWnXlWe3uHpuscBLoqNx7Zxyqh8ktWl39wWVP97e6A6ujvPAUcs3iPLSnUqVqeOz34OmC4SX9rDLuylnutiUQKbd+2M6YDRmhc0O4NL/e7z4W3PULMNdbIHQMWAR2RBYSJcYxFiCUjFowBJP9DsiOK2okggkgnQgRCTBQDg6D1G2PMEAUASChQz5mjswlKkYHvvfd0qvU9sr53HhaV/zHJby33zOaiXh8AwMGtuwqlAIo9Cfj7r3yVtPrCuLU7rKh6AWcAFhfe+shFy5aebGw3o0dYjuPL3PaKiprpVQdYjn3xjSNfmr458xnO/fJfwTCT/hYd9R/nd2B+46+CS/+WKYsmTkhdXZJj2cD7rw3PeUA2MrqoXHjbI1PKoUaKYAEEgcABzdB7XL4MMoU4QoaiaQxC2CCWIc0gQoKoChJPpekkNIgxSOwPMAwDmsFCmJwNKVrUFMGwELvqpkyZVIwZAJ0E9KsA4qV+z78YeNw2nz/SZ+jfs6NhYIiI3Cyg9C+2DvjVz39k+ai27jzMc2xLZyAWjFAoBUCeyFu+fflllxw91UIi3iDntAsXzA0zDPPy28e+MrN/GVoQBVBw6T9AHTA4vPe7tJOnlinkQ8ygAAAAuekAAF5rjuqAJ75zKxi9XHjbI5M9pCEXY3qAWQYY2ECQjOINTEb6pHU2NHSDrAQYRc0F5hfLDPIkJiAIkdGTNEAmAD3DSM10CRuGKak9Y6ZMqR+4Anjzg09jyxUex9KzZ1vL1BCULhEsWfpHF4j0j98BJ6hWXHDpXzx++8SPt+w8RJbIjJREJ5gCuv8DX3//k5ze6BtfuAEAEEEVXS3HMFYXTw8YmDl5sKW+fiIAw1qAjnSWfz71VOPpD6JNZTPjunzlkgk5OGD6d5qNFliOwapuff/NAB+DSHHzq2ROpwFGpqWffK+Id9dK+MIY6BgYRF8Q+5ClAEimGDYAcSEQK4K1K0KQYUm6AA9yDgSKF/cp2bBlr7Wwd9MrVAdkzASOUwQARIf/vcI/Hxf9MKoRFJX+BPKXqFq2YZFkrEL+i3eFkKXeMAZzNIPMgQ0wk1wggEsmap+A+kmOHZDlX3pl5+dumBGOSC8cvv7W8r8WVt8kc9/Ur4Li4D22ZJhPAigFIV6qYoBEFnIQmZNn0qYXwh7LCgbEn4sBYhBJ9gLA0K1vQTTen6jTaLIYMPTod4fYgMwAUSsoiGHIvwjBECRxGbnSY6fuzS2I2aajEUpknAfjNUGJw2aS+tZCSuNPIkUc/hdDB/zud09+/vMPWA8mCU42bZoxMmg3K9Q8Ph00eZeePc3laGUJOLd2bzgEf/m79RUe21sfHps+2Tu5kn8rfNtVwvMZrlPXDfL1iqqc6APeo2/Iqc2N1ruSPyTOHzMYpNNP112+eNCugZLNyLdAkEcurOiSZg6bSJAPCfpkIDH4GBgqOuSI1Scq90kqANlOkn6JRMbQMhOZZ8KKDliEETI0HesYQsToGLCmZYhjIBbzeYyjk4jody9ufZ8dyC6f/1raKlIlqwBSGn/ihWEK44/1utjGnyLqADPtML7fdAE7jrbveEYKtI87/x8Pn9jGi+ymj1sevPvWWW3/8633xkiqOmFcxdzpznUd916g9ombiifaB7U3vynFU937cFv6pmdmkpMmmDmBaZKAxgFGBB4XaNie1ZTovqlf/ePhJ4oxccmSEaRpBktGW8SGDNF/rS8yJgZ6MlCwhgvWU2+tiQpHc34bk5I9//aOo62RjUFKOACOIa5dxcAiz3AIyirkmd7dBA7oOlQ0CEiNH7KWZ0mel6JGLUIsQ96BZxlZ1WWNnE3VyQ5kDmENb/Kk/8eeSv+0JiCeYxVVSzb+9KoEMHogOuDB+w3iiSrwqJlTTzbvWy8F2oGuNK79kRtilsG3XfdIY5sPAPCT5U3//A5WNCMiqZIKQQEjKuOnHwBsrL+33yOWj2XWHAcHmgDwAGQHyAUiGIizmSoRNHzWvxo4JE7GijJNb4i7BCuQL3UogVmDE/ZdQ1yI1mtrumPV5LMsaeZeab/UeWiaPfvao67gptRtnB0Ou7XQfpj0iikIr6874vV6u7q7rzmvN7LFBP/l/b0VFd7lZ5CSXH3meT3C2hTE0QXz5sIMMjoRc+9oRy1rp54ZbM+gIjZYjv5KHmz0TmPNC1Q0jAAUWKDoxBUssIhnEQOhokXtA9YgvOcUuK/qiM1Ue2w+CHIsMv9IskZAUNawmkO5rJz5/D/QsX8KBRB99hRVi/f9ety2nm1DNvwvqjPgd7//wz333GMOOQqGCzWfOLApEmgDuoKAxprSn2eA9ukPJwsQmJUrH7+y+dur0akWaSZW1zdNO/+smE+ikPyO6eezWMYxq7e1a04XZAS2DIhe4ABABSAsgBY/GDOHadrTjw5Q1ACGYN0p+cLxYvz6+KIMPeo1w5o4MdYjOqLx4HEy0Rqi9q6K/ht9PntlV5LFzDRBEGGESGR6dI+EXNO4+GYrIqBHC0V9mGSUYC3EDZ/JCa1lS1i/9P4+XVMgAJoU1jXFqm0muMuvveXWHVu3Bro6AZ4Y/XN7LHfhYGDmrNlrtjc2HD3GCTa7q+y6ZZP7zOqS72csgT/VxDCRvve4v536O4VB4jU13dChAbGOMYNJehdjhvQbxC2g6YikVRKLkHkLdWt+bfp4GbIrMiOBenQX2VkziFOY3EFDNxBxC5BKckUZalLpn1IBRO81z7Gy0mf4bwX/pPf9DmrkT5F0wJ///Gdr4Yabb+t35/Xbj5txyhyJ0Zybeuh+cve6iK8Z6DICOpH+CHMICCx0CKDKCWfVsOJicdMrgccuawQA/PPL5Rjgqj1zD9XMvqbyBRKmcnSpbca5mqoqcljbeopheSQ4wge3VJXbqt0cMZiaE2UzzcYKYCIzcQaTKQXPkFybZp+s1swiYmlqP3/Oxt37VacLGA5S18UNPAAsA6ANgK0ABB2gow1cdQ7zVsaacWqo21NZ1YWMVe+tr6lwLz6rNxA+Z5KlUHZKJNElk9piFu8RjAqkPtt67cbmJtPbHzss5vsxj4oe+dfVh3SNxK6zCCJE4kcrbALHOhmG4ZCh65qiKtsOHbpgydKjR46FwuHyCeCjpv1dbSFdEg2oMrwmBSK6znZ1dc2YM6+zvVUwVC3Q9tob7SpgSGA1YhAv3nTpDDBswADrpLoz1nTMELEPiY6L35wst1PZc7CBIXEZx/YxXckGME8OSVh5XobYDTtPmkolMXyrwu34w+9oiemMJqAE40/v8D+97zdB+pcam3b3qazw2gsko+LhO6aHu5qwriCsM9Ag0p8xpT8PKh1oWhUjLhLAwspzF9636dH/IVOBm7q++XKFcrLlCgTXcHdsagsGTuyaWefXVNmSQJqmYMNoD4QUTW3tIgMrgTE9w0RMIatuFlZlgRMkSVE0FWPc1BWur5rmb4vaOtIxac+OozrAAg81Bag2HAYKD06Z1bisL5+ugw39xUzu+mizs9KrBEOBDl+wq6OhuYNzVV+3bA4YvmQnWax+Mal46Z29nGHYHXYWG5qmSlJEliOKEpYUSdNIrivHsQxELMsxDMOL9iNHjoZDgUBXV0juRCyyu0SbXUQslCVVCsmyn2E41tA13uGCwYDX6zLfmTcw5nkeMWjNxmMRSdEMwIriivPNCcSQghBApHpbtP4P6DtJS7i75iTA1J0JN7MnJqfXBGU+yroZG2rN0157uTc+YlGactBZ0h0Y4qTxYQtrCXZL+icbf8jwP73xJ+H1IAT+D4fOkQxCIs+pJKUxageIbbpyqrb9ExJeNm0iEyf9gUMg0n9qFeOcL4CzvQBcBsAj537/kU2PVgMA/vOmzm+/6n3+0KnLFW2qWHWqdpw5RAKik9xPVZF4u1MQnAZClrNCJuEUvWnYGDMA2EPk0xMgI5DPrtzBig7W3k8Tpbb2VmxzAkEAUhiE7XozF/CAHeZXWAbA8AEsAbm/L44vGIpoRqirC0bkyvop11+6AIxSXv3gkBLw23gOaWqbr7NpfwuJV+E5jvzPsxznsDlYTsAY+7ra/MGAYRiI43lB8HW0jZs05R/uXZHuzD978vXWxgan293WdFpkOXIgA3XdgAgJPO90OJwOj8vhsdlt23d3B4LBoKyuWNabJFUQvvXdx7LZraxuFhmts1DXSYiP9aASC46RPKi3QhHMwhFRJ0LvNyUWlGkYZoIwJmcjcwmSX2btmnPB2gwkRHlQUjmBccZwIJDIUKV9Da0OYBhUXeFcMn9qhy/U5Q8HQlIookiKpuvGlVN7e9IeOhF9fM+dBew89NrR5ErGM0cA51WYVpZozchzv/8RAA9venTjYzd0fPv1mjdOtp9Rp1d6PXeJvwXigIrB7dlpekfv+s8M+2jBbqyrmEUkHI9DBixTNEHzAMgDrAAjDEAYGGEZMJnaxiFdU0MBb4V376bPmk8e3Lz27X6v7cfffxiMKP78yhY7y/AsCoe6jx5vVjV14rQ53/1c/7lfz7y59R+nWmW1joAD76Xb7UfnRxe+u2kBMCDH8zaH+74bSP72ky+tDwf9Ta1NJ5RjmqYjhqupqq0fN3Hj5oZ2f3DDusJUCskB0g6AjNExNot+msLdTAPusQRFHcHmyJ+UAuoxAJHJqinjeyrKRJ0sVvc1kiNG0gosOWNGGRVSAVDSwfYO/0Hi8L87/fD/4NbeErijjwyjhZoKl9spzppcoxtYVXVZUUMRJRCWX/jpP6Xcf5PZj/bb1zIVs3hwYRkA5wEQE8rNADwCwJFzl9k3rQ0/dl0LAOD/rXUsX+A+Wn7je9vDX5wYLclXLAzF6PADTQJK0JD8MOjE3S7DZgMcb7r7dKDIWJFA1fgM5zi5K+c4nIcf/XHK9Xc99I3YMscy8yPERzIQpElkLjUQnn3lE1EQsBI51tikqvLE6XO/fcel6XYWjvWGQlk8MLs6p2zWxiO9g6pvfdbbDyueugn1Gz9d53Z6JoydcNPKe9q6fB99NNhqwOwEAE0XN2DN8m+JxJJTeiK+LDNQ7LsVc/ZbrxBjpgyYViAzzDQavJTzhVlXFxdXRskMy3NsQuC/Jf3NF7GVQ2/8GSaTAJeDBPFAQLpEsQISBc7jsvebGP3YKgWsUn52yRUAxKLmZQD+EYCDYE1Qa9DGV7ENbWQC8W/Ljj/8nnreGeOuPbvupd2XA/C3gVxtzR1jM5SAtjMgGPHpkh9GPDDkgnYHEO1QEAHLkm8kKc6iIUPPrACKAcfmkwWajHj8VPLKti69qrzfMqzgwzXrbJjp7GwJhAL/dt0FAPSYXJKk/CBzcPtaAEDY1yyr0v7D+yaOnXj1ijvafX5bWfmat1IWdiwk0TAp84c1RYfpLzdDgcwynyYJvoG+p+jbAkwn/cWiLl+GQbrWp+hwrpS77TaR9zhthxvaSZ/hHnizvhMlGXJfEgL/Lbp90eH/8DH+DJoOyOAlTNn6LsvStV9f/vLP3vtNz6sHAdgH1gWME2pLwIiovbdZ18Gnu5vaOsPjq92XX3D2++u2gOJw+TmL//L6K7qq4EgAh5zQ7oCiDXMCYDkS1AIAo+tuQWgDo4pzz57T0toZDEU6uoNtre1330VKBkkfvrXTXmvtEI4o2zZ9AmV1X8PBH9xwMRiudLWeIC6iCZPXblpTU1VXx7IXLLt+3dpizwYYYqZnoKYirifsxyD2fogxkf9fOvNAlif67y3TSRlpMo8gB5I2mwBqmMSAkrpxgJw+VxbPnWgTubrq8nKP/WhDe1cgoqiayHNzp40hAXaUJBKG/1Hjj/Uq4d+CG3/eXLf76gvmghFFrJZIAo8+8dz3v9p/a4GvL6/82XvtANwPwG6wMQCOac2m9NcNUO5CXQEyp358xelvvjX26Mmutraw12NmDRSHFyqmuUSuWwnpho59MvZ3kuE/L0CWxaYG4ABou/VrI65ffAbeDogLGMZmEyCE3V1dRNyHInaHTbz4qsUA/OlPz4e7AjyCx04ce/SH34WbUxthhhX7t38EAAh2N3b6Ohw2xxVX3ixxaO2qIoo7hKCuY5a4qUn9RAMbbDS7Iv8UeuIGNqcFJHaCFJczlUruVJaTbn0OkV88d+K08VUnm7uOnGqfUFu+YMYIe1AHDRQf+B+T/ubw31zV3/EDHP6/uW533scOTfv4AZsWv768EoDt4BMfOKo2+fWwgkkhIgw0Uholus9/XnU6IultXaHN2/eDYhK6/esukWOwhgwF6jII+0B3G+5ohp3NQrBLzyj9C0u8A4D0pBuwAyCBtqmLjCWXLJgzhRjrec7jdkiSfO01xKAfDkWsH7nb7/N1NbQ2PfTVh1qaW/GSS8DIoav12KkTuzd8vJkNKededHWR3oWM980UrZ4wHhIU17s5F6EdP46CVj3RnpXRFsIDgGOYqgrXwpnjLlsyY9HM8XaBH9DpRi+xDy8xLzLh35TD/4FI/5joH4gO2LvrVTC4FKTU2teXnwSH1GafEVKBJf11HUdkwx82mjui+vhn1zT99Jrmgb9Xv4N06e7v1tdW21jAAY3FGgs0ERleh2jc/70BnnloHQDxtE0lDQA+/ChqTLPbxGAgIMQJhT88+fTvfv3rw8cPj5k04b4H77ZWjsSeVMGuU1u2b+1oal16Xtqo04FhVW0wC/ebP70iIvfblZDYTbSLVRnUeqOBAQFgEfJ6HCJPHQD9KYB440+3L5zSe1NA6b9ub7PbWwkGzGvPvguKw9otB1NvSP+UP/rEc9mf/+t/joQUkk5pVlE3pX/ICIQMSSl89EK/kvrUDd8wHnqc+fJ/ffnuB9kv/wx88fHAXf8PDD/m3nJF3tLfKs1NGsW8+9HGDZ9BCM8/P1pI7v+e/WNz4/HPf/GL//bDPjqvubkVnDOSJgEW/s6Ggwe27D10YMXymwp+cjOQhxj7k+MeYi7gXE6XNOo0y8wBjJFVBpEyOKUg4kz/UQYzimogzoC9u16dPY80XRlaH0Ae/PJt5QuXc7qBJXPsHwwbEQXLKt51VJk3eWhmrL9xZ5vBW1Trf8jftQkkPpDL8W9OPPvUefc//dM/rs/+VFNnzTT/xTt2H5k9YwLAoOHEkfHjqo8f8be3HTt1uuVUQ1OFt+5fv/tda/9bbr7mxZfeuGjZubEzbFITryQnloOhoavt6Edb1LkLlzo9FZvXrirGWyQX0MqpgE+CzcEKDLWyCUjeGI3jHHwncPzwv3jGn3V7o5YNt7fS39E+QB1QvElAGjJJ/3cOs/HpYP3y2/fVO89H/rARCBthGcsqUDQg53CCwsSD5nE2UBwHQEr7z3IcC50Cu/5wF4AP5Sr9e/vBQvDAQ/e1tZDIpukzp5n+gEy6trm59dwLzt20bpP18pPt+5pOnZBl2ekq93ornQ7HnJlDX5shHaGuht1dDeOnzjpn2cWb135YkHOSBu6m0d8aBSWM0nOKvo/fmeQLW+H/VoW4gQXyN7Z111WV5X14SYH6BP73kHDviyH9LQpiCCq4JyDDo9fv6P+dw7kZHJ9dTyw/ERkrRPpjRQNKtMl2gSmU1B7MyJ/l+Dfx0t/iG/f1JM5mxFXhjX2Yu/YenTktms0QL/0TwGZm0y03X7NmbVTiW5xx9hnWwuKFs1Zec+WtN628avmFS86cXXDpP3baWXPOuqyw52w4vG/PngMXr7j8iht7J8rnXHTDxSvuzPeUxE5PUoAN0gNSN0gHYE3XzebuOdRxtnKAdZ3UBjVINQkzPdiqhE3OVcyS0JQeULz0t4b/qYP/i0/e3uDXnn138L3BBdQBPWN/Iv1lHSo6en8X9F3ylO+Spwp7YQOX3YMm/T9/x4XJor93EpAdPp8/+3dMJ7yam1sFsYjBuPGcPvTpnk9XF+RUM89e7q6ZguxVtsoJzgpvh1/69ONt9bMXnnfZpectu1QJ+z98+9k8TosBcPCYY7BAfoDIArvAiTwncCzDMDn5zSGELKmDxwg8J/IMzwAOkbYwHMI2rnBdmSgZSSWq0hl/zIpOmYf/qoG59LX11+1tNpvl9pneFcQQVFgyPXvpm5PkzSfHmVk1mqKTsb+qIxUzCuPk373LvEnM8LEFFUn6n7P87+Jf3nHNvBPP3nni2dTS3+Ib952f2RPQM/wHPl+ASMOe4b9F3bg6a0GWZF3T7PZoB5jRxP4t0dJDkTCItIMmM6ah4zQ4NrBWuAghzSC1QA3LD0w6AJAiDgiR+P2czDbmMB+SEkBmFgC2ukWa3y+d9FegTuDBAKUY/qcz/gxY+vMc47TzyXbeeENQfvOAwZsEwKJMAkzTP1QMU/oj58VT/TYO2rli2YJyFeV5HJI90+qj4vie6xcu9f34xLP9myaynwSQakWne3OZLfuPJfplSbaWw+FwKBSKP+Saqy9LsAKde0GvW7iUMQyzsQuDOIa0/7WK+zAMKeFDgvlzkdpmPwvzGIisstAsJH0ikVlSVDcKHw1MSSbpA8vX+JNZ+psl5xivxz62yu1xigmVxAvlDBhWvHOYzV4NHOlgifQ3GAU5HJXjbMUPAspSphdV9Me45/qFF4Uez0b0x0hnIIoh9thtkof/hmHISmKRtnCYJD9GIlIkklhGm8SDUkwCzXt8MgzpbECBQQVKGpBVRlKRpCJZQ7KegwJQdKTo5BBzgZE1EFZBWEURnVWQ88SewnitKZlh+wz/8zX+vLNp/6VLMvUt2rC/pbLMPr7G43YKDIMiMimimW7CmJ8hyJoEFCQktIBmnuzjgkzpb7NV1J1XuS+/dkh5kCDcLevQYLp5V7/9tGnwyefY2Vp3uk0NIGoCOnm6LcH9Gw5HLN0gK4oVAhSzXPh8fr4nKOiaqy97483V8fGg515wru/D/otdJzLqBrInO8GUGpK1brVG00gyMGAYUsMqJxMQZyoLhICqkbORWtGYOJc1AHS2vHjXT4mnV2PnbfyxpH9m449NYMd4XTUVzjKnWFPu8HrsbBEMQYWKCCqs9M1yHqBAUSirW1h2gBs6kTE44/0YT37OyGnUn8A4prdjVDwN1aTeQ8rhv8NFHmNJkqUe+4+FapLhvegkIEagea9Zt5nE7FtfetIYIK9vjXWIlTwAIfEHWPr4xF5S4IgyCLC9sf95GX/6Hfuv+ZRUB/SHwWcd7Z/tiNtQhECj1559d+Wd+SSLFpts5gG8p3Zh2SGPaDafHe08+bniBvlZY/zk4X90vI+JvdrSAUZ6W3PyJIBicaw5Mr2aeKjMOs66ORcg6QGY1HbOFhL7j6wzmAUgoKVNsIpoCP/gwfbv+80Y+H/pYiL9M1v/LWQxCyu/1BRbzDsiaOCGoCJE+kTnARnUQCU6zLPEmwZGNYUV/eOY59myLySs/KytgzQvLHPN6jv81zEx8iCEOI7lWBZCGPAH7HZbzFtgTQ7SvVdzc2vtxSvKd27M6QrTm6lGMBCxZBLARMU38QBbfR+jmbzZnQQapPkv6S1DOkoSnYzM481eFJTBNQHFaYCDn2Yr/SWVFG3NIP3f3d2h2CpR2Rh33aQJY8qmT6wcP6ZMFNh08pUtHxP/ciB14oYn7xxmJ49L8SWZU4+dArSP6pKFq99+utgDf4sbK8jT3OXv9eW2msN/RVH9gVB7hy8SISYgSZI1TQ+HI8FgOByORO3Xfc3YF16wJCEciEKcJU37ovE+5lyKKIB8EndJJSAyHYv28CIeBQjB6YMf05s8ZKUgCmj8Oejnp9WPq6tyX7l0hqd+BjCru3z6/ouaprd2hg6fbG/tDGnEfNj37auq/G1tA7QCFalAUEOrf+DGKwOD+ZPxhHJk64nyVHXcFgQuETKjdPjf4+nN39yfAa37t34+Wr8zDlvC8L+9K6DrejAUIcIGAqfDpmlkNsayrO4P2e2iJJO4ILfLiRAi8zCYxhMw/zycS58ANxidHGpWZ4xhdSWaHG/FceZZQNVqDmwAhgNdcupmbd/74c++952vD/SiKUmQqVw8ZPgP+x/+v7Np/yX9GX9aGk857XyFKH+6XXEfOhirocYwqMxtq/G6QhHFH5LN8n99rsIdpwOGxBDUz1M8MCmNMZA0HFSwwJKaJ+QzYIDXAfnROPEtqugnvqUUop+AzXZV8cN/fyCka6quGwjBSASpqqabLQOt/rGhcITjOAhwMBjyeEhfQZY1w9FHqUouAJBhGTYCVNZgzJxeq5JbTtlb5g0GDDZIEpkGAY/Y1hOjudn4MATlYfx5Z9P+C86YlvmrsW5/K8cib5m9ttLltPHxFTQhhCLPVlU4KssddpEzq3QlpQVUVeVtCBr08nA5o+ogJPdWToQACsQiDUefwaeooj9Z+uukpAz5SRj+HzhySlUU3YxWMQwsy3IgEIhIckSSwxEpIsk+n7+72+cPhLq7g42NLacbW4Ih0k0QY3zh+YvjrUDNza1wBNaILji+5v17GjWbQEwIpIUoQ5K5cswEJl98xECGIfMtG880hQdUeJWSB0kau99iZ5v2nz23njeDeNMN/9ftb2UZVOay1Va6rMD/xHdF0GUXxlS5qyucNoErkugrbG5w1P5jmoBeWb11IKfCAEik6BsYlRRd9HN3+7lE0Y8x7gzJTb5wcyCyVvMkDP+xoceXKdB1UrXA0DXzRzdIITOsqrosq6FwJBAM+/zBhlNNDaeaLDWQ54UORUGtwcTXtG/faZl0iNTInxrXgz1bSBUJ8ptUGD3lYzpOxocJ5kyVl4YP5QxKMfzHaYf/72zaP3faOKeZqJrB+ENsrHZ+bI27qtzBpmnswLLI67GPq/FUlNnMnICiTAL2Hn0fFPybO2B1hTGQNbMT5Oii2KL/lH57g357QFZDihpRNEnVFU1XdUPTjbagdKDVd7QzcLTDnzD833+INE/P/AmbJSiJVtB1Q1W1cFgKBMJ+f+jUqeZDh46decb8BFcwPOcSVTcUTbeuQdF063qCshpWtIiiBSTVvM4ilPYeTgRaD5E6PpA09M21fKdV9tM0A+GT3bqvad9AroRK//xg+0h/mCnt651NpD+t122LbwKazLr9rTzHVFU4x1S67CKXzrIBIRQE1ltmlxQtFFa6A5LVa7TAzoBQEOQFLnKUqIGBpGKXQOw/YBSZ+4t08o/armYgVHQyCWMgZJD5QxaQVVakyR/2SSSTS5wyO2H4n6t+x6RDJylxrOu6LMscx/KHdiXGg9ZWN3SHVJ2UMGYQIjubRXRV3WBMm6aiGwgSH8+Y0VbiJJFte/efPW+6GQWKASkTly3YIIewwJChEWo/lt+7t3X6qypGq6N9MMi2WI0l/c9fNJVl+zH+IATdTqHW63TaBZRRVyAIeZ6tLHcEQrIka2HyBc705cxJB8SSwvYefX/25MtBLiR6pZM3D4DffDTagj1Xv/00AKB40v/Fo5dAEDGbRJFYE6uxixkySGIRrRGGrEUNEAnD/30HjsKs48oxNnrLmZFWnRqGsCzYcQyCGgDWrN0UnxQ25eqV7//5act1o5sjWTICNmMZe8a25pWOdgUAANiy6+DSM2YyKDcfADTLP5DP6FCe0j8GHf4PSAH0Mf6kr/pw1pxJghmqktn447BxNV5XudtmdQ7KDILQIfI1Xqc/JCuarlkt0tNMAvImVx2AB6sr5Ein2KL/+SOXJH4s0c/GDB83/2fMgba1Onn47w53++1lcToAZ6kDMCYaxRHo6M0m4BInAWqfnujJ7zDanQBxbPhs/3kLZ+bkBuAYUlz6092HwKCw/s0/nX/1vWAk87Xv/AQA8PMffquA52T7RP6kN/7MnTbO1V+e0rr9rTaBrfG6xlS6RIHLcqTMMJC4i71OEhUalJIHEXkbggZSGSJjaXMq/QdZ9GeCYxg9WpkezJo2IV7snt72WbwOsCI+szmnJf1dwc6E9QmTAEo8G7fvX3ZWDkZaQWA+3kHsCpQs5X5hRX+SCSiNWHtn0/7p9WMqPXarq2p6408LQiTAf2y1x+MUoy1Ys4A4A3i2qtzhNw1BMvGbFcwQlMckwMpItOIFU/4VxEBk5S+CUqTYov8XJ28ik3rQlc3OEACRZQJyvPWffCytLX0KOFs6gIzumf5tnoauQMQkS3932Oe398YXNTe3XnXf3W/9scBd20Y0az/dfbfVhjkLqPQfQrkfo8dKYwqz5OH/O5v2TxxbXVvhJLG6mU+EkMsu1HpdFR5bytbeGY+FTodQ43WWuUTzjTJFBOUcDtTjB846IshqVko8gbFVplqKbtMMIxiWJEW11JSsahFTAI16Vr/9dDaJXfPuJxoiD544vvKJ4yuxkUPkDDKLBxg4NvxPDQTAK/nIx6drsZ/4HbChxX5Sjv0pw5behJrRwt1fengQRL9Fr8srpfQHANR6nbFQzgy+X5vI1Va5qr1OnmPyGBuzDKpw26u9TrvIq8FONdiV8BO/c/7ForPTAeYAH6txFs2wROoEaLoRltT2rmCHL7x9/ylF1XTD2L7/1J6jTZZNehSTfU5vTu26LB54Bj3wTB+PURuXVUV4hnQoJOK/r/W/NWb/sUAQsAi5I37rx1oZ1QQ9Qt8d9sV+0r2dO+xLSAq76r7UqciUQSNkfjdHDXd/6eGnfvXjQXu7fmbEC2dOcJBQzkzS/4MtRE90B0B3S/PulInchbCV2GxsJKLlYQginoAvEKtCNlhjf+s/SdbsInF7GBg3NHfbbEIwLIcllYz3Md62/5TAszVe975jLbPqayzD0ca3nzpvxSiUCLlG98+7/+ks1UCC3M8VFkGNVJLMNPy3vLFynDqP6QDKSAdjcKKxs358TcL6xrbuuqqRlxd29+BK/94ZwKSaxOH/6i1H5kwdV+4i9ekHRNaHq3xFih+u94d1V4MB0+8koEf+k1G/5TMkxQNUrdMf7g5ErJUYgGBY3ne0Zd1nR9xOcUZ9zagv45wT2Uj/5FF/jOytQKpuyKqeefhvZV0Uyk5AJwHDCsMwuvxmO5PRYvkZZNj00r+u3CnEvKDphv/v7mxForfSYx9Dav5wdpETeVYUOIFjBI7hWIZjEUmNMevFYoytNAKzE7RF3BhN0RrbAydb/E3tgebOoKyYCebWDj3fXtZdrflbc54E/Pbl+ElAOoewNfyPRhgCIMnExMOxTGtnIBiWddwnhsTlEANhCUK4Yulsj1MEo5oHnkG5VnLOMAlIJ/e3r1+18PxrYy/buPIqtR9XsGZgBGHm4X+xEcVR/ukPZ3QdmxWFR4n0f2pwh/+ZTEDjqlyS0o9v872drW67sGh67ZzJ1S47Hz8KbmltxypQVJCTfc7BglljHbPGkpKwiqo3tgcOn+rs9Ef6JAmXV/i7OouhAyzhH3unsKRYCsDyAMdLf5ZBPMd0+yMXnjl1bLVn1GTzFpBcpX8M8yGyKswb0WIxGXZGkJs8N3H4v3ULKCbuYOeaNRsuumip9fL48ZM3/v0XXvnl/xb1TSkp0cyc7dFxc54adOlPxuIph/+XLJ4WH8mTbvjvFnBdGVtuA5Ggv62to6W1PfYz8CsjxUQ99uoKB6k42Pf93eUVeZzwtd++3M8e0Wjy6D8RWTPrQQIpqaILw6Auf9jAhqJZPsjRTx7G+oRwoAw2nxgIIYZheJ5zOR3ZqFVmqIf/MW788t8P9SWUIqTA98iPArp70E3/MVJ/IW08mRm47GIG6b9pf3OFSxxT6XLZ+1R7LhQQQrvA1pQ7Ktw2LqmiXEwH5BQRFK8Dkp0BCEUTSk2wpuvhiGoYhtQ3ypNlkKrpThvpI7j7UNOB462WH9JyF2d/MaUzCchG9Fs47Da3y1HmJnVEpkypD5ZPYFie5wWO41mOZ1gOsRxkyA9gWMCwY5ZckHCGU9u2YcQO8AfELUR/mOgCySRgWHckuGbNhtibHj9+0tIB1rXFrpPlRlvZj+GGqo74mrp3D530T2ECWr3lyOXnTO9XoK/f0+h2iGOr3FVl9nT1PgcOwyCPU6ypcAYjqi+YolpcPunBcbagBENQgvgmQZ+yEpZUKwbUgtSfQVBgueXnzfx414mG5q6Pdx7nOWbKuEoGIX+ot3DA6CM/T8DiS+7Jfv917/zl+jse0nUNQqhEgueePU9WZtWc3qMbJP1CM4jBVzOwbi6rU+d5y5yxY/fu3S+dTC782QcIgJ0nvRcY83MkTxTpaYoMjAWWMTDWDSywpLS9jjFpVQHJAoKQI/GmBoSAZxjSdQBgG8skGIImTZowu7aM7MyQnUmJuvJJIJJPPVpKNmAM8i/WXcKO30wK4LwF9Xx/xp91exoFjqkqs9dUOO2kmn+xLOBP/3V9lnsWRAf0yP9oLTjDwJKkdgXCKik0Fl3JsUg38Divq77OCyFUNb253b9pxzG3Q6zxuhvb0oaQlyBk1P9MDtLfAmOD1BA0674dPnxkzuyZ4RlnVR39DGPGKrJm/T7knZIg/c1Prp8ZGMegMS7RKXCcWURUx5hoArMBpKUGDIw5s7eJgTFLilmRNCOr4KhhagIEISmATFIQYJ0bvBanAwAAM66/OfC3N6yQh0Plk53QrGJHKQ4Y42Tz7MiisbFp9V//PFwUwOotR648b2ZMoEuSnPIYjmUq3PZar9Nhs/p55cPEcYmhu8n8y0PXHzjZsWHnyZbOULJ1hRWB5s+zTlyCT9gizs0bFfdhWcWkCGVPM0uEDANXuO1mshucOKZCmaev33aktTN4+GSbwybsPtQIRjVZTgIGEt3/2v/9buXffT7Wi3HP3v1zZs9sn3xG9bFt5gqyfrdnUrL0l08ehwDwLHkiDRxTFWa72d7ma8SCV+kQeIaxor16qouam63dYssJCylW9MGaBLguvUZa89Y+Tz0EIOCnA4Iioqp6iSThD4YCWL3lyLIzp1idUDOwfm+T120bX+2u8NiSW31lz4lTLdns5hbAlWeNjyjq6Vb/6bZAZyCiqDrAIKyTaQrrrorpgFxrBMV0QJqoUFKRQlF1VYtadSCEHMcAjMvddsPA/mDE7bRNHV+pqNquQ40HT7Rikn5CM4wGmtvVqwN6BK2lA1rrF/XogBRY0t/rECodIjZ7Q5Ia/QbWMQ7Kql+K1u3QDKMrLFc5RJ4YLpMedZhqOeNet0/y/CXJELTPUw9KVfqf+dBz2e76zOcG+F5hiSTjj9yiXJddn/P8uIgKYMGMsQ6Ri93NlMP/dbsbRYGtrnBUV5Dv0ODceAiByLHV5U4AoG4YHf6IoWM7ow8kKjSBdJkBmtk70Fp22viIrNZVeUj3QV94887jSxdNdtj4mfU11RWujTuObTOLQ4DRToZJwMBFf6IO6CFeByQP/+WTx4kDWWCnV3kcPGvZiKxJgI5xoy8cMqO1rIywgKR2RWQH0QCFeX6TdcCiRbM+WrsZlCRbf3NH1vsO6GnBGIQldaTXZl89pPaf3s9g9ZYjVWWOWP+WdMYfUrrZKdaUE9P/YOa+kmpxNq6mwlFd7rTxiT2E84sK7TcoyHIDxJuF7DaeJXlhwS5/eP8xkgbc3BFAEFaVO8+cNd6sEloSUUDJgj77OJ/see3/fhf/co9p50mW/tY9RwCM9zg8Is8xiGeQwDI2jrHzrIPnPDayMnaIrOu+iBIL3CoSFy47p6jnpxiGIavaaC/BVXSiX4ylC+sFjsks/TfsbTLrfTo9cRnCgwbDIIdIdIDXY/UQTgDm2T243+SAHiKS5rQJrZ0BXzBiEO+TuudI0+pN+z/edbzDF/I4RdICofQohuhPpwNaO7uSpb/SQCJ/HAJb67Ynj0kQBHaOdZHxSnSNgUFI0YIKyfAoFLdP8iRHhQIAnnvyfwr2HpTkHOCeNnCUvCFf3dVbjpQ5RWsmlU76r9vTaBO4ukpXbYVZ73MoYBjkdgg1FQ6SddxXA7nLy4utA+wi1+ELBcO990fXjcZ2/8Ydxw6dbOM51qocV1Jyv3iiP1kHVNXWJDp+MbCkP2lV7RDFNEXIbRzjdYh83CQgpGidIVkt6CRgwYKFCTqATgKKiqbrI7oIxGXDwAFAFMDqLUeuOG9GZulvZT95Pba6Spezb8mHQYbnmEqPo6rMIfDJ6cG9OiBXstEBkqLGugL0grGuGycbO4MR2WknqWGUYs8DYhino9KfQ7DGaUv3VLIIldt4l8DHtqu60RmWQ3LBJgGW1zeZXz+VtUeUkiOqNnqKQAwh7EVnTeWYfkb0G/c1lTnF2gpi/Hn+zeJ7t/L6WoZkaz5ITPF5eIP71QEk+igNp1t9n+45OZJ9UcOd5OF/RIYdbHmVQvz/lU6xzMZn6lPNsx4b30kq+kVrfQQVtTMiOwU2Ock8V2IxP5Pr69es2bDsoqUwLiJogCenpIMU6hrhRSBWD7UHmCgAZ883J93wf41Z7r816GttbunthWEi23Jp1AWBQ+Qddt5qthWRVYkMwQr2EbJ2oHU1D0QH5I2m6/uPtdhIDBWl8Dz4T494SQxYlL17yAPZ0dGOIdfKVYwzuieUOTOH9LAIOQVW5JhQzxxO0YzOkOy1Cxk0RzYkRHxOrq9fu2bD2YvP5HkSJXH06IkZAzk7JUMOsFIqZbgGIww0g/EnhZSHJCWKY5kynrWJnNPOux2Cphtd/gjGgOMYnkU8xwo8w3OsomqdvgiAgGcZnmMEnhU4lueZiKR2+iIIwehKnhE4luOYYEjuDkik3CYf3VngGZZBvqCkqoamG80dgdOtfhJwmfTps+W18ToADCKKSirHDeY7lrL0b2v3m417FMjwYNz0MjGQ+SQQArfAeUQuolrhoOThCMhqQFbdYv7xbMnx/pbXt7ZunN0usixjE6lVsCgYBqnDSEOACqMAMkj/d/d0uJ3CzPrqBdPHVJY7Wlryy7xN2ZpHBOMTC5ESPCwApBx0AuV2m7UwY5yzK1B2pKHjdItPVvREQV9V6W+zapHCN9ftufqCOXldMGU4Sn8LSdKVnkLlDNAnjh/T4Z6WIUfMwsaxFXaxO6KEe0x5iq4HZFXVDSHHFtaZpT9pFdl4SnB4EEKiwC8uociAwUM3sKqNYAfAgvNWtLVG+5oMLSiD9F+3v9Ut4HFeYWwFr0jBfKV/gUEIWt3nPU7RTOdM7iBfGVt+c92eQb9ASrHYu2f/quf/9P5rT1tzf1LZzS6eajgFEWqbcmbsJ+WxCMFyO+8SuNjjgjHwRZSArGZvSDg1bl7sJ530BwBs/mCVHPIpiuoPhAbw51LSQmoCjuQY0B0b3wbDA9RP+pWdr/WSgs9MT47YcIBjUYXHVlvp8rhE04mXaQpPdcCoMf6sev5P1vL29W+YYcGMw24DEOzatWfXrl5Nn1IZQHMS4BSsKnCgxxWsdYYVJWM0YYLQtzh65Gj8CC453n/zB6t0Oehxp5jLUgaOqmojOgZ0+JC2I9j6A60OG5H+3jL7QGr+FAMIoU3garwuK+m/O5BYKdrdawjK/y0y76BrKsNSr+8gmv77DtS3r39j6fJbOI41dFKlGUAYrwPmzYua/mI6oOrIVgZCj8jbOCYom91GzYFkZ1j2ktpwQvwHHi/rYxw9cjRhjSTJoihkyPaaNGHMJ8Z3Dd0gUQ+KIcvKK3/6eY53gpIEBiFJ1eO+8i+t3nnzZfPpnSqYAli3v1XgmRqvs67abReLWPA5bxiE3E7ypVUUPSJrEUlNcAbE64BcnQFxvYp7YRGMOhApQyD9waoXEmPmNrz3olk31PLqEv1gdZMkvWiSlIGlCQxVM46cREd298aDympXWHYJHM+gZLmfLPQTyCD9N3+wCgCwbNlS6+Iwxr5gWnMrJXs03TDzMXu/jIo0IvvCLzhvxZDbgti0URMOcUyVu8xlK1TNrAIDAccwpAp/pTMQllTVSgscuA4gsp80rDeb1o/0QOORyIP/9EhyCc5VL0SNPxlyxK67/UHyjxVZTNoJRPV4TBnMmzeH49jJk8YdUNXmprZYCHKXauu2ja2prIBZy/1Xnv1NNn+LpQMuumip1ei4wmP7yrf//RePfTebYynpkBVNUfoPAWps666rShl+QsmoANbtb7UJbLXX6fXYSXOYYSn/CZAUpyt32Wq8rlBE9QelAYcFR6U/QpBBSKfCf4iojBv+74kz/Wfm9b/8vlcTWFO4qDIwP1fTVWCuBLqqejzO7m4SPIoxDobCDaeaO9taxf6iNrOU+xmYM3Pyt/79P3/y3W+CUcogVAeRFHUU5ADv2Pj2gvNWDDsFsG5/K88x3jJHjdeZXG5huAEhFHi2qtzhD8qSrJmlGvKfBFjSn2EQwyCdFIIu8tVTknjwnx5JkP553KSYJuidFphy3/zPrB4MoRwOQKwQ3WBuioT8IudOWVt4gEI/NgmwXp440VA/afy//fQX/+8bXxnIaUsWjIGsWgY8SqEVwLr9LQxC5W7b2Bq3xykOfsnP/KNCK53BsNzeHdJJbFieOgBByDIIMYhBUFZ0CIem5l3Jkiz9McZvJJn+B6YMohmCDLFsQqspmBlXbujmo1OokX4GHXD8eAMtEZE3hoEVtZAVBEqcvjMACG0iN6bKXT10JT/zgGVRhdte7ZVDkhIKK3k/GwyDWKIBoGoOMSDpCFvgS6XkNPYfoPRPpwyuvuU+8jFDGJMjGOPVrz9bvE9n8wer4lsHW3Xivnh39r1TKFEMwzC7gOXD+jf/dP7V9w6rW7lgqP3AvQa7dQdaRZ6tLCfGn+EZ+ZMOCKEostUVxGnBkazOPFPDOJZhGOL9VTSN5D3kJf1X3npXPoeVNpbjN4EsTf958OaLf1z79vNbP3p92/o3rJ/PPiKD9KKyZs0GXTdM8LFjpI4prRWabwpYjwOAhP+CkcuOoQ4B6lUA6/a3mAWf7WOr3U47P4KkvwUDSauA2kqX2ylwLMpPB5ijf6SM5C6jpeb4HUFs/mDVunWbWtu7Ozp9Xd2B7Tv2UjtGHmhmUsVIF/3DzgQEIUn6HVvjrixzkPoKI4qnX/iwIOdBCFpVBlkGqZqO0Igxgo0+0z8YjWz+YJXDXQER+Y9hUCCs/Pfv/vSPnx9eRonhi+mxUTV9VLmAdWXBkkt3fPy3oVQAH5gFn9v8oO10IxjFxFUITekNhhDKZpUxbJLfm8T6KlPyNP0P2PE7nAn5O93lldYj1tbW/tZ7G//53x5//P/981Bf14hB1XQ8irJzdnz8twVLLh1CHcBa0n94otnHWQssg2wiX+ay1VS4xtWUlbmilUEtxldGK65gQPpzdQcjuw417jrU2B2IxMtxxizcoAda0ukAjLGsagjB/MqMIITGVLonjKl49S95HF2KJEh/i+SM39HE5g9WnXPJtfMXzkcQ6jpxCuzcc3ioL2rEQFpAq6MtP2eHqQOG6t1ZXaweV122YMbYqeOrnHaBJMyMWCCALMNUepwXnjl19uTabftP7T3aHAhL8e5cxlWTTgeQZ8uKFc99+A8hnD6x+oIzpnicYmH+mNIjnen/0Z/8/Pvf+hoYLWz+YNWyi5aazyR50BYumF1VWfbMr34y1Nc1UmJAkyrAjxYdsGMoJgGozGWbO61uVn2ta4RL/3gYiGoq3JedM+PW5YsWzhhn79v1iXHVpDxKVjQESanxnN8PwjGV7gvOmFzhtg+rsqnDnN//1w8iESn2c8MNV//hLy8m7PPoT0j1tK9+61Ewovjmoz/45qM/eDLpzwEAfPGfvxeRyEQTEWDj6aalS2ghs6zQdMMy0o4+dgzRPIBdPHfS1PFVJOm3aNJ//5EGMERggMd57YbqaWjuCkskRcDK/eE8lZKvPWESYBgkuiCP4b/HKS47a1q5216Ev2CU8/PHvve1b3/PWt6xY8+CBXP+9YePx7ayLNHcXZ0dYKRh1iIC+/YeePIvLx45cjx+UzAkO2zROrIQwjUfrN304VtDdJkjCQxAWFLyTgIY/uwYinkAywKtqaWtuWWUjP0TgACKAldV7gxFFANjSdFAT4kHMbUOyO38CCGPUzxv4eSx1R4IoKyOzuHJIOgAqwzDxk1bq6u9sU3xov+r33r0iZ98H4wEzr3oSgijOmDvngNC3xJDMelPRX9OYEwUwChyAKdmkHUAEkmPpKGX/ubAPPbZ9vmQrZgcYppPWG9t6XNg3KaelQxCLodYW+mqLCM5An06iPX9u3uaxWYFhHDimPIl8yZetmT65LEVmq5T6Z83P3/se599us0wjC6zQFsyT/zk+yNF+gMANq155z+//8jGdetJ4RpZSbkPlf65ohtGihawo4sdpugfTFsQm3dH7MLSVwn1uaR0WWnkkDTXnrCBZxmvxwEhVDWtrYvEXljqQXRXSv52gKOTgOyNPwjBmgrXZefMsIsCwwyLGzjSWb/6dZKv562OX4kx+MV/jBi5n8CmNe9sWvPO338zMcl5zQdryVZq9skRXTe0uHpNo1sHnHnupVs3DcY8oCQ8lqRWBM9VuB1V5S67jU9UKObL3Q1dWZ4NIVTjdddUul0OkUr/ArJ+9ev+jj6dskeu9I/xv//5g4Q1mz58i0r/PDAL9o3m4X88Mel/5rnFnQ2UhAKIFY6uLHOQekEcG5si2DxV5mZw4nhWSXAMQrWV7poK1zCZOY3KeQCFkoymGaOgDcBANEExlEHansCjDwShwyZUl7uCYaVTD6nhILEC2cSItTkLec6xTI3XXVVOrElFv1wKAL/883MCnzRjyw5V1bLcU1FUDIDAJ3Z4xhirmmb2FSP/t7R2trR1+fzBQCDc3tFthaNAABCxAZLCogPIH6f0j1raPTq2FsciVEIKwCocXea2V4XlluZmS/oTu3NZub87K/vP2JqycpfNKiNMdUAxuP7OL+zdeyj2Mn45xnlLz8lwBrutn65eKYDws+0Hdu890q90if/QYU/CR3W1d8Vl58ZmhIcPHUw4SopExxiUgUCKtI/6GKBBp7QUAATw7Q+3kIeJ8/AMiBgYAiCi1HEayVjSv8jXWNL89dnfXn/nFzLvs3HD5gxbx9b1cSNniRT0iaye3wAeK6Gjh1MoKkrhqwDRe1poSkgBvPT2BjJ+XFg/derU5nb/qZbu7kCktdMPcLaTgJj0z6AGPt51sqBXXXLESWFsmVZ67nbvy7hGLiSs1zBw9DeCpxtbxtbV5PGmeVsXYuX/ojHJsJ8QNUp+qKQNAFUBI0EBfLQlbdMVAEBLW+ctV10AhkL6f/MLN1ovK9yO+rGVwYi88+CpQ2aiprusfJAviZKSOOVKFsxXMGkr+deS/mYrN6ISGMY01+cld1UtsZt09sQ6p1qt5ylFYhTnAA8h7Itvrb/lqvMLcq4Tp6MxfBPrSGjNica2+K0tbb1D7BffWpdwbPFUgiX6r7ty2fQJvSmmLItcrOByCLsO5PNUxUQPpeD4urrDkXC/u42pq4PQnBMAYseL0xbgdGNrToYgjLE2AOES01h7du/rd+d3//p/eb9RiZOHAmhs666rKivO5YyiGcCLb603RfD5Axf96SS+xVfuuZoM0iD8nz8mRvslq4SBa4WUoj+eYw3Njr514rIkg/Qv4TiFwvDhWy9cfNWt/eqApsZMYbtyFiokhoFxKBTB2SUZJaSjAwACvq49u/3Zvx0lP0gjMEqhgSe3vgYA+PGvXsxDB7z41oZ0E+ev3nuNKepJcU2MsWFgswt3NKTOMrkaJKoLI0ja8KbUCtYVxr/I/gpfentDZtFvLbR3hT7ecUCTSQUCwdnbNtI0LCTax+bPiPYnsLh4yczkM3+86yQOtHz7x7/O8jopKclGB2SgojyHcZ+q6eFwn9YR2SsAEltszy40AIL36PB/AKy86dbU9xUSH8wd16SODUuYAQy3pvBDTlTGPfylW378qxezmQqYQj+xz9Y/3H9tysQoTHq5JK4nfjzTbmpArBtGzMb7D/dfZ4VS63pUW/ziT28kvTu5wgQSLjjB3J9O9EevHgJL+heWxx7+ItUBA8FgbfMXTOkJwY96hiGEBrYMPtGZFllP3L8GIpuAqqoYY0HgWYapqfHGd4GLX0ig4XTrgUMns1EAlpMhnjKP68LzFsbcABn4l698Mcu/nUIZNHoHuQ9/6RZrKpCgBpIkfpQv3X2VKaZTfCtipFwf290siR7tu2t9/RBCmm6Ytl1y7D/cdy3x9ZEkQBIBBgH81VNvJp8wXitACMeNH3f7isX9iv7Y/rIQnSXkHkBOKSLxzTV7n5neQKzov1Z6Nvltdo7rjdSKHZu0kABruo/zM96RJ5McSD1ClBEJe/h449RJdbHX1lQAAPDCG2shSrSBfO3+azTTpmN9A0kapGXWGTCxk5CW9GZX+tiILBr2Yb7jV+69BpkqxzCwaVsi372oVsD4aw9cpxv4tQ927Dpwct6MCZlF/wBJaf+Jh04CBsLMqX2sbbk+RbXm8D9LGEsBZEFyAiBp66gbjPnEZuY/fvFrOgmgDDeIiD/cUwbH0gS3XnvxmCrnf//+r9bKL99zFYya6clLjo2O2YtN/DeN59jEeG1I4j8MEo2Dv3Jv1Lds7TBncm1Oot9l50NhpRgOJqoDRgQMQzpzgbzigKy+voW/JkqOvLR6582X0cZqOdNnjG9pAsTwGIOv3r9SJ9aYvpPooQ5+jM03GAgBil69QRLEo6bh1z/caSmAtZ/snTCmIptzTquxn2xWJdJqLrEaDGVIWHbdPYOtAPJ9sHUD6yVQo3j4o0j5hwyUMimmru2d3QhBlkECz/Icy7GMNUQacumfDqu9qjUJGON1dvoCnT5S6C1LarxOr8cmkqaYBQD3dJy3eOxh6vob7ljPd37HYsMYSA4BpXAMU+k0zEn93O/cP2RdfAuB5dDL1qdX5hLrqtzeMkeRrobqgGGONYDI71gD4+zLjv7HL2hwcLHI9wMsdQapFtBP/5gY0Flh4++7fXmx3i+XyYrIs9UVDghBUzudRY5gD3DeIIRQFl7cdGSvACjFgx0s3+QooygK4L+eeltVFIbJ9JF0RpR4rVBhZuQWUSWkB0FoF7mq8mLNAKg3eDg7ACwFYAWS5oeiqgW9HEo+OPMoA04plAJIGOCjjKI/JZ0RJf48ZUL0wh6448pB+JgQhDahHw9wQhpwrtCIoGELgjCbOM50yCR8gDI0WGnAAACPk/T2SAgaJIkacS56Nne5NOphC2vY6UO0NmJPspj5Ujfj9+OTOVPSLWuGGV335HPvDI4aYLJI5qSMSiCCmWermVFkqgCGHqddiJf7KSmFnvKDpAAef/K1WBJvOmwCR+r8EA8bZMgP0dWSopHoUgR13ZDk/o2n3eY+lhoYtAlBkaCTgGHoALBmACw7SDMAmg5WHEjH7yKdenTDFmrU3zPMJyN9BJGBYFW5nUGIZRDDRH9jjINhRTcMhKAka03tgWiNFpyVGhjMCUGRoDpgOAIhqQaRb0MAVdVoefChBcE+Zh9K9rAFMPgQiU80sMCReGqGgaRyD9bGVLqtsT+ZBJjJxBgDh4238uklWfMHZd3AsTLfpDioWduLgPufEIxcNUAZPh5gK4CcZbP9IiRXgzCjgGg5oKEEITS9vreeDSV72FxFP4SkXI+s6BxL5LpVzJllkccpOu08w5CAimBY1TXN7RSsr0rv/xhzPXNtUWBrK12KpodN9y8GQDLt/hBCTTc03QBWgn2qgZmlBn7/8nooBweuBp55NdqKQHCTPjbFhk4Cik1OhYAIkAQRxneazAlV0wyD9CPL41hKltz6ufvlSNrsTlJAjFJYBRCWFAThL597N7bGsuazDHLa+c5uo9xts8Q9w0CeYzxO0S7y1mAfgEi3n0RXJ57U1BbmEuYRU1ftVlQtImkIEedwICSTtgEQRmQ1LKs+f4RhiOeAlFu0uq0mgQWnomqKqhsGVjT92dc3nj13Ehj2UB0wrDBnAEzKoUY2mNWAsqoHZ0HdAAVnIC6cEietArCqb0aXIZlk2QRWFDiBY8o9NklW66rdlvRHCLGImIBYllTqtJIy+iuuQra67LxucE47qThkGNjtEKwRWERWQxE1EJRsAicKvKxoikqKkJo1f9L0CTILweV3C3SbVzfPnCGQmMYIjVYPcHw5ICOvR0jXDd0waBmpopJh+J8Q9xlPmctWtCsa9SYgq7+2ad8XBVbkWY/L5nYIHMs47XxHV6jCY7cqM1s9O/KoFASJ8w2i6jO8ZXYAwKmGU9Z6HgAPAHVTybLVTUxVNVlRIxH5yP5tmmaQ6UDsq0quABs6jm/Ive94a+x3MrE0hcsXTyMVsO+9MBhRJFl77s3P0l1qwWMM6CRgmDgA+rQEyAuiAGi3wiEl70Je194Q7TL24M2Zus+uvPOroARnAJKi2kWOY5lyl81h5112wS5yxK/LQJFn+Yy512ddvDIS6ExeP3782PiXls83JvpTXAZJ0oEI8TzH2kS+bMkFuz/bqKg6cRKYcKTlGGMToJJvRn5Tc3u/+/BFCDKjOmDYVYTOC2NgPeUpA8fpoGnAeZJWrgk8izGuLncKPFvmFm0CKQuqOiYbAGgATJxdL6U/qcsGUkp/0n6v4XRsefz4sRlEfzxkhsEgngSSguoKZyiiRHqybwbHOMPRHMJRXxC0J6c0r4KgtBzQkAEhLHcREwIlDzI99BFJGVPlqvY63A5h0wFJdUxO3icYJjE8+RGvDOLhxbQfJ4SgttI1pspVWbTinSkRe0pTZE5gzhVaKHSYQCIW8i4HBElWY05H0LKgBYRBpJZXIc9YSrDtvlDKDTsOkIrQdnsZmeQCcP4cOwil6K7lAQCkPkH+6CHS3zUdNpvHZgPeclAfd50WyxbPyEknvbtpv8dpC0Y8dlumBwhC4HHGeZMKOuWghqAYCOutrf2b4/olFmqcPZKsGLoKcXaWnD4jAGizCZFwuKWZFoQYGhiGoUV+8maIwqdw0u/krdYiThx0p17Tcypdx03tvryuJi0sy7gcvaWmaLz36AObJaryORKCigo3z+c2Av3pDx7N570oqeDY/P03lD4KIOErQILvY2ssIRu/A071k4b4FBsrpj8a2d+zTBast4tt7Xn32O+YoE+xpudUHb5QICTn+rn2+UuT4BjEc1kVC/t418lc35oagoYDhpmGnseBLMNUlLmp/BlCsvxuAgAuuu6BIl/LyIO1gutjMXAJL60crFi/9Z7xbzS3l9T1jNuztwBo7GVvaB00jOjOCeF2MP1CSnreoHe/2JqIrLZ0BHL/JpvXlf4gM9+tuEMMaggCAHy46pkbPvcQGArMKiS5KwAI3S6HKJJWFpShYiRWAXp/1bMYgHFzzj+06fWhvRI2vtUvEap9w6H7vEqKlEbptibIebNFY2znwlTO6nsGjIGsaKdbu/MOBs2gdkheW3YeQozB7oauuePL87wGyhBhJvPmrAAQhNWVZfl7jymFQOyvk0eReH/Vs7HlsXOWJW1P9CfFy/qwnwRJDrn0txRAehkYX+GqP6mdsDFxmJ/hXQYOJoUrWjsC/mCG2NT84YkCyOGq89MBdBJAqjz5QmWeQY3v6nUCm/0ncoJlGafTTs3PQ4tN4MbkWv0pR1579onklZYQtzi06VUwMsk4e+ozFwDDFknRmtr8/lBRpD+ApNdE/FwnQ+HHAc5sqA4YEnTDCAbzaQfNMiR/INejqAd4qNKAr73+5gdv6R2qt3UFX/vbtkJdRnKZ2BHBMJi9xmKB8orCMAzc1hX0h6T8nHj9wiCYUFEEAnDxkpmZj9rd0FWMixn1fLjqmcF/U0lSwpF8Rg+ma2jkfedHExBCMccQrCIBR6D0L1ZT+OyJCm0ShEOW+ishlwJZ0boDkSJJf2uUl1OayYkTjRMn5l+anE4CXn3mN0uWDajEd2PDCdBfu7r8iXvSfHJww4bU9abS8fGat4pwTaULzzFD5QMYHQzlDABjLMmqrGik/rOk5OGFwxj4Q1KspUwxsHqZZbnzG29/MPBJAE0P/njtO6I4sDqOxnAszkOlf8GxCRyXdRgopegzAASzysI1MD52urO5PZhg99FZV5nLdt7CCRPGlEXF6J5MkfW6bhR1+G8Vm8u13cQAJwEUAMDad4lXbdkVN0hSJM8bYuhFnAfkCBX9RULkuZyMBr9/cW28G4DSjwKYO2dCirWmvFU1vTsgBcIyVOJLdffGREektMpg/R7Tgc46yQGYfMNtIjd7SvXWPafD/sjqj8jWumr3jImVmS4Og0BYCpkNxYqHw85b7ezz0AF5h4RSQ5DF2ndfHV46IK+hBpX+xUOIK9KVAG0GMFAFgKCyd+9hUDhsIv/hlqMAgCvOJnXlrGWLiKTKSp9pe1jqp7iKTty/oaIO/yEE5S579u4dd4XX39mR/TzgjY92XXPhvJSbqA6I1wGmqzYy4uYBVPQXGxt1AAwMtjIp7LrTR0LixtWWn2pJUf1tgDAIWu234jEMvbrCue9oq66TNC6GYT1Ofsq4MgwAz/QJz1B0se/wP+eqD6lJE9qJELKbeZ6N7YFsTqMGE+3+mScB6aQ/JdkcBADI0zk8RDqASv/iM1xCgEbnDIBBhqySVsAAgMMNXW2dfgDA0kWTDx5v8ThF4nvBoK6mbMvuk3On1NhswonGTtIuBsEKd9p6zoqq1tWUrf30iMNGBKuuawxDrqHCbevw9cp6hkE6afKVSEwfdPmlwyf9D964NLoBwlBE+f2La62ewOl6gaUlzRCfQTBXF5ONRxElmlJEJwGF5eO17xT4jJSRDCSdmoaLm2eE0o9128yBJdh6bG2nWn12kfe4bDaBc9iJEHc7BKsGkNshWF3DIrJKPKfQsH4wkfu9FRoCIZljmfgmi2QeB+PbPFqp+VlkZpptK/MOxb7i3JnTJ1Q1dwUbOwKdgRQWBtIDOZdeYKqtSrVVsZ6a2JoTJxozhwNdc+G8Nz7alW4rjQiiUNIBIeTZkVcIaCQpAEskQ9MXaq1pagtUVUStRqS+GyAdWqx0GIEnpeUghAjB+MgZaNpSYi8lWRtT5fK4egss20Wuy99H/uq6kZ3lfWAawOSOK+ffePHsq5ZOT97kdohcxs6XCdgZzfpxV/Tmpp840TiQy6NQKCkhcibr3g+xUqC/f3EtvZ8x0urP2qqyprbGmGSNdeDSdUPgWKstu6UehJ5qfAhCm8hatd4SUrri9YFh4MoyR3z0TkhSVFWP10Ysg3KSvANh/Jk9dqTn1iUXgchJtyBEip4mO4TfXL/n6vPnZJ4EUG8whZITDEK5hmiPYlbm1bmezaXoNuRYZMk3MuRPlQUPs8uIZvtW2I9IGgA4Pt/KYeMZJpfic0WyMOboAOA5RpJ7jV3xOiAzN12x5OV3P6Y6gELJ/gsqCqQXTLErwY0IOZ432SoASEb6DM+zYSKsC0Bf52piXFBe2X15xoN++tZfAQBnXXV9wnqEYGxykyUep6hqIV1PcSWZJwFyJJ9iZBRKyULs0rbcJuir/vrStdffDEajHC98T+BPdh0FQIzP7EVMzs2vM8AMs9pJlhqIh2dZW47NpmsqnGFJDYblWHJC9oYgOgmgULKHYVAsMqXYrBwh0jwPcriDiqIbGKOsQ6oPtbJWo9VpNSlGxGZs0fDSAQk47HyuUcbV5Q5/SJIVTSEujdwMQdYkIIMzgEKhxGAYlEcv+FV/fSn2GwBw7Q23lvgtJQrAGq5a0jhxOU5E6+njMuMjOGMY/U0XMsh/EkRqvndv5peJJGuHTrZa4hVC+OQrG+K3Op32nDMA0mMX+VybTbsdQnW5s8svxSuAeLKZBKTTATQ3mEKJwaIcqjRS0oF6GqyTkgqx5QwtshkGSUqiGyAQSlGQxybyxJTfV4TGRwdl8ODHMrwT+sJ3BcLpZGsevLtp/5a9J1/68NArHx19dd2xvhtTOwAyF57gWKbcbXM5EjVHfFTom+v35O0JoGkBFIoFyxa9U3cpYLaE7Gn7bjlSe5v3pjrALnKNrb7J43olmqrrp1p81V5Xwp6BrlZJVjXdADW9iVE8z6pa9MQCz0ak1LUcYuG98Y24NE33BYrT9quni30MCIGY0sKYOTQJArvAVbjtXX4pJCnxdzBLQ5AVEkoNQRRKRiDLMDBHBXDbnXeTvJ+rzh71lv3sMWVcmoa9MJUGQBC2doXiFUAorARMw/fs2VPji8fNqo+WdI7H4xTbu6M5X1z6JA5V1aMDcPOCLJUUllRJ7qdCXKGAZhJAivX9HciyqLLM3hWIKKqmakYehiALagiiUNJ+Pc1AwTz6R8Xz+188lmVLyOSewKNGeWSsBop6DTsRSVm6kJTZSSAiKWUu25J5E7BhxEv/5FrQs2dPPXb05LxpYwzOuXbjznE1ngXTx3y4hSgDhmHnTavddajZqgtEaqgdabvwzPr4ww0Dd/pCGfwQhYXpKQOX+4HQ7RBqK5yhsNIdkEwveG4RQZmLQ1BnAIUCEeQzJoom14K++fbP6bpRKLfBa6naxGfJsFIeg1dJY+/ewzZLpCqksmaHLzwN9E4jlOSuXn21e0RSfUVq+54EhJBJ42LK0BE+hsCxlWUOf0iOyKTZWfymLA1BFy6Z99HH1BBEoaQGQVIEIqfx//CJOXxtAMqj4PqjHwWAAY4l/KZbjtu5d1WGna1PIpYxyyDi7z18ottaiLHvSGd9ndc6DmPc6Q8XMAshM1Y5o5RPTOaH6NnX+kQlJROK9OqDDJMAd4/xiRqCKJQ0MaCoptKT/c2BkFgRBqOEwDDQH9kriUwKwMBkwB7vCMCmKaYrEGluI6GOVrqsGT6EOZZxO8QKj90mcopKmoV1+cNyUryQhSXrP9rakCD044l3yiqqHhis4b/1rBAFkONRH+86KbMpnkhLkVhOZtYFtEB7Njrg6ovPfPPDrRnejkaFUkqEWz93vxyJbzuYc62wi6574NY77hr5wr8ASiJBN+RmAsKYyGJfICKb5Z0ZBjEIaZpZ+qBnqC4rWjhCmrwPsFe7XeSj+hqDYFhOjj0tIAgiA8emF5BMMPOyFX7l5sW6gQNhuaHFv/toa5c/QuJp+8YXsa7KeB2QDqhHnSg0IohCSUDg2Ty+oSUj/3PQDbndRMPAgRDpw4VJSjASeY58DD33FQMgq7ovKPlDUkRSY3Ux88Au8t4yhzUI1w3c4QsXr/UjxzJ11b0jdwiJYsvDYogxCAcDcjjIA3VSlbh0VtXMsc4yEdhZ3c70/HDYzmF3WXk2aQE3LF9sLaTzCdO0AEoJAiEkZXrNb2iOleBgLAaUkrMCwBjIiuoLSqpGivWLAmsXud6MJ0wqRQfDsj8kDXD4jxCsrXRzloYvbOvHVO/lLXPMnVIbWwPN4X8GBXDxkpkp1/cewTAMx7rd9tpqj9sh9BqTrKYISYFM6XSAGtcFl+oASsmSYP/hWcYmcHkM54eNG3hkKgDDMPwhOSQpGGOWQS67wPfNlZVVLRiRJVkboPGnzGlzO0VrYmFgTAwpA5hMZEbkOY9TdDnEWNAng2B8+5qc6alPwrGM18lXlztIySqzJ44a7CI/YZ8a9tn4rN7ipiuW5H8lFMpohOfysf+YfUqoBkiEzcG+Iam+YETTdAihw8Y77UJ8VQbDwLpBGjmCAcAySOC5mkp3rFaorGiy6W0uEhyLECQP0+zJtR/vOEwCQPPNL/nb39bkfRnpvMHxxSFoRBCFYnYlYXIdot12x90p65VR2EpPtNVXjE4fETrzZ44HAOw5dDC2vsINKtxuAMDptki3zydwnqgCMG9ssrczVxCEDrtQXeGyWgRHjU6qxgxkPN4/Zsv7ky3146o+3mEGgJo/eaiA73/jdksRWnehOxDZsvf0tgNNgZCc8r6wItD8bf3qAKtCnLVMdQClxLHaNOVapdEMVCTdaot2XSOVfBLBrGYtpj4QJtamqJfQ1B5saOxYNHtchpMcOdk2eXxlqi0GAFHb97HTHeVlVblW/MgPS80Q6Z/v2x063GCeiAF6dGJ05bnTLls8paHFt377icOnOpPL2LHuqngdkBI5Ei6rrO5uL1iVUwpl5AIRFHN3AMDUdW0og5gJnB9eN/a6nQA4E9aHIuqOQ131YysuPWe29TQEI/LvX/zo7LmkXkWWRaGvOHdmpy8c7OlObHUoQGYOWP5X3CP9YypB1Yzx5Zwc4lq71BS5bGXl/u6uzJOAS8+sf/nd6F9EJwGUUoZ4gMWcFYBZ6Xg0ZIEVHLRq84k3Pjn95pbTBT5xkW91rJDc3zbvHeBbzhlvr6/k9HCnFRSUXGO2sZ3UrsgblkHlLltNhdNh4/rVLCkjguRIeOqU3kJMmSsFUSijF2gTuZxSwHqhU4BUsD/7DysxDJs/GrHAGLra3Ikx5jl22vQpQDe2Hzi1YfuxQFjyOG3Lzpx6/bQ6RVLe2bj/s/2nrARdTU3007Jc5fp9EoRg6cw+HV2yp9PXGwSZDIPQpLrywuobCAGpMVtoQyGpLM2zVWZ1IOLTVvWEZ9EdNwlIx4KpNYePHM+8D80NpoxuIAQ2gc8jSOOF556OlYOmpDQBQcu/QhYR4JyRaGkfw2hs7tpxsDEYlgWemzGxevJYL+AYRkVej73cZZP8JKmV6+umJZm0SNd1Q9eNj3enHT6372nvjZgsc3g9DoE3+xOYXLN86YETra/+bVuCuGRY7sLZZpBobEpXIImNICkwknmfTz/b9elnKQbg3/ziLZnOjKDLztdUOANhxSxqlDgacfdnCJIj4YvPnf/hpp3WS2oIopQgDEK85YHMi+effer5Z5+KX3Ntdi0hU5bWGWBNt2EC++If/89auuW+v+u7yezIGJJ3HW5q7yaJGDae3bF738mTJ0SeDUWUpnZ/MJypnUt8JeR+LoLpW3rTlOwfbfpM043JVUJLR6BPFWhd2rSrj1L5/csb8msJ+fGh7usvmS/yHFE8ALAbj2cY/qeU+zH+89cvqnKmKUtOpNQBDq7P/aQlIiilBoOg9VUdDqwsXFXOG667jvRKcScGZKbD5er1iV52/vzCOIFjmoAog5suBwD85YV327tDLR0BK7Er4ufK3KTKtm7gkKSkK85jBukiVdWzjAqNlt6MvcZEc2iaoai6Pxjp8IWL1wNAj3Rv27EnNupPlwFQV+l6/b2NYBhww/LFr773SWYdQA1BlFHGay+/QATuTbcKPGvNADL7cxOaAVx03QNgePPq669n2HrDdddZusFpyv2EP3z1+p0DUQapo4BefPl9K/OrOxCxpD8ZGve8s6JqobCSsjgzqaPAMmZSWLZSG2Osm4m+EJJl1TQchWW12x8JRmRtYEnFGWAQqvDYESTVTCEEuo7NCtCpn6vrlp8HAMisBibNmGk1qoYQ+IJyWFLMoFLSuRRCGIzI/pCcIaWZtQHN15J5EhBfHCIDVAdQRqsaSOCqK69It/+/fCkr887wp1/1AAAYN7YmP2XAxobpZn233sI+mq77gpGI1Ne7i0miUyiiRGRSECJ1Yi2COfVtN7uvkAYsCEFNNyRZDUtqKCL7glLxCsBZzY09TltU5GNipp8xuXZsdfm6rb25bynVQP24FK0uswdjkiO241DznqOtwTC5jbE/kvXU9KsD4vPCqCGIQknHf/wqhcIYfbyaRj088b/RhRuuu+7e24hFJyXEoBadT1ljYXOOgUmgvRIIydZAngyTew6QFNUfIvXgks+FEOJYVtNzKwgBrfYOLFI1Iv1DESUUUdI1EiggbqfNLPrWexkWF5w5nYfWQDt1CYqWln5St7Jhep2jxj2muT1wqs1POkfGZgYVpLphhq5hciRs83gjvt4dqCGIQknJW++8S+/Mq6+/nmEOQeqURUNpLBFkKgRVI1WdrZYvDMm7hpbBR9MNOaSFzXpwyeciKdoQaGk6oacDIlLcwzCwP0iqfkZkVdX04lV/iyFwQ+lNYhB0OQQIgULUnhZOmGllnARcuqDujY9MBZAxHo4agiijHiriB0hc7I0VCGpG7wTCciBEhqUMKfpP6qVZNiJJUYNhOaX1n2UQzzLEgp+jz9as6wEDYbk7EAlGFEUdDOkfFZ5wKPNEOLOiaq3XWe62WXFTMdzmPCBDatjt15wfL/3fWLe7yBdLoVBGIShV0X+t2x9WVFL10yZyAs9ZhhLDwOlKPUNIwrOI0SQX63/PsaTNQCAkRWSFNBcrpt0/43UMwXsyDPI4xVqv02njE0bz8TogmaDfl835accYCoWStQLAQDcMX1AKRoiRh+cYp12wii5gUuTG0OIK3cTDsohlGdm03YBcgBAaBo7I6qAN/KPvaxaV7bNqKPQOJM3tmMoyR1WZQ+jbXCGelJOAay6YG/8y3SSA6gAKhZKVAsAASHK06D+C0GkT7CIfi4xMl9iFIBQ41orcB7lj6hVSTBoMIiyLqir6FpgbIoeAeZ/5Gq+zzCUyDMzJEJSlDqBQKJSU9Mm/1XUy/I/IxCEpCKzHZcum7hJpzwOhrGp5CHGMcfYJwwXE63FUuO1geMCyqNxtq65wmuo2B0MQSdlbQSJTM0MnARQKpR8FQEI/JcUflHTdMG3TNoeN762MmUZKIwQFjtHM7C1QBPLszpURBqGJY8qL3GcmByDpTMlWVzgqPfYMGjflJCAS9Me/pIYgCoWSPT1CEJP83u5ARFJUCKFd5D1mmHx0I6mljdMGU5LMr3yG/5mxmrPzHJvQeXjguByCtyzbshuDA4Og2y7UeJ0ep5DQkabfSUCCIYhCoVBAdvx/xcAQswsYwTIAAAAASUVORK5CYII="}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 1. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The forward arrow clears the rack once hook 1 is released; no other item intersects that path.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-1292eae75edb", "created_at_unix_s": 1788846011.067589, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 2, "requested_group_seed": 2, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [2], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "assistant": "The brass band is on the sword’s hilt, identifying item 0 as the nominated target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 0 Sword", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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oa3wMJNPChoVf6dATNUEu0wJmIxpJUkV/R3egptI7oicx4UBjZ/j/4/s/D8Yb//ixeYf37fDXTQIQA0MHmqrq0ROHdu/fueMHv1032mc38fnsVx/I9NLePXvjD6raV1XBVVXwmmp0XQ2HIIQQQAgQhAKHZIG7qUG+vl66rk7ANgDgrVs+ij8yHeXvv/PDkn04xgCZBv6MMT0DGC/Gn0H5JnlGGT/7X1+9+XMPLTn3svYTBwHGUNMwj9uO73/rhb7vP/b29++5pHRnW+ZQ6a8oqixLaUf68cE+gkN8peRlCHiyGbxpkkxVgGZar7Tr9K1pZwY0Nenvv/PDf//hd0A5cdUtn8cYdOxfyyFgGIYo8Bhjw4JEbWLsFKGiA5nHCALTAopmYgwkkSRv6SZWDCAgHNWJ5Q0DwJE3kYKLAHIYgxnLroQQvPbs6BsDyoSxFWY+6pOAgVzDfHjuV9/cvv7VxlmLAYcwhMQhYKqR7uZ3Xnzm+7/8S9FPkpE09k8c6VO5fFUlGelf2z/Yz+uKQUCmBRyCDoG7eZK8ukG6vl5aVUtkHAaDZgZlOA+onHLaOcsWte9Z27Fvrd03KTZoMk3SRikOh4CFgWaQy0VnWoA8tTAGfKLIwdiyyGWNbQPAkQ9fP7zl9flzZ89efumIf7hyBI214f+o64DC+MuvH3jliYenTV+MeB4gZFiWaeFgb9fGN1//+kN/GO2zm8jSv7Oj3eNxAwCuroTXVCFq4eHzlfqZQaQWLJR57qZG+QZiIxKvqeUxwAihbR/tKDcdgAGph5I6eSIGtaSVdvpu0puTQi3IClv0J29okQ6t+Z5bR1dvvm9hlEoBjDvjT1F44Y8PzZh5miQ7MUImME3T6O3pXvvqy9NOv2G0T23CSn8AwIVS5NpqZNv1S1gkAELAI+IwEG1lQOVTuekAiA3MQR0Ay74giMoPk4x3INY5YHDQBAgDUwXAJIIck1eBZdnSHgNsAEuDCPPQRMCEwCQzBdMisy57VxZ15SMILVLijVFeJqBRmQSsuumOVbd8YtUtn8hlyPGZW1dWVJDxZiae+90/1zROdbjcGAALWpZlRsK9XW3H6xawVOGSSP+LpMgIV4ahZo/V9UI56oCUHwk1sXF2ohadBuDB5qA0+7BH91Rb07elTtgSS7/lzsI5UyoqPKmPXRuf3bXx2T2bnitgnxMbfte+N4jiHfyVnbbwiiHf+dNfPhS7H+xv6spLT1+y4IpiDf/HSFRoJtLqgMTb64q7H5w+/7RQoBdDS5Z5BMxgT2vN3IsdLi8AzCtQTOkPRgmiA+qE59tI8Oi2j3YsW34aXV8+PmFanI1OuiAgFn+fm1O0AVESm5DFzUPU7dv/ksCjYMQQbdVhewHIJCCuOyCEq275RNHPec+m5xaed3PRdzt+4aOqmbwOg83bXqWLZy2L1Tb4j/95OK6TbaHfL/n7v9HX3/rw3/7roSsvPf0XD/92HBl/Vt1cksTdN37/wCV3fH/5GWd2dnZhCB2yHAiGosEuaJG809wZsTHL2P9VjCnpT4GQzAOebyGRQhNAB+za+OyQ21z0sQeIpKYjd9ujayKygDAxjvEI2T5e4ta1XbtkQxSzE0EEkUk9vhhwiJjs6L8YYw4CiAAJBerfc2w2wSgx8PXX07oo+2X9wDwsJv/jkp8u98/mYl4fAMCBrTuLpQBKPQn42y99mbT6wri9N6LppgxjZkcIwMqz5qZ9yxUXpU8OSstFtz9w8coVJ5o729s7Wts6EUSCIPz0koxB5amc98WJNl0oTNMUXfq3zVo+bWr66pICzwffyKPPM8aAzgNIwmq/DiCdJEqgA3KR0SXloo89MMsPDVIECyAIJAEYltnv8uXIEN4ECFmaYXAIYYsM70nsD1UVGFvYMC3MIc7CRGZYlgWhHQhE9oY0I2aK4HiIPY2zZk2PH/ec+TVpzycpEWzluUuynHy8i8DYH+6Mbh5AqvWNfIU+r6MvEE3aiIp+y8IQEblZROlfakPQL3/2I+qj2rrjUL1XaOsufqbJO089uPK2b11x+aWRSAR1dBKHgFXuRWtHbE7z+iMZD9Q2awgt7rlide46IDYPaI3Zgn7+ndvp+nuuGWVhXSJIiA6J34+5bZFtwccYEz9vfxINh+zcC3tyIKDYOJKIXzInsIhpKBbmE7P8DGyAiA4g+yowJDuZl97aEl+u9LlWnLWQGYKyK4DEMf+ghb5AlM4BUjakqhUXV/qXlP/9+b9s3nGQLNnWR3smQwKWh+SFNz7I60D/8NmbSGEJbXFLS7uiRmv95a4ARowr70s/yvvDW7GmstnxXLH6nKl5hEgM7TSbKPACh3WT/v7tAB8LIzubi4Tz2FYBhC17bA8w0Q3U8o8xMDGwgEX0Bxn1k7eQTDFsAUxCgUhqtr0pQpDjoYmBCLThiPu0bNy8hy4wZ8BQmcAJigCA/uH/YJUwApRoEhCT/gTySXQjxRGSAZoMFFeEMbOYPQ5K2ILcxNRWRisOXDSPP3x42jR/25Hm/JrI56tvsnPDFWcXcW+MMiFRqmKAZB4KENmTZ9KmF8J+ywom0wKyBYewPRGwTPoriMX7E3UKOaoMLDP22yE2IDtAlHqMOY78RQiGIWnUky/9duqB3IK4bToWoUTGeTBRE5Q5fDapTxf6jT8pft9Bbyzd8L8UOuCRRx6777576I1JgpNtm2acLNqNhprb91Km+UJ/yAMNjohNiuHHLoOHj8kfbD4MBqzEQ2OaFvl5xVRO7Abv1zdk1/aL9KjkgyTGXJRanWSBaZoRZsiRb5Egt1xEMxWS4gt0kwQCYdPioEUC9zHUTEgMPrYH2IZMFEyLVH0gEhlDC/dPqjDWTMAjjJBlmNjEECLOxIC3y0kIHMRyIU7g2CQi9ttLWD9oA7LJfV/JWEWqbBVAWuNPojBMY/yhz0tt/CmhDrBtmvZwZoBixR90bn9cCXZOvuCrh45/JMr8ngPB+7/yGXBgoJbkkMT6oA7kN6W5qwdubqpv+mcm8Xs9xZ1PVcggbR9z8Pf/sefjsUbcxMIbi+yA8TlN/GeWFqZpRk9GU+JDhthf+tX2f5s4ZoSPWd4Thg72/DZ+58TvoIEYD1uUW5jIdIEjrl3NwrLICQiqOhS5gc0kAZgm1AwISI0fslbkSZ6XpscsQjxHjiDynKqbqkH2pptkAzKHSAgYzZ+hf75M+mc0AYkCr+lGqvFnQCWAiQPRAfd+xsK4eIUDYrisEyd2bVCCncDUmtf9yAsxz+GP3VD6QUfi9CPzGrqcsib1IiTqGKox4hvGZMNBx0ysaXPMpqSdDUigAeUTr/oCB68hLkT6nE53qH6hljR6lEw/6gI0ze69nTFXcEv6Ns4ul5MudB7KL2Y3Cy+sP1xVVdXT23vd+QORLTb4T2/sqaysuvL0ukRlTUVkTDTTS0SviD3/zCKjk7G3jnXUohv1z2AT74KBbz8+v024HQamsfYJagZGAEo80EwSvinxSOQRB6FmxOwDdHTQvws8WHXEZ6r9Nh8EBR7ZH5KskRBUDawPkUk2LO77Ozb2T6MAYveephuJvl+f19H/2qgN/0vqDHjk0V9/8pOftIccRcODWo/v2hQNdgBTQ8DgbekvcsDY8kO3BEGscuU4pP8i7QWNvT1doUC35HRU1QcxBOtPqhdNkQdvm6BfkucqqWsSxFi/6IjFgyfIRDpEHVgV+xu7PwdkV4rFzDZBEGGESBRKbIukXNOE+GYaEdCvhWI+TDJKoAsJw2eyQ7pMhfUzb+w1DQ0CYCgR09DscBYsef3X33b79q1bgz3dAE+Lfdx+y10kFJy/YOHabc1NR44KksPpqbhh5cxBs7rU6xlP4E83Mcz0reW20VC7sEi8pmFaJrQgNjHmMIna4eyQfhIeZBkmQhwAxCJkX0KTzq/tKSVHNiVxPuQ99CYgGxsWcQqTK2iZFiJuAQ7SWKOiw6R/WgUQu9aiwKvaoOE/Df7J7Psd0cifEumA3/3ud3Thpls/NuTGG7Yds+OUBRKkv3hS2m1O7Fof7WsFpoqASaQ/wgICEg9dEqhxwwV1vHy2vOm5YC7ntmbrSY4XkeSKHNhc43fUegViMLUnynaaDQ1gIjNxDmPNsESO5Nq09ql63QKMsaqQ2FZdVfyN9bLTZRz8oMHvtGfu5LcX1XQ6fLMsrBsmMdLaoRo8Qk5Z0A1TN0xNUzRd5zkOI4FGapziqvyz3ZYe5QD2OGU93OurrulB1prXN9RVes8+Mx8XRxIZZyBDKJHk6Ut6i1miRzAmkAa9NmA3tl+ipT/73xb3/djvir3zL28eNA0Su84jiBBJIKh0SALv5jhOQJZpGpqufXTw4IXnrDhy+Gg4EvFPBe+07OvpCJuKbEGdEw0lGDVNvqenZ96iJd2d7ZKlG8GO51/s1AFHAqsRh0T5lsvmgTEDBtgk1Z2xYWKOiH1oh2wmvJwqt9PZc7CFIXEZx7exXckWsHcOSVh5QYbYjTtO2EolOXyr0uv69SNjt7LAmDABJRl/Bob/mX2/SdK/3Ni06xRdeOeWh1vDRhhjEyGL4x7c+J/Y1BA2OWgR6c/Z0l8E1S40p4aTl0tgWfV5yz696Xv/OeQhMACGoWHL6gyGNUNv7yEDK4mzPcNETCFaNwvrqiRIiqJpho4xbumJzKiZE+joNOzCjRAAZzQKIWw61dbb56h0S6ZlAoh0wxAgMeYqOsnVIUk7sVouAzYC0zJIiDfpmUXGbLJDOqmET3Urhq65vJ6ullYl0OeurtJC4WBXX6inq6m1S/DU3rByERi75CZZ4hWKU3jm1T2CZTldTh5bhqErSlRVo5oWUTTFMEiuqyDwHEQ8L3AcJ8rOw4ePRMLBYE9PWO1GPHJ6ZIdTRjxUFV0Jq2qA4wTeMg3R5YGhYFWVxz6yaGEsiiLi0Np3j0YVzbAAL8urLrAnEKMKQgCR6m2x+j9g8CQt6epSt1FqudDYnUak/cA3giA0be8Unac9/+yT8c2Xf+drwznn3uAoJ42PWXgq2Kn0TzX+2JlfGY0/Sc9HIPB/LNQI4hCSRTJAJlITgw23/qQtZEZs6Y85Dgjggc8eAQKAEnj436pt6Q9cEpH+s2s492kSOKsKgMsBeOC8Hzyw6Xu12Y8lOz26pohOtyS5LYSos0Il4RQDadgYcwA4w+TbkyAnke/O7+JlF+9URE4k49BoxNRNBBDgXVEDnerWiEi3LMM0YnshGWoWydO0ZT0ktY7JKIxsRMK8Y9YK+mM1RIegmrqutR0/xguC7HZHDSvc0wOjavWMWTdethRMUP781kEtGHCIAjL0jr7uln1tJF5FFATyv8gLgsvh4gUJY9zX0xEIBS3LQoIoSlJfV8fk6bP+7lOrMu35p4+90N7c5PZ6O1pOybxA3shB07QgQpIoul0ut8vncfkcTse2Xb3BUCik6qtWzijup/vmd3+cy2YVjQvIaJ2HpklCfOyxPIntt7O3kgb1/XlhdjrYYIveQFCmZdkJwpjsjcwlSH4Z3TTXyOxcSIryYKRzAuOs4UAgmdFK+xpdHcBxqLbSfc5ps7v6wj2ByGNn/ENryAxjy4RE+mMeAIE8oASgA3zzoU7OBx77Rk2VE82s5nyLJHB+JQArAYjVjDzvB+8AcP+m772b6XBv/d9/F3aeu3eUNuLzvFW39/V2R6Nh0zT0cLCqsmrPpg9bTxx4b90rQ773X35wPxhX/O65zU6eE3kUCfceOdaqG/q0OYu+e9fQuV+Pv7T1q7NpWa3DYP/rmTb70QWxhe9uWgosKIiiw+X99E3nAAAee2ZDJBRoaW85rh01DBNxQl1N/YzJ0959r6kzENq4fsQrhZB2AGSMjrFd9NMW7kSOxy1BMUewPfInpYD6DUBksmrL+P6KMjEnC+2+RnLESFoBlTN2lFExFQAjmwKIDf9B8vC/N/Pw/8DWgRK4E48so4W6So/XLS+YWWda+Dbl6paA2fP4/0N3ft+i0l+0FYAIoAygG3A+cn2//HDHez9vrFwggosqADgfgH/t31krAA8AcPi8lc5N68bZFHXTK0/lvvFjdw2O6sgcCLvW9Q26IPDcadFmMDyU6ZOHuYcnnvtAliSsRY82t+i6Om3u4m/dcVmmjaWjA6FQlHsW1uaVzdp8eGBQ9c0P30q7TePUGe9uWe91+6ZOmnrL6k929PS9885IqwG7EwCkFRv4QS2++oknp/RHfFEzUPy3FXf202eIs1MGbCuQHWYaC17K+8To2SXElTGyw4sCnxT4T6W//SS+cvSNP2NkEuBxkSAeCOD8n64HYANdaT3x/QEP4zcehA5b+lcQ6S8A4ABg9ZebawB4HnwOgJ/370kF4KsAHABrQ0aTMaWGb+pgVSJiCHwhWaCpyMdOpq7s6DFr/K4h3/v22vUOzHV3twXDwX+64UIA+k0uKVJ+hDmwbR0AINLXqurKvkN7p02adu2qOzr7Ao4K/9qXS957LhYmZT94W3TY/nI7FMgu82mT5BsYvIvBLcBM4qaKuXw5DpnE5ZR4qPzwe10OWfC5HYeaOkmf4X5EvrTNz8cv5LokBf5Tevtiw/+xY/wZMR2QxUtIfVmz//WdjBs8TKKM0SMPxqW/B4BqAOoB+B741Q8GNryXhFOuD1rH9bagFdXT3+5XXHjWG+s3D+/TMAZx3lmL2tq7Q+FoV2+oo73z7k+QkkHK2y/vcNbTDSJR7aNNH0BV39t04MGbLhmzl6+n/TgJ8Zo6c92mtXU1jY08f+HKG9evK/VsgCNmeg4aOhL6w34sYu+HGBP5/4Uz9ue4o//YPJeUkSbzCPJG0mYTQAOTGFBA1AnZfb6cvXiqQxYaa/1+n/NIU2dPMKrphiwKi+c05DFjLSeShv8x4w99lvS36Mafl9bvuvbCxWBcEa8lkh3jvgcMADyPPOgBoMqW/rWAJAB8GwDb/PEZAHaBd4PgqNFqS3/TAn4P6gkmJ8AEgzlFizJy5JWgvJTjHA4JQtjb00PEfTjqdDnkS645G4Df/vbJSE9QRPDo8aPf++F34XvpjTBjin3byFgk1Nvc3dflcriuuvpWRUDr1pRQ3CEETRPzxE1N6ida2OJj2RWFp9ATN7A9LeA4En1gR4QWsrNqP+nU5JLFsxdPmzOl5kRrz+GTnVPr/UvnpY/YZqDEwP+49LeH//aqoa7QMIf/L63fVfB7R6d9fD4T0/b7HohLfxmACADtAFxLXtkGPugDR/SWgBnRSIQlwMAgpVGS9/Detn2gLBm+AyCJjtnLrXMuXbpoFjHWi4LP61IU9frriEE/Eo7Sh9ob6OvraWpv+dyXP9fW2o7PuRSMH3raj548vmvj++/xYe28i+27rASQ8b6dotUfxkOC4gZezkdoJ46jIK0n2r8y1kJ4GAgcV1PpWTZ/8uXnzFs+f4pTEoe1u4lL/MtLzotM+pt2+D8c6R8X/cPRAXt2/hmMLLkM/xN5/74H6mxDUNSW/m0AdAOwGHwIDuqtfVZYB1T6myaOqlYgYrV2lbsnoFgOgEQ6ZpMGAG+/EzOmOR1yKBiUEoTCrx/7wyP/8z+Hjh1qmD710/feTVeOx55UoZ6Tm7dt7WppX3F+xqjT4UGrNhA/LX0MiIj8L1dSYjfRLrQyKD3Q8ICApDRW+VyyyBwAQymARONPb18krfemiNJ//Z5Wb1U1GDbPP/EaKA3rNh9I/4J9bx76+kW57+pX9z3w7/c90GFL/y4AQgAoAEy9symskXRKu4q6Lf3DVjBsKRqLXiiJ9Kdl7EijmNfeeXfjhxDCCy6IFcf+vyd+09p87L7Pf/6ffkic+XFaW9vBueNpEkAJdDcd2L95z8H9q668peg7pxUC+9u5JL2U/607+C00q8XOAsCIlkFkjEwpiATTf4yRlEPDcQbs2fnnhUtI05Ux5QNI5dn7Hpj3yINBeyqg23VyV3yxee3PGkwLK/bYPxSxohpWdbzziLZkZlnPWMOBnk1k1lQEZi+Yb//F23cdXjhvKsCg6fjhKZNrjx0OdHYcPXmq7WRTS2VV47e/+126/W23Xvf0My9evPK8+B426cM6kyvB6NDTceSdzfriZSvcvsr31q0pxSFSC2jlVcAnyeZAA0OpE4zkjbE4zpF3AicO/0tn/Fm/p5UueKuqA12dw9QBpZsEZKBwu8D++x7wP/KgDoABgKkDHAYX3tHy6mO1gYgVjFgRFas60AyglrERqLj2n7j0H+gHC8E9n/t0R1sHAGDu/Dm2PyCbrm1tbT/vwvM2rd9En36wbW/LyeOqqro9/qqqarfLtWj+6NdmyES4p2lXT9OU2QvOXXnJe+veLso+7ZIhdmcX+3ImjdLzir5P3JjkC9Pwf1ohbniB/M0dvY01FQW/vaxAgwL/+0m69qWQ/pSiGIKK7gnIcuvFR/95WYHi9NjRQZYBcAjgKDEGXXVLezBsRVWsEemPNQNosSbbjGHhqayKf5k79xyZP2cKXZ8o/ZOw+9qSScDadTGJTzn9rNPpwtnLFqy+7urbb1l9zZUXnXPGwqJL/0lzzlx05uXF3WfTob27d++/ZNUVV908MFE+9+KbLll1Z6G7JHZ6kgJskR6Qpl1HxDBNu7l7HnWcaQ6wacbqjZBMglgrGVqRpJQloRn9oETpT4f/6YP/S0/B3uDnn3ht5L3BBaPe94BlS3+sAKySx6f+pkXtl/6qCTUTvbGT6YAi0NcXyH3jTMKrtbVdkkeohPepg1t2b3mzKLuaf9aV3rpZyFnjqJ7qrqzqCihb3v9oxsJl519+2fkrL9MigbdfeaKA3WIAXCIWOCyRB5B54JQEWRQkgec4Li+/OYSQJ3XwOEkUZJETOdI+XiD/YodQvK5MjKyk849nMv7YFZ2yD/91CwuZa+uv39NqN8sdNL0riiGouGS79xJabxz6+kVZMsIy8d5XLzqfSn8FNPveBD577YNPp9/68btAmREOkPD8Ig3/QV8fSaSID/8pjZMb6YKqqKZhOJ2xDjATiX2bY6WHohEQ7QQtdkxD1ylwdHitcBFChkVqgVrUD0w6AJAiDrR6YF5mG1p4kJQAsrMAMO0Waf++TNJfgTmBR1YBDAz/Mxl/hi39RYGTJV5RDU0fVOlp+DqATgJGwhs8+PMVoAM6nrzr+f7lrUNvzn4Gw+XEqY4k+w8V/fENIpEIxtjpHJgKX3ft5S++9GaiKzjRE1DOWJZdhRxBjghwksJiAcDHGpfmFx1hFzOn9zcy7L7BpJao3Q/Gskin4VJ9BkY2+VKo8Se79LdLznFVPuekGq/PLSdVEi+WM4DBiCP3221Sh/+WZalacpG2SIQkP0ajSjRKuugkx4MybIKtu/tUGDb5oAZDGlQMoOqcoiNFR6qBVDOP8YpmIs0kb7EXONUAER1EdBQ1eQ25j+8ujteakdMMIDb8L9T48+qmfZedk61v0cZ9bdUVzil1Pq9b4jgUVfVwVMs0YRz1SUDmHrSDIDX0ATj49Qvn/Ov64R+UYZe+CC02eod/KZpIAY70w/9IJEp1g6ppNAQobrno6wuI/UFBaScBfW8PXew6mQk3kD3RDWbVkax12hrNIMnAgOOAaaZrB5YZgQ7+EdANsjdSKxoT5zKJkeP9pTt/RiIDGrtg4w+V/tmNPw6Jb6jy1FW6K9xynd9V5XPyKQF/iZOAgh3CRfEG5yL9LYx3H259b8exPYdbX7x1DDXtG9cs1mMmmuHQVEvqPaQd/rs85DZWFFVJMAGR+atNln2ySUCcYOseu24zMf7QHz1pDFCQ15a+hSYPQEj8AVQfH9+Tt2uNUfgMIBb7X5DxZ8ix/9otpDpgIAI+7Or8cHvCCyUINHr+iddW33kVGBECYeX9ncf6QlEauv61qdxPT7Ak3rECHeOntf7T9iMQQaoDrMy25tRJAINytDU6t1bo7/ZodxAlXeFJB/jcLxEx+CO6B7sABKTaBOuIhfCPHPzQvt+sgf+XnU2kf3brP0WVc7DyKy3xxYIjgoZvCBrSBIQBPtXeGwgppmmZ9vhHUfX7qsAjXSMULzhRmV5Fg6KGxYcdXaR5YYVnweDhv4mJkQchJAi8wPMQwmAg6HQ64t4COjnItNvW1vb6S1b5d2Rs35aWYhi0xhwQ8WQSwMXEN/H+0r6PsUze3HYCLdL8l/SWIR0liU5G9vvRhLOajQMTUIIGOLAlV+mv6KRoaxbp/9quLs1RjSoavI3TpzZUzJ1WPaWhQpb4TPKV9zckPh1OnbiSoqjGjgPNiR0nKLV/fXSUzogxwM2V5G7uCQz4ctvt4b+m6YFguLOrLxolJiBFUQ3DjESioVAkEonG7NeDzdgXXXhOUlIYgzhLWvbGojTtuRRRAIUk7pJKQGQ6FuvhRTwKEIJTB95nF3nUSkEU0fhzICDOmTG5scZ79Yp5vhnz7JqYYMsbTxuG2d4dPnSis707bJDh8+DD19QEOjqGaQUqUUhoU7udWIRBV1+4vTt9pf7avz7aftm9RT90mbDNO31qa1EqYDuShv+dPUHTNEPhKBE2ELhdDsMgNTd4njcDYadTVlQSF+T1uBFCiIiiDJ6A087H+fQJ8IKJycFWfV4Db2qx5HgivgsuoEqbA1uAE0CPmr5Z2/d/+NPvf+drwz1pRgpkKpcIGf7DoYf/r27ad+lQxp+25pNup1gpq1u2ad6DB+JRwhyHKryOuipPOKoFwqpd/m/QWXgTdMCoGIKGsv+AXYdaEvooJMN0wKiD7XZVicP/QDBsGrppWgjBaBTpumHaEzjaPzYciQqCAAEOhcI+n9fpkHmeRKWPy5LQIwPkeI6PAp23ODunl1Zyyyttxb7AgMMWCfw3IBAR3358IjcbH4OgAow/r27ad+Hpc7L/NNbvaxd4VFXhrK/2uB1iYo4IhFAW+ZpKV7Xf5ZQFu0pXSlpATU3BhqBSl4fTdONYc1f2rMfavz7KzEEFMMwSkCYpKUMeScP//YdP6ppGvTWWhVVVDQaDUUWNKmokqkQVta8v0NvbFwiGe3tDzc1tp5rbQmHSTRBjfNEFZydagVpb2+E4rBFddPpa9+1uNhwSMSHwAkQcogX9c8ce+UHEQY4j8y2HyLVEilMClpE7KRp7qBHPq5v2nbV4hmgH8WYa/q/f185zqMLjqK/20MD/5KMi6HFKDTXe2kq3QxJKNMwqboGgmP0HgHBU6+nNqVMj0wH50tMbOFw9Jy7HKbEe5BS6nCJoMMbdYbWlL9IajK4zfEnDf2yZiQrbNMmuLdOwH8SPjy2s66aq6uFINBiK9AVCTSdbmk62UDWQ98eIn+qEpq9l795TKukQaZCPmuIRGxpSRYL8SyqMnuzjuk4khgnmTU0VCx/KG5Rm+I8zDv9f3bRv8ZzJboeY3fhDbKxOcVKdt8bviqWJp8DzqMrnnFznq6xw2DkBJZkE7DnyBijqL9c0rVBEzb3oCZsK5MWho01NJ1tbApG2YLQzrHSF1Z6o1qdoQVUPq3ogvqzpUc1QdFMzTN20DNPqCCn72/uOdAePdAWShv/7Dh4f8hu2S1ASrWCalq4bkYgSDEYCgfDJk60HDx494/TTklzB8NxLddPSDJOeg2aY9HxCqh7RjKhmBBXdPs8JXto72H6Q1PGBpKFvvuU7adlP2wyET/SafS17h3MmTPoPKxM4Jv1htrSvVzcR71yV15HYBDSV9fvaRYGrqXQ3VHucspCpQAiEUJL4qgqnohnhiNYbVGiv0SI7A8IhUBCZBHxU1aNqtoyhLFMB5hwekkhEOaW2IyuIIAkL5+yyMwhCDkEOQs224XD0qb2GQ4iWFWkJRPoU8r3IsxYmDf9z/m77XyYdOkmJY9M0VVUVBF48uDM5HrS+tqk3rJukhDGHENnYLqKrmxZn2zQ100KQ1LVvmOglTj7as++sJXPtKFAMSJm4XMEWeQsPLBVa4c6jhR29oztQUzlRHe0jQa7dMqn0v2D5bJ4fwviDEPS6pfoqt9spoay6AkEoiny13xUMq4pqRMgPONuPMy8dEE8K23PkjYUzrwD5kOyV7gdj3BuMFtyqImYRKrv6nnmALcMEfCTB6kIdhbSZC7ny/ct2yCCJRaQjDNWIGSCShv979x+BOceVY2zRCmX2E2BaBoawItR1FII6ANau25SYFDbr2tVv/O4PdkgRMO2RLNFOdixj/9jWPtOJrgAAAJt3Hlhx+nwO5ecDgHb5B/IdHSxQ+sdhw/9hKYBBxp/MVR/OXDRdErkhjT8uh1BX5fF7HbRzUHYQhC5ZrKtyB8KqZpgGbZGeYRJQMPnqgEy3saIaSv7D/yTueZwV+ByCdsFfo8eKQid0p7D/xJf7X+DsgTZdnTr890Z6A86KBB2Ac9QBGBON4gp2DWQTCMmTAH1QT/TUI0x0J0ACGz/cd/6y+Xm5AQSOFJfesusgGBE2vPTbC679FBjPfOU7DwEAfvbDbxZxn2hQ5E9m48/iOZM9ziEa1a7f1y6LfF2Vp6HaI0tCjsVBOA4Sd3GV20WChdJsULAzYDjhQKnDfIxBOKraiQuMMYSQ0IdkwZypiS+d+uhDqgOw7W3MvVwxlf6eUHfSepYUloV3t+0jOZ45I0nch3tGSPqPa77ynYfior+40n9wFFCGX8erm/bNndFQ7XPSrqqZjT9tCMEKr2NSrc/nlmMtWHOAOANEvsbvqvG7RIEfMg6psPTg3L3Bccmf5JMwTSsQzlgngDEqQABknqPfVP/wnwQXJln/qQ6wjOQS0GmxSHZTGunvjfQlPm1tbb/m03cP/yNMJNZtyeO3+f72omT8TVi+Ukq5n6IAbKmbOvx/ddO+aZNq6yvdJFY3+44Q8jil+ipPpc+Rb2tvhKDbJdVVuSs8sn2gbBFBeUEmAf1+4Jx1QCz4kIaNU1TNCEYUvd/QzCgd2MojcgbZxQMsW2knDf8TgQBUKUR8Y9OIP5IOGn+klf6MMQtOCR4Z79z9hftLLffjDLi80kp/AEB9lTseypnF9+uQhfoaT22VWxS4AirD8hyq9Dprq9xOWdRD3XqoJ+mRuHHhxaJz0wF26DnWEyya4agWJEnLE+1WG7N0CDlVhOdIh0LyrQy2/rfH7T8UBAGPkDcaoA+6MqYJ+oW+N9IXf2Q6nDfSl5QUxiYBo05YyWliN164+wv3//6X/zJihxvCZrds/lQXCeXMJv3f2kz0RG8Q9La17kqbyF2MPC+Hg49GjQIigkg40GdvyfEo/YlH5D9FNZwycXtYGDe19Sja0O7f1bd/4vmn/pDjsRjDh0fQIJUksw3/qTdWTVDncR3AGO9gDI43d8+YUpe0vrmjt7Fm/OWF3T2y0n9gBjC9Lnn4/+bmw4tmT/Z75OGm6eb8dl2sTPMQBh68txYMmyEnAf3yH0QUjQ75LdPSNCM1TYEx6lYg3bRU3cw+/KctR4r17bFJwJjCsqyegN3OZKJYfkYYPrP0b/S7pbgvN9Pw/7Ud7UiuqvY5G0jNH8EpC7LIy5IgCZwkcALPCTwiqTF2vViMMU0jsNN8KAljNM1o7gyeaAu0dAZbu0OqZieY0w36f728t9YItOc9CfjfZxMnAZmiQunwPxZhaJf4N0xL4Lm27lAoOqGmmeOCjoRg0EwYFkYQZh/+lxpZlkfx6GWOaeKJEZh3ty39R3j4n80ENLnGM6TR4/Ud7V6ntHxu/aKZtR6nSMR8P23tnVgHmg7yEpwuHiyY5FowiZSE1XSzuTN46GR3dyA6aPTtrwz0dJdCB1DhHz9SRNEM0+I5LhhO7hLOKB32TUQrzFuxYjFZNkZQmLk4efi/dXNJvyBvqHvt2o0XX7yCPj127MTNf/vZ5/7rv0t6UEZaDDtne2JcnN+PuPQnY/G0w/9Lz56TGMmTafjvlXBjBe93gGgo0NHR1dbeGX8M/8xIMVGfs7bSRSoODj6+119ZwA6f/99nh9giltYT+xNVDU0nmV+9wWgBh2MUAEKI4zhRFDxuV/qS/IPhRnv4H+fmL/7taJ9COUIKfI9/2+zdI276j5M+K9UhkpmBxylnkf6b9rVWeuSGao/HOajac7GAEDolvs7vqvQ6hJSKcnEdkF9qWIIOSHUGIBRLKLXBhmlGonpXXzgX9y+jKLicDq/HVeEldURmzZoR8k/leFEUJUEQeUHkeAHxAuTIA3A84PiGcy5M2sPJjz7CiB/mAyQsxB5cbAHax/VGQ2vXbowf9NixE1QH0HOLnycvDJE7yRgmuj7uI7PvHj3pn8YE9Obmw1ecO3dIgb5hd7PXJU+q8dZUODPV+xw+HId8brmu0h2K6n2hNNXiCqkRlGALSjIE0XDyOIZphaKqXftzGJ+BkQ/rX/3TjXd8zjQNCKEWDZ131hJVW1B3ardpYcOyDIsYfA0Lm/ayPntJVYU7/t49e/YpJ1ILfw4CAuAUSTdgWmaO3FGkpymyMJZ4zsLYtLDEk9L2JsakVQUkCwhCgcSbWhACkeNItWqAHTyXZAiaPn3qwvoKsjFHNiYl6vzTQbSQerSMXMCYNOcY19fq7tFw/GZTAOcvnSEOZfxZv7tZEriaCmddpdtJqvmXqmvSH/6yIccti6ID+gV9rBacZeFAWInE3L+ZCsQxhuaMz/0x18v0+F0YW6SGoF337dChw4sWzo/MO7PmyIcYc7TIGv33YNWsJOlvf0lDfEcChxo8slsSBLuIqIkx0QR2A0iqBiyMBbu3iYUxT4pZkTQjWnDUsjUBgpAUQCYpCLDRC55P0AEAgHk33hr864s05OGgf6YbQsDMhyUDY6yM84Lbzc0tb/7ld2NFAby5+fDV58+PC3RFSV/5QOC5Sq/Trt5D+3kVwrTJyaG7qfzj527cf6Jr444Tbd3hpOE5OXUZGIEC68Ql+YQpCXleMXEfjmgmmXawvoDDYuuv7sh5W/T8/z2y+m/ui5f32b1n36KF8ztnnl579KP+rwbs8k1Plf7qiWMQAJEnd6SF46rCbjfb/8VCO+Ww2iWJHEejvfqri9I7IH6EWGHEQQtpVgyCTgI8l12nrH15r28GBCAYyJhTxhg+um4WUJudkV4BvLn58MozZtFOqFnYsKelyuuYUuut9DlSW33lzvGTbbls5pXA1WdOiWr6qfbAqY5gdzCq6SbAIGKSaQrvrYnrgHwbBsR1QIaoUFKRwpb+jJEmpgP6BS3VAe0zlvfrgDRQ6V/lkqpdMrZ7Q5Ia/RY2MQ6pekDR6RdpWFZPRK1xySIxXKbc6jDdctatPj7d96cUQ9Be3wxQrtI/r9neMI8VUTS7Ost4HZ9dfuMnx5ACWDpvkksW4lcz7fB//a5mWeJrK121leQ3NDIXHkIgC3yt321LZKsrELVM7OTM4USFFlAvmvb9KGDnjMJ1QD+JOiB1+K+eOEYcyBI/t8bnEnlqI6KTABPj5r5IWDMMW5dbGAQVvSequogGKM79m6oDli9f8M6690BZktdsbzgHwhhEFJ30XSiZCXoEGF37z8B38ObmwzUVrnj/lkzGH1K62S3X+YnpPzHqv+RniaDbIdRVumr9boeY3EO4sKjQIYOCkiAdqhgjyPP/90ji0922nSdV+lPDHQJgis/lk0WBQyKHJJ5zCJxT5F2i4HOQlfG3qKbZFyWJ3SU9+YtWnlvS/TMsy1J1gznmhknsh7Fi2QxJ4LJL/417Wux6n25fQobwiMFxyCUTHVDloz2Ek4AFdg8eMjmgn8TioIxR0QHt3T2p0l9rIpE/Lomv9zpTxyQIAqfAe8h4JbbGwiCsGSHNKGJw18en+1KjQgEAf3zsP4t2DEZqDjCrzlsUBfDm5sMVbpnOpDJJ//W7mx2S0Fjtqa+0632OBhyHvC6prtJFso4HayCv319qHcBKgY6uDqipr0t2/GJApT9pVe2S5QxFyB0CV+WSxYRJQFgzusOqXtRJwNKly5J0AJsElBTDNMd1EYjLx4ADgCiANzcfvur8edmlPy3XXOVzNFZ73INLPowwosBV+1w1FS5JTE0PHtAB+ZL7PIAx6vOAOOapmPQXEKxzOzLdlTxCfofokcT467ppdUfUsFq0SQD1+qbyP7/P2SPKyBPdmDhFIEYR/uIzZ5Omell5d29LhVuuryTGnydfKr13q6CfZVg14xGcBXiDmQ4Yy6QO/6Mq7OL9NRrx/1e75QqHmK1Ptcj7HGJ3RDVtkY8BCGl6d1R1S3xqknm+xGN+Zs6YsXbtxpUXr4AJEUHD3DkjE6RQ1zgP0ntztD3ARAG4+385mYb/a+1y/+2hvvbWtoFeGDaqI59GXRC4ZNHlFGmzraiqK2QIVrSvkHcCo6d1ODqAMTa59+8fqCIxYDH27CY3ZFdXJ4ZCu1A52eqdWuHOHtLDI+SWeFngwv15Q5phdYfVKqeURXPkQlLE58wZM9at3XjW2WeIIomSOHLk+Lzh7J2RJQdYo62AGMUIA81i/Ekj5SGJiRR4rkLkHbLgdopel2SYVk8gijEQBE7kkSjwksiJAq/pRndfFEAg8pwocJLISwIvilxU0bv7ogjB2EqRkwReELhQWO0NKjyHRDG2sSRyPIf6QoquW4ZptXYFT7UHSAp4yrfP++sTdcDwrgxj7Er/js6A3bhHg5wIJs+tkIPZdwIh8EqCTxaiOg0HJTdHUNWDqu6VC49nS433p17f+sbJTqfM85xDlgrbMyM7lmVpBgsBKpICyCL9X9vd5XVL82fULp3bUO13tQ3utZ0zaVvzyGBKciFSgo8HgJSDTsLvdNCFeZPdPcGKw01dp9r6VM1MFvQ11YEOWosUvrR+97UXLirohBljUfpTFMXU+svzccCcNqWhyzsnS44YxSHwlU65N6pF+suHaaYZVHXdtKQ8W1hnl/6kVWTzScnlQwjJkng2KwdXAkwL68Y4dgAsPX9VR3usr8nogrJI//X72r0SnlwlTaoUNSVUqPQvMghB2n3e55btdM7UDvLV8eWX1u8e8RNklIo9u/etefK3bzz/Bzr3J5XdnPLJppMQoY5ZZ8Qfad+LEPQ7RY8kxG8XjEFfVAuqeu6GhJOTl8QfmaQ/AOC9t9ao4T5N0wPB8DA+LiMjpCbgeI4B3f7uK2BsgIZIv3KK9VWk4DPXnyM2FhB4VOlz1Fd7fB7ZduJlm8IzHTBhjD9rnvwtXd624UU7LJhzOR0Agp07d+/cOaDp0yoDaE8C3BKtAgf6XcFGd0TTskYTJgl9ypHDRxJHcKnx/u+9tcZUQz5vmrksY/joujGuY0DHDhk7gm3Y3+5yEOlfVeEcTs2fUgAhdEhCXZWHJv33BpMrRXsHDEGFHyL7Bqahc7yQ1z5Pu/QuyzRMg5QX3bvxOVAM7vz2I6oSsSzL0JQXfvktMIFN/4MH6ts2vLjiytsEgbdMUqUZQJioA5YsiZn+4jqg5vBWDkKfLDoELqTa3UbtgWR3RK0iteGkxC88UdbHOXL4SNIaRVFlWcqS7TV9asMH1nct0yJRD5qlqtpzv/1ZnleCkQIGYUVPrNP1zJs7br38NHaliqYA1u9rl0SursrdWOt1yiUs+JzIExsDFinsgRCHLMPgEEeq6prEQkU8fhDyvIAQFz8XUuzLkjnkQhVA5AVBlOyWjpAUbYDQPcnClgUgkdSbWtRoNKKpir13CsfzAi/wpmmKsgMhRPoVV1ZwPPmwpmFgbDa3tPf09FqmbluZyFHnT3IO+SlmnnkttizbRUmqCyOEPB6XoWtA8NTWVB7ataW7u1v0eqtmn2khTlejpGqNaVmWhS2shkOkDyL1Wtq1LCHkZLfbIcukjbJd3AZCZFkmqWjP8063b+2ax+nh9DFcGP2ex9GwpT9Y81RyzNzG15+264ZSry65XrSbJABkTpCkDKgmsHTDOnwCHd41EA+q6j0R1SMJIodS5X6q0E8ii/R/7601AICVK1fQk8MY94UymlsZuUMadUTUROefpozLvvBLz1816rYgPmPUhEtuqPFWeBzFqpk1JHeu8KZbnXkSjcmt0N4TOnSis70rrJApYbI1N3EeMNghbAGg2g8ykqOr3t3fZdhDf9qRtsHJNZCeaAMtv2MRJFk5suWltOsXn31py+E2WRIgQiIAiqEgxPPAAnYbMkwkJBSgnNAS1xb3dk8SSydnSNbZGpIIFAgtEwXViKpqlmXxRP9NkNbk9/79A6klONc8FTP+ZMkRu+Hj95I/NLLYHjHAwcpgyZJFgsDPnD55v663tnTEQ5B7dEevY1JddSXMWe4/98SvcvksVAdcfPEK+q1W+hxf+tY//+LH383lvYxMqJqhaUOHADV39DbWpA0/YWRVAOv3tTskvrbKXeVzkuYwY7YGGiTF6fweR12VJxzVAyFl2GHBMEGA4OLWu9j1wVvx5aKXCZ5gA8vqhOH/7gTTf3Ze+NOjA5qAzhNj3yXV6v2aAANT130+d28vCR7FGIdCkaaTrd0d7fJQUZs5yv0sLJo/85v//K8PfffrYIKS72yvABRNnwA5wNvffWXp+avGnAJYv69dFLiqClddlTu13MJYA0IoiXyN3xUIqYpqqCTNJ6MzYMioUHvcH5P+JT5xRnru/fsHkqR/AVcqrgkGpgW23Lf/s6dQEKqRIMQa0Q32i9FwQBa8aWsLD1PoxycB9Onx400zpk/5p3/7xf/7hy8NZ7dlC8ZA1akBj1FsBbB+XxuHkN/rmFTn9bnlkS/5WXhUaLU7FFE7e8MmiQ0rXAcwxpT0xxi/mGL6H54yiGUIcsSySbwq9CXSPca+dYo10s+iA44da2IlIgrGsrCmF7OCQJkzeAYAoUMWGmq8taNX8rMAeB5Vep21VWpY0cIRreB7g91UY23sP0zpn0kZXHvbpzniXIFxOYIxfvOFJ0DJeO+tNYmtg2mduM/fnXvvFEYMy7LsLmCFsOGl315w7afG1KVcOtp+4AGD3fr97bLIV/uJ8WfEIn+KAoRQlvnaSuK0EEhWZ8GpYUUYVqy+/RPD30m5QR2/SeRo+i+Al57+zbpXntz6zgsfbXiRPj58hwzSS8ratRtNO9zLsvDRo6SOKasVWmgKWL8DIBadN17ZPtohQAMKYP2+Nrvgs3NSrdftFMeR9KdwkLQKqK/2eN2SwKdJDWPpwRPS8TuOeO+tNevXb2rv7O3q7uvpDW7bvofZMQrAsJMqxrvoH3MmIBI47xQn1XmrK1ykvsK44g9PvT3ap8Aosul/Ql7Q995a4/JWQkT+4zgUjGj/8chvv3rf2DJKjF1sj41umBPKBWxqS8+5bPv7fx1NBfCWXfC5IwA6TjWDCUxChVDmDR67pv9hO37HMuFAt9dfbTsdcEdH58uvv/uNf3r44f/3jdE+r3GDbph4nLcBSGT7+39des5lo6gDeCr9xyaGczJd4DnkkMUKj6Ou0jO5rqLCE6sMSplSHUsWwwCbptUbiu482LzzYHNvkOTZxjfj7MINZrCNPmU6YKxJf0pqxu9E4r231px76fWnLTsNQWiaxCmwY/eh0T6pcQNpAa2bE0f8J+gAMErwplw7ubZi6bxJs6fUuJ0SSZgZt0AAeY6r9rkvOmP2wpn1H+07uedIazCiJDp3OU8d0wFjk0ym/+899LMffPMrYKLw3ltrVl68wr4nyfhk2dKFNdUVj//yodE+r/ESA5pSAX6i6IDtozEJQBUex+I5jQtm1HvGufRPhIOortJ7+bnzbr9y+bJ5k52Duz5xnrrROzXGAI/++4PRqBJ/3HTTtb/+09NJF+h7D5HqaV/+5vfG14X7+vce/Pr3Hnws5eMAAD7/je9HFQMhWpYKNp9qWXEOK2SWE4Zpqf2tICYY20dpHsCfvXj67Ck1JOm3ZNJ/3+EmMEpggCdXOS3d19TaE1FIigDN/RF81UofyQ5jhqDR5Wc//v5XvvV9urx9++6lSxd9+4cPx1/leaK5e7q7wHjDrkUE9u7Z/9ifnj58+FjiS6Gw6nLE6shCCNe+tW7T2y+P0mmOJzAAEUUrOAlg7LN9NOYBPA+MlraO1rYJMvZPAgIoS0KN3x2OahbGimaA/kaiMtMBY0kH0DIM727aWltbFX8pUfR/+Zvf+/lDPwDjgfMuvpqWc4UQ7Nm9XxpcYigu/ZnozwuMiQKYQA7g9IywDkCkPuUYsPzYA/P4dzvoSyYBE7SebtJ6+sqgNya81L+SQ8jjkuurPdUVJEdgUAex0f/cDMLPfvz9D7d8ZFlWj12gLZWfP/SD8SL9AQCb1r76rz944N31G0jhGpW0f0iFSf98MS0rTQvYicV2W/SPpC2IL7gjdnEZrIQGnVKmrDTylgznnvSCyHNVPheEUDeMjh4Se0HVg+ytVgKdABdgCBoTF20iseHNF0i+XlVt4kqMwS9+Mm7kfhKb1r66ae2rf/v15CTntW+tI68ys0+emKZlJNRrmtg64IzzLtu6aSTmAeMs56vwWhGiUOl11fg9ToeYrFDsp7uaenLfX9HPkEF1QKBrUKfs8Sv94/z3vz6YtGbT2y8z6V8AdsG+iTz8TyQu/c84r7SzgbJQAPHC0dUVLlIvSODjUwSHr8Z+GRw/lmMSHJP+JZ8HMBipGAZpnVduV2ZrgiYohTLI2BN44oEgdDmkWr8nFNG6zbAeCRErkEOO5iHYmfQfUf7rd3+UxJQZW27k3iNT03QMgCQmd3gmjTYNw+4rRv5va+9u6+jpC4SCwUhnVy8NR4EAkNalgBQWpS4pRonQiem2fK/v1tJYhMpIAdDC0RVeZ01EbWttpdKf2J0r/IHe3O0/jBJy452f3bPnYPxp4nKc81ecm2UPTscQXb3SAOGH2/bv2nN4SOmS6I6CKDZ7rq2tWnX5eXFf2qGDB5LepURjYwzGcND1CVUEYoxQXgoAAvjK25vJzST4RA5ELQwBkFH6OA3GyPOXJ/73xjs/m32bdze+l+XVSY2D3Mg5ooT6ZN4sbACPtfCRQ2kUFaP4VYDYNS02ZaQAnnllIxk/Lpsxe/bs1s7Aybbe3mC0vTsAcDEnAbI0KOuYkS8JUhhT00r/uHvgaUIjFxLWa1k49i+Cp5rbJjXmnek9HOsN6p8KxGKS4RAhaozC0EkbAKYCxoMCeGdzlqYroK2j+7ZrLgSjIf2//tmb6dNKr2vGpOpQVN1x4ORBO1HTW+Ef4VNiDGVmIQv2M5jyKvlLpb/d35GoBI6zzfUFyV3dSO4mnTvxzqm09TyjREzgHOBRhH/65Q23XXNBUfZ1/FQshm9aIwmtOd7ckfhqW8fAEPvpl9cnvbd0KoGK/huuXjl36kCKKc8jDy95XNLO/eyuGlv09fRGopEhN2tobITQnhMAYsdL0BbgVHN7XoYgjLExDOES11i7d+0dcuPX/vJ/BR+ozClAATR39DbWVJTmdCbQDODplzfYIviC4Yv+TBKf8qVPXksGaRD+52+So/1SVcLwtUJa0Z/I0aZW1+A6cYxR5+2Xn7rkmtuH1AEtzdnCdtUcVEgcC+NwOIpzSzJKSkcHAAT7enbvCuR+OEZhkEZgjGIDT2x9HgDwL798ugAd8PTLGzNNnL/8qetsUQ9I6BbGloXtLtyxkDpqcrVIVBdGEKIMWoGeYeKT3M/wmVc2Zhf9dKGzJ/z+9v2GSioQSO6B1sG2YSHZPhbvRRrbgE9jQJMlcdkk4Vv/8j85nicjLbnogCxU+vMY9+mGGYkMah2RuwIgscVOR04tVCF4nQ3/h8HqW25Pf10h8cHccV362LCkGcBYawo/6sRE2P1fuO1ffvl0LlMBW+gn99n6u89cn7akBCa9XJLXEz+ebTe1IDYtK27j/bvP3EBDqU0zpi1+8dsXU45OzjCJpBNOMvdnEv2xs4eASv/i8uP7P890wHCweMdpS2f1h+DHPMMQQgtTgw9xAMT8rsT9ayHyEtB1HWMsSSLPcXV1VYld4BIXkmg61b7/4IlcFAB1MiRS4fNcdP6yuBsgC//4pc/n+NkZjBFjYAx7/xduo1OBJDWQIvFjfOHua2wxneZXESft+vjmdkl0ji7Tnx9CyDAt27ZL3vt3n76e+PpIEiCJAIMA/vL3L6XuMFErQAgnT5n88VVnDyn649urUmyWkH8AOaOExKNrBt0z/Qvxv7TeH/nX7hwX33KQK3nwQhK87T6OqZQ8IXcmeSNz/jLGJfyhY82zpzfGn9OpAADgqRfXQZRs4vjKZ64zbJsO/QWSNEhq1hk28Z2QlvR2V/r4iCwW9mEf8Uufug7ZKseysG1bIr+9mFbA+Cv33GBa+Pm3tu/cf2LJvKnZRf8wSWv/iQsjNgkYJvNnx7qBFnYX1dvD/xzhqALIgYSY1BikraNpcfYdm52f/OJ/2CSAMdYgUuxQfxkcqgluv/6Shhr3fzz6F7ryi5+8BsbM9OSpwMfG7KUm8ZcmCnxyvDYk8R8WCQTEX/pUzLdMN1g0sz4v0e9xiuGIViwHk0MSAIjtjBmCxgUcRzpzgYLigGhf3+KfEyNPnnlzx62Xs8ZqeTNoGEs1AeJEjMGXP7PaJNaYwZPo0a4dHZ9vcBACFDt7iySIx0zDL7y9gyqAdR/smdpQmcs+59Q5T7TqCmk1l1wNJl8Qgn6vA4DwMPdT5qy84ZMjrQAKvbFNC5tlUKN47KMphYcMlDNppq6d3b0IQZ5DksiLAi/wHB0ijbr0zwRtr0onAQ1V7u6+YHcfKfSWI3VV7iqfQyZNMYeL3+Oo4gdJ/x/fz1x/Yx16fxf2XmxZw8khYBSPMSqdxjjp7/sd+0ati28xoA69XH16FR65scZbVeEa9lHBpLo00YdMB4xx6ACisPdaGOdedvQnv2DBwaWi0C+w3BmhWkD/9pvkgM5Kh/jpj19ZquPlM1mRRb620gUhaOkc1ixSloS5U2tPHukbzk4YBXuACwYhhHLw4mYidwXAKB38SPkmJxglUQD//vtXdE3juGxfSXdUS9QKlXZGbglVQmYQhE5ZqPEPdwbQWOOrr/aePJLmJeYNHrMOAKoA4rFbBaDpelFPh1EI7gLKgDOKpQCSBvgoq+hPS3dUS9xPhRQ7sXvuuHoEviYEoR29k42kNOAkOA7NmFRFYlgzwHTAmAVBmEscZyZUEj7AGB1oGjAAwOcmvT2SggbJzzbBRc/nL5cmPHxxDTuDiNVG7E8Ws5+advx+YjJnWnpVw7Kj6x7746sjowa4HJI5s1Bd4Zo5uYo5osYjEMHss9XsaCpTAKOP2yklyv20lENP+RFSAA8/9nw8iTcTDkkgdX6Ihw1y5EF0taIZJLoUQdO0FHVo42mvvQ1VAyM2IcgXCMGUer9DHqKuHJsEjEEHAJ0B8PwIzQBYOlhpIB2/S7TriQ1frFF//zCfjPQRRBaCNX4nhxDPIY6L/YsxDkU007IQgopqtHQGYzVacE5qYCQnBHnhdkhzp9bk4nhmOmAsAiGpBlFoQwBdN+KdCRijAoKDzD6M3OGLYPAhEp9oYEkg8dQcB0nlHmw0VHvp2J9MAuxkYoyByyHSfHpFNQIh1bRwvMw3KQ5q1/Yi4KEnBGNHDUyqq6iucI/2WUwERt4DTON3+QyFPXKpBmFHAbFyQKMJQmjujIF6Nozc4fMV/RCScj2qZgo8keu0mDPPI59bdjtFjiMBFaGIbhqG1y3Rn8rA/xgL/XNtWeLrqz2aYUZs9y8GQLHt/hBCw7QM04rVU0g3MKNq4NFnN0A1NHw18PifY60IJC/pY5MvAs/NnFyVSz1ICpsElJq8CgERIAkiTOw0mRe6YVgW6UdWwHsZOXL7XZ9RoxmzO7MEXzAKVAARRUMQ/tcfX4uvodZ8nkNup9jda/m9DiruOQ6KAudzy05ZpIN9AKK9ARJdnbxTW1vYS1hEXGOtV9ONqGIgRJzDwbBK2gZAGFX1iKr3BaIcRzwHpNwi7baaApbcmm5oumlZWDPMJ15496zF08HIUlflmdZQmZf7l+mAMYU9A+DSDjVywa4GlFM9OApzAxSd4bhwypyMCoBW34wtQzLJcki8LAmSwPl9DkXVG2u9VPojhHhETEA8Typ10qSMoYqrkFc9TtG0BLeTVByyLOx1SXQEFlX1cFQPhhSHJMiSqGqGppMipHbNnwx9guxCcIVdAtNRZdp7LiCQGEE4rcEvMfvjuPUAJ5YDsgq6hUzTMi1ruGWkGFnJMvxPivtMpMLjYNe1UBMQ7a9t2/dliZdF3udxeF2SwHNup9jVE670OWllZtqzo4BKQZA43yCqPb2qwgkAONl0kq4XAfAB0DibLNNuYrpuqJoejaqH931kGBaZDsR/quQMsGXixIbcTicR5p196YuyzZgUM/UEQyT19/5PXRSKaopq/PGlD/P9CD6PY/bUmgKiP9kkYIw4AAa1BCgIogBYt8JRpeBCXtffFOsydu+t2brPrr7zy6AMZwCKpjtlQeA5v8fhcooep+SUBeLX5aAs8mLW3OszL1kdDXanrp8yZVLiU+rzjYv+NKdBknQgQqIo8A5ZrDjnwl0fvqvpJnES2Aik5RjnkKCWmJFPfXKp/6ajpbUT5InAo9pqbzCs1Fd7fO4CRxlMB4y5itAFYQ2vpzxj+LhdLA242ApAEnmMca3fLYl8hVd2SKQsqO6aaQFgADBt4Qwl8049DpBW+pP2e02n4stTpkzKIvoTITMMDokkkBTUVrrDUS3an32TNoGLTA/sKQKkUwU6XaBrhu2rkyW+scZ34Rlz2rqCXEILKsb4Lgjan1NaUEFQVg5o1IAQ+j3EhMAogGw3fVTRGmo8tVUur0vatF/RXTNTtwlFSAxPYSQqg0REOePXCSGor/Y01HiqsxbvtE1S/UYs+iiG6LcTDsWp9f4Kj6Pa754/o25ObuH/mWCFQscIJGKh4HJAkGQ15vUOVha0iHCI1PIq5h7LCT6ToXz7flIR2ukk9Y0tAC5Y5AThNN21fKD47U/MMOnvmgmHw+dwgCo/mJFwnpSVZ8/LqJMGd7ah0M9+tC3LZGYQHpc0td4f9zgNp4BMHGYIioOw2d6etzkulXioce4oqmaZOsS5WXIGuYqhwyFFI5G2VlYQYnTgOI4V+SmYUQqfSrDJpIm8SFhD44LwkGv6d2WauKUz54LMmU4gHV63PLXBL4k8M/lMPLBdoqqQd0JQWekVxfxGoP/24PcKORYjHQJfuP+GMUgBJP0ESPB9fA0Vsokb4HSPDCSm2NCY/lhkf/8yWaCHi7/af/T4v3FBn2ZN/666+sLBsJrxNFKeDpzMUD//qfVDh3u+v/MEyB9mCBp1LDsNvYA38hxXWeFl8mcUEYVcC/ldfMM9JT6X8QdPg+vjnsykpzQHK95vPdGUAu34h8QtBwqAxp8OjJahZcU2ThpBw8wLaek/wMB28e1V1WjrCmb5JSftuT8pLX2+ccH3WQEwQxAA4O01j9901+fAaGBXIclfAUDo9bjkoYoAMkrKeKwC9MaaJzAAkxddcHDTC6N7Jnxiq18iCQcHtQx6lhLvgjK9miTn7RaN8Y2LUzkrZQ+WhVu7AoOCQVNIHwtKVxVjEokx2NXUs3iKvwj7YowgdjJv3goAQVhbXVG495hRDOShOnmUiDfWPBFfnrRoZcrryf6kRFkfCZAgyVGX/lQBDDyBWeTlUFI7/eA63dMSmet6g9GewLB6OhaLwnQAmwSQL7EvXOEbbmu2AiBOYLv/RF7wPOd2O5n5eXRxSEJDvtWf8uT5J36eupIKccrBTX8G45Oss6dBcwEwlrGLSweGHMQV5UNkKfw4zJkN0wGjgmlZITsnPF94juQP5Psu5gEerTTg62+89d7bBobqHT2h5//6UbFOI7VM7LhgDMxe46E4BdbyIcaftq6AkkNjpkKPMAgIwCXnzM++za6mnmIcqux4e83jI39QRdEi0VxDgRNBJAtx/P3mJxIQQjnPEKwSAceh9C9VU/jciTneSBAOWRqqhFx6IooWUXKKwh6Br+j48eZp0wovTc4mAX9+/FfnrBxWie/mpuNgqHZ1hZPgK+5TQxs3tuf17vfXvlyCcypfRIEbLR/AxGA0ZwAYY0XVVc0g9Z8VrQAvHN2JbljD7OhbLF585a3hTwJYVOj7616V5eHVcbTGYnEeJv2LjkMShFKG5014ijwDQDCnyhAWxkdPdbd2hpKsMibvqfA4zl82dWpDRUyM7h4ish5jux1EblMHPB4mAQwAwLrXiFdt5VU3KUq0wAtimSWcB+QJE/0lQhaFvH7Rjz69LtENwBhCASxeNDXNWlto64bZG1SCERVqiaW6B2Kio0pGZbBht+1A50kbRRGTX7hDFhbOqt26+1QkEH3zHfJqY6133rTqIb+hWBvi3BixaQLVAQWHhDJDEGXda38eWzqgoGQxJv1LhyRllGCsGcBwFQCC2p49h0DxcMji25uPAACuOovUlaPLlKiiq9qgaXspzPqxhOGS+eu9lVWB7q7c5wEvvrPzuouWpH2J6YBEHWC7aqPjbh7ARH+pcTAHwPDgq1PCrrv7SEjc5Hr/ybY01d+GCYcgbb+ViGWZtZXuvUfaTZOkcXEc73OLsyZXYABEblB4hmbKhR8bk4z/cFTrDkSqkj71ULah0+bl1KlKDyXb/bNPAjJJf0aqOQgAUKBzeJR0AJP+pWeshABNzBkAhyxVJ62AAQCHmno6ugMAgBXLZx441uZzy8T3gkFjXcXmXScWz6pzOKTjzd2kXQyCld6M9Zw1XW+sq1i35bDLQYxFpmlwHDmHSq+jq29A1nMcMkmTr2Ti+qAnoOw9Frj35hWxFyAMR7VHn16XqScwxkDVjUBI6QlEwop+/tLBpa2LNx9wiCiqxVKK2CSguLy/7tUi75ExnoEAiOJYcfNMzCgg3u6VCiF09NvaTrb3OWXR53E4JMHlJELc65JoDSCvS6Jdw6KqzpG2wBZ9YCL3Byo0BMOqwHNSQvoGmcfBxDaPNDU/h8xMu+L/EOLbLvQWiqhtncG2rkAoorZ3h0x759U+FwLwSHNPc1ewO1iooTkB3VGjO2p4X118zfHjzdnDga67aMmL7+zM9CqLCGIwMgEhFPnxVwhoPCkAKpIhAFTWkwaKHcGaypj9hNR3A6RDC02HkURSWg5CiBAkkTn9QLvhRvypohoNNR6fZ8CY45SFnsAg+WuaVm5m+qE0AAaGZfUGIs0dfd2BsKYTN4OFcXP7QMnoO64+7eZLFl6zYm6mfeRebNbJGfThrRzITT9+vDnHtzMYjNwhcibn3g/xUqCPPr2OXeQ4GfVnfU1FS0dzXPLFO3CZpiUJPG3LTtVDvEgygtAh87TWW1JKV6I+sCxcXeEKRwdihMKKphPRPLANzyEha8/hobFPT9WMvlC0s4ccIfHF/cfbp9THTPNTzui3I/1xfdo9eXLuOIoQKXqa6hB+acPuay9YlH0SwLzBDEZecAglCpYyZ3VBnetznUDZxZChwCMq38iQP10WPGm/mMNwmedQYnXlqGIAgBPLqrgcIsflU3wu3cA/HNV6ApFASNFTenYHw0qSSsgEQnBSbSwpYUhEgVPUAWNXog7Izi1XnfPsa+8zHcBg5AyUJdILptSV4MaFHC+YXBUAJCN9ThT5CBHWRWBw/l5yXFBB2X0DO1HsgX9PIBolVR7TxG5jAI6c7JpS6wUAbHn5LwCAM6+5Me1O3U6prtLTNdhClQmfW9aNsGmmOWL2SYAaHRN1TBmM8QKxSzukvKI31vzlmetvvBVMRDle/J7AH+w8AoCcmNmLuLybX2eBK2XtpNauQCCs2GaljBw9FVMAFKoGkoGgttKT6K/OTl2lO6LooYgaTxjK3RDEJgEMRu5wHIpHppSa1eNEmhdAHldQ00wLY5RzSPXBdp42Wp1Th4eZwZsvPYHIkJWF1KytYygiz9X48yhPX+t3BcKKqhnU25yXIYhOArI4AxgMRhyOQwX0gl/zl2fi/wIArr/p9jK/pMTsntgRN2k5ETNz39TECM44FtEX2ZrtZpH/JIjUNvNrppz24XJWnLlo6rZ9J7btPbHhw0NPvbb1T69scbude4+17z3WXlhduVSqKlxuZ64eYBoRW+t3Z8lOfGnD7ixvv+Wqc6gOSPsqCwllMOLwCBXQjIGRBOpvsD4gqzHGWVpkcxxStOSxczCcpuyPQxaJKX+wlE+MDsriwY/L0KS+8MXltU37Nu858czbB59758if1x9NepXn0aTaCm5ww7/s5yDwnN/r8LjEpMjRxKjQLDpgSE8A0wEMRvznOUZqAI9r7JaQ/W3fqXd0oHlvujc4ZaG5vW/m5AGJppvmyba+2ipP0pbBnnZF1Q3TAnUDiVGiyOtGbMeSyEcVNf1p9Yf3DnzD/Togh/7thZCq8dxOKU09qeyhSRA4JaHS6+wJKGFFSzzRHA1BNCSUGYIYjKxAnuNgngrgY3feTfJ+rjlrwlv28/QBZGjYC9MJWgRhe084UQGEI1rQNnwvXDg7sXjcghlpoid9brmzNxZRI2RO4tB1M5ZeYJ8QVUnxcxv0tZdCG9gHqa30pM5RhrzjeB5VVzh7glFNN3Qjvc88uzeYkkkHsCJxDAa0AwUL6x8V59Ff/DjHlpCpPYEnjPLIWg0UDRh2ooq2YlmaMjtRRavwOM5ZMhVbVqL0T60FvXDh7KNHTiyZ02AJ7nXv7phc51s6t+HtzUQZcBy/ZE79zoOttC4QqaF2uOOiM2aAYgMhlERe4HkyPM8KQrC2klSrzhcOQa9Lqq90hyNab1CxveD5RQRlLw7BdACDAREUsyaKps7db/34XaZpFctt8Hy6NvE5MqaUx8hV0tiz55BDtutJaEEAQFdfZA4YmEZoKblaRS/ez3HI7ZD8XkdlhXvb/lPZN+ZJkNlAb4O8WspIAl9d4QqE1ahKmp0lvpSjIeiic5a88z4zBDEY6UGQFIGA47Nn7/PDUB5F1x9DKAAMcDzhN9NywsYDq7JsTL+JeMYsh4i/99DxXroQZ+/h7hmNVUVRA3Tg73PLfo/TIQtT6v3bDzRndHPb23McSlsCKPvpPPH8xuxnEo4O6IMskwBvf+QRMwQxGBliQFFdtS/3iwMhKUIzgk2hRlN/5K4ksikAC5MhcKKYxHYln55gtLUjQEspIATt8CEs8JzXJVf6iITVdNIsrCcQUVPihShU1r+ztSlJ6CdSrJif+MDf45IFnuSfCRxX6XV2ZciAox+qAPvi+ztPqHyaO5IqPKpveA8wgp256IBrLznjpbe3ZjkccwYwyoTb7/qMGk1sO5h3rbCLb7jn9js+Mf6FfxGURJJuyM8EhDHQdLMvGKVZVByHOIQMwy59YF9cjLGqGZEoafKeWoEnL5yyOHx9LUtChdtR4SXFqxNF+vwZdRu3DfQjQxBZOO6whWSCWZCt8Eu3nm1aOBhRm9oCu4609wSiJJ52sCrjPdWJOiAT0Ix5KVhEEIORhCTyBfxCy0b+56Eb8ruIloWDYSUcVTFJCUayKNgN2WOvkuqbutkXUgJhJarow8nGcspiVYUr90F4qj0HQuh2SvVVnppKt1O2A/NJy4EYNX63bBd4gBDKktBYOzByh5AotgIshhiDSCioRkIi0KfXyCsW1Myf5K6QgZM3nVz/Q8BOAXsr/LmkBdx05dl0gaWGMRhJP236C82zEhyMx4Ay8lYApKmWpveR4pqkWL8s8U5ZGDCUY1IpOhRRA2FlmMN/hGB9tVfITcNjAAzTTLI18RzyueXGGp/f6xRtsw8lLtQRhLOm1HAc8rrk+irv4ln18fdCe/ifRQFccs78tOsH3sFxnMB7vc76Wp/XJQ3MPGhOWUqjm0w6QE/ogst0AKNsSbL/iDznkIQChvNjxg08PhWAZVmBsBpWNIwxzyGPUxL7OwFQVN0IRVVFNYZp/KlwO7xuOZcJm2VhRdW7+yJJR6z2uxtqfG6nRAf+cRKnCTMaK6sr3A01XuoecNEIJTuOM7F9Td701ycReK7KLdb6XaRkld0TRw/1kEekT4/0OcScDkGLQzAYjDiiUIj9x+5TwjRAMnwe9g1F7wtFDcOEELocotspJZY8syxsWqSRIxgGPIckUair9g5ZKxTTCUdU7emL9KV0c6z1u4mPKOs+RIGvrSSb0UMtmFn//vZDNP6nsPvkr39dCwolkzc4sTgEiwhiMOyuJCREL69L8bE77k5br4zBV/uSq1129xGhc9r8KQCA3QcPxNdXekGll9RPPtUR7e3rkwRfTAHYFzbV25kvCEKXU6qt9NAWwRnB5FjqQMV/PfW4maR/0jqSS2KvOnSibcbkmve3kzECsh8FqIAf/MPHqSKkZ9MbjG7ec+qj/S3BsJr2uvAyMAIdQ+oAWiaaLjMdwChzSCN4gXQqz+tddqAi6VZbsvMarxSSCEabtdj6QJpWn6ZYZktnqKm5a/nCyVl2cvhEx8wp1elesQCIjeiPnuyaOmlymoF/RO0JRIIRUmko/d4zfNHJYUUJT2jRNyL9C60wdfBQk70jDpixs7r6vDmXnz2rqa1vw7bjh052J06YKLy3JlEHpEWNRiqqa3s72ws7KwZjIgERidrI9ycK09e1YeQZBTTyiJyS+JB4xSlptX44uVbUdGN6o/+em1fcaz8+vurMUCiyYHrtgum1mfaWdN9U+1wDMUyxFgWkf/2wRgr90p+qhKNHT0UDgSl+YYpf8AiWExlJj1wigi5LqIrBvMGMcoZ4gOW8FYBd6ZipgDSgNe8df/GDUy9tHqI0Qt6UeLIVLyT31/f2DPOQM6qFGdWCGemOp4AlbdDcSWpXFAzPIb/HUVfpdjmEITVLWh2gRiOzZw0UYspeKYjBmLhAhyzklQI2AJP/6eB/+hOaGIZpRCWxwFim3tqNMRYFfs7cWcC0tu0/uXHb0WBE8bkdK8+YfeOcRk3RXn1334f7TgbDCgDA0JMbrPNC9Ya9CoRgxXwZFER3X7Y2vBxC0xsHxs45Cv/smWUQAlJjttiGQgiBLPI1dnUgVTNU3Uy6F70V/kBvT/adLJ1dd+jwsezbsNxgxsQGQuCQxAKCNJ764x/i5aAZaX0AkPpXyCICgjsak5aW1dzas/1AcyiiSqIwb1rtzElVpJaCjqp8Tr/HoQRIUqsw2JJEMmmRaZqWaVrv78o4fO7c3TkQMVnhqvK5JNHuT2Bz3ZUrTAvvOniyLxQ90doTCCmqbqqaznHCRQvtING4OM/5fsi+IYKkwEj2PWz5cOeWD9MMwL/xt7dl2zOCHqdYV+kORrTuQCS1a3yiDkjrDVajkUvOO+3tTTvoU+YNZpQhHEIi9UAWxJNP/P7JJ36fuOb63FpCpi2tM8yabmME/unf/B9duu3TfzP4JbsjY1jdeails5ckYjhEfvuuvSdOHJdFPhzVWjoDoUi2di6JlZCHOAlucHc3W7K/s+lDI8HfS8JPASCzCVPZtHOQUnn02VgJNtoSEgBwxkISwjTkDGDBgnlRVael4iAA/LvHsgz/08r9OA//99O6mm3KkhdpdYBLGHQ9WYkIRrnBodhPdSywunhVOW+64QbSK8Wba/txj2egUv3lF5xWnCiguCYgyuCWKwAAf3rqtc7ecFtXkAbbRANChZdU2TYtHFa01MaQsT2SIF2k62aOUaEk8jJxUmdHeRqGpelmIBTtDkS0nHc19LEGP13/3oeJTzNlADRWe154/V0wBrjpyrP//PoH2XUAMwQxJhjPP/sUEbi33C6JPJ0BZLflJjUDuPiGe8DY5s8vvJDl1ZtuuIHqBrct95M++JsbdgxHGaQPA3362Tdo5ldvMEqlPxka9x9Z041wRDPNNDlfpI4Cz9lJYblmhGGMTbtqEIRkWbcNRxFV7w1EQ1Ey8Aeloz8yAEJgmphE/2SYAdxw5fkAgOxqYPq8+bRRNYSgL6RGFM0OKiWdSyGEoagaCKtZ6iPxDmD0tWWfBCQWh8gC0wGMiaoGkrjm6qsybf+PX8jJvDP2GVI9AAAmT6orTBnw8bG1Xd9toLCPYZp9oWhUGezdxSTRKRzVoiopCJG6O4EnUZSpAe9ZIMm3JPaGvNEwLUXVI4oejqp9IaVYA/84aQcOdrFmYqafN7N+Uq1//daB3Le0amDG5IphnQMmOWLbD7buPtIeipDLGP+QvK9uSB2QmBfGDEEMRiZ+8ss0CmPi8ecM6uHn/x1buOmGGz71MWLRSQsxqMXEIiSV/e0FsiYc1YJhlQ7kERxIo1A0PRAm9eBS94UQEnjeMPMrCAFpewce6QaR/uGoFo5qmRoJZFEhpPt8DsdKep7aZ/jCM+aKkA60k0ObKG1tQ6Ru5cLcRledt6G1M3iyI0A6R8ZnBpWkumGWrmFqNOLwVUX7BjZghiAGIy0vv/oauzJ/fuGFLHMIUqcsFkoTs4YQhaAbpKozbflCemNBSA0+hmmpYSNi14NL3RdJ0YbAyNAJPRMQkeIeloUDIVJoOqrqumHmXkqa5zmXLBqmaUSHaPM7puAQ9LgkCIFG1J4RSZppZZ0EXLa08cV3bAWQNR6OGYIYEx4m4odJQuwNDQS1o3eCETUYJsNSjhT956lpCGOsaHoooqa1/vMcEnmOWPDzrAdn1/WAwYjaG4yGopqm5yr9IYROWazxu6t8zhyLQ+GxlCci2BVV66vcfq+Dxk3F8drzgCypYR+/7oJE6f/i+l0lPlkGgzEBQemK/hu9duwNhCTvThJjvbTs2svpSz3TSEpiNMnH+t//XtJmIBhWoqpGmovlZvfnea7C46iv9tb43cLgqtTZjlXga6WCs/sW1Fe53Q4xaTSfqANSCQX6ctn/j+///HBPkcFglIsCwMC0rL6QEooSI48ocG6nRIsuYFLkxjISCt0kwvOI5zlSmy3P4T+E0LJwVNXzHfjX+t0N1V6fW86xb8wYnAFQpSOJXHWFq6bCJWVWY2knAddduDjxaaZJANMBDAYjE4OkJwZAUWNF/xGEbofklMV4ZGSmxC4EoSTwNHIf5I+tV0gx6Vw2FvoH/tV+tywl9CMbnzMAevXcDrGuyl3hkTkO5mUIylEHMBgMRloG5d+aJhn+R1XikJQk3udx5FJ3ibTngVDVjQKiNjHGOSYMxy3+8YH/hCnuzfPI73XU2r2L8zIEkZS9VSQyNTtsEsBgMIZQACT0U9ECIcU0Lds27XA5xIERdgYpjRCUBM6ws7dACaBjfDrwb7At/rGBP5w4BQFJiQuRr610VfucWTRu2klANBRIfMoMQQwGI38FgEl+b28wqmikNo5TFn1uR7wyml1LO73wlAQeksyvQob/2aHN2R2SUON304G/1y3z/LAG/mN2zsAh6HVKdVVun9vuY5zPJCDJEMRgMBggN/4/QRAAtT4JTiIAAAAASUVORK5CYII="}], 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"}], 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"}], 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"}], 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bbvaXTnT0DO0OY3afKQH7UhPx1945NhAe6sV60uolJ61ekvr4LD2wc++RnXuPpD21qult3cG+YJTjsCjwmm784dc//utTj/z1qf9jJ5x64fX3/ei3U/KyAQAoYC6bwCp+VmQCZjIElBb/qTFHxlu0tvUwMQiEpNe27JdkjXkADOYHYNOus+MLG8o3rmmUpWQaYPmS5EznHXsOj3QBJ65aTAhq7wn+c8tBTTNWLa5dOr/K63GKAocxHgwN2X7wxz2l3tLa2qqH7vnMFLwNAADMMjblZ9wLPwpUKB5AJpYrUO511lR4wolhoX8GSUnTHO8Ibt3TEY6ywTK0MJR9ZXoDFtt3H96648CBlt5gWPrkh89419pFf3px59/f2HukvV9RNYHnTIdAv/nipTe8Z15z856peaEAAMwyHn92aAbirHYCZswDSFv+sxtpTgCj2ud6+Ml/DcRUyewnUebkUqNA1m2EUIVHWLe8rtTjoLkBMw2w9oQltGjUhAx6A7pBuvvD/oGYbhjmfm9yw+Xnpj3pn17cuaChfEljlcshMj+DBYs+fPWtk/xGAAAwC9mUh3EvfA+ggJLAObj03DVvNB/fc6QnNQfgtPGmVR/CMEgkrtjNdhEsFdy8+3BSBkxvYK3pCmiGoTYfjMTl2MjtLj5w3ok04fOP7YsbKxfUl/tKaZmpKPBscrTA8xddfuPUv24AAAqUx3NuCit8019AISBW/5MDnsdrltZ6zaU9w2njdU1LXf7T/kVqcrGe5kk07z68fddhKgZmVEjgueVLm2SDRvoRQpnLf4srLlh38srGLbtbNH3YE2m6/swTD//h1z8e80sFAGBO8KRZDpTJZVd/drZY/xkTgMz4D9MAKxZkUe1z/fJPb7OKoCqfO/UuxRg2hM3n4mVVa+mVKip9rW3UV0iFCUNSBhDylTpPWT0/LONAPGM7cgbvf9eqWCTL5mTmEDCfAACAYuOy4YZ+dpn+AgoBpZZ+ZnLR2avNqAu3clF1e08okdNd6PVH2rpDPpewYukKTNO8h7LKAMZo1fKFRzv888pppelE6OhMFxsAAIqKy2ab3S8gAcia+M0KDe6LQkLRWPwncwyzz8UH43p7T0isKd++6xDGaO0JS80QUBYZ2HvgmNeB3j7YxybMjFQpRLMOQTp6fufeI5n7yMD6A0Axc9msNf0zJgAjlf9nbgVg8R/mARBCegPRWEIZKf5j3R4IhnaEJIyxwyZ29Yd9pa41KxchhA4daU27EpfDVl2S7CnNCoRyyAA9Z+8RbO4eGP+LBwAAKOYy0Nz7v1Kp9rl6g8mNXYqqv/jW4eOdQWJmgCVFzyoAwbhe7uJFcShdzBAFXufsLNNw2soaSz1C0URrV3DN0qEmf2y0gKUEbPnPaOn0pzYXSl3+33rHf47jrQAAACjqKqDMxG9WpIQaicnM+pvxn2EQE3b72af/mPnjKu0lHReREonLL2xrfX5ba1vPQExSnHbR7bKlnsm0YfvuQyNtIWbNhVJbSoD1BwBgNsIVwv6v5G2CFIUOiUmr/2F090dCMTk1/lPismfGf9jtp3//m6d//5usF1DuwuUu+uP728Nv7u3edbgzGJa27WtLO43D2No4ZtFUn2+6AgAAoPCZeQ+AbeAq9ZYeafe/vPXoWztbW7sG4pKi6waL/rP4T3tvSNOGivFvuWLjsgWVeDAPPCAZA5KR2izIkoGsSsBkoNxFy0AD8SxNJixXIAf+ATrAYF5D7eMPf3usLxkAAKCoq4BYRF43yNY97f0Dsf6BeExSMML7jvV5PY6G6pL6am+p224T+VBU7gvECCJW/Q/GqL6qdLfYI6vDmsT9JVv8h2nAzbfcHI4mZEUjKfuHmTcQiJPnt7Wev35+2g8SM+6fuvBvqq/AokNKqD09vexIS1fg7R3DqowAAABmBcIMxn8IIcGwdKQ9qOl6MKYzq0wQkVUtEpP2HIrsPdrntNO+DrpuhAa7vFn1P7UVJU31vr6+5JyAtFhQJj/9yU8RQl+/54uapoeiiUAoHksohjlehslAJplHDYMcPty646hf0wyWdXAKdE7ZBz/y8ad///j43xoAAICi8gCa5te6ArEjXdFAOFncmQo19IYmm0OA07j1IxvppQvcSctqX+gLsPL/PJ/0P+97ACF0/z1frCyj+4p1g8QTSiAUH4jQTIDTbvOVOstKXA5bsjwUpTgBPYFI8/72eEINx4YkJ3OyMQAAwKxgWstAUz0Ai/5g7PXtx7v6I6kHMws9rf4/ioGZANDaHlXf9EKzpiej/28+t2msl3T/PV9kNyRZPd4ZiA/uM0AIcxg7HWK51+X1OOdVl20/0N4biA4WHVG/IRgnaT4HOAEAAMwihBms/2E7ADQ50VhT1hugzZnZvSNt9M3c/9XvD7I2bbmDPzm4e9AhsNtEr8cpyepgOSkxCIlJtGNoGwruOdyV3EBsgjFHiOEbHjjiWH0oAADALGFGq4BMS+su8fT46fIfj2zoU7ng9KWHW/2HW/2HWvpffvsos9f5h4Cycvd9D3z1aw94PQ42kj7blY4U6KHygzGqqfB8+qr3TuQaAAAA5mwIaMQJMAQFwvGuvsiRNn9Xf8QgxMaRTAGw4j+iwA+Jh43+B2McjOvjiP9k5epPXJfiBwwD46Re+kqd61fN33e0KxiSBIFfu6KhyufxlpWxey+54pOTciUAAABzQQDSrL+saNZaO9kKgqCYpOw61N3aFYxE41kFILP9Q2r9T9YC0PFx/z1fNAhJKFowHB8IS1blKMYcz+EVC2uWNlXjwc4QCKFF86pKy7ypjwAaMLNcf9tX0o54vSUTfMwffONLE3wEACg0ZrIKaFgXIIzcLtspJ8xbvbjq2VcPKINFnzmwrP9UhN45jF120VXlbajyEoQ0zQjHEqpmnLS8wTEoXU31Fb5yHxswkEpvb//Pf/RfN3zmy1NwXUBe5n4q+H9f/qZ1+4f/dRf8JoA5gDBTdj9zZos5+Yt7bdvRk1c1vLG9JbUANGta2Ofi4you9dCdYk8+9tgkXuHd9z1gVQcxgREFrsJLp0Ja1p/RvPswmzcJzDFznxsQgxnh2pturq30PvCt/56Zp5+LCDPV/nN+Y03W/s8IocWNFZpuvLWzLVUDsvZ/dtiFjWvmE11+Es0Y23cfptOGB2Wgt7d/5q5lznLBh25sqK/N82TP8O5+ScgoY0dHBSNCUJZiMxCDqeYH37l/x4F2hFBdFQ203nUXXZx985u0fg+YBTmAcfR/1g3yzt6O5gOdrMozMy1s1X2WlJasair76td+OOmXneoE5GgJZ2UC2PT5VAGAKNDEueBDN7IbIwlAdnM/nMX9h1hWn7DGsQi1168a23UMLwrIqgSpQIxosvi3y68+b+PK9aubzN91ss273U1/6as2XDZpT1OszEAIKBKTS9zZBzFa4194DjfVl+071qtJykj9n9kA+Nbugd89+igqDJp3H66vTtYCAZNh+qnFTg395WPuF/UftLoEWrBPi/l/+s+C7v1juhjdMNrqVg49GhqqR8jtFoASTBxm/RFC0XjC43Iw60+H+m3dBBpQ6AKQWf0Zj0ZL3PaR4j8MQkiPP6oMlv1kLv+tMs1xbwEbaybAIrM9XKoTYAHL/wmv+odM/wI6NZrY7VnWDYv7D2bUAUx+WQDPcQt6DrDbhkFa61bkKQYf/9Sdjz/83Um/nqIiEk1OhUIIVVSVpt61dyst/gYZmH1VQFkDQan9n7v6IppO/7Qy07/BuF7m5AghA5IxdQKQP6ka0Nk7UF9dBtZ/wqY/acdN059EluVwKLyRGxj+Q9O9AZvj8JAYENJaO4oYfPxTdyKEQAbGxy0fPWuku/q785olBRTKTmDW/5kNYqTF9cPjqqnjXwYiiR5/lM3/yvpQbADASP2fJwvWKCJrj+jMM7OeDOTJBR+68YIP3ZBi+vGCxppU6x8Oh1dFWjOs/wzDYSoGC3oOOA432w43p96Fka6pQ9XMTAaAidPf7Wdf8GYWehI4Lf6z/1if2yn6Q5KUUHmbo6G6tL661FfqtIl8TbnbSgAYBtm+v/OdvR2aYXicYlmJK0xngRHz3ync/zUSX7/ni5mLzNQo0DU3/8dUX8Mc5pRzP+jzlWcEfIYIh8Mb8bTa/e6IxG6sqSsf3yO8aRuaMk1XPOEYuwF+wFj565O0izsjGo3XmoVAmZx1yQ1jfmhgmkNAe1tT/4zlzr6w45BQ7nU11JQum1/xntOXszsSstbdH2G94TRVXdpUuWJh1eFW/1u72sKx0TeITTpWB+msmQCw/pMEHtH040kw5WOy5lUeZ+bBvujQQ43KUnT0pbhz3rykDHhL3UwDIBw0VqIpCQBgFgvAH19JH5tlEBKX1XhvqLMv3Ly3rb62fM2SmkqfOxKX+4MxM/4jYGT4Sulf4+JGam3fbG5BSLecgGlY/ltYEZ6RykOBiZCP6R+HNc9qysdqzcfHuS7ppfauTA2AzPBE6O4LZXUCXn325+AEFJYApMZ/Ojv7cpxpFzld0453BLr7I9XlnnKvUzXH/3IcqqkoYwKAMVo0r1yKxdq6ByLxZCwIzQRMCb5uygAs/yfOymWLHI6h8p6ziflRoZ17nFNhzfVTznG6HFYxqfLK39GksllN1qrTjYoiaWvvagQNAIrWA5ASqiRrW3a3557bxQo9JVlt6Qp29IbY/i9V1XxemiFg53Ac9nrs7oXVLZ3BkBRGMwqLCwET5JRzP0iDfgmq6CL7RXOTuTzXTzkn9wm2s9/PbhDzI8dQX/9HpjUfH06R9Hd3JjTMXAFvKZ1DZ7kCwKh85Po7fv+L78MbNfsEwD8QD0USB1r6onFFN+t/RIHXtOTs31TSCj2Z9Wf7v453BE89oXHYFfN4yfwK0en86UND2SFgzjBZ1r938Vr6n8BARXmZLFOBsZBl2WYT084nRvKD6XI5xXdfkDz60qsTv5I1JIJ49HY7yhoOAsYKRIFmgQDcevstz28+lFA0SVZ9Lp5N06ryuQfCUlxWU8/MutGXoRjYHxpKAfX0+Oc31rS29dhEvqlqaNcYMGdYE+/M57TcC/PFK6zCfGrT/YGgxz3Kp8Wy/nSjYjypQLqurduwjrZ72rp92EUaw8aXWjSYU6azQY83IP35gaivzMMUyGGfyUa8AMCYqk/hvmO9uk6MwSmPbqctLqndwwf/5p7/xdyCmy8/zTpSWeljXeRSR8oAs5qVyxalfjvxkEtpRVVfn7+qaliKOBqLj6oBqSQSQ8Vmoiis27Cu5uC2lPtHMvRDhCRruPQQBOuBYMhtNrR55EEIIY4/CjSSEwAUhAB87zt39/qjkqwGQlIoImlKIhKTMcZGtjFbWcf/Wm7BW7taVyys9nocHIff2tW2fEGVr9TJcfjzd30HTT033nzrNZee/uZ22kqsPxD+/g/+ZxqetNgSAIxkAmCSiESiNJFcQpfbo2pA6vI/zfqzRFQ4HHFkM+hj4i1Ey9io9Se0s9AEHw3ICtQCFYQAVPnclaY7rKp6LKHIitbVFzlwpCPzzNzxH4TQjgPdrV2hZU2VHjsy54UNLJ1fWeaejg3M13/yFnbjjHVLEUKb/rl1Gp4USGOkeMtIsZc3EXIM1vikyUCqBiiKmpkGSDP90egoYXprK7tOiGEQ1TAUzdAJkTVdUnXNMBKqkdA0wyCaQdq4uCgKAs9JCTlNcoDJSQXDm1o4ISAW9LfZeJuNlu7VVJScuKxW1YxgWPIPxF7bfjyf+A+bxh4Ix9/e017m5Hwu3h+Rtu5tryyZ8vjpnXd+TtONm696j3Xkk1e+xzDID3/44FQ/ddHGfxYGW7LZdPeokZbUeMtqqe2NgdKSErd70NanykA0Fk/OlKZT3syVh2nEnU5nqvVPNf2arrsO7uzS9biiaYQomhFXNJ0Q3TT3hqkCWV3bNDRB13SdY38YwIRhUSDDMBRV7w1EOvtCwbAUiSX++5tf/Y+7vgZv8Iy1gkgt/88Ka8wuK3ogFO/oDW/dS0c9ZLoFDqeD9X5ILflnhaSTNf99TALAePg3//wBBIImj49efxtt8ux2VVdXSXFpQe/BcT9UXNWkpGkmim4E6xZzHMeCi0wMmOlnLUVtNpHnefZZVAd9UEVOColuGOFwrLunT2lJn/c5bnoEr2X9t73yp8l62OLha/d8SVF1gecUVcMY7ztu7hcZbCDCJBhj2ve7qbbse9/7wUxfb7EKwDfu/ZyVqh0VQoaUoLM33NlPq/udNl5WjZOW123fn6wJSdvzNdUbgO+8k74EXTc+lSEAP/vtPxFC3/8+JAMmWQPovKeaaroluPcgoY5f8l5zhEsy3KIZRNVpjMUwrTxbhtMbBumOSKFEljB9rKyW5zi73VbicdlEQbSJLqeDo0fEkhJPQkpYU2KikVi/f8AfDOmsCjnYNXkvkVp/1jmOfQsCkMn7PnAle4vsNoHjmFFPmnfzs5D8QBBCrXyKx2XG4QbdKp7jOIzrKks5DnMc/j7IwEw1g2MywMhHDEwl0AIhiSmBPxRPnQdZ7uINcwJMME77QEyPABgG+eSV52Xe+/Bv/okxaMBkcu7FV4miIIoCxwvhSCyRkKvkABmMsQ/a/xFx24S4quc8hZIop2X4GGHRJhoGDdsQQnRdT5obhOz+vIpQGQI/tChRtMwdjmwCDeIx7hZ8BjEwMnhqlTibKL7+3BOoCPjQldeyHk/sFzM4k40eIIT2Bh4KnQ0ephrAczaBp78nhHnOXNJnPPK9n74kx/NyPP2JR558TTfID38w+bMC5xJTHky324TU+e8jiQHG2GEX66vFuqqSxNKazt7wniO9gVCcfUrWrajr6IsEQnEOG9PW/yfToAT6Agihy89f98rb449UAJm89Jff0MFPZ9MJf4TQv95OrrRay6sDKIcxz3Ekj5G/jsDQop7Las15PpspT4VaI2yuNJlxd4g8h7HgtJU6bPQGh8ucdAWrGbpIV6PoQGlTNUKGbsgJSRQFjNBjP/vBrDboFKtpd9JZG4zCDI/GGDTXnbzXGuHHU9tO3z3OISZkVU9uDCU8n8zJY4yXL6q75MzV5d4J7fW56YozEUI3f/SsVad/eCKPM7eZwnbQzAmw26jG1NRUGAZp7+hNPSGXZ0BQQtGC4XhL50BPr/+y89cRQsJR+WhL1/e+/zCaSr74hTs0s0hDN8inBj0AZvotXn77AEb4K/eOku0AxkqqBtAhcflpALUsg2Gj0RbmWX6aWSseU+w0/IB5jD12QeTp7VKHTdN1nsNOkR1BAsfRlWlKSCcr+0oXsBvxWDSZHisw0//hK69Nm3ScatOTN9k91kqd2nH6hlGP3Hzn6VtnyuGYFumM/cd7f/u3bVJm7A7jE5fVX/iulRXe0bdcjOQBMP71zoEbPnPXOB6kSJhyD0BWNGboOQ5XVPr8/cG8PAOMHHahrqq0uqKkq4s1g8PeEkf98IFwU8ED3/7+F79wh7m+S7f7KVcHtRxTwrZXNq0/+zKMWdQH9wplGKM6sxJ0BGtOfxE2nmvwujpDcVnTRzX6LKhQ43FUehwuUbCZYWOBflGjni3eMGZm0PRnt+mD7qy1Nmc3dHOhY9l0eg5GAjXnlkHHYzLoY6W63ON22rIIACE7D3Z0dAdu+vC7KybgB4D1n0kByFzgu502f8YJsqL19PhHEgOew6LAGQZhgdrp2f/FgKriGWHbK5tOPe9DtK6e/gqwzSZEHPMcdtsJer9D5GkY3VyhsxsxRWWBF5dNIAZpHUgaXIywTaALdHYyZ37FVU025YEQ0htNaAZZXFHisafvBigo0/+hK6+1YuhJYz1YHm2dk5oXtWw6i6ETQjgKffmZDz4VNn1MOO02r8fZH4xWeIf6vOqGwZmexYoFVa9vO9C8v/3uWy5hLlf+/OsdOrMT1v4zPBHs/nv+H1tSpYlBa1uP3SZYY4GlhNpnrrVZvMiaHGkdYUGknh7/9AgA8wDY4unSc07Mes5LWw5wGKJAU8jl19zK1q2LFy80DULSBNS27Mg8+XggcqA3zMxitcd5Ur0vMy4RTiiH+yP9sYSVe7Tx3Lwy9/wyt0MUpsf0Dwuj41FselrshcVnMKb1LUNvRyEZ9LGiG+QPLzRv3dNaWWojOBm7I4Somr5qYbUwuGOjeX/7LR89p7qixHrJ/3gtOX2TZfIz346yUteSplqYEDDDIaC77/thVjFI0wOnI7kKkxXNbhPYvcwnYEemeUnOokDJYoURgB09U81Tj/3Yun35NbeyG0uWLOpuOsk6zsQgKqvHAlFmQF2isKqmLGtUutRhW9dQHpSUg32hAbaDrGxeCKGOlo55Xvd8n9s+aHHypK2eTjA9dmQ//UZqGx5vwR+68lrr82N9jnTDSJr7ZDgmuVI3LxjPdoM+VjgOV5Z5MEc9FYx0gmmxX0N12XUfO+fpP79p/e2vXTHvJ0+8TBDauHqeVbKFEJpXm1xBQl+gcTN9LQmHi8HnmGJbSmDdsLq8sSV/6iPMQNwd9m0WvBgYhrFtx74AKq1BQYHjTqjzOTLaCv22TbXbWX8ITJADOR1s2AxmpSlNy9sJ6m8/vKiipMrteF1O+aMwbfqgHU8uCYbW5of20VqX1CW8wfKiOFm+iIvOpqOMRbphLtJNpyX9HEJQW3ewxIakhOJ02OnEJ0W/7mN0hMMHLz0DIfT7p19j2/fWrpi3fX/7v3a3I4ye+s2j0BtudoSAxrRjIDNnYCUGWLzomhu/PG1XlU8UiBWDQi3QDPKhj98SDkeDYWlhQ0nWJYKi6t1ButgXBzv/UBNu2XOEyrwe879YJPG0HGnydNPbM216IYbRp43UqAvLyeWzOvr3L9+f5+Nf/YnreZ67+uJTGxvpfkDG4SPJNgG7DnezWBD71Vx85iq305rqRoFh8eNj5puSf+XeH4wkBqz7Pzsyzf2frSgQMOlccfV1mQdz7+IaMRanS14P73V72PbdZE2ndS9GLI583tmnNM1vyPoAguku/PmvL/M6V1QGPdWm67rBUguTaNDHivXsbW20WLyxsdqy/rQn4JJa2hC+1PHClsOEoL+8tue8U5exjpPA7BaAPMVgpgYADFqWIrfXyT/O3Ltt0+8dbo3p/eZ7Obj3J+3OoZZqrHpnuEnKZZvu/fSlOe597MWjaDQuveicjY3T0WJ2Shk1NTrNNn1MYIzXLqvfcyhp9Pccal9Ct9Chrt5he0Hee+oShNALWw6/uOXgxhMXzKspY54ZTAiYrSGgfPjGvZ9L1YbpjAIxckSBguH4d75XKNvNr7j6+hEN9aAtoHcNZiCzGtZkkGQE22HWZ6b+NLUygxHevCxObns96TABGMkDsJyAAhQAy6Czt312GfSx8onrb1y7rJ527FCGTQzMsRfshS2Hz9mwpMqXbPcNUaBZ7wGMxPRbfxYF+sLnP5e72ocg0tKV3No2Vq64+vohO53xJKwzyshmnJrpLNOVaZEJZaQGxSyWPZijHAk8U8a6iFKjpkHPp85glhr0sfKrX/zfivu+lHncH4qNJAPMG7CaTIATMGc9gJniA1dc5XLay72uKy5Yb9Cuk4auGz97evPw0EXKejoD1mky9cjwmMiw6u/UU9j/2YebtpvJY+0Hxnr6PYDUqEvylzd3F+nTwzezyUBuV4A2DPZSPwCcgLnpAUwF77vsY/Q/pmXleY7ugM9W6iGrRmd/9Ae/fnnwQEbcBNOZgqx5Ct1xSh8m7amymwSw1wVIatTFKgoaFTDok8hd93wrqwyM5Aow0w+MjyL1AC7+8FWalpLdNS0/XWjTzhO8wKcvBsFYFzJ/e/md1G+JOTCABrlMLSaE+IkvhwdwYD9tG0ArT0qSW9Bx+UKE0B033zjFFw5M1BvItP5ZnQDYEjwSxSgAF3/4KsNAmMvY9mku9qrLPVecv25Bffmk9AUDchvrZFbZ3Ek1wTf8zruyxFUuvepm+iFfNv/48fayshE7CbpdDjYpjO5ArB2qQwcNKHANyFMAQANGorgE4OIPX1XlK+nx0+6SWQRgkKryko9cABqQxVizTgbMUE/EXmc11pOOZf2tI/99b67OwF+6b6i/N8hA4csAMcjSBXR/QBrgBORPEQnAww/S2GI4lthxoKM/FMODzafMlgB6bWWplbPtCcSrykuuuGDtwvqKWeQHpEdCEG2pOYvs9aRz6VW35G/9LUAGZosM6LpxuK3/4rPWpJ0GApA/RSQAjIcf/FZMknce6uodiBODYEL7A9dXl9HpgGYfWqsC/syTF1WWly2aVzk9i2uN7saksRA22LrYjPWkc+lVtyCELAHI0/pbgAwUvgzIina8M5CnAEAUKCtFJwCMH3z7a89vpk0cly1MupCpH6P2rj52o62LNql22dNrpdhEWdaVdyKXAcZ6eqx/OBT+yfep/zcpGrBz71FCiCTJ/f6BUDimappV6LXj5Scn4/KBPDSAoL5gtLMvRAi5/IL1aSeAE5AnRSoAX73r8+zGBy44hd1wuGl/yM5Of6YAWBrQG4yNtCX45S0HMIwHKAwuuepmjPCqQesfGrT+13/2K0Mn5S3cNeWerDLgdLlVVevtDzbvPNDTF5TlobFWIAPTwNe/+h/tPQOBUJzuqXxfXgIATkAmRSoAlgZounH5hacx68/Ytm2fPmgeLAFACEXiCULQZecONaNPBTqDFggs8btqWZN15IF7v3zjHf/Jbp9yMu3gnw+EkFhMstttojkr5vjRY1llAHNCNBZv7+zbf/B4JCpp1BtIAjIw1Xzm07f0BaOEZBEAcALypHgFwNIAgtCy+VWZzW8isYQ/LKUdwRhDd+hC5tKrbkm1/n3BGO17gdAZp61lR1R1WKsZBs0AmT3qVFWTZaW9raOvP9jvH2hqrK2vqzIHK2JCkJ5i31NloHn3UVXVOrv7wxG6WUnXk3OJMcLNL/9+Kl9usXPFxz6evJGfEwAbAtIoagFgfPWuzy+dX5V6JBJLWLfTNCAal0ccEvnWfo7jYDzAjIf+Vy1vYv01QqGQikTL9COE+nt7I9GYpmmKokkJOR5PxKWElJATCUVVNVYIgDH2lroHQlFRFJYubozHE7puCAJPe3LwXG2VL6sGOBz2n/3q2dSLsWSA57h3XnxiOl5/EWsARIHGBwjAMA1INf2pWDIQiSf+7RyIAhW29TcJDaRb/+PHWzq7+trae2RFwQiLNoHjOJfDTnd+07HpHJ1Gq2oejzORUNxuZ02VzyZmHxmvKEMR/5raaofDPjS87JlXdF3XdEOWFV03LBngOLz9RfAGpoQrPvbx/KNAB452fvKzd0/Nhcw+QACS/O9378v9TjENgCjQbLH+p5yWTO8TQlpbWg8eafP7Bxrqqxvqq1CyV/4I7bAR+tjFrfk86a+eHtyFhFFTU2Pavc27jxw+2j4QirL4knUccgPTpgEjCQBCCDSAAQIwZg3IEQWCWqAZ4cbP3dXbG7CsP63uXb6MrdNbWlr3HTjOcdz8eTW1NRUcpl2eLj3HHOM+SfzqT8M2o6Ypwev/2n30eIeiqCQlKGS327Y89+tJvAaADhD+xfcz3wfQgNyAAIxNAxBCLd0Dk5UHLms6daBlS54nA1m56XO0uLNicCoIs/6GYRzYf2j/4ZaaqvJ59dUup52t9SfX9OdQgkyH4NHf/l1RaQ6ZyQDP86Ulrpf/9POpu54iJFMDcggAAj8ABGAcMtDSTWfU5dCA/AWgZN46jHG4LX1XMJC/6c+0/gMD4be37ayp8tVUl/ODHZ/GafpThzUMjkIbfi9BimH1jiYEPf7CvNRT0pTgkcf+wuZDMBkQBL660ve33/14PNcGjFcAIBNgAR7A2DSAWf8cAvDSWwc4DuepASUNa3Vdi3fvzudkYFTrP39B07ZtOwkhSxY1Cjwz/bQ19GVnHUA6QawBeMoInoSiywrd1M0sufkvNc+RmCbJRnLmGp2vSyesBcNqQjHM05IzfszRuygQVjTdHKNJaD9ZNo83Etcqq85KubBGOvSYbiAnsqyqmvbkJjphQtd1juM9Htdrz4ArMK1RIKgHLfaBMLn59J335PADso3xSpIyx3x0CCLcyE1JgTEt/I8dPfb6628vXdzocjkURQ0OhIMDkROajhCE/vYikRVjIKKxSZnWpBdZMWIJ3fplYg7x5q9P1UjquAi7jc4LGhSE5M+WuASbSM/mMLaJNKGMOVxeKtrF5EQgjHcghP/0koMQ1Lz7kNnxGmHBJkm00sxb4kQIB0IxgxC2ewCYOB+5/o6sGgCMBHgAubjiymvOXr/Y6viTUNSeAP1bZZMaJx4F8s5fbxh6pH1oChUwDuvvKyvt7PZrmsbzfCAYCoVjcSmxuLGPVfkIPC5xC+w3aJX+2EWurFQ0hxAkD2KMnHa+rEQwu/Ehmiw278AIuRy8y0l1mv0sa9ZnFzlB5NKriAQ6Fm7oW5007x74166gy8F3BZZyPMdz9KthXj3bWCDwPG8O/Lzj5hvgVz8pgBMwJsADyMWTv30MoWvsIu+wix6XDSFst9HC8Nw94AxC7r/nS3ffN3r3MZ4XDD3LxlQgT9NfW1+/e/f+d3YeUlVVEASft6S2puL2K2IIOzBqtKauu518crSj9XvDCAkZ5pt6cJPZ/bu3M/56c6CzL0EQ4nGzbpCVy89HCMVjUXZCU1MyYfDjRx9HCN16bXJfKwBMDyAAoyOruiSrwTBtO4UQWjJ/aP9nVjiM+waSf+G50TWVF+3OmhVSzxRWp8xV628QFAoGKsu95b5Sj9tpE0WO48xkrwMVBlFJ13QiChwhtOsUQmjfgefZXUwJWlraLRkwDPL9n/68py/Q7w898v2vz/S1z1YgCjQmIAQ0OldceQ1rAc2+baqvEAWephZHaAyHEHrwNy+9uOUYJ5juAsdrikQDyHRYrU4M3dA1VUnouubxVuiqijAKtU5CIdCSNaeJNidBXCAsOUvKOEyObfs7mpMxn4rKjvaOefXDuphMaYnnONFJbEDp6E1E4tqR9nhLlxQMKyyR4PSsxxgrUpTuFjZ0wyAGzRHzBjFKPK5ynxc0YNxAFCh/ilQA3nflZzkafOUwjcTSICwxaFkebRyGuUQiEQiE/H1+OaEYukZnjNM/Tv3sk+lWI4OgmoqSUrcjaxpA043jnYFnXtqBEHru1WaCEMcLyTQjMXSdVoITw9A1mRDiq5qnqrIg2HVDVxIxTrSrmkKfTVfNhCM2dBUP/ripHwbmeLvbU+IrM1vT8ETXHaVejnYr49wer9dbgTDyeEpcbreu63/8YbLr9Vyy/janx+1ydLR3IISYBhSi6U+F/fY148a7+lhUqqqid+26D1nBpn6/38ZjNl4CY9zU1PiZ6+maYxaxYMW7rdsGpuue1n0vFX496FmXFHvqZfaFgD706a8jc4wXxywgIQKPbWbPXoyxoetyQrLZbaZNN2Tas4WmbA2zfo9uyqcNHYkixQxFNljVn2lrzQo9gxgkEon2+wPReEJTFbNwzzCrRugf8fNv7GaFfuduXB2OJmoqSljln1k3ovf4Ix19oZ0HOwciEm8epNafE+hgRvPRqdTwvKEbCGOOEzSVagAxHQtNVQhvM7BAOIJpxYnNFCRiJKjHQB8IY0IMM79AOGwgTSYY6SrWNU2VYsSguhKx2WJllTS4LQgOp0ubzdmFrKb/uZe3V5R7McYnrFzE3s/2zp5brwqiAsf8MNx4V99N133YOtbemZw5wfLKLLeEEBoIxwrf+s9fde5g6RRpEDsJwpreaxhmsIsWtmFFQzVrlkumt0MICeM6+pIPvDqD19zdF8rUgFef/XmRa0ChC8BNX/lfzPOGQVRVjscishRPxCNETyRrMYjhdHlUHUtRmZpSXTeIoakqx3MG/U43NF3XtH/7+GfNRb6MkIZtJYgYbhvyOG0P3PsVapQ52gqM1gAiIghiNBoNhSLELAxBmKOJQswKAKlWYIQNRE12TFKOdvhfevvQ4sbKbn+4+UBHJEpzfQydYJ5abBr35QU74Qxs/iUYmmLeMOjSnqMXSTsWyHGshM0QMW0aQ+8ynRI6q1iR6emxntT3JBZAI5UNdqO5gGX9K1Osv9Pj/eAl5yCEDh08tK35wPx6X7mPjnGeLbzrtBP27TtAo/8rl6fd5XZS6z8QTv5Wb/38f/74OwWXA5i3nG5rqOJ7zDVQJy9gkUeGrmOe0xVD4LBq0E8vz3N03YM02m6Px2zznM/oEnjOu2JpjKdTejBCx/b8c6ZfEFCoIaCTzrmsfv6iklKfKNgEQdQNQzMNZSQUUGTajQcTlecFWlSHsdvjxRwydC25A4cYiqLY7LaTN17Y030cIdTVFXN4HK0HDmOMVTmBMeEE0VNSghHvdDuWn7CC/oik7Nn+CtUIQo63tHV19VIB0Ona39z3Q9fdLEDElvxnnrzYulpMvZAsr4Kn04XRK1uP0Z9PNoEhyKBN52kESJWJoYs2Jy/a5HhE6i3sIMa0W/9U07902ZLU9fKRw4cPHO6orvQuaKqn5nIaPIDUXzD9BbKDCKkGsuqL8sQgL7zS8+Lb/atWXGAdc5WUdbTRbHAqhaMBC1a/R9f1ar6T5zhV0zkOcRx9S0QBG5pG10iIwwSrOi2hwtjcMY1pWoPjOEIwz+OEqRCaQQQbr6qIGFiy07YZvCAefOcfU3TZEAWarR7Ajpc30Qj6IJ/4wndddqdot5V4aeBbEERDl5m5NwyNGIYo8DQEZC6bXSXzDuzZoavkqUd/ZRg6woZG/6Wn81Q4NGx2/Q0KHNLpMvzgjq0cL9hdpXXz5s1bML+mvqb98f+jYXeDCA4HL9gMjSZpzZ+iTgUi5Ky1DYZhaIOGIKv1t/AfeWOku2yVyzhB1FXF7eCHzRwoetKsf9r7sWDB/AUL6EJye/PeB748NMpt0lANKaoaBgqZ/xJCAmFVUc0NwwQNRFSJbh6mCwOOwxe+q8rmtY3hwTncWOsUBbrNwMKqLyg0Fp5wHiG4guu020kiQTAhIo91g9hFXlWJmc/iDB3xPF0s8aYqJMOl1IHlzKgr1jQk0PwG1QCkGwLPaYjY5S5BwDI/ratPiALNDgFI41ffvnPUc774vd/F47G+nr7wgeMBf0CKRhNSTNc1zlyrEDNboCPdIDSGg2lKQKdrd8zrSlw3DEmKhYO9Rw/td5ZUrll31gc/tuirNw0t0NJZew192JQGv1lhUaAcJyj9B92eU1VN7e/Ym+M078KNHCcgRHieK68sd7gdTpc7Fg6VlJVvfvpnI/3UuVf/v3g0TKNhHPf2s79Cs4dHfvANhNDn7kpuw961a++aNatSTzh2tGXhoqZQMLioqe6nv0E3X5VszjFZ9PclnnqhS1aMuMwa9hCVLjOSv0rDagRh7vqur7KfckrlmB7fYe4fTiURC6NCYtWpF8uJeJnRQ/ROZG6WMOh6n6OrIp66wWwVT0+lgVOs6/SPSrV8I4R4msniFS2579owxZLniG7QagUabKVtMIhN7V5z6nt2bYFw0IxRiCGgyWLjRdfZXW5d1pSEokgRWZKwufdHN3f5UxWgBpqn5lXXMf2I8/RzScSSspqFixYsW73CaRe+8skLMx/5iiuvUdQUARihnQMTgD8+Qff4ZMVdt4bjhUj7dutI1YpzaV0QreZERFN1WhSUrB0ysxVYtAvllRVOt4vjeNaWhuP40rJKw9ATIX/dvAV9vZ1RKbllQeBFXdV2vvgkms1c95nPL1y0yPr20MFDa05c3W7GTKYi/nPkYPixZ9tlNa9VeWWZ7YbLGn21Y3BEjh0KP/pse2X1mTSw3pBsINrW2lZQIaDTTl6lq0ShUR0icnTPHIeQSldU1O5rGmfQ9RP9/PEc1g1U6uIjMY0WUdDgKcEc53EJ4bjOc0jTNMxTnRCoo0BklbAOfcRUBdlRse+d12e2HvSsIs4DzwIPYNz866+/zDx47r/dQAxuIBRNSFGNurUqsdlMM4pU3cD0o2uEA8d2DrQf3r+nrqHpjvsfczkc3/j8FakP8uRvH0vXgJH5+j1f+s8RdwWTEjuJIFR70sVEo3EtVUnQyJQiCaKD7hUwK4VuWEl3CVSacvLdg6f2dvWUlpWWeL0cT8NZuiZJsUhFTYOm620th1lsDHO8oeuSFLU5XWiW88sffeeu+x+wvp3fOKz//qQj8KxtT170Dyj/3NJ/+fsbkG1YVCcHrHNcgTNgVPpEv64bmNasJXue6gbmESlxihKmHpFZVKdjTD0aVUe8iJHOJV0lDsmaQd9J6ifRfXB04aUbDrugaarpLtAqPgMhUeo78fSLdm7+6wy+2FeLuBYo30/tnOGlZ37+8rOPNL/2u/1bn523aMWCxSf6yqoFzOmKrOuKpidUjXr+ui5Ho71HDu147q9/ffXl1z947dc+d//jGV0iRkEnSUPy9Xu+lHlvScNaRAcT0mmCihxLxCOKktDoFrG4Jksfq//HJxa/ef2Kbcz6W9y5bMvnFr3p7/UH+wJSOKJJtFxVkeK9nS1RKRGTZElRWcZaU2TqcY8w1HB28c27vzhtz0Xz/PkrAEK7D0f2HswvhqORvvb4niORwheAA82vIp3YeGyjTjOWNBq04WmYn2bBRZ7WWNM9CxxnNjRFkqw7HQLNDnNYJ5ggTBPkTp5+As0qPjN6hhVVM1MFhOeRQQyVjsnhFDn7HNaJ85Hr75iiR54zzGUPYFRe3fSQdfv086/XdEUnWmQgYBgqZ/CE4zVdwZra3R3r6Wlt62i78Or/XHbCclEQvvOFjw17IEMfKQpEc5X729etGNYjnmG3O1xup4hptT7dFKpIvGC7dvG/8rnyL6x4GyH0vYMbXG6Xp9RD3RZNFx0Ou8NFa6METo5HVDmhGxwfmBuloUm8vopQ0D+FT0BoL2jWtiFPZNV48e3+hfVOZ/nQZOBMEkGl+WB4696Brn45nlji371/5QkrUAETwJU1Yn+Ubjgx87k0Woo1A8myarfzZsUc/eQatLyCfoBVzRy0Sd1SFgjC5hg0qqgcbc1NMOY03eA5WiFEsICoTBB6K95x8rsueeeNZ6fndUEqOJWiFoBUNj//C3bj9Iuut9vd/t5OKRLChEaCNVrqyYcCichAd0fr0YqK2uu+8HDjoiU7//lI/o+fGQia3zT/nZfolPDPnKch9Dyie2XGxr8v24oQ+oX/vQLPcza7aLOpSiIiRUWe11TV0DTB7uaFgl9t5oeiJ71Vr69iCp8G0+bP5iaMMbxvnX2Jl7f5LzyvDvH42tsO8jZPQtF+8xAtVaIk9MMt0c07B462xxKKceSoU5UPJeLRXdu3iaL5ZLyAMW+ziYLACTzvsFMhWb/xfNaPdOvm59BMcLD51cq1q+iS36CV13RDpLn30jB007LTkmiaEqB1nwQjkoirTreDdtI2aFbAnH+AbAKWZY0mjgd9Kva2YsMwN99QbwshToqH0MxhjEXv5xhzOQk8QS648u5AT2uwt00nKs120aIhgUO8IPAcZ/P6amrnz6tbuLir+S+j5oEtD8DSANPojw2WA8jKS95PY16MRyOcwCOCJCmqqWpCCjvdZXtefhrNCT5/37dpkrzSx76duiTwnl3BJ/7RpabMA8hB+xG6Pu7u8xNdZV2eZCXh9dU0zG9UVMlACU7gDF0nmoE41NsftGP3wiXLQ/1dve2tbN+J2dJDEOxOgacBE1o+ydFNLthsHE0QkmVZU+n/2N5ARdNcbpfbad/+9otoilmx9myP3mfQCmi6w8Ym0mg+NnSMkSBwqmawompTDWiI324TDIPafbONNtEJtfy0zxGz+4MawGGs0T32dPVpmPE2w1GzbzudkDPNqWAjxfSfc9lNqPgAD2BE/vHb+xFC77/qK5FQT0/7EU1RkKHpiNc0HmNZ0eL9/uPHDuyqqK4vq6hpWrHi2FtPj1QMKiua3SaMz+7nQzQ6wHGCKitOT6mBDKe7VBBsCanEWVqG5pD1zwItMJzMBs6suDPrwJ/2w1xUksPhKEe3lVAjJ0lRl9NJy8ho0EPleLqMdzvEqrrqkzZubH7zdWeN4fI5NVmTogmb21a1zNO+bcBTUeFx2W02wWkoGPMOu0MQ7bKGdZpQZaVe1MCyJCsyRwaZG2p1hAl9aoRjCTkqSevWn6cZWkKmcUspoXQd2zm57wNCaH/zK6ecSJ0AzGGO8GZPLMTT0k/dzJQkO1SZ59JeRqqmm7EfWmtnVlzTi6aXr9OEgUGonpmjFwhH94/R+A/9cYy5RM+Jp75v55bp83WKedWfCgjAKPz9N99IbuW/5FP+3s5ELIwNlTYXUjXE85GIPxYb6O44fnDvjsYlS9ecfMrBN55Ke4S6vl92dpJwgvhc2MaaBE02mqYoiTBdTXGcqiZ0w7A7XKqSEO1TsE9q5qisKCOE7D94jH0bi0Q2b/GfurqMdwuTKAOcuaOQrVlbD5L2rl4eY6fd4XI53TZ7STW1+GaLENOKUYtNa95t2JBVRdOVjkCotqEx2N3Nc7jaaUihMDGWlor8+896F330d6Nf/fJ36991jq4qB/bvXlw3TyMoEZeWnnzSmMpAN2y4QOTtDpvL49Bcbpso2lYvWhSLxoLhAUlTS0s9u7ZNTm1ljK+yk16zf4m524tNWDAX/hyHNZ2mfM2ZCwSbZf52kTNrqhksaMQrqi6KGGmET+6apj+u0d0D9AFpRxeCVHWqUsGi2SgMyAqEgMbGmZfdFgr0RILdhDaKSBbn06lO1Aw43B5v44KF85csaf71VWbSDPmcuNyFZQ31xYw2ec3Z9XvGVGGSZwjoCfkSRDcpE3eJT9VkTVUVWeI43ltZt/Uvs2kXWA6+9dCwXW+WDJTadp6wpOS0E3yiZ3Jk4OKPbA6FIgLPeVxuj9Nlt4miIAqCjTM7D9Lt4GZPb4JURAjPCZULk033BtpESZKJ3b7+Xef293Zp6i7NWGZ2SkDvf69p/U2OHz1aWVme+bzHW7smvg9g3cnnIE4XbZzDaeNt2OkSQ8FYOCwZBnEIJXZRePONMa+yN5y0WtcNWslPCFvA0B2V9KNPP+Fss7SZ9yW8yCmKbrPZzKjPUB7FMHRsLvhpCZHZe4tgpCo0gmRGj8yeW8jYuXdKGqI8/fiDmQd9dCTnMIozBAQCMB7OvOimhKoO9HeossyO0NobXqRJAo5z2J3VjfNqGxdUVNe+8/MPeuzIxuM98RN5Ta2srzuJf3nSBeD3yqWaohk6KSnzcTwvRWhFKTJQTdOSN59+GM0Vrrju0+s3rMsqAyXiztWLSzauKbN5RLZMHSvnXfYSQprH4XU6HBzmBF4QaQG7Xrs0r90eFhymvydqEvGKNNN/+OAhQeCzWv9MDUgTgNLaFaqmO11O3szwB9t3o+li7aoVDnOkJjY3MegG1Vmdih/hbWbJj7mzgeaEBayqusMm0l0CyZ+mwSBBwDFJsYn80PZ5KtWEbsnENHlmIG73vlz74SddAzIFABWlBoAATIgzLrghFOqPx8KELmU4jDieE0SBdkfhbY6Sssr62tqmBUuf/MX9Ikeq62pOXe1T9v1p0gXgaXSFLCVE0e4qKdVUNRoaMKcccFXzFr76+6FS10nh5LMvUlWNQ0SJR/c1b0aFJAMeYeeS+a71K7wVNc48ZeAj174d9IdKS0o9HrfA8wlJ+vKdC373j049pbHBWOnpr6koL73ofbR9JuOer35L0wy32+N2uxVFufWWK0fVgMJpBkfjn2tXKAavESzQEk7C8XSmBUIGL4rU9LOtbTR9Tf81dCLYbBrd8JaUAZ7ndNrjluYHaJc4WlaE7HZekmgzUY7D23dNofUHJyAHEB0bJ8l0rvYwcqPmilsjsWAkFDQnfikcDW4KRJbC/d2GFAn1dS9fc077wdcuO3/BipW1z+1Dk051fRPGfDwaHujvtdnsbg/tk6xIEh1pMEmcdPr53YGI3e1KYN43f17U3xsdCC5cttJht+3bldq7b8rp6Rs4fuy43x+0ZGDFsoVMBqLaic1H0c5D29csLjl5hbeqbkQZuPKT2/p6g26X02131VRUPf7oUAj+yMGwwOFUAXjt5a54QhZFm9frVVV14+kjptZbOmiREseT+vo6VuFz/OhRhND1130kx8I/lQXz69JiQYXAG83J4MzJa1aZfc6xgTlscMRAtKebZtAUAHMSOKwoimguVszAEE0R6ITYbGI0LjvtomjuDyaEvLV9ao0+kA8gAJPA2sSP6WaYcvS28Akp3C+rdAoBxljXuRiK65oc7Nrz7jOWRoKR5x56FE0BwY6juoHK6psIwbKS4HneZnMaSOdoD5a8WLNuI8fRAg/MYVmWpYSMiC7w2GGnf6+yq8q3aFmt0253Opwlbl4QDL3p+DaeD7bxRHGtW7Nt+y40vVRU+I4fOx7wD5y8YW2aDBj8uh3H0c7D209Y7Fm3wltb70I8vu7m/T6fy+ZEgk0kSPWUcL/92XuzPrLTTtekqUfOPGf0PRrM9JvVMALNVB9qaaivioUC1HXLz/QXuAYw3hlhqX762pU0NY451SB2m41mAkSBbvSlW4YRT5O/NFWmGWT7rilYAQHjBQRgMjlF+xVyoeNl1/YHI+GohIhCCOlu2VPidfo8ZPvuIB0pOQVse/HPtKfVR2413W3RMFRFSRCzXnv1ujNY3QbmbdFoTJITPFFtvGETeY9DtNP8Jm8TeKeAPU4bm6mDsahpNFeHOZGWqAt8t72yamFTZCAQCQVtTtFbUcZxnKeiPNhznNe1bc3TF49Oo7yijMkAwujk9WtTZYAI63a1oF0taHXjoXUrvL/80fJkW0uG1qCFzY2qZvRCVo24ZK75CYpJusvJS3K+of9U0z90VJP279237qSV43tdC+aPfVvgjLK5ObtZX3/iKk7g3t4xY58QIDcgAJPPgoFHF2B0cP6nYzGp98ibLpft3FPr44HE0oYSJRmsnmROPOFENlHWLHBWeUFkDURpwwksLaj2ijx2iFgUvCLvMztLY5HnNAPR3J45LAEjbObuaJVLTIrH42bbVMTJZtYv3N+L2tvMVkVadCAUj4bdXp9u6Nu2bUMFQHkFjclQJQgMpMkA3djVtnRPG1o576DLwYejGhvxIytGKMq6FdNH0HQSk5LfEjrubXTr39ZJ1/W0HGa46fc4kt6DJOuqqo2vBrG/n7oOc4BtOyHOU9CAAIyHUfdzEYS6jmxOxCW703bO6fP0Q3/lEB5fPN7HJ8sVLJvEG8luzxZnraqLKmq320OtOS2uUAgizlIv5nivx+n1OAxiuESi64Y/EnMIvNdll2S1P5IQaECXotGW93Qw8jOvvJX9Ol5K7js9/YNXRfp7PRXl//oT7WNRUJSXl2XKAFOCfe3LCCGh4BbrZGbuJYU2mh4MX5sbAMy9qZqmDwxENC2LEhCzcIVV86ZOA7NMP0sfBILhPv9AfW1lcZr+wicYkbLWAhUVUAU0OaafVsWlGAPdIK90r+JE/uQT6zwunjvy9/EZ/RykysB27V2yrqtLzg50dkbDtK2KINp9leUNCxcdeuH3lV6nz21/6u8zOZJ7ErnlzuTE4NxYMmDx6hvD/JULL6Ad+XNw6Ejb5i27smpAJsz6p5UOVVWUbVi3ko6rG6Pp//bX7s7nR4D8gd0AWQEPYBIgCEUSJKzULPH0KXyyb6LDKZ6wus5tI3la/3yMfio657Jk4JlX6ML2knWXuZaV+Xu66I4bOqmDFmDs2Dn5HQJmBcwb6O8PbjhlXZ4WP42mxtrW9h5/T7752MzC0UAw0tc/0FA3uhMA1h+YEUAAJgUSMxqxOsx5P8O3E3XunHS7n1UGEKKbUZ998Kvnfvg6lJCRQXRd5Z0OJTb1A9MLm8pKWixEJzAL2bv1DduxOtSvLInbRnqpjOaxJ8DcG5t2TDf0lrauqsqyHE4AmH5gBgEBmAQwwpoUdnLUCtt0zJyAqTP6Fvc/n2xCYPHSH7IMQZtj/OS73/joDbf5fOmD/XKjmJEcSwbKyvL6cV4Ue/oG4tL429TQTEB/sKGuKvMuMP3AjAMCME7uOe/V+15MbvU8HvA5cNTOGzNi9IuQJ37+0EdvuI2+q+OSgeq8C/M9LmdVpa+lrXsCToDR0tZdVelLcwLA+gOFAAjAmCFmV6tUbCRu4w2HLXk01QkAuz9FPPFz2uJifDKQPxjj2pryvv7gJDoBYPoLh2DRFwJBFdDY+Mx5mqqT/6v5ZteJNKN434tntfU7nbziFI0yN/aV4IHQkB/gtjkm8umExX6ejFUG8vcA2Eaxw8c68nUC0ipDB6ksLzvl5JXhULjY6nxuP3eo+PnBl2wzei1QCJQF8ADGVfOTUhlo51Q7T9wOVOrGooAqK7h+f1IDYkpirGIARn+C3sCkOwQTcQLWJwY7f3Si3s5hd91+bkHYxEm078BsJN9eMYDFj8q/Ljs87PY9571q5w2nnZS6sF2k2jA4ICmdmJKwvnLcC+/zuHni5w8xJQgGQ8HgZM6YZZmAjBKh7KxPPGp9jXryv1+a3KpWDBTVi50tgAcwZiS7W7c7UVcbqmtECDlspMSFXXY63IJZ/3IfCuQsv8xh6O84W//+K9kLFoF8YBqQOy7U2x8YUxRoUjIBAFCAgAcwZjSHy7A5iJ0V4KNvvP+fJU7M8+ZcJLPPjBnznfTfFDAeh2CyvIExOQEAMFsAARgzzPpbAoB5OgHGDP6Y1t8sBeQwqq8ap7G44+yxjaAC8pSBiSgBcwJcTnvacR6nf42VogqMFOCLDUYkVMSAAIwZw+4iDhexO3QuaQ5uWveiufpHlvXnzXGpi+phwTj70gMDoVDWL01RStyOCZp7oPB5edMjqGiAHMCYYdYfCTYk2HSxhI/1I4w7BvR6L5e0/lQAsMAjgccnLMS7j+XaIAbMSHogGAzVVI8xTkcHGqYfe+1vv0s7stYs7xkT/37pwu/9eWoahRceM/hiP/jx27O2hCtmQADGzOffuOTbF76JRBsSRCQk38D73vPaT7eejc1h1zyHTQ+AaoCNx6ev4O0Cfnk37USfrUYcmLGy0ZE04H++9bVpvygAmAFAAMaM24bY8t8UAFGvXcR307mv3WEz/YtJWSUvcEjgkZj8wgKHLlwr/K2ZtpIeVQagCmiaHYJCAJwAYEaAHMCYETh81182JJf/PP0ymlaZewJeGTyBBn9s5vKfWX+Bo+0jzl3BnbYArD8AAIUCeADjwWXDzPQPagAylq/iDiSn3wnU7ietv2i6ApggRSMJhcgKWV1NVA3pOjkSSq/3L6laihB1JuYkOXaNzvYNsZMCOAEzRbCIOwKBAIyT+55afM81XYgX6HxAASEekZNW3YNe/dFLZ3Z30emyJywRbTQPjHhEzX1CptZf05Gu08mLBkHzPYZuII1wOuI1ZNcdlbetfaT6qfPQnACaBABA4QMhoHHCc/j+X9djWvFDrT82NQBtpLEghNBt5752du3fafAHU3OfUAxZJapGdJ2YU9aRbmCDIJ1wBuJ0JOhiyW0bHkPFSgGWh88IRfU+FNWLLVhAACbE139eiQREnQAefe07lfd9kw7/+8y5r9GiQVE8peafJ7k2KWbkR1WZ9UcGQQZh1h/rzPrz7ttOK7gB6wAwVryCO8dXIbyfH/z47aOec85lN6GiAUJAE+Xr/5s+8fVHL515+wN+4+80JYDdno3ul0gs+ufDF1LrbxDDSGpAcvmP7cRRMeGrmPXMmQj4mCwdHeg8m5mgWS+0X/o5xWT6GTAPYGx85jxaypmDSkyLQW//tp85V8azycxwEl3/9ZZzUkP/t777b9ad1XfPkQTAWHMAomc5QqigbMFEuPt8Mf+T4455aUdm1/tQVC927gEewJTw4Bcqbv+un4bYLlqFDMS8AQrPXX06DRBlMpes/9xYDwLAnAdyAFPFg3dWmC3iEDIQfs8qfBbLD2ffA7a6En4Rc4cxTfVxJdpndXZ0gi8WmFnAA5hKDPpFdITML3zaKqQhpCGsEazKnCrzquxQpUo90XfL2VN6IQAAAJnAwnMKefA/Kqj1N6jRZxqQ/NIUrCmcpoqaUqrLYbD+s3Pxm4OiWhcX1YudY4AATC0PfaUizfpjTeU0lddVQVc9WuLu7r9O8SUAAABkBwRgynnoaxUPfbMiaf11Hesqp6ucpv2rfOs/avdN/fPPMorTCSjaFztnft2zFMgBTBMPfbfirNt/SQXXbn5ZZJ8hDxQXrkR7ZonkXKWoXmyBAx7ATAMTAubuqhCcAKDAAQGYee6/90uoyLoCFGCTgBmnqLKjRfViCxkQgBnm9LWL0VxkgovfOeMEFBVF5fHMDUAACoCiTwPM4fUglEhahLRY6pcaPTCTvxjABJLAk4aPpzMldPM2b4ylzdccTQPc/7w6pkYxc5uQFsvnNLE4XiZQIIAHMFF8vJN9pR7UOZfOuYo8DTAm5rATwBa8M30VAJAFEIAJGf00u59GnjIwV9MAAAAUOCAAY+NHLwq5jX4OGXht6yFUZEAEHAAKGRCAMXP/8+o4qh3w6V/JcS+dEQwMBwqBAGCqAQGYQhlg5win3SGcdkfuMzE3RxPB4AQAQAEDVUATgmlAaq3L+EqhIQ0AAMD0AwIwCeQ2+vfe/9/sxj13/8drWw+duWFp2gnv+cAn0ZxmTPWg0CgGAKYNEIDp475BJQAAACgEIAcATAfQJAAAChDwAIAZYJSNUdEDomf59F0NABQrIADANAG7YQGg0IAQEAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAADADeAU3gTd+poF5AAAwA3gFd9bjcTQ3X1dW8FReCZAP4AEAwNTy4Es2r+D2Cu5S81/2NdLJrkR78cz+nO0vdg4AAgAA0wQseIFCAwQAAAprXTzbKaoXO9sBAQCAwqKoAiNF9WILEEgCA8B0cP/z6t3niznunUu/hjn2cuYwIAAAME2AWQQKDQgBAQAAFCkgAAAAAEUKCAAAAECRAjkAYObJ3Bg12zfEAsCsAAQAmBkrT2BjFADMNBACAmamBib3tlgoDweAaQAEAAAAoEgBAQCmD+gUBgAFBeQAgIIDNkwBwPSAVy6smaanAgAAAAoJCAEBAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAQJECAgAAAFCkgAAAAAAUKSAAAAAARQoIAAAAACpO/j+8pVXXOXrIUgAAAABJRU5ErkJggg=="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "assistant": "The selected sword is retained at its brass-banded hilt by its own rack hook, so release hook 0 before moving it.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":0}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 0", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "assistant": "Across the top view, the sword’s leftward path has depth clearance from the axe; its rightward path meets the central equipment. With the hook released, extract left.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 3 Left", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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Q2qQL62cy+hhfUYmg5fExkAwT6yZ+uV2L9wQZnAGLEY0Iyaa/vcsfPwlgjMY6gOyH/w/e/Xkw1vjmLbMP79vhq5kAIAa6BlRF0SLHD+3ev3PHA39eV9SXfuR289gTA97bEuSzX7033UN79+yN3ahrX1kBV1bAKyrRVVUcghBCACFAEAocsgncdXW2q2ulq2oEbAEA3rrlw9gt3av8+z0/LNofx+gn3cCfMapnAGMl+DOg3iTHLONnf/XV6z/30MIzL2o7fhBgDFUV87j12P43n++9/5G37r/rgoJf5CO3k44FlPff/MuyC+8EJWz9ZVmx2aSUI/3YYB/BQT5S8jAEPDkMXjfBRl2Aapgvt2n0qSlnBrQ06d/v+eF///AeUEpcdsPnMQbt+9dyCOi6Lgo8xlg3IXGbGDtEKGvAxmMEgWECWTUwBpJIirc0A8s6EBCOaCTyhgHgyJOI4CKAHMagYcmlEIJXnx35YECJUNxF4DwcwAgGgq6/8RYaAoIArDh9VspjYovACVz7qR+ceuGqpqN7yJcaY0EQECe4fDXnXXLJ/V+4thimP8Zdj6MSH/sfPXIk/iGM8eUViAzyC/FCJsYmBrppvtSqARo26hshnHLqkthhJeIDyictmllhqIpB3gMIeA5oui5yvG4YGHIxB6AZxLKbJuYQUDQTYyDySDdNAKCBAQR9DgBjCDCE0MTEAZgmwCZxC7yEdOfkQx++GXvd++/5WsrriQ8B0TUAtgicE2i0Df/HYiAIAPCPP9778hM/mTJ1AeJ5gMh33TBxoKdz4xuv3f3T/xv6+d94+bGU1j+dVygd69/R3uZ2uwAAl5fDKyoQjfDwBbL+5BdCtGChjeeuq7ddQ2JE4hXVPAYYIbTtwx2lFgvCgOihJE+eSEAtYadVvpvw5IRUC7LDCsQlHmiSDq25Xlt7Z0+uT2EUywGMueBPQXj+rw81TFsk2RwYIQMYhqH3dHe9u/bNL3//kSGafhbuT2f9AQDnSuErK60hf3SEXhQgBDwiCwai5QyofSo1HwCxjjmoAUAG8xAgaj8MMt6BWOOAzkEDIAwMBQCDGHJMHgVk7E+mBwDrwFQhwjw0EDAgMICJgWFCIu9DzmTSpXwEoUkk3hjFZjRGD4Z5ErDyultX3vDxlTd8PJshx6duXFFWRsab6XjuL9+vqp9sd7owACY0TdOQZf+uD975zD2/LJ7pL51JQLL1P08KD7MyjBX8AKtqhVL0AUk/Ehpq46xCLToNwMYg30a6dkK9NX1a8oQtXvote+bNnFRW5k6+7dr47K6Nz+7Z9Fwe5xzfQEMhX1/ioeNYNO+SQZ/5s18/FP0+WJ/UpReesnDuJQUc/g/bYsDK624FyBKEgdFBR4Y1gEvOG6RiKHrYHT+YOmdR0N/DQSDyHEJIkOxTZi2cNGnKbWdEF97f37Zm2ZLUPUubekK5DvlLYSUgpfUfwevBGKxuJcmjJFF96aJSWA8onzB3WhWSFcP6YpNhu6mbCJOVkohueF28rJoCBw1NRzxnGoBDSFENAIAgWGsA2DRNE3CcrpsCjwJhXeQ4SIZKpNbXMIGmkymBJCHTVjNj5rTY654xpyrl9SSkga44c2GGi6caovPOur5w78eYh48o5OMZAAabt71CN09fEtU2+J/f/iTmky2j32f5+/z0a29+8NNfPXTphaf88id/HkPBn5XXF6Vw9/VH773g1vuXnnpaR0enbhgCBIamNB3ey6uhlzxnXzE3+NxLf/O4+zsZDcX0Ux653Tzri//I+4JH/69itFl/OoZdVSusbiaZQts+3BHzAWM0L2jXxmcHPea8W+4l4Ro6cseAR8BAZANhEhzjEeLJOARjAMn/ITkQReNEEEFkECMCISaOgUOQ/hdjzBEHAEgqUN+Zo7MJRpGBr732WKr9fba+fx4Wtf8xy0+3+2Zz0VUfAMCBrTsL5QCKPQn41y99mbT6writJ6xqRgFnAJTzbr73/BXLjzd1WNkjvCCIZR5HeXnNrKr9vMA//cLhx35+d/zxm341pHyhoTiAYSM/T1Nw6986femUyanVJQWeD7y+enTOA7Kx0UXlvFvune6DOhHBAggCSQC6afQt+XLIMuIImaqucwhhk0SGdJMYCeIqSD6VbpDUIM4kuT/ANE1oJQthcjak6tFQBMdD7K6fPn1qMWYAY2K4M7IOIN7q9/2Lgddj7/VHBgz9+w40TQwRsZsFtP7F9gG//vl/0jWqrTsOiQLf2hWIJSMUygGQb+RN377k4guPnGglGW9QcDmkcxeEOY579uWjT/zi24Uy/WPLBwwPr/0hbcC3dTr5EDM4AABAbj4AgNUtUR/wi3tuBuOX8265d5qXNOTirBVgngMmNhEko3gTk5E+aZ0NTcMkOwFG0XCB9cOykjxJCAhCZPYVDZAJQN8wUreWhE3TstTeuunTG4buAF58c0tsu9zrXH76PLrNfEC6QrBk6x/dINY//gCc4Fpxwa1/8fj9L360ecdBskVmpCQ7wTLQgz/x+dffz+mF/uOz1wEAIqi8u/UoxtqyWQETc8cPtDY0TKGnqjrQrz3JKCCXfib1KO+xN6NNZTPjvmTVGZNzWFYZfNFsvMALHNYM+vu3EnxMYsWtn5I1nQYYWZF+8rsiq7u04AtjYGBgEn9B4kPUAZBKMWwCsoRAogj0UIQgx5NyARHknAgUb+5TsnHzHrqxZ9NzzAdkrASOcwQARIf//cY/4xL9O1/86aZGoIoAOAC0AUEA1V7w3YfvB6NGIyhq/QnkL9H0pIWQNJCxCvlf/FII2epPY7BGM8ga2ACryAUCeMYU/X3QMNW5HfLiM8/tuP262eGIfNrp8wue3LnpV9e2z0orZ3/NJcsK+3KMUiDeqmKAbDwUILImz6RNL4R9kRUMyHouBohDpNgLANOgv4Jovj9xp9FiMWAa0d8OiQFZCaI0KYjjyL8IwRAkeRm50hen7q8tiMWmoxlKZJwH4z1BicNnsvp0I2XwJ5H+4f+vlj/YtV3Hbh56AF8PIA80AE5GwFduuf/nT44WH/CHPzzymc/cRb+YJDnZimnGyODdaKp5fDlo8iF9R1rbUWUJuKB2TzgEf/WHDeVe++krbjn2xG3HnngeDC+5Tl/yhnmaYWbQkW+BIN/msGrIujVsIkk+JOmTgyTgY2KoGlAgUZ+o3SelAORxUvRLLDKGNExknQmrBuARRsjUDWxgCBFnYMBbkSGBg9iWzyJwdBIR/e3F7R9wADnkM19JqyJVsg4gZfAn3himCP4kWP/fLvxGR283dnshz/PlwM2DagBCAHTagYzBV6+//3+eG2U+wCo7tIYz/RQq/6Bj++NyoGPiOV89dOxD0cZveq/10T/9V/tTtxevqqt95ndiM5PYdz1pOZ9OWQZ4++gCf98/1nw82oibRHijmR0wNqeJ/cxSwjzNyNnoAZ9o7POnH23fp4mhNe6hHzfdEzWO1vw29s2JfYP6czwsU24SCQcgcGRpVzWxTeQEBBUNilz/YZIADAOqOgRE44fsFXlS56Vq0YgQz5FXEHlO0QxFJ2fTDHIAmUPQsFGeDP7zZdY/bQhIFHhV05ODP/0uAWSio7PDdHnI3M8EUAJOAM4H4CQA7wCg2UCkF/zwxvvveSYfH1AMiA/49KdMshJV4Jwzp3n8+K4NcqADGGrTuv/0QMxz+EtzUPtTt4PiMDDyE/f3wBR76HbSnuQ3oe/J9D90Jh8r/7e+DQft07CqzjQaE06G06QSWBqccOAesoRI75NoArU31EvR4EJ6fb48PM3uvR3RpeDm1G2cnU4H3eg4RHrFFITn1x+uqKjo7um56uz+zBYL/LfX95SXV1x6CpHkintX+o21ZYijG9abCzPY6ESso6MdtehBfTPY+G9B/6cfm9/GfR36p7HWBao6RgBKPFANshQs8UjkEQehqkfjA3R00HcKPNB10MuP3qMVZAKPrD+S7JEQVHSsFbOu8TP/xsb+KRxA9Lunanr82q/XY+97bPDh/+981+tKGEsSVBWgOM0u4K8Db1nzQA0ArAKsgdYg+OJn83IA+T1rMH4KwJe/9y1ryFEw3Kjl2K5NkUA7MFQEdN6y/iIHmv3YJUF3VLmyYGQI+heYvjdpL6jv6e4M+rskh72iNoAhWH9COW+SbeCxcf4l3o+k3hNnxvpMRzQfPM4m0iFq/67ov9HvZ7/twgMtKT2rNX1BJDM9ekRCrWlcfjPNCOjzQtE1TDJKoBtxw2dyQrpNjfUzr+81dBUCoMthQ1eptpnk8V19083bt24NdHcBPCX65/aFqMPBwJy589Zua2o8clSQ7A532TUrpsW9L7F3Kt5f9wVSEm11pk8tu4MGO4VJ8jV1gwzxIDYw5jAp7+KslH6TLAvoBiJllSQiZL2FBp1fW1NKjhyKrEygPt9FDtZNsihM3kHTMBFZFiBKcvlPATLArH9KBxB9r0WBV6y2avE7M679Dsj88Xd3mA4PkCQgSzgsGW3OkAiOVZCvj6EDsxvgCNDCYLR1cPjLX/5CN6678ZZBD96w7SMrT1kgOZcLJqQ85viu9ZHeFmAoCBjE+iMsICDx0CmBKhecW8Pbltk2PRcY+pV/d+N88s/G1eTHhfHEcufciWUmxi29ilYzF2OsyERFXVNkX32tzeHUD75f53NYM3fy24uoGh2+mSbWdIMEaa1UDR4hh03QdEPTDVWVVU3jOQ4jgWZqnOQqfDNcphbhAHY7bFqox1tZ1Y3MNa9tqCn3LDutPxE+Z9LOQAZxIonTl+RB7IC70fFnon5Zf9zYesha7Y89Lbb2Yz0r+sx/vHHQ0EnuOo8gQiR/tNwuCbyL4zgBmYahq5r64cGD556x/Mjho6Fw2DcZvN28r7s9ZMg2E2qcqMuBiGHw3d3ds+cv7Opok0xND7SvfqFDI31GEVlOFW03XDQbjBowwAZRd8a6gTli9iHxcfEPJ9vtVPEcbGJIloxjx1hLySawTg5JWnlegdiNO45bTiUxfavc4/zjH5jEdMYQUELwp3/4n37tNyHv05BDWFcBpIJOuql7ddlnNCMokJEQVgCIaKYcbt9Y6DHw0MhbDnvTrpN04+0bftIS0kMYGwiZHPeDjf+LDRVhg4Mmsf6cZf1FUOlEM6s421IJLKk8a8knN933v3lf83fXkzIFCA368+Kw4bLbTEPd/VELxri5O9xQNdPf3qFbwo0QAEckAiFsPNna02svd0mGaQCINF0XIAnmykSwF5OinaiWS3+MwDB1kuJNemaRMZvNLp2QQye7ZF1TnR53Z3OL7O91VVaowVCgszfY3dnY0im4q69ZYXmmUUp2loX2i0nFM6/sEUzT4XTw2NR1TZYjihJR1bCsyrpOal0Fgecg4nmB4zjR5jh8+Eg4FAh0d4eULsQjh9tmd9gQDxVZk0OK4uc4gTcNXXS6YTBQUeG2Xlk0MRZFEXFo7TtHI7Kqm4C32VaeY00gRhSEACLqbVH9HzBwkpbw7tJlo2S50Og3jVj7/k8EWV9o8jW0DPjqZ5+MHb40jRx0lvQERrhofNTCU8NOrX9y8IcM/9MHfxLuc6aKO9uxGoJKLwx3Q78Ld7ugZAOCSFeOSM8sTVX3DedqWOHhELKJZIBMrCYGG278cWvQCFvWH3McEMC9nz2y0/H6sSdu29dGrT9wSsT6z6jiXIskcHoFABcDcO9ZD9y76b7qXF/9NwfOwxjX1/SXYdNPDWKsWe1tyWfnc/I2J++QRU4k49BI2NAMBBDgnREdnexSiUk3Td3Qo2chYo6W7r1l60kBDyKjMHIQSfOORivoj1UX7YJiaJraeuwjXhBsLldEN0Pd3TCiVDZMv/aixWCc8vc3D6oBv10UkK6193Y172sl+SqiIJD/i7wgOO1OXpAwxr3d7f5gwDRNJIiiJPV2tk+cOv3fPrEy3Zl/9sjzbU2NLo+nvfmkjRfIEzloGCZESBJFl9PpcnrdTq/dYd+2qycQDAYVbeWK/iKpgvCt7z6YzWFl9XPJaJ2HhkFCu9ZYnuT2WzI/CYN6mopgCUdEFxH6LUYsKdM0rQJhTM5G5hKkvowemm1mdjYkZHkwUi0C44zpQCCR5LKv8nabv7vV7AjicA90uaHDBe12IEqQF6jYK9QNMhnY/wEYs3Acqi53nbFoRmdvqNsffuTU/2gJGiFsGpBYf8yDnWXE9B+zDv7WQx2cFzzyjaoKB5pWyXnnS+DscgBWABDVjDzrgbcBuHvTfe/kovi2IZsjd+8obsbnWStv7u3pikRChqFroUBFecWeTR+0HD/w7rqXB33ujx4YIIAx+vnLc5sdPCfyKBzqOfJRi6ZrU2bO/+7tg9d+Pf7i1q/OoLJah8H+19Id9p/nRDe+u2kxMKEginan55PXnUEknp7ZEA76m9uaj6lHdd1AnFBTVdswcco77zZ2+IMb1w974TdpB0DG6Bhbop+WcbfKgPsiQdGFYGvkT6SA+gJAyLpRWej+nDJrvcWKtplWWQG1M1aWUSEdACOTA4gO/0Hi8L8n/fD/wNZ+CdwY36/ad9dJUdYV3CvjUC92OKHNDkQRckQ2EFotfyZXVP7yPC8YGsXWCMowWqgpd3tctrnTagwT3yRf3uw3uh//f+i2+02O21HVb/opq39affMDbV/+Sfu7v6gvnyuC88oAOBuA/+p7vAWAewE4fNYKx6Z14bEl9rnp5aeyPzhRrTp98fNa5zfohsBziyJNYGjIUycO8QxPPPe+TZKwGjna1KxpypRZC75960XpDpaO9qdCUe6aV51TNWvT4f5B1bc+6O+HFU/95IZ3tqz3uLyTJ0y+YdWd7d29b7893G7A6gQArSVuwFvyb4n0LXHHMr5oGCj224ot9tN7iLNKBqwoEA0W0OSlnC+MXl1cXhkjM7wo8AmJ/9T6W3diOxOelXg/pvrQUFd78KPDuqFhRcFKiCQCCALmeKL4DYDLbv9mPVlEHSJFKg/OBreTLGBAAOf8bH1sML6t7q0E0x9DAMAOwKovN1UBsBp8DoBf9D2iAPBVAA6AtUG9UZ9UxTe2xxZgRrXpHwYEPp8q0GRsH51I3tnebVT5nIM+96216+2Y6+pqDYQC37vmXAD6Qi5JVn6YObBtHQAg3NuiaPK+Q3unTJhy5cpbO3r99jLf2pdSCjsWkmialHXjLdNhrZdbqUCWzKdFwtrAwFMMbAFmkGWq6JIvxyGDLDnFv1Ru+DxOu03wuuyHGjtIn+E+REvfiZEMeV8SEv8pPb3R4X82wZ/Y9r01xx9Qa4+eOK7p5JMkoT9DhUT3lS/zeH4+NwQKRFF9QIZVQrqWNeO/3qZ3d3x9hVXT+/t0xz9xX/XXHmirBKAWgPvA7x7of+TTJJ1yfcA8prUGzIiW4uv+o/dn+yMmiR4wCsdZp89vbesKhiKdPcH2to47Pk4kg+S3XtrhqKUHhCPqh5veh4q2t/HAD667YNS+991tZMihTZ62btPamqr6ep4/d8W169cVezbAkTA9B3UNCX1pPyaJ90OMif3/wqn7szzR/2yeRWSkyTyCPJG02QRQxyQHlOjGkbFTzixbMNluE+qrfT6v40hjR3cgomq6TRQWzKzLYcZaSiQM/6PBH3ov4d8MwZ947pvUCSY5v7HL7A0EiCgggg6b7dYb7zzjcGJr3BfX77ry3AVgTBHTEhnU9MeosKx/NQASAN8BwAp/fAqAXeCdADiqt1jW3zCBz426A9FQyddfmiCJ0MRhj1NsLfrfVEK8HLAt5ji7XYIQ9nR3E3MfijicdtsFVywD4M9/fjLcHRARPHrs6H0//C58N3UQZlSxbxsZiwR7mrp6O51252WX3ygLaN2aIpo7hKBhYJ4sUxP9RBObfLS6Iv8SerIMbE0LOI5kH1gZofmcrNJHuvU5beKyBVNmTqo63tJ9+ETH5Frf4tmpM7YZAxL/Y9bfGv5buwZ7h9JJfv5kAQIgLtafZP2H7gNGJhCEweWXnDa3NSvTT7nnvuoXH2izARAGoA2AKwF4EWwD7/eCI1qz3wirJMMSYKATaRRw56P0t9TsLauBAB9qjWaalhpDXwBIoH3G0ooK3+Jm4k8lUZBEoaW55eqrSEA/HIp+25Uef29vj6wpn/vy51pb2mrOuBC+NwZ8AKW77Wg3WbfrOWPpaWedf+WmtS8W41XIeN8q0epL4yFJcf0P52K04/NCIYK08ItGh6IthIeAwHFV5e7yMmfDhAqnXbKJLASUmtiHl1gXmfBvyuH/UASfX1y/K2EjD/bs/DsYXiCEr7yRcxqrHYCIZf1bAegCYAH4ABzUWnrNkAao9TcMHFFMf9j84croomFvT2tPD+l8UmoUagEgnvYZpAHAW29vpncddlswEJCk/o5sf3zksT/89reHPjpUN3XyJz99B905FntSBbtPbN62tbO5bfnZabNOhwZVbbCE+61bv4nI/e1KKOwm3oUqg9IXGhoQkJLGCq+TWf/BHUB88KenN5xy9aaA1n/9nhZPRSUYMqufeBUUh3WbD6R+IC+jcOF91e2W9e8EIAiADMDk2xpDKimntFTULesfMgMhU1ZZ9kJRrD+VsSONYl59+52NH0AIzzknKo79f0/8qaXpo898/vPf++EAuZGWljZw5oVgrOHvajywf/Oeg/tXXnpDwU9OFQItKYzkh3L/6g58Cq1qsaoAMOJKLvdhJKUg4kL/UYbTDg0lELRn59/nLSRNV4ZzDWDubY/vfSI3WbdnP3Pv7D/8IGBNBTRLH2n5F5vW/rzOMLFsjf2DYTOiYkXDXz1b/p93BujqlBohf/cmMmsqADPmzrH+xdt3HZ43ezLAoPHY4UkTqz867O9oP3riZOuJxubyivrvfPe79Pibbrzq6WdeOH/FWbEzbNKGdCWXgpGhu/3I25u1BUuWu7zl765bU4yXSBbQyknAJyHmQBND6SIYSR5heZzDvwgcP/wvXvBn/Z4WuuGpqPR3dgzRBxRvEpCGPOMCa+p+f/VngO8PP9AsoQxDAzgEzr21+ZVHqv1hMxA2wwpWNKDqQEmdDloSFDb+E7P+/f1gIbjrc59sb20HAMyaM9NaD+gPBCXT0tJ21rlnbVq/id59f9ve5hPHFEVxuX0VFZUup3P+nJHXZkhHqLtxV3fjpBlzz1xxwbvr3irIOS3JEKuo03o7E0bpOWXfxx9M6oVp+j9ViBtaIn9Te099VVneTy8p0IDE/z4S3vtiWH9KQQJBBV8JyPDVi8WF5972eK6n/eiec7s/c69OFBMBDhJpPCCDy25oC4TMiIJVYv2xqgM12mSbMSTc5RWxD3PnniNzZk6i++OtfwLYqmy66car1q6LWnzKKaefQjeWLZm76qrLb75h1RWXnnfGqfMKbv0nzDxt/mkXF/acjYf27t69/4KVl1x2ff9E+czzr7tgZd5NKUicnpQAm6QHpGHpiOiGYTV3z0HHmdYAG0ZUb4RUEkRbyVBFkmJKQjP6QPHWnw7/Uyf/F5+8V4NXP/Hq8K8G58HOP36cpJp85l7Tsv5YJgJ5WAGf+Jdmpc/6KwZUDfSxxWwloAD09vqzPzid8WppaZNswyRfePLglt1b3ijIqeacfqmnZjpyVNkrJ7vKKzr98pb3PmyYt+Tsiy86e8VFatj/1stP5HFaDIBTxAKHJXIDNh44JMEmCpLAcxyX07o5hJAnOnicJAo2kRM5ICDSFkZA2C4UrisTIyOpsqPSBX8sRafMw3/NxEJ6bf31e1qsZrkDpncFCQQVlkzfvbjWG7muBFzd/NnoOaj1jzoAFShhYv0NMvbXDKRhTuVcViO1UiTkJ+n5BRr+g95eIrsdG/5T6ifW0w1FVgxddziiHWDGE/s2R6WHImEQ6QDNVk5D50lwdGitcBFCukm0QE26DkxyNomIA1UPzClsQ4UHiQSQVQWAabdI6/dlkP4KbBF4OEAphv/pgj9Dtv6iwLkcYnKcNz4QlN88YPgmAXkNTK5u/mzM+hOtq/vupWN/IKtADmM5/M0btig6VE3L+iPXcx+UqPUvBsdPkphPfPyHmn5FVuh2OBwOhQa84VddeXFCFOisc/uXhUsZ07Qau3BI4Ej7Xyruw3FEwock8+ditcnBRCIGkqY6VuonD0mfSGRJihpm4bOBGckkfWD5xh4yW39Lco6r8DomVHm8LluCknihFgOGn2xWAuJNfz8/uxcoGlbCWAkDJQKUCLH+Jqcip7NyqBJmDCIE1Be3SR7+m6apqIkibeEwKQeLRORIhHTRScwHZVgEWnb3KjBk8AEVBlUo60DROFlDsoYUHSlGDg5ANZBqkKdYG5yig7AGwhqKGLyKXMd2F2bVmpFVCCg6/M83+PPKpn0XnZGpb9HGfa2VZY5JNV6PS+I4FFG0UERNN2HMLxBEJwEFSQlN34M2BRkCQalNfx/wke+ZN3wRyMT6AyXy0GXPfunVO+3l9Y+9nqYEoQQIBIIL9J6hn6eRCHAQjp9sT1j+DYcj1DcoqkpTgGKRi95ev9iXFHTVlRe/8OIb8fmgZ517Vu9bg4tdJzLuBrLHu8D0GlK1Tluj6aQYGHAcMIxU7cDSI1jOAiGg6eRsRCsak8VlkiPH+4p3/Yx4+j123sEfav0zB3/sEl9X4a4pd5W5bDU+Z4XXwRchEFSojKChrz8lxHzSokSAGgGqDOQwkMNSWf3j/yxp6bcFWn+4Jm8aq6fHthOG/043+RrLsiL3xX8omkWGc7JJQIxAyx5Lt5nk7NMfPWkMkNevhj6lTwSCrAdQf3xsT1RskTEcM4Bo7n9ewZ9Bx/5rtxB1QH8YfNDZ8cH2uAeKkOey+olXV912GRh24icBWdn9PrgX/mict8qy/hGkRP761tGiXWNpQcf4ycP/6Hgfk3g19QFm+lhz8iSAQTnaEplVLfR1e7Q6iJKu8KQDfPZvEcn9R/QMlgCEpRgPANYQS+EfPvjB134zJv5ftIxY/8zRf4piyyLKLzfHNvPOCBp6ICinEFCMubc9HpOJzgElAuQwVMJISQw9lyBTK4baLAgA8EF7J2leWOaeO3D4b2AS5EEICQIv8DyEMOAPOBz22GoBnRykO21LS1vtBSt9O7Jt30YpREBr1AERTyYBXNR8kxVg2vcxWsmb3UmgSZr/kt4ypKMk8cnIej4ad1GzMRACivMAB7Zka/1ljYi2ZrD+r+7qVO2VqKzOUz91cl3ZrCmVk+rKbBKfzr7yvrr4u0PRiRuR1eDqfz6c6xO5916DcoRTZF6JalIyhsj15eTb3O3vd6ht1vBfVTV/INTR2RuJkBCQLCu6boTDkWAwHA5HovHrgWHs8849IyEdiEEWS5r3RvN9rLkUcQD5FO4SJSAyHYv28CIrChCCkwfeY2/yiElBFDD4c8AvzmyYWF/luXz5bG/DbEsTE2x5/WldN9q6QoeOd7R1hXQSPhz48lVV/vb2IUaBiiQQ1NhmFRZlfLuq//lw20Wfzum0SJU5NeLS0o49S4dtnqmTW/YV4kz2hOF/R3fAMIxgKEKMDQQup13XieYGz/OGP+Rw2GSF5AV53C6EECKmKM1KwKKzcS59AjxgfHKwRZtdxxtqtDie5nHmKaBKmwObgBNAt5K6Wdv9P/zZ/fd8bagXzUiCTOXiIcN/OPjw/5VN+y4cLPjT2nTC5RDLbcqWbarn4AGq8kEbv5V57DUV7lBE9YcUS/5vwFV44nzAiASCBvkWD/Ylz9UH8ErEpSo1FRVd2T+HkRFstauKH/77AyFD1wyDdCiKRJCm6YbVMpD2jw2FI4IgQICDwZDX63HYbTxvpaOPRUno4QFyPMdHgMabnFXTS5Xccqrest5gwGGTFJHpEIiIbzs2SL8pRmFBeQR/Xtm079xTZmb+aazf1ybwqKLMUVvpdtnFmPWnPzmbyFeVOyt9TodNsFS6ksoCqqryDgQNuzxcCqr/+XD24SCnplT7fF2/+WqRL2oMMEQJSINIypBbwvB//+ETmqoaVraKaWJFUQKBQERWIrISjsgRWent9ff09PoDoZ6eYFNT68mm1mCIdBPEGJ93zrL4KFBLSxscgxrRBae3Zd/uJt0ukRACL0DEkWKuHCuByQ8fcZDjyHzLLnLN4cJIwDKyJ8ljDzbieWXTvtMXNIhWEm+64f/6fW08h8rc9tpKN038T3xVBN0Oqa7KU13usktCkYZZha0NjsZ/rBDQc29szeYpWfqAKq+n53dsekvo7vEfrpwZs+OUaA/yvjd/wN3Yboy7Qkpzb7glEFmnexOG/9g04mUKDIOc2jR062aYRMgMa5qhKFooHAkEw73+YOOJ5sYTzdQNgPwY73pOvc17955USIdInfypcT3Ys4WoSJD/EoXRE71c5/H4NMGcqapg6UM5g1IM/3Ha4f8rm/YtmDnRZRczB39IjNUhTqjxVPmcfJrGDjyPKryOiTXe8jK7VRNQlEnAniOvg4L/cmGBpwL+3389hzOOaw4dbWw80dLsD7cGIh0huTOkdEfUXlkNKFpI0fyxbVWLqLqsGapuaIapG2Z7UN7f1nukK3Ck058w/N93kDRPz/wJWxKUxCsYhqlpejgsBwJhvz904kTLwYNHTz1lUcJSMDzzQs0wVd2g16DqBr2eoKKFVT2i6gFZs65znEt7B9oOEh0fSBr65irfSWU/rTAQPt5j9DbvHcqVMOs/pErgqPWHmcq+XtlEVucqPPb4JqDJrN/XJgpcVbmrrtLtsAnxwZ94IISSxFeUOWRVD4XVnoBMe40WeDEgFAR5gQuaJUp9QMqFgTwSh8Yx4bB8UmlDZsCSiAEchAiRNBMOQQ5C1YrhcPSutYdDiMqKNPvDvTKp5LJNn5cw/M/Vv2PSoZNIHBuGoSiKIPDiwZ2J+aC11Y09Ic0gEsYcQuRgS0RXM0zOimmqhokg0bWvG5MSJznw4Z59py+cZWWBYkBk4rIFm+QpPDAVaIY68qyAae/yV5WP14X24SDbXsnU+p+zdAbPDxL8QQh6XFJthcvlkFBGX4EgFEW+0ucMhBRZ0cPkB5zpx5mTD4gVhe058vq8aZeAXEhclU5+OC+YrR8UbOoG4MNxURe6UEibuZAwTt+2lTJIchHpCEPRowGIhOH/3v1HYNZ55Rib/XJmpFWnjiEsC3YehaAGgLXrNsUXhU2/ctXrf3nMSikChjWSJd7JymXsG9taVzreHQAAYPPOA8tPmcOh3NYAoCX/QD6jg0Otf2TD/7xBWQZ/AACnzZ8qidygwR+nXaipcPs8dto5aJCXh9BpE2sqXGUeu+VaMgWC8ibXQFDmr/FQehUxsqFN8MVC/ZZcMKnhMqwYQ2xbN8lwWzFMzSA9RehHkjz894R7MI009316mV86FsjAmDzLGeiUdSOsGRHNSFaG0EysWtdALsbEhonphZE1DOsijZL5qmz8YJ8kQiHb8SRB4IAooC27DoJhYcOLfwZjnK/c89BX7nmosOdEAzJ/0gd/Fsyc6HZkap5Hh/82ka+pcNdVum2SkOVImeMgWS6ucDlJslCKA/JeDBhKOlBGK89SA0cRQlwfkrkzJ8c/dPLDD+J9QLpoZDLU+ruDiXm5rCgsA+9s20dqPLNGkrgP9gyT9R8fdv/nP/zWz3/4rcKePG6QnubX8cqmfbMa6iq9DtpVNX3wpxUhWOaxT6j2el22aAvWLCCLASJf5XNW+ZwiGUIM8sT8yoOznwTELH/ymkR/gMjKTElAJ/XsjGEFAmDjOfpJ9Q3/SXJhQvSf+gBTT5SATolJqptSWH9PuDf+bktL2xWfvGPof8J4Yt2WHH6b720vSMXfuOUrxbT7SQ7AsrrJw/9XNu2bMqG6ttxFcnUznwght0OqrXCXe+25tvZGCLqcEgkEuW3WCxUsEEQmAX3rwFn7gGjyIU0bpyh96RyYzPHNYFiWVQ0DoOlGdyDcE4gcOdnx9pZD+V0kY8C7b+aQOYMs8QDTctoJw/94IAAVMjHf2NBjt4QXjd1SWn/GqIWE28YXd3zh7mLb/Rh9UzYMptaksP4AgNoKVyyVM8Par9Mh1la5qytcosDlESPhOVTucVRXKGFZ62rtl4RLSf7lwdktCFsDfKwZhiBEPVlYJqNC3TAVVQ+EZFnVN+86Xu51NLZ0t3cFIQQ9wUjEykJhFIR2wVelDd4bkiMdCon5Hxj9b4vFfygIAh4hTyRayeG3k7yRqA/oiwslDPBT4gn3xi8F00nAS396NOc/j1E4QtZvc9xwxxfufvTXPxq2lxskZrdkzmQnSeXMZP3f3Ez8RE8A9LS27NpRrJi53c5HInoePoCkA332hixfpa/wiPxPVnSHjSx7mBg3tvTY7VIwTPxTRNEAxlt2H4MQJlQJrbr546ufeizHv4yRPzyCNPKWYfhPV36VuDqlmCdgjHUwBseauhom1STsb2rvqa8ae3Vhdwyv9e8PASUP/9/YfHj+jIk+t22oZbpZP10Ty1PchP4b76kGQ2bQQFCf/SejfroUTMQDNL3LT0I9dCcN/uRfI8ooUBSIJAJpRubhP205Uqg4AZ0ExO6ylYCRxTTNbr/VzmS8RH6GGT699a/3uaTYWm664f+rO9qQraLS66gjmj+CwybYRN4mCZLASQIn8JzAI1IaY+nFYoxpGYFV5kOJG6OpelNH4Hirv7kj0NIVVFSrwJwe0Pfr5T3Vur8t50nA75+NnwSkCwTR4T+1/kTsWtF0wxR4rq0rEAwrBrby0BmjKQqkmxhBmHn4X2xsNtsIvnqJYxjYUhQeJ9b/0eEd/mcKAU2scsvqIEHt13a0eRzS0lm186dVux0iMfN9tLZ1YA2oGsgpPufkwdwJzrkTiCSsqhlNHYFDJ7q6/JEBCTm+cn93VzF8ADX+sVcKyyp1AHQFmFn/4cH6ElGFeTMqFpPhYASFaQsSh/9bNxf1Cj3BrrVrN55//nJ696OPjl//r5997le/KeqLMlKiWzXb4+PNeXTYrT8Zi6cc/l+4bGZ8Jk+64b9HwvVlvM8OIkF/e3tna1tH7Db0KyNiol5HdbmTKA4OfH2PrzyPE67+/bODHBFVGYv+E1F0Sw8SyONd0WX0gBDiOE4UBbfLmVqSfyDcSA//Y1z/xX8d6UsoRYjA99jPArpj2EP/MVIX69pFMjNwO2wZrP+mfS3lbltdpdvtGKD2XCgghA6Jr/E5yz12IUlRLuYDcisNi/MByYsBCJEWRX1g3TDCEc00TVlh6T3DhNNh97idZR6iIzJ9ekPQN5njRVGUBEHkBZHjBcQLkCM3wPGA4+vOODfhDCc+/BAjfog3ELcRvXHRDWi9ricSXLt2Y+xFP/roOPUB9Npi18kLg9ROMoaINrBCeyxyx8hZ/xQhoDc2H77kzFmDGvQNu5s8TtuEKk9VmSOd3ufQ4Tjkddlqyl3BiNYbTKEWl49GUFwsKCEQRNPJY+iGGVbUsKzRHFDGMLD+lb9de+vnDEMnGVaR4FmnL1TUuTUndxsmKb/QTRLwtbQWLPWFGQsrylyx5+7Zs08+niz8OQAIgEMk3YCpzBz5RpGepsjEWOI5ExMVB4kn0vYGxqRVBSQbCEKB5JuaEAKR44jSA8B2nksIBE2dOnlebRk5mCMHE4k631QQyUePlpENGIOxnohxx0gs/GZyAGcvbhAHC/6s390kCVxVmaOm3OUgav7FkkZ47B8bsjyyID6gz/5HteBME8uy1h0Ia0RobBCBOEYGTv3cX7N9fx6/HWOTaAhaum+HDh2eP29OePZpVUc+wJijImv0vwcrpidYf+uTG+QzEjhU57a5JEGwREQNjIknsBpAUjdgYixYvU1MjHkiZkXKjKjgqGl5AgQhEUAmJQiw3gNWx/kAAMDsa28M/PMFmvJw0DfNBSFgnZ6LBsZ4rIdnm5qa3/jHX0aLA3hj8+HLz54TM+iynLpLrcBz5R6Hpd5D+3nlw5SJiam7yXzzc9fuP965ccfx1q5QwvCcXLoN6P48uwcnrAlT4pZ5o+Y+rGiYiFAy8Z8hsfV3t2Z9LFr9f39Y9S+fidVn7d6zb/68OR3TTqk++mHfRwN2eacmW3/l+EcQAJEn30gTx1yF1W6274OFVslhpVMSOY5me/Wpi9JvQOwV4kS/49S/k3YMgE4C3BddJa99aa+3AQIQ8A9eXMbIG00zSFEOoyAO4I3Nh1ecOp12Qs3Ahj3NFR77pGpPudee3Oore46daM3mMI8ELj9tUkTVTrb5T7YHugIRVTMABmGDTFN4T1XMB+RaHhzzAWmyQokihaoZmi5nf05GQYj6gD5DS31AW8PSPh+QAmr9K5xSpdOGrd6QRKPfkucMKppfJrodVK+pO6xUOW0iCVwmfdVhqu2MR31sqvdvSYGgvd4GUKrWP6fZ3hBfKyyrfbPzMcnF1945ihzA4tkTnDYh9m6mHP6v39Vkk/jqcmd1OfkNDc8bDyGwCXy1zwUANEyz0x8xDezgjKFkhSaQrjJAt3oHDunqGUPxAX3E+4Dk4b9y/COygCzxs6q8TpGnMSI6CTAwbuoNh1Rdt2YBJgYBWeuOKE7iAQrz/U32AUuXzn173bugJMlptjeUF8IYhGWN9F0oWgh6GBjZ+E//Z/DG5sNVZc5Y/5Z0wR8i3eyy1fhI6D8+67/oV4mgyy7UlDurfS67mNhDOL+s0EGTgugyALP/I8Xq//tD/N3dVpwn2frTTwgBMMnr9NpEgUMihySeswucQ+SdouC1k52xpyiG0RtRiy3det6KM4t6foZpmoqms4W5IRL9YSxf0iD1CZ+ls/4b9zRbep8ub1yF8LDBcchpIz6gwkt7CCcA8+wePGhxAGPU+IC2ru5k6682kswfp8TXehzJYxIEgUPg3WS8Et1jYhBS9aBKKjwKxcemepOzQgEAf33kfwv2GozkGuC+NnCMITmANzYfLnPZ6EwqnfVfv7vJLgn1le7ackvvcyTgOORxSjXlTlJ1PNADeXw+5gPGtw+oqq1JXPjFgFp/0qraabOlESG3C1yF0ybGTQJCqt4VUrSCTgIWL16S4APYJKCo6IYxpkUgLh4FCwDEAbyx+fBlZ8/ObP2pXHOF115f6XYNlHwYZkSBq/Q6q8qckphcHtzvA3KFzQPG0DwghnEyav0FBGtc9nTfSh4hn110S2Lscc0wu8JKSCnYJICu+ibz20ezXhFl5Iimjx8RiBGEP/+0GaSpXkbe2dtc5rLVlpPgz5MvFn91K6+fZUgxYhmceawGMx8wmkke/kcU2Mn7qlSy/l/pspXZxUx9qkXeaxe7iKJfVOsjqGpdEcUl8clF5rkSy/mZ1tCwdu3GFecvh3EZQUM8OSMdRKhrjItAvDHSK8DEAbj6fjnphv9rLbn/tmBvW0trvwyuhWLPpVEXBE6b6HSItNlWRNFkMgQr2EfIO4De3TIUH8AYnXz63++tIDlgUfbsJl/Izs4ODIU2oXyi2TO5zJU5pYdHyCXxNoEL9dUNqbrZFVIqHFIGz5ENCRmf0xoa1q3dePqyU0WRZEkcOXJs9lDOzshQA6zSVkCMQqSBZgj+pLDykHRiEniuTOTtNsHlED1OSTfMbn8EYyAInMgjUeAlkRMFXtX0rt4IgEDkOVHgJJGXBF4UuYisdfVGEILRnSInCbwgcMGQ0hOQeQ6JYvRgSeR4DvUGZU0zdcNs6QycbPOTEvCkT5/31cb7gKG9M4zRa/3bO/xW4x4VciKYOKvMFsh8EgiBRxK8NiGi0XRQ8uUIKFpA0Ty2/PPZkvP96apvbf1Eh8PG85zdJuV3ZkZmTNNUdZYCVCAHkMH6v7q70+OS5jRUL55VV+lztg7stZ01KVvz2MCkRCFSgpcHgMhBJ+Bz2OnG7Imu7kDZ4cbOk629imokGvqqSn871SKFL67ffeW58/O6YMZotP4UWTbUPqFyDhhTJtV1emZmqBGj2AW+3GHriajhPvkw1TACiqYZppRjC+vM1p90iWk6ITm9CCGbJC5jcnBFwDCxpo/hBYDFZ69sb4v2NRlZUAbrv35fm0fCEyukCeWiKgfztf4FBiFIu897XTarnDO5g3xlbPvF9buH/QIZxWLP7n1rnvzz66sfo3N/ouzmsJ1oPAERap9+auyW8rkIQZ9DdEtC7OuCMeiNqAFFyz6QcGLiwtgtnfUHALz75hol1Kuqmj8QGsKfy0gL0QQcyzmg2995GYwO0CDlVw6xtoIIPnN9NWKjAYFH5V57baXb67ZZi3iZpvDMB4yb4M+aJ/9Mt7dteMFKC+acDjuAYOfO3Tt39nv6lM4AWpMAl0RV4EDfUrDeFVbVjNmECUafcuTwkfgRXHK+/7tvrjGUoNeTYi7LGDqapo/pHNDRQ9qOYBv2tzntxPpXlDmGovlTDCCEdkmoqXDTov+eQKJStKc/EFQsDF3jeCGnpyy68HZD101dBRDs3fhcQS7jtu/8QZHDpmnqqvz8r78NxnHof+BAfduGF5ZfepMg8KZBVJoBhPE+YOHCaOgv5gOqDm/lIPTaRLvABRWr26g1kOwKKxVEG06Kn0nG2/oYRw4fSdgjy4rNJmWo9po6ue5987umYZKsB9VUFPW5P/88x3eCkQQGIVkz4n7yz7yx48aLF7F3qmAOYP2+Nknkaipc9dUeh62Igs/xPLHRbxJhD4Q4ZOo6hziiqmuQCBVZ8YOQ5wWEOHItlkwYEfsybRxyojIg8oIgSlZLR0j6BELommBi0wSQWOpNzUokElYV2To7heN4PhgMt7S2QXJOoj9Mngyt+QQmT7TUgk3T0K0oE7k/Z4Jj0L9i2mlXYqogQaSFyQbZ0nVPZb2mKu2NB8LhkA45sWomhpyhqxACXpCs18RKKEj6INJVS0vLEkLO5nLZbTbSRtkSt4EQmaZBFO153uHyrl3zOH05bRQLo9/1OBqy9QdrnkrMmdv42tOWbihd1SXvF+0mCQCZEyQ4A+oJTE03Dx9Hh3f154MqWndYcUuCyKFku59s9BPIYP3ffXMNAGDFiuX04jDGvcG04VZG9uiGGQwr8Yt/qjwm+8IvPnvliMeC+LRZE05bXZWnzG0vlGbWoNy23JNqd/pJNCZfhbbu4KHjHW2dIZlMCROjufHzgIELwiYg7Yp5MK8+tuud/Z19m2RVkOMS35xoBklGjmx5MeV+37ILsYkrK8qadZUzgSAJEPGGwQMILQtuAgAFaItriWuZe6sniakRRVKyz/KQxKBAaBoooIQVRTVNkyf+b5y0Jv/0v9+bLMG55qlo8CdDjdg1H/s0+YdmFlsjBjjQGSxcOF8Q+GlTJ+7XtJbm9lgKcrdm77FPqKksh1nb/eee+F02fwv1Aeefv5x+quVe+5e+/f1fPvjdbJ7LSIei6qo6eApQU3tPfVXK9BNGRgewfl+bXeKrK1wVXgdpDjNqtfYgEafzue01Fe5QRPMH5dGcFrzr/TeLd/JxNrCsjBv+744L/Wfm+b893O8J6Jw16gwsh2otFVg7gaFpXq+rp4ckj2KMg8Fw44mWrvY222BZm1na/QzMnzPtW9//r4e++3UwTsl1tpcHsqqNgxrg7e+8vPjslaPOAazf1yYKXEWZs6bClSy3MNqAEEoiX+Vz+oOKrOgKKfNJuxjAskJHP5/+93sTrH8eJ4l5gv5pgWX3rf9ZUygIlXAAYpX4BuvBSMhvEzwptYWHaPRjkwB699ixxoapk77301/+v//40lBOW7JgDBSNBvAYhXYA6/e1cgj5PPYJNR6vyzb8kp/5Z4VWuoJhpaMnZJDcMOYDxon1xxi/kBT6H5oziFYIciSySVZV6EOke4z11SnUSD+DD/joo0YmEZE3polVrZAKAiXOwBkAhHabUFflqR45yc884HlU7nFUVyghWQ2FVfbdGDdj/yFa/3TO4MqbPsmRxRUYsyMY4zeefwIUjXffXBPfOpjqxH3+jux7pzCimKZpdQHLhw0v/vmcKz8xqt7KxSO9DtwfsFu/v80m8pU+EvwZtsyfggAhtNn46nKyaCGQqs6RLA1bdfPHi3r+cQld+E0gy9B/Hrz49J/Wvfzk1ref/3DDC/T2wdtkkF5U1q7daBimBT56lOiYMq3QfEvA+hYAotl5Y5XtI50C1O8A1u9rtQSfHROqPS6HOIasP4WDpFVAbaXb45IEPkVpGCsPHpcLv2OId99cs379praOns6u3u6ewLbte1gcIw90q6hirJv+URcCIonzDnFCjaeyzEn0FcYUjz311khfAqPAof9x+Ya+++Yap6ccIvI/jkOBsPo/f/jzVz8zuoISoxdrxUbTjXG1BGyoi8+4aPt7/xxJB/CmJfjc7gftJ5vAOCZOIbR4GUGxvsqMPEP/Q174Hc2E/F0eX6W16IDb2zteeu2db3zvJz/5f98Y6esaM2i6gcd4G4B4tr/3z8VnXDSCPoCn1n90ojsm0g2eQ3abWOa215S7J9aUlbmjyqCUSZXRYjEMsGGYPcHIzoNNOw829QQi8WNJzhJuMAKtRfIBCKHaSs+UuvK//62AZy0h609JrvgdT7z75pozL7x60ZJFCELDIIsCO3YfGumLGjOQFtCaMX7Mf5wPACMEb9iqJ1aXLZ49YcakKpdDIgUzYxYIIM9xlV7XeafOmDet9sN9J/YcaQmE5fi8UM5dUyQfYJeE80+bWV+Vsp6ZMTjpQv/3PfTzB771lXHzDr775poV5y+3vpNkfLJk8byqyrLHf/3QSF/XWMkBTVKAHy8+YPtITAJQmdu+YGb93IZa9xi3/vFwENWUey4+c/bNly5dMnuiY2DXJ85dU4wXNUwTWa1yinHyccnD//2DSESO3a677so//u3phGPue4iop335W/eBMcXX7/vB1+/7wSNJfw4A4PPfuD8i6whRWSrYdLJ5+RlMyCwrdMNU+lpBjDO2j9A8gF+2YOqMSVWk6Ldo1n/f4UYwQmCAJ1Y4TM3b2NIdlkmJAK39EbyVcm9HYScBponHQYX6MPPzB+//yrfvp9vbt+9evHj+d374k9ijPE88d3dXTKNpzGBpEYG9e/Y/8renDx/+KP6hYEhx2qM6shDCtW+u2/TWSyN0mWMJDEBYVvMuAhj9bB+JeQDPA725tb2ldZyM/ROAANokocrnCkVUE2NZ1UFfI1FboX2AKHACzyna+ByhFNsHUBmGdzZtra6uiD0Ub/q//K37fvHQA2AscNb5l1M5VwjBnt37pYESQzHrz0x/TmBMHMA4WgBOzTD7AGQjPZJG3vpbA/PYZzvgQyYJE1RPN2E/fWTAE+Me6tvJIeR22mor3ZVlpEZgQAexgv7ddom3S7l1CGBQfv7g/R9s+dA0zW5LoC2ZXzz0wFix/gCATWtf+a8H7n1n/QYiXKOoKY9h1j9XDNNM0QJ2fLHdMv3DGQvi8+6IXVgGOqEBl5SuKo08Jc21Jzwg8lyF1wkh1HS9vZvkXlD3YPNUyv4OgPOYBCS+MASgqtxtRdIY+bDhjedJvV5FdfxOjMEvfzxm7H4Cm9a+smntK//69cQi57VvriOPsrBPjhiGqcfpNY1vH3DqWRdt3TQc84CSWLEkWhGiUO5xVvncDruY6FCsu7sau7M/X/IuSeSn1leMCfm8UcuGN573dw7olD12rX+M3/zXDxL2bHrrJWb988AS7BvPw/94Ytb/1LOKOxsoCQcQE46uLHMSvSCBj00R7N4q62Fw7KMsi+BSmHgOwbnTaqfWlxf0kkt3HsBgJKPrpAVgqb0zW+M8QTGcQQmFLBCETrtU7XMHw2qXEdLCQRIFstsi9OGsxu6JB3EIuogYteeUuZPGkH7qWOFXf/mrJCbN2LIj+x6ZqqphModLXL8hjTZ13eorRv7f2tbV2t7d6w8GAuGOzh6ajgIBIK1LAREWpUtSjCKhkdBt6b6/W4sTESohB0CFo8s8jqqw0trSQq0/iTuX+fw92cd/+oEQ1FV5T503qbLM6XYM0kyKkQ3X3vbZPXsOxu7Gb8c4e/mZGc7gsOf+QUD4wbb9u/YcHtS6xC9Hwb6Cj+rqipUXnxVbSzt08EDCs+RIdIzBGAqaNq5EIEYJpeUAIIAvv7WZfJkEr8iBiIkhADaUOk9jUKp8rrMWTa2vLhsd6+jjgX888ftrb/ts5mPe2fhuhkcn1A9YRs4SOdhr4438BvBYDR05lMJRMQqvAsTe00JTQg7gmZc3kvHjkoYZM2a0dPhPtPb0BCJtXX6A85kE2CT+nKXTk63/ezuPF/i6S4w4K4xpaKVv3N1/N66RC5mHmSaO/hfBk02tE+pzrvQeSvQmJv8XzUmGg6SoMfJDI20AmAsYCw7g7c2Zmq60tnfddMW5YCSs/9c/ez29W+5xNkyoDEaUHQdOHLQKNT1lvuzPxiE4uc43qYaN/QtPXJiFbFj3YNKj5F9q/a3+jsQlcJwVrs/L7mp6Yjfp7ImlftHW84wiMY5rgEcQ/umXNtx0xTkFOdexk9Ecvin1JLXmWFN7/KOt7f1D7KdfWp/w3OK5BGr6r7l8xazJ/SWmPI/cvOR2Sjv35/OtmlhT5rJLkCV9FoHe7p5wJDzoYXX19RBacwJA4nhx3gKcbGrLKRCEMdaHYFxiHmv3rr2DHvzqP/4v7xcqcfJwAE3tPfVVZcW5nHE0A3j6pQ2WCT5n6KY/ncWnfOnOK8kgDcL//VNitl+ySxi6V0hp+uM52tjiHKgTlw0kl7QsKkCdTAnnKRSGt1566oIrbh7UBzQ3ZUrbVbJwITFMjEOhCM6uyCihHB0AEOjt3r3Ln/3LMfKDNAJjFBp4fOtqAMCPfv10Hj7g6Zc2pps4f/kTV1mmHpDULYxNE1tduKMpdTTkapKsLowgRGm8Ar3C+DvZX+EzL2/MbPrpRkd36L3t+3WFKBBIrv7WwVZgITE+ppvAbhNmTamikp8XnDEn+czv7TyOA63f/tFvs7xORkqy8QEZKPflMO7TdCMcHtA6InsHQHKLHfasWqhC8Bob/g+BVTfcnPp9heTHeOtVqXPDEmYAo60p/IgTtXF3f+GmH/366WymApbRT+yz9W+fujqlpAQmvVwS95N1PCtuakIinxmL8f7bp66hqdSGEfUWv/zzC0mvTq4wgYQLTgj3pzP90auHgFr/bEAITqopy0bw+cG7P898wFAwefuixdP7UvCjK8MQQhPTgE90pkX2k+VfE5GHgKZpGGNJEnmOq6mpiO8CF7+RQOPJtv0Hj2fjAOgiQzxlXvd5Zy/JpgL8m1/6fJZ/O4MxbPQPcu/+wk10KpDgBpIsfpQv3HGFZaZT/CpipNwfO9ySRI8WT9GfH0JIN0wrtkue+2+fvJqs9ZEiQJIBBgH89aMvJp8w3itACCdOmvixlcsGNf2x4xUpOksYNIG8tsLtYvn+w0V8c83+70zfRuxfqvdH/mt1josdOWApeeBGAry1fJxf8I58M8kT2eIvY0zCH/qoacbU+th9OhUAADz1wjqIEmMgX/nUVboV06G/QFIGScM6QyZ2EtKS3upKHxuRRdM+rFf80ieuQpbLMU1sxZbIby/qFTD+yl3XGCZe/eb2nfuPL5w9ObPpz4PqCndsO2X8Jx42CRgKc2ZEu4Hm9y2qtYb/WcJRB5AFcTmpUUhbR8PkrG9sZn78y9+ySQBjtEFM/KE+GRzqCW6++oK6Ktf/PPwPuvOLd14Bo2F6clfgh0nwIP6XJgp8Yr42JPkfJkkExF/6RHRtmR4wf1ptTqbf7RBDYTWbBaZclVOZDxgTcBzpzAXyygOifX0Lf02MHHnmjR03Xswaq+XMgDE+9QSIEzEGX/7UKoNEYwZOoke65jU23+AgBCh69SYpEI+Ghp9/awd1AOve3zO5Lit1tpk1juMtmkxazTE1/1HBimvuHG4HkO8X2zCxUQIaxaMfVc4/ZaCUSTF17ejqQQjyHJJEXhR4gefoEGnErX86aHtVOgmoq3B19Qa6eonQW5bUVLgqvHZbgaT8cV/HecqDd7Olv9EO/X7n91xsmkOpIWAUjlFqnUY5qb/3O/aNWBffQkAX9LJd0ytz2+qrPBlS+4cI8wGjHDqAyO+5JsbZy47++JcsObhY5PsBljrDpAX00z8lJnSW28VPfuzSYr1eLpMVm8hXlzshBM0dbBY5hleA8wYhhLJYxU1H9g6AUTz44VqbHGcUxQH896Mva6rKcZk+kq6IGu8Vyq2K3CK6hPQgCB02ocpXrBkAWw0ezQsA1AFkU9uRDlXTCno5jHxw5SEDziiUA0gY4KOMpj8lXRE1/jxlUvTC7rr18mH4mBCEg/ZzXzR7SMNSlhE0akEQZpPHmQ6FpA8wRgZaBgwA8LpIb4+EpEFSqBG3RM/nbpfGPXxhAzsDiGoj9hWLWXcNK38/vpgzJT2KblrZdY/89ZXhcQMcU3YrVSCCmWermVEV5gBGHpdDirf7KSmFnvLD5AB+8sjqWBFvOuySQHR+yAob5MiN+GpZ1Ul2KYKGYcrK4MHTHusY6gaGbUJQJNgkYBQuANAZAM8P0wyAlYMVB9Lxu0inHt/whRr19w3zyUgfQWQiWOVzcAjxHOK46H8xxsGwapgmQlBW9OaOQFSjBWflBoZzQlAkmA8YjUBI1CDybQigaXqsMwFjREBwQNiHkT18AQI+xOITDywJJJ+a4yBR7sF6XaWHjv3JJMAqJsYYOO0iraeXFd0fVAwTx2S+iTiope1FwINPCMauG2CMnhVgmkDO89n+EJLVIKwsICYHNJIghGY19OvZMLKHz9X0Q0jkehTVEHhi16mYM88jr8vmcogcRxIqgmHN0HWPS6I/lf7/Yyz0zbVtEl9b6VZ1I2wt/2IAZCvuDyHUDVM3TEAL7FMNzKgbePjZDVAJDt0NPP73aCsCyUP62BQbNgkoNjkJAREgSSKM7zSZE5qumybpR5bHcxlZcvPtn1Iiaas7iYAYo7AOICyrCMJf/fXV2B4azec55HKIXT2mz2On5p7joChwXpfNYRPpYB+ASI+fZFcnntTyFtYWFhFXX+1RNT0i6wiRxeFASCFtAyCMKFpY0Xr9EY4jKwdEbpF2W00CSy5V01XNME2s6sYTz79z+oKpYNTDfMCowpoBcCmHGtlgqQFlpQdHYcsABWcoSzglTloHQNU3o9uQTLLsEm+TBEngfF67rGj11R5q/RFCPCIhIJ4nSp20KGMwcRXyqNshGqbgchDFIdPEHqdER2ARRQtFtEBQtkuCTRIVVVc1IkJqaf6k6RNkCcHl9xYY9grDOnOGRGKWIzReV4Dj5YDMvL5ChmEapslkpIpKhuF/Qt5nPGVue9GuaNyHgGh/bSu+b5N4m8h73XaPUxJ4zuUQO7tD5V4HVWamPTvyUAqCZPENoupTKsocAIATjSfofhEALwD1M8g27SamabqiapGIcnjfh7pukulA7KdKrgCbBo5vyO2wVPs7ekMpX7dhQjTUEwiS0t+7P3FeMKLKiv7XFz9Id6kFzzFgk4BRsgAwoCVAXhAHwLoVjih5C3ldfV20y9inb8zUfXbVbV8GJTgDkFXNYRMEnvO57U6H6HZIDptA1nU5aBN5MWPt9WkXrIoEupL3T5o0If4uXfONmf4Ul0GKdCBCoijwdptYdsa5uz54R9UMskhgIZCWY5xdgmp8RT5dk0v+byqaWzrAYIhFSDJjPmDUKULnhTm0nvKMoeNysjLgPElr1ySRxxhX+1ySyJd5bHaJyIJqzmkmADoAU+Y1yOlP6raDlNaftN9rPBnbnjRpQgbTHw+ZYXBIJImkoLrcFYqokb7qm5TBGTI9sKYIkE4V6HSB7snrZy6wGsJxLwjaV1OalyAokwMaMSCEPjcJITDyINOXPiKrdVXu6gqnxylt2i9rzmnJxwTDJIcnP+KdQTyiLe3HCSGorXTXVbkrM4p3WiGpviAWveVr+im2PmmKzAXMucKEQkcJJGMhbzkgSKoac3oGkwUtIBwiWl6FPGMpwacLlG/fTxShHY4yMskF4Jz5DhBK0V3LCwBIfYL8MUKkv2s67Hav3Q4qfKAh7jopK5bNTuuTBna2odC//WhrhsmM9SwIvK641aSC5vuxQFAMhI22tsHDcYMSSzXOHllRTUODOLtIzoARALTbpUg43NrCBCFGBo7jmMhP3oxQ+lRcTCZF5kXcHjrcxoPu6TuVYeDmjt58LiM9PM+5nf1SUyzfe/yBLYmqfJ4JQXm5RxRzG4H+9Af35fNajFQIfP7rN4wBDiDhJ0CS72N7qJGNPwCnuqUhvsSG5vRHM/v7tskGfbnYo32vHvtvzNCn2NN3qs7eUCCkpL2MpLv9F5P+4gUOiUJWYmHv7TwOcocFgkYc0ypDz+OJPMeVl3mY/RlBsvxtAgDOv+auIl/L2IOnyfWxHLiEu7QGK9ZvPT6UAq38h/gj+wVAY3f74+7QNKMHJ8TiYfqNlPS9QP9xseMVRW/tDGT4JSecua8oLXW9cQyr3q24QwwWCAIAvLXm8etu/xwYCSwVktwdAIQet9NmI60sGCPFWFQBen3NExiAifPPObjp+ZG9Ej6+1S+xhAPToQfcS8qURukeTbDzVovG2MGFUc5KOoNp4pZO/4Bk0CRS54LSXekvidS1ZbdCiDHY1di9YJIvm4MZowermDdnB4AgrK4sy3/1mFEIbIN18igSr695IrY9Yf6KpMcT15PibX3YT5IkR9z6UwfQfwdmsJeDWe3Ug+tUd4s0lu4JRLr9RenpKBIHkMNV5+cD2CSAfIi9oTJvEVuzZVoEtvpP5ATPcy6Xg4WfRxa7JNTlqv6UI6uf+EXyTmrEKQc3/R2MTTLOngbMBcBoxhKX9g86iMvnj4Ck10T8XCeD8OMQZzbMB4wIhmkGrZrwXOE5Uj+Q67PYCvBIlQFffe2Nn76pf6je3h1c/c8PC3UZyTKxY4JRMHuNpeLkm19vmri10y9n0Zgpj1fgEExQFIEAXHDGnMzP2tXYnftLMcgywPC/C7KshiODpAKnxFoaGnu/+fEEhNCWYwpWkYBj0PoXqyl89kQX3kgSDtkaTEIuNWFZDctZZWHncXae43IqMzl2rGnKlPylydkk4O+P/+6MFUOS+G5qPAYGa1eXP3Frxb1KcOPGtpye/d7al4pwTaWLKHAjtQYwPhjJGQDGWFY0RdWJ/rOs5rEKR0+i6WbxsnRoL7MsD37h5TeHPglgWaHvrXvFZhuajqM5GsV5mPUvOHZJELJOA2UUfQaAYFbKECbGR092tXQEE6IyBu8uc9vPXjJ5cl1Z1IzuHiSzHmOrHUR2U4c8+jYJPJdru4khTgIYAIB1r5JVtRWXXSfLkTzfENMo4jwgR5jpLxI2UcjpF/3w0+vilwEYgziABfMnp9hrGW1NN3oCciCsQDVeqrs/Jzoip3UGG3ZbC+i8izwBk1+43SbMm169dffJsD/yxtvk0fpqz+wplYN+QtE2xNmRxzTB6RBpO/s8fEDeKaEsEERZ9+rfR5cPyKtYjFn/4iHFiXQlwJoBDNUBIKju2XMIFA67TXxr8xEAwGWnE105uk2JyJqiDpi2FyOsHy0Yznq9HkLgczuydzCe8gp/V2f284AX3t551XkLUz7EfEC8D7CWaiNjbh7ATH+xsbMFgKHBVyalXXf1kpS4ibW+E60p1N+GCIcgbb8Vj2ka1eWuvUfaDIOUcXEc73WJ0yeWYQBEbkB6hmr0a/LkDCYV/6GI2uUPVyT81WliQwghh1Xn2dQRyOYVtGBi3D/zJCCd9Wckh4MAAHkuDo+QD2DWv/iMlhSg8TkD4JCpaKQVMADgUGN3e5cfALB86bQDH7V6XTay9oJBfU3Z5l3HF0yvsdulY01dpF0MguWetHrOqqbV15St23LYaSeG1TB0jiPXUO6xd/b223qOQwZp8pVIzB90++W9H/k/ff3y6AMQhiLqw0+vS9cTGGOgaLo/KHf7wyFZO3vxQGnrNEN8DsFcl5jsIoqo0ZIiNgkoLO+te6XAZ2SMZSDp1DRalnnGKINEt60aWIK9L9Z2oq3XYRO9brtdEpwOYsQ9TolqAHmcEu0aFlE0snIKTXrDxO73KzQEQorAc/FNFsk8Dsa3eaSl+VlUZlqK/4OEgSyht2BYae0ItHb6g2GlrStoWCev9DoRgEeaups6A12BFBEG0gM5l15gmr1Ks1fx3prYnmPHmjKnA1113sIX3t6Z7lGWEcRgpANCKPJjTwhoLDkAapKhtRZK9zS3B6rKo/ETou8GSIcWWg4jiURaDkKIEIzPnIFWLCV2V1b0uiq3190fzHHYhG7/APtrGGZ2kffBPAAGumn2+MNN7b1d/pCqkWUGE+Omtn7J6FsvX3T9BfOuWD4r+dkep03I2PkyAQen05unvL82/dixpuzPwGAwsoTYmax7P8SkQB9+eh17h2Ok9Z+1VWXN7U0xyxrrwGUYpiTwtC07dQ9SnxofgtBu46nWW0JJV7w/ME1cWeYMRfpzhEKyqhHT3H8Mz6GcLG8KrMtTVL03GOnoJq8Q/+D+Y22TaqOh+Umn9sWR/ro+WQQip0VmhIjoafKC8Isbdl95zvzMkwC2Gsxg5ASHUK4p2uOYVXl1rudzEd2GAo+ofSND/lRV8KT9YhYmkx+osB+RdQBwfL2V0y5yXC7ic6kG/qGI2u0P+4OyltSzOxCSE1xCylfIXmqcIgqcrPQHu+J9QGZuuOyMZ199j/kABiNroE0ivWCKrQQ3Jux43mTrACAZ6XOiyIeJsS4AAxdXE/OC8qru6z+JbA38u/2RCFF5TJG7jQE4cqJzUrUHALDlpX8AAE674tqEYxCCsclNlnhdNk0PGUaKV8w8CVAiRdExZTDGKyQubc9tgr7mH89cfe2NYDza8cL3BH5/5xEAbPGVvYjLufl1Brhiaie1dPr9IdkKK6Xl6MmoA6BQNxCPyPP2HJtN15S7wrIWDCuxgqHsA0FsEsBgZA/HoVhmSrFZNUaseR7k8A6qqmFijLJOqT7YxtNGqzNr8BAreHOl2x8eVFlIydg6htYA55plXO1z+kOyoup0tTmnQBCdBGRYDGAwGDE4DuXRC37NP56J/RcAcPV1N5f4W0rC7vEdcRO24zHS902Nz+CMYRJ/kanZbgb7T5JIrTC/athS3pyOstPmT9627/i2vcc3fHDoqVe3/u3lLS6XY+9HbXs/astPVy4Bh03Mtdm0xylV+1wZqhNf3LA7w9NvuOwM6gNSPspSQhmMGDzKQaWRkQ7U12C931ZjjDO0yOY4JKuJY+dAKIXsj90mklD+QBManx2UYQU/ZkMT+sIXllc37du85/gzbx187u0jf19/dOCDqRcAMl+DwHM+j93tTPQc8VmhGXzAoCsBzAcwGBSeL3qn7lLAagnZ1/adro72N+9N9QSHTWhq6502sd+iaYZxorW3usKdcGSgu01WNN0wQU1/YZQo8poePbEk8hFZSX1Zfem9Axpx0X7C+XeOyUSCx4MQ2FJGGDOnJkHgkIRyj6PbL4dkNf5CswwE0ZRQFghiMDICeY6DOTqAW267g9T9XHH6uI/sZ49l49I07IWpDC2CsK07FO8AQmE1YAW+582bES8eN7chKukcj9dl6+iJ1nwJ6Ys4NM2IDsCtC6IuKXZtAz72YngD6xVdDinF/sGeyPOosszRHYiomq7pqdfMM68GU9L5ACYSx2BAK1Ewv/5RMR7+5YNZtoRM7gk8bpxHRjVQ1B/Yicjq8iUpZHYislrmtp+xcDI2zXjrn6wFPW/ejKNHji+cWWcKrnXv7JhY4108q+6tzcQZcBy/cGbtzoMtVBeIaKgdbj/v1AZQaCCEksgLPE+G5xnh+mTgcoVD0OOUastdobDaE5CtVfDcMoIyi0MwH8BgQATFjIWiyVrQN37sdsMwC7VssDpVm/gsGVXOY/iUNPbsOWSnJlUlypqdveGZoH8aoSbVahW82SrHIZdd8nns5WWubftPZjgSQsilWWLKpqWMJPCVZU5/SIkopNlZ/ENZBoLOO2Ph2++xQBCDkRoEiQgEHJs9e1cPwXkU3H8M4gAwwLGC33TbcQf378pwMP0kYhWzHCLrvYeO9dCNGHsPdzXUVxTEDdCBv9dl87kddpswqda3/UBT2mVu63iqgZfioYwv9MTqjZmvJBTp9wcZJgGevuATCwQxGGlyQFFNpTf7NwdCIkJT+HHlqPQf2TuJTA7AxGTAHm8msaXk0x2ItLT7qZQCQtBKH8ICz3mctnIvsbCqRpqFdfvDSlK+EIXa+re3NiYY/XgKlfMTG/i7LWU3CIHAceUeR2eaCjj6XSEOIMcXem/ncYVP8Y2kjoT6G94N9EBHNj7gygtOffGtrRleji0GMEqEm2//lBKJbzuYs1bY+dfcdfOtHx/7xr8ATiLBN+QWAsIYqJrRG4jQKiqOQxxCum5JH1hvLsZYUfVwhDR5T1bgyQmHTRy6v7ZJQpnLXuYh4tXxJn1OQ83Gbf39yBBEJo4t2EIywcwrVvilG5cZJg6ElcZW/64jbd3+CMmnHejKeHdlvA9IBzSiqxQsI4jBSEAS+Tx+oSVj/3PwDbm9iaaJAyE5FFEwKQlGNlGwGrJHHyXqm5rRG5T9ITkia0OpxnLYxIoyZ/aD8OR4DoTQ5ZBqK9xV5a5oSRdpORClyueyWUL/EEKbJNRX94/cISSOLY+IIcYgHAwo4aAItKlVtuVzq+ZMcJXZgIM3HFzfTcAOAXvKfNmUBVx36TK6wUrDGIyEnzb9heaoBAdjOaCMnB0Aaaqlar1EXJOI9dsk3mET+iueMFGKDoYVf0ge4vAfIVhb6RGy8/AYAN0wEmJNPIe8Llt9ldfncYhW2IcSM+oIwumTqjgOeZy22grPgum1sedCa/ifwQFccMaclPv7n8FxnMB7PI7aaq/HKfXPPGhThKRGN+l8gBbXBZf5AEbJkhD/EXnOLgl5DOdHzTLw2HQApmn6Q0pIVjHGPIfcDkkcWCuraHowosiKPsTgT5nL7nHZspmwmSaWFa2rN5zwipU+V12V1+WQ6MA/Rvw0oaG+vLLMVVflocsDzr6kTw7B+PY1OdOnTyLwXIVLrPY5iWSV1RNHC3aTW7hXC/faxaxegopDMBiMGKKQT/zH6lPCPEAifA7xDVnrDUZ03YAQOu2iyyHFS56ZJjZM0sgRDAGeQ5Io1FR6BtUKxXTCEVG6e8O9Sd0cq30uskaU8RyiwFeXk8PoS82dVvve9kMkATTf+pJ//nMtyJd0q8Hx4hAsI4jBsLqScLkO0W659Y6UemUMvtIbbfUVo6uXGJ1FcyYBAHYfPBDbX+4B5R6in3yyPdLT2ysJ3qgDsN7Y5NXOXEEQOh1SdbmbtghOCyavpfQr/mvJr5vO+ifsI7Uk1q5Dx1sbJla9t91KALVuebiAB/7jY9QR0qvpCUQ27zn54f7mQEhJ+b7wNqD72wf1AVQmmm4zH8AocWibplxVGq1ERdKttmjXNVbJpxCMNmux/IE0pTaFXkJzR7CxqXPpvIkZTnL4ePu0SZWpHjEBiI7oj57onDxhYoqBf1jp9ocDYaI0lPrsaT7oxLSiuDucNaYg1j9fhamDhxqtE3HAiF7V5WfNvHjZ9MbW3g3bjh060RU/YaLwnqp4H5ASJRIuq6zu6WjL76oYjPEERCRrI9efKEyta8PIMQto+BE5Of4m8bJDUqt9cGK1qGr61HrfXdcv/7R1+9jK04LB8Nyp1XOnVqc7W8L3ptLr7M9hirYoIP3rhzRS6LP+1CUcPXoy4vdP8gmTfIJbMB1IT7hlkxF0UZwqBlsNZpQyZAXYlrMDsJSOmQtIAVrz7rEX3j/54uZM0gj5UOTJVkxI7p/v7hniSzZUCg2VghHuoglIyRqzTR1EuyJveA753PaacpfTLgzqWVL6ACUSnjG9X4gps1IQgzF+gXabkFMJWD/M/qeC/9mPaWEYphmVJAJjGlpLF8ZYFPiZs6YDw9y2/8TGbUcDYdnrsq84dca1M+tVWX3lnX0f7DsRCMkAAF1LbLDOC5Ub9soQguVzbCAvunoTl3bj4RCaWt8/ds7S+GeuLIMQEI3ZQgcKibK0yFdZ6kCKqiuakfBd9JT5/D3dmU+yeEbNocMfZT6G1QYzxjcQArsk5pGk8dRfH4vJQTNSrgFAur5CNhEQXJGotTTNppbu7QeagmFFEoXZU6qnTaggWgoaqvA6fG677CdFrcLASBKppEWGYZiGYb63K+3wuWN3R3/GZJmzwuuURKs/gcVVly43TLzr4IneYOR4S7c/KCuaoagaxwnnzbOSRGPmPOvvQ+YDESQCI5nPsOWDnVs+SDEA/8a/3pTpzAi6HWJNuSsQVrv84eSu8fE+IOVqsBIJX3DWorc27aB32WowowThEBLpCmRePPnEo08+8Wj8nquzawmZUlpniJpuowT+6T/9H9266ZP/MvAhqyNjSNl5qLmjhxRi2EV++669x48fs4l8KKI2d/iD4UztXOKVkAe5CG6g9KZl2d/e9IEet95L0k8BILMJQ960c4BTefjZqAQbbQkJADh1HklhGnQGMHfu7IiiUak4CAD/zkcZhv8p7X6Mn/zmaU3JNGXJiZQ+wCkMeD+ZRASj1OBQ9Kc6GlhVOFXO6665hvRK8SQmZKbD7XbFti8+Z1FhsoBinoA4gxsuAQD87alXO3pCrZ0BmmwT8QtlHqKybZg4JKvJjSGjZyRJukjTjCyzQqPSmwOzPHXdVDXDH4x0+cNq1qca/LUG3l3/7gfxd9NVANRXup9/7R0wCrju0mV/f+39zD6ABYIY44zVzz5FDO4NN0siT2cAmWO5Cc0Azr/mLjC6+fvzz2d49LprrqG+wWXZ/YQ//I0NO4biDFKngT797Ou08qsnEKHWnwyN+15Z1fRQWDWMFDVfREeB56yisGwrwjDGhqUaBCHZ1qzAUVjRevyRYIQM/EHx6MsMgBAYBrYUoFN/r6659GwAQGY3MHX2HNqoGkLQG1TCsmollZLOpRDCYETxh5QM+ki8Hei9rZknAfHiEBlgPoAxXt1AAldcflm647/5hazCO6OfQd0DAGDihJr8nAEfG1tb+m79wj66YfQGIxF54OouJoVOoYgaUYggRPLpBJ5kUSYnvA/WfYU0YEEI6oYpK1pY1kIRpTcoF2rgHyPlwMESayZh+tnTaidU+9Zv7a99S+kGGiaWDekaMKkR236wZfeRtmCYvI2xP5L31gzqA+LrwlggiMFIx49/ncJhjD/+nsY9/OI30Y3rrrnmE7eQiE5KSEAtahYhUfa3NsieUEQNhBQ6kEewv4xCVjV/iOjBJZ8LISTwvG7kJggBaXsHHmk6sf6hiBqKqOkaCWRwIaT7fBavlXA/uc/wuafOEiEdaCemNlFaWwcp3cqGWfXOGk9dS0fgRLufdI6MzQzKibphhq5hSiRs91ZEevsPYIEgBiMlL73yKntn/v788xnmEESnLJpKE42GEIeg6UTVmbZ84UjdNaQBH90wlZAetvTgks9FSrQh0NN0Qk8HRETcwzSxP0iEpiOKpulG9lLSPM85baJuGHpkkDa/owoOQbdTghCoxO3p4YSZVsZJwEWL619423IAGfPhWCCIMe5hJn6IxOXe0ERQK3snEFYCITIs5YjoP09DQxhjWdWCYSVl9J/nkMhzJIKfox6cpesBA2GlJxAJRlRVy9b6QwgdNrHK56rwOrIUh8KjqU5EsBRVaytcPo+d5k3F8FjzgAylYR+76px46//C+l1FvlgGgzEOQalE//UeK/cGQlJ3J4nRXlqW9nJqqWeaSUmCJrlE//ueS9oMBEJyRFFJc7Hs4v48z5W57bWVniqfSxioSp3ptfJ8rFhwVt+C2gqXyy4mjObjfUAyQX9vNud/8O7PD/USGQxGqTgADAzT7A3KwQgJ8ogC53JIVHQBE5EbU48TuomH5xHPc0SbLcfhP4TQNHFE0XId+Ff7XHWVHq/LlmXfmFE4A6BORxK5yjJnVZlTSu/GUk4Crjp3QfzddJMA5gMYDEY6BlhPDICsREX/EYQuu+SwibHMyHSFXQhCSeBp5j7IHcuvEDHpbA4W+gb+lT6XTYrrRzY2ZwD03XPZxZoKV5nbxnEwp0BQlj6AwWAwUjKg/tYwyPA/opAFSUnivW57NrpLpD0PhIqm55G1iTHOsmA4FvGPDfzHjbg3zyOfx15t9S7OKRBESvZWkszUzLBJAIPBGMQBkNRPWfUHZcMwrdi03WkX+0fYaaw0QlASON2q3gJFgI7x6cC/zor4Rwf+cPwIAhKJC5GvLndWeh0ZPG7KSUAk6I+/ywJBDAYjdweASX1vTyAiq0Qbx2ETvS57TBnN0tJObTwlgYek8iuf4X9maHN2uyRU+Vx04O9x2Xh+SAP/UTtn4BD0OKSaCpfXZfUxzmUSkBAIYjAYDJAd/x8srL5QdplY0AAAAABJRU5ErkJggg=="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - RackGuard\n\n\r\n\r\nReset context:\nSeed 2. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The configured leftward path is clear after releasing the retaining hook.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-730c4ca2457f", "created_at_unix_s": 1788845998.356343, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 3, "requested_group_seed": 3, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [3], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass nomination band is on the axe at the left of the rack, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The selected axe is held by its visible retaining hook; release its own hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}], 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U/QjZs3uHxWJTZMVis1k6VguCIIrS8pVrBUE8+vuf5pcGiEieFZqF/YLF4XQ1RN7m5wbGj54aTHm0cFgeGPXYbdJ7Z0aKuQIACABQq7h/KuX2f/7M8h8+eTD7xPtqMR8bnMvlcPC9w0dXXXx5XyDgcjWJolXdGAoyzqw2O6UC4eocnJLVJohSpMlI6TlxUDn352KqAM+/eoAFvSl3UhR2sCf1DD+cEJ8/NDQ6hQ6gUCQEAKirAPDoN27+1ne+lfIWO91hNFa7fV7nRZNjA22dFwmCaHOqa6yHgk4qCDZH5HnA126xTPftj32ygAk2b9q8+tdvHMlQCSCkIaxEw4NnKm0bF+d8zJNHv9eRkbxXqAczQACA6nX7kkOZ3j36kYQtz5GuoZ492vOsHTs5J6IoMlGSJMlitQqC2D949rYF+6Jvx8pehZALgWbP0FpiBH0WQLC5LUohq1nKSsmz1lD30AsI6sc/f2Z5wJt7s7h6Bx+ZC45yxjlTli6Ldskv26RAyV15wlPjsed+X+rWIY3dkuf0pwBJEACgZiSsgsV53BZ1ha/xfiXgufPKObkdj0UGZQmEUs4ZpQIv30iAuG8Ki46cPiPG1deRAIDiIQBAzUho1VEncNBtoZSE/OOchQhLO5FOAs4Y5+qaMNrjkuDjpPSS54QghCxYs74MXw2QAAEA6ocSmiZKmOYaADhjTI0BiqIOCFZku0UdDpy8MgBVV+Ay+lyTWoH6xv19437iaM72CRXaf8AQCABQRW5fckj/yLp/482P6F9SHqZcZky5uTt7owrnYmQCHiHyEAkVHBZql6hVopJIRWGmwKWUSAKVig4CCUsE661oj/4mWixiun3stplGf7T/gCEQAKDG2v0zYWrpHwopgaDyvnlpS9LooYioTct24aafWkV1igiHhTgtxGGhNik6PxBV148kDoNzrnHh5KU3Dt/z4c33fHjz6iVztG91u+wJH5jd6o5NAZSXaz50T3GnCnULAQBqrN0/EyYzWQmqAYAFQtl6SWrHii0Iw7koqPPEOSzUYaEuK3XbqNMS/UKBkvS35gWmAWL0Qe2y7oWLO1upQMfGxvT7bFq7ZJbb2dLowvQPYCAEAKgfnEVL/2CIBcN8kTNjDIiM/4pMBq12J4pNCyoJ6sMqqsHAbaN2C2WR7kaC0VNGpzxek8t+2dqF61fNT35LFIWOlgaX3YoEABgFAQDqh9r4oz7U2/9QmMsy6RDSDtelAuXqDKDaymCc6zqBakWz2vQvkka72voflLlixAScGdIAF76bzG1r3HLp0pRv2qzSnLZGJADAKAgAUD9it/8hmYcVLitqqd0op+4UJAqiOgAg8jyyMEDqG3yrSNz2yPQ7IeOnho695JE0wLrVCyOr1dN0bf2U0sak3ABAwRAAoH58f+/yQKTxR739V0t/oihEZtzqD6f/UPb7eqdFTQbEpugxUAGNSod7Bgw/DTAtBACoK9tPromU/kR9MC4zNQzIjDNPKNUosFiZrq0OmRqlajLAIpZgoLDukCVa4eWzX/pGaQ4M9QCTwUH9uDB0QMhlaoV3e3XVgvhZJZJpyQBihPi1AZJWh4n4p4df1L/ctHZJ7Hm+CYBwyNCmK6gvqAGASUVWghdid/5ZRhiog28NPoGEzqAA5YcAAPVppnEnzXZVdJyX+r/o6mDpD1iimeLyOuzBE6mXiEkH7T+QGQIA1KeZiRzSbFfkEKWiWu7rZ5Qr09mlOKG//8yNWfdF+w8YCwEATEpgQSHa2/LCYOByiR8NUNqgMz1d1MrJUN8QAMCkgmFFFNXpHSjhlKrdgMp/Dimnhk4QywDn2/6D0h+yQgAAk/r0yreoOhYsfkagiqCZugAV7C+/cK8hx4E6hgAA5uW020TRwpiiLS9Wzq/OPidEkqBcwCr0AJkgAIB5cc4ki/VCBaByNQBCc8kA5yuEEQCQDQIAmBdTgqIYHeBb5hpAvqMBCkgABANBb4Q/ECjo1KD+YSQwmBcLBwXBqU0EUdEaQMn19PRU+hSgGqEGAOb1ue59gqCt/ViBAJAyDZCQAW6d1RiONP0Hw+rEFYu6NuZ+/CmfP+51RfPcUJ1QAwBT45yLojVdEvi5k12VbQWa8PrtNsuc1sYCDvvqc49/8OOfEmJLGWvTDiEGgA5qAGBqcsgnSZHboKpsAmKcD49PFTwFdN/5If2PVZGxDlDNUAOAPPxV15GSXi9PfKNFGYRDAYe7gQUrOVz24lXqOjDphGVFJpEOoJwRmt8d29DIeEtLU7HnB/ULNQAwtf++6YggCFQtWCtwd1zAaIC80gDBYOj0mfOKtiYar8xcR1DNUAOA+lFYk/3ESL+7uZVzXp4W/3zZLRYtA1wASRQ9Xp8oiGrpr+YAtP8BRKEGAKZHmcVqq4b0aEIXICoWe382cmI353x4dIIxFjkiSn+IgwAAZieJkijZKjUQLKu4238eKcfzIYliKBQOywpnVfoDQgUhAIDZLWkTKzcQOEsGuPg0gIYxJstKdfZ0ggpCAAAgw+dP01h/+WpitxS7EOXIicgawpzIiqIoqAVAHAQAAEKoomZKq4yiGHrDHokBXEsGAEQgAAAQUZSEojOuxmJEDUhXb1hq0capFZEGmPko52pDEMAFCAAAhBJBlIptbClSynVg0jVMFZAGUHGioAYAOggAAGRJm1CdCdJf7zhc6VOoK1tu++SW2z5Z6bOoItVV7QWolN5ThyLdgcrqofu/WuZvrNbOrmWy44WfaGEg9tzkEAAAVKxai0ZJEsKRqRxm5DkpULB/vzBrmeEnVrsQBmIQAAAiqrT8JxfNmXWydzSszecDRoeB1VfcTAg5sutX5ry0yAEAqJa1S9XWBUhzKNVc0JTSxd2byntSUIeq6B89VL+X3p65D72yG3cPpe0CBGVwZNevVl9x8+orbjZnJQC/wwAmUI3DnKvL6khbkNkgAIAxjeexXpQpX2pbtBUJtbeSX5pNXl2AbOp8pTM4w3guwxwx5b2/BgEAjLm5jM00nPKltoVGXmpvJb+EdPzBAtcDiJEkEZWArDFgtfkqAcgBQB7Q7l8GFnUFGINn7An5p4w9INQH1AAAajUDjI5ABjpiykoAAgAULqHhPvd2/IR2/8wv8z14HbBZ8IsJ5YB/Z1C4hIb73NvxE9r9M7/M9+A1Id9JIHz+QMnOBcxbCUAOAKA22Ky2YCgYe8mZQqtvDYNad663j5gJagAAUCfEtpWVPoUagwAAUEVGpqO/kkgDFEAZOWbI30LDgnXEHBAAAGqjC1DKNAA6Ahlr6tyfiZkgBwC17bmTXaTWrNy4tdKnAKBCDQCg3P72E1flshtagaDUEAAAqsvAeNoen5gRCIyFAABQMzAaAIyFAAA17KcPfpfUUQLg1jvuKO+5gNkhCQw1phazvjl2AZKT1n20WYSsE8Mt7t506uDbmfdZf+nGfXv3FHqadcsx/xJ/77sp32pYsM4MPYJQAwColgzw6LhHe9Jz8J0ynhHEMUO5H4MAAFAtHUDPnhvMegRZMdOseFBiCAAA1aL3/FC+H8HSYMXwp2n/MQ/kAMCk7e/V5tIrr35jV4ryKJc0QFaygiUk8+DzRtvi6h5qAABVkQFubmqY9nljLw1PAwiEaQ9jD1vTXPPMfkODAABQFQkAp9Oe43GS0wCYEQgKgwAAUBVdgGhk1fa2ZkfWg1CCPLAxfH2HUlze5iVqcmXiJDEBBACAKkJTrXymnxSosNJ/YGi0uPOC+oQAAFClM4AmpwFSlv65dAQaPr4r328HM0AAAKi8s8f2R5+lv7/Xl/6KglwuGADdQAHKJDDtzXcdGAMb+5m337iDQZ1ADQCg8rq3figUDmfYwapLA6SEjkBQAAQAgDIlAL78nz+YboeR0clzvSmGAZ/EpEDlRSNdgMwDAQCg3O0/yRbMn33oaKTfYYpOQJArsW2lIReLm6MPKAIAQOWdPbZ/1fKFWnv/7bffwoiYy6f0eWDMCBS9JiPHSvR3VK9QAwCoPHeDc9WKhQKXn3r2N+n2sVhyCgxQDK4EuRI0zzVEAAAofQKAx/XaTO4C9OMf/eyNP75KCPF4vEsWdXZvvjH2FtIA5eOeR0wGAQCgxDgL+n0ZMsCrr7gh9txmtZw8fT4UTlwaLBfoCGQMbx8xDQQAgAo7suvlmReRqSAmJjJljGMwHMz423+viUp/BACA6hIKhS0i8U0HUr6LNAAYCzUAgBJauWFL5h2cs2brXyqMec7u5/kPAUZHoKK4zXj7jwAAUGG5lPTIA0OJoAYAUFpBvy9TFyBdBJjqPag9yX00GNIABnCb9PYfk8EBGGzTB+9K3vip6zNNMLDtb77COX9t5z5lzpKAb2LFdXf2njkT7Nyg3qCJwpKFnWvXLH3nzde0nZ12y3Qgbtagj9yl+8aP3fnA//ySYT+MGbhN1/VTD7OBAhjDvXA9IeTAkTOCQD919y3xb06l/xyXFSaKwvs3b+Ccnx8cOXCoZ9LPqaDWzjknPafP95w+v6A5urecNBH0Dx59lnAeq0g453U5Xe6R42/h7zUPXjPe/iMAABjMQgKEkeNHj+g3dl2xIN3+U1O+Ha/tiPudZMwphcPxQwGGJoWOJkvKnIGoZIgukJbY2Km2oXlMWvRrUAMAMB5TmCAKWgN9qkUeo0LUQRTG1T482p6UMUYptUgSY1xRZpb6YpEdwNCi/zyuJ5LAAMZjkSYZURREURAijTkpM8DqewIVBSpq+wlUENUJfwRRsFol/Qf1Llu7UP+ytUGtGeih/ScdsbFTbOxUPOdR+mtQAwAwHlNv66NuT9/+E6kcRP5T13tU/xMFQgTK1ScC4SQYCukPpRkazWmcMCRDuZ8AAQDAGN4z+xrmrdGeHzhw2G6zZg0Ao8MpFoFJz6b9b8wznfiOkmk1MYB00AQEYLxQyPgSeWAkeuMfCGY6+Oy2JsO/GuoVAgCAYab6DseeT/tTz+cTMzQeN0Asd8kdgVqbnIUdCkwOTUAARqIWV/RXy2a59gPvy7Bn+9wFgWBI3VMSmcI454IoMMYFSrnaj0jhkYG+lFJZpehn+9ly6ZIde+OWLeSiHX+RkC8EAICScLkcCVsSugDNndPq9aqt+aIochIt9ylVE8Nq0S8rgkDlSGBgjI2Ne6Z9M03/Q2Po+w8GQAAAKIlZTW5CyJVL0t6YS5LU3ORmXL3Hp7Hpf7TiP/InJ5wSqu5AqNNhP37idP94eO4stdPnyESBzUcAesgBABjGvWhT7HlLS2PW/emFEQBUoNEHnflT267uIAoJ9YnpgNp2lA6WBoMcIQAAlESjO5oMKAXG+C1Xd+m3tDXgdxnyhn80ACXhsEe77ac0f9nqYg7OOI9MIQFQFAQAgJIQ1UG9JZRu4XgsDQa5QwAAMJ4QKf31GeDEdWAK1T8eHQU27vGn3GFwMnVgAEiGAABgPIc9Og9E6QyOeW/d2q3fgjQA5AsBAMB47gbjM8Dr18WlDSa9qWsAGnQEglwgAAAYr2VWY+kywJlzAAC5QwAAMN6sZnUUmKKwUizarqUBuBoDZiaHACgAAgCA8aPAGlyOyxdG0wAlCgOEEI/PnzINgI5AkCMEAADj2Wxxq3R959EXCTc+BgyNYkYgKAoCAIDRqDqnW4rtRseAMQ9mBIKiIAAAGMwilXyORS0NEAylzgN7ZXX8AToCQVYIAAAGczrTT83P2dXXXWfUF0Xmik4xIcS0D01DkBMEAACDNTc2xDLA0QSAzsnTfUZ9EeOccY7hYFAwBAAAg7W2lG9VXn8g9frA6AgEucCCMAAGa2os4UTQCUYnfQ3OTNOOQn34u//66YQtT730dsKWs0ffzfewCAAABg8CcDjS5gCuvv4Go5qAtNXBRsZ9C+e2pNtncfemUwcTiwnzyKXQJAWVm3UDTUAABtu8zJEuAWC4KX+QEII0ABQGAQDA+ImgyyYUktXFgwEKgiYgACPwaJd8uyV9H1BCBgeHT586U/CXUJIw+Y9F4TwYku3WuIHHoNfsdsaef/bOrYSQHz37hn6HxWsuPXV4rzkvGgIAgKG/UZKYdR/OScqRwvkaGA/MmWWfmg6mDACcKVTIfjImxkkt+IevfzFhixbAxPi/XIUVMjMgAgCAkb5yz+Z0b628eL3hMYAQMuH1tzU33Lq1+/+9dtCYI5pAZAQd16btIIRctOqSwo6z/uKuHPfc2L0o6z6P/Puviglg6X6KDFluBACAUkmXAaZUDQBG8flDyRsVhWmLEpu8I1Cp7dt/SP/yku41CmOcEGsOFcH8S3y9lGPA84YAAGAA75l97oUzN/jlJMslmWsaCvDuwcP6l2u7oiv/8Mi8HQKlj/684BK/JBAAACrBoPafDFPCQe4Gg02zbZOGX7EDh47Enh/UPa8e6AYKYIAf3P+1zDvoEwAqg5qABsYD/lBYO1jCaADIyyTpqIMrNsbarB15LDiKAABgmH/867/ImgDoWrPc2BqALCuynKIHiKIwCb/fOWOcda656sqbPk5q1mCwiTMWDsvLNuY64yz+gQDUNk6IP5h6SjhME5Q7u93ucs2MGKhRnHNFVl12w0dz2R85AABjXL9xQYZ339y5O/ZcCavzNxRAEFMO+LJ7fIFGV4oBaLManRNTQXQEykoMjftGxne9cpZanITYW+2B3P9StMFlhXE2tn73R78gRhgMNgmCYLVabXabKIq/eeFF7QfJPNMRAgBAyY16U/TUNMrA8OS8jqb5Hc3Jb7kcM8sSQC4ZGB6efuzh72e+Vu+99axRF3PaM1pM/Ij59Ee3aE8efuqP4aGBnqEjrbacUk0IAADGZICvuXxNjvuLFlsBlYA0t/8qry96tIThYKKANt6UqL5ovP6qLv2MqgaW79UP/z4AjFfqSUAT+APhlOOC3jl8tpynUTty6oPFDBytV61QAwCogMIqAenIiiIrzJJq9Om6FXP/fLyfVJlt227TMpYnzo70DU4ylnYsm8tp8wfCAqUOu2VWo3N2a4PbZaPFTqMRVwNIRzBqso4qhgAAYIA/vLk/9yagUgiG5JQBoJpRSue2uYfHphI6MbU1ObuWzRbF+B+H83ODk3uP9OV4Y04J+cRN9dC1v6QQAAAMkKH0P3s87Rxt9raFseeBkcKnie4ZJJevjR4q5axwVdkRiBNCGxzW1iZX/4hXv6qBxxecmg41uWfW1VFRdYIdQaBMySkA8EgNo+i6Qp1DAAAweAxw7gmAYgr9BKFw6tmA9xw6K1mqbrWAJ554Ydu2WwnhVKBL5rf0DU1QQeCMSZKkMBaSleNnRrqXzbZZpVf/9F7/e0djH7xoVXes+YYzpn9r2923vvJ2D9G95dmyPjGKQDwkgQHqgceXR9f16kC18dBWi76pJ3p3P+0PeXxBQUi8f5cZS94Y/WRSxeD4maFwmrgIGgQAgHow4fWT2qO2AulnxYj1W2Wcj01OR2ZWTpQu1UGTCrPzw55zgxMGnm79QQAAKNYf3txfQALAWH964/XY84RZ4datmEuqFI39qT1raXJqTzghEx5/yiku0ue6k6oLivLe2WFiYmePpf2XqUEAADAyA1zmEQB6Ke+XtTQAqXpOu5qo6OxojLUIBULK2GRitYYmNhllMTVtWF/buoQAAFDCWaDLKRjOtDDA4u5NpIrNbnUTQppctrntjdoWTtRWoITdKKUuhzX3nj36zkWQDAEAoE7U9N3u7NaGSDu+0HkhAGj9QRN2o1StAaBzp1EQAABKpWwJAM2kLg9cc4vD2G3Rvqp220zf9OR1DjhX9zTDGF1DZF3sHgEAoChMqZYVGV9+6bekZulu6mcK95TNNw6blCYNgNaevGEgGIBhKpgBzkwOh6twOFhhJFGQJJEkdRCS5bQTClVbqPjx9pdjzz911w0VPBMEAIB6yACbBOfcYhFT1gCm/CFjJ4MrrEAnhIRFbTL+9HQ7PPz0a5n3NWTBgHQQAADqByc8bmBVDcwIlIJAaYYZ30RBaHBaRyemE/YYnfDl/A28dAV6RTz6zA7GIxWgpOzIRasuybAoGAIAQEkSAGXOAGv+9eEnv/DZbRlmhasJNovoD8lphw5T4rJb1Vnh4pt8kgcN5FgD+N3OQ9HSs2oK9IINh5oIIe3WyRz3RxIYoM4TABo5nHrh+CrU7HZk7OVDnQ6LRUosu6YDNfMDlicM5AI1AACozIIwMR9434rYnwpjim55M3VkwIo1rZEVAqTICgGcsYERbzAka2/FDqLoagQWi/TBm6/t6Z2Sldwzw3VlONQksGDW1e1RAwCAKiIKtH2WS4yf8nNqOjjtj97gU4E2OG2ZhwIwxqf8oTmtjRkyIoXdMlezAn4KBAAA47sAVSQBoHno4SdqZzhY9Eb/D28d179sdtsdF8aFaUJhNjo5HZnxWc0C2KyilNQEFHdczsNhZcWiDpfTmst5tLiIYqn9GOAfjnvkAE1AADU/BCx3VdIR6IknXkjcolvaJaW+42RnPl/Rd5xsvnTNsgXtWfe8/squhZ0tsZdO98zz0Unfb3YeHhnoJbWgvck6PDnTF5YFPcSeJf4hAADUfwa4/oaD5YJSetGcWfl+ato7FnvuEMhHtiwlZKk+Kmjdbf/lsedJFWtvyqnqgwAAAHXLksvc0TS/qKAfn6UPDN997DlSI+V+DAIAgMEqmABIVrujAWrCtC4wxIbsVltUyAABACBvP7j/a9WcAHjo4Sdiw8GgSqKC3RWXZP7eTxKzIBWBAABgigRAzQ0HK6UKTAYX8E3qI8EXP3lbNQQDBAAAc6mSjkAVZfBkcIVFgmoIBggAAHWbANAgDVC1KwcEDA0GGSZ9SwcBACBv1ZwA0CANULU1gHyDgX674RAAAOoqAwx1IFDKQl/v/wO0rKMHBeUsigAAAABJRU5ErkJggg=="}], 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Lv1I3lvhcTn1pra1rcHcY133uBPzL1BKvvL2/PzJYi/WoVYuPWrU4/fn58sCW7Xu3bN+b8dKmZR/qDPWEYoKAZUm0bPLXP/78qUd+/dQj/8cfcPy519z50z9NymUDAFDAXDSBUfyMWAmYzhBQRvyn1mkZn6LlUBc3g2BYfWXdTlW3+AyAw+cB2NF1fvuCxooTVjfpanIZYNniZE/nzduahzuBI1cuohS1doWeW7fbssjKRXVL5laX+T2yJGCMB0JDyo8e3VZaVlpXV/2T2z89CW8DAAAzjMfHJu6FHwUqlBlANqmpQEWZp7bSH9GGhP45NG2Z5kBbaP22tkiMN5ZhiaH8K3s2kGLj1ub1m3ftOtgdiqif/PBJJ69Z+Lfnt/zzte17W3sN05JEwZkQ2Decv+Tas+Zs2rRtci4UAIAZxh+eHOyBOKMnAdM2A8gY/vODjEkApybgve/h//THTdWpJ1HuEdKjQKljhFClX1q7rL7U72ZrA84ywJojFrOkUQc6MBuwCe3sjfT1x21CnP3e9NqLz8h40b89v2V+Y8XipmqvW+bzDB4s+vAVN+X5jQAAYAby+BjEvfBnAAW0CDwCF56x+rVNB7bt7UpfA/AooqPqgxBCownD5ZSL4EvBm7Y2J23AmQ2scaYCFiHmpt3RhB4fvtzFB848ki34/Hvjoqaq+Q0VgVKWZipLIu8cLYnieRd/YvKvGwCAAuUPI24KK3zpL6AQEM//GQFRxKuX1JU5Q3uORxFty0of/rP6RWZysJ4xk9i0tXnjO83MDJyokCQKy5bM0wmL9COEsof/KS45Z+3RK5rWbT1o2UNeyLLtvz9031//+PNxXyoAALOCh510oGwuuuJzM0X9p80AsuM/3ANSsaAUNQHv7/72Fs8Iqg740u8yyJAmbAGvqJvWwW61sirQcojNFdLhxpC0AYQCpZ7jVs2N6DiYyNqOnMX7Tl4Zj+bYnMwnBHxOAABAsXHRUKGfWdJfQCGg9NTPbM47bZUTdRFWLKxp7QprI04XuvuihzrDAa+0fMlyzJZ59+S0AYzRymUL9rX1zalgmaYToa0902wAACgqLpppul9ABpBz4TcnLLgvS5ph8fhPdhvmgFcMJezWrrBcW7HxnT0YozVHLHFCQDlsYPuu/WVu9NbuHt5hZrhMIbbqEGKt57ds35u9jwzUHwCKmYtmrPRPmwEMl/6fvRWAx3/4DIBS2h2MxTVjuPhP6rg/FN4cVjHGbkXu6I0ESr2rVyxECO3Z25JxJl63UlOSrCnNE4RGsAH2mO17sbN74PAvHgAAoJjTQEfe/5VOTcDbHUpu7DJM+/k3mw+0h6izAqwadk4DCCXsCq8oy4PLxRxZEm3BxVca3rWiNuUe4ZjW0hFavWSwyB9vLZByAj785xxs70svLpQ+/L/pC984jLcCAACgqLOAshd+c6JqZjSuc/V34j9DoA78+MnHHs3+cZPVkk7IyIgm9Gc3tDyzoeVQV39cNTwu2edV0h/JvWHj1j3DbSHmxYXSS0qA+gMAMBMRCmH/V/KYIsNgTWIy8n84nb3RcFxPj/+UeF3Z8R9+/NhfHnzsLw/mPIEKL67wsh/f2Rp5fXvnO83toYi6YcehjIcJGKc2jqWY1zDW5QoAAIDCZ/pnAHwDV2lZ6d7WvhfX73tzS0tLR39CNWyb8Og/j/+0doctazAZ/8ZLTlg6vwoPrAP3q6RfJenFglI2kNMJuA1UeFkaaDCRo8hEaiowAn39rIHBnMa6P9z3/fFeMgAAQFFnAfGIvE3o+m2tvf3x3v5EXDUwwjv295T53Y01JQ01ZaU+lyKL4ZjeE4xTRFP5PxijhurSrXKXbg4pEvePXPEf7gE33HhDJKbphkXT9g/z2UAwQZ/Z0HL2MXMzfpA6cf/0gf+8hkosu1XN7Orq5rcc7Ai+tXlIlhEAAMCMQJrG+A+lNBRR97aGLNsOxW2uyhRR3bSicXXbnuj2fT0eF6vrYNskPFDlLZX/U1dZMq8h0NOT7BOQEQvK5pe/+CVC6Fu3f9Wy7HBMC4YTcc0gTnsZbgPZZN9KCG1ubtm8r8+yCF918EisT9kHP/Kxx/7yh8N/awAAAIpqBjBvbp03GN/bEQtGksmd6TChJ5buNAHO4KaPnMBOXRKOWlr3bE+Qp/+P8UW/cefdCKG7bv9qVTnbV2wTmtCMYDjRH2UrAR6XEij1lJd43UoyPRSlTQK6gtFNO1sTmhmJD1pOdmdjAACAGcGUpoGmzwBS9Ibir2480NEbTb8xO9EzVf/HIJgbAMvtMe3Hn91k2cno/+v/eny8p3TX7V/lB6puHmgPJgb2GSCEBYw9brmizFvm98ypKd+4q7U7GBtIOmLzhlCCZsw5YBIAAMAMQprG/B++A8DStaba8u4gK87M7x1uo2/2/q/evhAv0zZy8GcEbh2YELgUuczvUXVzIJ2UEkrjKqsYegiFtjV3JDcQO2AsUEoCQwNHAs8PBQAAmCFMaxaQo7S+En9XHxv+4+GFPp1zTlzS3NLX3NK352Dvi2/t43o99hBQTm698+7bvnl3md/NW9LnOtPhAj3MfjBGtZX+T13+nomcAwAAwKwNAQ3bAYaiYCTR0RPde6ivozdKKFUEmm0AqfiPLImD5qGw/2GMQwn7MOI/Obni41enzQOGgHHSLwOlnmNWzt2xryMUViVJXLO8sTrgLysv5/decMkn83ImAAAAs8EAMtRfN6zUWDtZCoKiuGq8s6ezpSMUjSVyGkB2+Yf0/J+cCaCHx123f5VQqhlWKJLoj6ipzFGMBVHAyxfULplXgwcqQyCEFs6pLi0vS38G8IDp5ZrPfD3jlrKykgk+54++/V8TfAYAKDSmMwtoSBUgjHxe5bgj5qxaVP3ky7uMgaTPEUip/2SE3gWMvS7ZW13WWF1GEbIsEolrpkWOWtboHrCueQ2VgYoAbzCQTnd3729++t/Xfvprk3BewJjkfjL4f1/7Tur4x/99C/wmgFmANF26n92zxen8JbyyYd/RKxtf23gwPQE057JwwCsmTFzqZzvFHn7ggTye4a133p3KDuIGI0tCZRnrCplSf86mrc283yQwy+R+ZMAMpoWrrruhrqrs7u9+b3pefjYiTVf5z7lNtTnrPyOEFjVVWjZ5c8uhdA/IWf/Z7ZJOWD2X2vrDaNrYuLWZdRsesIHu7t7pO5dZyzkf+kRjQ90YH+wfWt0vCR2l7eioYEQpypFsBmYw2fzoB3dt3tWKEKqvZoHWW25hg7PvfIfl7wEzYA3gMOo/24S+vb1t0652nuWZvSycyvssKS1ZOa/8tm/+OO+nnT4JGKEkXGolgHefTzcAiAJNnHM+9Al+MJwB5Jb7oSzq3cNX9SkvHItQa8PK8Z3H0KSAnE6QDsSI8sX7L77izBNWHLNqnvO7TpZ5d/nYL33lsRfl7WWKlWkIAUXjeokvdyPGVPsXUcDzGsp37O+2VGO4+s+8AXxLZ/+ff/97VBhs2trcUJPMBQLyIf1MsdNDf2OR+4W9u1NVAlPwT4vzH/tnfufOcZ2MTcih+hWDz4YG8xFGnhaAE0wcrv4IoVhC83vdXP1ZU7/1j4MHFLoBZGd/JmKxEp9ruPgPh1La1RczBtJ+sof/qTTNw94CNt6VgBTZ5eHSJwEpYPg/4VH/oPTPZ12jqcuVY9ywqHd3Vh5A/tMCREGY37WLHxNCW+qXj9EMPnb9zX+473/yfj5FRTSW7AqFEKqsLk2/a/t6lvwNNjDzsoByBoLS6z939EQtm/1pZS//hhJ2uUeglParZPIMYOyke0B7d39DTTmo/4SlP6njjvQn0XU9Eo6cIPQP/aGp3oAtCHjQDChtqRvFDD52/c0IIbCBw+PGS08d7q7ezjH1kgIKZScwr//MGzGy5PqhcdX09i/9Ua2rL8b7f+V8Kt4AYLj6z/mCF4rIWSM6+5E5HwyMkXM+9IlzPnRtmvTj+U216eofiURWRluy1H+aETAzg/ldu9zNm5TmTel3YWRb5mA2M7cBYOL0dvbxL3gzC30ROCP+s3N/j88j94VVVTNFxd1YU9pQUxoo9SiyWFvhSy0AEEI37mx/e3ubRYjfI5eXeCOsFxh1/p3E/V/DkTMQlB4FuvKGr0z2Ocxijjvjg4FARVbAZ5BIJHICnlLd74yq/GB1fcXhPcPrymCXaTbiicT5AcwDxstTD7Mq7pxYLFHnJAJlc+oF1477qYEpDgFtb0n/M9bbeyLuPVJFmbextnTp3MqzTlzG79B0q7M3ymvDWaa5ZF7V8gXVzS19b75zKBIffYNY3kkVjMu5EgDqnyfwsNKP8yDl41Lzar8n+8ae2OBTjcoStO+FhGfOnKQNlJX6uAdAOGi8xNIWAIAZbACPvpTZNotQmtDNRHe4vSeyafuhhrqK1YtrqwK+aELvDcWd+I+EEQmUsr/GRU1MbV/fdBAhOzUJmILhf4pUhGe49FBgIoxF+g9DzXNK+XjV/PA4w6u+0NqR7QGwMjwROnvCOScBLz/5G5gEFJYBpMd/2tt7RnikSxZsyzrQFuzsjdZU+CvKPKbT/lcQUG1lOTcAjNHCORVqPH6osz+aSMaC0HTAneBbjg3A8H/irFi60O0eTO85jTofFVa5xzMZam4fd7rH604lkxov/RPllTfMZK4626go00OtHU3gAUDRzgBUzVR1a93W1pH7dvFET1U3D3aE2rrDfP+XaVqBMrZCwB8jCLjM7/ItqDnYHgqrETSt8M5iwAQ57owPsqCfxhxd5r9oIZ/Dc/u400d+gHLa+/gBdT5yHPPVf2er+eHhkWlvZ7tmYT4VKCtlfehSUwFgVD5yzRf+8tt74I2aeQbQ158IR7VdB3tiCcN28n9kSbSsZO/fdDISPbn68/1fB9pCxx/RNOSMRbx4bqXs8fzyJ4OrQ8CsIV/q371oDftfsL+yolzXmcGk0HVdUeSMx1OS/GB6vR75lHOSt77w8sTPZDWNIhG91YpyhoOA8QJRoBlgADd99sZn3tijGZaqmwGvyLtpVQd8/RE1oZvpj8y50ZdjENwXHlwC6urqm9tU23KoS5HFedWDu8aAWcPqRPtYHjbywHzR8lRiPtP0vmDI7xvl05JSf7ZRMZF0INu21h67lpV7Wr9xyEmSIe1LUzQ6XaZzwW5vRPYz/bFAuZ87kNs1nYV4AYAzWZ/CHfu7bZuSgS6PPo+SUM3OoY1/R+7/xacFN1z8rtQtVVUBXkUuvaUMMKNZsXRh+rcTD7mUVlb39PRVVw9ZIo7FE6N6QDqaNphsJsvS2mPX1u7ekHb/cEI/SFhNNZcehGI7GAr7nII2v74XQoiHHwUabhIAFIQB/PAHt3b3xVTdDIbVcFS1DC0a1zHGJFebrZztf1PTgjffaVm+oKbM7xYE/OY7h5bNrw6UegQBf+mWH6DJ5xM33HTlhSe+vpGVEusNRu750f9OwYsW2wIAJ7kAkCei0RhbSC5hw+1RPSB9+J+h/nwhKhKJunMJ+rh4E7E0Nqb+lFUWmuCzATmBXKCCMIDqgK/KmQ6bph3XDN2wOnqiu/a2ZT9y5PgPQmjzrs6WjvDSeVV+F3L6hfUvmVtV7puKDczXfPJGfnDS2iUIocefWz8FLwpkMFy8ZbjYy+sIuQdyfDJsIN0DDMPMXgbIkP5YbJQwfWoru00pIdQkxLCITalu2appW4RoJtEsixBqEXpISMiyJImCqukZlgPkZykY3tTCCQHxoL+iiIrCUvdqK0uOXFpnWiQUUfv6469sPDCW+A/vxh6MJN7a1lruEQJesS+qrt/eWlUy6fHTm2/+vGWTGy4/K3XLJy87ixD64x/fO9kvXbTxnwWhg7k03TdqpCU93rJKPfRaf2lJic83oPXpNhCLJ5I9pVmXN2fk4Yi4x+NJV/906bds27t7S4dtJwzLotSwSMKwbEptR+6J4wI5p7YZWJJt2bbA/zCACcOjQIQQw7S7g9H2nnAookbj2ve+c9tXbvkmvMHTVgoiPf0/J7wwu27YwXCirTuyfjtr9ZA9LXB73Lz2Q3rKP08kzVf/93EZAOe+B5/7EQSC8sel13yGFXn2eWtqqtWEOr9792E/VcK01KQ0U8MmofpFgiDw4CI3Ay79vKSoosiiKPLPojkwBzX0pJHYhEQi8c6uHuNgZr/Pw6ZLKkup/4aX/pavpy0evnn7fxmmLYmCYVoY4x0HnP0iAwVEuAVjzOp+z6sr/+EPfzTd51usBvDtOz6fWqodFUoHnaC9O9Ley7L7PYqom+SoZfUbdyZzQjL2fE32BuCbb2aXYNvk+iwD+NWfnkMI3XMPLAbk2QNYv6faGrYluHs3ZRO/5L1OC5dkuMUi1LRZjIU4Ks+H4eyA0M6oGtZyhOnj5XWiILhcSonfq8iSrMhej1tgt8glJX5N1VJdYmLReG9ff18obPMs5FBH/i6RqT+vHMe/BQPI5r0fuIy/RS5FEgQu6kl5dz4LyQ8EpUzl02ZcThxuYFolCoKAcX1VqSBgQcD3gA1MVzE4bgOcsZiB4wRWMKxyJ+gLJ9L7QVZ4ReJ0gAklWB2IqTEAQugnLzsz+977HnwOY/CAfHLG+ZfLsiTLkiBKkWhc0/RqPUgHYuwD+j8sPkVKmPaID2FoFSwNHyMsKzIhLGxDKbVtOyk3CLn6xpSEypHEwUGJYWXvcOQdaJCIcacUIJRgRESmSoIiy6/+6yFUBHzosqt4jSf+ixnoycZuoJTVBh4MnQ3czDxAFBRJZL8nhEXBGdJnPfMdn7pghNcVRPYTv374FZvQH/8o/70CZxOTHkx3KVJ6//fhzABj7HbJDTVyfXWJtqS2vTuybW93MJzgn5K1y+vbeqLBcELAZMrq/2QLSrAniBC6+Oy1L711+JEKIJsX/vEga/x0GuvwRyn7620XSmusMVUAFTAWBYGOoeWvOzg4qBdyqrko5pLydJgaYWekycXdLYsCxpJHKXUr7EDA5R42grWILbPRKNpVOq8GIWITXVNlWcIIPfCrH81oQWekinYnJ2sDUZih0RjC1rqT96Za+IlM29m7J7hlTTft5MZQKorJNXmM8bKF9Re8e1VF2YT2+lx3ybsRQjdceurKEz88keeZ3UxiOWg+CXApzGNqaysJoa1t3ekPGGlmQJFmWKFI4mB7f1d330Vnr6WURmL6voMdP7znPjSZfPXLX7CcJA2b0OsHZgBc+lO8+NYujPDX7xhltQMYL+kewJrEjc0DmLIMhI1GG5jn+GmuViJmuFj4AYsY+12SLLLjUrdi2bYoYI/Mb0GSILCRaVpIJyc7Sufzg0Q8llweKzDp//BlV2V0Ok7X9OQhvyc1Umc6zt4wNiN33nn21jl2OK5BOmfnge4/Pb1BzY7dYXzk0oZzT15RWTb6lovhZgCc/7y969pP33IYT1IkTPoMQDcsLvSCgCurAn29oTHNDDByu6T66tKaypKODl4MDpeVuBuGNoSbDO7+/j1f/fIXnPFdpu6nnR3kckwKG156/JjTLsKYR31wt1SOMap3MkGHUXP2i1BEobHM2x5O6JY9qujzoEKt313ld3tlSXHCxhL7YqKeK94wbqZR+nNr+sB0NjU25we2M9BJaTp7DEYSk/OUoONxCfp4qanw+zxKDgOgdMvutrbO4HUfPqVyAvMAUP/pNIDsAb7Po/RlPUA3rK6uvuHMQBSwLAmEUB6onZr9XxzIKp4WNrz0+PFnfojl1bNfAVYUKeqe43YpR9i9bllkYXRnhM4P4obJAy9eRaKEtvQnBRcjrEhsgM4fLDhfCdPSHXuglHbHNIvQRZUlflfmboCCkv4PXXZVKoaeFOuB9OjUY9LXRVOazmPolFKBwS4/+8knQ9PHhcellPk9vaFYZdlgnVebEMGZWSyfX/3qhl2bdrbeeuMFfMo1dv7zNuvZCWP/ae4Idtft/48PqTLMoOVQl0uRUm2BVc3sccbaPF6U6hyZuoUHkbq6+qbGAPgMgA+eLjz9yJyPeWHdLgFDFGgSufjKm/i4ddGiBY4gJCWg7uDm7AcfCEZ3dUe4LNb4PUc1BLLjEhHNaO6N9sa11NqjIgpzyn1zy31uWZoa6R8SRsejaHpG7IXHZzBm+S2Db0chCfp4sQn967Ob1m9rqSpVKE7G7iilpmWvXFAjDezY2LSz9cZLT6+pLEld8r9fSXbf5Cv52W9Heal38bw66BAwzSGgW+/8cU4zyPADjzs5CtMNy6VI/F4+J+C3TPGQnEeBkskKwwA7eiabRx74eer44itv4geLFy/snHdU6nZuBjHd3B+McQH1ytLK2vKcUelSt7K2sSKkGrt7wv18B1n5nDBCbQfb5pT55gZ8rgHFGSOHGlgH0/17d7Jv1END4y34Q5ddlfr8pD5HNiFJuU+GY5IjdeeE8UwX9PEiCLiq3I8FNlPByKaYJfs11pRf/dHTH3vi9dTf/prlc37x0IsUoRNWzUmlbCGE5tQlR5BQF+iwmbqShEPN4PPcsVNOkDpIVXnjQ/70Z5iGuDvs2yx4MyCEbNi8I4hKa1FIEoQj6gPurLJCfzpkuly8PgSmyI08bt5sBvPUlHnLWinqbW1eWFlS7XO/qqf9UTiaPqDjySHB4Nh8zw6W65I+hCd8XRQn0xdx0Wk6yhqkE2eQ7kxaMh9DKTrUGSpRkKoZHreLdXwy7Ks/ylo4fPDCkxBCf3nsFb59b83yORt3tv5nayvC6JEHfw+14WZGCGhcOway1wxSCwM8XnTlJ742ZWc1ligQTwaFXKBp5EMfuzESiYUi6oLGkpxDBMO0O0NssC8PVP5hEp7Sc4TKy/zO/7FMExlrpMmHO7M9R9MLMYw+ZaRHXfia3FhGR1/82l1jfP4rPn6NKApXnH98UxPbD8hp3pssE/BOcyePBfFfzfnvXunzpLq6MaBZ/OEx/UXJv37Hj4YzA179n98yxfWfU1EgIO9ccsXV2TeOvItr2FicrZb5xTKfn2/fTeZ0pu7FiMeRzzztuHlzG3M+geRMF5546kXRFopK0NM13bYJX1rIo6CPl9SrHzrEksWbmmpS6s9qAi6uYwXhS93PrmumFP3jlW1nHr+UV5wEZrYBjNEMpqsBwICyFLleJ/84R95tm3nvUDVm9zvv5cDen4w7B0uq8eydoZI0kjbd8akLR7j3gef3odG48LzTT2iaihKzk8qoS6NTrOnjAmO8ZmnDtj1J0d+2p3Ux20KHOrqH7AV5z/GLEULPrmt+ft3uE46cP6e2nM/MoEPATA0BjYVv3/H5dG+YyigQZ4QoUCiS+MEPC2W7+SVXXDOsUA9oAbtrYAUyp7AmgyTDaIeTn5n+00xlBiK8Y1KckfU673ADGG4GkJoEFKABpASdv+0zS9DHy8ev+cSapQ2sYocxpGPgCHvBnl3XfPqxi6sDyXLfEAWa8TOA4Zh69edRoC9/6fMjZ/tQRA92JLe2jZdLrrhmUKezXoRXRhlexplM5+iuzJJMGMMVKOax7IE1yuHA0yXWRbQ06gj6WPIMZqigj5f7f/t/y+/8r+zb+8Lx4WyAzwZSRSZgEjBrZwDTxQcuudzrcVWUeS855xjCqk4S2ya/euyNoaGLtPF0FrzSZPotQ2MiQ7K/0x/C/+MfblZuZgxjPxDrqZ8BpEddkr+82TtInxq+k8sGRp4KsILBZWweAJOA2TkDmAzee9FH2f8cZRVFge2Az5XqoZukvTf2oz++OHBDVtwEs56CvHgK23HKnibjpXJLAuh1AZIedUklBY0KCHoeueX27+a0geGmAlz6gcOjSGcA53/4cstKW911lJ8NtFnlCVESMweDINaFzNMvvp3+LXUaBrAgl+PFlNI+GhhhBrBrJysbwDJPSpJb0HHFAoTQF274xCSfODDR2UC2+uecBMCW4OEoRgM4/8OXE4KwkLXt0xns1VT4Lzl77fyGirzUBQNGFuvkqrKzk2qCb/jNt+SIq1x4+Q3sQ7507oEDreXlw1YS9HndvFMY24FYN5iHDh5Q4B4wRgMADxiO4jKA8z98eXWgpKuPVZfMYQADVFeUfOQc8IAcYs0rGXChnohe5xTrvJNS/9Qt37tjpMrA/3XnYH1vsIHCtwFK6JL5bH9ABjAJGDtFZAD33ctii5G4tnlXW284jgeKTzklAey6qtLUmm1XMFFdUXLJOWsWNFTOoHlAZiQEsZKaM0iv886Fl984dvVPATYwU2zAtknzod7zT12d8TAwgLFTRAbAue/e78ZVfcueju7+BCUUU1YfuKGmnHUHdOrQpjLg3330wqqK8oVzqqZmcG2x3ZgsFsIbWxebWOedCy+/ESGUMoAxqn8KsIHCtwHdsA60B8doABAFyknRGQDnR9//5jNvsCKOSxckp5DpH6PWjh5+cKiDFan2ujJzpXhHWV6VdyKnAWI9NeofCUd+cQ+b/+XFA7Zs30cpVVW9t68/HImblpVK9Nr84sP5OH1gDB5AUU8o1t4TppRefM4xGQ+AScAYKVIDuO2WL/GDD5xzHD9w+1h9yPb2vmwDSHlAdyg+3JbgF9ftwtAeoDC44PIbMMIrB9Q/PKD+13zu64MPGrNx11b4c9qAx+szTau7N7Rpy66unpCuD7a1AhuYAr5121dau/qD4QTbU/neMRkATAKyKVIDSHmAZZOLz30XV3/Ohg077AF5SBkAQiia0ChFF50xWIw+HagMWiDwhd+VS+elbrn7jq994gvf4MfHHc0q+I8FSmk8rrpciuz0ijmwb39OG8CCFIsnWtt7du4+EI2pFpsNJAEbmGw+/akbe0IxSnMYAEwCxkjxGkDKAyhCS+dWZxe/ica1voiacQvGGKpDFzIXXn5juvr3hOKs7gVCJ71rDb/FNIeUmuGwFSCnRp1pWrputB5q6+kN9fb1z2uqa6ivdhorYkqRnabv6Tawaes+07TaO3sjUbZZybaTfYkxwpte/MtkXm6xc8lHP5Y8GNskADYEZFDUBsC57ZYvLZlbnX5LNK6ljjM8IJbQh20S+eZOQRCgPcC0h/5XLpvH62uEw2ETySnpRwj1dndHY3HLsgzDUjU9kdASqqZquqYZpmnxRACMcVmprz8ck2VpyaKmREKzbSJJIqvJIQp11YGcHuB2u351/5PpJ5OyAVEQ3n7+oam4/iL2AIgCHR5gAEM8IF3600nZQDShvf90iAIVtvo7hPsz1f/AgYPtHT2HWrt0w8AIy4okCILX7WI7v1nbdIF1ozUtv9+jaYbP56mtDihy7pbxhjEY8a+tq3G7XYPNy/7+km3blk103bBtkrIBQcAbn4fZwKRwyUc/NvYo0K597Z/83K2TcyIzDzCAJD/7nztHfqe4B4waBertj9/zo//N++8JGK/6H/eu5PI+pbTlYMvuvYf6+vobG2oaG6pRslb+MOWwEfro+S1jecPvf2xgFxJG8+Y1Zdy7aeve5n2t/eEYjy+lboe1gSnzgOEMACEEHsABAxi3B4wUBVq3a/Putkf+9MDIzwPkl098/pbu7mBK/Vl277KlfJx+8GDLjl0HBEGYO6e2rrZSwKzK04WnO23c88T9fxuyGTXDCV79z9Z9B9oMw6RpQSGXS1n3rz/m8RwA1kD4t/dkvw/gASMDBjA+D0AIHezsTxkAIazumGWThGoYlv3iul3tPeFHH/rDqE8C5IvrPs+SOysHuoJw9SeE7Nq5Z2fzwdrqijkNNV6Pi4/18yv9IzhB9oTg93/6p2GyNWRuA6IolpZ4X/zbbybvfIqQbA8YwQAQzAPAAA7DBg52sh51F5x+ZGtX/wvr9qi6qZuWadmsQwtFIqZgAFMp/dnq398feWvDltrqQG1NhThQ8ekwpT+9WcNAK7Sh91JkkFTtaErRH56dk/6QDCf49QP/4P0huA1IklhTFXj6zz8/nHMDDtcAYCUgBcwAxucBXP2ZAZx25PNv7X57x2Dfao7Iakkg8IBpUf+58+dt2LCFUrp4YZMkculnU7SLTt2FbIp4AfC0FjyaYesG29TNldz5l8lzNG6pOkn2XGP9dVmHtVDE1AziPCzZ48dpvYuCEcNxf3aMWQ8fdnc0YVVVn5p2Yk2s6THbQE513TQt6+HHWYcJ27YFQfT7va/8HaYCUxoFgnzQYm8IMzKfuvn2EeYBFNFoQmtu6Z3akwJGGvjv37f/1VffWrKoyet1G4YZ6o+E+qNHzNtLEXr6eaobpD9q8U6ZqU4vukHimp0yBCwg0dk0YFo0vV2ES2H9ggYMIfmzJV5JkdmjBYwVmS0oYwFXlMouOdkRCOPNCOG/veCmFG3ausepeI2wpKgqyzQrK/EghIPhOKGU7x4AJs5HrvlCTg8AhgNmACNxyWVXnnbMolTFH1U3u0POTh9CEprZFYxbdlpXmQEgCjTF6h8oL23v7LMsSxTFYCgcjsQTqraoqYdn+UgiLvFJ/DeYSv1xyUJ5qew0IUjeiDHyuMTyEsmpxofYYrFzB0bI6xa9Hjaf4D/Li/W5ZEGShcwsIom1hRv81qabtvb/552Q1y12BJcIoiAK7KtxTgPfWCCJoug0/PzCDddO4rtWTMAkYFyAAYzCJZdd6ZJFt0v2uZXOvsj8xirTshOa0R9VVSf4mw1EgaZM+usaGrZu3dnS2mWapiRJgbKSutrKK97Tnt5hBmPk84jJ1o4pccYISVnyzdL181n9u7s1/pdnOtp7NMo+FdgmdMWys9MfMG/ekAWDm65K7msFDhswgHEBIaDR3iBR0E1b1c1QhJWdam7pLivxxhK6blho+JYywBSoP6EoHApWVZRVBEr9Po8iy4IgOIu97gJ5/2OqbdlUlgRKWdUphNCOXc/wu7gTHDzYmrIBQug9v/xNV0+wty/863u+Nd3nPlOBKNC4gBnA6Fx//fWhqEpIcrxPEbZ55Gd4A4Ao0OTGfCqr2lrb5jQMqWIyqSmeh4lN4/1GW7cWTVh7WxMHO9RQxOALCR7/MRhjQ42x3cLEZvnEbI1YJJSU+L0VgTLwgMMGJgFjBwxgTHz0io87a4Msdc9iRcMGGMYDIAo0eeqvePw+r7uttQ0hxD2gEKU/Hf55scgnbunhUanqyu41az+UCjb19vUpIubtJTDG8+Y1ffqaK9GMYv7yU1LHBLP6GS07Xij8fNBTLyj2pRcIAY2JP//x/ksuu1ISRcu2JREP8QBgqqT/Xy9urKwowxgfsWIhdco5tLZ33XR5qNB/A47Sf+KWnuuu/nDqttb2ZM8Jvq7sUpJFh/oj8cJX/7krzxhInaKNcjtF2LK7CXGCXYhVTjUsVLt6merMdiilEVzPLnnXy9N4zp094WwPePnJ3xS5BxSFAfzw4fVsdEV1hCyslCBKfArye5RLz8hsJjcCD//pgUsuu1IQhFQsaARsivkkAJiI+lelqb/HX/bBC05HCO3ZvWfDpl1zGwIVAdbGeaZw8ruO2LFjF4v+r1iWcZfPw9S/P5JMBr3pS9/4+Q8Kbg1gzjK2raFa7HI2O7SLEpZFRGwbi4JtEEnAJmElj0RRoAgJyGLl9kTMN88FSIckCmXLl8RF1qUHI7R/23PTfUHArA4B3f37F7s6DyCEOjribr+7ZVczxtjUNYypIMn+khKMRI/PveyI5ZQSQzW++8UPjeVpL7nsSkIIRIEmW/3TpX/J0sXp4+W9zc27mttqqsrmz2tgcjkFM4BU/r/j7QM3ImQSlMovGiOEPvtS1/Nv9a5cfk7qNm9JeduhzB2FheMB81edZdt2jdguCoJp2YKABIG9JbKEiWVhQWC5UxSbNkuhwtjZMY3ZsoYgCJRiUcSa4xAWoZIimiaiBKsuVjZDlOTdb/97kk4bokDFOAO49d5/7Nq2mVr0kd/fT4iNMLHYv+zzKiJMTQs7VX9DksCS+W1z9+b1gii5vKUXX//dOfPn1jbUfu3qs0Z+CUEQJDTUA4C8kqH+GffOnz93/nw2kNy4afvdXxts5ZY3TKLGTEJQ2PmXUhqMmIbpbBimqD9qqmzzMNsQLAj43JOrlTJlHE8u4KY6jyyxbQYpxjKnnBYWHHEmpbhSaHe5qKZRTKkssmRWlyyaJrVZZSOB2EgUkU2Q6LiCE/ThGbiCs2caWxaS2PoG8wBkE0kULERdeockYV2c0tEnRIFmrQF89Yd/TiTiwe6ezetfDfYF1VhMU+O2bQnOWIWyVh6ijWxCKRujUMy8AWERi7aRsAlR1Xgk1L1vz05PSdXHv/yLxcsW3nbd4AAtZyAIkcEdpIcXBfrsGYM15RFC976QW0eOPffjiqIgRE3TFEXRtE1JVuKRcEl5xRuP/Wq4Jz/jiv+XiEUs0xQE4a0n70czh1//6NsIoc/fktyG/c4721evXpn+gP37Di5YOC8cCi2cV//LB9ENlyeLc+SL3h7tkWc7dIMkdF6wh5oWJQOFgEiqEIRT4r+h2nXccVXjen63s384HS0eQYXEyuPP17VEOemidjtyNksQNt4XEKGSiAVM+SiePZRVV8W2zf6ozLQhkUjZHjfDSu67Jo5ZigK1CRXYHw/707Ftqpidq48/6511EA6aNmZtCAghdMJ5V7u8Plu3DM0w1KiuqtjZ+2M7u/yZCzCBFtmA3rYx+4iL7HNJ5ZLy2gUL5y9dtdzjkr7+yXOzn/mSy640WVkBOmouUGP3WGu8yP5lP3xi/9mXfy4ci/pKA5QiU1P1RJwQW9d1NoMRRSwgQkxRkpwhFltwQ6xboVhaXkWIrYX76ufM7+luj6mJgU0Msm1aW55/GM1krv70lxYsXJj6ds/uPauPXNXqxEwmI/6zd3fkgSdbdXNMo/KqcuXai5oCdeOYiOzfE/n9k61VNe9mgfXGZAHRQy2HCioE9K6jV9omNVhUh8oC2zMnIGSyERXTfcsSCBs/sc+fKGCboFKvGI1bTm1ctkSABcHvlSIJWxSQZVlYZD4hsYkC1U3KK/RRxxV0d+WOt1+d3nzQU4t4HXiWzABy8p+nfpd94xnvv5YSoT8c09SYxaa1JlUUR0aRaRPMProkEty/pb+1eee2+sZ5X7jrAa/b/e0vXZI9DzDMwS4feeHcj98ci4Yty4xF+r0lZfFYWIvFCCWiKHv9/ngs7vW5DdN0OtM60VbezNZS1Xi0srbRsu1DB5udaTjBgkhsW1VjiseLZji/++kPbrnr7tS3c5uG1N/PO5LIy/aMid5+47l1vRe/rxEpQ6I6I8ArxxU4/aQqIPfZNsHE+aA5NU9tgkVESzyyitmMiNVGsm2M2YzGtJEoY2QLyamSgHSLsHfSCZmy68XItonbJVmW6UwXWA0lgpCs9hx54nlb3nhqGi/25SLOBRrrp3bW8MLff/Pik7/e9Mqfd65/cs7C5fMXHRkor5GwYBu6bRuWrZkWm/nbth6Lde/ds/lfTz318ouvfvCqb37+riFV/h8eQ9cXm46vrsCK0KNxLREJBePhkEcUZFHw+Lz+Er+oyAlNkz0umwq6asfCcVO3JCzZmm5rumUahprobj8YU7W4qquG6egLtgydzbiHaWo4s/jOrV+dstdiRT3H7gAIbW2Obt89thiORXtaE9v2RgvfAHZtehnZVBGxwibNWLVY0EZkYX62Ci6LmDrNk7EgOAVNkarbHrfEVocFbFNMEWYL5B6RfQJFkbsCodgwLWepgIoiIpSYrE2OYOi5+7BOnI9c84VJeuZZw2yeAYzKy4//JHV84tnXWLZhUyvaHyTEFIhIBdGyDWyZnZ3xrq6WQ22Hzr3iG0uPWCZL0g++/NEhT0TsfJWFME3TNq2yyhqXy1Vb2xCJhvv6um2KZcUtK3J/Ty/Pu8AYWaZuW85yJEZs2mLZstvtcntFQcSSoCeipq7ZRBCDnWgWURaoDIf6JvEFKKsFnbPG33DoJnn+rd4FDR5PxWBn4Gy0kLFpd2T99v6OXj2hLe7bunPFEctRARPEVbVyb0xnoxgm/Cxaii2CdN10uUSM+TyGJXqKTkTStJxGm4IoJANB2GmDxhxVYKW5KcaCZRNRYBlCFEuI2QRlR4m2o0++4O3Xnpya64Kl4HSK2gDSeeOZ3/KDE8+7xuXy9XW3q9EwpiwSbCG2hhwOatH+zraWfZWVdVd/+b6mhYu3PPfrvJ+GKCkut9vr83V2tJUHKmRJNk3D7Sstr6gI9vWaho4pFbBELMtbVo0QtYltaColRFBcsqKYhhZVY7IoWqZJLEty+USp4EebY8Owk7PVskDlJL4MZsWfnf2643jf2nu0Fzf0nXtmPRLxVZ/ZLSp+zbAe/AlLVWJodvPB2Btb+ve1xjWD7N3nMfU9WiL2zsYNsuy8mChhLCqKLEmCJIpuFzOSY044m9cjXf/Gv9B0sHvTy1VrVrIhP0EIU0oQFVg2BSG2o+yYB/ERy/ukGFEtYXp8blZJm7BVAaf/AVIkrOsWWzgemFPxtxUTwtpzMg9gcQg1EUbTBxmP388ywAAyeeOppBOcc9mtwa6WUPchSg1iI5slDUmmoWvxYEdr845NtXVzl9cvWNSx6R95/H2cKb++of69/f0hNZFQXG6EsN9fXlXbIMsyptjvK+NbKw01alNLkGW37K2srBFYjoWciEUFj6fEX6KqMVEyNWS5Pa5tLz6Wx9MrBsy0TgCj0rqXjY87e/rWvdrzgx9vJ7alG1pZoLZxbtPHP7uHIE2QBGLb1CJIQN29IRf2LVjcFO7tUPsiPkWklG2YEhGSFJmtkdqsMpBq25LASkOIgkAQWrn6FIu1nDN51WrDsrw+r8/j2vjW82iSCaJqv9iDBTbAIJRI7LOHKcW2TSWJTTopZQEdxyBYqo9lWJQgTJCMmcSz1ArsLArzVgpOUhxhCaOCxRJsneRQZ3kAsVnE1NWGS00CSBFLPwcMYFj+/ae7EELvu/zr0XBXV+teyzBY9j8SLUvEWDesRG/fgf273qmsaSivrJ23fPn+N/MjtaIgyopC2cRZsUxDwJIa7e+N91P2p8O6iBi6Zpmax+c1NM1QY6ZbFUVJECRTNzz+UoKIx1cqSYqmlnhKy9Gs4Et3fj/3HSzBMJ8FnHlyZ1rTsEFam4WYqkciMYEpGlsbVdWY1+NhaWQs6GEKIhvG+9xydX3NUSecsOn1Vz21xBvwWLqlxjTFp1Qv9bdu6PdXVvq9LkWRPMTAWHS73JLs0i1sswVVFlV3JiASX2RFTCb5hlobYcpeGuG4psdUde0xZ1rE0nQWt1Q1o2P/lvy+DwihnZteOu5INgnAAhaoSFnFOsRy0ZDtrJTwLITkmF4QMGuMymI/LNfOCU2yk2anb7MFA0LZJmGnTDcV2P4xFv9hP46xoHUdefx7t6yburkOSD8HDGAU/vngt5Nb+S+4vq+7XYtHMDEpxcS0kChGo33xeH9n24Hd2zc3LV6y+ujjdr/2CJoYaxJPvaidQAgJB3ts27JtS42bWBA8Hp9uWrZlqYkoq4nmUkRZIiZTDcsyDC2CCPsrNE3NJsTl9pqGJrsmYZ/U9FFVWU4p3bl7P/82Ho2+sa7v+FXlok/Kow0IGDs9vZiuteymrR3dIsYel9vr9fgUV0kNU3wmX1zFmGKznHcFE900LNtoC4brGptCnZ2igGs8RA1HKFlSKovvO/Vk9uynoPt/9+djTj7dNo1dO7cuqp9jUaQl1CVHHzWuNNBjjz1HFl1uxet3W16fIsvKqoUL47F4KNKvWmZpqf+dDfnJrYyL1S7azd4OJxbk5PU4KcgscQ5bNlvydXouUOyk+btkwcmp5vCgkWiYtixjZFExuWua/bjFdg+wJ8RsJQCZ5mQtBcsyqFxR7gOYDN590WfCwa5oqJParD0sG7wIAuvqxGTA7fOXNc1fUNvU1LnrNf74uT3/N8Znlv2DJWL+HV7rlI+0JEmuqKrr6dqnyC6P15/QrFikn2+1dHvcHp/XJSuaGk8kIshmgzFfScC0dMs0DV0VBLGsqn79P2bSLrAR+O5Phux6S9lAqbLliMUl7zoiIPvzYwPnf+SNcDgqiYLf6/N7vC5FliVZkhRBFCllRZttYlNiU2QiSkVBqlpg8h/sPySrqk5drmNOPqO3u8My37HIUqdSAnrfexz1dziwb19VVUX26x5o6Zj4PoC1R5+OBFtWBLdHERXs8crhUDwSUQmhbqnEJUuvvzbuUfaxR62ybcIy+Sl1DMDZUck++mz3L98s7az7UlEWDMNWFMVJ+xlcRyHExs6An6UQsZ1gbEnANAh7Cicw5LxLZMv2Sanq+tgf7s2+McBacg7h9IuuQ8UHGMDh8O7zrtNMs7+3zdR1fgvLvRFlzEIxLGMiGIkoHr/i9l/g++NhGMA/gyvZKFQQ3V5vqK/L4/aVV9XriVioP8SDqUhEHo+XElIWKCeWHQ51W4ZFbFpSHhBEUY1GDUNDBNXOW/z6Y/eh2cIlV3/qmGOZNWbbQIm8ZdWikhNWlyt+mQ9Tx8uZF72AkOV3l3ncbgELkijJLIHdrlsyvt0eAmaKxiQRL8+Q/ubdeyRJzKn+2R6QYQCldctNy/Z4PaKzwh9q3YqmijUrl7udlprY2cRgE+azNjM/KipOyo+zs4GtCUvYNG23IrNdAsmfZsEgScJx1VBk0dlZ7cCsmrItmWwFBBEkbN2xffIuIdsDsg0AFaUHwOTocHjlqWT+z0nnXBsO9ybiESdFhc2HRSpRauqJUCIWlBQvGtzBOi6oIClcxjyeEpfLo8Yihhbn9eJFUbKpZVsmIVTX1IqqukQ8QmzN5XYpbo9lmjYhkuxiKiaPp0zN2Dj6tPNM0xIQNRKxHZveQFPIw7/72cO/G2IDy5cu4DYQNY/8z060tXnL4rneY5aXVdZ6xmgDH7nqrVBfuLSktKmmRhJFTVW/dnPTn//dbjuVbg4DQmlXb21lRel57x2U/ttv+65lEZ/P7/P5DMO46cbLcv7s/Ln1GfOAFJFONjpW0TSwafvOk9csN4hoseRNm5VSEZHIxvgEUTE10nci/0hRJMsmkqJYbImY2wBmH2dZZBVZJKdKHGFvk8slqpazuiDgLe9MovrnJBRVc3pAsQEGMCFe/zer9HDyeZ+MxkPRcIiy0IAhC8jn84b7I5aWLPA7XrAk2YaORVGRXUq5WxAk27aY9LtZDNU0dAEJbpb17/H6/QjjmoZ5GIuJWKS/t1tRXD4/q5NsqKplDik3NBGOOvHszmDU5fNqWAzMnRPr6471hxYsXeF2KTve2YymkK6e/gP7D/T1hbJtIGYduWkf2rJn4+pFJUcvL6uuH9YGLvvkhp7ukM/r8bm8tZXVf/j9YAh+7+6IJLBEl9Qtr7zYkdB0WVbKyspM0zzhxGGX1g+2BZzKILShoZ53LDiwbx9C6JqrPzLCwH+MHjCNvLYpGZw5evVKkaWEYoIFTARKEKvpZhG2BMAnCQI2DEN2VgGcwBBbIrApVRQ5ltA9Lll29gdTSt/cONWiD2QDBpAHqrzxKq+Cqms7IkiN9Oqm5fG4E/G4YRxmrQjqVDBVFLcWDxPbNrUY+9c0MBJkl4vYtiSJphEzwzTWKzpINkHlDfMoxbqhiaKoKB6CbIHVYBkTq9eeIAgswQMLWNd1VdMRZa1v3C7296p7qwMLl9Z5XC6P21PiEyWJ2PMObBDF0CGRGt61qzdsfAdNLZWVgQP7DwT7+o8+dk2GDRBx7eYDaEvzxiMW+dcuL6tr8CIRX33DzkDAq3hYwiVFpr9E+NOv3pPzmT0uFqROv+Xdp7N+JiPDpd8ZCEtspXrPwcaG6ng4yD4eY5P+AvcAztvDDNVPXLOCLY1jwSTUpShsJUCW2EZftmUYiWzxly2VWYRufGfHlJ81UHwGMO/GH+7riSBJqvFIfb/7r6l50fpShMprLYv0hqKGbgSDg6XKyiTfyD+brN/mIIjs94IFSQ33+EorFTkgsPQegzVdcnvX/fPPOZ/h1I/cxFYjRJkQ0zA0SmyM0aq1J/G8DSwqsVhc1TWRmopIFFn0u2UXW98UFUn0SNjvUSjL3SYYy5bF1uqwIIuiJElip6uqesG8aH8wGg4pHrmsslwQBH9lRajrgGhbGzZNXTw6g4rKcm4DCKOjj1mTbgNUWvvOQfTOQbSqac/a5WW/++myZFlLjtVoRZyNqs6iim6ShOqM+SmKq7bXI6r6WM07XfoHb7XUndt3rD1qxeFd1/y5o1tOQfHGptyyfsyRKwVJeGvztH1CgKIzANdl32qXSrtcDXSpH0lyLzHFL/65JB50/fJTk/SKjz/y4EUXX86P/c2/US1UaVLP0ht2724eVfdTeLXWhHsOP5YkxbJY1c+ullavt4/nTYgCq1hHbWvJitW8vi7rxEGJ6GQi8o6yToKzKUpyKoTtwur8mjJZxG4Zy1KZLAYQoqKIZVGwCGJre06zBIyws3bHslziaiKRcMqmIkF3Vv0ivd2o9ZBhaJZlxfrDiVjEVxawib1hwwZUAFRUspgMc4Jgf4YNIIS2HVqy7RBaMWe31y1GYpYTmKa6QcIxXq2YPYNl07ia/JYi5gGjvuihdjauZ+kwQ6Xf707OHlTdNk3r8HIQe3vZ1GEWsGELxHkKmtlmANoHbuuqWGiX11GXB8kKVhQkSRSR/nif58t/Kv1+7sW3cXH0l36+qaUnQQVTUmxJsRUPcXnuU7xUVpCk0OXXshiwIJ7zt0+77fRh/TiwbdOyDElSGmsCyxp9ZR7JZhuBeNUfzLaHOtkmiohZTgbCooBjhtnp8zM1Zx5hUEQ9pWVYEMv8njK/m1Dilaltk75o3C2JZV6Xqpu9UU1iAV2GxUreI0Lo3196M/c5vZDcd3riBy+P9nb7Kyv+87e/oAKjoqI82wa4E+xoXUopDYfWpR7M5V412DL9QPja2QDg1IKzLLu/P2pZOZyArYEOdPpN7waWkn6+fBAMRXr6+hvqqopT+gufEKwDz7I00Mqrv7vd02hXN1GXF7k92OVGLhcWRWdzpyUEW0v6WjzfuTT9R4764s+2tPaoVDQlxZJdRHYTl5e4PBQJdmsrJVRYtAR5/SzFQRAp+1dwjlnVw4GD5C0sl5nF0UVWGE7AZ33vdEESSwPes9zjiI/zScDfuxcqLk/T4lWH1v171RyfZROVlf9kCwMszRSLlGJFZFXabYuaTla2btvm4tOC7e2xCCurIsmuQFVF44KFe579S1WZJ+BzPfLP6WzJnUduvDnZMXhkUjaQ4uXXhsxXzj2HVeQfgT17D72x7p2cHpANV/+MNnHVleXHrl2hjG0SkC793//mrWP5EWDswG6A2T8DaDZlMreBevzI48U+P/Yp2ONUGWEZazKVm+LUCN3xMsICFUSu2m2CgGpzqDkyDXN/K+3vl4+qEJrmseYXTHidrGVWusU5kBGraSg7BwrCLudLZCW2MULue+UjVtVTy0DjL15ZUd3gLSktq6petaD/h09sHONPXbD2Iu/S8r6uDrbjhnXqYAkYm7fkv0LAjIDPBnp7Q8cet3aMip/BvKa6ltauvq6xrsdmNwkNhqI9vf2N9aNPAkD9gWlhVhmAWV5FPX7s9SF/iVAuibVIdP707CiiMYRkiaiVqLISs6g50/FBNZeS/zI1V5iaIwPZmwJ2OCI1zPGe5lThcpon4LQvHgbgf/U07cCphIhe/uvzLzkHiacTP/3b4CavsawE1DTOFWVl52vPzhlP/siT9952xoevRpqOCLVtU/S4jfjkN0wvbKqqWLIQq5wh5S43NmTH6sCvNYVPod10bJVBnb2xGbfZxD54qKO6qnyESQBIPzCNzB4DqL/ue10eH3J7kNcnlEhiPfLWoVJWzBlFK5HhRqyJqdfnPxFnS3m2mtsIGfMXkb4+8+0N9Mx3sfwR58YMuXci7k543kI3XVnXWGp//ds9VEVIQ9RENKGiaIT2ZlbkZ8/vxPGH9gYfJB7qIsSunrcARXeN60144a85mqDNMn7xP9++9NrPBAKZjf1GxnAiOSkbKC8f04+LstzV059QD79MDVsJ6A011ldn3wXSD0w7s8cAmH6LAlv1dSvYh8RaVI7QyQh1IrQFIcvHJwFysiH1gIizLwNRFdEEImFEdUR7ddLbTXq7SOtBwUVox+7Y773ySauZk1iIahbSVaSpNBZBagKFg9g0sGlc+8J1WCYJk37uc5WqycaCHhmVutk6LcZoV5hU+jwYoahBVYM1GSAEVftxQylfdRyCV2tdi557q7viyBpYD8zNQ7/5yaXXfoZt6D8sG6gZc2K+3+uprgocPNQ5gUkAOXios7oqkDEJAPUHCoHZYwAdv/oK/srDrBCLwsI4LJEDoW6Ews7gnWEh2tsVeVhHukr6epCpMxE3DGRoyDIxsTCx2aZ1xJIjRcwKXfkUqcyj1Abf2f/es4d73QuPqaB6P6FEs0lfnDU79SjIr2CvwlrQcyp92OVUKvMorFouq4iFkUfieZu5OQ7Uf0Qe+g3r5nZ4NjB2MMZ1tRU9vaE8TgJA+guHUNEnAs0eA2D11NSIrkcQCmABkQSKeNFGZ7CvWch4VbO3/EfYt+2UueX7fnLz2J8zgVCy3tgwYDPqkWyXRF0u7HbhaJT6XVhmjVQHH+ORcVTX6qD2yAy0gbxMAg60sElAJBwptjyfz54xWIzk3hfyX5lq7HzwY5/NmQiUwYuP/7qoSsLNKgOYG+7Y/WavXV6Ly2uR28f2ctoW6emksRCKBQVbK3Ghcan/WHAJpluiJV5UWSq4FWSXoWAwR5uhEtc4SlSmbwoDxmUDeXeCiUwCjtF+nzxqR93tQ+767BnTr4n51XdgJjKrDCB6/62BD349eCBk4b1IVJAgObv/bUQMgVpeifof+++8v6hLJB4XLfVil8xTSoYdJ8aNpIL4FHfeTwNI2UAoFM6vDYxrEjAo+mPgixcu+OETI88wZw9FdbEzhVllAAgh92Pfbrz0tq6obtom5W3EMZUEXF5Z6r5/UioCuRVa4sVeF0st5e3xKgIorQjQSE4wghl4tda4e06eux0OpeOPnckUWJ4RK6Da8+vQrLCBkeNC3b3Bsa8D52slAAAKkNlmACwM+9A3axCq+8b9XUSwZZff60585cLJe7lfvyHe+X4qisnO17wEelUFGuOW/nQzsIQhm07l/J1k2/c2IlFipXtFEUkyO5ByPH3X0wMZqwPprjPXEvK7PDC+lQAAmCHMQgPgdH7r46xgztAqm5OELDnppFz9nVVAAaOGatzeMz6xkIiY7gF8j9JE6LjtOVYVw6ktenh0PdnJsqkMhNVYzVWLUbEuDww3CTis5mPFGxgpwIsNFXci0FjrxQMjcMujTkk1R7O5+otOu9SFDROSh4n8cM/1v+q8eaLt6TOf8xebgj9+Hc1AHvrNT7gThEJhvkIwAv3hcM4vyzBKfG6WH5z2Bcw+Xnw82e+vGJhVxeCml299wE6qP0YSq7eMZBErEtq6P0dS0AikJgG8S/BhjJhil9+t+ivskgD1BfgMgApSjhCQjDLWAAbHA+k7ngdmAFiNiXrCq0XdX30vmsnwCUFtzfj6tFCKWtu7e3v702985ek/TyQxJtUIutDGxWNkxl0slIQrlhDQlHHahVcjhF56gtVgYEUm2PAfOzMALIlIEfGJy0WXhF/cyirRj7Dza7hA0Hgpv+o7MdM5GjH+VBprznl7bNHMi/McdlxoOA/43+9+c8pPCgCmATCAifLSE787+rQPnnDOR7/xtz//94dsETP1lwQksRkA/2Jbgs9dIz29iTVpGdUGJqL+AzhD97RXKfemdSd2aloMh39vc8YCRGzO7LSEVL5QIVCAwfHJo6gutsCBNYD8EI9FEUJfe1QcUH9Wr19hgSCm/pKAKKFnLBfeNX8U9TfzoP6OyTjyXVrjKiuzy/z6+J9i8Cz9rc3+7mZfNLOkHQAAMx0wgHyAkay4+OEX/yLwBQCu/rIzFcAUmRbVDKobdFUNWVpBFpXlEPqS6iX5WVbEgvfIFSUL8pzB6bViLnF86xnA2Pnihcn+ZcVAQV1sKKqiYgVCQHnA5XYb+uAo+wVylR6zrqh8kDXtElnHAdumms7U37KRbbPOi4SiuX5iE2RRwUaihVy2uyrUtzMfp4PwSSejScP2jbXLMQAABQ7MAPKALMuCMDh2/8uD969aXP8cvpEFfzCTe80guklNi9o2dbqsI5tgQpFNBYIEG0m2XBIKD4mK/jN4xj/a5j2ws3bFaR8d18m0HXcqmi18+kyLf91+lnn7WXxpezZTUOPiyaaoLrZggRlAHtA1TVaUtadeuPHlJ/gtiiTWV5WuQ18mhBzX933doKbJ1R8Righl6v/Tg6dgjLEgROJqQjUorhdkWXF5JEWpa5zfuLrW6/UeV1EpiHJtXf2LD92DioNPnznsCjX3gDufy+MWaSCflEkjzQ6nYEtmXmqCng7VQIHhOOKUC10ul8fjEQWREhIK9cajEX9pSSIWKykrP/LkC7a89iRC6MGn/kOJjbBAKP5Z5BhVNwhFgiTJbq8gSoIoegPVqNxW3G6MaFmlWSkrgiAQ07B1PRHtt414276tlFBZVhrmLx6X+vs+c0r8J6/OGtGfcTYwsggWoCZO2cUWfjrQ6cUk/RzYCJaDk867XGLIkqSw8p6EWDbRtASxicC6yeNENGzqmm3baiJGbOuk08/pD/UauuEvq2g9tJ/6q5C/DFFqJKIYUdvUbU0jtim7PcQwjUTMtG1CiaGpiBBimZauEnaDjSi5btWO1Gkc9kYwlpU0aR7g+8wpU6/7VTj3pobCtIFbzx7HWWXX/S4oTRyVorrY2UeRhoDed9lnsSAIooTZpi3RtmzbtmzLtG2bUpvYpmFoaiLiCDWxCREVb1l5hRqP65pm2xZzBSzYtunyePVETFZk0zQFUYxFwqZFvCINBzvivT2J/pBl6sS2KCVOqSD6xC+/8rcfXzUFFzhJ84CJq/+4BvujcvtZZmF6AADMCGatAZx1yY2CKGJBKikplUTJ0FXT0AxdM1TVImakv5s4Wk8JYWNL7HQOcLLfCRNrxB6p64riEgRBkmV/aSnzC1GQfX7btkxDFwVJEsVEPIwxe7Blmj5/ycF9e3e89Qw/gTtu/UrqZN597JKpfwe4WOfFBiao+/kV/cLnrmfMsY+Ls5v/FFpgZFIvFpheZrYBnHflzZLEWusKksiC8pRYpsG+LJPahFqmZiZMNUYs09R1jClmVR2xTSzDMHRdo9SW2bqrG4uCgAXqNA8WWKxeUhTJ9rpZRj3rMozVaMTl8Sgy+6DLkiIQkwjI6y6RZGwaRjgUlGQpGu5XY0MKxUyQiVcDzdDu8ZoB6D4AzG4K3QDee+mNHq+vpKzS7fFQQjTdsGzLkXiDEmLqmhoJO31YWICFlTmwqc4G76xmryiKNqV+f4koSoIbU2IRy6KsTQzxuF1ej5sgImBBdnkk2S2KoijJ7JGiiJkTULZllyJJViRJ9vhKZFnRNLW7q13XtNf+9lD6Sc659EZFEEN9PQd2vJXHa8cFHL4fjmIb7A9HUY2Li+piZxnTbwBnfuTTbrfH7fVKouhxK9gplUBtOxYNI0yxgCkhsWhvPIqwIFkW0bWEmkhomqqpqsvlkgTsdntkReFyKUpYUWRcUsoKc4qibtlutxvZNpsliIIgsK5dWBBcXq+j+IogitTZ3Mpi/ZZlWeZDv7hrvJdgmaZlWYnoYMvvogJEHwBmKNNgABd/9juCIAoYW6ZmGZojvQa1NCy6o+GgaeixaERNxDU14WaD/xK3x+Nxe5l4C6IoIlkSS0pLnVgOtg3DclZfWdRGUmTFJUmSKEsspdJ5LTWhulxur9tDbDsRj/7+nq9PxhUZhqlrmmkMKY17x13f48sAFKFX1u+ZlmWASQV0P4/j4qK92Jm14DH7mCID+OStP/N4/bF43NDVYF9XX1d7LNpPLNPtUnwlZV6f1+cvlSTRGc27ywMVkiSqibggiorLJbGGVpgdK24kyJKsYCzE41Fi24TYtmlapvnHe29D04dt24ah79++Lue9s69rCEh/3imqwEhRXWxRG8Ap7796zvxFXm+px+NVFMVlmaIo1MiN5YFKNqDHLM4uSTJLqmQgt0sRBZEQYpsWoqJpapFg6NHfFvwmWCesNN0nARQcMAkAitoAXv07a5My65Fk2TOWVi8AMDxFNS4uqostZGDcmgckxfXa03/Mvv2Ou76Xj6cvdgJi8fbsnlnc9czsL9g3ywADyAPP/fW+UR/zyvo9aObDa3NOwQsFRE/qi62yCF7+hWa1Jnq1VjR7CVvx9C8ztmu6zwgogDRQYFI3guWLKdP9kR/APUAkM6+KWthK68o5PDM9bWiMlwkUCGAAhUvhqH+hRXhmlg2AJgIFCxgAMFPD+rbghaAzAEwEWAOYXNLXgce+DFAm+SZYab0wSQ/rT4S7njH5V57OCwCKFJgBTDVjV/bZkSqXrxwekHsAyDtgAED+AdEHgBkBGMBUc8anf/bCTz81K/MCQfcBYGYBBjB1ywC33/qVWVkVLgVPyzm8bH2I8ADA1AMGMHXcmbYgPIvrRKayM0d1AhB9AJhewACmh2LQvuEmBMVw7QAwIwADAKbCBqB1OwAUILAPAAAAoEgBAwAAAChSwAAAAACKFDAAAACAIgUMAAAAoEgBAwAAAChSwAAAAACKFDAAAACAIgUMAAAAoEgBAwAAAChSwAAAAACKFDAAAACAIgUMAAAAoEgBAwAAAChSwAAAAACKFDAAAACmgTLJR+GNn26gIQwATANlki/n7cl2mrPuunKCJ/NMgLEAMwAAmFzufUEpk3xlkq/U+Zd/Dfdgr9Y6o38f4+r3OdMvdhYABgAAUwQMeIFCAwwAAAprXDzTKaqLnemAAQBAYVFUgZGiutgCBBaBAWAquOsZ89az5RHunU2/hll2ObMYMAAAmCJAFoFCAwwAmCgB0cMPbOdfkcz0VEYAKBbAAIAJiX42tuAFGwCAGQEYAJAf3c8AbAAACh/IAgLGxE+fl376/LiHC7bg5U4AAEABglcsqJ3ucwBmGCNks2QAy54AUMiAAQCHSfEkNQLAbAUMAMiPDYDoA8CMAwwAAACgSIFFYAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAgCIFDAAAAKBIAQMAAAAoUsAAAAAAihQwAAAAAFSc/H92wPI3FnX7BgAAAABJRU5ErkJggg=="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "assistant": "With its hook released, the axe has its clear corridor toward the left; the other directions intersect the rack or central weapons.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":3}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 3 Left", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 3. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The direction is set; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-218cdf46232b", "created_at_unix_s": 1788846012.596542, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 4, "requested_group_seed": 4, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [4], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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L5AcI4vxPoXDUChTD1gEbX/nDJTc9OJJnO46gM4BM3HVJxa7t65edfxUviNjQgGloutrddeat19/48bOtE0n6R2cAEMoGBJYTWJHDEJFcYPIaxqqqREJ+XVMZBEXRwXKkZBAJHKLF4CjFB2NgWyaj96qJD59osRahrqkppT8lG+gMYIBDx1vmzWxIuED/840HAYhEpEWdLWf6etqACXSo+v0d+w8ov5bnfPKGcpAjj92fQ/W0h54YOQ0NsYEg0E0yzLfCQFkDGyyDTB0jBDmOQ8B0iqLAsrqqOtxO0/pVchxxCFMoxb05ATBJhFoUhAZZHan0HzcK4FMf+1D801/97kkwHrhhEfP1N7cvWn5Zx9njCBi6qYcM7eAh6f/0+UvPW7Kqvn3za49ffAMpNTF+wabBMlAjvy6idQReiETCDCM4XCI2saHruq6xIqeqMjCxpiqkTBAE0SkChVLUmxMDJi7WgDqdxqUCSJD+02s8YDxMAmy++8W7Hv7BU+etvOnsib2MYeqmrgN88uh+yCAAl6yqawXjHNMwWAAlRWOAgxRNEgSB51mWZRlWViXD0HlBME1TU2SG5YCuQ5Yh8n98eoA/+9VHGSvalbUcHqLT+5//9rnRPilKWjDAy2en/mGSr49nZZV8m5SxqwBSSv9HH/701773y3GhA/oC4Yc/dfO3f/LcZdd84MShdyHAqmkahn7i4O5Qn9+4/GowztGITCcTbWTJRJfHixAk/QB0gyH5AcAhCIGAH0NgYANbxleW41/93RDdg8cU//Twf/QvDpIXciTwpUf+017+0Xf+beRPjJKZlKOM5tauKfVV9NKNbx/AGNQBCfQFBtrefvQD1/zmySeuu/0jh/dsQqZukmiEUNuZoxvWSI/8ZM13Pn89GNdRQDzCpOg/mWvLkXAo4Nc1BWPM8wLDsFb9n04AISZqwWRYrqpmIOlmPAj9rIhpAqoMxg5wsNE/LMkkTd3BN7d2AQCm1FfRScCYVgCZjT9jRAeQUe3gSUC86I/x2u+/AQD4Gc8f2bNVlSVsYtkwO9pPbX7r1X/67p//7+v3gfGJYfWERChq1dd1TZbCAGMIoSJLHm85QkQJyJEwYlnEMOWVNba7eJwK/YMHDpJ8ZlUDAJx73vLMyoDaiMYOtvSPh04FxrQCGPumf5t4YTZ3em1K6R/jszfU/Ug9t/n4fjnkB1jTNdzTe2b7xjc+/vVf/fa7nwLjEH9IgRrmOBYJxvKrP6Qpqq7oJjZ5jgMQqIqmuyBALCs4eUFUFcmQI2FFAmPPrJ+N3I+BAbFu7dj+vv00nSagNqLRZdns+nTS36a5tau6smxkT2oiMPomoLE2CbDJLP1tvnTblP96KtLVcTbY2wmxhg0cDvv3bF171ye+PX3u/B9++R4wTrjjC9/ubm8J9fYYmkZi7SDQtQBCAmIYniXGH0h6RvJS2M/zoig6I5EQ2Q2b9Q1Tx6xZP5PQx/jGKgQtjY+BYJhYN/FrnVq8JshmWkBtRCNJsujv7AlUV3hH9CQmHOzYGf6PER2QE//6gbl3ffa1uUvO621vBroGDF0BuOnYfkXVvv2Hdd98cFDVoDFLR0tz+8mTrB3WCaEdcsdyXGVdAwBYscpscYIIIexsPVNXP43XdVkmCjISGeXsm0/+8yNZj/TBjZVE4pNk5rj1JJCJIbnPd9QzBsl2xqvbSGIRhFkpgy9+/bv/892vF+SzUDKQbuBPGdMKYLwYf2LAOE9Aljz3s3++81PfX3zhVR1NR4nFXFUxi9tPH377Jf+3HnvnWw9dAcY8Kin1Q7wACDEQQtPQEWLDgYAshTlO0E0NQsA7ncQ/bBoAYYQAA4FqGj19PaMu/WVZEUUhpdCPDfaHjFW1NANgyWbwjkmipQiAapivdZDPDtLMDMhGJakDrrvr0xiDzsNrGURKhvAcizHWTYita+LkoawBkcWIlBAHskqKjAs8Sd7SDCzrgENY0sgdhwFgyE6k5BSADMagcdm1EILXnxtnA8Hxy9jK4nn04U+P7gnkV9j++V99dfeGNQ0zFwESLwmJQ8BQIj0t619+9ls//xsY81jyjnx0hiFpvRwvErmvkYbvhqGzLOd0+UTRFQkHBcFhGjrLC4LDhW3z+RgY+x88cNB+xD7OdRXwxip0UxW6uZrJNVMBAkCqoiLo4Jg7J4m31Qu31Ak31BAZh8kNgndsf99+xHb54te/C0qDiilLVixb2HFgbeehtfY8yR40JZQEZxAwMVB1crkgmVaSbVRSUxaw8SIHY9O0a0xZ2wBwYucbx7e/MW/OrFnLrxzxD1eKoLE2/B91HZAff/vdI689+YNp0xchlgUI6SRQEgf7uje99cbDP/wLGNtACJxuD0RIkoKqIum6SqQ/gKZBIoM0TYYQqaqsqYoiR/yd7WF/j2katqhdeftHRlf6d3V2eDxuAMD1FfDGSnSjJfTZwuWnIQhZBEWWuaNBvLVOuLmWv7GGxQAjhHa9v6fUdAAmCeM4efKUEA9mP4mr3RDdOSHUgqywRH/ihibpzpfruXV29+W6C6VYCmDcGX8Kwkt//n7jjCWC6MQIGSSwXu/r7dm69u3P/8eY7pnu8Tir66rdvnKH4BJ4B8JQlWRBdEFMzOMOp4+Uhoak/INuYEVRTQMTq5FpQohIqOioSn/SyUCI3FSFyFDfduwWBwgBiyDHIN5SBrZ8KjUdALGOrXohpnVBonVADDLegVhjgM5AYiQEhgKAQQQ5tjpLkO5yZDYAsA5MFSLMQgMBAwKDzBQMk8y6rEOZtiufdBoinhhKqUQBjaI3+IY77rPrX2Iw9D33sbuJX7evzwqDScXzf/yPm+7/cndXqxQJm9CEpiHLgX07N3/i6z/9zXfHaLGBt//8KwCAr3ZaVZnTMNGpo/vTbXnejfdFQiHB4SCTd10zoEbKQoyq9L9MGGlHtK1jbqvjXmwjgUO73t+zbPkS+6WJ7w9IGpcTvYvJX9Oy9hAz2VAd4mx7I9HWpJ8oWRqc5jWwTa4smD0l5fp9m56zS8gtuOjOPA47gYGGQoYwVv/XAZYsuGbIPX/08+9H7wfrm7r2ynMWz7+mgMP/EdMBMQUgwqgCgACsOn9Oyo2vuSx1NEjiZg98Z/q8JaFAHwMBzzIIIU5wTJuzeMqUaTO2321vw6x8yNj02NipBlo/ddacSWWtfeqRgwOj2iG5+v7PvfXET0HJSP94MAYvthMdQALV+3UAqSA7cXVAxaT5M6qRrBjWjU2G7aZuIkwqiEi64XOzsmpyDDQ0HbGMaQAGIUUl1kKOI3ZRgE3Sa5phdN3kWBSM6DzDkEqfVlUpwwSaTqYEgoBMsXbW7Bmx910xrzrl+SSEga66cHGGk7driFIdEA8rKeTrGQQG23atsRfPXxatbfC/v/xBTCdbQr9f8vfr6Tfe3vnDn33/2ivP+ekP/jCOjD833FmUxN03H3/kivu+tfzc87q6unXD4CAwNKXl+EFWDZdf+Vb521e3Nn7QG9RcQx3nwJbnwYiw4KI7WQZ5HHxHgGTGZk/JSn97DEvmAa0kUmgCzAPsYXJmLvvAI8RcY0+DLI+ugcgCwsQ4xiJk+XiJW9dy7ZIN7coiVjUHRAJtSS0RohgYBO2/GGOrrCAgoUD9R446hSlFBr7xxkCX8zj6Zf3APCwq/2OS317un81FvT4AgCM79hZKARR7EvCPn/s8afWFcUdfRNWMAs4AbC6795HLV61saukini7Achxf5nVWVNTOqT7McuwzLx//zJytmY9w0WdHLojo5g8/vHha5ale/Jef/Wvx3iW/8VfBpX/7zOXTpqauLsmxbPDNF8fmPCAbGV1ULvvAIzPLoU6KYAEEgcAB3TT6Xb4MsoQ4Qqaq6wxC2GSsDhNESBBVQZqM6oaJGcSYpMIIMIkbyQoEIkdDqh41RTAsxJ6GmTOnF2MGQCcBQ/oAkq1v5Cv0eR3+gJSwkS36TRNDRORmAaV/sZ0BP//xf9o+qh17jtV5ufaewmearH/6O6vu+do1V1954kw7Jl2FoSzLMysOAsA89dKxz817F4wl7Gg8xBBrWPEYsTnNG79J+0btM4fQ4p5rbsteB0TnAf3+gJ98/V57/UM3jrKwLhIkRIfE70fdtqRrHEkexMTP259EQ4ye5GYiYoJD0XEkEb9kTkB6SMNomA8RHyh+A0R0ADlWniHZibzy9vbYcoXPtfL8BfZNSA1B6RRA/Jh/0II/INlzgKQNbdWKCyv9i8qvf/K9bXuOkiVL6lkzGeLCGpKX3nwvpzf6l0/eAQCQUEVv+0mMtQvmBE3MNB1pb2ycBsDYUgA2SSF545VrP5F6qvGnt6NNZTPjuea2FVNzcMAM7TSbKLAcgzXD/v1bAT4mRlY2FwnnsawCyLL0k98V0Q12whfGwMDABCbRH2TUT3YhmWLYBMSFYGVoW5siBBkWGhjwWQRlZBD3Kdm07YC9QHXAUFFAcYqAhIjYw//BKmEEKNIkICr9CeSTaHqSIyQNdjJQTBFGzWLWOChuC3IT27Yy666GK6bp74HG6a7dkOWffX7P/XfMjUjy08duv7f8b4XVN5m59ZoLMryqGYZpYiMhHoBS8sRLVQyQyEIOImvyTNr0krbR/QZg4s/FADHIju4xDftXEI33J+oUMrYyMI3ob4fYgKwAUTuEl2HIvwjBMMxnJtpvpx7ILYjZpu0fqDXOg/GaoMRhM0l9e6Hf+JPk9x20Y/GG/8XQAb/5zWOf+MRD9o1JgpMtm2aMDNrNDjW37qV084Xoeus2s25Da2FR3YFIGP7sNxsrfI5X3zk5Z0bljCr+1cgHbhSeynCehkHqM/ernOgN3q9vyKGtF+13JR8kzh+TszpxCrxhmiJUhq91MmsaSsEZcuRbIMgtF1ENmaT4Ao0E+ZCgTwaaJHAfQ9WAxOBjeYAtyETBMEnCuBUhCk3cP6nCWDUAS8qKmDppMAQhYgwMWKucBMdALObjBI5OIqK/vbj1gzYgm3ziC2mrSJWsAkhp/IkXhimMP/bzYht/iqgDLJtmfL/pAnYc7dr9hBzsmnzJPx87/T4vslvebf/4A/fO7/y/r75RL2va1MkVi+a4N3Q/eKk2KG4qnmgf1IH8phR39cDNbeub/plJ7F5PcufbKmSQtre9cPZBdN2w5uPkt4QxUTbRQ1pr7DlN7Gc2AhOXiaFpRkpG28SGDNF/7R9y/7eJo0b4qOU9buhgzW9jd07sDhqI8bBEuYmJTOcY4tpVTSzyDIegokGeGdhM4IBhQFWHgNT4IWt5luR5qVrUIsQy5B14llE0Q9HJ0TSDbEDmEPbwJk+G/vlS6Z/WBMRzrKrpycafAZUAJg5EB3z8Y6QJYqFjzlxmU9O+jXKwCxhqy7r/9ELMMvgDtz7S0ukHAHz/2tavrMGqbkqyJmsQTCrcG8dPP9KvsZcT1uhWQiaDoN0md9DO9h97Jh9L/x80JBi4L/rLLCePJ2JVX+DgNcSFaD+3pzu2frEtadF3gQXTNPsPdkVdwa2p2zi7XE57oetYbhGxGXhpw/HKysrevr6bLx6IbLHAf33zQEVF5bXn1MYra1tERkWzfYnsK2LNPzPI6ESsraMdteyN+mew8XfBwIw2Nr+Nuz8GprHWCao6RgAKLFANEr4psIhnSRFBVY/aB+zRQf8h8GDVEZup9tt8EORYZH1IskZAUNGxVkwz5Cf+iY79UyiA6L2nanq879fnJf3B0/l+7ecj4/stkjPgN7/93Uc+8hFryFEwPKjt9L4tUrATGCoCOmtJf54B+vbvzhAgsCpX/uD6tq+9hc60y/OwtrF19iXnxXwSo4b1o8MMk94tlHyRBmRE4mvxa/qVT4Y1cWKsX3RE48HjZKI9RB1YFf03en8OyK4ki5llgiDCyJ7l2Fsk5JrGxTfbEQH9WijqwySjBHshbvhMDmgv28L62TcPGrpKtKkcMXTVCmfBgrf8lnvu3b1jR7C3B+Bp0Y/bb7mLhILz5i9Yu6ul+cRJTnA4PWW3rpoxaFaXfD1jFVxSTQzTfUfZbTTUIUwyU9QN04AmxAbGDCZRO4wV0k/Cg0yd1JQFgFiErEto2PNra0rJkE1JnA/Zx74JyMak7ZBpxRKZhomIW4CBdqxRwaHSPwH71x691jzHKuqg4b8d/JPe9zuikT9F0gF//OMf7YU77v7AkBtv3HXKilMm9Q8uWpR66N60b4PkbwOGgoBBpD/CHAICC10CqHbD+bWseIG45fngo1e3AAC+8lw5Brh6/6KjtQturnoajB5WMB8x1I7aGaRXMJmVSKJLJrXFLN4jGBVIg14bsBtbL9mlP/t3i/l+rL2ie/7traOGTmLXWauPJseyFQ6BY90Mw3DINAxd1dT3jx69dMXKE8dPhiOR8qlgfeuh3s6wIYsm1Bhel4OSYbC9vb1zFy7u6eoQTE0Pdr74cpcGGBJYjRjEi3ddNReMGUihB1LdGesGZojYh1bIZtzLyXI7lT0HmxgSl3FsG8uVbALr4JCEledliN20p8lSKonhWxVe1+9+Q0tMp4BNZ/wZGP6n9/0mSP9SY8u+s/FPX3yapNQ9fN+cSG8rNlSEDQaaRPozlvTnQZULza5mxOUCWFZ10bKPbvnm/5GpwF29X36uQm1qvw7Btdx9l/v+HH/Mh7/5ve99++GR+Th2zo4VqT2xyU6yxCoUJ/HsmgOcaTpdThabpHOyLCmKpKoRWZV1neS6chzLQMSyHMMwvOg8fvxEJBwM9vaGlR7EIqdHdDhFxEJF1uSwogQYhmNNQ+ddHhgKVlZ6rHfmTYx5nkcMWrv5pCSruglYUbzhEmsCMaogBBAxFhLrTfx6e5KWcHVtt1FyudDolI9I+4FvBEFoWPehfQ+++NxAfMTyr39pOOfcFxzlpPExC2sLdlv6Jxt/rMyvtMafhOcjEPg/FrqGMQiJPKeRlMaoHSD20vWz9F3vkfCy2dOYOOkPXAKR/rOqGfcSAZxfCcDVADxy0bcf2fLNGgDAf9/V87UXKp86euYaVX+77Korp/09/u0e/ub3RuZzzZ67MPp7pKTihbePqsGAg+eQrnX6e1oPtZN4FZ7jyP88y3Euh4vlBIyxv7czEAqapok4nhcEf3fn5Okz/+nBG9Jd1x899lJHS7Pb6+1sPSuyHNmRgYZhQoQEnne7XG6Xz+PyOZyOXfv6gqFQSNFuWNVY2G/pq994NJvNyhrmk9E6Cw2DhPhYY3kS229lbyUM6vvzwqx0sMEWvYGgTNO0EoQxORqZS5D8MnvTbCOzsyEhyoMSg80YAhoXDgQSGa20r9HVAQyDaircK5bM6vaHewORYFgOS6qs6oZhXj9roCft0dPR2/ei+cDJw0onmlHF+BYK4OIKAFYBEK0bfNG31wPw8JZvbn70ju6vvVT7clPXubrxduXlV7pJt40RBkGrMIuirl53cNPfc6iFkCsjNqcpFH98fpuTZXgWRcJ9J061abo2bfbCb9w/dO7XE6/s+OdZdlmt4+DwG+k2+89Logvf2LIUmJDjeYfL+9E7VgAAHnt2YyQUaO1oPa2e1HUDMVxtdV3j5GmbtzZ3BUKbNox4uyHSDoCM0TG2in5awp3I8ZglKOoItkb+pBRQvwEIWQ+7LPRATFl/MyKSI0bSCmw5Y0UZFVIBUDIpgOjwHyQO//vSD/+P7MihYOS4I8NoobbC43WL82fUGibWNENRtbCkBiPK0z/8Ysrtt5AuVfhrtzAV83lwWRkAFwPw3/0vtgHwCADHL1rl3LIu8uit7QCA/7fOda2DPVl+1/YT5j3lL4ARhJQsZRmXiNKWus6Xx+4fHNVxZKCpegJrXV+xFziWWSIRH8lwkKdPHuYRnnz+PVEQsCqdbGnVNGXanEVfu++qdBsLJ5sT1jy0oCanbNaW4wODqq/ufDvlNg1TGzdv3+B1+6ZOmnrXbR/p7PWvXz/SasDqBADtig3soBZf/cSSU/ojvmwzUOy3FXP2288QY6UMWFYgK8w0GryU84nZZxcXV0bJDMtzbELgvy39rSexlaNv/BkjkwCPiwTxQEC6RLECEgXO53EOmRj96GoVrFZ/dOV1APykf50CwD8DcASsDenN+pRqtrmTTCD+fdWph9/QLj5n8sULGl48dD0AaYeNhQdCVTUgQlZZl9GEG4hDHRbiqTPJKzt7jeryIcuwgnfWbnBgpqenPRgO/vutlwLQb3JJkvIjzJFd6wAAEX+bosmHjh2cNmnaTTfc1+UPOMrK176asrBjIYmGSVkP1hIdlr/cCgWyynxaJPgGBh9icAswg/QXi7p8GQYZ+qCiw7lS7nU5RM7ndhxr7iJ9hvvh2bHY+GQsQK5LQuC/TZ8/OvwfO8afEdMBGezgKVvfZVm69kvXPvejN0jrFYuPA3AQbAiap7X2oClpA5fZMMD2fa2dPZEpNd5rLj3/zQ3bwAhB6tWJIp/g3JtgXHT+wvaOnlBY6u4LdXZ0PfBhUjJIfufVPc46e4OIpL6/5T2oaAebj3znjivAWKW34zRJx506Y92WtbXVDQ0se+mq2zesK/ZsgCFmegbqGuL6w35MYu+HGBP5/5lzD2d5oP/dNoeUkSbzCLIjabMJoI5JDCgg6iSfRkMXLJrqELmGmvJyn/NEc1dvUFI1XeS5RbPrRzPAbgwTrd6dYPyxnyX8W3Djzysb9oHxRrqp5Td/Mih6Jx1furbK+vdjAOwDm4PgpN5mSX/DBOWe6FT6BzecjUSME029O/e3BYNBMFJgjHVDE+wOHROU14IiyzAOh+BxOw2NWGciYVLgVrzixgtWnHPg4NHtm3fu3rzt5KmTH/vcx8ay9I9xaNf6UF/L8aPb9x3affLUseuuv3vVLdFypEUCIWgYmCVuaqufY9x8Me8bh/gQiDOZ1AKyisvFUslyo6rc7XIILpG/YNG06y6ef/HSxil15ecumHLBotGPnhqboPjA/5j0t4b/1qqh9h/m8H84OmB02scP27Ro6YBd4D0/OKG1BoyIikkhIgx0Uholus1/33hWko3O3vDWXYfACGKYJinjPgbsp8N3ACTQOWu5ueLKpQtnEmM9z/m8LllWbrmZGPQjYcl+KH0Bv7+3uaP1U5//VHtbB15xJRg/9HacPHN636Z3t7Jh9aLLbyrSu5DxvpWi1R/GQ4LiBl7ORWjHT5uhXU+0f2W0hfAw4BimusKzbN7kq1fMXT5vilPgh3W4iUvsy0vMi0z4N+XwfzjSPyb6h6MDDuwdUTdp5lJr2fOla5vAUa3Nb4Y1YEt/w8CSYgYiZlt3VB//6ObWH97cBkaQaIoTcaCZE8ABEE/nLNIA4J31UWOa0yGGgkEhTij87rE//eaXvzx26lj99Kkf/fgD9srx2JMq1Htm264d3a0dKy9OG3U6POyqDWS0bj8GRETulyshsZtoF7syqP1GwwMCwCJU6XOJPHUADKUA4o0/ff5o0gQumvTfcKDNW2kbQ4bFi0++DorDum1HUr+Q/i7P0gpk86U/SmGVpFNaVdQt6R82g2FTVkdt9I0B0k0k6Xap9omDLf3tMnakUczr6zdv2gkhvOSSaCG5vzz5+7aWU5/49Kf//bvfit+xra0DXDieJgE2gZ7mI4e3HTh6+IZr7yr4we0Kgf3tXBJeyv22GbyLXa/OygLAyC6DSBmZUhBxpv8oIykDXtmw76ZLF+W374G9LyxYTJqujAyxeuLD52evqZ+8hjNMLFtj/1DElFSsaHjvCXXxjNGYsVr5lw6n0BsaZQUQDvRuAYk3ZH7Mmj/P+hfv3nd8wdypAIPm08enTK45dTzQ1XnyzNn2M82tFZUN//aNb9jb33P3zc88+/Llqy6KHWGLNqwzuRaMDr2dJ9Zv0xYtW+n2VWxdt7oYb5EcK5BTAZ8Em4MdGGrPPUne2BiwQ5YCg2JA44f/xTP+bDgQtWx4K6sC3V3D1AHFmwSkIZP0X3OMjU8HG5Jfv6l96BIUiJjBiBlRsKIBVQdKDgcoJCQIzzBDIUnTi9sVcsTsPzHpP9APFoKHPvXRzvZOAMCcebMtf0AmXdvW1nHRpRdt2bDFfvreroOtZ04riuL2lFdWVrldroXzxq53MdzbvK+3ecqs+ReuumLruncKckzSwN0y+tujoIRRek7eo/iNSb6wHf5vV4gbXiB/S2dfQ3VZ3ruXFOygwP9+Eq59MaR/sg7Im4JPAtKXHx7aLpyrDnhyo3n5XFNSsEqkP1Z1oEabbI80qrOqanKlwbHSGSIfxzueisrYvbz3wIl5s6fY6+OlfwK28yN5EnDO+efs3LaTRBkumw+WzS/eOU+afV6Zr2z/9rcKeMzmYwf7+vxX3HAN73C//nzUZ3bh5Xc4HM53Xnsyr0NaCtUqAmFF2pPmj2buZQTtHGDDIHUkiFUpWu6aHGZQajGlmLDx0t8e/qcO/i8+w5kEjLAhqLA6oH/sT6S/YkDVQG/uhQ//1PpxPnE/GDEQx7rLIAScc4JUzvL7Az5ftoOVdK7vtraOujpSsmkEOHt0+6D6gsNg3vnXtjQdDwUDgtPhrvB1B+Sze442LlhW31AJNCBHAlvX5hNAgQFw8aSZl93YnaQEM5xd3EEzSBRZ9oci5aFJ73jAAgZgbOiWFrF60jAIhMehB348kso/ns74YynozMN/zcRc+jSiDQfarGa5g6Z3BTEEFRaY3+wgX947xcyv1VWDjP01A2mYURn3jl/dZ704cq6wcLDP3+0u83l0VR6xN01xGoHewg3/gd9PEiliw3+bhskN9oIiK4auO53RDjATiUPbojnkUgRIXaDVimnoPgtODq8VLkKIRAlYUUDRZgsmKeKAEInfz8lsQ9KHMSQlgEgxOIjtbpHW78sg/RWoE3gkQCmG/+mMP8OW/jzHuJ18sp03PiIov6hQexIARoDspP+aY7lFnlmmf6ialvRH7psWjFz+l82Ftz2kqYoiSaTUI8dfc//nwESh6eyARcu2/9iiX5EVezkSiYTD4fhdbr7p6rXronZ/m4suHbAIlTKmaTV2YRDHkPa/dnEfhiElfEgwfy5Sm2wMrX0gssvQspD0iURWIqJhjpojqqRI+sLyNf5klv5WyTmm0uecVO31ucXkYgMFiQodU6w5xmavBo53s0T6m4yKXK6q4ZYwy4NQXzdiGYbjgn09uiG3nBz9DmXDRBSt1muphv+maSpqYpG2SIQkP0qSLEmJEyASD0qxCLbt9yswbLBBFYZUKOtA0RhZQ7KGFB0pRg4KQDWQapBdrAVG0UFEAxENSQarIvfp/YXxWlMyww4a/udr/Fmz5dBVKzL1Ldp0qL2qzDml1ud1CwyDJIUU0Uw3YczPEFRAT0ABzTzZ+wMs6e9wVDRcXjui2b82siKxHMdyLGQYwEhwlKKwg8HQIr1v+MdpBlETUNPZzgT3byQi2bpBUVU7BChmufD7A3x/UNDNN1398itvxbuCL7r0Iv87r+V8KhNuINvUA2bWkqx1uzWaTpKBAcOQGlY5mYA46xZDCGg6ORqpFY0BRkAHwGDLi3f+lHgGfud5G39s6Z/Z+OMQ2PpKT22Fu8wt1pa7Kn3OuM7jBTMEFSo3uLBG/iznASoUhbKGK+pHZ+jt9niBaYT8fT3trV1NZ3Qpah4ZYRZpBQhAaq4h9R5SDv9dHnIby7Ii99t/bDSLDMekk4AYwbYDVt1mErNv/+hJY4C8fjX2LnbyAITEH2Dr49MH1ud+MEo+sAOx/3kZf4Yc+6/dTqoDBiJgZ3fXzt1xLxQh0OjFJ1+/7UPXgbFHNvMA3ld3bsXRkfT6xrPrzWfqZy9v6WxDpOWHeeYgiXocv9hj/OThf3S8TxpfQlsHmOltzcmTAIrNyTZpTg3X3+3RsOYCJD0Ak9rO2UJi/5F9BKsABLS1CdYQDeEfOdihfb8ZA/+vuoBI/8zWfxtFzMLKL7fGFvOOCBq+IagIkT7ReUAGNVCFjjFoNO0FDXX1TUc6NcMor6gOkOY0o8D0St/wD7Kzs5s0LyzzzB88/DcwMfKQvjfE1sVCCIOBoNPpiHkL7MlBusOSeNArbijfszmnkymEQWvMARFLJgFMVHwTD7Dd9zGayZvdQaBJks9JbxnSUZLoZGTtP6q/glKjf7wZpwGObM9W+ssaqQSeQfq/vq9bdVShsnpvw/Sp9WVzplVNqS8TBTadfGXL68d7vejMrDnGzpic4keysBG7Begc1ZKFOza8Wl1VzXPs6SPju93bnRXkbu4NDPhyO6zhv6pqgWC4q9svScQEJMuKrhuRiBQKRSIRKWq/HmzGvuzSFQnhQBTiLGk9GI33seZSRAHkk7hLKgHZ6V924zASSQTB2SPv0os8aqUgCmj8ORLgZzdObqj2Xr9yrq9xLrCqu2x/8xldNzp6wseaujp6wjoxHw5+++rqQGfnMK1ARcoLa+4IDN94ZWKwZAaeWo4cZA5N0AzcGQIeETKjXX/ywP73Zy9Y3tE6ak2vdnmnT20riA/ckTD87+oNGoYRCktE2EDgdjl0nczGWJY1AmGnU5QVEhfk9bgRyU6yW1Sl8gQsuRhvTd2sMSVeMDE52qbNrWcNNZocb8dx5llA1W4ObAKGA71K6mZt3/ruj7719S8N96QpSZCpXDxk+A+HHv6v2XLoyqGMP+0tZ9xOvkJUtu9SvUePxGqoMQwq8zpqKz1hSQ2EFSuDfNBZeON0wKgYgoa4i4cnpTEGso5DKhZYUvOEfAcMqHRBfmxMfI8eeB+Mf7DVrip++B8Ihg1dMwwTIShJSNN0w2oZaPePDUckjuMgwKFQ2OfzOh0iy1rh6KOtkscukGEZVgIaazIQQutq2U3icziE9T+DTZJEpkPAI7bj9PiefY47UB7GnzVbDl16zuzMP40Nhzo4FlWWOeuqPG4HH19BE0Io8mx1hauq3OUUOatKV1JaQHV13oagES8PlzOaAcLKQOVECKBALNJU1uQ5GY1hkMZS5JEw/D98/IymqoYVrWKaWFGUYDAoyYokKxFJlmTF7w/09fkDwXBfX6ilpf1sS3soTLoJYowvu+SCeCtQW1sHHIc1oguOv+3Q/hbdIRATAstBxJBkrhwzgckPHzGQtKCDwMEzrZHClIClZE+Sxh6y2NmWQ+cvauStIN50w/8NhzpYBpV5HHVVHjvwP/FdEfQ4hfpqb02F2yFwRRJ9hc0Njtp/LBPQ82/tGM6hMAAyKfpWmBObSPT2BY5XzY7JcZtoD3IbezlJ0GCMe8JKqz/SFpTW6b6E4T82jfgyBYZBDm0auvUwTFKQDGuaoShaOCIFQxF/INR8prX5TKutBvL8MBO9nrG/9eDBswrpEKmTjxrXgz1bSBUJ8pdUGD3jZ7qb4sMEc6a6koYP5QxKMfzHaYf/a7YcWjR7stvBZzb+EBurk59U660ud7FpUopYFlX6nJNrfRVlDisnoCiTgAMn3gQF/+UOW11hDBTd6gRJGcyxk83NZ9paA5H2oNQVlrvDSq+k+mU1qGhhRQvEllVNUnVZM1Td0AxTN8zOkHy4w3+iJ3iiO5Aw/D909PSQ3zDRKaQiDTYMU9P0SEQOBiOBQPjMmbajR0+ee86SBFcwvPBKzTBV3bDPQdUN+3xCihZRdUnVg7JmnecolfYeKYIdR0kdH2hV8MzxfrauuVVBFOKmPsPfenA4Z0Klf36wg6Q/zJT2tWYL8c5Veh3xTUCT2XCog+eY6gp3fZXHKXLpLBsQQkFgK8ucsqqHI2pfULZ7jRbYGRAOgbzARY4SNTGQNewRiP1nuMeaQEQi8lmlA5lBq0QMYCBEiISZMIh4yFXLhsPYT601DEJ2WZHWQMQvk0wuceaChOF/rvodkw6dpmGahmEoisJxLH90b3J90Oa+sGaYwKqPTza2iuhqBmmqDEmdAxNB4uOpn2glThJ5/8Ch8xfPsaJAMSBl4rIFm2QXFpgKNMNdJ/N7986eQHXFRHW0jwTZFquxpf8ly2ex7BDGH4Sg1y3UVbrdTgFl1BUIQp5nq8pdwbAiK3qE/IAz/Thz0gGxpLADJ95cMOMakAuJXunkl4fBr9bT/tRpwaZuADYSZ3WxHYV2MxdixulftkIGSSyiPcJQ9KgBImH4f/DwCZh1XDnG5kA5M9KqU8cQloW6T0JQC8DadVvik8Jm3nTbm3/8k+26MayRLNFOVixj/9jWOtOJrgAAANv2Hll5zjwG5eYDgFb5B/IdHc1T+segw/+8QVkafwAA5y2cLlihKpmNPy4HV1vpKfc67M5BQ7w9hC6Rr610l3kdlmrJZAjKm1wNQZlv4+H0KqJkQwdXHjP1W+WCSQ6XYdkYYsu6SYbbimFqhmH0Tx6Th//eSB+2Lc39317mt44ZMjAme7mC3bJuRDRD0ozkyhCaiVXrHMjJmNgwsX1ixIdhnaRRMrfKpp2HBB5yudTA5RjAc2j7vhGqfbLxlT+Acc4Xvv79L3z9+4U9JhoU+ZPe+LNo9mTPUHlKGw51iDxbW+mpr/KIApflSJlhIHEXV7pdJFgoxQZ5OwOGEw6UUcpTo80YgmOYWLDm/NlT4186+/7OeB2QfZyVLf09oZ6E9TQpLAObdx0iOZ5ZIwjMzgPjvujsSMr9H3/3qz/+7lcLe/C4QXqaX8eaLYfmNNZX+Zx2V9X0xp92hGCZ1zGpxudzi9EWrFlAnAE8W13uqi538WQIMcSO+aUHZz8JiEn+ZJ/EgIHIikyhjDoQAJFl7G+qf/hPggsTrP+2DjD1xBLQKTFJdlMK6e+N+OOftrV13PjRB4b/ESYS67bn8Nt8d/coVL0dR3yhmHI/SQFYUjd5+L9my6Fpk2rqKtwkVjfzgRDyOIW6Sk+Fz5Fra2+EoNslEEOQR7TeqGCGIDIJ6PcDZ60DosGHdti4jdIfzoHJHN8MRWRZ1WwdoGi6pGQqJEnJCWzmEDmDrOIBpqW0E4b/8UAAKmUivrGhxx4Jbxp7pJT+lDHLQELNROGBzzxcbLkfY8DllVL6AwDqKt2xUM4Mvl+HyNVVe2oq3TzH5GEjYRlU4XXWVLqdIq+FerRQb8IjfuP8i0VnpwOsAT7W4gKbIzIZFeqGGZG1rt5Qtz+y69AZVdMN09x16Mz+E610SlBYOrmsKsIzpEMhEf+Drf8dMfuPDWk8i5BXCtgPe2VUE/QLfW/EH3ukeztvxJ+QFEYnAaNO2PptThge+MzDj//8eyP2dkPY7JbNm+oioZyZpP/b24ie6AuCvva2fXuKZTN3OFhJ0vOICCLhQJ+8K8t36U88Iv/Jiu4UidvDxLi5rc/hEEIRJSJrZLyP8fuHzgjE4eE9eLJ9fmOtXcpq82uPX3wDNQuMHCyCutWIPMPw3/b8KnHqPKYDKOMdjMHplp7GKbUJ61s6+xqqx19e2AMjK/0HZgDTaxOH/29tO75w1uRyjzjcNN2sd9f4ihQPbuDBemvAsBlyEtAv/8mo33YFk+IBmt4TiPQFJXslBiAUUQ6eaN+w87jXLc5trLWr+lBG2ApEAoE0I/Pw3866KJSdgE4CxhSmafYGrHYmE8XyM8Kw6aV/Q7lbiPly0w3/X9/TgcTKKp+zntT84ZwiJ/KsKHACxwgcw7EMxyKSGmPVi8UY22kEVpqPTdwYTdVbuoJN7YHWrmBbT0hRrQRze4P+Xy/rrdEDHTlPAn79XPwkIF1mgD38t6W/1SWRmHg4lunoCYYiioGtOPR+PC4xGJEhhDesXOBzi9mcBiUnOrnyam2Q6S8Z3cQIwszD/2IjivTbHzUMA1sVhSeI9H98ZIf/mUxAk6s9sjqEb/ONPR1ep7B8Tt3CGTUeJx8/Cm7v6MIaUDWQk33OxYL5k1zzJ5GSsKpmtHQFj53p6QlIgwJyyisCvT3F0AG28I+9U0RWbQVge4DjpT/LIJ5j+gLSZefOmlTjo9m8BcS6iewK82a0WEyGjRHkZixKHP7v2AaKiTfUs3btpssvX2k/PXWq6c5//OTzP/tFUd+UkhLdytmeGBfn8RGX/mQsnnL4f+UFs+MjedIN/70Cbihjyx1ACgU6O7vbO7pij+GfGSkm6nPWVLhIxcHB7+8tr8jjgC/++rkhtohWGYv+Iym6VQ8SyEkVXRgG9QYiJjZV3fZBUgoDQohhGJ7nPG5XNmqVGe3hf4w7P/uPo30KpQgp8D3+o4AeGHHTf4zUyboOnswMPE4xg/TfcqitwiPWV3k8zkHVngsFhNApsLXlrgqvg0uqKBfTAbmlhsXpgGRnAEKkRVE/WDeMiKSZpikPjvJkGaTphttB+gjuO9p6+FSH7Ye03cXZnwwlGZfT4fW4yrykjsjMmY2h8qkMy/O8wHE8y/EMyyGWgwx5AIYFDFu/4tKEI5x5/32M2GE+QNxC9MFEF6D1vl4ptHbtptibnjrVZOsA+9xi58lytOxHcdEGZ2iPRx4YPemfwgT01rbj11w4Z0iBvnF/i9clTqr2Vpc509X7HD4Mg3xusbbCHZI0fyhFtbh8agTF2YISDEEJ4psEfSpqRNbsGFAbUn8GQYHlrr143rt7Tze39b675xTPMTMnVzEIBcIDbQgpebBhzV9vv+9ThqFDCFUpdNH5ixV1fu3Z/YZJ0i90kxh8rVoLVvWFWYsry9yxfQ8cOCQ3JRf+HAQEwMmT3gt2mTlyR5GepsjEWGAZE5MqDgJLStsbGJNWFZAsIAg5Em9qQgh4hiGVHgB2sEyCIWj69KkL6srIxgzZmJSoK58OpHzq0VKyAWOQf7HuEnb8ZlIAFy9t5Icy/mzY3yJwTHWZs7bC7STV/IsVAPOnv23McsuC6IB++R+tBWeaWJa13mBEI4XGois5FhkmnlzpaWyohBBqutHWFdiy+6TXJdZWels604aQlzLnfurP2W76xP0Ym6SGoFX37dix4wsXzIvMPa/6xE6MGbvImv33aOXMBOlvfXNDzMA4BtV7RLfAcVYRUQNjogmsBpC2GjAx5qzeJibGLClmRdKM7IKjpqUJEISkADJJQYANXvBinA4AAMy9/e7g31+2Qx6Ols9wQwikPC8aZUgwxsnm2fFFS0vrW3/741hRAG9tO379xfNiAl2WlZT7cCxT4XVa1Xvsfl75MG1yYuhuMv/6qdsPN3Vv2tPU3hNOtq6wItADeXYPTvAJ28S5eaPiPqJomBSh7G9miZBp4gqv00p2g9PqK9TFxsb3j3f0hI41dbocwr6jLfmdz8Rmx6/uy3pb9OJffnPbP3wiVt5n/4FDCxfM65pxTs1Ju1clWb/PNz1Z+itNpyAAPEvuSBPHVIXVbnag+Rqx4FW5BJ5h7Giv/uqi1sv2ZrHlhIUUKwZhTwI8V90sr331oK8RAhAM0AFBEdE0gybhF0wBvLXt+KpzZ9qdUDOw8UBrpdcxpcZb4XMkt/rKntNn2rPZzCuA68+bIqna2Y7A2c5gT1BSNQNgEDHINIX1Vsd0QK4NA2I6IE1UKKlIoWqGpketOhBCjmMAxuVep2niQEjyuh2zplSpmr73aMuR0x2YpJ/QDKMCENUB/YLW1gEdjcv7dUAKbOlf6RKqXCK2ekOSGv1Wec6QogXkaN0O3TR7I0q1S+SJ4TLpVoepljNu9cHpvr8mGYIO+hpBqUr/nGZ7w3yviEyS8cdvccarb//IGFIAS+dOcolc7GqmHP5v2NciCmxNhaumgvyGRubCQwhEjq0pdwMADdPsDkimgZ2MMZyo0ATSZQboVu9Ae9nt4CVFa6j2ke6D/sjWPadWLp/hcvDzGmtrKjybd5983yoOkf/npCTrgH7idUDy8F9pOkUcyAI7p9rn4lnbRmRPAgyMW/yRsBWtZWeEBWWtV1JcRAMU5v5N1gHLl89fv25raX6fOc32hvNGGIOIrJG+C+M5B/OtUbX/DHwHb207Xl3mivVvSWf8IaWb3WJtOTH9j2TuK6kW5+BqK1w15W4Hn9hDOL+o0CGDgmw3QLxZyOngWZIXFuoNRA6dJGnAbd2kd1V1ufvc+VOsKqE0CqhgvPiX38Q/3W/ZeZKlv33NEQBTfC6fyHMM4hkksIyDY5w86+I5n4OsjO2iGIZfUmOBW0XislUXFvX4FNM0FU2nVXmHSfSHsXJZo8AxmaX/pgOtVr1Pty8uQ3jEYBjkEokOqPTZPYQTgHl2Dx4yOaAfSdbdDqGjJ+gPSSbxPmn7j7e+tfXQ8TMk6cHnFkkLBEoxdUBHT2+y9FebSeSPS2DrvM7kMQmCwMmxHjJeia4xMQirekglGR6F4oPTfclRoQCAPz/2fwV7D0pyDnB/GzjKsBTAW9uOl7lFeyaVTvpv2N/iELiGKk9dhVXvczRgGOR1CbUVLpJ1PFgDecvLi60DnCLX7Q+HIgPXxzDMls7A1j2nmtp6eY61K8dRiqQDqutqEx2/GNjSn7SqdolimiLkDo6pdIl83CQgrOo9YUUr6CRg6dJlCTqATgKKim4Y47oIxNVjwAFAFMBb245fd/HczNLfzn6q9DkaqjzuwSUfRhieY6p8ruoyl8AnpwcP6IBcyUYHyKoW6wowAMZtXYF3954KSYrbSVLDKMWeB8QwzkalP4dgrduR7q5kESp38B6Bj72uGWZPRAkrBZsE2F7fZH75eNYeUUqOaPrEKQIxirCXnzeLNNXLyOaDrWVusa6CGH+eeqX43q28fpZhxZ4PElN8Ht7gIXUAiT5KBcb4TFvf9v1N49kXNdZJHv5LCuxmy6tV4v+vcotlDj5Tn2qe9Tn4HlLRL1rrI6RqPZLiFtjkJPNcicX8zGhsXLt206rLV8K4iKBhHpySDlKoa5wXgXhrtD3ARAG4+3856Yb/a61y/x0hf0db+0AvDAvFkUujLghcIu9y8nazLUnRZDIEK9hXyDqB3ts2HB2QN7phHDrZ7iAxVJTC8/EvPlJJYsCiHNhPbsju7i4MuQ6uYrLZN7XMnTmkh0XILbAix4T753CqbvaElUqnkEFzZENCxOeMxsZ1azedf8G5PE+iJE6cOD13OEenZMgBVmkZrsKFgWYw/qSQ8pCkRHEsU8azDpFzO3mvS9ANszcgYQw4juFZxHOswDM8x6qa3uOXAAQ8y/AcI/CswLE8z0iy1uOXEILRlTwjcCzHMaGw0heUSblNPrqxwDMsg/whWdNM3TDbuoNnOwIk4DJJd7DldfE6AIwgqkYqx43kO5ay9O/sCliNe1TI8GDynDIxmPkgEAKvwPlETtLscFBycwQVLahoXjH/eLbkeH/b61vXMNnpFFmWcYjUKlgUTJPUYaQhQIVRABmk/+v7u71uYV5jzdI59VXlrvbBvbazJmVrHhFMSSxESvCxAJBy0AmUOx32wtzJ7t5g2fHm7rPtfkU1EgV9dVWg065FCl/ZsP+mSxfmdcKUsSj9bWTZUPsLlTPAmDalvts7O0OOmI2DYyucYp+kRvpNeaphBBVNM0whxxbWmaU/aRXZckZw+RBCosBfQCMDioBhYk0fxw6ApRff0NkR7WsyuqAM0n/DoQ6vgCdXCpMqeFUO5Sv9CwxC0O4+73OLVjpncgf5qtjyKxv2j/gJUorFgf2HVj/1hzdf/JNdF4RUdnOKZ5rPQIQ6Z54be6TcFyFY7uQ9Ahe7XTAGfkkNKlr2ZsgzkxfHHumkPwBg69urlbBfVbVAMDyMj0tJC6kJOJ5jQHdvfg2MDdAQ6VdOvq6SFHxm+nPExgIciyp8jroqj88jWk68TFN4qgMmjPFn9VN/sJd3bXzZCgtmXE4HgGDv3v179w5o+pTKAFqTALdgV4ED/a5gvSeiqhmjCROEvs2J4yfiR3DJ8f5b315tKCGfN8VcljJ8NE0f1zGgY4e0HcE2Hu5wOYj0ryxzDqfmTzGAEDoErrbSYyf99wUTK0V7BwxB+b9F5g0MXWPY3Ly+S66839B1U1cBBAc3PQ8KwQ2f+O6xg/sdLtekqVNe+803wQQ2/Q8eqO/a+PLKa+/hONY0SJVmAGG8Dli8OGr6i+mA6uM7GAh9Iu/gmJBidRu1BpI9EaWS1IYT4r/weFkf48TxEwlrZFkRRSFDttf0qfXvmd8wDZNEPaimoqjP/+HHOV4JShIYhGXNiPvJP/vWnruvXkKvVMEUwIZDHQLP1Fa6G2q8TrGIBZ/jeXJTwCSFPRBikKnrDGJIVV2DWKiIxw9CluUQYmLnQop9mSKDXKgM8CzH8YLV0hGSPoEQuieZ2DQBJJJ6S6siSRFVIS18URSGZTmWYw3D4EUHQoj0K64oY1jyYQ1dx9hoa+/s7e0zdLuOGHnXeZOcQ36KGefdhO0KEqS0MFkgS7rurWrQVKWz+UgkEjYZtm7BxZzLpUgRjE3DsP7HWJMkcqkhUT+kISLGiGERw0Jskg9lXQfyEilMD1mOc3nK9m/7e7CvLxKR2psqwVjloSfQsKU/WP10YszcpjeeseqG2l5doh/sbpIAkDlBgjKwNYGp6ebxJnR830A8qKL1RhSPwPEMSpb7yUI/gQzSf+vbqwEAq1attE8OY+wPpTW3UrJHN0wrH3NAAajyuOwLv/TiG0bdFsSmjZpwifXV3jKPo1A1s4bkQyu9qVann0Rjcit09IaONXV1dIdlMiVMtObGzwMGO4RNABTrQUZy9qrNh7t1a+hvN6uvFVFtvQDAQCBHNIIkIye2v5JyffkFV2ITV1WWteoqw3BqJKDJEcMgb2gpDDKfhYYWdWkQMW8VNDYNU1ftL4BUmbc0JBEoEOq60ieFFEU1DZNhWV2ZIIXnP/7FR5JLcK5+Omr8yZAjdusHP07+sSOLrREDHKwMFi9eyHHsjOmTD2taW2tnLAS5V3P0OSbVVlXArOX+80/+KpvPYuuAyy9faTc6rvA5Pve1//jpo9/IZl9KOhRVV9WhQ4BaOvsaqlOGn1AyKoANhzocAltT6a70OUlzmDGb3wRJcbpyj6O20hOWtEBIHnZSAfmotlwwMS6s4Wvfe2+DYjIuh0BpqIob/u+PM/1n5qW//nZAE9h6NKoMbK3erwkwMDTN53P39ZHgUYxxKBRpPtPW09khDhW1maXcz8DCeTO++h///f1vfBlMUHKd7eWBrGoTIAd49+bXll58w5hTABsOdfAcU1nmqq10J5dbGGtACAWerS53BUKKrOhWqYa0zoAho0KtRk728HF8ZxiOXz7+xUcSpH8eB4lpgoFpgSX3rf+sKRSESiQIsUp0g/WiFA6InDdlbeFhCv3YJMB+evp0c+P0Kf/+w5/+v3/53HAOW7JgDBTNNuBRCq0ANhxqZxAq9zom1Xp9bnHkS37mHxVa5Q5FlK6+sEFiw/LXAZQxJf0xxi8nmf6HpwyiGYIMsWwSM5v9EukeY906hRrpZ9ABp0410xIReWOaWNUKWUGgxBk8A4DQIXL11d6a0Sv5mQcsiyq8zppKJSyr4Yia971Bb6qxNvYfpvRPpwxuuuejDGn4DmNyBGP81ktPgqKx9e3V8a2D7Tpxn34g+94plCimaVpdwPJh4yt/uOSmB8fUpVw62n7gAYPdhsMdIs9WlRPjz4hF/hQECKEosjUVxGnBkazOvFPDBlRH3h//tns/nN+OpYzt+E0gS9N/HrzyzO/XvfbUjvUvvb/xZfuxcz0ZpBeVtWs3GYZpgU+eJHVMaa3QfFPA+h0AJPwXjF92j3YI0IAC2HCo3Sr47JxU43U7+XEk/W0YSFoF1FV5vG6BY1OkhuWaHjzeLkCJOn7HEVvfXr1hw5aOrr7uHn9vX3DX7gPUjpEHupVUMd5F/5gzAZHAeSc/qdZbVeYi9RXGFX96+p3CHhAR4wDVAaNp+gcTka1vr3Z5KyAi/zEMCkbU//3NH/75E2PLKDF2sTw2mk5SZsCEwVCXrrhq97t/H00F8LZV8LkzADrPtoAJTFyF0IzeYBIlQpLI8iLWV5mSp+l/2I7fsUw40OMtr7KcDrizs+vVNzZ/5d9/8IP/95XRPq9xg6YbeJy3AYhn97t/X7riqlHUAawt/ccmunOyvcAyyCHyZR5HbYVncm1ZmSdaGdRmSlU0WQwDbBhmX0jae7Rl79GWvqAUP5ZkrMINRrA9sw6IBYPmCkKovso7tb7ihb/msXcpkiD9bZIzficSW99efeGVtyxZtgRBaBjEKbBn/7HRPqlxA2kBrRkTR/zH6QAwSrCGWDO5pmzp3EmzplS7nQJJmBm3QABZhqnyuS87d9aCGXXvHzpz4ERbMCLHx4UyntqMOoAY/+1ik7m9NYRzptVces5Mn1sc9ucoUdKZ/r/5/R9/+6tfABOFrW+vXnX5SuueJCONZUsXVFeVPfHz74/2eY2XGNCkCvATRQfsHo1JACrzOBbNbpjfWOcZ59I/Hgai2grv1RfOvffa5cvmTnYO7vrEeGrT7UiG/3m8H4T1Vd5Lz5lR4XWOqbKpY5zf/s93JEmOPe6446bf/fWZhG2++X1SPe3zXx1nde6+/M3vfPmb33ks6eMAAD79lW9Jso6QXZYKtpxtXbmCFjLLCt0wlf5WEBOM3aM0D2AvWDR91pRqkvRbNOl/6HgzGCUwwJMrnabma27rjcgkRcDO/eF8VbK/K+UkIA/7j88trjpvdrl36FJxlAR+/Oi3vvC1b9nLu3fvX7p04b999wexV1mWaO7enu5xd93sIIKDBw4/9tdnjh8/Ff9SKKy4HNE6shDCtW+v2/LOq6N0muMJDEBEVvNOAhj77B6NeQDLAr21vbOtfYKM/ROAAIoCV13uDkuqibGs6jH7jphCB5Dgn5xACPnc4sXLZkyq8UEAFW1iDk9GQAfYZRg2b9lRUzNQ1jRe9H/+q9/8yfe/DcYDF11+veVJIjrgwP7DwuASQzHpT0V/TmBMFMAEcgCnZoR1ABJJj6TRl/7WwDz23Q76kknAhF1PN2G9/cqgHeNe6l/JIORxiXVVnqoykiMwKLozxefO9v6CEE6rL1+xeNrVK+bMmFShGwaV/nnz40e/tXP7+6Zp9loF2pL5yfe/PV6kPwBgy9o1//3tRzZv2EgK1yhqym2o9M8VwzRTtICdWOy2RP9I2oLYvDtiF5bBSmjQKaXLSrMK52dzNNKPvtLnghBqut7ZS2IvbPUgeqvkQBfAsUlAtvcWQrC2wnP1hXOdosAwY+ICjnc2vvUSyderrIlfiTH46X+NG7mfwJa1a7asXfOPX05Mcl779jryKjX75IhhmHpcvaaJrQPOveiqHVtGYh5QEh5LUiuC5yq8rupyj9PBJyoU6+m+5t4sj4YQqq301lZ5PS6RSv8CsvGtlwLdgzplj1/pH+MX//2dhDVb3nmVSv88sAr2TeThfzwx6X/uRcWdDZSEAogVjq4qc5F6QRwbmyI4fNXWy+D0qayS4BiE6qq8tRWeMTJzmpDzAAolGV0nLQBL7crsiNMExVAGaXsCTzwQhC6HUFPuCUXUHiOsRULECuQQo520spDnHMvUVnqry4k1qeinSwHgZ3/8s8AnzdiyQ9P0LLdUVdL1U+ATOzyTJp26bvUVI/+3d/S0d/b6A6FgMNLV3WeHo0AASOtSEj4QdVVRioRGTLele313FMciVEIKwC4cXeZ1VkeU9rY2W/oTu3NZeaAvK/vPpNqyco/DLiNMdUAxuP1Dnzxw4GjsafxyjItXXpjhCE7HEF29UgDhzl2H9x04PqR0if/SYX/CR01N5Q1XXxSbER47eiRhL1maIN06RxdNm1BFIMYIpaUAIICvvbON3Eycj2eAZGIIgIhSx2kkY0v/Ip9jSfO3J399+4c+mXmbzZu2Znh1UsMgN3KWyCG/yBr5DeCxGj5xLIWiohS+ChC9poWmhBTAs69tIuPHZY2zZs1q6wqcae/rC0odPQGAs50ExKR/BjXw7t6mgp51yREnhbFtWum/2gNP4xq5kLBe08TRvwiebWmf1FCbx5vmbV2Ilf+LxiTDIULUKPmhkTYAVAWMBwWwflumgvvtnT333HgpGA3p/+VP3mk/rfC6GidVhSRlz5EzR61ETW9Z+QifEiUlccqVLFjPYNKr5N9YyW6GISqBYSxzfV5yV9MTu0lnT6xzqt16nlIkJnAO8CjCPvPqxntuvKQgxzp9NhrDN62BhNacbumMf7W9c2CI/cyrGxL2LZ5KsEX/rdevmjN1IMWUZZGHFTwuYe/hfO4q2i2gePh7+yJSZMjN6hsaILTmBIDY8eK0BTjb0pGTIQhjrA9DuMQ01v59B4fc+PW//SXvNypx8lAALZ19DdVlxTmdCTQDeObVjZYIvmT4oj+dxLf53EduIoM0CP/v94nRfskqYfhaIaXoj+dkc5trcJ24LMngCCjhOIXC8M6rT19x471D6oDWlkxhu0oWKiSGiXE4LOHskowS0tEBAEF/7/59gezfjpIfpBEYpdDAph0vAgC+9/Nn8tABz7y6Kd3E+fMP3myJekBCtzA2TWx14Y6G1NkmV5NEdWEEIUqjFewzjH+S/Rk++9qmzKLfXujqDb+7+7CukAoEgnugbaRlWEi0jy2ZG+1PYHPFinnJR353bxMOtn/te7/M8jwpKclGB2SgojyHcZ+mG5HIoNYR2SsAElvszC40AII36PB/GNx2172pryskPpj7bk4dG5YwAxhrTeFHnaiMe/gz93zv589kMxWwhH5in61/+tgtKROjMOnlkrie+PEsu6kJsWGaMRvvP33sVjuU2jCi2uKnf3g56d3JGSaQcMIJ5v50oj969hDY0r+wPPrwp6kOGA4m61iydGZ/CH7UMwwhNLFt8InOtMh64v41EXkJaJqGMRYEnmWY2trK+C5w8QsJNJ/tOHy0KRsFYDsZ4inzeS67eFnMDZCBf/3cp7P87BTKiDEwyH34M/fYU4EENZAk8aN85oEbLTGd4lcRI+X62OZWSXTGXrZ/fggh3TAt2y7Z958+eotdoF+3IsAggD9//JXkA8ZrBQjh5CmTP3jDBUOK/tj2ihCdJeQeQE4pIvHNNQfumYFArOi/dno2+Wt1jhuI1Irtm7SQAGu5j/Mz3pE7k+xInb+UcQl77FTLrOkNsef2VAAA8PTL6yBKtIF84WM365ZNx/4FkjRI26wzbGIHIS3pra70sRFZNOzDesfPPXgzslSOaWLLtkR+e1GtgPEXHrrVMPGLb+/ee7hp8dypmUX/MElp/4mHTgKGw7xZg6xtud5FddbwP0sYWwFkQXICIGnraJiMdcdm5r9++ks6CaCMNYiIP9ZfBsfWBPfeckV9tft/f/s3e+VnP3IjjJrpyVOOjY7Zi038L43n2MR4bUjiP0wSCIg/92DUt2xvsHBGXU6i3+PkwxG1GA4mqgPGBQxDOnOBvOKA7L6+hT8nSo48+9aeu6+mjdVyZtAY39YEiOExBp//2G0GscYMnkSPdh5sbL7BQAhQ9OxNkiAeNQ2/9M4eWwGse+/A1PqKbI45u9bZ1KbJpNVcYjUYyqiw6taPjLQCyPfGNkxslECN4rGPKucfMlDKpJi6dvX0IQRZBgk8y3MsxzL2EGnUpX867Paq9iSgvtLd4w/2+EmhtyyprXRX+hwiaYpZAHB/x3mbRx+mrr+xjn1/57cvNs3h5BBQCscYlU5jnNT3/Z5Do9bFtxDYDr1sfXplHrGh2ltZ5irS2VAdMMaxBxD57WtinH3Z0f/6KQ0OLhb5foGlzgjVAvrh7xMDOisc/Ec/eG2x3i+XyYrIszUVLghBaxedRY5jD3DeIIRQFl7cdGSvACjFgx0p3+QEoygK4H8ef01TVYbJ9JX0SGq8VqiwMnKLqBLSgyB0ilx1ebFmANQbPJYdALYCsANJ80PVtIKeDiUf3HmUAacUSgEkDPBRRtGfkh5JjT9OmRA9sYfuu34EviYEoUMYwgOckAacKzQiaMyCIMwmjjMdCgkfoIwOdhowAMDnJr09EoIGSaJGnIuezV0uTXjYwhp2BhGtjdifLGY9Naz4/fhkzpT0KbppRdc99uc1I6MGmCySOSkTEohg5tlqZlSFKoDRx+0U4uV+Skqhp/wIKYAfPPZiLIk3HQ6BI3V+iIcNMuRBdLWs6iS6FEHDMGVlaONpn7WNrQZGbEJQJOgkYAw6AOwZAMuO0AyApoMVB9Lxu0iHntiwhRr19w/zyUgfQWQiWF3uZBBiGcQw0b8Y41BENUwTISgremtXMFqjBWelBkZyQlAkqA4Yi0BIqkHk2xBA03RaHnx0QXCQ2YeSPWwBDD5E4hMNLHAknpphIKncg/X6Kq899ieTACuZGGPgcvB2Pr2s6IGQYpg4VuabFAe1ansR8NATgvGrBihjxwNsB5CzbLY/hORqEFYUEC0HNJoghOY0DtSzoWQPm6voh5CU61FUg2OJXLeLObMs8rlFt5NnGBJQEYpohq573YL9Uxn4H2Ouf64tCmxdlUfVjYjl/sUAyJbdH0KoG6ZumMBOsE81MLPVwG+f2wiV0PDVwBMvRFsRCF7Sx6bY0ElAscmpEBABkiDC+E6TOaHpummSfmR57EvJknvv/5gipc3uJAXEKIVVABFZRRD+7M+vx9bY1nyWQW4n39NnlnsdtrhnGMhzjM8tOkXeHuwDIPUFSHR14kEtbWEtYR4xDTVeVdMlWUeIOIeDYYW0DYBQUrSIovkDEsMQzwEpt2h3W00CC25V01XNME2s6saTL20+f9F0MOahOmBMYc0AmJRDjWywqgFlVQ/OhroBCs5wXDglTloFYFffjC5DMslyCKwocALHlPscsqI11Hht6Y8QYhExAbEsqdRpJ2UMVVyFvOpx8obJuZ2k4pBpYq9LsEdgkqKFJS0Ykh0CJwq8ouqqRoqQWjV/0vQJsgrB5XcJDEelYR05QyAxjRGaqB7g+HJAZl63kGGYhmnSMlJFJcPwPyHuM54yj6NoZzThTUB2f23Lvi8KrMizPo/D6xI4lnE7+e7ecIXPaVdmtnt25FEpCBLnG0Q151SWOQEAZ5rP2Ot5AHwANMwiy3Y3MU3TFVWTJOX4ofd13STTgdhPlZwBNg0c35D74KmO2N9kYmkK11wwm1TAfvCykKTKiv7nV3amO9WCxxjQScAYcQAMagmQF0QB0G6Fo0rehbxuuSPaZezjd2fqPnvbhz4PSnAGIKuaU+Q4lin3OFxO3uMUnCJH/LoMFHmWz5h7fd4Vt0nBnuT1U6ZMin9q+3xjoj/FaZAkHYgQz3OsQ+TLVly6b+dmVTOIk8CCIy3HGIcA1Xwz8lvbuobchi9CkBnVAWOuInRemMPrKU8ZPm4XTQPOk7RyTeBZjHFNuVvg2TKv6BBIWVDNNcMEQAdg2oJGOf1BPQ6QUvqT9nvNZ2PLU6ZMyiD64yEzDAbxJJAU1FS4w5Iq9WffjIxxhqM5hBO+IGh/TmleBUFpOaBRA0JY7iEmBEoeZLrpJVmtr/bUVLq8LmHLYVlzzUjeJhQhMTz5Ea8M4uHFtF8nhKCuylNf7akqWvHOlIj9pSkyJzDnCi0UOkYgEQt5lwOCJKsxpz1oWdACwiBSy6uQRywl2C5/OOULuw+TitBOZxmZ5AJwyUInCKforuUDAKQ+QP4YYdLfNR0Oh8/hAJXloDHuPG1WXTA3J530+pZDPrcjJPmcjkw3EITA547zJhV0ykENQTEQNjo6hjbHDUks1Dh7ZEU1DQ3i7Cw5g0YA0OEQpEikvY0WhBgdGIahRX7yZpTCp3DS3+RX7UWcOOhOvab/UIaBW7v8eZ1NWliW8bgGSk3ReO+JB7ZKVOWzJwQVFV6ez20E+sPvfDOf96KkgmPz999QBimAhJ8ACb6PrbGFbPwGONUjDfEpNnZMfzSyv3+ZLNhvF3u1/91jf2OCPsWa/kN1+8PBsJLr9zrokybBMYjnsioW9u7eplzfmhqCxgKmlYaex44sw1SUean8GUWy/G0CAC6/9aEin8v4g7WD62MxcAlP7RysWL/1/vFvNLeX1PWM23KgAGjs6UBoHTTN6MYJ4XYw/UJK+t9gYLvYGknR2ruDuf+SrfNKv5OV71bcIQY1BAEA3ln9xB33fwqMBlYVktwVAIRej0sUSSsLymgxHqsAvbn6SQzA5IWXHN3y0uieCRvf6pcI1cHh0IOeJUVKo3SvJsh5q0VjbOPCVM4afASMgaLqZzv68g4GzaB2SF5bdh5CjMG+5t5FU8rzPAfKKGEl8+asABCENVVl+XuPKYVAHKqTR5F4c/WTseVJC1clvZ7oT4qX9ZEACZIcdelvK4D0MjC+wtVQUjvhxcRhfoZ3GT6YFK7o6A4GQhliU/OHJwogh7POTwfQSQCp8uQPl/lGNL5rwAls9Z/ICZZl3G4nNT+PLg6Bq8+1+lOOvPjkT5JX2kLc5uiWF8D4JOPsadBcAIxZZFVv7QwEwkWR/gCSXhPxc50MhR+HObOhOmBUMEwzFMqnHTTLkPyBXPeiHuDRSgO+5fa7P37PwFC9szf04t/fL9RpJJeJHReMgdlrLBYorygM08SdvaFAWM7PiTckDIIJFUUgAFesmJd5r33NvcU4mQnPO6ufGPk3lWU1IuUzerBcQ+PvNz+RgBCKOYZgFQk4DqV/sZrCZ09UaJMgHLI0VAm5FCiq3heUiiT97VFeTmkmp0+3TJuWf2lyOgl44YlfrVg1rBLfLc2nwVDt6vIn7k7zK6FNm1LXm0rHu2tfLcI5lS48x4yWD2BiMJozAIyxrGiKqpP6z7KahxcOYxAIy7GWMsXA7mWW5cYvv/b28CcBND343XVrRHF4dRzNsVich0r/guMQOC7rMFBK0WcACGaVhWtifPJsT1tXKMHuY7CeMo/j4mVTp9aXRcXo/kyR9YZhFnX4bxeby7XdxDAnARQAwLrXiVdt1XV3yLKU5wUxjSLOA3KEiv4iIfJcTkaD3z6zLt4NQBlCASxaODXFWkvearrRF5SDEQWq8aW6B2KiJTmtMti433Kgs26yAya/cIfILZhZs2P/2UhAems9ebWhxjt3WlWmk8MgGJHDVkOx4uFy8nY7+zx0QN4hodQQZLPu9RfGlg7Ia6hBpX/xEOKKdCVAmwEMVwEgqB44cAwUDofIv7PtBADguvNJXTl72UaSNUUdNG2PyEMUVzGI+zdc1OE/hKDc48zeveOtqAz0dGc/D3h5/d6bL1uc8iWqA+J1gOWqlcbdPICK/mLjoA6A4cFWJYVd9/hJSNzkuvIz7Smqvw0TBkG7/VY8pmnUVLgPnugwDJLGxTCsz83PnFyGAeCZQeEZqiEOHv7nXPUhNWlCOxFCTivPs6UrmM1htFCi3T/zJCCd9Kckm4MAAHk6h0dJB1DpX3zGSgjQxJwBMMhUNNIKGABwrLm3sycAAFi5fMaRU+0+t0h8Lxg01JZt29e0aGatwyGcbukh7WIQrPCmreesalpDbdm67cddDiJYDUNnGHIOFV5Ht39A1jMMMkiTr0Ri+qA3IB9rCnz8zpXRFyAMS+pvn1ln9wRO1wssLWmG+AyCubqYHDyS1GhKEZ0EFJZ3160p8BEp4xlIOjWNFTfPOGUI67aVA0tw9NvaznT4nSLv8zgcAudyEiHudQl2DSCvS7C7hkmKRjyn0LQfmMj9gQoNwbDCsUx8k0Uyj4PxbR7t1PwsMjOttpV5h2Jfd9G8OVOr23pDLd3BnmAKCwPpgZxLLzDNUa05qllfbWzN6dMtmcOBbr5s8cvr96Z7lUYEUSjpgBDy7PgrBDSeFIAtkqHlC7XXtHYGqyuiViNS3w2QDi12OozAk9JyEEKEYHzkDLRsKbGnsqLXV3t8noECy06R6w0Mkr+GYWZneR+eBrC47/old16x4MaVc5Jf8rpELmPnywScjG4/vBUDuemnT7cM5/QoFEpKiJzJuvdDrBTob59ZR69njLT6s666rLWzJSZZYx24DMMUONZuy26rB6G/Gh+C0CGydq23hJSueH1gmriqzBUfvROWVU0z4rURy6CcJO9wmHJuvx3pzxuSi0DkpFsQIkVPkx3Cr2zcf9MlCzNPAqg3mELJCQahXEO0JzC35dW5ns2l6DbkWGTLNzLkT5UFD7PLiGYHV9iXZB0AHJ9v5XLwDJNL8bkiWRhzdADwHCMrA8aueB2QmbuuW/Hc6+9SHUChZP8DFQXSC6bYleDGhRzPm2wVACQjfYbn2QgR1gVgsHM1MS4or+y+PONBt7/6NwDAeTfenrAeIRib3GSJzy1qetgwUpxJ5kmAIuVTjIxCKVmIXdqR2wR99d+eveX2u8FElOOF7wn83t4TAIjxmb2Iybn5dQaYMVY7yVYD8fAs68ix2XRthTsia6GIEktOyN4QRCcBFEr2MAyKRaYUm9vGiTTPgxyuoKoaJsYo65Dqox2s3Wh1dm2KEbEVWzS2dEACLiefa5RxTbkrEJYVVVeJSyM3Q5A9CcjgDKBQKDEYBuXRC371356N/QUA3HLHvSV+SYkCsIertjROXI4T0Ub6uMz4CM4Y5lDThQzynwSRWu89kPllISv60aYOW7xCCB97flP8q263M+cMgPQ4RT7XZtNel1BT7u4NyPEKIJ5sJgHpdADNDaZQYrAohyqNlHSg/gbrpKRCbDlDi2yGQbKa6AYIhlMU5HGIPDHlDxah8dFBGTz4sQzvhL7wvcFIOtmaB69vObTtQNOz7xx9fv2JFzacHPxiagdA5sITHMuUex0eV6LmiI8KfWXj/rw9ATQtgEKxYdmid+ouBayWkP1t321H6kDz3lQ7OEWupcM/Y/KARNMM40y7v6bSk7BlsLdDVjTdMEHtQGIUz7OaHj2wwLOSnLqWQyy8N74Rl64b/mBx2n71d7GPASEQU1oYM4cmQeAUuAqvszcgh2U1/gpmaQiyQ0KpIYhCyQhkGQbmqAA+8KEHSN7PjedPeMt+9lgyLk3DXphKAyAIO3rD8QogHFGDluF7wYJZ8cXj5jdGSzrH43OLXX3RnC8ufRKHphnRAbh1QrZKisiarAxRIa5QQCsJIMX6oXZkWVRV5uwNSqqma7qZhyHIhhqCKJS0P08rUDCP/lHx/Panj2bZEjK5J/CEUR4Zq4GiAcOOJKsrl5EyOwlIslrmcaxYPBWbZrz0T64FvWDBrJMnmhbPrjc597rNeybX+pbOqX9nG1EGDMMunl2392ibXReI1FA73nnZuY3xu5sm7vGHM/ghCgvTXwYu9x2h1yXUVbjDEbUvKFte8NwigjIXh6DOAAoFIshnTBRNrgV99wfvNwyzUG6DF1O1ic+SMaU8Rq6SxoEDxxy2SFVJZc1uf2Q2GJhGqMldvQZrd0nW/EVq+54EhJBJ42LK0BE+hsCxVWWuQFiRFNLsLP6lLA1Bl61YvP5dagiiUFKDICkCkdP4f+zEHL44DOVRcP0xhALAAMcSftMtx208sCrDxvY3EcuYZRDx9x473WcvxDh4vKexodLeD2PcE4gUMAshM3Y5o5R3TOab6MkXB0UlJROWBvRBhkmAt9/4RA1BFEqaGFBUW+XL/uJASKwII1FCYAzoj+yVRCYFYGIyYI93BGDLFNMblNo6SaijnS5rhQ9hjmW8LrHC53SInKqRZmG9gYiSFC9kY8v69TuaE4R+PPFOWVUzgiM1/LfvFaIActzr3b1NCpvijrQVie1kZj1AD3ZlowNuuuLcV97ZkeHtaFQopUS49/6PKVJ828Gca4VdfutD99734fEv/AugJBJ0Q24mIIyJLPYHJcUq78wwiEFI163SB/1DdUXVIxJp8j7MXu1OkY/qawxCESU59rSAIIhMHJteQDLBzMtW+Lm7LzBMHIwoze2BfSc6egMSiacdHF/EeqridUA6oBF1otCIIAolAYFn8/iFloz8z0E35HYRTRMHw6QPFyYpwUjkOfI19F9XDICiGf6QHAjLkqzF6mLmgVPkK8tc9iDcMHG3P1K81o8cyzTUDIzcISSKLQ+LIcYgEgoqkRAPtOnV4sr51fMmuctE4GQNJ9P/4LCTw96y8mzSAu649gJ7IZ1PmKYFUEoQCCEp02v9QnOsBAdjMaCUnBUAxkBRNX9I1nRSrF8UWKfIDWQ8YVIpOhRRAmF5mMN/hGBdlZezNXxhWz+meq/KMteimXWxNdAa/mdQAFesmJdy/cAeDMNwrNfrrKvxeV3CgDHJboqQFMiUTgdocV1wqQ6glCwJ9h+eZRwCl8dwfsy4gcenAjBNMxBWwrKKMWYZ5HEK/OBcWUXTQ5IiK/owjT9lbofXLdoTCxNjYkgZxmQiMyLP+dyixyW6+oM+GQTj29fkTH99Eo5lKt18TbmLlKyyeuJooV7yiPi1iN/BZ/UWd123Iv8zoVAmIjyXj/3H6lNCNUAibA72DVnzhyRdNyCELgfvdgrxVRlMExsmaeQIhgHLIIHnaqu8sVqhiqorlre5SHAsQpDcTPNn1L27+xgJAM03v+Tvf1+b92mk8wbHF4egEUEUitWVhMl1iPaB+x5IWa+Mwlb5oq2+YvT4idBZMm8KAGD/0SOx9RVeUOH1AgDOdkp9fr/A+aIKwLqwyd7OXEEQupxCTYXHbhEcNTppOjOc8fjQWC3vm9obJ1e/u9sKALUeeaiAb//LB21FaF+FvqC07cDZ9w+3BsNKyuvCikAPdA6pA+wKcfYy1QGUEsdu05RrlUYrUJF0qy3aeY1X8kkEs5u1WPpAmFaXol5Ca1eouaV7+YLJGQ5yvKlzxpSqVK+YAERt3yfPdpeXVeda8SM/bDVDpH++b3f0WLN1IAYY0YnR9RfNvvqCmc3t/o27Th8705Ncxo71VsfrgJQoUqSsqqavq2BVTimU8QtEUMzdAQBT17WhjGAmcH5UenGl1w2AO2F9WNJ2H+1tnFRx1YUL7LshJCm/fWb9+YtIvYosi0Jfd9G8Hn8k1N+d2O5QgKwcsPzPuF/6x1SCpptTyjklzHX0aily2crKA329mScBV53b+Nzr0U9EJwGUUoZ4gMWcFYBV6XgiZIEVHLR66+mX3zv7yrazBT5wkS91rJDc37ceGOZbLpzibKzijEiPHRSUXGO2pYvUrsgblkHlHkdthdvl4IbULCkjghQpMmvmQCGmzJWCKJSJC3SIXE4pYAPQKUAq2B/9l50Yhq2HTiwwpqG19WCMeY6dPWcmMMxdh89s2nUyGJF9bseqc2fdPrtBldU1mw/tPHTGTtDVtUQ/LctVbTwoQwhWzhvU0SV7evwDQZDJMAhNbygvrL6BEJAas4U2FJLK0jxbbVUHIj5tzUi4F71xk4B0LJ1Ve+z4qczb0NxgysQGQuAQ+DyCNJ7+859i5aApKU1A0PavkEUEOLcULe1jmi1tvbuPtIQiisBzc6fVzJhUCTiG0VClz1nuccgBktTKDXbTkkxaZBiGaRjmu/vSDp+79ncNREyWuSp9LoG3+hNY3HztysOnO174+/sJ4pJhucsWWEGisSldgSQ2gqTASOZttu/cu31nigH4V/7xnkxHRtDj5Gsr3MGIahU1ShyNeIcyBClS5IqLlryzZY/9lBqCKCUIgxBveyDz4qknH3/qycfj19ySXUvIlKV1hlnTbYzAPvP7v9hL93z0Hwa/ZHVkDCt7j7V29ZFEDAfP7t53sKnptMizYUlt7QqEIpnaucRXQh7iJJjBpTctyb5+y07dMGdUC+3dwUFVoA15y95BSuW3z23KryXku0f7br9yichzRPEAwG4+lWH4n1Lux/jBL57RlExTlpxIqQNc3KDrSUtEUEoNBkH7pzoWuK1wVTnvuPVW0ivFmxiQmQ6PZ8AnevUlSwrjBI5pAqIM7roGAPDXp1/v6gu3dwftxC4pwJV5SZVtw8RhWU1XnMcK0kWaZmQZFRotvRl7jonm0HVT1YxASOr2R4rXA8CQ+t7fvT826k+XAdBQ5Xnpjc1gDHDHtRe88MZ7mXUANQRRJhgvPvc0Ebh33SvwrD0DyOzPTWgGcPmtD4GxzQsvvZTh1TtuvdXWDW5L7id88Lc27hmOMkgdBfTMc2/amV99QcmW/mRo3P/OqqaHI2rK4sykjgLLWElh2UptjLFhJfpCSJY1y3AUUbS+gBSSFH14ScUZYBCq8DkRJNVMIQSGga0K0Knvq1uvvRgAkFkNTJ87z25UDSHwh5SIrFpBpaRzKYQwJCmBsJIhpZl1AN3fnnkSEF8cIgNUB1AmqhpI4Mbrr0u3/b9+JivzzthnSPUAAJg8qTY/ZcDGhulWfbeBwj66YfhDkiQP9u5ikugUllRJIQUhUifWIphT33ar+wppwIIQ1A1TVrSIrIUlxR+Si1cAzm5u7HM7oiIfEzP93Bl1k2rKN+wYyH1LqQYaJ6dodZk9GJMcsd1H2/af6AhFyGWMfUjWVzukDojPC6OGIAolHf/18xQKY+LxQhr18JNfRBfuuPXWBz9ALDopIQa16HzKHgtbcwxMAu3VYFixB/JkmNy/g6xqgTCpB5d8LIQQx7K6kVtBCGi3d2CRphPpH5bUsKSmayRQQLxuh1X0beA0bC49dw4P7YF26hIU7e1DpG5lw5wGV623vq0reKYzQDpHxmYGFaS6YYauYYoUcfgqJf/ABtQQRKGk5NU1r9Mr88JLL2WYQ5A6ZdFQGlsEWQpB00lVZ7vlC0PyrqFt8NENUwnrEaseXPKxSIo2BHqaTujpgIgU9zBNHAiRqp+Somm6UbzqbzEELoU3SdUNbGI+RXZzgWEQ9LgECIFK1J4eSZhpZZwEXLW04eX1lgLIGA9HDUGUCQ8V8cMkLvbGDgS1oneCESUYJsNShhT9J/XSbBuRrGqhiJLS+s8yiGcZYsHP0Wdr1fWAwYjSF5RCkqpqIyH9o8IzXn5a79njD5/t7BuBd7cqapCKqnWV7nKvw46biuG15gEZUsM+ePMl8dL/5Q37inyyFAplAoJSFf3X+wIRVSNVPx0iJ/CcbSgxTZyu1DOEJDyLGE1ysf7370vaDATDsqSopLlYMe3+Gc+D/GnvDja1DpGTVUAYBvncYl2l2+3gE0bz8TogmVDAn83xaccYCoWStQLAwDBNf0gOScTIw3OM2ynYRRcwKXJj6nGFbuJhWcSyjGLZbkAuQAhNE0uKNmID/+j7WkVlB62y3rwvKIX7SwONyGkAgWeqylzVZS5hcHOFeFJOAm6+dFH803STAKoDKBRKVgoAAyAr0aL/CEK3Q3CKfCwyMl1iF4JQ4Fg7ch/kjqVXSDFpMIKwLKquGFxgzvqUqqaP8JlY15mvrXSXeUSGgTkZgrLUARQKhZKSQfm3hkGG/5JCHJKCwPo8jmzqLpH2PBAqeYlOjHH2CcMFpNLnqvA6k9eP5CwkBsuicq+jpsJtqdscDEEkZe8GEpmaGToJoFAoQygAEvopq4GQbBimZZt2uBz8QGXMNIIRIShwjG5lb4EikGd3rowwCE2rL0/ZZ2ZUPBCQdKZkaypcVT5nBo2bchIghQLxT6khiEKhZE+/EMTE+tEXlGRVgxA6Rd5nhclHXyS1tHHaYEqS+VV4y4ndnJ3n2ITOw8PH4xIqy1KX3RiNCQCBQdDrFGor3T63kNCRZshJQIIhiEKhUEB2/H/pB0CTj258yQAAAABJRU5ErkJggg=="}], 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"}], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass nomination band is on the Axe (item 1).\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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aWSY/QJDgfwpHYlagOLYO2PDSHy664f6RPNtxBJ0BZOOOi/w7tq1bdu4VvCBiQwOmoelqd9fpN197/cdPt04k6R+bAUAoGxBYTmBFjkBEcoHJaxirqhINB3RNZRAURQfLkZJBJHCIFoOjlB6MgW2ZjN2rJj54rMVahLqmppX+lFygM4ABDhxtmTezMekC/c837gcgGpUWdbac7utpAybQoRoIdOzdp/xanvPJ6ypBnjxybx7V0x54bOQ0NMQGgkA3yTDfCgNlDWywDDJ1jBDkOA4B0ymKAsvqqupwO03rV8lxxCFMoZT25gTAJBFqMRAaZHWk0n/cKIBPfexDiU9/9bvHwXjgukXM19/Ytmj5JR1njiJg6KYeNrT9B6T/0+cvPWfJqob2Ta88euF1pNTE+AWbBstAjfy6iNYReCEajTCM4HCJ2MSGruu6xoqcqsrAxJqqkDJBEMSmCBRKSW9ODJiEWAPqdBqXCiBJ+k+v9YDxMAmw+e4X73jwB0+cs/KGM8d2M4apm7oO8PHDeyGDAFyyqr4VjHNMw2ABlBSNAQ5SNEkQBJ5nWZZlWFmVDEPnBcE0TU2RGZYDug5Zhsj/8ekB/uxXH2asaFfWcniITu9//tvnRvukKBnBAC+fnf6HSb4+npVV8m1Sxq4CSCv9H37w01/73i/HhQ7oC0Ye/NSN3/7JM5dc9YFjB96BAKumaRj6sf07w30B49IrwThHIzKdTLSRJRNdHi9CkPQD0A2G5AcAhyAEgwEMgYENbBlfWY5/+XdDdA8eU/zTg//RvzhIXsjR4Jce+k97+Uff+beRPzFKdtKOMppbu6Y0VNNLN759AGNQByTRFxxoe/vRD1z1m8cfu+bWjxzctRGZukmiEcJtpw+vf1V66Cevfufz14JxHQXEI0yK/pO5thyNhIMBXVMwxjwvMAxr1f/pBBBiohZMhuWqaweSbsaD0M+JuCagymDsAAcb/SOSTNLUHXxzaxcAYEpDNZ0EjGkFkN34M0Z0ABnVDp4EJIr+OK/8/hsAgJ/x/KFdW1RZwiaWDbOj/cSmN1/+p+/++f++fg8YnxhWT0iEYlZ9XddkKQIwhhAqsuTxViJElIAcjSCWRQxTWVVru4vHqdDfv28/yWdWNQDA2ecsz64MqI1o7GBL/0ToVGBMK4Cxb/q3SRRmc6fXpZX+cT57Xf2P1LObj+6VwwGANV3DPb2nt214/eNf/9Vvv/spMA4JhBWoYY5jkWAsv/JDmqLqim5ik+c4AIGqaLoLAsSygpMXRFWRDDkaUSQw9sz6ucj9OBgQ69b2be/bTzNpAmojGl2WzW7IJP1tmlu7aqoqRvakJgKjbwIaa5MAm+zS3+ZLt0z5ryeiXR1nQr2dEGvYwJFIYNeWNXd84tvT587/4ZfvAuOE277w7e72lnBvj6FpJNYOAl0LIiQghuFZYvyBpGckL0UCPC+KojMaDZPdsNnQOHXMmvWzCX2Mr69G0NL4GAiGiXUTv9KpJWqCXKYF1EY0kqSK/s6eYI3fO6InMeFgx87wf4zogLz41w/MveOzr8xdck5vezPQNWDoCsCnjuxVVO3bf1j7zfsHVQ0as3S0NLcfP87aYZ0Q2iF3LMdV1TcCgBWrzBYniBDCztbT9Q3TeF2XZaIgo9FRzr755D8/lPNIH1xfRSQ+SWZOWE8CmRiS+3xbA2OQbGe8uo0kFkGYkzL44te/+z/f/XpRPgslC5kG/pQxrQDGi/EnDkzwBOTIMz/759s/9f3F51/RceowsZirKmZx+8mDb70Q+NYjb3/rgcvAiGBuaUt8is6vz31flZT6IV4AhBgIoWnoCLGRYFCWIhwn6KYGIeCdTuIfNg2AMEKAgUA1jZ6+HjDa0l+WFVEU0gr9+GB/yFhVSzMAlmwGb5skWooAqIb5Sgf57CDDzIBsVJY64Jo7Po0x6Dy4hkGkZAjPsRhj3YTYuiZOHsoaEFmMSAlxIKukyLjAk+QtzcCyDjiEJY3ccRgAhuxESk4ByGAMmpZdDSF47ZlxNhAcv4ytLJ6HH/z06J5AYYXtn/3VV3euf7Vx5iJA4iUhcQgYSrSnZd2LT3/r538DI0JQGfTIC0vekY/OMCStl+NFIvc10vDdMHSW5Zwunyi6opGQIDhMQ2d5QXC4sG0+HwNj//379tuP+Me5xg+vr0Y3VKMba5h8MxUgAKQqKoIOjrl9knhLg3BTvXBdLZFxmNwgePu29+1HfJcvfv27oDzwT1myYtnCjn1rOg+ssedJ9qApqSQ4g4CJgaqTywXJtJJso5KasoBNFDkYm6ZdY8raBoBj771+dNvr8+bMmrX88hH/cOUIGmvD/1HXAYXxt9899MrjP5g2fRFiWYCQTgIlcaive+Obrz/4w7+MwAno7KBHXkAInG4PREiSQqoi6bpKpD+ApkEigzRNhhCpqqypiiJHA53tkUCPaRq2qF1560fAqEr/rs4Oj8cNALjWD6+vQtdbQp8tXn4agpBFUGSZ2xrFm+uFG+v462tZDDBCaMf7u8pNB2CSMI5TJ09J8WD2k4TaDbGdk0ItyApL9CdvaJLufPmeW2d3X767UEqlAMad8acovPDn7zfNWCKIToyQQQLr9b7eni1r3vr8f5S+Z7pz8CMfPB5nTX2N21fpEFwC70AYqpIsiC6IiXnc4fSR0tCQlH/QDawoqmlgYjUyTQgRCRUdVelPOhkI0RuqERnq247d0gAhYBHkGMRbysCWT+WmAyDWsVUvxLQuSKwOiEHGOxBrDNAZSIyEwFAAMIggx1ZnCdJdjswGANaBqUKEWWggYEBgkJmCYZJZl3Uo03blk05DxBNDKZcooFH0Bl932z12/UsMhr7nPnYn8ev29VlhMOl49o//ccO9X+7uapWiEROa0DRkObjnvU2f+PpPf/PdEhYbQL7C933rz78CAPjqplVXOA0TnTi8N9OW51x/TzQcFhwOMnnXNQNqpCzEqEr/S4SRdkTbOuaWeu75NhI4tOP9XcuWL7Ffmvj+gJRxOdG7mPxrWtYeYiYbqkOcbW8k2pr0EyVLg9O8BrbJlwWzp6Rdv2fjM3YJuQUX3F7AYScw0FDIEMbq/zrAkgVXDbnnj37+/dj9YH1TV19+1uL5VxVx+D9iOiCuAEQYUwAQgFXnzkm78VWXpI8GSd7svu9Mn7ckHOxjIOBZBiHECY5pcxZPmTJtxrY77W2YlQ8YGx8pVjXQ4LFBTmDvjDycwDYNU2fNmVTR2qce2j8wqh2SK+/93JuP/RSUjfRPBGPwfDvRASRQvV8HkAqyE1cH+CfNn1GDZMWwbmwybDd1E2FSQUTSDZ+blVWTY6Ch6YhlTAMwCCkqsRZyHLGLAmySXtMMo+smx6JQVOcZhlT6tKpKGSbQdDIlEARkinWzZs+Iv++KeTVpzycpDHTV+YuznLxdQ5TqgERYSSFfzyAw2LrjVXvx3GWx2gb/+8sfxHWyJfT7JX+/nn79rfd++LPvX335WT/9wR/GkfHnuttLkrj7xqMPXXbPt5affU5XV7duGBwEhqa0HN3PqpHKy9+sfOvK1qYPekOaa6jj7Nv8bI7vOKvQHW0WXHA7yyCPg+8IkszY3Clb6W+PYck8oJVECk2AeYA9TM7OJR94iJhr7GmQ5dE1EFlAmBjHWIQsHy9x61quXbKhXVnEquaASKAtqSVCFAODoP0vxtgqKwhIKFD/kWNOYUqJga+/PtDlPIF+WT8wD4vJ/7jkt5f7Z3Mxrw8A4ND23cVSAKWeBPzj5z5PWn1h3NEXVTWjiDMAm0vufujSVStPtXQRTxdgOY6v8Dr9/ro5NQdZjn3qxaOfmbMl+xEu+GyuQUSL6y5MfLq7fRPIkxs//ODiaVUnevFffvavoGQUNv4quvRvn7l82tT01SU5lg298fzYnAfkIqNLyiUfeGhmJdRJESyAIBA4oJtGv8uXQZYQR8hUdZ1BCJuM1WGCCAmiKkiTUd0wMYMYk1QYASZxI1mBQORoSNVjpgiGhdjTOHPm9FLMAOgkYEgfQKr1jXyFPq8jEJSSNrJFv2liiIjcLKL0L7Uz4Oc//k/bR7V915F6L9feU/xMk3VPfmfVXV+76srLj51ux6SrMJRleaZ/PwDMEy8c+dy8d4r4XrFI+GFgR+MhhljDSke+U5OCef03Gd+ofeYQWtxz1S2564DYPKDfH/CTr99tr3/g+lEW1iWChOiQ+P2Y25Z0jSPJg5j4efuTaIjRk9xMRExwKDaOJOKXzAlID2kYC/Mh4gMlboCIDiDHKjAkO5mX3toWX/b7XCvPXWDfhNQQlEkBJI75By0EgpI9B0jZ0FatuLjSv6T8+iff27rrMFmypJ41kyEurCF54Y1383qjf/nkbQAACfl7249jrJ03J2Ri5tSh9qamaQAUUwEUa8KcEpI3Xrn6E+mnGn96K9ZUNjueq25ZMTWPGLmhnWYTBZZjsGbYv38rwMfEyMrmIuE8llUAWZZ+8rsiusFO+MIYGBiYwCT6g4z6yS4kUwybgLgQrAxta1OEIMNCAwM+h6CMLOI+LRu37rMXqA4YKgooQRGQEBF7+D9YJYwAJZoExKQ/gXwSTU9xhGTATgaKK8KYWcwaByVsQW5i21Zm3dVwxTT9XdA03bUTsvzTz+6697a5UUl+8sitd1f+rVj6Rr33wiF3vPmq87IcQTMM08RGUjwApexJlKoYIJGFHETW5Jm06SVto/sNwMSfiwFikB3dYxr2ryAW70/UKWRsZWAasd8OsQFZAaJ2CC/DkL8IwQgsZCbab6ceyC2I26btH6g1zoOJmqDMYbNJfXuh3/iT4vcdtGPphv+l0AG/+c0jn/jEA/aNSYKTLZtmnCzazQ41h9nG3LH11m1m3YbWwqL6fdEI/NlvNvh9jpffPj5nRtWMav7l6AeuF57Icp6GQeoz96uc2A3er2/Ioa0XCbmU5cyuTpwCb5imCJV8Zzn5ahpK0Rly5FskyC0XVQ2ZpPgCjQT5kKBPBpokcB9D1YDE4GN5gC3IRMEwScK4FSEKTdw/qcJYNQBLyoqYOmkwBCFiDAxYq5wEx0AsFjKnjU0iYr+9hPWDNiCbfOILGatIla0CSGv8SRSGaYw/9vNSG39KqAMsm2Ziv+kimlO6dj4mh7omX/TPR06+z4vs5nfaP37f3fM7/++rrzfImjZ1sn/RHPf67vsv1gbFTSUS64M6kN+U5q62/yRVf4iPdNK5820VMkjb2144+6103bDm4+S3hDFRNrHJjrXGntPEf2ZpGb4KmXiaZqRktE18yBD7a/+Q+79NHDPCxyzvZDkmHK35bfzOid9BAzEelig3MZHpHENcu6qJRZ7hEFQ0yDMDmwkcMAyo6hCQGj9kLc+SPC9Vi1mEWIa8A88yimYoOjmaZpANyBzCHt4UyNA/Xyr9M5qAeI5VNT3V+DOgEsDEgeiAj3+MNEEsdsyZyzx1as8GOdQFDLVl7X96IWYZ/IGbH2rpDAAAvn9161dexapuSrImaxBMKsI7JgVvDqTmJ05I+tfYy0lrdCshk0HQbpM7aGf7H3smH0//HzQkGLgv+sssp44n4lVf4OA1xIVoP7enO7Z+sS1psXeBRdM0e/d3xVzBrenbOLtcsUTqriP5RcRm4YX1R6uqqnr7+m68cCCyxQL/9Y19fn/V1WfVJSprW0TGRLN9iewrYs0/s8joZKytYx217I36Z7CJd8HAjDY+v024PwamsdYJqjpGAAosUA0SvimwiGdJEUFVj9kH7NFB/yHwYNVhn37smX2/cSyyPiRZIyCo6FgrpRnyE/9Ex/5pFEDs3lM1PdH36/OS/uCZfL/285Hx/ZbIGfCb3/7uIx/5iDXkKBoe1HZyz2Yp1AkMFQGdtaQ/zwB923dnCNCO1/nBtW1fexOdbpfnYW1D6+yLzon7JArE6BzuaVs/Oswwmd1CqRdpQEYkv5a4pl/5ZFmTIMb6RUcsHjxBJtpD1IFVsb+x+3NAdqVYzCwTBBFG9izH3iIp1zQhvtmOCOjXQjEfJhkl2AsJw2dyQHvZFtZPv7Hf0FWiTeWooatWOAsWvJU33XX3zu3bQ709AE+Lfdx+E3U0HJo3f8GaHS3Nx45zgsPpqbh51YyE6xK/Uon6ut+Qkm5imOk7ym2joQ5hkpmibpgGNCE2MGYwidphrJB+Eh5k6qSmLADEImRdQsOeX1tTSoZsSuJ8yD72TUA2Jm2HTCuWyDRMRNwCDLRjjYoOlf5J2L/22LXmOVZRBw3/7eCfzL7fEY38KZEO+OMf/2gv3HbnB4bceMOOE1acMql/cMGi9EP3U3vWS4E2YCgIGET6I8whILDQJYAaN5xfx4rniZufDT18ZQsA4CvPVGKAa/YuOly34MbqJwv+FOawC2FZwXzEUAtGi8wKJrsSSXbJpLeYJXoEYwJp0GsDdmPrJbv0Z/9ucd+PtVdsz7+9edjQSew6a/XR5FjW7xA41s0wDIdMw9BVTX3/8OGLV6w8dvR4JBqtnArWtR7o7YwYsmhCjeF1OSQZBtvb2zt34eKerg7B1PRQ5/MvdmmAIYHViEG8eMcVc8GYgRR6INWdsW5ghoh9aIVsJrycKrfT2XOwiSFxGce3sVzJJrAODklYeUGG2I27TllKJTl8y+91/e43tMR0GthMxp+B4X9m32+S9C83Nu85k/j0+SdJSt2D98yJ9rZiQ0XYYKBJpD9jSX8eVLvQ7BpGXC6AZdUXLPvo5m/+H5kK3NH75Wf86qn2axBcw91zqe/Picd88Jvf+963H8zlZExiWxoWds6OFak9sclNssQrFKfw9Kv7ONN0upwsNknnZFlSFElVo7Iq6zrJdeU4loGIZTmGYXjRefTosWgkFOrtjSg9iEVOj+hwioiFiqzJEUUJMgzHmobOuzwwHKqq8ljvzJsY8zyPGLRm03FJVnUTsKJ43UXWBGJUQQggYiwk1pvE9fYkLenq2m6j1HKhsSkfkfYD3wiC0LDuQ/sefP6ZgfiI5V//0nDOuS80yknjYxbWFuy29E81/liZXxmNP0nPRyDwfyx0DWMQEnlOIymNMTtA/KVrZ+k73iXhZbOnMQnSH7gEIv1n1TDuJQI4twqAKwF46IJvP7T5m7UAgP++o+drz1U9cfj0Var+VsUVl0/7e+LbPfjN7+VyVviR+wvYK5HZcxfGfo+UdDz31mE1FHTwHNK1zkBP64F2Eq/Ccxz5n2c5zuVwsZyAMQ70dgbDIdM0EcfzghDo7pw8feY/3X9dpuv6o0de6Ghpdnu9na1nRJYjOzLQMEyIkMDzbpfL7fJ5XD6H07FjT18oHA4r2nWrmor7LX31Gw/nsllF43wyWmehYZAQH2ssT2L7reytpEF9f16YlQ422KI3EJRpmlaCMCZHI3MJkl9mb5prZHYuJEV5UOKwWUNAE8KBQDKjlfY1ujqAYVCt371iyazuQKQ3GA1F5IikyqpuGOa1swZ60h4+Gbt9L5gPnDyscqIZ1YxvoQAu9AOwCoBY3eALvr0OgAc3f3PTw7d1f+2FuhdPdZ2tG29VXXq5m3TbyAsc7B3mR0PQKsyiqKvX7t/49zxqIeRLjnOascMfn93qZBmeRdFI37ETbZquTZu98Bv3Dp379dhL2/95ll1W6yg4+Hqmzf7zotjCNzYvBSbkeN7h8n70thUAgEee3hANB1s7Wk+qx3XdQAxXV1PfNHnapi3NXcHwxvUj1G5oANIOgIzRMbaKflrCncjxuCUo5gi2Rv6kFFC/AQhZD7ss9EBMWX8zIpIjRtIKbDljRRkVUwFQsimA2PAfJA//+zIP/w9tz6Ng5Lgjy2ihzu/xusX5M+oME2uaoahaRFJDUeXJH34x7fabSZcq/LWbGP98HlxSAcCFAPx3/4ttADwEwNELVjk3r40+fHM7AOD/rXVd7WCPV96x7Zh5V+VzeZxzeLhOAFKylGVcIspY6rpQHrl3cFTHoYGm6kmscX3FXuBYZolEfCTDQZ4+eZhHePzZd0VBwKp0vKVV05RpcxZ97Z4rMm0sHG9OWvPAgtq8sllbjg4Mqr763ltpt2mc2rRp23qv2zd10tQ7bvlIZ29g3bqRVgNWJwBoV2xgB7X46qffxR2P+LLNQPHfVtzZbz9DjJUyYFmBrDDTWPBS3idmn11CXBklOyzPsUmB/7b0t57EV46+8WeMTAI8LhLEAwHpEsUKSBQ4n8c5ZGL0w6tVsFr90eXXAPCT/nUKAP8MwCGwJqw361Nq2OZOMoH491UnHnxdu/CsyRcuaHz+wLUAZBw2JoEjwWF+NAChqhoQIausy2jCDcShDgvxxOnUlZ29Rk3lkGVYwdtr1jsw09PTHoqE/v3miwHoN7mkSPkR5tCOtQCAaKBN0eQDR/ZPmzTthuvu6QoEHRWVa15OW9ixmMTCpKwHa4kOy19uhQJZZT4tknwDgw8xuAWYQfqLxVy+DIMMfVDR4Xyp9LocIudzO440d5E+w/3w7FhsfDIWINclKfDfpi8QG/6PHePPiOmALHbwtK3vcixd+6Wrn/nR66T1isXHAdgP1ofMk1p7yJS0gctsGGDbntbOnuiUWu9VF5/7xvqtuRwcR0O5bJb9GLquiyKf5NybYFxw7sL2jp5wROruC3d2dN33YVIySH775V3OWAeFqKS+v/ldqGj7mw9957bLwFilt+Mkyf+YOmPt5jV1NY2NLHvxqlvXry31bIAhZnoG6hri+sN+TGLvhxgT+f+Zsw/meKD/3TqHlJEm8wiyI2mzCaCOSQwoIOqkkEZD5y2a6hC5xtrKSp/zWHNXb0hSNV3kuUWzGwoPsJvQxKp3Jxl/7GdJf4tu/Hlp/R4w3sg0tfzmTwZF72TiS1dXW38/BsAesCkEjuttlvQ3TFDpiU2lf3DdmWjUOHaq9729baFQrmIdR8OJjxz3GnQEjHVDE+wOHROUV0IiyzAOh+BxOw2NWGeiEVJEQ7zs+vNWnLVv/+Ftm97buWnr8RPHP/a5j41l6R/nwI514b6Wo4e37Tmw8/iJI9dce+eqm2LlSEsEQtAwMEvc1FY/x4T5YsE3DvEhEGcyqQVkFZeLp5LlR3Wl2+UQXCJ/3qJp11w4/8KlTVPqK89eMOW8RaMfPTU2QYmB/3Hpbw3/rVVD7T/M4f9wdMDotI8ftmnR0gE7wLsBcExrDRpRFZNCRBjopDRKbJv/vv6MJBudvZEtOw7kel7RUOKjsHMzTJOUcR8D9tPhOwCS6Jy13Fxx+dKFM4mxnud8XpcsKzfdSAz60YhkP5S+YCDQ29zR+qnPf6q9rQOvuByMH3o7jp8+uWfjO1vYiHrBpTeU6F3IeN9K0eoP4yFBcQMv5yO0E6fN0K4n2r8y1kJ4GHAMU+P3LJs3+coVc5fPm+IU+GEdbuIS//KS8yKT/qYd/g9H+sdF/3B0wL7debhJi0JyOHNBfOnqU+Cw1hYwIxqwpb9hYEkxg1GzrTumj390Y+sPbxzU5XEIouFBj/yJpTgRB5o5ARwAiXTOIg0A3l4XM6Y5HWI4FBIShMLvHvnTb375yyMnjjRMn/rRj99nrxyPPanCvae37tje3dqx8sKMUafDw67aQEbr9mNAROR/uZISu4l2sSuD2m80PCAALEJVPpfIUwfAUAog0fjTF4glTeCSSf/1+9q8VbYxZFg8//hroDSs3Xoo/QuZ7/IcrUA2X/qjFFFJOqVVRd2S/hEzFDFltcD7vggmIIB0E0m6Xap94mBLf7uMHWkU89q6TRvfgxBedFGskNxfHv99W8uJT3z60//+3W8l7tjW1gHOH0+TAJtgT/Ohg1v3HT543dV3FP3gdoXA/nYuSS/lf9sM3sWuV2dlAWBkl0GkjEwpiATTf4yRlAEvrd9zw8WLCtt33+7nFiwmTVdGhoEqm8PmZ6+on7yKM0wsW2P/cNSUVKxoePcxdfGMvGesWBp29KaVf+lwCr3hUVYAkWDvZpB8QxbGrPnzrL94556jC+ZOBRg0nzw6ZXLtiaPBrs7jp8+0n25u9Vc1/ts3vmFvf9edNz719IuXrrogfoTN2rDO5GowOvR2Hlu3VVu0bKXb59+ydnUp3iI1ViCvAj5JNgc7MNSee5K8sTFghywHBsWAJg7/S2f8Wb8vZtnwVlUHu7uGqQNKNwnIQDbp/+oRNjEdbEh+/Yb2oYtQMGqGomZUwYoGVB0oekGnZQ43cYYE4RlmOCxpemm7Qo6Y/Scu/Qf6wULwwKc+2tlOKufNmTfb8gdk07VtbR0XXHzB5vWb7afv7tjfevqkoihuT2VVVbXb5Vo4b+x6FyO9zXt6m6fMmn/+qsu2rH27KMckDdwto789CkoapeflPUrcmOQL2+H/doW44QXyt3T2NdZUFLx7WcEOCvzvJ+nal0L6p+qAgin6JCBz+eGh7cL56oDHN5iXzjUlBatE+mNVB2qsyXae4OEqANVZXT25yuBY6fSwK4uOATz+qvi9vHvfsXmzp9jrE6V/ErbzI3UScNa5Z7239T0SZbhsPlg2v3TnPGn2ORW+ir3b3iziMZuP7O/rC1x23VW8w/3aszGf2fmX3uZwON9+5fGCDmkpVKsIhHXPkeaPZv5lBO0cYMMgdSSIVSlW7pocZlBqMaWUsInS3x7+pw/+Lz3DmQSMsCGouDqgf+xPpL9iQNVAb+yGD/7U+nE+dm+OByGx08MEcay7AkLAOSdI5axAIOjz5TpYyeT6bmvrqK8nJZtGgDOHtw2qLzgM5p17dcupo+FQUHA63H5fd1A+s+tw04JlDY1VQANyNLhlTSEBFBgAF0+aedmN3UlKMMPZxR00g0SR5X4oUh6a9I4HLGAAxoZuaRGrJw2DQGQceuDHI+n845mMP5aCzj7810zMZU4jWr+vzWqWO2h6VxRDUHGBhc0OCuXdE8z8Ol01yNhfM5CGGZVxb//VPdaLubrCSHul4REJ9QW63RU+j67KwzzUsE5j2EWNEob/IBAgEbHx4b9N4+RGe0GRFUPXnc5YB5iJxIGtsRxyKQqkLtBqxTR0nwHHh9cKFyFEogSsKKBYswVie8QIkfj9vMw2JH0YQ1ICiBSDg9juFmn9vgzSX4E6gUcClGb4n8n4M2zpz3OM28mn2nkTI4IKiwq1JwFgBMhN+r96JL/IM8v0D1XTkv7IfcOCvAP5BRYlPvLd/fxbHtBURZEkUuqR46+693NgonDqzIBFy7b/2KJfkWNtNKPRaCQSSdzlxhuuXLM2Zve3ueDiAYtQOWOaVmMXBnEMaf9rF/dhGFLChwTz5yO1ycbQ2gciuwwtC0mfSGQlIhrmqDmiyooUOVXoODK79LdKzjFVPmeFR+zqi/YEosbggIGiOAPGFLYOyNEWdLSbrfVgzWRU5HJVTwYg1/yvODUVvuF8jeG+bsQyDMeF+np0Q245PtwOZaOOKFqt19IN/03TVFQ1yf0bjUpOp0OS5NG1Ao1xQm17A/4FHMeaqm438MWYIbksgAzb87rrVMPuQW2noPfHoiCEGQQZ4eTe4nitKTkpgNjwv1Djz6ubD1yxIlvfoo0H2qsrnFPqfF63wDBIUkgRzUwTxsIMQUX0BBTRzJO7P8CS/g6Hv/HSugOFnPML8SpDhSArEstxLMdChgGMBEcpCjsUCi/Sh93bDIBmEDMBnTrTmeT+jUYlWzfEdUDcchEIBPl+rXDjDVe++NKbia7gCy6+IPD2K3mfyoQbyJ7qATPrSNa63RpNJ8nAgGFIDau8TECcdYshBDSdHI3UisYAI6ADYLCVpTt/SiIDv/OCjT+29M9u/HEIbEOVp87vrnCLdZWuKp8zofN40QxBxcoNLq6RP0dzkApFoaLxsobRGXq7PV5gGuFAX097a9ep07oUM4+MMIu0IgQgNdeSeg9ph/8uD7mNZVmR++0/NppFlmOSpDCKRahtn1W3mUwA7B89aQxQ0K/G3sW2BUBI/AG2Pj65bx292CMDOxD7X5DxZ8ix/5ptpDpgMAre6+56b2fCCyUINHr+8ddu+dA1YOyRyzyA99Wf7T+cu9e3uOx446mG2ctbOtsQaflhnt5Poh7HL/YYP3X4Hxvvk8aX0NYBZmZbc+okgGJzvE2aU8v1d3s0rLkASQ/ApLZzrpDYf2QfwSoAAW1tgjVEQ/hHDnZo32/WwP8rziPSP7v130YRc6j9ILfGFwuOCBq+IagEkT5DuwSq0REGjaa9oLG+4dShTs0wKv01QdKcZhSYXjXIk1EY73V2k+aFFZ75g4f/BiZGHtL3hti6WAhhKBhyOh1xb4E9Och0WOIJuOy6yl2b8jqZYhi0xhwQsWQSwMTEN/EA230f84lFJiN+4gcGCJGOkkQnI2v/Uf0VlBv9480EDXBoW67SX9ZIJfAs0v+1Pd2qoxpVNHgbp09tqJgzrXpKQ4UosJnkK1vZMN7rRWfn1SPsjMlpfiQLm7BbgM5RLVm4ff3LNdU1PMeePDS+273d7id3c29wwJ3bYQ3/VVULhiJd3QFJIiYgWVZ03YhGpXA4Go1KMfv1YDP2JRevSAoHohBnSev+WLyPNZciCqCQxF1SCchO/7Ibh5FIIgjOHHqHXuRRKwVRROPPoSA/u2lyY4332pVzfU1zgVXdZdsbT+m60dETOXKqq6MnohPz4eC3r6kJdnYO0wpUoryw5o7g8I1XJgZLZuCplchB5tAEzcCdYeARITPa9Sf37X1/9oLlHa2j1vRqh3f61LZCfOApOJKG/129IcMwwhGJCBsI3C6HrpPZGMuyRjDidIqyQtoDeD1uRLKT7BZV6TwBSy7EW9I3a0yLF0xMDrdpcxtYQ40lx9txnAUWULWbA5uA4UCvkr5Z27e++6Nvff1Lwz1pSgpkKpcIGf7DoYf/r24+cPlQxp/2ltNuJ+8XlW07VO/hQ/EaagyDKryOuipPRFKDEcXKIB8cEpqgA0bFEDTEXTw8KY0xkHUcVrHAkpon5DtgQJUL8mNj4nt43/tg/IOtdlWJw/9gKGLommGYCEFJQpqmG1bLQLt/bCQqcRwHAQ6HIz6f1+kQWdYKRx9tlTx2gQzLsBLQWJOBEFpXy24Sn8chrP8ZbJIkMh0CHrEdJ8f37HPcgQow/ry6+cDFZ83O/tNYf6CDY1FVhbO+2uN28IkVNCGEIs/W+F3VlS6nyFlVupKP5a2pKdgQNOLl4fJGM0BEGaicCAEUiEWaypoCJ6NxDNJYijyShv8Hj57WVNWwolVMEyuKEgqFJFmRZCUqyZKsBALBvr5AMBTp6wu3tLSfaWkPR0g3QYzxJRedl2gFamvrgOOwRnTRCbQd2NuiOwRiQmA5iBiSzJVnJjD54SMGkhZ0EDh4pjVanBKwlNxJ0dhDFjvbfODcRU28FcSbafi//kAHy6AKj6O+2mMH/ie/K4Iep9BQ4631ux0CVyLRV9zc4Jj9xzIBPfvm9uEcCgMgk6JvQ25WdhVxe/uCR6tnx+W4TawHuY29nHJhMMY9EaU1EG0LSWt1X9LwH5tGYpkCwyCHNg3dehgmKUiGNc1QFC0SlULhaCAYbj7d2ny61VYDBX6Yif7tBVr37z+jkA6ROvmoCT3Yc8WuYGsapMLo6QDTfSoxTDBvaqpo+FDeoDTDf5xx+P/q5gOLZk92O/jsxh9iY3Xyk+q8NZUuNkNKEcuiKp9zcp3PX+GwcgJKMgnYd+wNUPRf7rDVFcZA0a1OkFmxw6vLiiPHm5tPt7UGo+0hqSsid0eUXkkNyGpI0SKKFowvq5qk6rJmqLqhGaZumJ1h+WBH4FhP6Fh3MGn4f+AwaZ6e/RsmOoVUpMGGYWqaHo3KoVA0GIycPt12+PDxs89akuQKhudfrhmmqhv2Oai6YZ9PWNGiqi6pekjWrPMsVHmME0Idh0kdH2hV8MzzdrWuuVVBFOJTfUagdf9wzoRK/8JgB0l/mC3t69XNxDtX5XUkNgFNZf2BDp5javzuhmqPU+QyWTYghILAVlU4ZVWPRNW+kGz3Gi2yMyBSYJsUXOIoURMDWcMegdh/Mm1ThgogGpXPKB3IDFklYgADIUIkzIRUB4BQta4IYz+11jAIMdZApDUYDcgkk0ucuSBp+J+vfsekQ6dpmKZhGIqicBzLH96dWhmiuS+iGSaw6uOTja0iuppBmipDUufARJD4eBqK0PhuTPP+vgPnLp5jRYFiQMrE5Qo2yS4sMBVoRrqOF/bunT3BGv9EdbSPBLnWLLOl/0XLZ7HsEMYfhKDXLdRXud1OAWXVFQhCnmerK12hiCIrepT8gLP9OPPSAfGksH3H3lgw4yqQD8le6dSXh8Gv1tH+1BnBpm4ANppgdbEdhXYzF2LG6V+2QgZJLKI9wlBINRlC0vB//8FjMOe4cozNgXJmpFWnjiGsCHcfh6AOgDVrNycmhc284ZY3/vgn23VjWCNZqzQOmf72j22tM53oCgAAsHX3oZVnzWNQfj4AaJV/IN/R4QKlfxw6/C8YlKPxBwBwzsLpghWqkt3443JwdVWeSq/D7hw0xNtD6BL5uip3hddhqZZshqCCydcQlP02Hk6vIkoudHCVcVO/VS6Y5HAZlo0hvqybZLitGKZmGEb/5DF1+O+N9uFBvdKGHP7Hpl3YarDjCnXLuhHVDEkzUitDaCZWrXMgJ2Niw8T2iREfhnWSRtncKhvfOyDwkMunBi7HAJ5D2/aMUO2TDS/9AYxzvvD173/h698v7jHRoMifzMafRbMne4bKU1p/oEPk2boqT0O1RxS4HEfKDAOJu7jK7SLBQmk2KNgZMJxwoKxSnobrjCE4hokHa86fPTXxpTPvv5eoA3KPs7Klvyfck7SeJoVlYdOOAyTHM2cEgXlv37gvOjuScv/H3/3qj7/71eIePGGQnuHX8ermA3OaGqp9TrurambjTztCsMLrmFTr87nFWAvWHCDOAJ6tqXTVVLp4MoQYYsfC0oNznwTEJX+qT8LGKmA74UM8xgcQAJFl7G+qf/hPgguTrP+2DjB1kuo1JCbJbkoj/b3RQOLTtraO6z963/A/wkRi7bY8fpvv7CxKxt+E5QullPspCsCSuqnD/1c3H5g2qbbe7yaxutkPhJDHKdRXefw+R76tvRGCbpdADEEe0XqjohmCyCSg3w+csw6IBR/aYeM2Sn84B6ldbprhqCyrmq0DFE2XlGyFJCl5gc08ImeQVTzAtJR20vA/EQhAlUzENzb0+CPpTeOPtNKfMmYZSKiZKNz3mQdLLffjDLi80kp/AEB9lTseypnF9+sQufoaT22Vm+eYAmwkLIP8Xmdtldsp8lq4Rwv3Jj0SNy68WHRuOsAa4GMtIbA5KpNRoW6YUVnr6g13B6I7DpxWNd0wzR0HTu891kqnBMWlk8upIjxDOhQS8T/Y+t8Rt//YkMazCHmloP2wV8Y0Qb/Q90YD8Uemt/NGA0lJYXQSMOpErN/mhOG+zzz46M+/N2JvN4TNbtm8qS4SyplN+r+1leiJvhDoa2/bs6tUNnOHg5UkvYCIIBIO9Mk7cnyX/sQj8p+s6E6RuD1MjJvb+hwOIRxVorJGxvsYv3/gtEAcHt79x9vnN9XZpaw2vfLohddRs8DIwSKoW43Iswz/bc+vkqDO4zqAMt7BGJxs6WmaUpe0vqWzr7Fm/OWF3Tey0n9gBjC9Lnn4/+bWowtnTa70iMNN0815d433p3lwAw/WW4S2fENOAvrlPxn1265gUjxA03uC0b6QZK/EAISjyv5j7evfO+p1i3Ob6uyqPpQRtgKRQCDNyD78t7MuimUnoJOAMYVpmr1Bq53JRLH8jDBsZunfWOkW4r7cTMP/13Z1ILGq2udsIDV/OKfIiTwrCpzAMQLHcCzDsYikxlj1YjHGdhqBleZjkzBGU/WWrtCp9mBrV6itJ6yoVoK5vUH/r5f11urBjrwnAb9+JnESkCkzwB7+xyswyAox8XAs09ETCkcVA1tx6P14XGIoKkMIr1u5wOcWczkNSl50cpU12iDTXyq6iRGE2Yf/pUYU6bc/ahgGtioKTxDp/+jIDv+zmYAm13hkdQjf5uu7OrxOYfmc+oUzaj1OPnEU3N7RhTWgaiAv+5yLBfMnueZPIiVhVc1o6QodOd3TE5QGBeRU+oO9PaXQAbbwj79TVFZtBWB7gBOlP8sgnmP6gtIlZ8+aVOvLks1LyRfrJrIrzJuxYjFZNkaQm7Eoefi/fWtJL7s33LNmzcZLL11pPz1x4tTt//jJZ3/2i5K+KSUtupWzPTEuzqMjLv3JWDzt8P/y82YnRvJkGv57BdxYwVY6gBQOdnZ2t3d0xR/DPzNSTNTnrPW7SMXBwe/vrfQXcMDnf/3MEFvEqozF/kiKbtWDBHJKRReGQb3BqIlNVbd9kJTigBBiGIbnOY/blYtaZUZ7+B/n9s/+42ifQjlCCnyP/yig+0bc9B8nfbKugyczA49TzCL9Nx9o83vEhmqPxzmo2nOxgBA6Bbau0uX3OriUinJxHZBfaliCDkh1BiBEWhT1g3XDiEqaaZry4ChPlkGabrgdpI/gnsOtB0902H5I212c+8lQUnE5HV6Pq8JL6ojMnNkUrpzKsDzPCxzHsxzPsBxiOciQB2BYwLANKy5OOsLp99/HiB3mAyQsxB5MbAFa7+uVwmvWbIy/6YkTp2wdYJ9b/DxZjpb9KC3a4Azt8ch9oyf905iA3tx69Krz5wwp0DfsbfG6xEk13poKZ6Z6n8OHYZDPLdb53WFJC4TTVIsrpEZQgi0oyRCUJL5J0KeiRmXNjgG1IfVnEBRY7uoL572z+2RzW+87u07wHDNzcjWDUDAy0IaQUgDrX/3rrfd8yjB0CKEqhS84d7Gizq87s9cwSfqFbhKDr1Vrwaq+MGtxVYU7vu++fQfkU6mFPwcBAXDypPeCXWaO3FGkpykyMRZYxsSkioPAktL2BsakVQUkCwhCjsSbmhACnmFIpQeAHSyTZAiaPn3qgvoKsjFDNiYl6iqnA6mQerSUXMAYFF6su4wdv9kUwIVLm/ihjD/r97YIHFNT4azzu52kmn+pLOB/+tuGHLcsig7ol/+xWnCmiWVZ6w1FNVJoLLaSY5Fh4slVnqbGKgihphttXcHNO497XWJdlbelM2MIeTlz9qf+nOumj92LsUlqCFp1344cObpwwbzo3HNqjr2HMWMXWbP/PVw1M0n6W9/cEDMwjkENHtEtcJxVRNTAmGgCqwGkrQZMjDmrt4mJMUuKWZE0I7vgqGlpAgQhKYBMUhBgoxc8n6ADAABzb70z9PcX7ZCHw5Uz3BACqcCLRhkSjHGqeXZ80dLS+ubf/jhWFMCbW49ee+G8uECXZSXtPhzL+L1Oq3qP3c+rEKZNTg7dTeVfP3XrwVPdG3edau+JpFpXWBHowQK7Byf5hG0S3LwxcR9VNEyKUPY3s0TINLHf67SS3eC0Br+62Njw/tGOnvCRU50uh7DncEth5zOx2f6re3LeFj3/l9/c8g+fiJf32bvvwMIF87pmnFV73O5VSdbv8U1Plf7KqRMQAJ4ld6SJ46rCajc70HyNWPCqXQLPMHa0V391Uetle7P4ctJCmhWDsCcBnitulNe8vN/XBAEIBemAoIRomkGT8IumAN7cenTV2TPtTqhZ2LCvtcrrmFLr9fscqa2+cufk6fZcNvMK4NpzpkiqdqYjeKYz1BOSVM0AGEQNMk1hvTVxHZBvw4C4DsgQFUoqUqiaoekxqw6EkOMYgHGl12maOBiWvG7HrCnVqqbvPtxy6GQHJuknNMOoCMR0QL+gtXVAR9Pyfh2QBlv6V7mEapeIrd6QpEa/VZ4zrGhBOVa3QzfN3qhS4xJ5YrhMudVhuuWsW31wuu+vKYag/b4mUK7SP6/Z3jDfKyqTZPzxW5zxyls/MoYUwNK5k1wiF7+aaYf/6/e0iAJb63fV+slvaGQuPIRA5NjaSjcA0DDN7qBkGtjJGMOJCk0iU2aAbvUOtJfdDl5StMYaH+k+GIhu2XVi5fIZLgc/r6mu1u/ZtPP4+1ZxiMI/JyVVB/STqANSh//KqRPEgSywc2p8Lp61bUT2JMDAuCUQjVjRWnZGWEjWeiXFRTRAce7fVB2wfPn8dWu3lOf3mddsbzhvhDGIyhrpuzCeczDfHFX7z8B38ObWozUVrnj/lkzGH1K62S3WVRLT/0jmvpJqcQ6uzu+qrXQ7+OQewoVFhQ4ZFGS7ARLNQk4Hz5K8sHBvMHrgOEkDbusmvatqKt1nz59iVQmlUUBF4/m//Cbx6V7LzpMq/e1rjgCY4nP5RJ5jEM8ggWUcHOPkWRfP+RxkZXwXxTACkhoP3CoRl6w6v6THp5imqWg6rco7TGI/jJXLmgSOyS79N+5rtep9un0JGcIjBsMgl0h0QJXP7iGcBCywe/CQyQH9SLLudggdPaFAWDKJ90nbe7T1zS0Hjp4mSQ8+t0haIFBKqQM6enpTpb/aTCJ/XAJb73WmjkkQBE6O9ZDxSmyNiUFE1cMqyfAoFh+c7kuNCgUA/PmR/yvae1BSc4D728BRhqUA3tx6tMIt2jOpTNJ//d4Wh8A1Vnvq/Va9z9GAYZDXJdT5XSTreLAG8lZWlloHOEWuOxAJRweuj2GYLZ3BLbtOnGrr5TnWrhxHKZEOqKmvS3b8YmBLf9Kq2iWKGYqQOzimyiXyCZOAiKr3RBStqJOApUuXJekAOgkoKbphjOsiEFeOAQcAUQBvbj16zYVzs0t/O/upyudorPa4B5d8GGF4jqn2uWoqXAKfmh48oAPyJRcdIKtavCvAABi3dQXf2X0iLCluJ0kNo5R6HhDHOBOT/hyCdW5HpruSRajSwXsEPv66Zpg9USWiFG0SYHt9U/nlozl7RCl5oukTpwjEKMJees4s0lQvK5v2t1a4xXo/Mf488VLpvVsF/Swjij0fJKb4ArzBQ+oAEn2UDozx6ba+bXtPjWdf1FgndfgvKbCbraxRif+/2i1WOPhsfap51ufge0hFv1itj7Cq9UiKW2BTk8zzJR7zM6Opac2ajasuXQkTIoKGeXBKJkihrnFeBOLN0fYAEwXg7v/lZBr+r7HK/XeEAx1t7QO9MCwURz6NuiBwibzLydvNtiRFk8kQrGhfIesEem/bcHRAweiGceB4u4PEUFGKz8e/+FAViQGLsW8vuSG7u7sw5Do4/2Szb2qFO3tID4uQW2BFjon0z+FU3eyJKFVOIYvmyIWkiM8ZTU1r12w897yzeZ5ESRw7dnLucI5OyZIDrNIyXMULA81i/Ekj5SFJieJYpoJnHSLndvJel6AbZm9QwhhwHMOziOdYgWd4jlU1vScgAQh4luE5RuBZgWN5npFkrScgIQRjK3lG4FiOY8IRpS8kk3KbfGxjgWdYBgXCsqaZumG2dYfOdARJwGWK7mAr6xN1ABhBVI1UjhvJdyxn6d/ZFbQa96iQ4cHkORViKPtBIARegfOJnKTZ4aDk5ggpWkjRvGLh8Wyp8f6217e+cbLTKbIs4xCpVbAkmCapw0hDgIqjALJI/9f2dnvdwrym2qVzGqorXe2De23nTNrWPCKYklyIlOBjASDloJOodDrshbmT3b2hiqPN3WfaA4pqJAv6mupgp12LFL60fu8NFy8s6IQpY1H628iyofYXKmeAMW1KQ7d3dpYcMRsHx/qdYp+kRvtNeaphhBRNM0whzxbW2aU/aRXZclpw+RBCosCfRyMDSoBhYk0fxw6ApRde19kR62syuqAs0n/9gQ6vgCdXCZP8vCqHC5X+RQYhaHef97lFK50ztYN8dXz5pfV7R/wEKaVi394Dq5/4wxvP/8muC0IquznF082nIUKdM8+OP9LuixCsdPIegYvfLhiDgKSGFC13M+TpyYvjj0zSHwCw5a3VSiSgqlowFBnGx6VkhNQEHM8xoDs3vQLGBmiI9CsnX19FCj4z/TliYwGORX6fo77a4/OIlhMv2xSe6oAJY/xZ/cQf7OUdG160woIZl9MBINi9e+/u3QOaPq0ygNYkwC3YVeBAvytY74mqatZowiShb3Ps6LHEEVxqvP+Wt1YbStjnTTOXpQwfTdPHdQzo2CFjR7ANBztcDiL9qyqcw6n5UwoghA6Bq6vy2En/faHkStHeAUNQ4W+RfQND1xg2P6/vksvvNXTd1FUAwf6Nz4JicN0nvntk/16HyzVp6pRXfvNNMIFN/4MH6js2vLjy6rs4jjUNUqUZQJioAxYvjpn+4jqg5uh2BkKfyDs4JqxY3UatgWRPVKkiteGExC88UdbHOXb0WNIaWVZEUciS7TV9asO75jdMwyRRD6qpKOqzf/hxnleCkgIGEVkzEn7yT7+5684rl9ArVTQFsP5Ah8AzdVXuxlqvUyxhwedEHt8YNElhD4QYZOo6gxhSVdcgFiri8YOQZTmEmPi5kGJfpsggF6oAPMtxvGC1dISkTyCE7kkmNk0AiaTe3KpIUlRVSAtfFINhWY7lWMMweNGBECL9iv0VDEs+rKHrGBtt7Z29vX2GbtcRI+86b5JzyE8x45wbsF1BgpQWJgtkSde91Y2aqnQ2H4pGIzpk+JrZGDKGTjoNWFuR4Yyp93eesQoKIwa5fBUsx0Nskg9lXQcIISaF6SHLcS5Pxd6tfw/19UWjUvupKjBWeeAxNGzpD1Y/mRwzt/H1p6y6obZXl+gHu5skAGROkKQMbE1garp59BQ6umcgHlTReqOKR+B4BqXK/VShn0QW6b/lrdUAgFWrVtonhzEOhDOaWym5oxumlY85oABUeVz2hV964XWjbgtiM0ZNuMSGGm+Fx1GsmllD8qGV3nSrM0+iMbkVOnrDR051dXRHZDIlTLbmJs4DBjuETQAU60FGcvaqTQe7dWvobzerrxNRXYMAwEAgRyyCJCvHtr2Udn3leZdjE1dXVbTqKmMCTuAgYg2DBZZAtxWAoWkJLXEtcW9opqnbXwBZZ2lIIlAg1HWlTworimoaJsOyujJBCs9//IsPpZbgXP1kzPiTJUfs5g9+nPyxI4utEQMcrAwWL17IceyM6ZMPalpba2c8BLlXc/Q5JtVV+2HOcv/Zx3+Vy2exdcCll660v1W/z/G5r/3HTx/+Ri77UjKhqLqqDh0C1NLZ11iTNvyEklUBrD/Q4RDY2ip3lc9JmsOM2fwmSIrTVXocdVWeiKQFw/KwkwrIR7XlgolxcQ1fe959C5SScTkEykB1wvB/b4LpPzsv/PW3A5rAnifGlIGt1fs1ASaK1udz9/WR4FGMcTgcbT7d1tPZIQ4VtZmj3M/Cwnkzvvof//39b3wZTFDyne0VgKxqEyAHeOemV5ZeeN2YUwDrD3TwHFNV4aqrcqeWWxhrQAgFnq2pdAXDiqzoVqmGjM6AIaNCLbuLPXwc3xmG45ePf/GhJOlfwEHimmBgWmDJfes/awoFoRINQawS3WC9KEWCIudNW1t4mEI/Pgmwn5482dw0fcq///Cn/+9fPjecw5YtGANFsw14lGIrgPUH2hmEKr2OSXVen1sc+ZKfhUeFVrvDUaWrL2KQ2LDCdQBlTEl/jPGLKab/4SmDWIYgQyyb0G4KZsWVm4Z16xRrpJ9FB5w40UxLRBSMaWJVK2YFgTJn8AwAQofINdR4a0ev5GcBsCzye521VUpEViNRteB7g95UY23sP0zpn0kZ3HDXRxnS8B3G5QjG+M0XHgclY8tbqxNbB9t14j59X+69UygxTNO0uoAVwoaX/nDRDfePqUu5dLT9wAMGu/UHO0Sera4kxp8Ri/wpChBCUWRr/cRpwZGszoJTwwZUR8Ef/5a7P1zYjuWM7fhNIkfTfwG89NTv177yxPZ1L7y/4UX78d46MkgvKWvWbDQM0wIfP07qmNJaoYWmgPU7AEj4Lxi/7BztEKABBbD+QLtV8Nk5qdbrdvLjSPrbMJC0Cqiv9njdAsemSQ3LNz14vF2AMnX8jiO2vLV6/frNHV193T2B3r7Qjp37qB2jAHQrqWK8i/4xZwIigfNOflKdt7rCReorjCv+9OTbxT0gIsYBqgNG0/QPJiJb3lrt8vohIv8xDApF1f/9zR/++RNjyygxdrE8NppuTCgXsKEuXXHFznf+PpoK4C2r4HNnEHSeaQETmIQKoVm9wSRKhCSRFUS8rzKlQNP/sB2/Y5lIsMdbWW05HXBnZ9fLr2/6yr//4Af/7yujfV7jBk038DhvA5DIznf+vnTFFaOoA1hb+o9NdOdke4FlkEPkKzyOOr9ncl1FhSdWGdRmSnUsWQwDbBhmX1jafbhl9+GWvpCUOJZkrMINRqg9uw6IB4PmC0Koodo7tcH/3F8L2LscSZL+NqkZvxOJLW+tPv/ym5YsW4IgNAziFNi198hon9S4gbSA1oyJI/4TdAAYJVhDrJ1cW7F07qRZU2rcToEkzIxbIIAsw1T73JecPWvBjPr3D5zed6wtFJUT40IZT11WHUCM/3axyfzeGsI502ovPmumzy0O+3OUKZlM/9/8/o+//dUvgInClrdWr7p0pXVPkpHGsqULaqorHvv590f7vMZLDGhKBfiJogN2jsYkAFV4HItmN85vqveMc+mfCANRnd975flz7756+bK5k52Duz4xnrpMO1olGPIHwoZq78VnzfB7nWOqbOoY57f/8x1JkuOP22674Xd/fSppm29+n1RP+/xXx1mduy9/8ztf/uZ3Hkn5OACAT3/lW5KsI2SXpYItZ1pXrqCFzHJCN0ylvxXEBGPnKM0D2PMWTZ81pYYk/ZZM+h842gxGCQzw5Cqnqfma23qjMkkRsHN/OF+1HOhKOwkowP7jc4urzpld6R26VBwliR8//K0vfO1b9vLOnXuXLl34b9/9QfxVliWau7ene9xdNzuIYP++g4/89amjR08kvhSOKC5HrI4shHDNW2s3v/3yKJ3meAIDEJXVgpMAxj47R2MewLJAb23vbGufIGP/JCCAosDVVLojkmpiLKt63L4jptEBJPgnLxBCPrd44bIZk2p9EEBFm5jDkxHQAXYZhk2bt9fWDpQ1TRT9n//qN3/y/W+D8cAFl15reZKIDti396AwuMRQXPpT0Z8XGBMFMIEcwOkZYR2ARNIjafSlvzUwj3+3g75kEjBh19NNWm+/MmjHhJf6VzIIeVxifbWnuoLkCAyK7kzzuXO9vyCE0xoqVyyeduWKOTMm+XXDoNK/YH788Lfe2/a+aZq9VoG2VH7y/W+PF+kPANi85tX//vZDm9ZvIIVrFFLxOxUq/fPFMM00LWAnFjst0T+StiC24I7YxWWwEhp0Spmy0sguGc496QWeZap8LgihpuudvST2wlYPordaDnYBHJ8E5HpvIQTr/J4rz5/rFAWGGRMXcLyz4c0XSL5eVW3iSozBT/9r3Mj9JDaveXXzmlf/8cvJSc5r3lpLXqVmnzwxDFNPqNc0sXXA2RdcsX3zSMwDysJjSWpF8Jzf66qp9DgdfLJCsZ7uae7N8WgIoboqb1211+MSqfQvIhvefCHYPahT9viV/nF+8d/fSVqz+e2XqfQvAKtg30Qe/icSl/5nX1Da2UBZKIB44ejqChepF8Sx8SmCw1djvQxOnsgpCY5BqL7aW+f3jJGZ04ScB1Aoqeg6aQFYbldme4ImKIUyyNgTeOKBIHQ5hNpKTziq9hgRLRomViCHGOuklYM851imrspbU0msSSU/XQoAP/vjnwU+ZcaWG5qm57ilqpKunwKf3OEZY6zputVXjPzf3tHT3tkbCIZDoWhXd58djgIBIK1LSfhAzFVFKREaMd2W7/XdXhqLUBkpALtwdIXXWRNV2tvabOlP7M4VlcG+nOw/k+oqKj0Ou4ww1QGl4NYPfXLfvsPxp4nLcS5ceX6WIzgdQ3T1SgOE7+04uGff0SGlS+KXDvsTPmprq6678oL4jPDI4UNJe8nSBOnWObpo2oQqAjFGKC8FAAF85e2t5GbifDwDJBNDAESUPk4jFVv6l/gcy5q/Pf7rWz/0yezbbNq4JcurkxoHuZFzRA4HRNYobACP1cixI2kUFaX4VYDoNS02ZaQAnn5lIxk/LmuaNWtWW1fwdHtfX0jq6AkCnOskIC79s6iBd3afKupZlx0JUhjbppX+qz3wNKGRCwnrNU0c+xfBMy3tkxrrCnjTgq0L8fJ/sZhkOESIGqUwNNIGgKqA8aAA1m3NVnC/vbPnrusvBqMh/b/8ydvtp36vq2lSdVhSdh06fdhK1PRWVI7wKVHSkqBcyYL1DKa8Sv7GS3YzDFEJDGOZ6wuSu5qe3E06d+KdU+3W85QSMYFzgEcR9qmXN9x1/UVFOdbJM7EYvmmNJLTmZEtn4qvtnQND7KdeXp+0b+lUgi36b7521ZypAymmLIs8rOBxCbsPFnJX0W4BpSPQ2xeVokNu1tDYCKE1JwDEjpegLcCZlo68DEEYY30YwiWusfbu2T/kxq/97S8Fv1GZU4ACaOnsa6ypKM3pTKAZwFMvb7BE8EXDF/2ZJL7N5z5yAxmkQfh/v0+O9ktVCcPXCmlFfyLHm9tcg+vE5UgWR0AZxykUh7dffvKy6+8eUge0tmQL21VyUCFxTIwjEQnnlmSUlI4OAAgFevfuCeb+dpTCII3AKMUGntr+PADgez9/qgAd8NTLGzNNnD9//42WqAckdAtj08RWF+5YSJ1tcjVJVBdGEKIMWsE+w8QnuZ/h069szC767YWu3sg7Ow/qCqlAILgH2kZahoVk+9iSubH+BDaXrZiXeuR3dp/Cofavfe+XOZ4nJS256IAs+CvzGPdpuhGNDmodkbsCILHFztxCAyB4nQ7/h8Etd9yd/rpC4oO558b0sWFJM4Cx1hR+1InJuAc/c9f3fv5ULlMBS+gn99n6p4/dlDYxCpNeLsnriR/PspuaEBumGbfx/tPHbrZDqQ0jpi1++ocXU96dnGESSSecZO7PJPpjZw+BLf2Ly8MPfprqgOFgso4lS2f2h+DHPMMQQhPbBp/YTIusJ+5fE5GXgKZpGGNB4FmGqaurSuwCl7iQRPOZjoOHT+WiAGwnQyIVPs8lFy6LuwGy8K+f+3SOn51CGTEGBrkPfuYueyqQpAZSJH6Mz9x3vSWm0/wq4qRdH9/cKonO2Mv2zw8hpBumZdsl+/7TR2+yC/TrVgQYBPDnj76UesBErQAhnDxl8gevO29I0R/fXhFis4T8A8gpJSSxuebAPTMQiBX7a6dnk3+tznEDkVrxfVMWkmAt93FhxjtyZ5IdqfOXMi5hj5xomTW9Mf7cngoAAJ58cS1EyTaQL3zsRt2y6di/QJIGaZt1hk38IKQlvdWVPj4ii4V9WO/4uftvRJbKMU1s2ZbIby+mFTD+wgM3GyZ+/q2duw+eWjx3anbRP0zS2n8SoZOA4TBv1iBrW753Ub01/M8RxlYAOZCaAEjaOhomY92x2fmvn/6STgIoYw0i4o/0l8GxNcHdN13WUOP+39/+zV752Y9cD2NmevKUY2Nj9lKT+EvjOTY5XhuS+A+TBALiz90f8y3bGyycUZ+X6Pc4+UhULYWDieqAcQHDkM5coKA4ILuvb/HPiZInT7+5684raWO1vBk0xrc1AWJ4jMHnP3aLQawxgyfRo50HG59vMBACFDt7kySIx0zDL7y9y1YAa9/dN7XBn8sxZ9c5T7VpMmk1l1wNhjIqrLr5IyOtAAq9sQ0TG2VQo3jso8qFhwyUM2mmrl09fQhBlkECz/Icy7GMPUQademfCbu9qj0JaKhy9wRCPQFS6C1H6qrcVT6HSJpiFgHc33He5uEHqetvrGPf34Xti01zODkElOIxRqXTGCf9fb/rwKh18S0GtkMvV59ehUdsrPFWVbhKdDZUB4xx7AFEYfuaGOdedvS/fkqDg0tFoV9guTNCtYB++PvkgE6/g//oB68u1fvlM1kRebbW74IQtHbRWeQ49gAXDEII5eDFzUTuCoBSOtiR8k1OMEqiAP7n0Vc0VWWYbF9Jj6QmagW/lZFbQpWQGQShU+RqKks1A6De4LHsALAVgB1IWhiqphX1dCiF4C6gDDilWAogaYCPsor+tPRIauJxKoTYiT1wz7Uj8DUhCB3CEB7gpDTgfKERQWMWBGEucZyZUEj4AGV0sNOAAQA+N+ntkRQ0SBI1Elz0bP5yacLDFtewM4hYbcT+ZDHrqWHF7ycmc6alT9FNK7rukT+/OjJqgMkhmZMyIYEIZp+tZkdVqAIYfdxOIVHup6UcesqPkAL4wSPPx5N4M+EQOFLnh3jYIEMeRFfLqk6iSxE0DFNWhjae9lnb2GpgxCYEJYJOAsagA8CeAbDsCM0AaDpYaSAdv0t06IkNW6xRf/8wn4z0EUQmgjWVTgYhlkEME/sXYxyOqoZpIgRlRW/tCsVqtOCc1MBITghKBNUBYxEISTWIQhsCaJpOy4OPLggOMvtQcoctgsGHSHyigQWOxFMzDCSVe7DeUO21x/5kEmAlE2MMXA7ezqeXFT0YVgwTx8t8k+KgVm0vAh56QjB+1QBl7HiA7QByls31h5BaDcKKAqLlgEYThNCcpoF6NpTcYfMV/RCScj2KanAsket2MWeWRT636HbyDEMCKsJRzdB1r1uwfyoD/2PM9c+1RYGtr/aouhG13L8YANmy+0MIdcPUDRPYCfbpBma2GvjtMxugEh6+GnjsuVgrAsFL+tiUGjoJKDV5FQIiQBJEmNhpMi80XTdN0o+sgH0pOXL3vR9TpIzZnaSAGKW4CiAqqwjCn/35tfga25rPMsjt5Hv6zEqvwxb3DAN5jvG5RafI24N9AKS+IImuTj6opS2sJcwjprHWq2q6JOsIEedwKKKQtgEQSooWVbRAUGIY4jkg5RbtbqspYMGtarqqGaaJVd14/IVN5y6aDsY8VAeMKawZAJN2qJELVjWgnOrB2VA3QNEZjgunzMmoAOzqm7FlSCZZDoEVBU7gmEqfQ1a0xlqvLf0RQiwiJiCWJZU67aSMoYqrkFc9Tt4wObeTVBwyTex1CfYITFK0iKSFwrJD4ESBV1Rd1UgRUqvmT4Y+QVYhuMIugeGoMqwjZwkkpjFCE9UDnFgOyCzoFjIM0zBNWkaqpGQZ/ifFfSZS4XGU7IwmvAnI7q9t2fdFgRV51udxeF0CxzJuJ9/dG/H7nHZlZrtnRwGVgiBxvkFUe1ZVhRMAcLr5tL2eB8AHQOMssmx3E9M0XVE1SVKOHnhf100yHYj/VMkZYNPAiQ2595/oiP+bSjxN4arzZpMK2PdfEpZUWdH//NJ7mU616DEGdBIwRhwAg1oCFARRALRb4ahScCGvm26LdRn7+J3Zus/e8qHPgzKcAciq5hQ5jmUqPQ6Xk/c4BafIEb8uA0We5bPmXp9z2S1SqCd1/ZQpkxKf2j7fuOhPcxokSQcixPMc6xD5ihUX73lvk6oZxElgwZGWY4xDgGqhGfmtbV1DbsOXIMiM6oAxVxG6IMzh9ZSnDB+3i6YBF0hGuSbwLMa4ttIt8GyFV3QIpCyo5pphAqADMG1Bk5z5oB4HSCv9Sfu95jPx5SlTJmUR/YmQGQaDeBJICmr97oikSv3ZNyNjnOFoDuGELwjan1NaUEFQWg5o1IAQVnqICYFSANlueklWG2o8tVUur0vYfFDWXDNStwlHSQxPYSQqg0R4MePXCSGor/Y01HiqS1a8My1if2mK7AnM+UILhY4RSMRCweWAIMlqzGsPWha0iDCI1PIq5hHLCbYrEEn7ws6DpCK001lBJrkAXLTQCSJpumv5AADpD1A4RoT0d82Ew+FzOEBVJWhKOE+bVefNzUsnvbb5gM/tCEs+pyPbDQQh8LkTvElFnXJQQ1AchI2OjqHNcUMSDzXOHVlRTUODODdLzqARAHQ4BCkabW+jBSFGB4ZhaJGfghml8Cmc8m/qq/YiTh50p1/TfyjDwK1dgYLOJiMsy3hcA6WmaLz3xANbJaoK2RMCv9/L8/mNQH/4nW8W8l6UdHBs4f4byiAFkPQTIMH38TW2kE3cAKd7ZCAxxcaO6Y9F9vcvkwX77eKv9r97/N+4oE+zpv9Q3YFIKKLk+70O+qQpcAziuZyKhb2z+1S+b00NQWMB00pDL2BHlmH8FV4qf0aRHH+bAIBLb36gxOcy/mDt4Pp4DFzSUzsHK95vvX/8G8vtJXU9E7YcKAAafzoQWgdNM7ZxUrgdzLyQlv43GNguvkZStPbuUP6/ZOu8Mu9k5buVdohBDUEAgLdXP3bbvZ8Co4FVhSR/BQCh1+MSRdLKgjJajMcqQG+sfhwDMHnhRYc3vzC6Z8ImtvolQnVwOPSgZymR0ijTq0ly3mrRGN+4OJWzBh8BY6Co+pmOvoKDQbOoHZLXlpuHEGOwp7l30ZTKAs+BMkpYybx5KwAEYW11ReHeY0oxEIfq5FEi3lj9eHx50sJVKa8n+5MSZX00SIIkR1362wogswxMrHA1lNROejF5mJ/lXYYPJoUrOrpDwXCW2NTC4YkCyOOsC9MBdBJAqjwFIhW+EY3vGnACW/0n8oJlGbfbSc3Po4tD4Bryrf6UJ88//pPUlbYQtzm8+TkwPsk6exo0FwBjFlnVWzuDwUhJpD+ApNdE4lwnS+HHYc5sqA4YFQzTDIcLaQfNMiR/IN+9qAd4tNKAb7r1zo/fNTBU7+wNP//394t1GqllYscFY2D2Go8FKigKwzRxZ284GJELc+INCYNgUkURCMBlK+Zl32tPc28pTmbC8/bqx0b+TWVZjUqFjB4s19D4+81PJCCEYp4hWCUCjkPpX6qm8LkTE9okCIcsDVVCLg2KqveFpBJJf3uUl1eaycmTLdOmFV6anE4CnnvsVytWDavEd0vzSTBUu7rCSbjTAkp448b09aYy8c6al0twTuULzzGj5QOYGIzmDABjLCuaouqk/rOsFuCFwxgEI3K8pUwpsHuZ5bjxi6+8NfxJAE0Pfmftq6I4vDqO5lgszkOlf9FxCByXcxgopeQzAARzysI1MT5+pqetK5xk9zFYT4XHceGyqVMbKmJidG+2yHrDMEs6/LeLzeXbbmKYkwAKAGDta8Srtuqa22RZKvCCmEYJ5wF5QkV/iRB5Li+jwW+fWpvoBqAMoQAWLZyaZq0lbzXd6AvJoagC1cRS3QMx0ZKcURls2Gs50Fk32QGTX7hD5BbMrN2+90w0KL25jrzaWOudO60628lhEIrKEauhWOlwOXm7nX0BOqDgkFBqCLJZ+9pzY0sHFDTUoNK/dAgJRbqSoM0AhqsAEFT37TsCiodD5N/eegwAcM25pK6cvWwjyZqiDpq2R+UhiqsYxP0bKenwH0JQ6XHm7t7x+quCPd25zwNeXLf7xksWp32J6oBEHWC5aqVxNw+gor/UOKgDYHiw1Slh1z0BEhI3ub7ydHua6m/DhEHQbr+ViGkatX73/mMdhkHSuBiG9bn5mZMrMAA8Myg8QzXEwcP/vKs+pCdDaCdCyGnlebZ0hXI5jBZOtvtnnwRkkv6UVHMQAKBA5/Ao6QAq/UvPWAkBmpgzAAaZikZaAQMAjjT3dvYEAQArl884dKLd5xaJ7wWDxrqKrXtOLZpZ53AIJ1t6SLsYBP3ejPWcVU1rrKtYu+2oy0EEq2HoDEPOwe91dAcGZD3DIIM0+Uomrg96g/KRU8GP374y9gKEEUn97VNr7Z7AmXqBZSTDEJ9BMF8Xk4NHkhpLKaKTgOLyztpXi3xEyngGkk5NY8XNM04Zwrpt5cASHP22ttMdAafI+zwOh8C5nESIe12CXQPI6xLsrmGSohHPKTTtByZyf6BCQyiicCyT2GSRzONgYptHOzU/h8xMq21lwaHY11wwb87UmrbecEt3qCeUxsJAeiDn0wtMc9RojhrWVxdfc/JkS/ZwoBsvWfziut2ZXqURQRRKJiCEPDv+CgGNJwVgi2Ro+ULtNa2doRp/zGpE6rsB0qHFTocReFJaDkKIEEyMnIGWLSX+VFb0hhqPzzNQYNkpcr3BQfLXMMzcLO/D0wAW91y75PbLFly/ck7qS16XyGXtfJmEk9Hth9c/kJt+8mTLcE6PQqGkhciZnHs/xEuB/vaptfR6xsmoP+trKlo7W+KSNd6ByzBMgWPttuy2ehD6q/EhCB0ia9d6S0rpStQHpomrK1yJ0TsRWdU0I1EbsQzKS/IOhyln99uR/rw+tQhEXroFIVL0NNUh/NKGvTdctDD7JIB6gymUvGAQyjdEewJzS0Gd69l8im5DjkW2fCND/nRZ8DC3jGh2cIV9SdYBwIn5Vi4HzzD5FJ8rkYUxTwcAzzGyMmDsStQB2bnjmhXPvPYO1QEUSu4/UFEgvWBKXQluXMjxgslVAUAy0md4no0SYV0EBjtXk+OCCsruKzAedNvLfwMAnHP9rUnrEYLxyU2O+NyipkcMI82ZZJ8EKFIhxcgolLKF2KUd+U3QV//t6ZtuvRNMRDle/J7A7+4+BoCYmNmLmLybX2eBGWO1k2w1kAjPso48m03X+d1RWQtHlXhyQu6GIDoJoFByh2FQPDKl1NwyTqR5AeRxBVXVMDFGOYdUH+5g7Uars+vSjIit2KKxpQOScDn5fKOMaytdwYisqLpKXBr5GYLsSUAWZwCFQonDMKiAXvCr//Z0/F8AwE233V3ml5QoAHu4akvj5OUEEW1kjstMjOCMYw41Xcgi/0kQqfXeA5lfFrKiHz7VYYtXCOEjz25MfNXtduadAZAZp8jn22za6xJqK929QTlRASSSyyQgkw6gucEUShwW5VGlkZIJ1N9gnZRUiC9naZHNMEhWk90AoUiagjwOkSem/MEiNDE6KIsHP57hndQXvjcUzSRbC+C1zQe27jv19NuHn1137Ln1xwe/mN4BkL3wBMcylV6Hx5WsORKjQl/asLdgTwBNC6BQbFi25J26ywGrJWR/23fbkTrQvDfdDk6Ra+kIzJg8INE0wzjdHqit8iRtGertkBVNN0xQN5AYxfOspscOLPCsJKev5RAP701sxKXrRiBUmrZf/V3s40AIxLQWxuyhSRA4Bc7vdfYG5YisJl7BHA1BdkgoNQRRKFmBLMPAPBXABz50H8n7uf7cCW/Zzx1LxmVo2AvTaQAEYUdvJFEBRKJqyDJ8L1gwK7F43PymWEnnRHxusasvlvPFZU7i0DQjNgC3TshWSVFZk5UhKsQVC2glAaRZP9SOLIuqK5y9IUnVdE03CzAE2VBDEIWS8edpBQoW0D8qkd/+9OEcW0Km9gSeMMojazVQNGDYkWR15TJSZicJSVYrPI4Vi6di00yU/qm1oBcsmHX82KnFsxtMzr12067Jdb6lcxre3kqUAcOwi2fX7z7cZtcFIjXUjnZecnZT4u6miXsCkSx+iOLC9JeBy39H6HUJ9X53JKr2hWTLC55fRFD24hDUGUChQAT5rImiqbWg7/zgvYZhFstt8Hy6NvE5MqaUx8hV0ti374jDFqkqqazZHYjOBgPTCDW1q9dg7S7JWqBEbd9TgBAyGVxMWTrCxxE4trrCFYwokkKanSW+lKMh6JIVi9e9Qw1BFEp6ECRFIPIa/4+dmMPnh6E8iq4/hlAAGOB4wm+m5YSNB1Zl2dj+JuIZswwi/t4jJ/vshTj7j/Y0NVbZ+2GMe4LRImYhZMcuZ5T2jsl+Ez3+/KCopFQi0oA+yDIJ8PYbn6ghiELJEAOK6qp9uV8cCIkVYSRKCIwB/ZG7ksimAExMBuyJjgBsmWJ6Q1JbJwl1tNNlrfAhzLGM1yX6fU6HyKkaaRbWG4wqKfFCNrasX7e9OUnoJ5LolFU1IzRSw3/7XiEKIM+93tl9SmHT3JG2IrGdzKwH6KGuXHTADZed/dLb27O8HY0KpZQJd9/7MUVKbDuYd62wS29+4O57Pjz+hX8RlESSbsjPBIQxkcWBkKRY5Z0ZBjEI6bpV+qB/qK6oelQiTd6H2avdKfIxfY1BOKqkxp4WEQSRiePTC0gmmAXZCj9353mGiUNRpbk9uOdYR29QIvG0g+OLWE91og7IBDRiThQaEUShJCHwbAG/0LKR/3nohvwuomniUIT04cIkJRiJPEe+hv7rigFQNCMQloMRWZK1eF3MAnCKfFWFyx6EGybuDkRL1/qRY5nG2oGRO4REsRVgMcQYRMMhJRrmgTa9Rlw5v2beJHeFCJys4WT6Hxx2cthbUZlLWsBtV59nL2TyCdO0AEoZAiEkZXqtX2ieleBgPAaUkrcCwBgoqhYIy5pOivWLAusUuYGMJ0wqRYejSjAiD3P4jxCsr/ZytoYvbuvHdO9VVeFaNLM+vgZaw/8sCuCyFfPSrh/Yg2EYjvV6nfW1Pq9LGDAm2U0RUgKZMukALaELLtUBlLIlyf7Ds4xD4AoYzo8ZN/D4VACmaQYjSkRWMcYsgzxOgR+cK6toelhSZEUfpvGnwu3wukV7YmFiTAwpw5hMZEfkOZ9b9LhEV3/QJ4NgYvuavOmvT8KxTJWbr610kZJVVk8cLdxLHtGAFg04+Jze4o5rVhR+JhTKRITnCrH/WH1KqAZIhs3DviFrgbCk6waE0OXg3U4hsSqDaWLDJI0cwTBgGSTwXF21N14rVFF1xfI2lwiORQiSm2n+jPp3dh4hAaCF5pf8/e9rCj6NTN7gxOIQNCKIQrG6kjD5DtE+cM99aeuVUdhqX6zVV5yeABE6S+ZNAQDsPXwovt7vBX6vFwBwplPqCwQEzhdTANaFTfV25guC0OUUav0eu0VwzOik6cxwxuNDY7W8P9XeNLnmnZ1WAKj1KEAFfPtfPmgrQvsq9IWkrfvOvH+wNRRR0l4XVgR6sHNIHWBXiLOXqQ6glDl2m6Z8qzRagYqkW23Jzmu8UkgimN2sxdIHwrT6NPUSWrvCzS3dyxdMznKQo6c6Z0ypTveKCUDM9n38THdlRU2+FT8Kw1YzRPoX+naHjzRbB2KAEZsYXXvB7CvPm9ncHtiw4+SR0z2pZexYb02iDkiLIkUrqmv7uopW5ZRCGb9ABMX8HQAwfV0byghmAhdGlRdXed0AuJPWRyRt5+Hepkn+K85fYN8NYUn57VPrzl1E6lXkWBT6mgvm9QSi4f7uxHaHAmTlgBV+xv3SP64SNN2cUskpEa6jV0uTy1ZRGezrzT4JuOLspmdei30iOgmglDPEAyzmrQCsSscTIQus6KDVW06++O6Zl7aeKfKBS3yp44Xk/r5l3zDfcuEUZ1M1Z0R77KCg1BqzLV2kdkXBsAyq9Djq/G6XgxtSs6SNCFKk6KyZA4WYslcKolAmLtAhcnmlgA1ApwDpYH/0X3ZiGLYeOrHAmIbW1oMx5jl29pyZwDB3HDy9ccfxUFT2uR2rzp516+xGVVZf3XTgvQOn7QRdXUv207Jc9Yb9MoRg5bxBHV1ypycwEASZCoPQ9MbK4uobCAGpMVtsQyGpLM2zNVZ1IOLT1oyke9GbMAnIxNJZdUeOnsi+Dc0NpkxsIAQOgS8gSOPJP/8pXg6aktYEBG3/CllEgHNLsdI+ptnS1rvzUEs4qgg8N3da7YxJVYBjGA1V+ZyVHoccJEmt3GA3LcmkRYZhmIZhvrMn4/C5a2/XQMRkhavK5xJ4qz+BxY1Xrzx4suO5v7+fJC4ZlrtkgRUkGp/SFUliI0gKjGTfZtt7u7e9l2YA/pV/vCvbkRH0OPk6vzsUVa2iRsmjEe9QhiBFil52wZK3N++yn1JDEKUMYRDibQ9kQTzx+KNPPP5o4pqbcmsJmba0zjBruo0R2Kd+/xd76a6P/sPgl6yOjBFl95HWrj6SiOHg2Z179p86dVLk2YiktnYFw9Fs7VwSKyEPcRLM4NKblmRft/k93TBn1Ajt3aFBVaANefPuQUrlt89sLKwl5DuH+269fInIc0TxAMBuOpFl+J9W7sf5wS+e0pRsU5a8SKsDXNyg60lLRFDKDQZB+6c6FrileFU5b7v5ZtIrxZsckJkJj2fAJ3rlRUuK4wSOawKiDO64CgDw1ydf6+qLtHeH7MQuKchVeEmVbcPEEVnNVJzHCtJFmmbkGBUaK70Zf46J5tB1U9WMYFjqDkRL1wPAkPre37k3PurPlAHQWO154fVNYAxw29XnPff6u9l1ADUEUSYYzz/zJBG4d9wt8Kw9A8juz01qBnDpzQ+Asc1zL7yQ5dXbbr7Z1g1uS+4nffA3N+wajjJIHwX01DNv2JlffSHJlv5kaNz/zqqmR6Jq2uLMpI4Cy1hJYblKbYyxYSX6QkiWNctwFFW0vqAUlhR9eEnFWWAQ8vucCJJqphACw8BWBej099XNV18IAMiuBqbPnWc3qoYQBMJKVFatoFLSuRRCGJaUYETJktLMOoAeaM8+CUgsDpEFqgMoE1UNJHH9tddk2v5fP5OTeWfsM6R6AABMnlRXmDJg48N0q77bQGEf3TACYUmSB3t3MUl0ikiqpJCCEOkTaxHMq2+71X2FNGBBCOqGKStaVNYikhIIy6UrAGc3N/a5HTGRj4mZfu6M+km1leu3D+S+pVUDTZPTtLrMHYxJjtjOw217j3WEo+Qyxj8k66sbUgck5oVRQxCFkon/+nkahTHxeC6DevjJL2ILt9188/0fIBadtBCDWmw+ZY+FrTkGJoH2aiii2AN5Mkzu30FWtWCE1INLPRZCiGNZ3civIAS02zuwSNOJ9I9IakRSMzUSKCJet8Mq+jZwGjYXnz2Hh/ZAO30Jivb2IVK3cmFOo6vO29DWFTrdGSSdI+MzAz+pbpila5giRR2+KikwsAE1BFEoaXn51dfolXnuhReyzCFInbJYKI0tgiyFoOmkqrPd8oUhedfQNvjohqlE9KhVDy71WCRFGwI9Qyf0TEBEinuYJg6GSdVPSdE03Shd9bc4ApfGm6TqBjYxnya7ucgwCHpcAoRAJWpPjybNtLJOAq5Y2vjiOksBZI2Ho4YgyoSHivhhkhB7YweCWtE7oagSipBhKUOK/pN6abaNSFa1cFRJa/1nGcSzDLHg5+mztep6wFBU6QtJYUlVtZGQ/jHhmSg/rffsCUTOdPaNwLtbFTVIRdX6Knel12HHTcXxWvOALKlhH7zxokTp/+L6PSU+WQqFMgFB6Yr+633BqKqRqp8OkRN4zjaUmCbOVOoZQhKeRYwm+Vj/+/clbQZCEVlSVNJcrJR2/6znQf5p7w6dah0iJ6uIMAzyucX6KrfbwSeN5hN1QCrhYCCX49OOMRQKJWcFgIFhmoGwHJaIkYfnGLdTsIsuYFLkxtQTCt0kwrKIZRnFst2AfIAQmiaWFG3EBv6x97WKyg5aZb15X0iK9JcGGpHTAALPVFe4aipcwuDmComknQTcePGixKeZJgFUB1AolJwUAAZAVmJF/xGEbofgFPl4ZGSmxC4EocCxduQ+yB9Lr5Bi0mAEYVlU4x9cYM76lKqmj/CZWNeZr6tyV3hEhoF5GYJy1AEUCoWSlkH5t4ZBhv+SQhySgsD6PI5c6i6R9jwQKgWJToxx7gnDRaTK5/J7nanrR3IWEodlUaXXUet3W+o2D0MQSdm7jkSmZodOAigUyhAKgIR+ymowLBuGadmmHS4HP1AZM4NgRAgKHKNb2VugBBTYnSsrDELTGirT9pkZFQ8EJJ0p2Vq/q9rnzKJx004CpHAw8Sk1BFEolNzpF4KYWD/6QpKsahBCp8j7rDD52IukljbOGExJMr+Kbzmxm7PzHJvUeXj4eFxCVUX6shujMQEgMAh6nUJdldvnFpI60gw5CUgyBFEoFArIjf8P5VCm5ZjTwsIAAAAASUVORK5CYII="}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "assistant": "The bow is interlocked with the selected axe in all three projections, so it must be quarter-turned clear.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":2,\"rotationSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nROTATED ITEM 2 TO SLOT 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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aWSY/QJDgfwpHYlagOLYO2PDSHy664f6RPNtxBJ0BZOOOi/w7tq1bdu4VvCBiQwOmoelqd9fpN197/cdPt04k6R+bAUAoGxBYTmBFjkBEcoHJaxirqhINB3RNZRAURQfLkZJBJHCIFoOjlB6MgW2ZjN2rJj54rMVahLqmppX+lFygM4ABDhxtmTezMekC/c837gcgGpUWdbac7utpAybQoRoIdOzdp/xanvPJ6ypBnjxybx7V0x54bOQ0NMQGgkA3yTDfCgNlDWywDDJ1jBDkOA4B0ymKAsvqqupwO03rV8lxxCFMoZT25gTAJBFqMRAaZHWk0n/cKIBPfexDiU9/9bvHwXjgukXM19/Ytmj5JR1njiJg6KYeNrT9B6T/0+cvPWfJqob2Ta88euF1pNTE+AWbBstAjfy6iNYReCEajTCM4HCJ2MSGruu6xoqcqsrAxJqqkDJBEMSmCBRKSW9ODJiEWAPqdBqXCiBJ+k+v9YDxMAmw+e4X73jwB0+cs/KGM8d2M4apm7oO8PHDeyGDAFyyqr4VjHNMw2ABlBSNAQ5SNEkQBJ5nWZZlWFmVDEPnBcE0TU2RGZYDug5Zhsj/8ekB/uxXH2asaFfWcniITu9//tvnRvukKBnBAC+fnf6HSb4+npVV8m1Sxq4CSCv9H37w01/73i/HhQ7oC0Ye/NSN3/7JM5dc9YFjB96BAKumaRj6sf07w30B49IrwThHIzKdTLSRJRNdHi9CkPQD0A2G5AcAhyAEgwEMgYENbBlfWY5/+XdDdA8eU/zTg//RvzhIXsjR4Jce+k97+Uff+beRPzFKdtKOMppbu6Y0VNNLN759AGNQByTRFxxoe/vRD1z1m8cfu+bWjxzctRGZukmiEcJtpw+vf1V66Cevfufz14JxHQXEI0yK/pO5thyNhIMBXVMwxjwvMAxr1f/pBBBiohZMhuWqaweSbsaD0M+JuCagymDsAAcb/SOSTNLUHXxzaxcAYEpDNZ0EjGkFkN34M0Z0ABnVDp4EJIr+OK/8/hsAgJ/x/KFdW1RZwiaWDbOj/cSmN1/+p+/++f++fg8YnxhWT0iEYlZ9XddkKQIwhhAqsuTxViJElIAcjSCWRQxTWVVru4vHqdDfv28/yWdWNQDA2ecsz64MqI1o7GBL/0ToVGBMK4Cxb/q3SRRmc6fXpZX+cT57Xf2P1LObj+6VwwGANV3DPb2nt214/eNf/9Vvv/spMA4JhBWoYY5jkWAsv/JDmqLqim5ik+c4AIGqaLoLAsSygpMXRFWRDDkaUSQw9sz6ucj9OBgQ69b2be/bTzNpAmojGl2WzW7IJP1tmlu7aqoqRvakJgKjbwIaa5MAm+zS3+ZLt0z5ryeiXR1nQr2dEGvYwJFIYNeWNXd84tvT587/4ZfvAuOE277w7e72lnBvj6FpJNYOAl0LIiQghuFZYvyBpGckL0UCPC+KojMaDZPdsNnQOHXMmvWzCX2Mr69G0NL4GAiGiXUTv9KpJWqCXKYF1EY0kqSK/s6eYI3fO6InMeFgx87wf4zogLz41w/MveOzr8xdck5vezPQNWDoCsCnjuxVVO3bf1j7zfsHVQ0as3S0NLcfP87aYZ0Q2iF3LMdV1TcCgBWrzBYniBDCztbT9Q3TeF2XZaIgo9FRzr755D8/lPNIH1xfRSQ+SWZOWE8CmRiS+3xbA2OQbGe8uo0kFkGYkzL44te/+z/f/XpRPgslC5kG/pQxrQDGi/EnDkzwBOTIMz/759s/9f3F51/RceowsZirKmZx+8mDb70Q+NYjb3/rgcvAiGBuaUt8is6vz31flZT6IV4AhBgIoWnoCLGRYFCWIhwn6KYGIeCdTuIfNg2AMEKAgUA1jZ6+HjDa0l+WFVEU0gr9+GB/yFhVSzMAlmwGb5skWooAqIb5Sgf57CDDzIBsVJY64Jo7Po0x6Dy4hkGkZAjPsRhj3YTYuiZOHsoaEFmMSAlxIKukyLjAk+QtzcCyDjiEJY3ccRgAhuxESk4ByGAMmpZdDSF47ZlxNhAcv4ytLJ6HH/z06J5AYYXtn/3VV3euf7Vx5iJA4iUhcQgYSrSnZd2LT3/r538DI0JQGfTIC0vekY/OMCStl+NFIvc10vDdMHSW5Zwunyi6opGQIDhMQ2d5QXC4sG0+HwNj//379tuP+Me5xg+vr0Y3VKMba5h8MxUgAKQqKoIOjrl9knhLg3BTvXBdLZFxmNwgePu29+1HfJcvfv27oDzwT1myYtnCjn1rOg+ssedJ9qApqSQ4g4CJgaqTywXJtJJso5KasoBNFDkYm6ZdY8raBoBj771+dNvr8+bMmrX88hH/cOUIGmvD/1HXAYXxt9899MrjP5g2fRFiWYCQTgIlcaive+Obrz/4w7+MwAno7KBHXkAInG4PREiSQqoi6bpKpD+ApkEigzRNhhCpqqypiiJHA53tkUCPaRq2qF1560fAqEr/rs4Oj8cNALjWD6+vQtdbQp8tXn4agpBFUGSZ2xrFm+uFG+v462tZDDBCaMf7u8pNB2CSMI5TJ09J8WD2k4TaDbGdk0ItyApL9CdvaJLufPmeW2d3X767UEqlAMad8acovPDn7zfNWCKIToyQQQLr9b7eni1r3vr8f5S+Z7pz8CMfPB5nTX2N21fpEFwC70AYqpIsiC6IiXnc4fSR0tCQlH/QDawoqmlgYjUyTQgRCRUdVelPOhkI0RuqERnq247d0gAhYBHkGMRbysCWT+WmAyDWsVUvxLQuSKwOiEHGOxBrDNAZSIyEwFAAMIggx1ZnCdJdjswGANaBqUKEWWggYEBgkJmCYZJZl3Uo03blk05DxBNDKZcooFH0Bl932z12/UsMhr7nPnYn8ev29VlhMOl49o//ccO9X+7uapWiEROa0DRkObjnvU2f+PpPf/PdEhYbQL7C933rz78CAPjqplVXOA0TnTi8N9OW51x/TzQcFhwOMnnXNQNqpCzEqEr/S4SRdkTbOuaWeu75NhI4tOP9XcuWL7Ffmvj+gJRxOdG7mPxrWtYeYiYbqkOcbW8k2pr0EyVLg9O8BrbJlwWzp6Rdv2fjM3YJuQUX3F7AYScw0FDIEMbq/zrAkgVXDbnnj37+/dj9YH1TV19+1uL5VxVx+D9iOiCuAEQYUwAQgFXnzkm78VWXpI8GSd7svu9Mn7ckHOxjIOBZBiHECY5pcxZPmTJtxrY77W2YlQ8YGx8pVjXQ4LFBTmDvjDycwDYNU2fNmVTR2qce2j8wqh2SK+/93JuP/RSUjfRPBGPwfDvRASRQvV8HkAqyE1cH+CfNn1GDZMWwbmwybDd1E2FSQUTSDZ+blVWTY6Ch6YhlTAMwCCkqsRZyHLGLAmySXtMMo+smx6JQVOcZhlT6tKpKGSbQdDIlEARkinWzZs+Iv++KeTVpzycpDHTV+YuznLxdQ5TqgERYSSFfzyAw2LrjVXvx3GWx2gb/+8sfxHWyJfT7JX+/nn79rfd++LPvX335WT/9wR/GkfHnuttLkrj7xqMPXXbPt5affU5XV7duGBwEhqa0HN3PqpHKy9+sfOvK1qYPekOaa6jj7Nv8bI7vOKvQHW0WXHA7yyCPg+8IkszY3Clb6W+PYck8oJVECk2AeYA9TM7OJR94iJhr7GmQ5dE1EFlAmBjHWIQsHy9x61quXbKhXVnEquaASKAtqSVCFAODoP0vxtgqKwhIKFD/kWNOYUqJga+/PtDlPIF+WT8wD4vJ/7jkt5f7Z3Mxrw8A4ND23cVSAKWeBPzj5z5PWn1h3NEXVTWjiDMAm0vufujSVStPtXQRTxdgOY6v8Dr9/ro5NQdZjn3qxaOfmbMl+xEu+GyuQUSL6y5MfLq7fRPIkxs//ODiaVUnevFffvavoGQUNv4quvRvn7l82tT01SU5lg298fzYnAfkIqNLyiUfeGhmJdRJESyAIBA4oJtGv8uXQZYQR8hUdZ1BCJuM1WGCCAmiKkiTUd0wMYMYk1QYASZxI1mBQORoSNVjpgiGhdjTOHPm9FLMAOgkYEgfQKr1jXyFPq8jEJSSNrJFv2liiIjcLKL0L7Uz4Oc//k/bR7V915F6L9feU/xMk3VPfmfVXV+76srLj51ux6SrMJRleaZ/PwDMEy8c+dy8d4r4XrFI+GFgR+MhhljDSke+U5OCef03Gd+ofeYQWtxz1S2564DYPKDfH/CTr99tr3/g+lEW1iWChOiQ+P2Y25Z0jSPJg5j4efuTaIjRk9xMRExwKDaOJOKXzAlID2kYC/Mh4gMlboCIDiDHKjAkO5mX3toWX/b7XCvPXWDfhNQQlEkBJI75By0EgpI9B0jZ0FatuLjSv6T8+iff27rrMFmypJ41kyEurCF54Y1383qjf/nkbQAACfl7249jrJ03J2Ri5tSh9qamaQAUUwEUa8KcEpI3Xrn6E+mnGn96K9ZUNjueq25ZMTWPGLmhnWYTBZZjsGbYv38rwMfEyMrmIuE8llUAWZZ+8rsiusFO+MIYGBiYwCT6g4z6yS4kUwybgLgQrAxta1OEIMNCAwM+h6CMLOI+LRu37rMXqA4YKgooQRGQEBF7+D9YJYwAJZoExKQ/gXwSTU9xhGTATgaKK8KYWcwaByVsQW5i21Zm3dVwxTT9XdA03bUTsvzTz+6697a5UUl+8sitd1f+rVj6Rr33wiF3vPmq87IcQTMM08RGUjwApexJlKoYIJGFHETW5Jm06SVto/sNwMSfiwFikB3dYxr2ryAW70/UKWRsZWAasd8OsQFZAaJ2CC/DkL8IwQgsZCbab6ceyC2I26btH6g1zoOJmqDMYbNJfXuh3/iT4vcdtGPphv+l0AG/+c0jn/jEA/aNSYKTLZtmnCzazQ41h9nG3LH11m1m3YbWwqL6fdEI/NlvNvh9jpffPj5nRtWMav7l6AeuF57Icp6GQeoz96uc2A3er2/Ioa0XCbmU5cyuTpwCb5imCJV8Zzn5ahpK0Rly5FskyC0XVQ2ZpPgCjQT5kKBPBpokcB9D1YDE4GN5gC3IRMEwScK4FSEKTdw/qcJYNQBLyoqYOmkwBCFiDAxYq5wEx0AsFjKnjU0iYr+9hPWDNiCbfOILGatIla0CSGv8SRSGaYw/9vNSG39KqAMsm2Ziv+kimlO6dj4mh7omX/TPR06+z4vs5nfaP37f3fM7/++rrzfImjZ1sn/RHPf67vsv1gbFTSUS64M6kN+U5q62/yRVf4iPdNK5820VMkjb2144+6103bDm4+S3hDFRNrHJjrXGntPEf2ZpGb4KmXiaZqRktE18yBD7a/+Q+79NHDPCxyzvZDkmHK35bfzOid9BAzEelig3MZHpHENcu6qJRZ7hEFQ0yDMDmwkcMAyo6hCQGj9kLc+SPC9Vi1mEWIa8A88yimYoOjmaZpANyBzCHt4UyNA/Xyr9M5qAeI5VNT3V+DOgEsDEgeiAj3+MNEEsdsyZyzx1as8GOdQFDLVl7X96IWYZ/IGbH2rpDAAAvn9161dexapuSrImaxBMKsI7JgVvDqTmJ05I+tfYy0lrdCshk0HQbpM7aGf7H3smH0//HzQkGLgv+sssp44n4lVf4OA1xIVoP7enO7Z+sS1psXeBRdM0e/d3xVzBrenbOLtcsUTqriP5RcRm4YX1R6uqqnr7+m68cCCyxQL/9Y19fn/V1WfVJSprW0TGRLN9iewrYs0/s8joZKytYx217I36Z7CJd8HAjDY+v024PwamsdYJqjpGAAosUA0SvimwiGdJEUFVj9kH7NFB/yHwYNVhn37smX2/cSyyPiRZIyCo6FgrpRnyE/9Ex/5pFEDs3lM1PdH36/OS/uCZfL/285Hx/ZbIGfCb3/7uIx/5iDXkKBoe1HZyz2Yp1AkMFQGdtaQ/zwB923dnCNCO1/nBtW1fexOdbpfnYW1D6+yLzon7JArE6BzuaVs/Oswwmd1CqRdpQEYkv5a4pl/5ZFmTIMb6RUcsHjxBJtpD1IFVsb+x+3NAdqVYzCwTBBFG9izH3iIp1zQhvtmOCOjXQjEfJhkl2AsJw2dyQHvZFtZPv7Hf0FWiTeWooatWOAsWvJU33XX3zu3bQ709AE+Lfdx+E3U0HJo3f8GaHS3Nx45zgsPpqbh51YyE6xK/Uon6ut+Qkm5imOk7ym2joQ5hkpmibpgGNCE2MGYwidphrJB+Eh5k6qSmLADEImRdQsOeX1tTSoZsSuJ8yD72TUA2Jm2HTCuWyDRMRNwCDLRjjYoOlf5J2L/22LXmOVZRBw3/7eCfzL7fEY38KZEO+OMf/2gv3HbnB4bceMOOE1acMql/cMGi9EP3U3vWS4E2YCgIGET6I8whILDQJYAaN5xfx4rniZufDT18ZQsA4CvPVGKAa/YuOly34MbqJwv+FOawC2FZwXzEUAtGi8wKJrsSSXbJpLeYJXoEYwJp0GsDdmPrJbv0Z/9ucd+PtVdsz7+9edjQSew6a/XR5FjW7xA41s0wDIdMw9BVTX3/8OGLV6w8dvR4JBqtnArWtR7o7YwYsmhCjeF1OSQZBtvb2zt34eKerg7B1PRQ5/MvdmmAIYHViEG8eMcVc8GYgRR6INWdsW5ghoh9aIVsJrycKrfT2XOwiSFxGce3sVzJJrAODklYeUGG2I27TllKJTl8y+91/e43tMR0GthMxp+B4X9m32+S9C83Nu85k/j0+SdJSt2D98yJ9rZiQ0XYYKBJpD9jSX8eVLvQ7BpGXC6AZdUXLPvo5m/+H5kK3NH75Wf86qn2axBcw91zqe/Picd88Jvf+963H8zlZExiWxoWds6OFak9sclNssQrFKfw9Kv7ONN0upwsNknnZFlSFElVo7Iq6zrJdeU4loGIZTmGYXjRefTosWgkFOrtjSg9iEVOj+hwioiFiqzJEUUJMgzHmobOuzwwHKqq8ljvzJsY8zyPGLRm03FJVnUTsKJ43UXWBGJUQQggYiwk1pvE9fYkLenq2m6j1HKhsSkfkfYD3wiC0LDuQ/sefP6ZgfiI5V//0nDOuS80yknjYxbWFuy29E81/liZXxmNP0nPRyDwfyx0DWMQEnlOIymNMTtA/KVrZ+k73iXhZbOnMQnSH7gEIv1n1TDuJQI4twqAKwF46IJvP7T5m7UAgP++o+drz1U9cfj0Var+VsUVl0/7e+LbPfjN7+VyVviR+wvYK5HZcxfGfo+UdDz31mE1FHTwHNK1zkBP64F2Eq/Ccxz5n2c5zuVwsZyAMQ70dgbDIdM0EcfzghDo7pw8feY/3X9dpuv6o0de6Ghpdnu9na1nRJYjOzLQMEyIkMDzbpfL7fJ5XD6H07FjT18oHA4r2nWrmor7LX31Gw/nsllF43wyWmehYZAQH2ssT2L7reytpEF9f16YlQ422KI3EJRpmlaCMCZHI3MJkl9mb5prZHYuJEV5UOKwWUNAE8KBQDKjlfY1ujqAYVCt371iyazuQKQ3GA1F5IikyqpuGOa1swZ60h4+Gbt9L5gPnDyscqIZ1YxvoQAu9AOwCoBY3eALvr0OgAc3f3PTw7d1f+2FuhdPdZ2tG29VXXq5m3TbyAsc7B3mR0PQKsyiqKvX7t/49zxqIeRLjnOascMfn93qZBmeRdFI37ETbZquTZu98Bv3Dp379dhL2/95ll1W6yg4+Hqmzf7zotjCNzYvBSbkeN7h8n70thUAgEee3hANB1s7Wk+qx3XdQAxXV1PfNHnapi3NXcHwxvUj1G5oANIOgIzRMbaKflrCncjxuCUo5gi2Rv6kFFC/AQhZD7ss9EBMWX8zIpIjRtIKbDljRRkVUwFQsimA2PAfJA//+zIP/w9tz6Ng5Lgjy2ihzu/xusX5M+oME2uaoahaRFJDUeXJH34x7fabSZcq/LWbGP98HlxSAcCFAPx3/4ttADwEwNELVjk3r40+fHM7AOD/rXVd7WCPV96x7Zh5V+VzeZxzeLhOAFKylGVcIspY6rpQHrl3cFTHoYGm6kmscX3FXuBYZolEfCTDQZ4+eZhHePzZd0VBwKp0vKVV05RpcxZ97Z4rMm0sHG9OWvPAgtq8sllbjg4Mqr763ltpt2mc2rRp23qv2zd10tQ7bvlIZ29g3bqRVgNWJwBoV2xgB7X46qffxR2P+LLNQPHfVtzZbz9DjJUyYFmBrDDTWPBS3idmn11CXBklOyzPsUmB/7b0t57EV46+8WeMTAI8LhLEAwHpEsUKSBQ4n8c5ZGL0w6tVsFr90eXXAPCT/nUKAP8MwCGwJqw361Nq2OZOMoH491UnHnxdu/CsyRcuaHz+wLUAZBw2JoEjwWF+NAChqhoQIausy2jCDcShDgvxxOnUlZ29Rk3lkGVYwdtr1jsw09PTHoqE/v3miwHoN7mkSPkR5tCOtQCAaKBN0eQDR/ZPmzTthuvu6QoEHRWVa15OW9ixmMTCpKwHa4kOy19uhQJZZT4tknwDgw8xuAWYQfqLxVy+DIMMfVDR4Xyp9LocIudzO440d5E+w/3w7FhsfDIWINclKfDfpi8QG/6PHePPiOmALHbwtK3vcixd+6Wrn/nR66T1isXHAdgP1ofMk1p7yJS0gctsGGDbntbOnuiUWu9VF5/7xvqtuRwcR0O5bJb9GLquiyKf5NybYFxw7sL2jp5wROruC3d2dN33YVIySH775V3OWAeFqKS+v/ldqGj7mw9957bLwFilt+Mkyf+YOmPt5jV1NY2NLHvxqlvXry31bIAhZnoG6hri+sN+TGLvhxgT+f+Zsw/meKD/3TqHlJEm8wiyI2mzCaCOSQwoIOqkkEZD5y2a6hC5xtrKSp/zWHNXb0hSNV3kuUWzGwoPsJvQxKp3Jxl/7GdJf4tu/Hlp/R4w3sg0tfzmTwZF72TiS1dXW38/BsAesCkEjuttlvQ3TFDpiU2lf3DdmWjUOHaq9729baFQrmIdR8OJjxz3GnQEjHVDE+wOHROUV0IiyzAOh+BxOw2NWGeiEVJEQ7zs+vNWnLVv/+Ftm97buWnr8RPHP/a5j41l6R/nwI514b6Wo4e37Tmw8/iJI9dce+eqm2LlSEsEQtAwMEvc1FY/x4T5YsE3DvEhEGcyqQVkFZeLp5LlR3Wl2+UQXCJ/3qJp11w4/8KlTVPqK89eMOW8RaMfPTU2QYmB/3Hpbw3/rVVD7T/M4f9wdMDotI8ftmnR0gE7wLsBcExrDRpRFZNCRBjopDRKbJv/vv6MJBudvZEtOw7kel7RUOKjsHMzTJOUcR8D9tPhOwCS6Jy13Fxx+dKFM4mxnud8XpcsKzfdSAz60YhkP5S+YCDQ29zR+qnPf6q9rQOvuByMH3o7jp8+uWfjO1vYiHrBpTeU6F3IeN9K0eoP4yFBcQMv5yO0E6fN0K4n2r8y1kJ4GHAMU+P3LJs3+coVc5fPm+IU+GEdbuIS//KS8yKT/qYd/g9H+sdF/3B0wL7debhJi0JyOHNBfOnqU+Cw1hYwIxqwpb9hYEkxg1GzrTumj390Y+sPbxzU5XEIouFBj/yJpTgRB5o5ARwAiXTOIg0A3l4XM6Y5HWI4FBIShMLvHvnTb375yyMnjjRMn/rRj99nrxyPPanCvae37tje3dqx8sKMUafDw67aQEbr9mNAROR/uZISu4l2sSuD2m80PCAALEJVPpfIUwfAUAog0fjTF4glTeCSSf/1+9q8VbYxZFg8//hroDSs3Xoo/QuZ7/IcrUA2X/qjFFFJOqVVRd2S/hEzFDFltcD7vggmIIB0E0m6Xap94mBLf7uMHWkU89q6TRvfgxBedFGskNxfHv99W8uJT3z60//+3W8l7tjW1gHOH0+TAJtgT/Ohg1v3HT543dV3FP3gdoXA/nYuSS/lf9sM3sWuV2dlAWBkl0GkjEwpiATTf4yRlAEvrd9zw8WLCtt33+7nFiwmTVdGhoEqm8PmZ6+on7yKM0wsW2P/cNSUVKxoePcxdfGMvGesWBp29KaVf+lwCr3hUVYAkWDvZpB8QxbGrPnzrL94556jC+ZOBRg0nzw6ZXLtiaPBrs7jp8+0n25u9Vc1/ts3vmFvf9edNz719IuXrrogfoTN2rDO5GowOvR2Hlu3VVu0bKXb59+ydnUp3iI1ViCvAj5JNgc7MNSee5K8sTFghywHBsWAJg7/S2f8Wb8vZtnwVlUHu7uGqQNKNwnIQDbp/+oRNjEdbEh+/Yb2oYtQMGqGomZUwYoGVB0oekGnZQ43cYYE4RlmOCxpemm7Qo6Y/Scu/Qf6wULwwKc+2tlOKufNmTfb8gdk07VtbR0XXHzB5vWb7afv7tjfevqkoihuT2VVVbXb5Vo4b+x6FyO9zXt6m6fMmn/+qsu2rH27KMckDdwto789CkoapeflPUrcmOQL2+H/doW44QXyt3T2NdZUFLx7WcEOCvzvJ+nal0L6p+qAgin6JCBz+eGh7cL56oDHN5iXzjUlBatE+mNVB2qsyXae4OEqANVZXT25yuBY6fSwK4uOATz+qvi9vHvfsXmzp9jrE6V/ErbzI3UScNa5Z7239T0SZbhsPlg2v3TnPGn2ORW+ir3b3iziMZuP7O/rC1x23VW8w/3aszGf2fmX3uZwON9+5fGCDmkpVKsIhHXPkeaPZv5lBO0cYMMgdSSIVSlW7pocZlBqMaWUsInS3x7+pw/+Lz3DmQSMsCGouDqgf+xPpL9iQNVAb+yGD/7U+nE+dm+OByGx08MEcay7AkLAOSdI5axAIOjz5TpYyeT6bmvrqK8nJZtGgDOHtw2qLzgM5p17dcupo+FQUHA63H5fd1A+s+tw04JlDY1VQANyNLhlTSEBFBgAF0+aedmN3UlKMMPZxR00g0SR5X4oUh6a9I4HLGAAxoZuaRGrJw2DQGQceuDHI+n845mMP5aCzj7810zMZU4jWr+vzWqWO2h6VxRDUHGBhc0OCuXdE8z8Ol01yNhfM5CGGZVxb//VPdaLubrCSHul4REJ9QW63RU+j67KwzzUsE5j2EWNEob/IBAgEbHx4b9N4+RGe0GRFUPXnc5YB5iJxIGtsRxyKQqkLtBqxTR0nwHHh9cKFyFEogSsKKBYswVie8QIkfj9vMw2JH0YQ1ICiBSDg9juFmn9vgzSX4E6gUcClGb4n8n4M2zpz3OM28mn2nkTI4IKiwq1JwFgBMhN+r96JL/IM8v0D1XTkv7IfcOCvAP5BRYlPvLd/fxbHtBURZEkUuqR46+693NgonDqzIBFy7b/2KJfkWNtNKPRaCQSSdzlxhuuXLM2Zve3ueDiAYtQOWOaVmMXBnEMaf9rF/dhGFLChwTz5yO1ycbQ2gciuwwtC0mfSGQlIhrmqDmiyooUOVXoODK79LdKzjFVPmeFR+zqi/YEosbggIGiOAPGFLYOyNEWdLSbrfVgzWRU5HJVTwYg1/yvODUVvuF8jeG+bsQyDMeF+np0Q245PtwOZaOOKFqt19IN/03TVFQ1yf0bjUpOp0OS5NG1Ao1xQm17A/4FHMeaqm438MWYIbksgAzb87rrVMPuQW2noPfHoiCEGQQZ4eTe4nitKTkpgNjwv1Djz6ubD1yxIlvfoo0H2qsrnFPqfF63wDBIUkgRzUwTxsIMQUX0BBTRzJO7P8CS/g6Hv/HSugOFnPML8SpDhSArEstxLMdChgGMBEcpCjsUCi/Sh93bDIBmEDMBnTrTmeT+jUYlWzfEdUDcchEIBPl+rXDjDVe++NKbia7gCy6+IPD2K3mfyoQbyJ7qATPrSNa63RpNJ8nAgGFIDau8TECcdYshBDSdHI3UisYAI6ADYLCVpTt/SiIDv/OCjT+29M9u/HEIbEOVp87vrnCLdZWuKp8zofN40QxBxcoNLq6RP0dzkApFoaLxsobRGXq7PV5gGuFAX097a9ep07oUM4+MMIu0IgQgNdeSeg9ph/8uD7mNZVmR++0/NppFlmOSpDCKRahtn1W3mUwA7B89aQxQ0K/G3sW2BUBI/AG2Pj65bx292CMDOxD7X5DxZ8ix/5ptpDpgMAre6+56b2fCCyUINHr+8ddu+dA1YOyRyzyA99Wf7T+cu9e3uOx446mG2ctbOtsQaflhnt5Poh7HL/YYP3X4Hxvvk8aX0NYBZmZbc+okgGJzvE2aU8v1d3s0rLkASQ/ApLZzrpDYf2QfwSoAAW1tgjVEQ/hHDnZo32/WwP8rziPSP7v130YRc6j9ILfGFwuOCBq+IagEkT5DuwSq0REGjaa9oLG+4dShTs0wKv01QdKcZhSYXjXIk1EY73V2k+aFFZ75g4f/BiZGHtL3hti6WAhhKBhyOh1xb4E9Och0WOIJuOy6yl2b8jqZYhi0xhwQsWQSwMTEN/EA230f84lFJiN+4gcGCJGOkkQnI2v/Uf0VlBv9480EDXBoW67SX9ZIJfAs0v+1Pd2qoxpVNHgbp09tqJgzrXpKQ4UosJnkK1vZMN7rRWfn1SPsjMlpfiQLm7BbgM5RLVm4ff3LNdU1PMeePDS+273d7id3c29wwJ3bYQ3/VVULhiJd3QFJIiYgWVZ03YhGpXA4Go1KMfv1YDP2JRevSAoHohBnSev+WLyPNZciCqCQxF1SCchO/7Ibh5FIIgjOHHqHXuRRKwVRROPPoSA/u2lyY4332pVzfU1zgVXdZdsbT+m60dETOXKqq6MnohPz4eC3r6kJdnYO0wpUoryw5o7g8I1XJgZLZuCplchB5tAEzcCdYeARITPa9Sf37X1/9oLlHa2j1vRqh3f61LZCfOApOJKG/129IcMwwhGJCBsI3C6HrpPZGMuyRjDidIqyQtoDeD1uRLKT7BZV6TwBSy7EW9I3a0yLF0xMDrdpcxtYQ40lx9txnAUWULWbA5uA4UCvkr5Z27e++6Nvff1Lwz1pSgpkKpcIGf7DoYf/r24+cPlQxp/2ltNuJ+8XlW07VO/hQ/EaagyDKryOuipPRFKDEcXKIB8cEpqgA0bFEDTEXTw8KY0xkHUcVrHAkpon5DtgQJUL8mNj4nt43/tg/IOtdlWJw/9gKGLommGYCEFJQpqmG1bLQLt/bCQqcRwHAQ6HIz6f1+kQWdYKRx9tlTx2gQzLsBLQWJOBEFpXy24Sn8chrP8ZbJIkMh0CHrEdJ8f37HPcgQow/ry6+cDFZ83O/tNYf6CDY1FVhbO+2uN28IkVNCGEIs/W+F3VlS6nyFlVupKP5a2pKdgQNOLl4fJGM0BEGaicCAEUiEWaypoCJ6NxDNJYijyShv8Hj57WVNWwolVMEyuKEgqFJFmRZCUqyZKsBALBvr5AMBTp6wu3tLSfaWkPR0g3QYzxJRedl2gFamvrgOOwRnTRCbQd2NuiOwRiQmA5iBiSzJVnJjD54SMGkhZ0EDh4pjVanBKwlNxJ0dhDFjvbfODcRU28FcSbafi//kAHy6AKj6O+2mMH/ie/K4Iep9BQ4631ux0CVyLRV9zc4Jj9xzIBPfvm9uEcCgMgk6JvQ25WdhVxe/uCR6tnx+W4TawHuY29nHJhMMY9EaU1EG0LSWt1X9LwH5tGYpkCwyCHNg3dehgmKUiGNc1QFC0SlULhaCAYbj7d2ny61VYDBX6Yif7tBVr37z+jkA6ROvmoCT3Yc8WuYGsapMLo6QDTfSoxTDBvaqpo+FDeoDTDf5xx+P/q5gOLZk92O/jsxh9iY3Xyk+q8NZUuNkNKEcuiKp9zcp3PX+GwcgJKMgnYd+wNUPRf7rDVFcZA0a1OkFmxw6vLiiPHm5tPt7UGo+0hqSsid0eUXkkNyGpI0SKKFowvq5qk6rJmqLqhGaZumJ1h+WBH4FhP6Fh3MGn4f+AwaZ6e/RsmOoVUpMGGYWqaHo3KoVA0GIycPt12+PDxs89akuQKhudfrhmmqhv2Oai6YZ9PWNGiqi6pekjWrPMsVHmME0Idh0kdH2hV8MzzdrWuuVVBFOJTfUagdf9wzoRK/8JgB0l/mC3t69XNxDtX5XUkNgFNZf2BDp5javzuhmqPU+QyWTYghILAVlU4ZVWPRNW+kGz3Gi2yMyBSYJsUXOIoURMDWcMegdh/Mm1ThgogGpXPKB3IDFklYgADIUIkzIRUB4BQta4IYz+11jAIMdZApDUYDcgkk0ucuSBp+J+vfsekQ6dpmKZhGIqicBzLH96dWhmiuS+iGSaw6uOTja0iuppBmipDUufARJD4eBqK0PhuTPP+vgPnLp5jRYFiQMrE5Qo2yS4sMBVoRrqOF/bunT3BGv9EdbSPBLnWLLOl/0XLZ7HsEMYfhKDXLdRXud1OAWXVFQhCnmerK12hiCIrepT8gLP9OPPSAfGksH3H3lgw4yqQD8le6dSXh8Gv1tH+1BnBpm4ANppgdbEdhXYzF2LG6V+2QgZJLKI9wlBINRlC0vB//8FjMOe4cozNgXJmpFWnjiGsCHcfh6AOgDVrNycmhc284ZY3/vgn23VjWCNZqzQOmf72j22tM53oCgAAsHX3oZVnzWNQfj4AaJV/IN/R4QKlfxw6/C8YlKPxBwBwzsLpghWqkt3443JwdVWeSq/D7hw0xNtD6BL5uip3hddhqZZshqCCydcQlP02Hk6vIkoudHCVcVO/VS6Y5HAZlo0hvqybZLitGKZmGEb/5DF1+O+N9uFBvdKGHP7Hpl3YarDjCnXLuhHVDEkzUitDaCZWrXMgJ2Niw8T2iREfhnWSRtncKhvfOyDwkMunBi7HAJ5D2/aMUO2TDS/9AYxzvvD173/h698v7jHRoMifzMafRbMne4bKU1p/oEPk2boqT0O1RxS4HEfKDAOJu7jK7SLBQmk2KNgZMJxwoKxSnobrjCE4hokHa86fPTXxpTPvv5eoA3KPs7Klvyfck7SeJoVlYdOOAyTHM2cEgXlv37gvOjuScv/H3/3qj7/71eIePGGQnuHX8ermA3OaGqp9TrurambjTztCsMLrmFTr87nFWAvWHCDOAJ6tqXTVVLp4MoQYYsfC0oNznwTEJX+qT8LGKmA74UM8xgcQAJFl7G+qf/hPgguTrP+2DjB1kuo1JCbJbkoj/b3RQOLTtraO6z963/A/wkRi7bY8fpvv7CxKxt+E5QullPspCsCSuqnD/1c3H5g2qbbe7yaxutkPhJDHKdRXefw+R76tvRGCbpdADEEe0XqjohmCyCSg3w+csw6IBR/aYeM2Sn84B6ldbprhqCyrmq0DFE2XlGyFJCl5gc08ImeQVTzAtJR20vA/EQhAlUzENzb0+CPpTeOPtNKfMmYZSKiZKNz3mQdLLffjDLi80kp/AEB9lTseypnF9+sQufoaT22Vm+eYAmwkLIP8Xmdtldsp8lq4Rwv3Jj0SNy68WHRuOsAa4GMtIbA5KpNRoW6YUVnr6g13B6I7DpxWNd0wzR0HTu891kqnBMWlk8upIjxDOhQS8T/Y+t8Rt//YkMazCHmloP2wV8Y0Qb/Q90YD8Uemt/NGA0lJYXQSMOpErN/mhOG+zzz46M+/N2JvN4TNbtm8qS4SyplN+r+1leiJvhDoa2/bs6tUNnOHg5UkvYCIIBIO9Mk7cnyX/sQj8p+s6E6RuD1MjJvb+hwOIRxVorJGxvsYv3/gtEAcHt79x9vnN9XZpaw2vfLohddRs8DIwSKoW43Iswz/bc+vkqDO4zqAMt7BGJxs6WmaUpe0vqWzr7Fm/OWF3Tey0n9gBjC9Lnn4/+bWowtnTa70iMNN0815d433p3lwAw/WW4S2fENOAvrlPxn1265gUjxA03uC0b6QZK/EAISjyv5j7evfO+p1i3Ob6uyqPpQRtgKRQCDNyD78t7MuimUnoJOAMYVpmr1Bq53JRLH8jDBsZunfWOkW4r7cTMP/13Z1ILGq2udsIDV/OKfIiTwrCpzAMQLHcCzDsYikxlj1YjHGdhqBleZjkzBGU/WWrtCp9mBrV6itJ6yoVoK5vUH/r5f11urBjrwnAb9+JnESkCkzwB7+xyswyAox8XAs09ETCkcVA1tx6P14XGIoKkMIr1u5wOcWczkNSl50cpU12iDTXyq6iRGE2Yf/pUYU6bc/ahgGtioKTxDp/+jIDv+zmYAm13hkdQjf5uu7OrxOYfmc+oUzaj1OPnEU3N7RhTWgaiAv+5yLBfMnueZPIiVhVc1o6QodOd3TE5QGBeRU+oO9PaXQAbbwj79TVFZtBWB7gBOlP8sgnmP6gtIlZ8+aVOvLks1LyRfrJrIrzJuxYjFZNkaQm7Eoefi/fWtJL7s33LNmzcZLL11pPz1x4tTt//jJZ3/2i5K+KSUtupWzPTEuzqMjLv3JWDzt8P/y82YnRvJkGv57BdxYwVY6gBQOdnZ2t3d0xR/DPzNSTNTnrPW7SMXBwe/vrfQXcMDnf/3MEFvEqozF/kiKbtWDBHJKRReGQb3BqIlNVbd9kJTigBBiGIbnOY/blYtaZUZ7+B/n9s/+42ifQjlCCnyP/yig+0bc9B8nfbKugyczA49TzCL9Nx9o83vEhmqPxzmo2nOxgBA6Bbau0uX3OriUinJxHZBfaliCDkh1BiBEWhT1g3XDiEqaaZry4ChPlkGabrgdpI/gnsOtB0902H5I212c+8lQUnE5HV6Pq8JL6ojMnNkUrpzKsDzPCxzHsxzPsBxiOciQB2BYwLANKy5OOsLp99/HiB3mAyQsxB5MbAFa7+uVwmvWbIy/6YkTp2wdYJ9b/DxZjpb9KC3a4Azt8ch9oyf905iA3tx69Krz5wwp0DfsbfG6xEk13poKZ6Z6n8OHYZDPLdb53WFJC4TTVIsrpEZQgi0oyRCUJL5J0KeiRmXNjgG1IfVnEBRY7uoL572z+2RzW+87u07wHDNzcjWDUDAy0IaQUgDrX/3rrfd8yjB0CKEqhS84d7Gizq87s9cwSfqFbhKDr1Vrwaq+MGtxVYU7vu++fQfkU6mFPwcBAXDypPeCXWaO3FGkpykyMRZYxsSkioPAktL2BsakVQUkCwhCjsSbmhACnmFIpQeAHSyTZAiaPn3qgvoKsjFDNiYl6iqnA6mQerSUXMAYFF6su4wdv9kUwIVLm/ihjD/r97YIHFNT4azzu52kmn+pLOB/+tuGHLcsig7ol/+xWnCmiWVZ6w1FNVJoLLaSY5Fh4slVnqbGKgihphttXcHNO497XWJdlbelM2MIeTlz9qf+nOumj92LsUlqCFp1344cObpwwbzo3HNqjr2HMWMXWbP/PVw1M0n6W9/cEDMwjkENHtEtcJxVRNTAmGgCqwGkrQZMjDmrt4mJMUuKWZE0I7vgqGlpAgQhKYBMUhBgoxc8n6ADAABzb70z9PcX7ZCHw5Uz3BACqcCLRhkSjHGqeXZ80dLS+ubf/jhWFMCbW49ee+G8uECXZSXtPhzL+L1Oq3qP3c+rEKZNTg7dTeVfP3XrwVPdG3edau+JpFpXWBHowQK7Byf5hG0S3LwxcR9VNEyKUPY3s0TINLHf67SS3eC0Br+62Njw/tGOnvCRU50uh7DncEth5zOx2f6re3LeFj3/l9/c8g+fiJf32bvvwMIF87pmnFV73O5VSdbv8U1Plf7KqRMQAJ4ld6SJ46rCajc70HyNWPCqXQLPMHa0V391Uetle7P4ctJCmhWDsCcBnitulNe8vN/XBAEIBemAoIRomkGT8IumAN7cenTV2TPtTqhZ2LCvtcrrmFLr9fscqa2+cufk6fZcNvMK4NpzpkiqdqYjeKYz1BOSVM0AGEQNMk1hvTVxHZBvw4C4DsgQFUoqUqiaoekxqw6EkOMYgHGl12maOBiWvG7HrCnVqqbvPtxy6GQHJuknNMOoCMR0QL+gtXVAR9Pyfh2QBlv6V7mEapeIrd6QpEa/VZ4zrGhBOVa3QzfN3qhS4xJ5YrhMudVhuuWsW31wuu+vKYag/b4mUK7SP6/Z3jDfKyqTZPzxW5zxyls/MoYUwNK5k1wiF7+aaYf/6/e0iAJb63fV+slvaGQuPIRA5NjaSjcA0DDN7qBkGtjJGMOJCk0iU2aAbvUOtJfdDl5StMYaH+k+GIhu2XVi5fIZLgc/r6mu1u/ZtPP4+1ZxiMI/JyVVB/STqANSh//KqRPEgSywc2p8Lp61bUT2JMDAuCUQjVjRWnZGWEjWeiXFRTRAce7fVB2wfPn8dWu3lOf3mddsbzhvhDGIyhrpuzCeczDfHFX7z8B38ObWozUVrnj/lkzGH1K62S3WVRLT/0jmvpJqcQ6uzu+qrXQ7+OQewoVFhQ4ZFGS7ARLNQk4Hz5K8sHBvMHrgOEkDbusmvatqKt1nz59iVQmlUUBF4/m//Cbx6V7LzpMq/e1rjgCY4nP5RJ5jEM8ggWUcHOPkWRfP+RxkZXwXxTACkhoP3CoRl6w6v6THp5imqWg6rco7TGI/jJXLmgSOyS79N+5rtep9un0JGcIjBsMgl0h0QJXP7iGcBCywe/CQyQH9SLLudggdPaFAWDKJ90nbe7T1zS0Hjp4mSQ8+t0haIFBKqQM6enpTpb/aTCJ/XAJb73WmjkkQBE6O9ZDxSmyNiUFE1cMqyfAoFh+c7kuNCgUA/PmR/yvae1BSc4D728BRhqUA3tx6tMIt2jOpTNJ//d4Wh8A1Vnvq/Va9z9GAYZDXJdT5XSTreLAG8lZWlloHOEWuOxAJRweuj2GYLZ3BLbtOnGrr5TnWrhxHKZEOqKmvS3b8YmBLf9Kq2iWKGYqQOzimyiXyCZOAiKr3RBStqJOApUuXJekAOgkoKbphjOsiEFeOAQcAUQBvbj16zYVzs0t/O/upyudorPa4B5d8GGF4jqn2uWoqXAKfmh48oAPyJRcdIKtavCvAABi3dQXf2X0iLCluJ0kNo5R6HhDHOBOT/hyCdW5HpruSRajSwXsEPv66Zpg9USWiFG0SYHt9U/nlozl7RCl5oukTpwjEKMJees4s0lQvK5v2t1a4xXo/Mf488VLpvVsF/Swjij0fJKb4ArzBQ+oAEn2UDozx6ba+bXtPjWdf1FgndfgvKbCbraxRif+/2i1WOPhsfap51ufge0hFv1itj7Cq9UiKW2BTk8zzJR7zM6Opac2ajasuXQkTIoKGeXBKJkihrnFeBOLN0fYAEwXg7v/lZBr+r7HK/XeEAx1t7QO9MCwURz6NuiBwibzLydvNtiRFk8kQrGhfIesEem/bcHRAweiGceB4u4PEUFGKz8e/+FAViQGLsW8vuSG7u7sw5Do4/2Szb2qFO3tID4uQW2BFjon0z+FU3eyJKFVOIYvmyIWkiM8ZTU1r12w897yzeZ5ESRw7dnLucI5OyZIDrNIyXMULA81i/Ekj5SFJieJYpoJnHSLndvJel6AbZm9QwhhwHMOziOdYgWd4jlU1vScgAQh4luE5RuBZgWN5npFkrScgIQRjK3lG4FiOY8IRpS8kk3KbfGxjgWdYBgXCsqaZumG2dYfOdARJwGWK7mAr6xN1ABhBVI1UjhvJdyxn6d/ZFbQa96iQ4cHkORViKPtBIARegfOJnKTZ4aDk5ggpWkjRvGLh8Wyp8f6217e+cbLTKbIs4xCpVbAkmCapw0hDgIqjALJI/9f2dnvdwrym2qVzGqorXe2De23nTNrWPCKYklyIlOBjASDloJOodDrshbmT3b2hiqPN3WfaA4pqJAv6mupgp12LFL60fu8NFy8s6IQpY1H628iyofYXKmeAMW1KQ7d3dpYcMRsHx/qdYp+kRvtNeaphhBRNM0whzxbW2aU/aRXZclpw+RBCosCfRyMDSoBhYk0fxw6ApRde19kR62syuqAs0n/9gQ6vgCdXCZP8vCqHC5X+RQYhaHef97lFK50ztYN8dXz5pfV7R/wEKaVi394Dq5/4wxvP/8muC0IquznF082nIUKdM8+OP9LuixCsdPIegYvfLhiDgKSGFC13M+TpyYvjj0zSHwCw5a3VSiSgqlowFBnGx6VkhNQEHM8xoDs3vQLGBmiI9CsnX19FCj4z/TliYwGORX6fo77a4/OIlhMv2xSe6oAJY/xZ/cQf7OUdG160woIZl9MBINi9e+/u3QOaPq0ygNYkwC3YVeBAvytY74mqatZowiShb3Ps6LHEEVxqvP+Wt1YbStjnTTOXpQwfTdPHdQzo2CFjR7ANBztcDiL9qyqcw6n5UwoghA6Bq6vy2En/faHkStHeAUNQ4W+RfQND1xg2P6/vksvvNXTd1FUAwf6Nz4JicN0nvntk/16HyzVp6pRXfvNNMIFN/4MH6js2vLjy6rs4jjUNUqUZQJioAxYvjpn+4jqg5uh2BkKfyDs4JqxY3UatgWRPVKkiteGExC88UdbHOXb0WNIaWVZEUciS7TV9asO75jdMwyRRD6qpKOqzf/hxnleCkgIGEVkzEn7yT7+5684rl9ArVTQFsP5Ah8AzdVXuxlqvUyxhwedEHt8YNElhD4QYZOo6gxhSVdcgFiri8YOQZTmEmPi5kGJfpsggF6oAPMtxvGC1dISkTyCE7kkmNk0AiaTe3KpIUlRVSAtfFINhWY7lWMMweNGBECL9iv0VDEs+rKHrGBtt7Z29vX2GbtcRI+86b5JzyE8x45wbsF1BgpQWJgtkSde91Y2aqnQ2H4pGIzpk+JrZGDKGTjoNWFuR4Yyp93eesQoKIwa5fBUsx0Nskg9lXQcIISaF6SHLcS5Pxd6tfw/19UWjUvupKjBWeeAxNGzpD1Y/mRwzt/H1p6y6obZXl+gHu5skAGROkKQMbE1garp59BQ6umcgHlTReqOKR+B4BqXK/VShn0QW6b/lrdUAgFWrVtonhzEOhDOaWym5oxumlY85oABUeVz2hV964XWjbgtiM0ZNuMSGGm+Fx1GsmllD8qGV3nSrM0+iMbkVOnrDR051dXRHZDIlTLbmJs4DBjuETQAU60FGcvaqTQe7dWvobzerrxNRXYMAwEAgRyyCJCvHtr2Udn3leZdjE1dXVbTqKmMCTuAgYg2DBZZAtxWAoWkJLXEtcW9opqnbXwBZZ2lIIlAg1HWlTworimoaJsOyujJBCs9//IsPpZbgXP1kzPiTJUfs5g9+nPyxI4utEQMcrAwWL17IceyM6ZMPalpba2c8BLlXc/Q5JtVV+2HOcv/Zx3+Vy2exdcCll660v1W/z/G5r/3HTx/+Ri77UjKhqLqqDh0C1NLZ11iTNvyEklUBrD/Q4RDY2ip3lc9JmsOM2fwmSIrTVXocdVWeiKQFw/KwkwrIR7XlgolxcQ1fe959C5SScTkEykB1wvB/b4LpPzsv/PW3A5rAnifGlIGt1fs1ASaK1udz9/WR4FGMcTgcbT7d1tPZIQ4VtZmj3M/Cwnkzvvof//39b3wZTFDyne0VgKxqEyAHeOemV5ZeeN2YUwDrD3TwHFNV4aqrcqeWWxhrQAgFnq2pdAXDiqzoVqmGjM6AIaNCLbuLPXwc3xmG45ePf/GhJOlfwEHimmBgWmDJfes/awoFoRINQawS3WC9KEWCIudNW1t4mEI/Pgmwn5482dw0fcq///Cn/+9fPjecw5YtGANFsw14lGIrgPUH2hmEKr2OSXVen1sc+ZKfhUeFVrvDUaWrL2KQ2LDCdQBlTEl/jPGLKab/4SmDWIYgQyyb0G4KZsWVm4Z16xRrpJ9FB5w40UxLRBSMaWJVK2YFgTJn8AwAQofINdR4a0ev5GcBsCzye521VUpEViNRteB7g95UY23sP0zpn0kZ3HDXRxnS8B3G5QjG+M0XHgclY8tbqxNbB9t14j59X+69UygxTNO0uoAVwoaX/nDRDfePqUu5dLT9wAMGu/UHO0Sera4kxp8Ri/wpChBCUWRr/cRpwZGszoJTwwZUR8Ef/5a7P1zYjuWM7fhNIkfTfwG89NTv177yxPZ1L7y/4UX78d46MkgvKWvWbDQM0wIfP07qmNJaoYWmgPU7AEj4Lxi/7BztEKABBbD+QLtV8Nk5qdbrdvLjSPrbMJC0Cqiv9njdAsemSQ3LNz14vF2AMnX8jiO2vLV6/frNHV193T2B3r7Qjp37qB2jAHQrqWK8i/4xZwIigfNOflKdt7rCReorjCv+9OTbxT0gIsYBqgNG0/QPJiJb3lrt8vohIv8xDApF1f/9zR/++RNjyygxdrE8NppuTCgXsKEuXXHFznf+PpoK4C2r4HNnEHSeaQETmIQKoVm9wSRKhCSRFUS8rzKlQNP/sB2/Y5lIsMdbWW05HXBnZ9fLr2/6yr//4Af/7yujfV7jBk038DhvA5DIznf+vnTFFaOoA1hb+o9NdOdke4FlkEPkKzyOOr9ncl1FhSdWGdRmSnUsWQwDbBhmX1jafbhl9+GWvpCUOJZkrMINRqg9uw6IB4PmC0Koodo7tcH/3F8L2LscSZL+NqkZvxOJLW+tPv/ym5YsW4IgNAziFNi198hon9S4gbSA1oyJI/4TdAAYJVhDrJ1cW7F07qRZU2rcToEkzIxbIIAsw1T73JecPWvBjPr3D5zed6wtFJUT40IZT11WHUCM/3axyfzeGsI502ovPmumzy0O+3OUKZlM/9/8/o+//dUvgInClrdWr7p0pXVPkpHGsqULaqorHvv590f7vMZLDGhKBfiJogN2jsYkAFV4HItmN85vqveMc+mfCANRnd975flz7756+bK5k52Duz4xnrpMO1olGPIHwoZq78VnzfB7nWOqbOoY57f/8x1JkuOP22674Xd/fSppm29+n1RP+/xXx1mduy9/8ztf/uZ3Hkn5OACAT3/lW5KsI2SXpYItZ1pXrqCFzHJCN0ylvxXEBGPnKM0D2PMWTZ81pYYk/ZZM+h842gxGCQzw5Cqnqfma23qjMkkRsHN/OF+1HOhKOwkowP7jc4urzpld6R26VBwliR8//K0vfO1b9vLOnXuXLl34b9/9QfxVliWau7ene9xdNzuIYP++g4/89amjR08kvhSOKC5HrI4shHDNW2s3v/3yKJ3meAIDEJXVgpMAxj47R2MewLJAb23vbGufIGP/JCCAosDVVLojkmpiLKt63L4jptEBJPgnLxBCPrd44bIZk2p9EEBFm5jDkxHQAXYZhk2bt9fWDpQ1TRT9n//qN3/y/W+D8cAFl15reZKIDti396AwuMRQXPpT0Z8XGBMFMIEcwOkZYR2ARNIjafSlvzUwj3+3g75kEjBh19NNWm+/MmjHhJf6VzIIeVxifbWnuoLkCAyK7kzzuXO9vyCE0xoqVyyeduWKOTMm+XXDoNK/YH788Lfe2/a+aZq9VoG2VH7y/W+PF+kPANi85tX//vZDm9ZvIIVrFFLxOxUq/fPFMM00LWAnFjst0T+StiC24I7YxWWwEhp0Spmy0sguGc496QWeZap8LgihpuudvST2wlYPordaDnYBHJ8E5HpvIQTr/J4rz5/rFAWGGRMXcLyz4c0XSL5eVW3iSozBT/9r3Mj9JDaveXXzmlf/8cvJSc5r3lpLXqVmnzwxDFNPqNc0sXXA2RdcsX3zSMwDysJjSWpF8Jzf66qp9DgdfLJCsZ7uae7N8WgIoboqb1211+MSqfQvIhvefCHYPahT9viV/nF+8d/fSVqz+e2XqfQvAKtg30Qe/icSl/5nX1Da2UBZKIB44ejqChepF8Sx8SmCw1djvQxOnsgpCY5BqL7aW+f3jJGZ04ScB1Aoqeg6aQFYbldme4ImKIUyyNgTeOKBIHQ5hNpKTziq9hgRLRomViCHGOuklYM851imrspbU0msSSU/XQoAP/vjnwU+ZcaWG5qm57ilqpKunwKf3OEZY6zputVXjPzf3tHT3tkbCIZDoWhXd58djgIBIK1LSfhAzFVFKREaMd2W7/XdXhqLUBkpALtwdIXXWRNV2tvabOlP7M4VlcG+nOw/k+oqKj0Ou4ww1QGl4NYPfXLfvsPxp4nLcS5ceX6WIzgdQ3T1SgOE7+04uGff0SGlS+KXDvsTPmprq6678oL4jPDI4UNJe8nSBOnWObpo2oQqAjFGKC8FAAF85e2t5GbifDwDJBNDAESUPk4jFVv6l/gcy5q/Pf7rWz/0yezbbNq4JcurkxoHuZFzRA4HRNYobACP1cixI2kUFaX4VYDoNS02ZaQAnn5lIxk/LmuaNWtWW1fwdHtfX0jq6AkCnOskIC79s6iBd3afKupZlx0JUhjbppX+qz3wNKGRCwnrNU0c+xfBMy3tkxrrCnjTgq0L8fJ/sZhkOESIGqUwNNIGgKqA8aAA1m3NVnC/vbPnrusvBqMh/b/8ydvtp36vq2lSdVhSdh06fdhK1PRWVI7wKVHSkqBcyYL1DKa8Sv7GS3YzDFEJDGOZ6wuSu5qe3E06d+KdU+3W85QSMYFzgEcR9qmXN9x1/UVFOdbJM7EYvmmNJLTmZEtn4qvtnQND7KdeXp+0b+lUgi36b7521ZypAymmLIs8rOBxCbsPFnJX0W4BpSPQ2xeVokNu1tDYCKE1JwDEjpegLcCZlo68DEEYY30YwiWusfbu2T/kxq/97S8Fv1GZU4ACaOnsa6ypKM3pTKAZwFMvb7BE8EXDF/2ZJL7N5z5yAxmkQfh/v0+O9ktVCcPXCmlFfyLHm9tcg+vE5UgWR0AZxykUh7dffvKy6+8eUge0tmQL21VyUCFxTIwjEQnnlmSUlI4OAAgFevfuCeb+dpTCII3AKMUGntr+PADgez9/qgAd8NTLGzNNnD9//42WqAckdAtj08RWF+5YSJ1tcjVJVBdGEKIMWsE+w8QnuZ/h069szC767YWu3sg7Ow/qCqlAILgH2kZahoVk+9iSubH+BDaXrZiXeuR3dp/Cofavfe+XOZ4nJS256IAs+CvzGPdpuhGNDmodkbsCILHFztxCAyB4nQ7/h8Etd9yd/rpC4oO558b0sWFJM4Cx1hR+1InJuAc/c9f3fv5ULlMBS+gn99n6p4/dlDYxCpNeLsnriR/PspuaEBumGbfx/tPHbrZDqQ0jpi1++ocXU96dnGESSSecZO7PJPpjZw+BLf2Ly8MPfprqgOFgso4lS2f2h+DHPMMQQhPbBp/YTIusJ+5fE5GXgKZpGGNB4FmGqaurSuwCl7iQRPOZjoOHT+WiAGwnQyIVPs8lFy6LuwGy8K+f+3SOn51CGTEGBrkPfuYueyqQpAZSJH6Mz9x3vSWm0/wq4qRdH9/cKonO2Mv2zw8hpBumZdsl+/7TR2+yC/TrVgQYBPDnj76UesBErQAhnDxl8gevO29I0R/fXhFis4T8A8gpJSSxuebAPTMQiBX7a6dnk3+tznEDkVrxfVMWkmAt93FhxjtyZ5IdqfOXMi5hj5xomTW9Mf7cngoAAJ58cS1EyTaQL3zsRt2y6di/QJIGaZt1hk38IKQlvdWVPj4ii4V9WO/4uftvRJbKMU1s2ZbIby+mFTD+wgM3GyZ+/q2duw+eWjx3anbRP0zS2n8SoZOA4TBv1iBrW753Ub01/M8RxlYAOZCaAEjaOhomY92x2fmvn/6STgIoYw0i4o/0l8GxNcHdN13WUOP+39/+zV752Y9cD2NmevKUY2Nj9lKT+EvjOTY5XhuS+A+TBALiz90f8y3bGyycUZ+X6Pc4+UhULYWDieqAcQHDkM5coKA4ILuvb/HPiZInT7+5684raWO1vBk0xrc1AWJ4jMHnP3aLQawxgyfRo50HG59vMBACFDt7kySIx0zDL7y9y1YAa9/dN7XBn8sxZ9c5T7VpMmk1l1wNhjIqrLr5IyOtAAq9sQ0TG2VQo3jso8qFhwyUM2mmrl09fQhBlkECz/Icy7GMPUQademfCbu9qj0JaKhy9wRCPQFS6C1H6qrcVT6HSJpiFgHc33He5uEHqetvrGPf34Xti01zODkElOIxRqXTGCf9fb/rwKh18S0GtkMvV59ehUdsrPFWVbhKdDZUB4xx7AFEYfuaGOdedvS/fkqDg0tFoV9guTNCtYB++PvkgE6/g//oB68u1fvlM1kRebbW74IQtHbRWeQ49gAXDEII5eDFzUTuCoBSOtiR8k1OMEqiAP7n0Vc0VWWYbF9Jj6QmagW/lZFbQpWQGQShU+RqKks1A6De4LHsALAVgB1IWhiqphX1dCiF4C6gDDilWAogaYCPsor+tPRIauJxKoTYiT1wz7Uj8DUhCB3CEB7gpDTgfKERQWMWBGEucZyZUEj4AGV0sNOAAQA+N+ntkRQ0SBI1Elz0bP5yacLDFtewM4hYbcT+ZDHrqWHF7ycmc6alT9FNK7rukT+/OjJqgMkhmZMyIYEIZp+tZkdVqAIYfdxOIVHup6UcesqPkAL4wSPPx5N4M+EQOFLnh3jYIEMeRFfLqk6iSxE0DFNWhjae9lnb2GpgxCYEJYJOAsagA8CeAbDsCM0AaDpYaSAdv0t06IkNW6xRf/8wn4z0EUQmgjWVTgYhlkEME/sXYxyOqoZpIgRlRW/tCsVqtOCc1MBITghKBNUBYxEISTWIQhsCaJpOy4OPLggOMvtQcoctgsGHSHyigQWOxFMzDCSVe7DeUO21x/5kEmAlE2MMXA7ezqeXFT0YVgwTx8t8k+KgVm0vAh56QjB+1QBl7HiA7QByls31h5BaDcKKAqLlgEYThNCcpoF6NpTcYfMV/RCScj2KanAsket2MWeWRT636HbyDEMCKsJRzdB1r1uwfyoD/2PM9c+1RYGtr/aouhG13L8YANmy+0MIdcPUDRPYCfbpBma2GvjtMxugEh6+GnjsuVgrAsFL+tiUGjoJKDV5FQIiQBJEmNhpMi80XTdN0o+sgH0pOXL3vR9TpIzZnaSAGKW4CiAqqwjCn/35tfga25rPMsjt5Hv6zEqvwxb3DAN5jvG5RafI24N9AKS+IImuTj6opS2sJcwjprHWq2q6JOsIEedwKKKQtgEQSooWVbRAUGIY4jkg5RbtbqspYMGtarqqGaaJVd14/IVN5y6aDsY8VAeMKawZAJN2qJELVjWgnOrB2VA3QNEZjgunzMmoAOzqm7FlSCZZDoEVBU7gmEqfQ1a0xlqvLf0RQiwiJiCWJZU67aSMoYqrkFc9Tt4wObeTVBwyTex1CfYITFK0iKSFwrJD4ESBV1Rd1UgRUqvmT4Y+QVYhuMIugeGoMqwjZwkkpjFCE9UDnFgOyCzoFjIM0zBNWkaqpGQZ/ifFfSZS4XGU7IwmvAnI7q9t2fdFgRV51udxeF0CxzJuJ9/dG/H7nHZlZrtnRwGVgiBxvkFUe1ZVhRMAcLr5tL2eB8AHQOMssmx3E9M0XVE1SVKOHnhf100yHYj/VMkZYNPAiQ2595/oiP+bSjxN4arzZpMK2PdfEpZUWdH//NJ7mU616DEGdBIwRhwAg1oCFARRALRb4ahScCGvm26LdRn7+J3Zus/e8qHPgzKcAciq5hQ5jmUqPQ6Xk/c4BafIEb8uA0We5bPmXp9z2S1SqCd1/ZQpkxKf2j7fuOhPcxokSQcixPMc6xD5ihUX73lvk6oZxElgwZGWY4xDgGqhGfmtbV1DbsOXIMiM6oAxVxG6IMzh9ZSnDB+3i6YBF0hGuSbwLMa4ttIt8GyFV3QIpCyo5pphAqADMG1Bk5z5oB4HSCv9Sfu95jPx5SlTJmUR/YmQGQaDeBJICmr97oikSv3ZNyNjnOFoDuGELwjan1NaUEFQWg5o1IAQVnqICYFSANlueklWG2o8tVUur0vYfFDWXDNStwlHSQxPYSQqg0R4MePXCSGor/Y01HiqS1a8My1if2mK7AnM+UILhY4RSMRCweWAIMlqzGsPWha0iDCI1PIq5hHLCbYrEEn7ws6DpCK001lBJrkAXLTQCSJpumv5AADpD1A4RoT0d82Ew+FzOEBVJWhKOE+bVefNzUsnvbb5gM/tCEs+pyPbDQQh8LkTvElFnXJQQ1AchI2OjqHNcUMSDzXOHVlRTUODODdLzqARAHQ4BCkabW+jBSFGB4ZhaJGfghml8Cmc8m/qq/YiTh50p1/TfyjDwK1dgYLOJiMsy3hcA6WmaLz3xANbJaoK2RMCv9/L8/mNQH/4nW8W8l6UdHBs4f4byiAFkPQTIMH38TW2kE3cAKd7ZCAxxcaO6Y9F9vcvkwX77eKv9r97/N+4oE+zpv9Q3YFIKKLk+70O+qQpcAziuZyKhb2z+1S+b00NQWMB00pDL2BHlmH8FV4qf0aRHH+bAIBLb36gxOcy/mDt4Pp4DFzSUzsHK95vvX/8G8vtJXU9E7YcKAAafzoQWgdNM7ZxUrgdzLyQlv43GNguvkZStPbuUP6/ZOu8Mu9k5buVdohBDUEAgLdXP3bbvZ8Co4FVhSR/BQCh1+MSRdLKgjJajMcqQG+sfhwDMHnhRYc3vzC6Z8ImtvolQnVwOPSgZymR0ijTq0ly3mrRGN+4OJWzBh8BY6Co+pmOvoKDQbOoHZLXlpuHEGOwp7l30ZTKAs+BMkpYybx5KwAEYW11ReHeY0oxEIfq5FEi3lj9eHx50sJVKa8n+5MSZX00SIIkR1362wogswxMrHA1lNROejF5mJ/lXYYPJoUrOrpDwXCW2NTC4YkCyOOsC9MBdBJAqjwFIhW+EY3vGnACW/0n8oJlGbfbSc3Po4tD4Bryrf6UJ88//pPUlbYQtzm8+TkwPsk6exo0FwBjFlnVWzuDwUhJpD+ApNdE4lwnS+HHYc5sqA4YFQzTDIcLaQfNMiR/IN+9qAd4tNKAb7r1zo/fNTBU7+wNP//394t1GqllYscFY2D2Go8FKigKwzRxZ284GJELc+INCYNgUkURCMBlK+Zl32tPc28pTmbC8/bqx0b+TWVZjUqFjB4s19D4+81PJCCEYp4hWCUCjkPpX6qm8LkTE9okCIcsDVVCLg2KqveFpBJJf3uUl1eaycmTLdOmFV6anE4CnnvsVytWDavEd0vzSTBUu7rCSbjTAkp448b09aYy8c6al0twTuULzzGj5QOYGIzmDABjLCuaouqk/rOsFuCFwxgEI3K8pUwpsHuZ5bjxi6+8NfxJAE0Pfmftq6I4vDqO5lgszkOlf9FxCByXcxgopeQzAARzysI1MT5+pqetK5xk9zFYT4XHceGyqVMbKmJidG+2yHrDMEs6/LeLzeXbbmKYkwAKAGDta8Srtuqa22RZKvCCmEYJ5wF5QkV/iRB5Li+jwW+fWpvoBqAMoQAWLZyaZq0lbzXd6AvJoagC1cRS3QMx0ZKcURls2Gs50Fk32QGTX7hD5BbMrN2+90w0KL25jrzaWOudO60628lhEIrKEauhWOlwOXm7nX0BOqDgkFBqCLJZ+9pzY0sHFDTUoNK/dAgJRbqSoM0AhqsAEFT37TsCiodD5N/eegwAcM25pK6cvWwjyZqiDpq2R+UhiqsYxP0bKenwH0JQ6XHm7t7x+quCPd25zwNeXLf7xksWp32J6oBEHWC5aqVxNw+gor/UOKgDYHiw1Slh1z0BEhI3ub7ydHua6m/DhEHQbr+ViGkatX73/mMdhkHSuBiG9bn5mZMrMAA8Myg8QzXEwcP/vKs+pCdDaCdCyGnlebZ0hXI5jBZOtvtnnwRkkv6UVHMQAKBA5/Ao6QAq/UvPWAkBmpgzAAaZikZaAQMAjjT3dvYEAQArl884dKLd5xaJ7wWDxrqKrXtOLZpZ53AIJ1t6SLsYBP3ejPWcVU1rrKtYu+2oy0EEq2HoDEPOwe91dAcGZD3DIIM0+Uomrg96g/KRU8GP374y9gKEEUn97VNr7Z7AmXqBZSTDEJ9BMF8Xk4NHkhpLKaKTgOLyztpXi3xEyngGkk5NY8XNM04Zwrpt5cASHP22ttMdAafI+zwOh8C5nESIe12CXQPI6xLsrmGSohHPKTTtByZyf6BCQyiicCyT2GSRzONgYptHOzU/h8xMq21lwaHY11wwb87UmrbecEt3qCeUxsJAeiDn0wtMc9RojhrWVxdfc/JkS/ZwoBsvWfziut2ZXqURQRRKJiCEPDv+CgGNJwVgi2Ro+ULtNa2doRp/zGpE6rsB0qHFTocReFJaDkKIEEyMnIGWLSX+VFb0hhqPzzNQYNkpcr3BQfLXMMzcLO/D0wAW91y75PbLFly/ck7qS16XyGXtfJmEk9Hth9c/kJt+8mTLcE6PQqGkhciZnHs/xEuB/vaptfR6xsmoP+trKlo7W+KSNd6ByzBMgWPttuy2ehD6q/EhCB0ia9d6S0rpStQHpomrK1yJ0TsRWdU0I1EbsQzKS/IOhyln99uR/rw+tQhEXroFIVL0NNUh/NKGvTdctDD7JIB6gymUvGAQyjdEewJzS0Gd69l8im5DjkW2fCND/nRZ8DC3jGh2cIV9SdYBwIn5Vi4HzzD5FJ8rkYUxTwcAzzGyMmDsStQB2bnjmhXPvPYO1QEUSu4/UFEgvWBKXQluXMjxgslVAUAy0md4no0SYV0EBjtXk+OCCsruKzAedNvLfwMAnHP9rUnrEYLxyU2O+NyipkcMI82ZZJ8EKFIhxcgolLKF2KUd+U3QV//t6ZtuvRNMRDle/J7A7+4+BoCYmNmLmLybX2eBGWO1k2w1kAjPso48m03X+d1RWQtHlXhyQu6GIDoJoFByh2FQPDKl1NwyTqR5AeRxBVXVMDFGOYdUH+5g7Uars+vSjIit2KKxpQOScDn5fKOMaytdwYisqLpKXBr5GYLsSUAWZwCFQonDMKiAXvCr//Z0/F8AwE233V3ml5QoAHu4akvj5OUEEW1kjstMjOCMYw41Xcgi/0kQqfXeA5lfFrKiHz7VYYtXCOEjz25MfNXtduadAZAZp8jn22za6xJqK929QTlRASSSyyQgkw6gucEUShwW5VGlkZIJ1N9gnZRUiC9naZHNMEhWk90AoUiagjwOkSem/MEiNDE6KIsHP57hndQXvjcUzSRbC+C1zQe27jv19NuHn1137Ln1xwe/mN4BkL3wBMcylV6Hx5WsORKjQl/asLdgTwBNC6BQbFi25J26ywGrJWR/23fbkTrQvDfdDk6Ra+kIzJg8INE0wzjdHqit8iRtGertkBVNN0xQN5AYxfOspscOLPCsJKev5RAP701sxKXrRiBUmrZf/V3s40AIxLQWxuyhSRA4Bc7vdfYG5YisJl7BHA1BdkgoNQRRKFmBLMPAPBXABz50H8n7uf7cCW/Zzx1LxmVo2AvTaQAEYUdvJFEBRKJqyDJ8L1gwK7F43PymWEnnRHxusasvlvPFZU7i0DQjNgC3TshWSVFZk5UhKsQVC2glAaRZP9SOLIuqK5y9IUnVdE03CzAE2VBDEIWS8edpBQoW0D8qkd/+9OEcW0Km9gSeMMojazVQNGDYkWR15TJSZicJSVYrPI4Vi6di00yU/qm1oBcsmHX82KnFsxtMzr12067Jdb6lcxre3kqUAcOwi2fX7z7cZtcFIjXUjnZecnZT4u6miXsCkSx+iOLC9JeBy39H6HUJ9X53JKr2hWTLC55fRFD24hDUGUChQAT5rImiqbWg7/zgvYZhFstt8Hy6NvE5MqaUx8hV0ti374jDFqkqqazZHYjOBgPTCDW1q9dg7S7JWqBEbd9TgBAyGVxMWTrCxxE4trrCFYwokkKanSW+lKMh6JIVi9e9Qw1BFEp6ECRFIPIa/4+dmMPnh6E8iq4/hlAAGOB4wm+m5YSNB1Zl2dj+JuIZswwi/t4jJ/vshTj7j/Y0NVbZ+2GMe4LRImYhZMcuZ5T2jsl+Ez3+/KCopFQi0oA+yDIJ8PYbn6ghiELJEAOK6qp9uV8cCIkVYSRKCIwB/ZG7ksimAExMBuyJjgBsmWJ6Q1JbJwl1tNNlrfAhzLGM1yX6fU6HyKkaaRbWG4wqKfFCNrasX7e9OUnoJ5LolFU1IzRSw3/7XiEKIM+93tl9SmHT3JG2IrGdzKwH6KGuXHTADZed/dLb27O8HY0KpZQJd9/7MUVKbDuYd62wS29+4O57Pjz+hX8RlESSbsjPBIQxkcWBkKRY5Z0ZBjEI6bpV+qB/qK6oelQiTd6H2avdKfIxfY1BOKqkxp4WEQSRiePTC0gmmAXZCj9353mGiUNRpbk9uOdYR29QIvG0g+OLWE91og7IBDRiThQaEUShJCHwbAG/0LKR/3nohvwuomniUIT04cIkJRiJPEe+hv7rigFQNCMQloMRWZK1eF3MAnCKfFWFyx6EGybuDkRL1/qRY5nG2oGRO4REsRVgMcQYRMMhJRrmgTa9Rlw5v2beJHeFCJys4WT6Hxx2cthbUZlLWsBtV59nL2TyCdO0AEoZAiEkZXqtX2ieleBgPAaUkrcCwBgoqhYIy5pOivWLAusUuYGMJ0wqRYejSjAiD3P4jxCsr/ZytoYvbuvHdO9VVeFaNLM+vgZaw/8sCuCyFfPSrh/Yg2EYjvV6nfW1Pq9LGDAm2U0RUgKZMukALaELLtUBlLIlyf7Ds4xD4AoYzo8ZN/D4VACmaQYjSkRWMcYsgzxOgR+cK6toelhSZEUfpvGnwu3wukV7YmFiTAwpw5hMZEfkOZ9b9LhEV3/QJ4NgYvuavOmvT8KxTJWbr610kZJVVk8cLdxLHtGAFg04+Jze4o5rVhR+JhTKRITnCrH/WH1KqAZIhs3DviFrgbCk6waE0OXg3U4hsSqDaWLDJI0cwTBgGSTwXF21N14rVFF1xfI2lwiORQiSm2n+jPp3dh4hAaCF5pf8/e9rCj6NTN7gxOIQNCKIQrG6kjD5DtE+cM99aeuVUdhqX6zVV5yeABE6S+ZNAQDsPXwovt7vBX6vFwBwplPqCwQEzhdTANaFTfV25guC0OUUav0eu0VwzOik6cxwxuNDY7W8P9XeNLnmnZ1WAKj1KEAFfPtfPmgrQvsq9IWkrfvOvH+wNRRR0l4XVgR6sHNIHWBXiLOXqQ6glDl2m6Z8qzRagYqkW23Jzmu8UkgimN2sxdIHwrT6NPUSWrvCzS3dyxdMznKQo6c6Z0ypTveKCUDM9n38THdlRU2+FT8Kw1YzRPoX+naHjzRbB2KAEZsYXXvB7CvPm9ncHtiw4+SR0z2pZexYb02iDkiLIkUrqmv7uopW5ZRCGb9ABMX8HQAwfV0byghmAhdGlRdXed0AuJPWRyRt5+Hepkn+K85fYN8NYUn57VPrzl1E6lXkWBT6mgvm9QSi4f7uxHaHAmTlgBV+xv3SP64SNN2cUskpEa6jV0uTy1ZRGezrzT4JuOLspmdei30iOgmglDPEAyzmrQCsSscTIQus6KDVW06++O6Zl7aeKfKBS3yp44Xk/r5l3zDfcuEUZ1M1Z0R77KCg1BqzLV2kdkXBsAyq9Djq/G6XgxtSs6SNCFKk6KyZA4WYslcKolAmLtAhcnmlgA1ApwDpYH/0X3ZiGLYeOrHAmIbW1oMx5jl29pyZwDB3HDy9ccfxUFT2uR2rzp516+xGVVZf3XTgvQOn7QRdXUv207Jc9Yb9MoRg5bxBHV1ypycwEASZCoPQ9MbK4uobCAGpMVtsQyGpLM2zNVZ1IOLT1oyke9GbMAnIxNJZdUeOnsi+Dc0NpkxsIAQOgS8gSOPJP/8pXg6aktYEBG3/CllEgHNLsdI+ptnS1rvzUEs4qgg8N3da7YxJVYBjGA1V+ZyVHoccJEmt3GA3LcmkRYZhmIZhvrMn4/C5a2/XQMRkhavK5xJ4qz+BxY1Xrzx4suO5v7+fJC4ZlrtkgRUkGp/SFUliI0gKjGTfZtt7u7e9l2YA/pV/vCvbkRH0OPk6vzsUVa2iRsmjEe9QhiBFil52wZK3N++yn1JDEKUMYRDibQ9kQTzx+KNPPP5o4pqbcmsJmba0zjBruo0R2Kd+/xd76a6P/sPgl6yOjBFl95HWrj6SiOHg2Z179p86dVLk2YiktnYFw9Fs7VwSKyEPcRLM4NKblmRft/k93TBn1Ajt3aFBVaANefPuQUrlt89sLKwl5DuH+269fInIc0TxAMBuOpFl+J9W7sf5wS+e0pRsU5a8SKsDXNyg60lLRFDKDQZB+6c6FrileFU5b7v5ZtIrxZsckJkJj2fAJ3rlRUuK4wSOawKiDO64CgDw1ydf6+qLtHeH7MQuKchVeEmVbcPEEVnNVJzHCtJFmmbkGBUaK70Zf46J5tB1U9WMYFjqDkRL1wPAkPre37k3PurPlAHQWO154fVNYAxw29XnPff6u9l1ADUEUSYYzz/zJBG4d9wt8Kw9A8juz01qBnDpzQ+Asc1zL7yQ5dXbbr7Z1g1uS+4nffA3N+wajjJIHwX01DNv2JlffSHJlv5kaNz/zqqmR6Jq2uLMpI4Cy1hJYblKbYyxYSX6QkiWNctwFFW0vqAUlhR9eEnFWWAQ8vucCJJqphACw8BWBej099XNV18IAMiuBqbPnWc3qoYQBMJKVFatoFLSuRRCGJaUYETJktLMOoAeaM8+CUgsDpEFqgMoE1UNJHH9tddk2v5fP5OTeWfsM6R6AABMnlRXmDJg48N0q77bQGEf3TACYUmSB3t3MUl0ikiqpJCCEOkTaxHMq2+71X2FNGBBCOqGKStaVNYikhIIy6UrAGc3N/a5HTGRj4mZfu6M+km1leu3D+S+pVUDTZPTtLrMHYxJjtjOw217j3WEo+Qyxj8k66sbUgck5oVRQxCFkon/+nkahTHxeC6DevjJL2ILt9188/0fIBadtBCDWmw+ZY+FrTkGJoH2aiii2AN5Mkzu30FWtWCE1INLPRZCiGNZ3civIAS02zuwSNOJ9I9IakRSMzUSKCJet8Mq+jZwGjYXnz2Hh/ZAO30Jivb2IVK3cmFOo6vO29DWFTrdGSSdI+MzAz+pbpila5giRR2+KikwsAE1BFEoaXn51dfolXnuhReyzCFInbJYKI0tgiyFoOmkqrPd8oUhedfQNvjohqlE9KhVDy71WCRFGwI9Qyf0TEBEinuYJg6GSdVPSdE03Shd9bc4ApfGm6TqBjYxnya7ucgwCHpcAoRAJWpPjybNtLJOAq5Y2vjiOksBZI2Ho4YgyoSHivhhkhB7YweCWtE7oagSipBhKUOK/pN6abaNSFa1cFRJa/1nGcSzDLHg5+mztep6wFBU6QtJYUlVtZGQ/jHhmSg/rffsCUTOdPaNwLtbFTVIRdX6Knel12HHTcXxWvOALKlhH7zxokTp/+L6PSU+WQqFMgFB6Yr+633BqKqRqp8OkRN4zjaUmCbOVOoZQhKeRYwm+Vj/+/clbQZCEVlSVNJcrJR2/6znQf5p7w6dah0iJ6uIMAzyucX6KrfbwSeN5hN1QCrhYCCX49OOMRQKJWcFgIFhmoGwHJaIkYfnGLdTsIsuYFLkxtQTCt0kwrKIZRnFst2AfIAQmiaWFG3EBv6x97WKyg5aZb15X0iK9JcGGpHTAALPVFe4aipcwuDmComknQTcePGixKeZJgFUB1AolJwUAAZAVmJF/xGEbofgFPl4ZGSmxC4EocCxduQ+yB9Lr5Bi0mAEYVlU4x9cYM76lKqmj/CZWNeZr6tyV3hEhoF5GYJy1AEUCoWSlkH5t4ZBhv+SQhySgsD6PI5c6i6R9jwQKgWJToxx7gnDRaTK5/J7nanrR3IWEodlUaXXUet3W+o2D0MQSdm7jkSmZodOAigUyhAKgIR+ymowLBuGadmmHS4HP1AZM4NgRAgKHKNb2VugBBTYnSsrDELTGirT9pkZFQ8EJJ0p2Vq/q9rnzKJx004CpHAw8Sk1BFEolNzpF4KYWD/6QpKsahBCp8j7rDD52IukljbOGExJMr+Kbzmxm7PzHJvUeXj4eFxCVUX6shujMQEgMAh6nUJdldvnFpI60gw5CUgyBFEoFArIjf8P5VCm5ZjTwsIAAAAASUVORK5CYII="}], 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"}], 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"}]], 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"}], 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"}], 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t7N8dxu0+VwL+pUZCGz846vH192I9Yem8E5bOS3x9nh7Yuffwzr2Hk95a1fTmDne3OyAIWJZETSd/+v3P//7CE39/4f/4A0688JYHfvbsuJw2AAA5zGVjWMVPiUzAZIaAkuI/lcbI+DjHmzu5GLi84Y2b94ejGvcAONwPwIZd57fPri05eXl9NBxLAyycF5vpvGPPoaEOYMWSuZSilk73vzcf1DSyZG7V/BnlRQ6rLAkY477QkOmxF/cUFhVWVZX/9L7PjMPHAADAFGNDZsY996NAueIBDCbuCpQUWStLHb7IgNA/hyakaY61urfuafUF+GAZVhjKv5K8AWrUoSoq60e0ffehrTsOHGjqcvvCt19x6mkr5/z59Z3/eHfv4ZYeRdUkUTAcAv2Oi+d/8ty6hoY943/SAABMAX73cv8MxCntBEyaB5C0/OcXkpwAToXT9vjz//EE1bDRT6LYKiRGgeKXEUKlDmnVwupCh4XlBow0wMpl85jJR8gbjLzwasMJc0rcvnC3O2C8FBcPjBD95JVnJ73pn1/fObOmpLqsUBIFURQKHRbB8AmuuP6ucfgwAACYYmzIwLjnvgeQQ0ngNFx69vJ3G47tOdyZmAOwmsQ+Ix6DEOoPKWajXQRPBTfsZvGflcvmSaIgicIbDS38kSW2+PgGmtKfuHT9MkTREy9tkkTBZjGdf8rC0iK7LIl8crQkihddeesEnDgAALnJ79JuCst9059DAhCv/hwKUcTL51e1dHq9fREeq0nUNc2jDBjDE1J5UoB5EnGvAiG0bWcjy/GS/u5GrhCNy8CFZ61u6/Zqmh4IKdRQAV0nnkCYUrpgZrnXHznS2tN4vLtkmS326sboyr889zg4BACQtzz/7FMpBWCqmP7JDAENLv9HCMV3ACQ+ssJpe/LPWy46a6mmkzc2Hz7UHDPrVpMYVvpzwgghp010h/TqssLzT5nX2+M2m6RIlNV3RqJqW7fPaxj0ROsfp8Bm5ndFVT2+90M32hax8JBxtaxAOu+0FTXl/Z1K04+TBgAg3wJBl00p059DHkBi6edgLjprqRF1ERbPqWjp9EYMtRiKrl5/c4fXaZMWzWezaDZu3t3c4QlHY4MdOHzhz2WgzC70BIfsexoXCkLolj3HP3TqYssgT6W1jQWmAADIWy6bgqY/VwQgZeI3JSy4L0sRRePxn8FjmLkT0NLplStLtu9qxBidsmZx6P2DbW0dgxsPcRnoCZJEPUhJqZ3lkz0u14FjnSsW1Ca+K1h/AMhnLpuypn/SykBT1v8kXU6M//DLlNIuVyAYia3lFTLA+jtt/Vt/PW7vjoOt2w+07D3S+erGBhNSVWwayr7Hs8ElNpyQGU4BofRA4/EtDQcyPlEAAICcJof2AaR0BXj8x9ivS5rbPbqeYjdAEvwxlNJwVGnr9h5p7RFJtMSGXSHKv9I8l8tAohLw5b+x6UwIK6ovGGnYc4hvJ44v/3/+6LdHeK4AAACTz+QLwOCFf0rCEdUfjNK++p+ke6kBv/zySy+mfIW4ZR9WBoZyCAghXn9Y1ZjAJLaUuOuL38zkFAAAAPJaANLFfyhSFDYkZnD8ByHU0eP39mVrefynwGYeHP/hl1/64zMv/fGZrMhA4lWMhVBECYSyMCwTAABg0pl8D4DX2BQWFR5u6X1z65H3dx4/3u4JhRVdJ/H4j6LqLV1ezVh6c+686uQFs8pwXx7YEyaeMElsFhSXgZRKEA/1ZCIDiWg68fQ5Ab0eNsCgrrbqd4//YLSnDgAAkJdVQLz8Xyd0656WHk+wxxMKhhWM8L6j3UUOS21FQU1FUaHdbJJFbyDa7QpSROP1PxijmvLC3XJnVB3QJO5vqeI/XAOu/8TNob4ccpzEEqCUSWBKCcb9MomxQEVzRUW52xVzX5raXVt2NGbvUwEAAJiOG8GS4j+RqOr2hQ+3uDVddwf1xHU4N/RYlK1m1tdB14nLFyaUxvd/3XX1yZpG3tp2tLs7NieAx39SCkAiH7v6upEedqIAcCI6CzTxrINVipWYvvTH3430lQEAAPI0BDRzRlVtTQUSZddA689RCI6qmicQ6XQFeryhxEL+u64+mTkvknDCgqqkHMCwpEkPDAWlyaVHoYgSiijhqGoRU0w2BgAAmBIIk1v0WV5iP2vtnOqygkyeMrj+p8hhkfgMYYNhl/+ZpAcywWnDzkHxoo9dfcPoXg0AAGCaC0BS/CdeAqRFI/WVxfFZ7SkNfZyk/V89vW7NqPp32sQR+QFxMpSBJCeAB4WSNEDgwycBAACmCJNaBWQET+wFjs5eP2/MP5ShT+SCU+YfOt576HhvY1PPm1uO8EB8Uv3PSBmDN8CEAWNUWer49HXnjeUYAAAApm0SeMgJMBS5fKH2bv/h5t72Hj+h1CTQwQLA3QKFsLG9/eJhYv9gjN0h/b1/bsjKcabJEidlg0UBiaJoMckrF9WWOx1FxcX89kuuuj0rRwIAADAdBGBw/+f4AIBYBwiKgmFlV2PH8Xa3PxBKKQAp+z/HL2eeABi1BiQJAEaourywsqRgbn15YfGATtGgAZPLLZ/9RtItRUUZ5ZnS8Nh3vjbGVwCAXEPKleY/GNltpnXL6pbOLX/57QNK3+CXNMSt/2SF3ilCJqsjqianK7q6en79s+9+8jNfn6TjyjsGm/vx4P99/eH45f/+7j0T8I4AMA0FgNv9480p2uiLorBx25HVS2rf3d6UaFhT9n922sSQigsdbKfY808/ncUjfOmPz6R0ApI2hSGEAl7P6etXm+Tk/HPidElgKpr79IAYTAo33XZHVVnRI9/7/uS8/XREmvj4DxeAGfWV8ctxKpw2hNDc+lJNJ+/vbE7UgJT9ny1m6eTlM6gefR5NApIk1FUV7zlwFPPp8wZdXT2qpr/bcHQyjmjacsHlt9bWsD0fmeCwmVLcStPNEcoEzMbFpag0AzEYbx774UM7DrBp3tXGSL577vkqQujhhx8Z9zfOAyYiB5BSAFJS4bR1uUO8P8QHe1sbDrTxKs/BaeF40WdBYcGSmcXfevC/s37Yw2YCLCbp7HXzO3t98btWLpvX1dXz/q5j3e4gQujF52Bv8Fi54PJb+YWhBCC1uR/I3J5G7pNR3jgWoZaaJSM7joEuXUolSARiRNniI1def87Ji9csnWn8rC38RrOd/dCXrL0sa2+Tr0xCCMgfjBbYY408k+Djf40CGzyzpnjf0S4trAzV/5mPaD/e4fnDb3+LJoOIonW5WD+4OA27D1WXF7l94Uk5nulo+pnFTgz9ZWLu5/QcjHcJjMN/W4z/2bdZHftHdDA6Ic3Vi/tfDfXXI6R3C0AJxg63/iziGoo4bBZu/RFCe7duAA3IdQEYXP0ZCgQK7Oah4j8cSmlnb0DpK/sZvPyPB9lHt/8rW5mA4gJrocPS1Nbf15oQajXL/lAUlv9jXvX3m/5ZbGo0NZtTrBvm9hwcVAeQ/bIAURBmdcaGwRFCj1cvylAMbvjU3b97/EdZP568wh9gUQFOaXlh4l17t7Lib5CBqVcFlH7+l6Lq7d1+TWd/WoPTv+6QXmwVKKWeMBk/ARgWAWOblS1GZtaUxjWgs9dXW1H08CM/nqyjmi6mP2bHDdMfIxqN+ry+kwXPwCdNdBWYIOB+MaD0eNUwYnDDp+5GCIEMjI47rzlzqLt6OjKaJQXkyk5g3v8ZIeQNRKKKllQqkzj+xeOPdPYG+PyvlC/FBwCMqP/PKBhqezDvDGE1y/FORPc+8Mi9D8SyUjNrSsbvkKYrF1x+6wWXfzLB9ONZ9ZWJ1t/n8y3xHx9k/ScZATMxmNV5wHKowXSoIfEujHRN7a9m5jIAjJ2ejl7+BR9mrnsASfGfo61uu1Xu9YbDEVU0WWorCmsqCp2FVpMsVpbY448khLZ0eiNRlReGlhYX+9gsMGp8T97/Na7HnyYQxN69iMWsKKWfuPOrcRkY7+OZfqw7+2NOZ8mggE8/Pp/vZOyZyIV+hz+WxVlenamWlzus7J/gcX71PVM1v1BUyH6xvT5WFADhoDESCIRS2v23X/71mZd8cqyvnpdMaAho7/HE5Vu0rdtnaZRKimy1lYULZpSee8pCfkckqnX0+HXCVtmaqs6fWbZodvmh473v72r2BYffIDZOfkCSDFBKZlQ7CaU39Vl/YGzg8TD9cVM+Gms+kO7ACHL789GRN0LWurp+GYhrAISDRkQgIQEATGEBePGt5LFZhNJQVA11edu6fQ17m2uqSpbPqyxz2v2haI87aMR/JIyIs5D9Nc6tZzmD9xqaENLjTsC4xn+GCgcZSoDNslhkt7yxGWaBZYdMTP8orHlKUz5Saz46zraF32hpH6wB4AqMhY5ub5WxGyAJcAJyTgAS4z9tbd1pHmmWBV3TjrW6Onr8FSWOkiIrH7orCKiytJgLAMZoTl1JOBhs7vD4jbHsk5X+5UpwxTXXb9p57MlfPzEpxzCdWLxgjsXSX95zFjV+VVjnHut4WHN93XqrzRIvJlXe+gfKKpvUWK062yki0+aW9nrQACBvPYBwRA1Htc27WxJj94PhhZ7hqNrU7m7t8vL9X6qqOYtYhoA/RhBwkcNsn13R1Ob2hvu3X00Kf3ru95N7ANODdWd/jAX9IkzRZf6DFrK5PNfXrU//ANNZH+YXqPErx1Hf+ddgaz46rDLt6WiLaJi7AokpASATrr7li3/8zaPwWU09Aej1hLz+yIGm7kBI0Y36H1kSNS3F9MekQk9u/fn+r2Ot7hOX1Q84YhHPm1EqW62//Okvx+/ggckiW9a/a+5K9o/LU1pSHI0ygYkTjUZNJjnp8ZTEfjFtNqt8+gWxW994e+xHspz6kYi2tKCU4SBgpEAUaAoIwF2fu/PVTY0RRQtHVadNNHZfonKn3eMLh4zyngznf/V6+1NAnZ29M+orjzd3mmRxZnn/rjFg2rA81JbJw9IvzOcuihfmM5ve63I77MP8tsStP9uoGIopkK5rq9auQght37p9wEESNr9oMLXF/ZVsA2G31yL9VU/AWezgCmQxT2YjXgDgjNdv4b6jXbpOiVHJw/4CrKZQWO3oSf2Xk3L+F3cL7rjypPgtZWVO3kUucaQMMKVZvGBO4tWxh1wKS8u7u3vLywekiAPB0LAakEgk0l9sJsvSqrWrKg9uS7h/KEPfjzesDL6RYt3l9tqNhjZP/AQqhkcfBRrKCQByQgB+/MN7u3oD4ajq8oa9/rCmRPzBKMaYpGqSPHijb6Jb8P6u44tmVxQ5LIKA39/VvHBWubPQKgj4y/f8EI0/t95x142XnvLedtZKrMfle/Sx/5mAN823BAAnlgDIEn4/69FUUMCW28NqQOLyP8n680SUz+e3pDLoI+J9xMrYmPWnrLPQGF8NSAnUAuWEAJQ77WWGO6yqejCiRBWtvdt/4HDr4EcOO/99x4GO4+3eBTPLHGZkzAvzzJ9RVmyfiA3Mt9x+J79w6qr5CKEN/946AW8KJDFUvGWo2Mt7CFn6anySZCBRAxRFHZwGSDL9gcAwYfr4VnadUkKoSoiiEZ3SqKaHVV0jJKKSiKYRQjVCm4WQLEuSKIQj0STJAbKTCoYPNXdCQDzobzKJJhMr3assLVixoErViNsX7vUEN24/lkn8x/iZUpcvtGVPS7FVcNrEXn94696WsoJxj5/effcXNJ3ccd258Vtuv/ZcQuh///dPxvut8zb+M9vdlMqm24eNtCTGW5aGm9/1FBYU2O19tj5RBgLBUGymNEIaX3kYRtxqtSZa/0TTr+m67eDOdl0PKZpGqaKRkKLplOqGuSeGCqR0bZPQJF3TdYH/YQBjhkeBCCGKqne5/G3dXrcv7A9Gvv/wt/7rngfhA560eQCJ5f8p4Y3Zo4ru8oZau3xb97JRD4PdAovVwns/JJb880LSbM1/H5EAcB5/5t+PQSAoe1xzy2dZk2e7raKiPBwKz+o6OOqXCqlaOGaaqaITd/VcQRB4cJGLATf9vKWoySSLosh/F9U+H1SJxoREJ8TnC3Z0ditNh7Jzngh1SkVx67/trT9n62Xzhwfv+5qi6pIoKKqGMd53zNgv0tdAhEswxqzv98yq4h//+LHJPt58FYDv3P+FeKp2WCjtV4K2Ll9bD6vut5rEqEpOWFi9fX+sJiRpz9d4bwC++252CrpOPjVIAH717L8RQo8+CsmALGsAm/dUWcG2BHcdpMzxi91rjHCJhVs0QlWdxViIYeX5MpxdILTDH/ZGUoTpg8VVoiCYzaYCh80kS7JJtlktArtFLihwRMKR+JSYgD/Y0+vpdXt1XoXsbs/eKTLrzzvH8asgAIP50Eev5R+R2SQJAjfqMfNu/C7EfiEoZVY+weMy4nB9bpUoCALG1WWFgoAFAT8KMjBZE8G4DHAyEQNDCTSXN8yVoNcbSpwHWWITiTEBxh1ifSAmRgAIobdfe87gex9/5t8YZ0cDPnd2v836yRvDjzqZrpx98XWyLMmyJIiSzx+MRKLlURfti7H32f8hsZukkKoPO4c5UsLK8DHCskkmhIVtKKW6rsfMDULm3oyKUDmS2L8oUbTBOxz5BBokYtwhOQklGBGRWSXBJMvv/PM5lAdcfu1NvMcT/8H0zWRjN1DKegP3h876bmYaIAomSWQ/J4RFwVjSD3rl+z99SZr3FUT2jCee36gT+t+PZX9W4HRi3IPpZpOUOP99KDHAGFvMck2FXF1eEJlf2dbl23O4y+UN8d+SVYuqW7v9Lm9IwGTC+v8MNiiubhdC6MrzV7215eAoTHx6vnTp7B//NU8nCb/xN9ZdY81ZbMIfpeyvt00orNAy6vwsYCwKAs1g5K/F1b+oF1Jac1FMZcoTYdYIGytNbtwtsihgLFlNhRYTuyDgYitbwWpEl9lqFB0onFmBENFJNBKWZQkj9PSvHpvSBp0Rb9odc9b6ojADozGE5bpj98ZH+InMtrNPT7DIkaiqxzaGUlGM5eQxxgvnVF9yxtISo9vuqLntqjMQQndcc+aSU64Yy+tMb8ZLAL5x/2NxJ8BskiorS1mT59auYcUAY2w1y3PrSmsrity+UFObp7Ord8G8uvlzqS8QPdLU/k6We7ck89WvfFHjRRoJKw9u+uNQRL9z/9e/cf8w2Q5gRGx7a8Oasy7DmNkKtpVXKs5EAwil/qiKEeZho+EW5oNh76XqRMQMh1lmisIuSLLI4gmFFpOm66KArTK/BUmCwFamCSGdVIj7CmexZANCoSDLQptkKddM/xXX3pQ06TjRpscu8nuMf5nXxOw4+9CYR278tWLMBrgaj8KZL9I5+491PfvKtvDA2B2ldPehdkHAF562uLRo+C0X6fnPB7HRPcDkeABRReOGXhBwaZmzt8edkWeAkcUsVZcXVpQWtLfzZnC4qMBSM3Ag3HjwyA8e/epXvmis75LtfsLRjUstRz47AUkawF2BLqkYY1RtVIIOYc3ZD8IkCrVFtjZvKKrpwxp9HlSodFjKHBabLJmMsLHEvpgZSxVvGDHc9KM+088qIybK9Ke26X3ubHxtzi/oxkInbtPZYzCSmHcTd5DwiAz6SKkocditpiQB4Ie782Bra4frtitOLx2DH/CfDw588jP3jPUopzXjKACDF/h2q6l30AOiitbZ2TuUGIgCliWBEMoDtROz/4sDVcWTwra3Npx4zuWsrp79CLDJJPktdRazaZneY5FFFkY3Vuj8QlBReeDFZpIoocc9MYOLETZJbIHOHywYXyFVixryQCntCkQ0QueWFjjMybsBcsr0X37tTfEYesxY95VHxx+TmBeN23QeQ6eUCgx2+oNffDxs+oiwmk1FDmuPO1Ba1N/nVSdEMDyLRbPK39l2oGF/y713XtLnZIxs4Q/WfzIF4MZbv/7Qff+PL6niNp1fSDT3ZpNUXl7Sbay1zSZp8L2sQVi3q7KydILbP2CM//rmzkvXrxh811nrFryx+QBEgcaJza+zNM+VN97F161z5842DMICghBBqKppR/yR1r79w8dc/mZPrHi/wmE9ocbJw/SJ+CLKoR5/TzBCjDqinmDEF1Hqiu0ziu0WWZoY0z8gjI6Hsek86tJ3ixFhN243DHoKizjpBn2kmGQx1uyd6hT3Nf3FWNX0JbNZSRhCaOWiuod+8fKd16yvKC2In/K/Nsamb/JMPo9EJVJcaJs3s2oCT2WqMr4hoHsf6E/BJ4pBknNgtcRWYVFFM5ukRJHgt0zwkpxHgWLFCkMwTjt6IAoU54Wnfx6/fOWNd/EL8+bN6Zh5Qvx2LgaBqHrUFeAG1CZLSyqLB1t/1ibIYlpVW+IOKwe7vR6+g6y4zotQa1NrXZF9htNu7tsjliHNNUsRQkcP72dXws0D4y348mtviv/+xH+PdEJi5j4Wjomt1I0DxlPdoI8UQcBlxQ4s8LQP0wBCaW1F8c0fX//SX9+L/+2vXFT3i+fepAidvLQuXrKFEKqrKo39GkBfoNEycS0JB4rBF7j9TPIMEru8DV7yT8IeSti3mfNiQAjZtmOfCxVWIrckCMuqnZZBbYWebVbNZl5liymyIKuFD5vBvDRl5sIWinpaDs0pLSi3W96JJvxRGDa9z47HlgT8l4Ld1riP1bokLuEJz4uy9blRMJR3Nh0NWqQTY5FuRHWSH0Mpau5wF5hQOKJYLWY28UnRb/44G+HwsUtPRQj98aWNfPveykV12/e3/Gd3C8LohWd+C73hcn0fQObEi4UG5wzisSBeR3TjrV+fsKPieWC+pEsZBUII8WLQ9LVAmZeByo6F/d308jsVnDmX33Cnzxdw+8KzawtSLhEUVe9wsx+B3Nf5h5nwuD1HqLjIYfyLZRpKypHGHm54e4ZNz8Uw+oSRGHXhOblMVkdf+vpDGb7+9Z+4RRSF6y8+sb4+FvxBCB06HGsTsOtQB0KoYX8L/9FcfMYSu3XAvpmhnAAYFp+eyW9K/o37HxtKDHj3f37LBCcA4lEgIOtcdf3Ng29Mv4tryFicHi5yiEV2B9++y416/08NI8lYP55z1rqZM2pTvoBkuAt//fuboi7klUFPtOm6TpjCZZBozdygj5T4uzc3dyGE6usr4taf9QScxwL6ZYWW1zYfohT9beOec05cwDtOAlNbADIUg8kaANBnWSaU69bPfObN/s5oOWCvY3+c6XfbJt870Bqz+43Psm/vT9Kd/S3VePXOQJOUzjbd/+lL09z79OtH0HBcetH6k+snosXsuDJsanSCbfqIwBivXFCzpzFm9Pc0tsxjW+hQe9eAvSDnnTgPIfTa5kOvbz548opZdZXF3DODCQHTQQDSiEHi1YlEFIU0tUBvbTn45S/9vx/+OJvbzTWCRi06V11/y5CGus8WsLv6MpApDWssSNJ3j1FZ2P9y8bqUvmczK8MjvEOESRKOQBjeXk8wmqpzJyCXDTr/2KeWQR81ESU2MXB3I2sgn3IvWFwGrGa53Nk/9WEwMCFgqgpAIpNi/R/5waNf+fIX0ps0imhTe2xrW7ZoKz4LFaOrrjfWcYPujR8N25Q56F6jyIQxVINiHsvuy1EOReL2nxwy1tMqNWoY9EzqDKa6Qc+Qp37zf4se+Nrg23u9wfQyEG8yAU7AtBWAyaKxqctmNZcU2XTCdiax3pM6+dVLmwaELgThiutuHhTsoGHW54Qg31MjekdVj0VI+Pqa/3KzdjMp7MTgChMw1pMZdeHkwyJ9nLjnvu8hhB4eQgaGagvh8TGFcBYN6QeAE5CG/BWAD132cfaPYVlFUWA74FOVekRV0tYTeOz3b/bdMChugtlMQd48he04ZS/DbrVZTMGBe9yLpHQ5q5Dx/a57f5Sd0wPGHHWJFwUNCxj0CZCBoVyBNKYfGJY8FYCLr7hO04xAuxFI0XTWHpgttFnnCVEShawsrrfuPX7wueczfLAt0hKy1I3iXYBX3vwg8UOgxkbfeEqiFNNe6kzzKe3Ztdf4juoLYi1FcclsZtbvuBU+2xyXgSTrD1GgqbcPYOK5+IrrCEFYGJT6MxZ7FSWOq85fNaumZOx9wcIR5aXv3pj547kA3Pb1H+WPsY5llY2dVGP8wO++J0Vc5dLr7mC/5AtmHDvWUlw8ZCdBu83CJ4WxHYhV/XXoXwQNmGxSRoTiGjB4+Q8bAkZEfgnAxVdcV+4s6Oxl3SVTCEAf5SUFV1+QHQ1oPN695f9iE68yIWSpyx0BGGyseScD/qmM5cNJaayzTtz6x2/5/v3pekN+7YH+PX0gA7kvA5TQ+bNSNPxJqQGwIyzfBeDxnzCn0heM7DjQ2uMN4r7mU7wXVVVZYXw6YKcrVF5ScNUFK2fXlI5RAzSd/PFBNutuAgQgORKCWOJ6CtnrrHPpdXdmbv3jgAxMFRnQdXKouefiM5cnPQwEIHPySAA4j//ke8FwdGdje5cnRAnFlPUHrqkoZtMBjT608Qr4M1bPKSspnlNXNsZ37HYHXn1sBNHk4vX/lYfGOutcet2dCKG4AGRo/eOADOS+DEQV7VibK0MBACcgJXknAJzHfvDgq5tYE8cFs2MuZOKvUUt7N7/Q3M6aVNvMyalyPlHWKBsa3lLrhPje+kHmx5aUCoa+QGO3/j6v7xePMv8vKxqwc+8RVuYbjvb0ery+oKpp8bqwHW9mmvMHxqoBlC2t2rq9lNIrL1iT9ABwAjIkTwXgW/d8mV/46AXr+AWLnfWHbGvrHSwAcQ3ocgeHagz35uYDGOM0jeHuPV8enQaAAIyUS667AyO8pM/6e/us/y2f/0b/gzJ2sSpLHCllwGqzq6rW1eNu2Hmgs9sdjfaX/IIMTADf/tZ/tXR6XF5WPn3VhzISAHACBpOnZaAPPvxDrgEvvPL+lReexK0/iwXVlG7btm+weQhFNX8okqYRTlZGCQLZSvwuWTAzfssvHv3erV/8Jr+8bjXr4J8JlNJgMGw2m2RjVsyxI6w/a2dHV1wGwiGjHtFZeNLapS1t3fsPHvMHwhrzBtAJ668CGRhvvvng9z/z6TsHtm7tB+pBMyRPPQAO1wCK0IIZ5QnNb2L4g5FeXzjpFozxqLtDZ+4EgAcwai697s5E69/tDvLZWaeetJLfoqqxVjOJsAyQsQNbVbVoVGlpbu3ucff0embWV9VUlxuDFTGlSDfs+2BvoGH3EVXV2jp6fH6mCroem0uMEW5484+jPxlgOK76+A2xC5k5AVALlESeegCcBx/+YVwGEq2/PxjhF0oLrYkaUGC3BELRoV6NECKkGkQ1CvimsMGaBGQS+uejE43gjxcLctz0I4R6urr8gaCmaYqihSPRUCgSCkfCkWgkoqiqxgsBMMZFhXaPNyDLkiRLx4636zqRJJH15BCFqvLYnrLOjq64BqxcNsdiMf/qqZf5VVFkBWaGotAT1l8lCsIHrz8HP7vx4Pk//C6uAZkAbSGSyGsPIM637vny/BnliaY/ibgM+EORj6zvH0k40vkwo3ACIAcwIuu/ZGFs+e/1eFU0wPofO9bU1t7d3NIZVRSMsGySBEGwWcxs57cxZZdSqqqaw2GNRBS73VpZ7jTJqX9eitIf8a+sqrBYYpvIWFDxL2/puq7pJBpVdJ3EvQFBwNtfB29gXLjq4zcM9gCGcgIOHGm7/fP3js+BTD1AAGL8748eSP9JcQ2YsCgQQihoqcMgAKO1/utOiqX3KaXHm44fPNzc2+upramorSlHsV75Q7TDRujjFx/P5E2feqlvFxJGM2fWJ93bsPvwoSMtHm+Ax5fit0OKeMI0YCgBQAiBBnBAAEasAYFQdJxqgbwaiyAnDYkED2BYbv3CPV1drrj1Z9W9CxfwdXpT0/F9B44JgjCjrrKqslTALEZ36XpjjHuWeOrPAzajJinBO//ZfeRYq6KoNCE3YDabNv/z91k8BoANEP7No4M/B9CA9IAAjEwDEEJNHZ6JmRIsORaCBzAst32BFXeWJkwFWbBwASHkwP7G/YeaKstL6moqbFYzX+tn1/SnUYLBDsFvn/2HorIcMpcBURQLC2xv/vnX43c8echgDUgjAAj8ABCAUchAUwebUZdGA7I7Jh48gPSmf7D193h8W7btrCx3VlaUiH0dn0Zp+hNLDPtGoQ28lyKFtZXlxYiUot+9NmAfX5ISPPH03/ggHy4DkiRWlDlf+cPPR3NswGgFADIBccADGJkGcOufRgDeeP+AIKSLAoEAjJ/1nzFr5rZtOyml8+bUS0YpjtExjF525gGkUzZsk/dI6nt8RNGjCtvUzS258Z2ZZ39QC0dJbOYam6/LJqy5fWpEIcbDYsPYjNG7yOVTNN0Yo0lZP1k+j9cf0srKz0w4sHo29JhtIKfRqKpq2vMb2IQJXdcFQXQ4bBv/Aq7AhEaBoB6Uk9dloGn49N33pfEDUs1qjJEwxxyY0IX/0SNH33lny/y59TabRVFUt8fn9viXzTxMEXrldRpViMev8UmZ8UkvUYUEI3r8h4kFJBo/PlWjsXERBmYTmxfUJwix5xbYJJPMHi1gbJJZQhkLuKRQNsuxoUIY70AI//kNC6WoYXej0fEaYckUDrNKs6ICK0LY5Q0SSvnuAWDsXH3LF1NqADAUIABD8um777vq2hvPWjM33vEnPq4aUZR+UvzQrwpk3/o7iwt37NijaVpZafHhoy1eXzAUjsyt7xYR2ncMSSIusEvMUgtI5LvCMDLLQnGhbAwhYLu4+QWrWSwukIzx9sb4ej7pHiGbRbRZmT/Bn8sbrJplQZKF5CoiiY2F67+q0yKH5z+73DaL2O6aL4iCKAii4Kytq+EbCyRRFI2Bn198AzYKjBcpdwXDhgAOCEA6nn/2aYRuNMuixSw7bCaEsNkkD9v4gVD6nfu/nj4TkCFBZejuE3lGStNfVVOze/f+D3Y2qqoqSZKzqKCqsvRzVwURtmBUH5+6breKsdGO8Z8bRkgaZL6ZB5dNB66rLfROg6utO0IREnGDTujiheezTR7BAH/AzJmxhMHPf/s7NhD0phHsaQKAsQMCMAwCxlFVD0dVt49P7UXzZlQM+xSUJXzMdACprT+hyOt2lZUUlTgLHXarSZYFQTCSvZYc+cgCYV3TqSwJlLLJEAihfQde5XdxJWhqaonLACH00V/+urPb1dPrfeLRb0/2sU9VIAo0IiAJPDxXXcvGOhISCwrPrCmVJZGlFs9OvSWY1wI99vt3BMlwFwRRU8IsgMyG1eo3zNqY4c9Gdixs9ZLn3m7K8PHzlp8km6wUCd3ugKOoRBDQ0W3/QNMy5lNa1trSWlczYBP7uJZ4jhKdBj1Ka1fEH9IOt4Sa2sNun8ITCVbHGoyxEg6w3cJEJ4QSYy41oaTAYStxFoEGjBpIBWdOnnoAH7r28wILvgqYRWJZEJYSVpbHQsTGXiEsiJgQSZQikXCHx9PR0RXw+09dUYsQOtraW1laUGi3DJUG4LUkt35s3U+e/hdFSBClWJqREl3v7yaWIWL5Iv50SqkoSY7SMkk28UCFKIpU1y2FRQLrVibYHUWFhSVtzU0oKNbPX1ZRXTXVBSCl9TdZY5db2jq5BuSi6eeI2F5qXlDCGkWsWeG89Z5uHpUqL+1aOH9m3E/s6e01iZiPl8AYz5xZ/5lbRjBKOheYtej0+GWC2brn+L43JutgwAmYzgJw+ae/jYwxXgJrziVSSiURm4yevRhjouvRSNhkNhk2nURZzxZKKSZG/R7blM8aOlIlHCRKlPCqP6O4w6jQI8ZDjII+SSaqSnRN11RE9RKHZBYsm3YcCUcUXdPOPnmpLxCpLC3glX+8ItzlDZpNssNqiqUPMbtRECQ2mNF4dSY1sdrEEcCUibWFw5QVNLJJxgImVFMRorqKdU1Tw0FKmK74TeaQs4woihp27W/Y5HUl70Wa6qb/n29uLy0pwhgvWzyHf54tbZ13XedGOY7xG3LrPd233XxF/LaWttjMCZ5X5rklhJDHF8x96z9jydl9pVO0Vm6jCGt6FyFGsAuxzqmKhiqXLwwb3g6l1Ier2SkfeHsSjxlSwVNSAG77xv9iUSSEqmo0FPRHw6FIyE/1SKwWgxKrzaHqOByIUkqIrhNKNFUVRIGwazrRdF3TPnLD541FfhQhDZsKECV2E3JYTY/c/w1mlAXWCsxY+FNZliUWSmZWVpJEJIpEkhDRKSVmi9nusLV39Hg8bCtAMKwcae19Y0vjotmVlaUFwXD0r2/tXrd05pK5VdioBXpl425KWNxXlMxUIKxDJaVEU0a6CYlGfDTYyW/REOrtSrfa7Ui43HNwylv/sgTrb3UUfeyS9QihxoON2xoOzKhxljjZGOepwmknLdu37wCL/i9m+/sSsVuZ9ff4YsWgd335mz//Yc7lAOoWsm0N5WKnsdmhTZSwLCKi61gUdIVIAlYJa3kkigJb9yCNtdsTMd885yTtkigULZofFNmUHozQ0T3/nuwTAnJVAE5Yf1nNjDkFhU5ZMqnRsE6IZsTfw0G/EmXdeDBVRZG1chQFQZZlo3w7ti2HUqIoislsWn3yhZ0dxxBC7e3BXTt2HD9wCGOsRiMYU0GSHQUFGIkLlq9fuGwRe0pY2bP9rb6iEWMssCQRReErd0o0XdMRooJJ02mvxVYQO1CKtu05tutQW32VEyNU7iyYN6M87tfrhFy4fs1bW4+y4eyxJjAUjcID6LP+eQK3/ommf/6CeYnrZbbAjIR8PnniBCBx5gibGsBvREglKF5fNByfvNI4BUJfe2vX61t6liy6IH6Xo8jZ2syywXFySgNmLT1X1/UKsU0UBFUjAkaCyD8SjKhOdCqIAmb+mLGJgiKiIYwFTScsxkqxKOKIQjCiIkIlYquqIkrw3BVnswiZJB/84F/jccwQBZrCArDjzQ07Eq5+4is/spmtstlUUFTMSqclmehRbu4J0SghsiSyEJARy7cV1B3Ys0NX6Qu/fYoQHWGise/s4SITDg0bXX/dksAstK4e3LFVECWzrbC6rq5u1ozKmsrd29/RNFXTSaGz1Gpz+Lwej9sb0cKE6F5/CAnms9bMIIRofYZAEHCvJyiKwpXnnWCW++07cyAQ6j38bvLpzc30c0gzgGykXP75B7w93bquBT3uksDRVqFUMlktjiJBEAglOvujZBEm9scqS4Ike7u7LA671Wqvq6n+4/8M3x8puyRZ/6R7Z82aMWsWW0hub9j7yNdjo9yyiUrCAZUQ5DW+U0pdPlVRjQ3DFHn8aphtHmZ7AQUBX3hauamIBf0yRcD1VVZZGjA3Il5fkGvMXnYOpbhUaDObaSRCMaWyiHVCzbKoqtTIZwlEZ6sanbBFGP+IeMSSbaVgV7GmIYnlN5iXgHQiiYKGqDnaLkk4Kk5oL3qIAk0NAUjiqR/cPexjvvrjP4RCwe7Obt+BY65eVzgQiISDuq6xCS1GtFgURR3pLIjOoumYaQPCIhZ1JaQTEg4Hfe6uI437rQVlcxfOW7h8zrdu61+gJbPsRkEQrDJb42CEixzWLpe/yGE92uqaU1fqsJp59H/5grq9hxNDMiNmGAG46gG2sZX5K2z5Vb2GpaNdX7ss5WOP7NqlKapF6Z5dLGK7GYd6G12WaDhitloFSQz6/IIoyiYLFgWrxWK2WIudBYIgFFjlqN97ySc+9/JTP0ETxROPfQch9IV7Yqqza9fe5cuXJD7g6JGm2XNmet3uOTOrf/kMuuO6WHOObNHTHXnhtfaoQkJR3rCHqmyZEfthkHgjCEP7a8rN69aVjej1Lcb+4UQiQR/KJZaceHE0EiomnVRvQ8ZmCUIREy3C8m0CpnwVzx7KAqdY19kflRr3jRASKdvjpmixfdfEEEtRoDph1QrMXWdtMKhJ7Vh+4rm7NkM4aNKYAgKQCY986eODbzz5opvNNrse1ZSIooT90bCC2d4fqvE/a6yrWEVIFERJ16NYR6xNS9i9ufPYvp2VV9/1gwVLF1nN0jduvzDpZZ9/9umrrr1RNX7fBYzcHn9VWaHHF37tPwccNvPsutLVi+pKi+zNRtegy6+54cXn2B6fUZDe/nssRZSlLpj155VLaaiZN7e3vd3XreyPSFZ/Uy8uks0WUZJYvkTXi0pKC8urJJM51NOqaZGwO6BrGiEkGgzamdclfOgTX/jnU4+hCeSxh++LX775M1+ePWdO/CpFyO5wuN3s4x2PDLA3oHX0RKPq8KtyXadvf+CaV293Vo3AEWGuBEYePxstV1cbayDq9sS2huUCezf/7aTVS3SVqhgLRugGEyRirLIlFMu+6RolbP3EFviigHWCCqySP6gRo06B3Uyx1Syqui4KSNM0iXk8zL1EmEZVXWQVeEwYBIKj2pAj9sYjCgSzgqenAKTkP39/cvCNZ3/kk5QIHm8gEg5ozK1VqcmEWBklUnWCKRYE4nMd3elpObR/T3XtzC8+9LTNYvnOl9mY7yQNKC8psJplQcCXnrWi1xdqbOo6cKxrV2NbZ49v+fyafUdjy/9v3/e1bz7wvVEcf/rQcrS0ltl9o5UB+6MzKPnehpROgIDFirp62WQO9rZFHFVmTTPy3LokyQLLciv+zmaT2YIIc5NkUbTK5oKCgqgaDUcVTVFlYTJ/T5782Q/veeiR+NUZ9QP672cdidmpTB/c41H+vbnnyg/XIlOm00B557gcx0PKnHKvrhNMjByH0fNUJ1hEtMAqhzHziIyiOh1j5tGoOhJljHQh5ioJKKoR9kkyP4ntg2OyoROLWdI01XAXWBUfQUgOd6845aKdm/4+iSf79su/ztvecNmZYTuFeOMvv37z5ScaNv5h/9aX6+YsmjV3hbO4QsKCrkR1XdH0iKoxz1/Xo4FA1+HGHf/8+9/ffvOdj9304BceGrCQf/7Zp1s7PW1d3qAREi4rsp9ywuyPf3j19RetXcasf2ePO6jTmCH59n1fG82xpjVDxO4k9iJiY1/UOkw69OXHH/Z2tfp7WqNKFAmSbLZiQaSEqNFIJOAP+b0hf9DrckuSSWNZPtFiswTDQZ/fF/T5opGIz+s558pb0eTx8L1fnbD3MhboI9jLvfuQf+/BzGI4Gu1uCe057M99ATjQ8DbSqUnEJuY047DGHC9j3c6y4LLIaqxZqbMgGA1NUTiqWy0Sa7ckYJ2ysCRLkFtFFvMxqvgM1wcrqmas/akoIkKJysbkCEo09RzWsXP1LV8cp1eeNkxnD2BY3t7w0/jlU86/RdMVnWp+j4sQVSAiFURNV7CmdnQEOzuPN7c2X3j9NxcsWyhL0g+/Eos4hSJKKKL89Nk3Tz5h7owqZ5HDUua0V5YWlBTaXn57dyjCmsdt39+yatGAHvEZksYItT2wsf/+2PLfn/7VCkvLdV0PBwPGQERBVRVJlLi7bmTLBYQEf8BvtxcIkujx+4zdC0gyW1ilB9EUdQQFrONHkbPU6+4dxzegrBc0b9uQIVGVvL6lZ3aN1Wps+BqKiFtpOOjbutfT3hMNReb17t6/eNkilMO4cFml3BOIslUMM/wsWoo1gqJR1WwW2b4UoxCIsPIKlvRVNWPQpiAKsUAQNsagMUU1tr/QeGkQYZEkCRnZK3Yp1Lr6tEs+ePfliTkvSAUnktcCkMimV3/DL5xy0S1ms723qy3s92LKIsEaC1+KXlfE7+loPX6ktLTq5q88Xj9n3s5/P8Gfoqr6xg8OCwI2yWJ9pXPxnMr6SuecurLdh9rjrz+KQFAaAaCSYWv6htouF2ITjtLw8q9+wC+cdvpZdiEc1nFrjzpr8Rqfu4ciKsky0VVKlGDAVTNjXndPl6ayVZ8kSmYL26VqK5jkontFj3mrRc7ScXwbzJo/GwXBI1ilt3VH3tzWe+E51UjEN332oGhyRBTtmZ+yUiVGRD/UFNi003OkJRhRyOEjVjXaGAkFdm3fJrNN3RgzJRZNJlmSBEkULWZj5/DJ5/MNhVs3/RNNBgcb3i5buYQt+QmrvGa7JI29l4TohmXHLIjPds6zrAZGNBJSrXYL66RNWFbAmH+ATBKORjWWOO77beYfKybE2HzDvC2EhHDIiyYPMhK9n2aAACSz6e8xJbjg2ntdncfdXc2UKiw2zoqGJFWJRoKu9pZD+xoqq2Ysqp49t73hb/zxhNBIVGs83n2ktbemrLCowGqSRWPj8Sg1IJ0FkuWYQPC/q/6R48Pz7jtvnXXqOhPWkSQEAz6VkEg4jKNRUZSMTXGiNxBg5XqCILK/d5a+o5TN10X5gZowCWBYWg6z9XFHd+/md7p/+N97ia5FlUiRs7J2Rv0nPtdIUESQBKLrVCNIQF09bjO2z55X7+1pD/f67CaRUrZhSkRIMsmSyObD6EQP67okMNEVBYEgtGT56ZrK/uNdqxVNs9ltdqt5+5bX0TjjQuUOsRsLlBBWLiyx1TymFOs6lSSBlUJTFtAxBIKV+miKkR0mSMbMxOs6pkZ+iu+vZw9ifyZIFAS2s4dtGTZ2irHf4RHvj8lKKpjksenngAAMyb+efQgh9OHrvuH3dna2HNYUBRFNR6KmiRhHFS3U03vs6IFdpRU1xaWVMxctOvr+S/yJuk6aOz0tnR4+d0TENB4F+vZ9X+t6+8EMfzbpOhNLcv/+I4wTBWCoPDDnyq8+2t1yNNr4PhKRmZCepsOO6jqWjxPYnzA2Mnu+3k67w65Go+FQQBBFrOHC4hKTNJm/Kl9+IOa+JMMKDLM8gUdgH0UK8W05JATCUZ8vIDCLxnKj4XDAZrWKRrsPSlVBZMt4u0Uur6444eSTG957x1pJbE6rFtXCgYjJbipf4GjZ5nGUljpsZpNJshIFY9FitkiyOaphnSVUjY3pzNJLPMnKth+ytlSsbBJhyt4a4WAkGgiHV605RyNaJMriluGI0n50Z3Y/B4TQ/oa31q1gTgBmW7pEakQFRRYt1I1MCe9QFVvTCwJWNT1Wk2YUCLHEL++qpbOEAaFsk7AxeoEKmBVoEyYnrJuKEOlcceKHdm6eOF8HTD8HBGAY/vHMd2KRk0s+1dvVFgn6MFFZcyFVQ6Lo9/cGg56O1mMH9+6onzd/+ep1B999gT8+0YRIbB09YtJ5ACZTLFHJo0AZZ9FcnS3thxqLsGwRIibMgj6FBQ6z1axEwmazRdc0TYliqtuttg6vm8VzJZlQqisKJvqF19/1yu8nc3ptWWkxpXT/waP8atDv37S598SlxaJdyqIMGGoYCwEdP0hb2rtEjK1mi81mtZvMBRXM4jPzxa0Ys9is5t2ESVRVNF1pdXmrauvdHR2igCusJOz1UTK/UBY/fOZp7NVPR089+Yc1p63XVeXA/t1zq+s0iiKh8PzVyZ1l0+8EXrv2Alk0W0w2h0Wz2U2ybFo6Z04wEHT7PGFNLSx07Nr2TlY+jaBYbqZdRiMTY7dXbLM8200pCFjTWcrXWINQbJT5m2WBLfxjz+ZBI1FRdVnGSKNibNXCnq6x3QPsBVlHF4pUdbxSwbLRKAxICXw0mfLuy48jhM647LNeV6ff3UF1ndWBs8WMoLEdZ4FjewLdTUfqZ82eMW9e084B7rkoClWljsf+96mPX3N5VnIA2GTu8wAMIRgmAZxAwF1qJlGpqAc7reFmWlgjyiarJIW87mgwwCJAgijLZk3TrfZCipBsMqtKWGe7Yd0Wi231eR/74LWYozOR/PC+r3zvp7/ilxctmM0WpweP2gsKNu4u2HFw57J5BSctc8qO7MjAl+7f6/X6JVFw2OwOq21WdbUsyZJkEozOg6zHFNEpaw/FNgdjQXLOiM2JCzfLUYWUlpZV18/q6Wovq/IHIgvYil5EHz7PsP4GZ555YlmZZWblujPOXJf4vseO92eMhmXr1tQdFFatXm8VdNkknHnu+aIJW22y1x30+cKEUItUYJal994d2Sp77/a31p6wVNdZBwjM2puyGw0Tz/YIiCIL+BjtUphTJMtiJKqbTMxXiA9N0nVjVzxrIGFoCBNNJEgsRsSEhP3L3Ac5ml8tT3IEmAcwGs646LaIqnp6WtVobBuLyPx/GRsdiixma0V9XVX9rNKKqqNbWIZAxHTXzq2ippbVVK8SMg3dSo6FbUPMA2h/ythk0CcBS1veT7w3TQjowtu/Eew44g2EzBYrb4wqiqKmRCWTKRoOsc4QhMhmi8Xq8HtdrGRbEHluQJZEi82OEN74UordFRPDVTd/es3aVYm3xL2BAnnn0rkFJy8vNjnkmJUaIedc9gZCmsNSZLVYBCxIoiSzAna9ar4+UgeCt8AkmBX5JJr+QwcbJUksKysZ6rmJGpDkARRWLVI13Wqzsp+XqrpbdqOJYuWSRRY2UpP9umk6ZTYcsxiQKFDRZJT8GDsbWE5YwqqqW0wy2yUQezYLBkkSDoYVlhKLf5ZG2yBdY/vJWEAGCbv37R2/U3jpd8lb2Z1sJnMy6y+7DeUZ4AGMho1/j9X/nHrBJ73enlDQZ5SoMH9YpJKiBNqP7e9qO1ZQXFZTNXPmrPnP/+YhWaCltVXrlhYrezJdp/KphanvsvRb/xHxyq++s+7i6yOhkKZGWFyXOemCJJmCPh9LcmAkWy0WScYSi0ELomnXW39BOcPzT/7v808OkIG4N+BXV/xnP9p9aOe8GbY1i4pKK60ZysDVN21x93oLCwrrKyokUYyEw1+/u/4P/2obxeQGDqG0s6eytKTwog/1m/77vvU9TSN2u8NutyuKcted16Z87qwZ1UP5Ab4O1gWWtUKccBr27j9t5SKFiBor3tTZvkMRiSwtQfg2YR4uMyL/yGSSNJ1IJpPGUsRcBjBiDRhF1pFFYvkA1vqIULNZDGtGdkHAO3eNo/VPidsfTqkB+QYIwJh471+/ZumBi273B91+r5tN/KKKwIKbEo2GfT0dJOz3dncsXL6+5eDGy86ftWhx1a/eIZUF2CJn1EPS6K+YAmzj2TR+ZWTH7HDYbNb6SCgQDgQQIZrfvbNhE5o6dHZ7jh091tvrHiwDAW1FwxG0s3H78rkFqxcVlVcPKQPX3r6tu8ttt1ntZltlafnvftsfgj980CcJrNAlfsvGN9tDkagsm4qKilRVPfmU4qGOranVyVa3Iq2pqeYTC44dOYIQuuXmq9Ms/DPUgEnk3YZYE/LVy5eIrCQUEyxgIlCCWE83jbAUAHcSBKwoimxkAYzAEEsR6JSaTHIgFLWaZdnYH0wpfX/7RBt9YDAgAFmgzBYss5lQeWW7D4V9PVGVTSHAGOu6EEQhXYu62/ecfup8v9v/kct/v24O6fSjumKh3CHIRiuHlBRJ9hBCpiGq4wRelM81YIQC8MZzsWD6qaedUSyrfhtafMKJ+3ZsRlOK0lLnsaPHXL2e1WtXJskAEVftOIZ2Htq+bK5j1aKiqhobEvHNd+x3Om0mKyu4pEh1FAjP/uq8lK9sNbNCnMRbzljP5pmkh5t+YyEssUx1Y1NtTXnQ62K/HpmZ/hzXAM4HQyzVT1m5mKXGsaASajaZFEU3yRLb6Mu2DCORJX9ZMFEjdPuufRN+1MCQgABkk+pChIorNY30uP2+QBhRhVLa0bSnoMjqdNDtu91Llyx2Kcp55U3uMHH5kNMqFJiRMQ4whRm3RVoiEhtCORjBGTP9Rmn4KI/2vXc3nn36KRZBtYmjDHdMOiWlxVwGEEar16xMlAEqrdrVhHY1oaX1jasWFT35s4WxtpYcrVbzGRtVjTLGqEpCYWPNT1EwrNusYjiaaeg/0fT336qF9+/dt+qExaM7r1kzhpecnGJTQ2qzvmbFEkEStuyYuIwFMCJAALLAhheeuezK6/jlOZ1PhlUq+Wjhws8Eg+Guw+/ZbKazT6wJuSLzawuqS60tHZ4P3JYLyg5GNOoK0aMuZnasMnaYcJEFs7kGCa8sDxHBEO3s+9hLXqJEssh6sc28ZNmqvbu3o6lJSSmLyTAlcHmSZAAhtKd5/p5mtLjuoM0i+gKaEZimUYV4A7xbMXsFTafBcOwqZePehrf+zW0lsdaeA02/wxL7sYSjuqpqo6tB7OlhrsM0YNtOiPPkNCAA2WRO99PMagu40IIOH94UCYXNVtP6U+qk/f9oMF1ebe5EmDhsYk1F1cv/7nWr5BNr3ZUFyB+lvUHa6Sc2k2AaaNUdkZb0P7YxasB7720889STHWbsMGd5O9WkUFJSPFgGuBLsa1lAKfW6+yNd3NyHFdZoui98bWwAMCprNU33ePwamwSXDMuB9n30iRG8uOnn6QOX29fd66mpKstP05/7uCEPDGWg2YJ7AFwA+H73f3UvEmRx9Ypqh020HOwvvj5g+5BdUpwHjwdUvSWielTSQ+WbV3al3PpLKHr4tViZeSKRIwNGzWCEqv+YaSVoEqeeenqlVY+opMsf3ba9AeU8d94dmxicnrgMxHn73W2JVy+84Iz0r9B4uHnT5l0pNWAw3PonZI4Z5aXFa1ctZuPqRmj6f/DgvZk8BRhLJShKVQyab5Wg4AFkhw0vPHPpmhLUN8pQEJDFKi9bWm030UTrjxBaGDKuVqED9KNre/cijA8HI6/urzgSFu5a3THxXbzfe++d005cM7PUGs7M0k0tb6Cnx7123aoMLX4SM+urjrd09nZmmo9Nsv6GE+Dv7vHUVg/vBID1ByYFEIDs8NGTKkl4gPO+vnAXat6V5imr8Z9pGXr0LXH93Hkn1Ugnzq095J91pD3gD2v79qZ7IusEMadKGegEjIWeqFQWiRY6zPMWLTu0f1rl68rKWLEQ+8SMEc2D4d1p4qY7yQezm2gXzawzqNHTJuk2nehNze3lZcVpnAAw/cAkAgKQBa44fUbU120VUsRqhuWLZ+mPvnXozcNoXVQ859SZM2qtHV3R+bWnHTzu2X9gT5onxtsYjn24SHFVfbvmJ4Fg2bxl5tIaC9K3vZu7Y1p/8aPvXPPJzzqdrJtj5iiGfxOXgeLijJ4uynJntycUHn2bGpYJ6HHXVpcPvgtMPzDp5N1EsPEgEnCbccQsjrK17BfPYrZpy449j/z87zsbg3YrKrCQ8mJL+mfpxpfWd2EsEF2NUItz/lIBY6vDrrHxHTnNc7/+qdvtdbtH3ESedWvT9AytP4vs26zlZbzkNgNSDfrSCWlq7lDU5EJbsP5ALgAewFi5dE2JpHlNIrGYRr8WN/wAZnb/8fq7bJz9mlX/2TZMUSY3+lmZLbjltQ2nXHxNNBJlHaGRvuPt1I3Gcornfs2muV3zyc+yVN4IvYHMwRhXVZZ097iz6ASA6c8d3HlfCATN4MbKR1bIVlGxyqTYjp0F2OMd/YgJrgEZ4h6YA5jz3CbWnnHkVUBxzr7uVqJpb/3xt2iqMVIZqBjJ1lxK6aGjrU3NHZmqbaq93WUlxetWL/Z5fflW5/O5s/vnCP3kDdOkHgsUAqUAPICxYhZUs0jtFlRoZ9u4ykqFnt7Rx4Iy14AB1og35x0bbzzzf2hqkugNZN0hGIsTsCbSp6ZtqGvg1M7PnZ0TNjGL9h2YikAOYKyYRWI100IbNsvMKPcNSBp3aBTRiPEVZt+B5379U64Eo0sPZCsTsCby2/jXsA/+0qWxrWr5QF6d7FQBBGCsWEy0wIZtZr4EZ9a/JNYbZvQJ4UygQURD7Dsxvg6cfdLo33XayUD6LHHXCHfbcifAZmWz2gFgOgECMFae2CQWWLEoxiZf8xboI+z/OBrK1lbRAKIBhIIIBYyvMSQAph/DysCIGFk5EABMEUAAsoAs8eCPYf2N/UACRjXleNydgFDCVxixxrvA0DIwFiUYygkQcfLXSMmrwEgOnqzbPylTdnIFEIAscM+L2Fj9o7j1FwX2NadmnBeMYYIiBIV1FNFQWGNNpYExpwc8Xm/KL01RCuyWMZp7IPd5c0Ns3l8+AGWgWePbH9Vj1h8jScSyyJo5myS0++iIF+YZ1gL1PH3A2HxkzGM1BGhBxyEIAQ0LrxeqrBhZnI5S1NLW1dPjSbxx4yt/GEthjOxYyC/8+K+x4cZTiyl3stASLgkoA80m2OjoKQrY8ACwJLJhL6csEs0SfnM360Sf0RzIjCm7caHnl1vY+8ZlABhJ2ehQGvA/33sQPkggHwAByBrf/LP43ct1ETPrLwlIYh4A/8KSgC5cKb3SwPoBDCsDI9oOVhj2CYgKiIqUfYfl/0hlIEf40qWzp6gTMAry6mRzHBCAbPL1F8UfX00M649NxvKfW39JQJTQsxcJoQh5n/WmzI71ZzmcL5zLG8PxMFN0bMcPAEBeAWnDLPOlPwo8AcCtv2y4ApgiVaMRhUYVurSCLCghc4tSlPoUlM/P9uEAU4YcrJDJk5N153EhEHgA2adz1lcRQku6v28SmfUXEdJ1Goky66/pSNcRIZRQNMNBdII0KuhI1JBZt5Q9+MKRcTgcAACA1IAHkH3uf+j7CKG95f/Fgj+YmfuIQqIqVTWq61TTqU6QTjChSKcCQYKOJF0uePDPreNwLFObz5yj8a/7zlXvO3c04xamFjm1Lh5v8upkcxbwAMaRLUVfYZO/uh+JKlRVufVnY34J5dYf69z6i/YH/9ozngcylfjMOcmt8+NwDXjg3/LEHhGQKUWSPc29oRz4ID92w+dSVoLm7VhgEIBxx37hE3aEjv7+Zmb9CduuyzUgtvzHZmopRSibzcumk9GfcjKQ3gjmoE2csJPN/XKg9flk+jmwEWxcuP/e/4pfPmPtgNTuzl/dnBj6v+8f+Vu5k7ndL8Optzjkpgzce/4IjipkqUu6Jads4rDk1clOP8ADGBfuf+j7iRqQyIrbn4xf3tRwGP3jeyifGNFif1juO1fNTQ0AgCkBJIHHnY1bG1F+E8/lZtf65ywPvTqCfLUt0jKls6NjPFlgcgEPYFKZ1r0b8sTcA8DUBTyASQWjh+7/GppG5Ntifyjyal2cVyc7zQAPABgreW7rAWDqAh7A+G4H42GeodIAp6yci6YssNLP4ro4b092aiU8ph/gAYw7eNqlAWDJn3VskZbBJZLTlbw62RwHPIDJBqZKTV/ACQByHBCAyWea5YGB0ZFX2dG8OtlcBgRgItIAaZjSaYCJwSlaJ/sQgIzIK49negACkAPbwaZOGmDC6judojX+hRDSBRv/QlMNKJGM49WCiV9q4MBk/mAAA0gC5wBTIQ0wYXY//QO4Bohk6nVR82rBTB421ftaZHiaQI4AHkBOkM9pgKTFfiZMLW+AL3gn+ygAIAUgAJNPHqYBRmH0B6MLNugEBwBjAQRg4vLA0BVu7Eaf89CrKv8a888HAPIayAHkBGxG8DQlWzU8YO4BIOuAAOQEWJgKieCMAaMPAFMCEICcYHqkAcDuA8DUAgRg4tIA9937Xxu3NiZNiEQInfvR29G0gFdnjq4+ByI8ADDxgABMHA9ksDF4GhAv0h9WCcDoA8DkAgIATLRDAHYfAHIEEABgImQACvYBIAeBfQAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAMAkUSXYKH/xkAwNhAGASKJLsKW+PjdOcdueVEjyeRwJkAngAADC+/OQNU5FkL5LshcZ3/jXUg22Rlin98xjRvM+pfrLTABAAAJggYMEL5BogAACQW+viqU5enexUBwQAAHKLvAqM5NXJ5iCQBAaAieChV9V7z5fT3DudfgzT7HSmMSAAADBBgFkEcg0QAGCsOEUrv6Ab30Uy1UsZASBfAAEAxmT0B6MLNpABAJgSgAAA2bH7SYAMAEDuA1VAQEb87HXpZ6+PeLmgCzauBAAA5CB48ezKyT4GYIqRppolCUh7AkAuAwIAjJL8KWoEgOkKCACQHRkAow8AUw4QAAAAgDwFksAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAB5CggAAABAngICAAAAkKeAAAAAAOQpIAAAAAAoP/n/hZjqsu/JirMAAAAASUVORK5CYII="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The selected axe remains retained at its own highlighted rack position; release its hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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zp8+2JjY4cro9ZXlKc5gBAsYDAQBKlMUSVE8BqM1FVMhPR6DRrUDmx6BS2Phy34ctLR39pi1SVn8sAAsQAKDUJE31nJna+Z8TOU5bEqxyVr7PTTtk8ht/eGVH0jt9g+H3ms4GQtGkLkDTu38XjCECgG0QAKBE6fN6ZvhcbfnXO4LKhGOiyNdOnf03b/4yH+eSsjaASXTSKwH+snLtY3auve/D0x3J2zASiLKIlI9zBCfCegDgVCNDBNRcgCxFTxz+cMnFl+X/wCNrwjz3xkEaDZhupCi06aTJDD8yJQMRJvCcy2x0M0BWUAOAkvLY927M8htqVyDGmNvrnTXzgoHedpJPI1NDp68ESEr8k8Gh4QVqRospbDDCsuop2t3dk3hkf9ZQslADgMJ1y7xDWW3/7KnGzpO7733pX396w2tjbswYEQSBCqIoii63m+eFto7m/fc8SPLPuDow76lwKaMywFaEJeaOkWrbzwwcBjUAKB3/74sLI4Fuy5srw51qOEYZo8r8Bcvzd26mkwKl3sRLQ32J5+GgeeuQ/sWhkamDAHKEAABFI6kHJGOj3lFX+OprUyKDt10x3dr+qFqQcuoiAUxbDJLltxeo0agjSYLPynemdo3KJMsYLQzjhgAARSOpC3181V7Dy1i4j9EYoVYn0mGUMqauCaM/Wo/vyfaUvtBQn+1XUueEIITMWbYq2/0AjB8CAJQOJRYiisRZDQCMUqrGAEVRBwQrcsaZI8y1PvgZkpuUVqDWvnBrX5j40LAPEwcBAArILfMOGR9jbl9546PGlxyTOCZTqvy5+aYxv8uYoE3Aw2sPdZl4Ps8jgVNHAyQsqo//JrrQuxMmEAIAFFm7fyZULf1jMSUSVX6///ox9kwEpvXH0aYCVR/ZzrKwact9JHejgs0r2w7f/d/Wbap8aem86frCZBVlXmKTqz9xt127ghKDAABF1u6fCZWprETVAEAjsbEm99f3lVgQRp0ZdIJmWTCOBkjqC3Tp8oa5M2s5nuvt7TVu0xh6SsAvK9gN/6agdDAaL/2jMRqV2ENvXp1xa2026Pj/mJoFYBN2+582mFWVeS+9qGHVktlJ73tdpNyd5yYqcB4EACgdauOP+lBv/2MSkzMmg9X5n9UVY/T7fsaYOi1cvkv/DGmA4dMiM+oq118yP/WTcjfndSEEgJ0QAKB0JG7/YzKTFCYr7PsvbUi3scAL6gAA7bm2MAAny+rQsAm4909MDZ14ybQ0wMavqeGB5znRrLmH50mlhxPTL3IJkC0EACgdP9u7MKI1/qi3/wpTKFEU8q3nrsr4pZF2nyjnnZjSPyHbstwtkgpPhnWOAbKDAAAlZfOpZVrpT9QHZTJVw8DfP7M+zSiwROmvJgF8FdUb3x7VrzSvpb/KUJRbTECUuTlfNg1BX/rG97I/LXAKTAYHpUZv/FG0AV5qJUB7/Pffr5coe+yubYnNZMUwrb42qwRjbKA37VRCm7bcl+3kdKY6XvvHadf+wGxeuBFJ68Cslp5KPOc4UuHhJEWdE9TK4aRYbJwnDCUMNQAoNa/1XqzQRA2AqJGAqs8VSu759bp7nlinb6atBM/r68Lr3UGlWDQSCqbucNOW++y/90/pDGqdSyCVXn20AMC4IABACdoRXKGX+8N/qmtpqZOnaYX9PU+su1sPA1oZqg0EU1cHc3m9jCp3HHkhsZ9vvfsPVop+fcf6IehwnUM/KBv9SJVbMe4V1bagMTdD+w9khiYgKE37pFVLyb54WczUMJBoa9G6fRJFjnGcoK4KOVyQhgMDlNKO1rP3nr42EvL7q+v391eMeSC9WI+X/sPP9fd5ngjajHVMG3NsPpJruBHo21/cZP2n0xuCxtwM7T+QGWoAULKOkFWJ0n8k2zucb+VplI8XyfGqAScIbp9fcLlDQx6X1187Z1Hm/TOmRheZEkl7xBQWkVlYij9CEgtGWTCmPo/K6suwpG6spwEMu8mxKcfiwOBQKJTb/sEJUAOAUnbadYk6k35wT2qqNSopgqCuq8sRpt2ks8hgv9vj9Xs9guiuqJ+146DJkrw6Nny/H88uaBFEYWr5ro4tU5ca0EeXqV02OU5rr9fecCvE5+Jcw2X3DeuWvrTtiHki2CwDnC2U/pAZagBQ+jrLVifd/hNClkTe5NSxYCMzAjGqeD3e6ee2+ipr9p9LOyhMb+6XtVv+qMyiMonKJCyTmExkRe14KquTTKuRQN9MVpg6MI2ymKJWCAYjLCiNLvC5TF2AcvaZr95vy36ghCEAgCN0lq/uLF/dVR6PBDr1Zl9wUaroy4v5Xdwl7iaX1380fdO/FijUG39JLf1JVO2OqZX7WtGvZn2HW5SMjU56zND/jMpsKEoOvfDtfP/IAGNCExA4S1eFGgPqA+riX4xR0eWWYmG9AlBdVd3Kl5FIpq+rnXy0+3pJUR+KVuhbH8alb6mHCgPu21/8GLFbLBYTcljjBpwE/z7AiboqVndVrKZKVBAEvQGGEVYuGoaGmdEb/WOy2pgjaX1ME7f82UoEgJxHA4wpGokGNOFIxpgGDoYaADgXlaI87x9eC2DsYlxt+aFMG4UbTwJP0AIC43by5MnJPgUoRKgBgHNN9cd4ntdysJYCQLzlJz6+LNsFxJLt3vqtrCaBSPXrh3+c4dOhYHjU69yqKlDSUAMAR2OMCYJbTwKPubGs3f4nWn7sklUr0LOnGi1u+cazv/3Y7Z/nE1NG6L1NEQPAADUAcDQ5FhRF7TbIWhOQUlQtP63nO40/lmH2UwAVagCQhXsaj+T1eg2ObrSYAFIs4qsop1FLw2UlRS1DR3fgscHFSxpIfnR299XUVOVp51ACUAMAR5tby/M8z3G8lcYRvReQjUzTAJld2LjG+sbRaOzM2fOKvjamNttFtoeD0oYaADhdf3dbRXWtleaRHV3LbT965uKcv+Sfmo63RaXh/qlqoMqCKAiDgaDAC2rpr+YA9P8AxKEGAI7HUZfbUwjp0aQuQGsvmjfOHXYf38UY6+rpp/oURZZXvQeHQAAApxMFURA9BZvZHbn919axzPbroiDEYpIkK/bnLqD4IQCA082rE7RxAJNz9BwywFmlAXSUUllWU9jZfhFKGwIAAOk6f6Ywl1hsOp52SmqLuo/vUv/DiKwoioJaAIyCAABACKeomdICs+vgWTt3p8UAdaJqgGEIAABEEEReKKwecZSoAemq1fNd+ji1caQBRr7KmNoQBDAMAQCAcIQXRNfkXgjTdWDSNUzlkAZQqeMYUAOAEQgAAGReHV+YCdKX3jk82adQUtbf/Ln1N39uss+igBRWtRdgsrScPqR1B5pQv3jQuDr8hCjEMDdx3nnhCT0MJJ47HAIAwMgy7gXI5RIkfS6HBEazGhIcbTvAT1lg/5kVLYSBBAQAAE2Blv9kccPUphNtyTEAbAoDSy+/kRByZOefnHlFkQMAUC2oFwutC5Du/UPNqRtwHDd3+dqJPSkoQQX0jx4K3yvvj9yHXrEcdw/57QIEE+DIzj8tvfzGpZff6MxKAH6HARygEIc5F5alWluQ0yAAgD2N54lelKYv9Xf0FQn1j1JfOo31LkA+r8ujzlc6glGM57LNEUfe++sQAMCem8vETMOmL/V3OO2l/lHqS0ind8DSgmUZiKKASsCYMWCp8yoByAFAFtDuPwFcLldUsnm8biw8ZO8OoTSgBgBQrBlgdASy0RFHVgIQACB3SQ331tvxk9r9M7/MduclwOPCLyZMBPw7g9wlNdxbb8dPavfP/DLbnReFbCeBCIYjeTsXcG4lADkAgOLgcXuisWjiJaMKV3hrGBS7cy2txElQAwCAEiHULZ7sUygyCAAABaQ7FP+VRBogB0r3UVv+FsrnrCTOgAAAUBxdgEzTAOgIZK+hcx8QJ0EOAIrbs6caSbFZvGbDZJ8CgAo1AICJdt+nr7SyGVqBIN8QAAAKS3tf2h6fmBEI7IUAAFA0MBoA7IUAAEXs1w//mJRQAuDjt946secCTockMBSZYsz6WuwCJKes++hx8WNODDd3+drTTe9n3mbVJWv27d2d62mWLN/sFeGW/aYflc9Z6YQeQagBABRKBrinb1B/crJpzwSeEYzihHI/ATUAgELpANp8rkN/cqJLmZ9mG1lhb7/7AWFMliReFHzeMka42WX5OFkofQgAAIWi5Xyn/mRBfaZJfq65cpXxJccLYzYBgalwmvYf50AAAIe2vxeaS664attOk/LIShpgTLKCJSSzEAzE2+JKHnIAAAWRAa6uKg8FA4mXtqcBeEL1h727LWpls5x+Q4MAAFAQCQC/32txP7KSvDgOZgSC3CAAABREFyBOW7W9rto35k444qTV0fIp2Hoo9U2uep663EL/KeIACAAABYQzW/nMOClQbqV/e2fP+M4LShMCAECBzgCamgYwLf0ZHTvB23VsZ7ZHBydAAACYfM1HD8Sfpb+/N5b+ioJcLtgA3UABJkgkFMh2HRgbG/tpoM2+nUGJQA0AYIJ4XGnvt5Zv+ERMkjJ8121IA5hCRyDIAWoAAHm3eM2GK1fOW7N0RroNunsGzrXEhwEbnWraM2/56jyfHSR3AXIO1AAAJsJAf1+GT+fMnnboQ63foUknILBKqFtsy8VizugDigAAMMmNP3oGeMnCBr29/5ZbbqIk00RApnlgKx2BnEDpPjrZp1BkUAMAyLt7PzNGH9CKcv+SRQ08k5/e+nK6bVwuS4EBxoMpUaZEnXMNEQAA8mvxmg3RWMz4TmoXoF/98jfb3nyDEDI4GJh34czl6zYlPjqFtQEmTMUs4jAIAAD5dd1lC1/bceDay5el22Dp5dcnnnvcrlNnzsek5KXBrEBHIHsEWoljIAAA5NeyedMzb3Bk56sjL7SpIPr7R0YMZIDhYPbf/gccVPojAAAUllhMcgkkGIqYfoo0ANgLNQCAPPr5g9/JvIF/yjTjS4XSweYDLPshwOgINC4VTrz9RwAAmGRWSnrkgSFPUAMAyK/XdgxP9GbaBcgQAYZamvQn1keDIQ1ggwqH3v6r/9Juv2H9ZJ8DQOlY+7E7Ut/8/MZ56QKAr3ra2ktXM8be2r5PUWgk2N8wd27L2bP9QXVsFy/w8xpmXrRs/p4db+nbuwQ+FBk1a9Btd37G+PKh//UNu38mZ3T9DDgxAGAuIAB7VDSsIoQcPHKW57nP33nT6A+H0n+PyQoVBP6j69QwcL6j++ChkwNhxvFq7ZwxcvLM+ZNnzs+pjm8tp0wE/fPHthLGEhUJ/6xGf1lF97F38feahYATS38EAACbuUiEUHLswyPGNxsvn5Nu+6Gh4DtvvTPqd5JSvyhJo4cCdA7wU6tcpjkDQckQXSAtoXKm2oY26NCiX4caAID9qEJ5gdcb6M0WeYyLcT6iUKb24dG35CilHMe5RJFSpigjM/xQbQOwteg/j+uJJDCA/ajWJCMIvCDwvNaYY5oAUD/jOYHnBH07nuMFdcIfXuDdbtH4RaNLL2owvqwtV2sGRmj/SUeonClUzlQGz6P016EGAGA/qt7Wx92Svv1Hqxxo/1fXe1T/L/CE8BxTn/CEkWgsZtyVrrPH0jhhSIVyPwkCAIA9Amf3lc+KT/hz8OBhr8c9ZgDo6TJZBCY9j/6f3sFQ8idKptXEANJBExCA/WIx+0vk9u74jX8kmmnn0+qqbD80lCoEAADbDLUeTjwPhc3n80no7AvmdpTUjkC1Vf7cdgUOhyYgADtxrrL4r5bHdc21H8mwZf2MOZGouk6AKApUoYwxXuApZTzHMbUfkcK0gb4cx8kqxTjbz/pL5r2zd9SyhUzw4i8SsoUAAJAXZWW+pHeSugDNmF4bCKit+YIgMBIv9zlOTQyrRb+s8Dwna4GBUtrbNxgKjjT9d/ai7z/YAAEAIC+mVFUQQq6Yl/bGXBTF6qoKytR7fC4x/Y9e/Gt/MsI4wqkbEM7v8x47fqatT5oxRe302d2fY/MRgBFyAAC2qbhwbeJ5TU3lmNtzwyMAOJ6LP7iRP/X31Q0EPqk+EYqMWmMyCZYGA4sQAADyorIingzIB0rZTVc1Gt+pK8fvMmQN/2gA8sLnjXfbNzV7wdLx7Jwypk0hATAuCAAAeSGog3rzKN3C8VgaDKxDAACwH6+V/sYMcPI6MLlq64uPAusbDJtu0DFgHhgAUiEAANjP543PA5E/Hb2Bj29YbnwHaQDIFgIAgP0qyu3PAK9aOSptMBAwrwHo0BEIrEAAALBfzZTK/GWAM+cAAKxDAACw35RqdRSYotB8LNqupwGYGgNGJocAyAECAID9o8DKy3yXNcTTAHkKA4SQwWDYPA3AsHwYWIIAAGA/j2fUKl0/euzFfBTKnT3mMwJVlWNiOLAEAQDAbpw6p5vJ+3bHgN5B8xmBptWqDVAAY0IAALCZS8z7HIt6GiAaM88D7z4xiI5AYAUCAIDN/P70LTCMXnXddXYdSJsr2mRCiFAQk0WDJQgAADarrixPZIDjCQCDU2da7ToQZYwyhuFgkDMEAACb1dZM3Kq84Yj5+sArF82YsHOA4oUFYQBsVlWZx4mgk/QMBMv9maYdhdLw9b//QtI7T7/yftI7zR/uz3a3CAAANg8C8PnS5gCu2ni9XU1A+upg3X3Bhhk16baZu3zt6abkYsI55i5fk/SOIptUmJqzLzdLBpqAAGy2boEvXQLAdkPhKCEEaQDIDQIAgP0TQU+YWExWFw8GyAmagADswOJd8r2uTKNwOzq6zpw+m/NBOJI0+Y9LYSwak73uUQOPwejvbrki6YL8cus248u5yy45fXivMy8aAgCArb9RojDmNowR05HC2Wrvi0yf4h0KRU0DwMpFMz441mbDYUoWI8XgX757r2kAE/hR/9IUmsvMgAgAAHb61t3r0n20+OJVtscAQkh/IFxXXf7xDcv/+FaTPXt0AG0EHdOn7SCEXLBkRW77WXVxo8Ut1yy/cMxtHv39n8YTwNL9FBmy3AgAAPmSLgPMcWoAsEswHEt9c/eh5jWNF6AjUL7tO3DI+HLF8mUKpYwQt4WKYPYlvpHpGPCsIQAA2CBwdl9Fw8gN/kSSZUz+XCj2Nx02vryoMb7yD9Pm7eA57rGnci7x8wIBAGAy2NT+k2FKOLCuI1o1zTNg+xU7eOhI4nmT4XnhQDdQABv8/MHvZN7AmABQ2dQE1N4XCcckfWdJowEgKwNkaglcsV5a556axYKjCAAAtvnB1/5mzARA47KF9tYAZFmRZZMeILsPNWNGIOsoozOXXXnFDbeTotURrWKUSpK8YI3VGWcRAACKGyMkHDWfEg6s83q9ZWX+Yr9ijDFFVl16/SetbI8cAIA9Nq6Zk+HTHdt3JZ4rkjp/Qw54wXTAl3cwGKksSzsAzeEzAlkhxPqC3X07X2/mXH5CvLXeiPXvfum2DTkf119Z++Nf/oHYoSNaxfO82+32eD2CILz8wov6D5J5piMEAIC86wmY9NS0S3vXwKypVbOnVufvECUnbQaGSaHHH/lZ5i+feHerXecRGuwZT/xI+MIn1+tPHnn6Tamz/WTnkVqPpVQTAgCAPRngqy9bZnF7weXJoRKQ5vZfFQjG94bhYNZwxqJx45WNxhlVbSzfCx9yAAD2y/ckoEnCEcl0XNDuQ80TeRrFw1IfLGrjaL1ChRoAwCTIrRKQjqwoskJdZqNPC3NGoLvuulnPWB5v7m7tGKA07Vi2Mr8nHJF4jvN5XVMq/dNqyyvKPJy1aTS2f3DeSg0gHd6uyToKGAIAgA1e23HAehNQPkRjsmkAKGQcx82oq+jqHUrqxFRX5W9cME0QRv84jJ3rGNh7pNXijTlHiNtd9L168g0BAMAGGUr/5mNp52jz1jUknke6c58m+mQHueyihgxpgILsCMQI4cp97tqqsrbugHFVg8FgdCgUq6oYWVdHxakT7PA8RxVLAaD0m2/sgAAAYPMYYOsJgPEU+klikvlswLsPNYuuglst4MknX7jrro8Twjiemze7prWzn+N5RqkoigqlMVk5drZ7+YJpHrf4xnsn2k58mPjiBUuWJ5pvGKXGj+668+Ovv3+SGD5KXRISkiAJDFAKBoNZdF0vDJw+HtrtElJv3EPh2GAwyvPJrfAypalvxr+Je/7sIQAAlIL+QJgUH7UVyDgrhsDHSyTKWO9ASJtZOVm6VAeHwix7uGYA4/XajgM5JADs9d62txPPk2aFK+AZgbjEn/qzmiq//oQR0j8YNp3iIn2uu/Q77WSr+Wjaf5k6BAAAOzPAEzwCwMj0frlYRgP4vWqiYubUykSLUCSm9A6ETfr2jGoygnFBAADI4yzQEykqZVoYYO7ytaSATautIIRUlXlm1Ffq7zCitgIlbcZxXJnPjVt9uyAAAJSIoZBtI8sm3rTacq0dn585HAD0/qBJm3GcWgOwOBAMxoQAAJAvE5YA0A0Y8sBFtziM1xPvq+r1jPRNT13ngDF1SyeM0bXFmIvdIwAAjAtVCmVFxldf+TMpWoab+pHC3TSn4fOIadIA6AeaNQwEA7DNJGaAM5MlqQCHg+VGFHhRFEhKByFZTjuhUKGFil9tfjXx/PN3XD+JZ4IAAFAKGWCHYIy5XIJpDWAobH3RBUuTweVWoBNCJEGfjD89wwaPPPNW5m1tWTAgHQQAgNLBCBs1sKoIZgQywXNchhnfBJ4v97t7+kNJW/T0By0fgeWvQJ8Uj215hzKtApSSHblgyYoMi4IhAADkJQEwwRlg3b8/8ruvfumuYl8cxuMSwjE57dBhjpR53eqscKObfFIHDVisAfxl+6F46VkwBXrOumJVhJB694DF7ZEEBijxBIBOlopm4fjqCl/GXj6c3+dyicllVyhSND/gxIQBK1ADAIDJWRAm4dqPLEr8qVCqGJY3U0cGLFpWq60QIGorBDBK27sD0Zisf5TYiWKoEbhc4sduvObo2UHiVF2xKp5Gx1zdHjUAACggAs/VTykTRk/5ORSKhsLxG3yO58r9nsxDAShl2eSEifVb5kKWw0+BAABgfxegSUkA6H7xyJPFMxwsfqP/2rvHjC+rK7y+4XFhuphEewZC2ozPahbA4xbElCagUftlTEqzQIKpmjKiuIo/BoS7Rj0sQBMQQNEPAbOuQDoCPfnkC8nvGJZ2MdV6jGzP5hCtx4jFBWE2XtHYMLMm8dJfMfK8ZyD48vbD3e0tpBjUV7m7BkbqPTQ6SLzuzF9BAAAo/Qxw6Q0Hy59QoDfx3MeTv10/n5D5xqigd7f9yePPkQJWXzVG0a9DAAAAZ+OyiwrG8VnGwPDjx58lRVLuJyAAANhsEhMAqYp3NEBRCBkCQ2LIbqFFhQwQAACy9vMHv1PICYBfPPJkYjgYFEhU8JaNSjL/9InkLMikQAAAcEQCoOiGg+XTJEwGFwkOGCPBvZ+7uRCCAQIAgLMUSEegSWXzZHC5RYJCCAYIAAAlmwDQIQ1QsCsHRGwNBhkmfUsHAQAga4WcANAhDVCwNYBsg4HxfdshAACUVAYYSkAkn4W+0f8HT8cwSufphU8AAAAASUVORK5CYII="}], 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"}]], 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"}], 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"}], 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/dLSta2B3GLf7XAn4lxoNb3zvqNc/0Iv1xOULTly+IPn1eXpg597DO/ceTnlrVdNbOj09nqAgYFkSNZ386clf/O25R//23P/xB5x80S33/fzpCTltAADymMvHsYqfFpmAqQwBpcR/qoyR8QmOt3RxMXD7Ihs374/ENO4BcLgfgA27zm+fW1d66sqGWCSeBli8ID7TeceeQ8MdwAnL5lOKWrs8/958UNPIsvnVC2dVFDussiRgjPtDQ6aHn99TVFxUXV3xs3s+PQEfAwAA04wN2Rn3/I8C5YsHMJSEK1BabK0qc/ijg0L/HJqUpjnW5tm6p80f5INlWGEo/0rxBqhRh6qorB/R9t2Htu44cKC52+OP3Hbl6WesmvfnV3f+/e29h1t7FVWTRMFwCPTbL1n48fPrGxv3TPxJAwAwDfj9SwMzEKe1EzBlHkDK8p9fSHECOJUu2yPP/scbUiNGP4kSq5AcBUpcRgiVOaTVi2uKHBaWGzDSAKtWLGAmHyFfKPrcK40nziv1+CM9nqDxUlw8MEL041edm/Kmf3515+za0pryIkkURFEoclgEwye48oY7J+DDAABgmrEhC+Oe/x5AHiWBM3DZuSvfbjy253BXcg7AahL7jXgcQmggrJiNdhE8Fdy4m8V/Vq1YIImCJAqvNbbyR5baEuMbaFp/4rL1KxBFj76wSRIFm8V0wWmLy4rtsiTyydGSKF581a2TcOIAAOQnv8+4KSz/TX8eCUCi+nM4RBGvXFjd2uXz9Ud4rCZR1zSvMmgMT1jlSQHmSSS8CoTQtp1NLMdLBrobucM0IQMXnXNSe49P0/RgWKGGCug68QYjlNJFsyt8geiRtt6m4z2lK2zxVzdGV/7lmUfAIQCAguXZpx9PKwDTxfRPZQhoaPk/QiixAyD5kZUu22N/3nLxOcs1nby2+fChlrhZt5rEiDKQE0YIuWyiJ6zXlBddcNqCvl6P2SRFY6y+MxpT23v8PsOgJ1v/BE6bmd8VU/XE3g/daFvEwkPG1XKn9L4zTqitGOhUmnmcNAAAhRYIunxamf488gCSSz+HcvE5y42oi7B0XmVrly9qqMVwdPcFWjp9Lpu0ZCGbRbNx8+6WTm8kFh/swOELfy4D5XahNzRs39OEUBBCt+w5/v7Tl1qGeCpt7SwwBQBAwXL5NDT9+SIAaRO/aWHBfVmKKhqP/wwdw8ydgNYun1xVun1XE8botDVLw+8ebG/vHNp4iMtAb4gk60Fayuwsn+x1uw8c6zphUV3yu4L1B4BC5vJpa/qnrAw0bf1PyuXk+A+/TCntdgdD0fhaXiGDrL/LNrD11+vx7TjYtv1A694jXa9sbDQhVcWm4ex7IhtcasNJmeE0EEoPNB3f0ngg6xMFAADIa/JoH0BaV4DHf4z9uqSlw6vraXYDpMAfQymNxJT2Ht+Rtl6RxEpt2B2m/CvDc7kMJCsBX/4bm86EiKL6Q9HGPYf4duLE8v8XD317lOcKAAAw9Uy9AAxd+KclElUDoRjtr/9JuZca8MsvvfB82ldIWPYRZWA4h4AQ4gtEVI0JTHJLiTu/8M1sTgEAAKCgBSBT/IciRWFDYobGfxBCnb0BX3+2lsd/nDbz0PgPv/zCH5964Y9P5UQGkq9iLISjSjCcg2GZAAAAU87UewC8xqaouOhwa9/rW4+8u/P48Q5vOKLoOknEfxRVb+32acbSm3PH1acumlOO+/PA3gjxRkhys6CEDKRVgkSoJxsZSEbTibffCejzsgEG9XXVv3/kh2M9dQAAgIKsAuLl/zqhW/e09npDvd5wKKJghPcd7Sl2WOoqnbWVxUV2s0kWfcFYjztEEU3U/2CMaiuKdstdMXVQk7i/pov/cA244WM3h/tzyAmSS4DSJoEpJRgPyCTGAhXNlZUVHnfcfWnucG/Z0ZS7TwUAAGAmbgRLif9EY6rHHznc6tF03RPSk9fh3NBjUbaaWV8HXSduf4RQmtj/dec1p2oaeWPb0Z6e+JwAHv9JKwDJfPia60d72MkCwInqLNDEsw5WKV5i+sIffz/aVwYAACjQENDsWdV1tZVIlN2DrT9HITimat5gtMsd7PWFkwv577zmVOa8SMKJi6pTcgAjkiE9MByUppYehaNKOKpEYqpFTDPZGAAAYFogTG3RZ0Wp/Zy182rKndk8ZWj9T7HDIvEZwgYjLv+zSQ9kg8uGXUPiRR++5qNjezUAAIAZLgAp8Z9ECZAWizZUlSRmtac19AlS9n/19nk0o+rfZRNH5QckyFIGUpwAHhRK0QCBD58EAACYJkxpFZARPLE7HV19Ad6YfzhDn8yFpy08dLzv0PG+pube17cc4YH4lPqf0TIOb4AJA8aoqszxqevfN55jAAAAmLFJ4GEnwFDk9oc7egKHW/o6egOEUpNAhwoAdwsUwsb2DoiHif2DMfaE9Xf+sSEnx5khS5ySDRYFJIqixSSvWlJX4XIUl5Tw2y+9+racHAkAAMBMEICh/Z8TAwDiHSAoCkWUXU2dxzs8gWA4rQCk7f+cuJx9AmDMGpAiABihmoqiqlLn/IaKopJBnaJBA6aWWz7zjZRbiouzyjNl4OHvfG2crwAA+YaUL81/MLLbTOtW1C+fX/HSmweU/sEvGUhY/6kKvVOETFZHTE1NV3R39/7m59/7+Ke/PkXHVXAMNfcTwf/7+ncTl//7e3dNwjsCwAwUAG73j7ekaaMvisLGbUdOWlb39vbmZMOatv+zyyaGVVzkYDvFnn3iiRwe4Qt/fCqtE5CyKQwhFPR5z1x/kklOzT8nT5cEpqO5zwyIwZRw0ydury4vfvD7P5iat5+JSJMf/+ECMKuhKnE5QaXLhhCa31Cm6eTdnS3JGpC2/7PFLJ26chbVY8+iKUCShPrqkj0HjmI+fd6gu7tX1fS3G49OxRHNWC684ta6WrbnIxscNlOaW2mmOULZgNm4uDSVZiAGE83DP3pgxwE2zbvGGMl3111fRQh997sPTvgbFwCTkQNIKwBpqXTZuj1h3h/ivb1tjQfaeZXn0LRwoujTWeRcNrvkW/f/d84Pe8RMgMUknbtuYVefP3HXqhULurt73911rMcTQgg9/wzsDR4vF15xK78wnACkN/eDmd/bxH0yyhvHItRau2x0xzHYpUurBMlAjChXfPCqG847dema5bONn7WF32i2sx/6srWX5+xtCpUpCAEFQjGnPd7IMwU+/tcosMGza0v2He3WIspw/Z/5iPbjnd4//O53aCqIKlq3m/WDS9C4+1BNRbHHH5mS45mJpp9Z7OTQXzbmfl7vwUSXwAT8t8X4n32b07l/VAejE9JSs3Tg1dBAPUJmtwCUYPxw688iruGow2bh1h8htHfrBtCAfBeAodWf4WDQaTcPF//hUEq7+oJKf9nP0OV/Isg+tv1fucoElDitRQ5Lc/tAX2tCqNUsB8IxWP6Pe9U/YPrnsKnR1GxOs26Y33twSB1A7ssCREGY0xUfBkcIPV6zJEsx+Ognv/T7R36c8+MpKAJBFhXglFUUJd+1dysr/gYZmH5VQJnnfymq3tET0HT2pzU0/esJ6yVWgVLqjZCJE4ARETC2WdliZHZtWUIDuvr8dZXF333wJ1N1VDPF9MftuGH648RiMb/Pf6rgHfykya4CEwQ8IAaUHq8eQQw++skvIYRABsbGHdeePdxdvZ1ZzZIC8mUnMO//jBDyBaMxRUsplUke/+INRLv6gnz+V9qX4gMARtX/ZwwMtz2Yd4awmuVEJ6K773vw7vviWanZtaUTd0gzlQuvuPXCKz6eZPrxnIaqZOvv9/uXBY4Psf5TjICZGMzpOmA51Gg61Jh8F0a6pg5UM3MZAMZPb2cf/4IPM989gJT4z9E2j90q9/kikagqmix1lUW1lUWuIqtJFqtK7YlHEkJbu3zRmMoLQ8tKSvxsFhg1vqfu/5rQ488QCGLvXsxiVpTSj93x1YQMTPTxzDzWnfthl6t0SMBnAL/ffyr2TuZCvzMQz+KsrMlWyyscVvZP6Di/+o6phl8oLmK/2D4/KwqAcNA4CQbDae3+my/95uxLPz7eVy9IJjUEtPd48vIt1t7jtzRJpcW2uqqiRbPKzj9tMb8jGtM6ewM6YatsTVUXzi5fMrfi0PG+d3e1+EMjbxCbID8gRQYoJbNqXITSm/qtPzA+8ESY/oQpH4s1H0xPcBS5/YXoyGtha339gAwkNADCQaMimJQAAKaxADz/RurYLEJpOKaGu33tPf7GvS211aUrF1SVu+yBcKzXEzLiPxJGxFXE/hrnN7CcwTuNzQjpCSdgQuM/w4WDDCXAZlkstlte2wyzwHJDNqZ/DNY8rSkfrTUfG+faIq+1dgzVAHAFxkNnj6/a2A2QAjgBeScAyfGf9vaeDI80y4Kuacfa3J29gcpSR2mxlQ/dFQRUVVbCBQBjNK++NBIKtXR6A8ZY9qlK/3IluPLaGzbtPPbYbx6dkmOYSSxdNM9iGSjvOYcavyqsc491Iqy5vm691WZJFJMqb/wd5ZRNarxWne0UkWlLa0cDaABQsB5AJKpGYtrm3a3Jsfuh8ELPSExt7vC0dfv4/i9V1VzFLEPAHyMIuNhhts+tbG73+CID26+mhD898+TUHsDMYN25H2ZBvyhTdJn/oIVcLs/1deszP8B0zgf4BWr8ynHUt/451JqPDatMezvboxrmrkBySgDIhmtu+cIff/sQfFbTTwD6vGFfIHqguScYVnSj/keWRE1LM/0xpdCTW3++/+tYm+fkFQ2DjljEC2aVyVbrr372q4k7eGCqyJX1756/iv3j9paVlsRiTGASxGIxk0lOeTwl8V9Mm80qn3lh/NbX3hz/kaykASSiLa0obTgIGC0QBZoGAnDnZ+94ZVNTVNEiMdVlE43dl6jCZff6I2GjvCfL+V99voEUUFdX36yGquMtXSZZnF0xsGsMmDGsDLdn87DMC/P5SxKF+cym97k9DvsIvy0J6882KobjCqTr2uq1qxFC27duH3SQhM0vGkpdyUAl22DY7XVIf8UbdJU4uAJZzFPZiBcAOBP1W7jvaLeuU2JU8rC/AKspHFE7e9P/5aSd/8XdgtuvOiVxS3m5i3eRSx4pA0xrli6al3x1/CGXorKKnp6+iopBKeJgKDyiBiQTjQ4Um8mytHrt6qqD25LuH87QD+CLKENvpFh3e3x2o6HNoz+FiuGxR4GGcwKAvBCAn/zo7u6+YCSmun0RXyCiKdFAKIYxJumaJA/d6JvsFry76/iSuZXFDosg4Hd3tSyeU+EqsgoC/vJdP0ITz62333njZae9s521Eut1+x96+H8m4U0LLQHAiScAckQgwHo0OZ1suT2iBiQv/1OsP09E+f0BSzqDPireRayMjVl/yjoLjfPVgLRALVBeCECFy15uuMOqqoeiSkzROnoCBw63DX3kiPPfdxzoPN7hWzS73GFGxrww78JZ5SX2ydjAfMttd/ALp69eiBDa8O+tk/CmQArDxVuGi728g5Clv8YnRQaSNUBR1KFpgBTTHwyOEKZPbGXXKSWEqoQoGtEpjWl6RNU1QqIqiWoaIVQjtEUIy7IkiUIkGkuRHCA3qWD4UPMnBMSD/iaTaDKx0r2qMucJi6pVjXj8kT5vaOP2Y9nEf4yfKXX7w1v2tJZYBZdN7AtEtu5tLXdOePz0S1/6vKaT268/P3HLbdedTwj97//+6US/dcHGf+Z6mtPZdPuIkZbkeMvySMvb3iKn027vt/XJMhAMheMzpRHS+MrDMOJWqzXZ+iebfk3XbQd3duh6WNE0ShWNhBVNp1Q3zD0xVCCta5uCJumargv8DwMYNzwKRAhRVL3bHWjv8Xn8kUAo+oPvfuu/7rofPuApmweQXP6fFt6YPabobl+4rdu/dS8b9TDULbBYLbz3Q3LJPy8kzdX891EJAOeRp/79MASCcse1t3yGNXm22yorKyLhyJzug2N+qbCqReKmmSo68dTMFwSBBxe5GHDTz1uKmkyyKIr8d1Ht90GVWFxIdEL8/lBnV4/SfCg354lQl1ScsP7b3vhzrl62cLj/nq8pqi6JgqJqGON9x4z9Iv0NRLgEY8z6fs+uLvnJTx6e6uMtVAH4zr2fT6RqR4TSASVo7/a397LqfqtJjKnkxMU12/fHa0JS9nxN9AbgL32JnYKuk08OEYBfP/1vhNBDD0EyIMcawOY9VVWyLcHdBylz/OL3GiNc4uEWjVBVZzEWYlh5vgxnFwjtDER80TRh+lBJtSgIZrPJ6bCZZEk2yTarRWC3yE6nIxqJJqbEBAOh3j5vn8en8ypkT0fuTpFZf945jl8FARjK+z90Hf+IzCZJELhRj5t343ch/gtBKbPySR6XEYfrd6tEQRAwrikvEgQsCPghkIGpmgjGZYCTjRgYSqC5fRGuBH2+cPI8yFKbSIwJMJ4w6wMxOQJACL3tuvOG3vvIU//GODca8NlzB2zWT18bedTJTOXcS66XZUmWJUGU/IFQNBqriLlpf4y93/4Pi90khVV9xDnM0VJWho8Rlk0yISxsQynVdT1ubhAy92VVhMqRxIFFiaIN3eHIJ9AgEeNOyUUowYiIzCoJJll+6x/PoALgiutu4j2e+A+mfyYbu4FS1ht4IHTWfzPTAFEwSSL7OSEsCsaSfsgr3/upSzO8ryCyZzz67Ead0P9+OPezAmcSEx5MN5uk5Pnvw4kBxthilmsr5ZoKZ3RhVXu3f8/hbrcvzH9LVi+paesJuH1hAZNJ6/8z1KC4e9wIoasuWP3GloNjMPGZ+eJlc3/yYoFOEn7tr6y7xppz2IQ/Stlfb7tQVKll1flZwFgUBJrFyF+Le2BRL6S15qKYzpQnw6wRNlaa3LhbZFHAWLKaiiwmdkHAJVa2gtWILrPVKDpQNLsSIaKTWDQiyxJG6IlfPzytDToj0bQ77qz1R2EGR2MIy3XH702M8BOZbWefnmCRozFVj28MpaIYz8ljjBfPq7n0rOWlRrfdMfOJq89CCN1+7dnLTrtyPK8zs5koAfjGvQ8nnACzSaqqKmNNntu6RxQDjLHVLM+vL6urLPb4w83t3q7uvkUL6hfOp/5g7Ehzx1s57t2Syle/8gWNF2kkrTy46U9AEf3OvV//xr0jZDuAUbHtjQ1rzrkcY2Yr2FZeqSQbDSCUBmIqRpiHjUZamA+FvZeqExEzHGaZKQq7IMkiiycUWUyarosCtsr8FiQJAluZJoV00iHuK5rDkg0IhUMsC22SpXwz/Vded1PKpONkmx6/yO8x/mVeE7Pj7ENjHrnx14oxG+BqPApnv0jn7D/W/fTL2yKDY3eU0t2HOgQBX3TG0rLikbdcZOY/78VH9wBT4wHEFI0bekHAZeWuvl5PVp4BRhazVFNRVFnm7OjgzeBwsdNSO3gg3ETw4A8f+upXvmCs71LtftLRTUgtRyE7ASkawF2BbqkEY1RjVIIOY83ZD8IkCnXFtnZfOKbpIxp9HlSocljKHRabLJmMsLHEvpgZSxdvGDXc9KN+088qIybL9Ke36f3ubGJtzi/oxkInYdPZYzCSmHeTcJDwqAz6aKksdditphQB4Ie782BbW6f7E1eeWTYOP+A/7x34+KfvGu9RzmgmUACGLvDtVlPfkAfEFK2rq284MRAFLEsCIZQHaidn/xcHqoqnhG1vbDj5vCtYXT37EWCTSQpY6i1m0wq91yKLLIxurND5hZCi8sCLzSRRQo974wYXI2yS2AKdP1gwvsKqFjPkgVLaHYxqhM4vczrMqbsB8sr0X3HdTYkYetxY95dHJx6TnBdN2HQeQ6eUCgx2+kNffCJs+qiwmk3FDmuvJ1hWPNDnVSdEMDyLJXMq3tp2oHF/6913XNrvZIxu4Q/WfyoF4MZbv/7APf+PL6kSNp1fSDb3ZpNUUVHaY6y1zSZp6L2sQViPu6qqbJLbP2CMX3x952XrTxh61znrFr22+QBEgSaIza+yNM9VN97J163z5881DMIighBBqLp5R+KR1v79w8fcgRZvvHi/0mE9sdbFw/TJ+KPKod5AbyhKjDqi3lDUH1XqS+yzSuwWWZoc0z8ojI5HsOk86tJ/ixFhN243DHoaizjlBn20mGQx3uyd6hT3N/3FWNX0ZXNZSRhCaNWS+gd++dId166vLHMmTvmfG+PTN3kmn0eikikpsi2YXT2JpzJdmdgQ0N33DaTgk8UgxTmwWuKrsJiimU1SskjwWyZ5Sc6jQPFihWGYoB09EAVK8NwTv0hcvurGO/mFBQvmdc4+MXE7F4NgTD3qDnIDapOlZVUlQ60/axNkMa2uK/VElIM9Pi/fQVZS70Oorbmtvtg+y2U39+8Ry5KW2uUIoaOH97MrkZbB8RZ8xXU3JX5/Er9HOiFxcx8Px8RX6sYB4+lu0EeLIODyEgcWeNqHaQChtK6y5OaPrH/hxXcSf/urltT/8pnXKUKnLq9PlGwhhOqry+K/BtAXaKxMXkvCwWLweW4/UzyD5C5vQ5f8U7CHEvZt5r0YEEK27djnRkVVyCMJwooal2VIW6GnW1SzmVfZYoosyGrhw2YwL02ZvbiVot7WQ/PKnBV2y1uxpD8Kw6b32/H4koD/UrDbmvaxWpfkJTzheVG2PjcKhgrOpqMhi3RiLNKNqE7qYyhFLZ0epwlFoorVYmYTnxT95o+wEQ4fvux0hNAfX9jIt++tWlK/fX/rf3a3Ioyee+p30Bsu3/cBZE+iWGhoziARC+J1RDfe+vVJOyqeB+ZLurRRIIQQLwbNXAuUfRmo7Fg80E2vsFPB2XPFR+/w+4Mef2RunTPtEkFR9U4P+xHI/Z1/mAlP2HOESoodxr9YpuGUHGn84Ya3Z9j0fAyjTxrJUReek8tmdfTFrz+Q5evf8LFbRFG44ZKTGxriwR+E0KHD8TYBuw51IoQa97fyH80lZy2zWwftmxnOCYBh8ZmZ+qbk37j34eHEgHf/57dMcgIgEQUCcs7VN9w89MbMu7iGjcXpkWKHWGx38O273KgP/NQwkoz143nnrJs9qy7tC0iGu/Di314XdaGgDHqyTdd1whQui0Rr9gZ9tCTevaWlGyHU0FCZsP6sJ+ACFtAvL7L8a/MhStFfN+457+RFvOMkML0FIEsxmKoBAP2WZVK5fv3sp14f6IyWB/Y6/seZebdt6r2DrTG73/gs+/f+pNw50FKNV+8MNkmZbNO9n7osw71PvHoEjcRlF68/tWEyWsxOKCOmRifZpo8KjPGqRbV7muJGf09T6wK2hQ51dA/aC/K+kxcghP61+dCrmw+eesKc+qoS7pnBhICZIAAZxCD56mQiikKGWqA3thz88hf/349+ksvt5hpBYxadq2+4ZVhD3W8L2F39Gci0hjUeJOm/x6gsHHi5RF1K/7OZleER3mHCJElHIIxsrycZTdW5E5DPBp1/7NPLoI+ZqBKfGLi7iTWQT7sXLCEDVrNc4RqY+jAUmBAwXQUgmSmx/g/+8KGvfPnzmU0aRbS5I761LVe0l5yDStDVNxjruCH3Jo6Gbcoccq9RZMIYrkExj2X35yiHI3n7Tx4Z6xmVGjUMejZ1BtPdoGfJ47/9vyX3fW3o7X2+UGYZSDSZACdgxgrAVNHU3G2zmkuLbTphO5NY70md/PqFTYNCF4Jw5fU3Dwl20Ajrc0KQ//FRvaOqxyMkfH3Nf7lZu5k0dmJohQkY66mMunAKYZE+Qdx1z/cRQt8dRgaGawvh9TOFcBUP6weAE5CBwhWA91/+EfaPYVlFUWA74NOVesRU0t4bfPjJ1/tvGBI3wWymIG+ewnacspdht9osptDgPe7FUqacVdj4fufdP87N6QHjjrokioJGBAz6JMjAcK5ABtMPjEiBCsAlV16vaUag3QikaDprD8wW2qzzhCiJQk4W11v3Hj/4zLNZPtgWbQ1b6sfwLsDLr7+X/CFQY6NvIiVRhmkfdWX4lPbs2mt8Rw3OeEtRXDqXmfXbb4XPNs9lIMX6QxRo+u0DmHwuufJ6QhAWhqT+jMVeZanj6gtWz6ktHX9fsEhUeeF7N2b/eC4An/j6jwvHWMezysZOqnF+4F+6K01c5bLrb2e/5ItmHTvWWlIybCdBu83CJ4WxHYjVA3XoXwANmGrSRoQSGjB0+Q8bAkZFYQnAJVdeX+FydvWx7pJpBKCfilLnNRfmRgOajvds+b/4xKtsCFvq80cAhhpr3smAfyrj+XDSGuuck7D+iVt+cG+m3pBfu29gTx/IQP7LACV04Zw0DX/SagDsCCt0AXjkp8yp9IeiOw609fpCuL/5FO9FVV1elJgO2OUOV5Q6r75w1dzasnFqgKaTP97PZt1NggCkRkIQS1xPI3udcy67/o7srX8CkIHpIgO6Tg619F5y9sqUh4EAZE8BCQDnkZ9+PxSJ7Wzq6PaGKaGYsv7AtZUlbDqg0Yc2UQF/1knzyktL5tWXj/MdezzBVx4eRTS5ZP1/FaCxzjmXXX8HQighAFla/wQgA/kvAzFFO9buzlIAwAlIS8EJAOfhH97/yibWxHHR3LgLmfxr1NrRwy+0dLAm1TZzaqqcT5Q1yoZGttQ6If43fpj9saWkgqEv0Pitv9/n/+VDzP/LiQbs3HuElflGYr19Xp8/pGpaoi5sx+vZ5vyB8WoAZUur9h4fpfSqC9ekPACcgCwpUAH41l1f5hc+dOE6fsFiZ/0h29v7hgpAQgO6PaHhGsO9vvkAxjhDY7i7L5DHpgEgAKPl0utvxwgv67f+vn7rf8vnvjHwoKxdrKpSR1oZsNrsqqp193oadx7o6vHEYgMlvyADk8C3v/VfrV1et4+VT1/9/qwEAJyAoRRoGej93/0R14DnXn73qotO4dafxYJqy7Zt2zfUPIRjWiAczdAIJyejBIFcJX6XLZqduOWXD33/1i98k19edxLr4J8NlNJQKGI2m2RjVsyxI6w/a1dnd0IGImGjHtFVdMra5a3tPfsPHgsEIxrzBtCJ668GGZhovnn/Dz79qTsGt24dAOpBs6RAPQAO1wCK0KJZFUnNb+IEQtE+fyTlFozxmLtDZ+8EgAcwZi67/o5k69/jCfHZWaefsorfoqrxVjPJsAyQsQNbVbVYTGltaevp9fT2eWc3VNfWVBiDFTGlSDfs+1BvoHH3EVXV2jt7/QGmCroen0uMEW58/Y9jPxlgJK7+yEfjF7JzAqAWKIUC9QA493/3RwkZSLb+gVCUXygrsiZrgNNuCYZjw70aIURIN4hqDPBNYUM1Ccgm9M9HJxrBHx8W5ITpRwj1dncHgiFN0xRFi0Rj4XA0HIlGorFoVFFVjRcCYIyLi+xeX1CWJUmWjh3v0HUiSSLrySEK1RXxPWVdnd0JDVi1Yp7FYv714y/xq6LICswMRaEnrr9aFIT3Xn0GfnYTwbN/+H1CA7IB2kKkUNAeQIJv3fXlhbMqkk1/CgkZCISjH1w/MJJwtPNhxuAEQA5gVNZ/2eL48t/n9alokPU/dqy5vaOnpbUrpigYYdkkCYJgs5jZzm9jyi6lVFU1h8MajSp2u7WqwmWS0/+8FGUg4l9VXWmxxDeRsaDiX97QdV3TSSym6DpJeAOCgLe/Ct7AhHD1Rz461AMYzgk4cKT9ts/dPTEHMv0AAYjzvz++L/MnxTVg0qJACKGQpR6DAIzV+q87JZ7ep5Qebz5+8HBLX5+3rrayrrYCxXvlD9MOG6GPXHI8mzd9/IX+XUgYzZ7dkHJv4+7Dh460en1BHl9K3A4p4knTgOEEACEEGsABARi1BgTDsQmqBfJpLIKcMiQSPIARufXzd3V3uxPWn1X3Ll7E1+nNzcf3HTgmCMKs+qrqqjIBsxjdZeuNMe454vE/D9qMmqIEb/1n95FjbYqi0qTcgNls2vyPJ3N4DAAbIPzbh4Z+DqABmQEBGJ0GIISaO72TMyVYciwGD2BEPvF5VtxZljQVZNHiRYSQA/ub9h9qrqoora+ttFnNfK2fW9OfQQmGOgS/e/rvispyyFwGRFEsctpe//NvJu54CpChGpBBABD4ASAAY5CB5k42oy6DBuR2TDx4AJlN/1Dr7/X6t2zbWVXhqqosFfs7Po3R9CeXGPaPQht8L0UKayvLixEpRb//16B9fClK8OgTf+WDfLgMSJJYWe56+Q+/GMuxAWMVAMgEJAAPYHQawK1/BgF47d0DgpApCgQCMHHWf9ac2du27aSULpjXIBmlOEbHMHr52QeQTtmwTd4jqf/xUUWPKWxTN7fkxndmngMhLRIj8ZlrbL4um7Dm8atRhRgPiw9jM0bvIrdf0XRjjCZl/WT5PN5AWCuvODvpwBrY0GO2gZzGYqqqac9uYBMmdF0XBNHhsG38C7gCkxoFgnpQTkGXgWbgU1+6J4MfkG5WY5ykOebApC78jx45+tZbWxbOb7DZLIqierx+jzewYvZhitDLr9KYQrwBjU/KTEx6iSkkFNUTP0wsINH48akajY+LMDCb2LygfkGIP9dpk0wye7SAsUlmCWUs4NIi2SzHhwphvAMh/OfXLJSixt1NRsdrhCVTJMIqzYqdVoSw2xcilPLdA8D4ueaWL6TVAGA4QACG5VNfuufq6248Z838RMefxLhqRFHmSfHDvyqQe+vvKinasWOPpmnlZSWHj7b6/KFwJDq/oUdEaN8xJInYaZeYpRaQyHeFYWSWhZIi2RhCwHZx8wtWs1jilIzx9sb4ej7pHiGbRbRZmT/Bn8sbrJplQZKF1CoiiY2FG7iq02KH9z+7PDaL2OFeKIiCKAii4Kqrr+UbCyRRFI2Bn194DTYKTBRpdwXDhgAOCEAmnn36CYRuNMuixSw7bCaEsNkkj9j4gVD6nXu/njkTkCUhZfjuEwVGWtNfXVu7e/f+93Y2qaoqSZKr2FldVfbZq0MIWzBqSExdt1vF+GjHxM8NIyQNMd/Mg8ulA9fdHn6r0d3eE6UIibhRJ3Tp4gvYJo9QkD9g9ux4wuAXv/s9Gwh60yj2NAHA+AEBGAEB45iqR2Kqx8+n9qIFsypHfArKEX5mOoD01p9Q5PO4y0uLS11FDrvVJMuCIBjJXkuefGTBiK7pVJYEStlkCITQvgOv8Lu4EjQ3tyZkgBD60K9+09Xj7u3zPfrQt6f62KcrEAUaFZAEHpmrr2NjHQmJB4Vn15bJkshSi+em3xLMa4EefvItQTLcBUHUlAgLILNhtfpH52zM8mcjOxa3+cgzbzZn+fgFK0+RTVaKhB5P0FFcKgjo6La/oxkZ8ykrb2ttq68dtIl9Qks8x4hOQ16lrTsaCGuHW8PNHRGPX+GJBKtjDcZYiQTZbmGiE0KJMZeaUOJ02EpdxaABYwZSwdlToB7A+6/7nMCCrwJmkVgWhKWEleWxELGxVwgLIiZEEqVoNNLp9XZ2dgcDgdNPqEMIHW3rqypzFtktw6UBeC3JrR9e99Mn/kkREkQpnmakRNcHuolliVixhD+dUipKkqOsXJJNPFAhiiLVdUtRscC6lQl2R3FRUWl7SzMKiQ0LV1TWVE93AUhr/U3W+OXW9i6uAflo+jkitpeZF5WyRhFrTnDdelcPj0pVlHUvXjg74Sf29vWZRMzHS2CMZ89u+PQtoxglnQ/MWXJm4jLBbN1zfN9rU3Uw4ATMZAG44lPfRsYYL4E15xIppZKITUbPXowx0fVYNGIymwybTmKsZwulFBOjfo9tymcNHakSCRElRnjVn1HcYVToEeMhRkGfJBNVJbqmayqieqlDMguWTTuORKKKrmnnnrrcH4xWlTl55R+vCHf7QmaT7LCa4ulDzG4UBIkNZjRenUlNvDZxFDBlYm3hMGUFjWySsYAJ1VSEqK5iXdPUSIgSpisBkznsKieKokbc+xs3+dype5Gmu+n/x+vby0qLMcYrls7jn2dre9ed13tQnmP8htx6V88nbr4ycVtre3zmBM8r89wSQsjrD+W/9Z+17Nz+0ilaJ7dThDW9mxAj2IVY51RFQ1UrF0cMb4dS6sc17JQPvDmFxwyp4GkpAJ/4xv9iUSSEqmosHArEIuFoOED1aLwWgxKrzaHqOBKMUUqIrhNKNFUVRIGwazrRdF3TPvjRzxmL/BhCGjY5ESV2E3JYTQ/e+w1mlAXWCsxY+FNZliUWSmZWVpJEJIpEkhDRKSVmi9nusHV09nq9bCtAKKIcaet7bUvTkrlVVWXOUCT24hu71y2fvWx+NTZqgV7euJsSFvcVJTMVCOtQSSnRlNFuQqJRPw118Vs0hPq6M612O5Mu9x6c9ta/PMn6Wx3FH750PUKo6WDTtsYDs2pdpS42xnm6cMYpK/btO8Ci/0vZ/r5k7FZm/b3+eDHonV/+5i9+lHc5gPrFbFtDhdhlbHZoFyUsi4joOhYFXSGSgFXCWh6JosDWPUhj7fZEzDfPuUiHJArFSxaGRDalByN0dM+/p/qEgHwVgBPXX147a56zyCVLJjUW0QnRjPh7JBRQYqwbD6aqKLJWjqIgyLJslG/Ht+VQShRFMZlNJ516UVfnMYRQR0do144dxw8cwhirsSjGVJBkh9OJkbho5frFK5awp0SUPdvf6C8aMcYCSxJRFL5yp0TTNR0hKpg0nfZZbM74gVK0bc+xXYfaG6pdGKEKl3PBrIqEX68TctH6NW9sPcqGs8ebwFA0Bg+g3/oXCNz6J5v+hYsWJK+X2QIzGvb75ckTgOSZI2xqAL8RIZWgRH3RSHz8KuMUCP3XG7te3dK7bMmFibscxa62FpYNTpBXGjBn+fm6rleK7aIgqBoRMBJE/pFgRHWiU0EUMPPHjE0UFBENYSxoOmExVopFEUcVghEVESoV21QVUYLnn3Aui5BJ8sH3/jkRxwxRoGksADte37Aj6erHvvJjm9kqm03O4hJWOi3JRI9xc0+IRgmRJZGFgIxYvs1Zf2DPDl2lz/3ucUJ0hInGvrOHi0w4NGx0/fVIArPQunpwx1ZBlMy2opr6+vo5s6pqq3Zvf0vTVE0nRa4yq83h93m9Hl9UixCi+wJhJJjPWTOLEKL1GwJBwH3ekCgKV73vRLM8YN+ZA4FQ3+G3U09vfrafQ4YBZKPlis/d5+vt0XUt5PWUBo+2CWWSyWpxFAuCQCjR2R8lizCxP1ZZEiTZ19NtcditVnt9bc0f/2fk/ki5JcX6p9w7Z86sOXPYQnJ7494Hvx4f5ZZLVBIJqoQgn/GdUur2q4pqbBimyBtQI2zzMNsLKAj4ojMqTMUs6JctAm6otsrSoLkRifqCfGPuivMoxWVCu9lMo1GKKZVFrBNqlkVVpUY+SyA6W9XohC3C+EfEI5ZsKwW7ijUNSSy/wbwEpBNJFDREzbEOScIxcVJ70UMUaHoIQAqP//BLIz7mqz/5Qzgc6unq8R845u5zR4LBaCSk6xqb0GJEi0VR1JHOgugsmo6ZNiAsYlFXwjohkUjI7+k+0rTf6iyfv3jB4pXzvvWJgQVaKituFATBKrM1Dka42GHtdgeKHdajbe559WUOq5lH/1cuqt97ODkkM2pGEICr72MbW5m/wpZfNWtYOtr9tcvTPvbIrl2aolqUnrklIrabcbivyW2JRaJmq1WQxJA/IIiibLJgUbBaLGaLtcTlFATBaZVjAd+lH/vsS4//FE0Wjz78HYTQ5++Kq86uXXtXrlyW/ICjR5rnzpvt83jmza751VPo9uvjzTlyRW9P9Ll/dcQUEo7xhj1UZcuM+A+DJBpBGNpfW2Fet658VK9vMfYPJxMN+VE+sezkS2LRcAnpono7MjZLEIqYaBGWbxMw5at49lAWOMW6zv6o1IRvhJBI2R43RYvvuyaGWIoC1QmrVmDuOmuDQU1q58qTz9+1GcJBU8Y0EIBsePCLHxl646kX32y22fWYpkQVJRKIRRTM9v5Qjf9ZY13FKkKiIEq6HsM6Ym1aIp7NXcf27ay65s4fLlq+xGqWvnHbRSkv++zTT1x93Y2q8fsuYOTxBqrLi7z+yL/+c8BhM8+tLztpSX1Zsb3F6Bp0xbUfff4ZtsdnDGS2/15LMWWpC2b9eeVSBmoXzO/r6PD3KPujkjXQ3IeLZbNFlCSWL9H14tKyoopqyWQO97ZpWjTiCeqaRgiJhUJ25nUJ7//Y5//x+MNoEnn4u/ckLt/86S/PnTcvcZUiZHc4PB728U5EBtgX1Dp7YzF15FW5rtM333MvaLC7qkfhiDBXAiNvgI2Wq6+LNxD1eONbw/KBvZv/espJy3SVqhgLRugGEyRirLIlFMu+6RolbP3EFviigHWCnFYpENKIUafAbqbYahZVXRcFpGmaxDwe5l4iTGOqLrIKPCYMAsExbdgRexMRBYJZwTNTANLyn789NvTGcz/4cUoEry8YjQQ15taq1GRCrIwSqTrBFAsC8buP7vS2Htq/p6Zu9hceeMJmsXzny2zMd4oGVJQ6rWZZEPBl55zQ5w83NXcfONa9q6m9q9e/cmHtvqPx5f+37/naN+/7/hiOP3NoOVZWx+y+0cqA/dEZlH5/Q1onQMBiZX2DbDKH+tqjjmqzphl5bl2SZIFluZVAV4vJbEGEuUmyKFpls9PpjKmxSEzRFFUWpvL35LGf/+iuBx5MXJ3VMKj/fs6RmJ3K9sG9XuXfm3uv+kAdMmU7DZR3jstzvKTcJffpOsHEyHEYPU91gkVEnVY5gplHZBTV6Rgzj0bVkShjpAtxV0lAMY2wT5L5SWwfHJMNnVjMkqaphrvAqvgIQnKk54TTLt656W9TeLJvvvSbgu0Nl5sZttOI1/7ym9dferRx4x/2b32pft6SOfNPcJVUSljQlZiuK5oeVTXm+et6LBjsPty04x9/+9ubr7/14Zvu//wDgxbyzz79RFuXt73bFzJCwuXF9tNOnPuRD5x0w8VrVzDr39XrCek0bki+fc/XxnKsGc0QsbuIvZjY2Be1jpAOfemR7/q62wK9bTElhgRJNluxIFJC1Fg0GgyEA75wIORzeyTJpLEsn2ixWUKRkD/gD/n9sWjU7/Oed9WtaOr47t1fnbT3Mhboo9jLvftQYO/B7GI4Gu1pDe85HMh/ATjQ+CbSqUnEJuY044jGHC9j3c6y4LLIaqxZqbMgGA1NUSSmWy0Sa7ckYJ2ysCRLkFtFFvMxqvgM1wcrqmas/akoIkKJysbkCEos/RzW8XPNLV+YoFeeMcxkD2BE3tzws8Tl0y64RdMVnWoBr5sQVSAiFURNV7CmdnaGurqOt7S1XHTDNxetWCxL0o++Eo84haNKOKr87OnXTz1x/qxqV7HDUu6yV5U5S4tsL725OxxlzeO2729dvWRQj/gsyWCE2u/bOHB/fPkfyPxqRWUVuq5HQkFjIKKgqookStxdN7LlAkJCIBiw252CJHoDfmP3ApLMFlbpQTRFHUUB68RR7Crzefom8A0o6wXN2zZkSUwlr27pnVtrtRobvoYj6lEaD/q37vV29MbC0QV9u/cvXbEE5TFuXF4l9wZjbBXDDD+LlmKNoFhMNZtFti/FKAQirLyCJX1VzRi0KYhCPBCEjTFoTFGN7S80URpEWCRJQkb2il0Kt510xqXvvf3S5JwXpIKTKWgBSGbTK7/lF067+Baz2d7X3R4J+DBlkWCNhS9Fnzsa8Ha2HT9SVlZ981ceaZi3YOe/H+VPUVV943uHBQGbZLGhyrV0XlVDlWteffnuQx2J1x9DICiDAFDJsDX9Q21XCvEJRxl46dc/5BfOOPMcuxCJ6LitV52zdI3f00sRlWSZ6ColSijorp21oKe3W1PZqk8SJbOF7VK1Oae46F7R495qsatsAt8Gs+bPRkHwKFbp7T3R17f1XXReDRLxTZ85KJocUUV76mesVIkR1Q81Bzft9B5pDUUVcviIVY01RcPBXdu3yWxTN8ZMiUWTSZYkQRJFi9nYOXzqBXxD4dZN/0BTwcHGN8tXLWNLfsIqr9kuSWPvJSG6YdkxC+KznfMsq4ERjYZVq93COmkTlhUw5h8gk4RjMY0ljvt/m/nHigkxNt8wbwshIRL2oamDjEbvZxggAKls+ltcCS687m5313FPdwulCouNs6IhSVVi0ZC7o/XQvsaq6llLaubO72j8K388ITQa05qO9xxp66stLyp2Wk2yaGw8HqMGZLJAshwXCP53NTByfGTefuuNc05fZ8I6koRQ0K8SEo1EcCwmipKxKU70BYOsXE8QRPb3ztJ3lLL5uqgwUJMmAYxI62G2Pu7s6dv8Vs+P/nsv0bWYEi12VdXNavjYZ5sIigqSQHSdagQJqLvXY8b2uQsafL0dkT6/3SRSyjZMiQhJJlkS2XwYnegRXZcEJrqiIBCElq08U1PZf7xrtaJpNrvNbjVv3/IqmmDcqMIh9mCBEsLKhSW2mseUYl2nkiSwUmjKAjqGQLBSH00xssMEyZiZeF3H1MhP8f317EHszwSJgsB29rAtw8ZOMfY7POr9MTlJBZMCNv0cEIBh+efTDyCEPnD9NwK+rq7Ww5qiIKLpSNQ0EeOYooV7+44dPbCrrLK2pKxq9pIlR999gT9R10lLl7e1y8vnjoiYJqJA377na91v3p/lzyZTZ2JJHth/hHGyAAyXB+Zc9dWHelqPxpreRSIyE9LbfNhRU8/ycQL7E8ZGZs/f12V32NVYLBIOCqKINVxUUmqSpvJX5cv3xd2XVFiBYY4n8Ajso0gjvq2HhGAk5vcHBWbRWG40EgnarFbRaPdBqSqIbBlvt8gVNZUnnnpq4ztvWauIzWXVYlokGDXZTRWLHK3bvI6yMofNbDJJVqJgLFrMFkk2xzSss4SqsTGdWXqJJ1nZ9kPWloqVTSJM2VsjHIrGgpHI6jXnaUSLxljcMhJVOo7uzO3ngBDa3/jGuhOYE4DZli6RGlFBkUULdSNTwjtUxdf0goBVTY/XpBkFQizxy7tq6SxhQCjbJGyMXqACZgXahMkJ66YiRLtOOPn9OzdPnq8Dpp8DAjACf3/qO/HIyaWf7Otuj4b8mKisuZCqIVEMBPpCIW9n27GDe3c0LFi48qR1B99+jj8+2YRIbB09ajJ5ACZTPFHJo0BZZ9HcXa0dh5qKsWwRoibMgj5FTofZalaiEbPZomuapsQw1e1WW6fPw+K5kkwo1RUFE/2iG+58+cmpnF5bXlZCKd1/8Ci/GgoENm3uO3l5iWiXcigDhhrGQ0DHD9LWjm4RY6vZYrNZ7Sazs5JZfGa+uBVjFpvVvJswiamKpittbl91XYOns1MUcKWVRHx+ShYWyeIHzj6DvfqZ6PHH/rDmjPW6qhzYv3t+Tb1GUTQcWXhSamfZzDuB1669UBbNFpPNYdFsdpMsm5bPmxcKhjx+b0RTi4ocu7a9lZNPIyRWmGm30cjE2O0V3yzPdlMKAtZ0lvI11iAUG2X+ZllgC//4s3nQSFRUXZYx0qgYX7Wwp2ts9wB7QdbRhSJVnahUsGw0CgPSAh9Ntrz90iMIobMu/4zP3RXwdFJdZ3XgbDEjaGzHWfDYnmBP85GGOXNnLVjQvHOQey6KQnWZ4+H/ffwj116RkxwANpn7PQBDCEZIACcR9JSZSUwq7sUua6SFFtWKsskqSWGfJxYKsgiQIMqyWdN0q72IIiSbzKoS0dluWI/FYjvpfR9+719xR2cy+dE9X/n+z37NLy9ZNJctTg8etTudG3c7dxzcuWKB85QVLtmRGxn44r17fb6AJAoOm91htc2pqZElWZJMgtF5kPWYIjpl7aHY5mAsSK5Z8TlxkRY5ppCysvKahjm93R3l1YFgdBFb0YvoA+8zrL/B2WefXF5umV217qyz1yW/77HjAxmjEdm6NX0HhdUnrbcKumwSzj7/AtGErTbZ5wn5/RFCqEVymmXpnbdHt8reu/2NtScu13XWAQKz9qbsRsPEsz0CosgCPka7FOYUybIYjekmE/MVEkOTdN3YFc8aSBgawkQTCRKLETEhYf8y90GOFVbLkzwB5gGMhbMu/kRUVb29bWosvo1FZP6/jI0ORRaztbKhvrphTlll9dEtLEMgYrpr51ZRU8tra1YL2YZuJcfi9mHmAXQ8bmwy6JeA5a3vJt+bIQR00W3fCHUe8QXDZouVN0YVRVFTYpLJFIuEWWcIQmSzxWJ1BHxuVrItiDw3IEuixWZHCG98Ic3uisnh6ps/tWbt6uRbEt6AU965fL7z1JUlJocct1Kj5LzLX0NIc1iKrRaLgAVJlGRWwK5XL9RH60DwFpgEsyKfZNN/6GCTJInl5aXDPTdZA1I8gKLqJaqmW21W9vNSVU/rbjRZrFq2xMJGarJfN02nzIZjFgMSBSqajJIfY2cDywlLWFV1i0lmuwTiz2bBIEnCoYjCUmKJz9JoG6RrbD8ZC8ggYfe+vRN3Ci/8PnUru4vNZE5l/eWfQAUGeABjYePf4vU/p1/4cZ+vNxzyGyUqzB8WqaQowY5j+7vbjzlLymurZ8+es/DZ3z4gC7Ssrnrd8hJlT7brVD61MP1dlgHrPype/vV31l1yQzQc1tQoi+syJ12QJFPI72dJDoxkq8UiyVhiMWhBNO164y8ob3j2sf999rFBMpDwBgLqCf/Zj3Yf2rlglm3NkuKyKmuWMnDNTVs8fb4iZ1FDZaUkitFI5OtfavjDP9vHMLmBQyjt6q0qKy26+P0Dpv+eb31f04jd7rDb7Yqi3HnHdWmfO2dWzXB+gL+TdYFlrRAnnca9+89YtUQhosaKN3W271BEIktLEL5NmIfLjMg/MpkkTSeSyaSxFDGXAYxYA0aRdWSRWD6AtT4i1GwWI5qRXRDwzl0TaP3T4glE0mpAoQECMC7e+edvWHrg4tsCIU/A52ETv6gisOCmRGMRf28niQR8PZ2LV65vPbjx8gvmLFla/eu3SJUTW+Ssekga/RXTgG08m8avjO6YHQ6bzdoQDQcjwSAiRAt4djZuQtOHrh7vsaPH+vo8Q2UgqJ3QeATtbNq+cr7zpCXFFTXDysB1t23r6fbYbVa72VZVVvH73w2E4A8f9EsCK3RJ3LLx9Y5wNCbLpuLiYlVVTz2tZLhja25zsdWtSGtra/jEgmNHjiCEbrn5mgwL/yw1YAp5uzHehPyklctEVhKKCRYwEShBrKebRlgKgDsJAlYURTayAEZgiKUIdEpNJjkYjlnNsmzsD6aUvrt9so0+MBQQgBxQbguV20yooqrDjyL+3pjKphBgjHVdCKGwrsU8HXvOPH1hwBP44BVPrptHugKovkSocAiy0cohLcWSPYyQaZjqOIEX5XMNGKUAvPZMPJh++hlnlchqwIaWnnjyvh2b0bSirMx17Ogxd5/3pLWrUmSAiKt3HEM7D21fMd+xeklxda0Nifjm2/e7XDaTlRVcUqQ6nMLTv35f2le2mlkhTvItZ61n80wyw02/sRCWWKa6qbmutiLkc7Nfj+xMf55rAOe9YZbqp61aylLjWFAJNZtMiqKbZIlt9GVbhpHIkr8smKgRun3Xvkk/amBYQABySU0RQiVVmkZ6PQF/MIKoQintbN7jLLa6HHT7bs/yZUvdivK+imZPhLj9yGUVnGZkjANMY8Zt0daoxIZQDkVwxU2/URo+xqN95+2N5555mkVQbeIYwx1TTmlZCZcBhNFJa1YlywCVVu9qRrua0fKGptVLih/7+eJ4W0uOVqf5jY2qRhljTCXhiLHmpygU0W1WMRLLNvSfbPoHbtUi+/fuW33i0rGd15xZI0tOXrGpMb1ZX3PCMkEStuyYvIwFMCpAAHLAhueeuvyq6/nleV2PRVQq+WnR4k+HQpHuw+/YbKZzT64Nu6ML65w1ZdbWTu97HsuF5QejGnWH6VE3MztWGTtMuNiC2VyDpFeWh4lgiHb2ffwlLzEiWWS9xGZetmL13t3b0fSktIzFZJgSuL0pMoAQ2tOycE8LWlp/0GYR/UHNCEzTmEJ8Qd6tmL2CptNQJH6VsnFvI1v/lvbSeGvPwabfYYn/WCIxXVW1sdUg9vYy12EGsG0nxHnyGhCAXDKv5wlmtQVcZEGHD2+KhiNmq2n9afXS/r83mq6oMXchTBw2sbay+qV/93lU8rG1nionCsRoX4h2BYjNJJgGW3VHtDXzj22cGvDOOxvPPv1Uhxk7zDneTjUllJaWDJUBrgT7WhdRSn2egUgXN/cRhTWa7g9fGxsAjMpaTdO93oDGJsGlwnKg/R99cgQvYfp5+sDt8ff0eWurywvT9Oc/HsgDQxloruAeABcAvt/9nz1LBFk86YQah020HBwovj5ge79dUlwHjwdVvTWqelXSS+WbV3Wn3fpLKPruv+Jl5slEjwwaNYMRqvljtpWgKZx++plVVj2qku5AbNv2RpT33PGl+MTgzCRkIMGbb29LvnrRhWdlfoWmwy2bNu9KqwFD4dY/KXPMqCgrWbt6KRtXN0rT/8P7787mKcB4KkFRumLQQqsEBQ8gN2x47qnL1pSi/lGGgoAsVnnF8hq7iSZbf4TQ4rBxtRodoB9a27cXYXw4FH1lf+WRiHDnSZ2T38X7nXfeOuPkNbPLrJHsLN308gZ6ez1r163O0uKnMLuh+nhrV19XtvnYFOtvOAGBnl5vXc3ITgBYf2BKAAHIDR86pYpEBjnv64t2oZZdGZ5yEv4zLUcPvSGun7/glFrp5Pl1hwJzjnQEAxFt395MT2SdIOZVK4OdgPHQG5PKo7Eih3nBkhWH9s+ofF15OSsWYp+YMaJ5KLw7TcJ0p/hgdhPtptl1BjV62qTcphO9uaWjorwkgxMAph+YQkAAcsCVZ86K+XusQppYzYh84Rz9oTcOvX4YrYuJ550+e1adtbM7trDujIPHvfsP7MnwxEQbw/EPFympbujQAiQYKl+wwlxWa0H6trfzd0zrL3/8nWs//hmXi3VzzB7F8G8SMlBSktXTRVnu6vGGI2NvU8MyAb2eupqKoXeB6QemnIKbCDYRRIMeM46axTG2lv3COcw2bdmx58Ff/G1nU8huRU4LqSixZH6Wbnxp/RfGA9HVKLW4Fi4XMLY67Bob35HXPPObn3k8Po9n1E3kWbc2Tc/S+rPIvs1aUc5LbrMg3aAvnZDmlk5FTS20BesP5APgAYyXy9aUSprPJBKLaexrccMPYGb376++zcbZr1n9n20jFGVyo5+T2YJb/rXhtEuujUVjrCM00ne8mb7RWF7xzG/YNLdrP/4ZlsobpTeQPRjj6qrSnl5PDp0AMP35g6fgC4GgGdx4+eAJslVUrDIpsWOXE3t9Yx8xwTUgSzyDcwDzntnE2jOOvgoowbnX30o07Y0//g5NN0YrA5Wj2ZpLKT10tK25pTNbtU23t7u8tGTdSUv9Pn+h1fl89tyBOUI/fc00pccChUBpAA9gvJgF1SxSuwUV2dk2rvIyobdv7LGg7DVgkDXizXnHx2tP/R+aniR7Azl3CMbjBKyJ9qtpO+oePLXzs+fmhU3MoX0HpiOQAxgvZpFYzbTIhs0yM8r9A5ImHBpDNGp8Rdh34Jnf/IwrwdjSA7nKBKyJ/i7xNeKDv3hZfKtaIVBQJztdAAEYLxYTddqwzcyX4Mz6l8Z7w4w9IZwNNIRomH0nxteBc08Z+7vOOBnInCXuHuVuW+4E2KxsVjsAzCRAAMbLo5tEpxWLYnzyNW+BPsr+j2OhfG01DSIaRCiEUND4GkcCYOYxogyMitGVAwHANAEEIAfIEg/+GNbf2A8kYFRbgSfcCQgnfUUQa7wLDC8D41GC4ZwAEad+jZaCCozk4cl6AlMyZSdfAAHIAXc9j43VP0pYf1FgX/NqJ3jBGCEoSlBER1ENRTTWVBoYd3rA6/Ol/dIUxWm3jNPcA/nP6xvi8/4KASgDzRnf/pAet/4YSSKWRdbM2SSh3UdHvTDPshao94kDxuYjYx6rIUCLOg9BCGhEeL1QVeXo4nSUotb27t5eb/KNG1/+w3gKY2THYn7hJy/GhxtPL6bdyUJLuBSgDDSXYKOjpyhgwwPAksiGvZy2RDRL+PXdrBN9VnMgs6b8xsXeX21h75uQAWA0ZaPDacD/fP9++CCBQgAEIGd888/i967QRcysvyQgiXkA/AtLArpolfRyI+sHMKIMjGo7WFHELyAqICpS9h2W/6OVgTzhi5fNnaZOwBgoqJPNc0AAcsnXnxd/cg0xrD82Gct/bv0lAVFCz10ihKPkXdabMjfWn+VwPn8+bwzHw0yx8R0/AAAFBaQNc8wX/yjwBAC3/rLhCmCKVI1GFRpT6PJKsqiUzC9OU+rjrFiY68MBpg15WCFTICfrKeBCIPAAck/XnK8ihJb1/MAkMusvIqTrNBpj1l/Tka4jQiihaJaD6ARpVNCRqCGzbim//7kjE3A4AAAA6QEPIPfc+8APEEJ7K/6LBX8wM/dRhcRUqmpU16mmU50gnWBCkU4FggQdSbrsvP/PbRNwLNObT5+n8a97zlfvOX8s4xamF3m1Lp5oCupk8xbwACaQLcVfYZO/eh6MKVRVufVnY34J5dYf69z6i/b7X+ydyAOZTnz6vNTW+Qm4Btz3b3lyjwjIlmLJnuHecB58kB/+6GfTVoIW7FhgEIAJx37Ro3aEjj55M7P+hG3X5RoQX/5jM7WUIZTL5mUzyehPOxnIbATz0CZO2snmfznQ+kIy/RzYCDYh3Hv3fyUun7V2UGp3569vTg793/P3wq3cyd7ul+P0WxzyUwbuvmAURxW21Kfcklc2cUQK6mRnHuABTAj3PvCDZA1I5oTbHktc3tR4GP39+6iQGNVif0TuOV/NTw0AgGkBJIEnnI1bm1Bhk8jl5tb65y0PvDKKfLUt2jqts6PjPFlgagEPYEqZ0b0bCsTcA8D0BTyAKQWjB+79GppBFNpifzgKal1cUCc7wwAPABgvBW7rAWD6Ah7AxG4H42Ge4dIAp62aj6YtsNLP4bq4YE92eiU8Zh7gAUw4eMalAWDJn3Ns0dahJZIzlYI62TwHPICpBqZKzVzACQDyHBCAqWeG5YGBsVFQ2dGCOtl8BgRgMtIAGZjWaYDJwSVap/oQgKwoKI9nZgACkAfbwaZPGmDS6jtdojXxhRDSBRv/QtMNKJFM4NNCyV9q8MBU/mAAA0gC5wHTIQ0waXY/8wO4Bohk+nVR82mhbB423ftaZHmaQJ4AHkBeUMhpgJTFfjZML2+AL3in+igAIA0gAFNPAaYBxmD0h6ILNugEBwDjAQRg8vLA0BVu/Eaf88ArKv8a988HAAoayAHkBWxG8AwlVzU8YO4BIOeAAOQFWJiaRHDbj3cKlZVCGatEqrqkOlcvC0YfAKYFIACFmwbouf3XyVc9D7/l+vyZ43lBsPsAML0AAZi8NMA9d//Xxq1NKRMiEULnf+g2NCPg1Zljq8+BCA8ATD4gAJPHfVlsDJ4BJIr0R1QCMPoAMLWAAACT7RCA3QeAPAEEAJgMGYCCfQDIQ2AfAAAAQIECAgAAAFCggAAAAAAUKCAAAAAABQoIAAAAQIECAgAAAFCggAAAAAAUKCAAAAAABQoIAAAAQIECAgAAAFCggAAAAAAUKCAAAAAABQoIAAAAQIECAgAAAFCggAAAAAAUKCAAAABMAcWSncIHP9XAQBgAmAKKJXva2+PjNGfceaUFT+SRANkAHgAATCw/fc1ULNmLJXuR8Z1/DfdgW7R1Wv88RjXvc7qf7AwABAAAJglY8AL5BggAAOTXuni6U1AnO90BAQCA/KKgAiMFdbJ5CCSBAWAyeOAV9e4L5Az3zqQfwww7nRkMCAAATBJgFoF8AwQAGC8u0cov6MZ3kUz3UkYAKBRAAIBxGf2h6IINZAAApgUgAEBu7H4KIAMAkP9AFRCQFT9/Vfr5q6NeLuiCjSsBAAB5CHgAwKjTmBmqWdI+HqFsHw8AwGQCAgDkXgag3AUApgUgAEBuZACMPgBMO0AAgHEBdh8Api+QBAYAAChQQAAAAAAKFBAAAACAAgUEAAAAoEABAQAAAChQQAAAAAAKFBAAAACAAgUEAAAAoECBjWAFjE5qNj0fkcxIkq1U8647c6oPCACASQUvnVs1ue8I5AWhK+4LVcwmxeXUbEOSiKkuRrz2QK/tfz851YcGAMAkAQJQiHivfTDasJTYiqnJgkwmLJuQJAmY4GCvra/V+cPrpvoAAQCYDCAHUHCEP3xPtH6x7nBRmwM5nNhZjIqKcVExLXLR6jmR6tnq15+e6mMEAGAygBxAwREqqyNFZcjqQDY7tjsFB8YWhERjpK8uU6EuoEdLp/ogAQCYBMADKCys139bd1Ugqw3bHUJRkViJ5YXIvByZliCxDmEXwiVmvbi04oHnpvpIAQCYcMADKCz8VEAWK7JYsc2Bi5DUgJwOVIpQGCFvCVIkRHSErPY+OdvZvwAATF/AAygwZAlhhM1mZMGCHYmG9T8XodnG2EbBgbCEkChgGYY4AsDMBwSgsKiVKNZjWKDYFJ/UG0ZoF0LdCBGEEEVIQ5jqC0x0qo8UAIAJBwSgsOj5zdckJYyiAWxc1WPIi9A+hLoQUhAiPoQiSFbCBz7z/qk+UgAAJhwQgIKjOuoRAj3IH0BhZvHVGIogFNOR7ka0FwnBSG3UO9XHCADAZAAbwQoR9atPeCtnkbJqXFyM7EbcnyAURUIoXObtRLefPtUHCADAZAACUKAUffPJlqJKtagUWW1IlASqS2p0QczTfce5U31oAABMEiAAhU71T15EotT5/y6a6gMBAGCyAQEAAAAoUCAJDAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAABQoIAAAAAAFCggAAABAgQICAAAAUKCAAAAAAKDC5P8DNYSMf2Ov/aAAAAAASUVORK5CYII="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "With the bow turned and retaining hook released, the clear corridor for the axe is forward, toward the FRONT camera.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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lTyb6HAuq9T/dP29KBHMVYL0qkGsC4NGTLTmkAcwZIbZub9q6vSnmHe7o8ZYjx055ysrvueX83M4HICsIAMXkKNXLjjMIudGIAXr7nUqYHGbhIAuHtJB/oKfr0OX3TvRpFobRynSz7Z+xSLMPx3Pjdtdt5iWe37iXDIWBw8dOHT/ZGt3hwPH2hOUpk8MMEDAWyAEUjcN/3aSoRHQQPyEbCTlJSFghdICxoJ+EQyQcZKGgFvS3KsGW/9pECttl38p/uoJleF9vpAD05iIq5KcjUNRVqxa/8u4BbniquWFUCfKSO/p018ctddXls6ZUJ7ZgZfpjAWQAAaBo9PcSjSeshrQR0m6U/vI+wvq7SWCQhfQaAJFDTA6pvFMaXonWjmKmeh69pNQ7/3MixxlLglXOGIfTS1wX4C+vbY3bpc8bfL/pZHWF+9dP/i329andT/pdXLkTEQCsgQBQNPQ+P4zQQaLyhIQI7SNady/z9rGAlwR9JDTIgoOcQCjR5J2BjI5oQTFSwCWROa9nmvf1W2mzI6hKOIco8hVVMz/19u9f/+Q/WH4uv3v4ya99ecQsQ4nRyawEeMrKjbfZqfa+j493xB2HMuILM5HnXJLl5wh2hABQNGgfoYrKGGWqSoIBNuijvgFmPgZ9bNAXGfSkaeqezXkpowu4tM/F8BABPRegKuEj+z9edPYF41kFeH7jXhr2Jd1J02jT0SQz/KiUDISYwHNSstHNAFlBACgevR0sGKCKzMJhFvKzQID5fczvJYM+FvAbe3DGRMdOdY/el9yeHvnh1TP2ZfUJvSsQY8zhcs2YfsZAbzvJfxogTSWAkHJFi4QH7+DQAjUjyRrzhsgkTxYBubvbyhXqoWQgABQNl7fH399L5TALBVgoSEIB5vcz/yCTw9HBTIQKosjLcoksDn79nOzK8ueONXYe3XHXKz/51VVvjrozY0QQBP2CiaLkcPC80NbRvPvO+8j4ZgF4Z4WkBbM9QlBhDplUW35mYDMIAEVj7ZkNz2w8oAYDLBQyY4B+46/3YoyOaOJ5TqxyOy7v30Vs6b++PN/beSzj3fUp8o0eORyjjFFt7rylu/N2biPSAEMRILESoAz2EXekYA/6k7cOmR8cHJ46CCBHGAdQNHr/bV1lwMv3tLOeNtbTzgb7iRYmVCaaQjSZowrPVJfIr9i7npSouO6TjI14RV/hq69NC3lvumhqZsczYienLxLAjMUg2fglOUZ8kyIMdwBNY3LXiOVlVIwWhjFDACgmK7f/oZIqYjjAUYXTC33Z/JNnqkA0t8TffNV1pHTF9X+PrNob81QO9jEqE5rpRDqMUsb0NWHMR+vhndme0pca6rP9SOKcEISQWUuWZXscgLFDACgyFx19bfa0yR6RkzhNYJpANJHTnAKpqyi77PS7Xb/7J2JjmhwgmsJlGgAYpVSPAZqmDwjW1LQzRyTXet+tJDdDESBah2ntC7b2BaPtPwDjADmA4rNw9/MLCZn5jz/fuPk1VdXOPGO2+42HSTexYda38uqHvS9/OfqUYwrHVEq115uv+dQZoyykzphgTMDDGw99mXg+z0NsE0cDRC2o598zNiRJUBSs3wvjBDWAYtXy22/Ob3p98cd/10v/0pXY7p8O1Ut/WdZCYe3Pu68c5chEYEY21pgKVH9kOP1O1Lpn7ya5GxFsXnt3/x3/a9W6ylcWz5lqLkxWUeYiFrn0ujusOhSUGAQAKLJ2/3SoSlUtrAcAGpJHm9zfPFZ0QRh9ZtBxmg7OTANExX7r+UsbZk+v5Xiut7c3dp/GwFMCflnBavg3BaWD0UjpH5ZpWGH3v31p2r2N2aAj/2N6FoCN2+1/ymBWVeY6/6yGZYtmxr3ukki5A7PAgcUQAKB06I0/+kO//ZcVfcqMNPT5n/UVY8z7fsaYPi1cvkv/EWsDJD8tMq2ucvV5cxPfKXdwLqnEpuOACYYAAKUjevsvq0zRmKqxH72yJtXOAi/oAwCMbWNhAE5VtfG59x9qBRouzZmRBlj7DT086MP5kjX38DypdHJi6kUuAbKFAACl48EP54eMxh/99l9jGiWaRr77fPq1B4bbfcKca3xK/6hsy3KHSCqcadY5BsgOAgCUlPXHlhilP9EflKlUDwP//MzqFKPAoqW/ngRwV1Sv3fTwuJX+upiiPMMERJmDc2fTEPSVb/8w+9MCu8A4ACg1ZuOPZgzw0isBxuMf/7xaoeyR24fnSVU1ZfgzxqwSjLGB3pTjKdY9e3e2wxSS6njzX6Zc/uOUq8MY/vOhEYMYlitPRbc5jlQ4OUXT5wTN5OsUWR7jCUMJQw0ASs2bvWdrNFoDIHokoPq2Rsmdj6+687FV5m7G0oq8uS682R1UkcOhyMTaI6x79m7r7/0TOoNmThJIpcscLQAwJggAUIK2+s8xy/2hP43F1IZ6/N/52Ko7zDBglKHGQDB9Mm3J5WJUu+XAi9HjfPe9b2ZS9JsHNr+CDtU5zC9lIx+JcivGXaLeFjTqbmj/gfTQBASlaZeybDHZFSmLmR4Gom0tRrdPoqkyxwn6IgpDBWnQN0Ap7Wg9edfxy0MBj6e6fnd/xahfZBbrkdJ/aNt8neeJYMxYp6/krvc7Svb5oUag7315XeY/ndkQNOpuaP+B9FADgJJ1gCyLlv7D2d6hfCtPw3ykSI5UDThBcLg9guQIDDoll6d21oL0x2dMjy4qJYrxkDUWUllQiTwCCvOHmV/Wt8Oq/jSo6DubaYCYw+TYlJPhwOBAILMFosGWUAOAUnZcOk+fSd+/MzHVGlY0QdDX1eUIM27SWcjb73C6PC6nIDoq6mds3ZtkSV4TG7rfj2QXjAiiMb1818eW6UsNmKPL9C6bHGe01xsvODTiljiJH7FCZPJEcLIMcLZQ+kN6qAFA6essWx53+08IWRR6m9PHgg3PCMSo5nK6pp7a4K6s2X0q5aAws7lfNW75wyoLqySskqBKZJWomt7xVNUnmdYjgbmbqjF9YBplsqZXCLwh5ldGFvhcui5AObv16/dYchwoYQgAYAud5cs7y5d3lUcigUm/2RckSjVzeTGPxJ3naJJcnoOpm/6NQKHf+Ct66U/CendMo9w3in496zvUohTb6GTGDPPPsMoGw2Tfi9/L948MMCo0AYG9dFXoMaDepy/+xRgVJYciB80KQHVVdStfRkLpPq538jHu6xVNf2hGoZ/5MC5zTzNUxOC+9+VPE6vJsizksMYN2An+fYAddVUs76pYTrWwIAhmAwwjrFyMGRqWjNnoL6t6Y45i9DGN3vJnKxoAch4NMKpwKOwzBENpYxrYGGoAYF9UCfO8Z2gtgNGLcb3lhzJjFG4kCTxOCwiM2dGjRyf6FKAQoQYA9jXZI/M8b+RgMwoAkZafyPiybBcQi7djw3ezmgQi0eMP/CLNu4P+4IjnuVVVoKShBgC2xhgTBIeZBB51Z9W4/Y+2/Fglq1ag5441Zrjnxuf+9Ombv8hHp4wwe5siBkAM1ADA1lTZL4rGbVBmTUBaUbX8tJ7ujP2xYmY/BdChBgBZuLPxQF6vl3dko8U4UOSQu6KchjMaLqtoehk6sgOPBc5e1EDyo7O7r6amKk8HhxKAGgDY2uxanud5juMzaRwxewFZKGkaIL0zG1dkvnM4LJ84eVoz18Y0ZrvI9uugtKEGAHbX391WUV2bSfPI1q6lln97+uKcP+9fmw63hZWh/ql6oMqCKAhen1/gBb3013MA5n8AIlADANvjqORwFkJ6NK4L0Mqz5ozxgN2HtzPGunr6qTlFUcar3oNNIACA3YmCKIjOgs3sDt/+G+tYZvtxURBkWVFUzfrcBRQ/BACwuzl1gjEOYGK+PYcMcFZpABOlVFX1FHa2H4TShgAAQLpOnyjMJRabDqeckjpD3Ye36/9hRNU0TUMtAEZAAAAghNP0TGmB2b73pJWHM2KAPlE1wBAEAAAiCCIvFFaPOEr0gHTJ8rmSOU5tDGmA4Y8ypjcEAQxBAAAgHOEFUZrYC5F0HZhUDVM5pAF0+jgG1ABgGAIAAJlTxxdmgvSVzfsn+hRKyuprv7D62i9M9FkUkMKq9gJMlJbj+4zuQOPqN/fFrg4/LgoxzI2fzS8+ZoaB6LbNIQAADC/jXoAkSVDMuRyiGM1qSHC4bQ8/aZ71Z1a0EAaiEAAADAVa/pOFDZObjrTFxwCwKAwsvvBqQsiBbS/b84oiBwCgm1cvFloXINMH+5oTd+A4bvbSleN7UlCCCugfPRS+1z4Yvg+9aCnuHvLbBQjGwYFtLy++8OrFF15tz0oAfocBbKAQhzkXlsVGW5DdIACANY3n0V6USZ+ar5grEppvJT61m8y7ALldklOfr3QYoxjPZZkDtrz3NyEAgDU3l9GZhpM+NV/hjKfmW4lPIZXegYwWLEtDFAVUAkaNAYvtVwlADgCygHb/cSBJUlixeLyuHBy09oBQGlADACjWDDA6AlnogC0rAQgAkLu4hvvM2/Hj2v3TP8324CXAKeEXE8YD/p1B7uIa7jNvx49r90//NNuDF4VsJ4HwB0N5OxewbyUAOQCA4uB0OMNyOPqUUY0rvDUMit2pllZiJ6gBAECJEOoWTvQpFBkEAIAC0h2I/EoiDZADrfugJX8L5bPOJfaAAABQHF2AkqYB0BHIWoOnPiJ2ghwAFLfnjjWSYrNwxZqJPgUAHWoAAOPt7s9dnMluaAWCfEMAACgs7X0pe3xiRiCwFgIAQNHAaACwFgIAFLHHH/gFKaEEwGduuGF8zwXsDklgKDLFmPXNsAuQmrDuo1PiR50YbvbSlcebPki/z7LzVuz6cEeup1my3DPPCbbsTvpW+axz7dAjCDUAgELJAPf0ec2No007x/GMYAQ7lPtRqAEAFEoH0OZTHebGkS5tbop9VI1teu8jwpiqKLwouF1ljHAzy/JxslD6EAAACkXL6U5zY159ukl+Lrt4WexTjhdGbQKCpIIp2n/sAwEAbNr+XmjOu+iSd7clKY8ySQOMStWwhGQW/L5IW1zJQw4AoCAywNVV5QG/L/rU8jQAT6j5sPawRa1sht1vaBAAAAoiAeDxuDI8jqrFL46DGYEgNwgAAAXRBYgzVm2vq3aPehCO2Gl1tHzyt+5LfJGrnqMvt9B/jNgAAgBAAeGSrXwWOylQbqV/e2fP2M4LShMCAECBzgCamAZIWvozOnqCt+vQtmy/HewAAQBg4jUf3BPZSn1/H1v6axpyuWABdAMFGCehgC/bdWAsbOynvjbrDgYlAjUAgHHilFLeby1dc52sKGk+64hJAySFjkCQA9QAAPJu4Yo1F587Z8Xiaal26O4ZONUSGQYc61jTzjlLl+f57CC+C5B9oAYAMB4G+vvSvDtr5pR9Hxv9DpN0AoJMCXULLblYzB59QBEAACa48cfMAC+a32C2919//TWUpJsIKGkeOJOOQHagdR+c6FMoMqgBAOTdXbeO0ge0otyzaEEDz9SnN7yaah9JyigwwFgwLcy0sH2uIQIAQH4tXLEmLMuxryR2AfrD7//47tsbCSFer2/OmdOXrloXfesY1gYYNxUziM0gAADk1xUXzH9z657LL1ySaofFF14Z3XY6pGMnTstK/NJgmUBHIGv4WoltIAAA5NeSOVPT73Bg2xvDT4ypIPr7h0cMpIHhYNbf/vtsVPojAAAUFllWJIH4A6Gk7yINANZCDQAgj3593/fT7+CZNCX2qUapt3kPy34IMDoCjUmFHW//EQAAJlgmJT3ywJAnqAEA5NebW4cmekvaBSgmAgy2NJkbmY8GQxrAAhU2vf3X/6XdfNXqiT4HgNKx8tO3JL74xbVzUgUAd/WUlecvZ4y9s2WXptGQv79h9uyWkyf7/frYLl7g5zRMP2vJ3J1b3zH3lwQ+EBoxa9BNt90a+/T+f/u21T+TPbp++uwYADAXEIA1KhqWEUL2HjjJ89wXb7tm5JuDqT/HVI0KAv/JVXoYON3RvXff0YEg43i9ds4YOXri9NETp2dVR/ZWEyaC/vUjGwhj0YqEZ0ajp6yi+9B7+HvNgs+OpT8CAIDFJBIilBz6+EDsi40Xzkq1/+Cgf/M7m0f8TlLqERVl5FCAzgF+cpWUNGcgaGmiC6QkVE7X29C8Ni36TagBAFiPapQXeLOBPtkijxEy5yYaZXofHnNPjlLKcZwkipQyTRue4YcaO4ClRf9pXE8kgQGsR40mGUHgBYHnjcacpAkA/T2eE3hOMPfjOV7QJ/zhBd7hEGM/GOv8sxpin9aW6zWDWGj/SUWonC5UTte8p1H6m1ADALAe1W/rI65P3f5jVA6M/+vrPer/F3hCeI7pGzxhJCzLsYcydfZkNE4YEqHcj4MAAGAN38ld5TMiE/7s3bvf5XSMGgB6upIsApOa0/xPrzcQ/46WbjUxgFTQBARgPVm2vkRu747c+IfC6Q4+pa7K8q+GUoUAAGCZwdb90e1AMPl8PlGdff7cviWxI1BtlSe3Q4HNoQkIwEqcVBb51XJKl13+iTR71k+bFQrr6wSIokA1yhjjBZ5SxnMc0/sRacwY6MtxnKrTYmf7WX3enM0fjli2kAku/EVCthAAAPKirMwd90pcF6BpU2t9Pr01XxAERiLlPsfpiWG96Fc1nudUIzBQSnv7vAH/cNN/Zy/6/oMFEAAA8mJSVQUh5KI5KW/MRVGsrqqgTL/H56LT/5jFv/EnI4wjnL4D4Txu16HDJ9r6lGmT9E6f3f05Nh8BxEIOAMAyFWeujG7X1FSOuj83NAKA47nIgxv+03xd30Hg4+oTgdCINSbjYGkwyBACAEBeVFZEkgH5QCm75pLG2FfqyvG7DFnDPxqAvHC7It32k5o5b/FYDk4ZM6aQABgTBACAvBD0Qb15lGrheCwNBplDAACwHm+U/rEZ4Ph1YHLV1hcZBdbnDSbdoWMgeWAASIQAAGA9tysyD0T+dPT6PrNmaewrSANAthAAAKxXUW59BnjZuSPSBgO+5DUAEzoCQSYQAACsVzOpMn8Z4PQ5AIDMIQAAWG9StT4KTNNoPhZtN9MATI8Bw5NDAOQAAQDA+lFg5WXuCxoiaYA8hQFCiNcfTJ4GYFg+DDKCAABgPadzxCpdP3vkpXwUyp09yWcEqirHxHCQEQQAAKtx+pxuSV63Ogb0epPPCDSlVm+AAhgVAgCAxSQx73MsmmmAsJw8D7zjiBcdgSATCAAAFvN4UrfAMHrJFVdY9UXGXNFJJoQI+DFZNGQEAQDAYtWV5dEMcCQBEOPYiVarvogyRhnDcDDIGQIAgMVqa8ZvVd5gKPn6wOcumDZu5wDFCwvCAFisqjKPE0HH6Rnwl3vSTTsKpeFb//yluFeefu2DuFeaP96d7WERAAAsHgTgdqfMAVyy9kqrmoDM1cG6+/wN02pS7TN76crjTfHFhH3MXroi7hVNTVJhas6+3CwZaAICsNiqee5UCQDLDQbDhBCkASA3CAAA1k8EPW5kWdUXDwbICZqAAKzAIl3yXVK6UbgdHV0njp/M+Us4Ejf5j6QxFpZVl2PEwGOI9Q/XXxR3QX6/4d3Yp7OXnHd8/4f2vGgIAACW/kaJwqj7MEaSjhTOVntfaOok12AgnDQAnLtg2keH2iz4mpLFSDH4jx/clTSACfyIf2kazWVmQAQAACt9945Vqd5aePYyy2MAIaTfF6yrLv/MmqV/fafJmiPagDGCjpnTdhBCzlh0Tm7HWXZ2Y4Z7rlh65qj7PPznl8cSwFL9FGmy3AgAAPmSKgPMcXoAsIo/KCe+uGNf84rGM9ARKN927dkX+/ScpUs0ShkhjgwqgtmX+LGSjgHPGgIAgAV8J3dVNAzf4I8nVcXkz4Vid9P+2KdnNUZW/mHGvB08xz3yVM4lfl4gAABMBIvaf9JMCQeZ6whXTXEOWH7F9u47EN1uitkuHOgGCmCBX9/3/fQ7xCYAdBY1AbX3hYKyYh4sbjQAZGWATC6BK9ZL6xyTs1hwFAEAwDI//sanRk0ANC6Zb20NQFU1VU3SA2THvmbMCJQ5yuj0JRdfdNXNpGh1hKsYpYqizluR6YyzCAAAxY0REgwnnxIOMudyucrKPMV+xRhjmqo7/8obM9kfOQAAa6xdMSvNu1u3bI9ua4o+f0MOeCHpgC+X1x+qLEs5AM3mMwJlQpD7/N19295q5iQPIa5aVyjzz37lpjU5f6+nsvYXv/8LsUJHuIrneYfD4XQ5BUF49cWXzB8k/UxHCAAAedfjS9JT0yrtXQMzJlfNnFydv68oOSkzMEwJPPrQg+k/fOS9DVadR8DbM5b4EfWlG1ebGw89/bbS2X6080CtM6NUEwIAgDUZ4EsvWJLh/oLkzKESkOL2X+fzR46G4WCZ4WKLxrUXN8bOqGph+V74kAMAsF6+JwGNEwwpSccF7djXPJ6nUTwy6oNFLRytV6hQAwCYALlVAlJRNU3VqJRs9Glhzgh0++3XmhnLw83drR0DlKYcy1bmcQZDCs9xbpc0qdIzpba8oszJZTaNxpaPTmdSA0iFt2qyjgKGAABggTe37sm8CSgfwrKaNAAUMo7jptVVdPUOxnViqqvyNM6bIggjfxzGTnUMfHigNcMbc44Qh6Poe/XkGwIAgAXSlP7Nh1LO0eaqa4huh7pznyb6aAe54KyGNGmAguwIxAjhyt2O2qqytm5f7KoGXn94MCBXVQyvq6Pj9Al2eJ6jWkYBoPSbb6yAAABg8RjgzBMAYyn048hK8tmAd+xrFqWCWy3giSdevP32zxDCOJ6bM7OmtbOf43lGqSiKGqWyqh062b103hSnQ9z4/pG2Ix9HP3jGoqXR5htGaexbt9/2mbc+OEpi3kpcEhLiIAkMUAq8/iy6rhcGzhwP7ZCExBv3QFD2+sM8H98Kr1Ka+GLkk7jnzx4CAEAp6PcFSfHRW4FiZ8UQ+EiJRBnrHQgYMyvHS5Xq4FCYZQ/XDGCs3ty6J4cEgLXef3dTdDtuVrgCnhGIi/5pbtVUecwNRki/N5h0iovUue7S77STreaDKf9lmhAAAKzMAI/zCIBYSe+Xi2U0gMelJyqmT66MtgiFZK13IJikb8+IJiMYEwQAgDzOAj2ewkq6hQFmL11JCtiU2gpCSFWZc1p9pfkKI3orUNxuHMeVuR241bcKAgBAiRgMWDaybPxNqS032vH56UMBwOwPGrcbx+k1gAwHgsGoEAAA8mXcEgCmgZg8cNEtDuNyRvqqupzDfdMT1zlgTN/TDmN0LTHqYvcIAABjQrVCWZHxjddeJ0Ur5qZ+uHBPmtNwO8UUaQD0A80aBoIBWGYCM8DpqYpSgMPBciMKvCgKJKGDkKqmnFCo0ELFH9a/Ed3+4i1XTuCZIAAAlEIG2CYYY5IkJK0BDAYzX3Qho8ngcivQCSGKYE7Gn1rMDg898076fS1ZMCAVBACA0sEIGzGwqghmBEqC57g0M74JPF/ucfT0B+L26On3Z/wNLH8F+oR45NnNlBkVoITsyBmLzkmzKBgCAEBeEgDjnAE2/fahJ7/+lduLfXEYpyQEZTXl0GGOlLkc+qxwI5t8EgcNZFgD+NuWfZHSs2AK9Jx1yVWEkHrHQIb7IwkMUOIJAJOqFM3C8dUV7rS9fDiPW5LE+LIrECqaH3B8wkAmUAMAgIlZECbq8k8siP6pUarFLG+mjwxYsKTWWCFANFYIYJS2d/vCsmq+FT2IFlMjkCTx01dfdvCkl9hVl1zF0/Coq9ujBgAABUTgufpJZcLIKT8HA+FAMHKDz/FcuceZfigApSybnDDJ/Ja5kOXwUyAAAFjfBWhCEgCm3zz0RPEMB4vc6L/53qHYp9UVLvfQuDCTrNCegYAx47OeBXA6BDGhCWjEcRlTUiyQkFRNGdGk4o8Bwa4RjwygCQig6IeAZa5AOgI98cSL8a/ELO2SVOshsiWbr2g9RDJcEGbtRY0N02uiTz0Vw9s9A/5Xt+zvbm8hxaC+ytE1MFzvoWEvcTnSfwQBAKD0M8ClNxwsfwK+3ui2myf/e/VcQubGRgWzu+0vH32eFLD6qlGKfhMCAADYG5ddVIgdnxUbGH7x6HOkSMr9KAQAAItNYAIgUfGOBigKgZjAEB2yW2hRIQ0EAICs/fq+7xdyAuA3Dz0RHQ4GBRIVXGUjksy/eiw+CzIhEAAAbJEAKLrhYPk0AZPBhfwDsZHgri9cWwjBAAEAwF4KpCPQhLJ4MrjcIkEhBAMEAICSTQCYkAYo2JUDQpYGgzSTvqWCAACQtUJOAJiQBijYGkC2wSD2dcshAACUVAYYSkAon4V+rP8PFUiHcmseKqoAAAAASUVORK5CYII="}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 4. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The extraction direction is configured and the required clearance actions are complete; attempt the extraction.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-551cfd0150e9", "created_at_unix_s": 1788845755.347132, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 5, "requested_group_seed": 5, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [5], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass-banded item is the Axe (item 1). Its retaining hook is engaged; release it, then set the clear forward extraction path.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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m1gYCyS1CGSABADlDAICSbv3PcF8/Mto3tpyukH3/nBn3bs0hDaD70bc+Pfp0Yolo5u9gLL+zBgHoEACghFv/M/3zZkTUVwFWqwK5JgAeO9uSQxpAnxFix66mHbuaEj6hJ0+3nDh1zldWfu/tl+R2PgBZQQCAEjVWma63/XMea/ahAp2wu249L/H8mwfJcBg4furc6bOt8Q2OnG5PWZ7SGGaAgPFAAIASZbIE1VMAanMRE/PTEWh0K5DxMZgUSny578OWlo5+wxYpsz8WgAkIAFBqkqZ6zkzt/E8dlGpLglXOyve5aYdMfuMPr+5IeqdvMPR+01l/MJLUBWh695OBKCIAWAYBAEqUPq9nhs/Vln+9I6hMKHc4hNqps//urV/l41xS1gYwiE56JcBXVq59zM+19314uiN5G078ER6W8nGOYEdYDwDsamSIgJoLkKXIicMfLrn40vwfeGRNmOffPMgifsONFIU1nTSY4UdmZCDMRYE6jUY3A2QFNQAoKY9+/4Ysv6F2BeKcuzyeWTMvGOhtJ/k0MjV0+kqApMQ+GRwaXqBmtKjCB8M8q56i3d098Uf2Zw0lCzUAKFw3zzuU1fbPnWrsPLn77pf/9WfXvz7mxpwTURSZ6HA4HE6XSxDEto7m/Z+7n+Rf4urAgrvCqYzKAJsRkrgrSqotPzOwGQQAKOISP8n/+9LCwc5TpjdXhjvVUM44Z8r8Bcv3k3z55SNPfvVLdyRFgNR5KqShPuKNFeyhgHHrkP7FoZGpgwByhCYgKBpJPSA5H/WOusJXX5sSHrz18unm9sfUgpSqiwRwbTFInt9eoIlGHUkSvWa+M7VrVCZZxmhhGDcEACgaSV3oY6v2JryMhvo4ixJmdiIdzhjn6pow+qP1+J5sT+mLDfXZfiV1TghCyJxlq7LdD8D4IQBA6VCiQaJI1GwA4IwxNQYoijogWJEzzhxhrPX+z5DcDEeAeB2mtS/U2heKt/8ATAAEAChilTc8kviScolymTHlz803jvldzkVtAh5Be6jLxAt5HgmcOhogblF97DfRid6dMIEQAKDI2v0zYWrpH40q4Yjy+/3XjbFnInItG6tNBao+sp1lYfOWe0juRgWbV7cdvuvv12+ufHnpvOn6wmQVZR5ikas+cZdVu4ISgwAARdbunwmTmaxE1ADAwtGxJvfX9xVfEEadGXSCZllIHA2QNCDgkuUNc2fWUoH29vYmbtMYfErELytYDf+moHRwFiv9I1EWkfgDb12VcWttNujY/7iaBeATdvufNphVlXkuuahh1ZLZSe97nKTclecmKrAfBAAoHWrjj/pQb/+jEpczJoPV+Z/VFWP0+37OuTotXL5L/wxpgOHTIjPqKjesnp/6SbmLepwIAWAlBAAoHfHb/6jMJYXLCv/ByxvTbSwKojoAQHuuLQxAZVkdGjYB9/7xqaHjL7mWBtj0dTU8CAJ1GDX3CAKpdFNH+kUuAbKFAACl46G9C8Na4496+69whRFFId9+/sqMXxpp94lQz8SU/nHZluUuB6lwZ1jnGCA7CABQUp4+tUwr/Yn6YFxmahj4h2c3pBkFFi/91SSAt6J60zuj+pXmtfRXJRTlJhMQZS7qzaYh6Mvf/H72pwV2gbmAoNTojT+KNsBLrQRoj//2+w0S44/euS2+mawkTKuvzSrBOR/o7U63281b7hnnVEW6jtf/ado1PzSaF25E0jowa6Sn4s8pJRVuKinqnKBmDidFo+M8YShhqAFAqXm992KFxWsARI0ETH2uMPK536z/3OPr9c20leAFfV14vTuoFI2Eg4HUHW7eco/19/4pnUHNc4qk0qOPFgAYFwQAKEE7Aiv0cn/4T3UtLXXyNK2w/9zj6+/Sw4BWhmoDwdTVwZweD2fK7UdejO/n2+/9o5miX9+xfgg2XOfQD8pHP1LlVox7HGpb0Jibof0HMkMTEJSmfdKqpWRfrCzmahiIt7Vo3T6JIkcpFdVVIYcL0pB/gDHW0Xr27tPXhIM+X3X9/v6KMQ+kF+ux0n/4uf6+IBBRm7GOa2OOjUdyDTcCfedLm83/dHpD0Jibof0HMkMNAErWEbIqXvqPZHuH860CiwixIjlWNaCi6PL6RKcrOOR2eny1cxZl3j/nanSRGZG0R1ThYZmHpNgjKPFAhAei6vOIrL4MSerGehogYTc5NuWYHBgcDAZz2z/YAWoAUMpOO1erM+kH9qSmWiOSIorqurqUcO0mnYcH+11uj8/jFh2uivpZOw4aLMmr48P3+7HsghZBFK6W7+rYMnWpAX10mdplk1KtvV57w6UQr5M6h8vu69cvfXnbEeNEsFEGOFso/SEz1ACg9HWWrUm6/SeELAm/RdWxYCMzAnGmeNye6ee2eitr9p9LOyhMb+6XtVv+iMwjMonIJCSTqExkRe14KquTTKuRQN9MVrg6MI3xqKJWCAbDPCCNLvBppi5AOfvM1+61ZD9QwhAAwBY6y9d0lq/pKo9FAp16sy86GVP05cV8Trra1eT0+I6mb/rXAoV64y+ppT+JqN0xtXJfK/rVrO9wi1Jio5MeM/Q/IzIfipBDL34n3z8ywJjQBAT20lWhxoB6v7r4F+fM4XRJ0ZBeAaiuqm4Vykg409fVTj7afb2kqA9FK/TND+PSt9RDRQL6nS99jFgtGo2KOaxxA3aCfx9gR10Va7oq1jAlIoqi3gDDCS93JAwNM6I3+kdltTFH0vqYxm/5sxUPADmPBhhTJBzxa0LhjDENbAw1ALAvJkUEwTe8FsDYxbja8sO4Ngo3lgSeoAUExu3kyZOTfQpQiFADAPua6osKgqDlYE0FgFjLT2x8WbYLiCXbvfXbWU0Ckeo3D/4kw6dDgdCo17lVVaCkoQYAtsY5F0WXngQec2NZu/2Pt/xYJatWoOdONZrc8s3nfvex274gxKeM0HubIgZAAtQAwNbkaMDh0G6DzDUBKUXV8tN6vjPxx0qY/RRAhQAAtiZFw6JTrQGY2lhRy9DRHXgscPGSBpIfnd19wRAywJAWAgDY2txaQRAESgUzMUDvBWQhwzRAZhc2rjW/cSQSPXP2vKKvjanNdpHt4aC0IQcAdtff3VZRXWumeWRH13LLj565OBdW/3PT8baINNw/VQ1UWXCI4qA/IAqiWvqrOQD9PwAxqAGA7VHmdLkLIT2a1AVo3UXzxrnD7uO7OOddPf1Mn6LI9Kr3YBMIAGB3DtEhOtwFm9kduf3X1rHM9usOUYxGJUlWrM9dQPFDAAC7m1cnauMAJufoOWSAs0oD6BhjsqymsLP9IpQ2BAAA0nX+TGEusdh0PO2U1CZ1H9+l/ocTWVEUBbUAGAUBAIAQqqiZ0gKz6+BZK3enxQB1omqAYQgAAEQUHYJYWD3iGFED0pVr5jv1cWrjSAOMfJVztSEIYBgCAAChRBAdzsm9EIbrwKRrmMohDaBSxzGgBgAjEAAAyLw6oTATpC+/e3iyT6GkbLjp8xtu+vxkn0UBKaxqL8BkaTl9SOsONKF+cX/i6vATohDD3MR598XH9TAQf25zCAAAI8u4FyCnU5T0uRziOMtqSHCk7YAwZYH1Z1a0EAbiEAAANAVa/pPFDVObTrQlxwCwKAwsvewGQsiRnX+y5xVFDgBAtaDeUWhdgHQfHGpO3YBSOnf5uok9KShBBfSPHorLzfMOTfYplA7DLkAwAY7s/NPSy25YetkN9qwEIABAFlDoFyt9OTBIb6ktYwCagCAXPOV5vBel4Uv9HX1FQv2j1Jd2Y74LkNfjdKvzlY7gDOO5LHPEfuV+HAIA5IKmPI/PNGz4Un+Hai/1j1JfQjq9A8FxXhyHQ8RCAGPGgKVaQthW0AQEUFicTmdEsni8bjQ0ZO0OoTSgBgBQrBlgdASy0BFbVgIQACB3SQ335tvxk9r9M7/MduclwO3ELyZMBPw7g9wlNdybb8dPavfP/DLbnReFbCeBCITCeTsXsG8lADkAgOLgdrkj0Uj8JWcKLbw1DIrduZZWYieoAQBAiRDrFk/2KRQZBACAAtIdjP1KIg2QA6X7qCV/C+VzVhJ7QAAAKI4uQIZpAHQEstbQub8RO0EOAIrbc6caSbFZvHbjZJ8CgAo1AICJds+nrzCzGVqBIN8QAAAKS3tf2h6fmBEIrIUAAFA0MBoArIUAAEXsNw/+hJRQAuDjt9wysecCdockMBSZYsz6muwCJKes++h2CmNODDd3+brTTR9k3mbV6rX79u7O9TRLlnf2ilDLfsOPyuestEOPINQAAAolA9zTN6g/Odm0ZwLPCEaxQ7kfhwAAUCgdQJvPdYy5B1mx06x4kGcIAACFouV8Z7ZfwdJg4xFK0/5jH8gBgE3b3wvN6suv3LbToDwykwYYk6xgCcksBPyxtriShxoAQEFkgKuryoMBf/yl5WkAgTD9Ye1ui1rZLLvf0CAAABREAsDn85jcT2oaADMCQW4QAAAKogsQ1VZtr6v2jrkTSpAHtkag9ZDB5a2epyZX+k8RG0AAACgg1Gjls8RJgXIr/ds7e8Z3XlCaEAAACnQG0NQ0gGHpb6YjUNexndkeHewAAQBg8jUfPRB7lv7+PrH0VxTkcsEC6AYKMEHCQX+268BY2NjP/G3W7QxKBGoAABPE7Ux7v7V84yeikpThu66ENIAhdASCHKAGAJB3i9duvGLlvLVLZ6TboLtn4FyLwTDgU0175i1fk+ezg+QuQPaBGgDARBjo78vw6ZzZ0w59qPU7NOgEBGaJdYstuVjcHn1AEQAAJrnxR88AL1nYoLf333zzjYyIZvaZmAfGjECxa9J9dJx/U3aDGgBA3t39mTH6gFaU+5YsahC4/MzWV9Jt43SaCgwwHlyJcCVin2uIAACQX4vXboxEo4nvpHYB+vWvfrvtrTcJIYOD/nkXzly+fnP8o1NYG2DCVMwiNoMAAJBf11668PUdB665bFm6DZZedl38udvlPHXmfFRKXhrMDHQEsoa/ldgGAgBAfi2bNz3zBkd2vjbyQpsKor9/ZMRABhgOZv3tv99GpT8CAEBhiUYlp0gCwbDhp0gDgLVQAwDIo5/f/93MG/imTEt8qTA22HyAZz8EGB2BxqXCjrf/CAAAk8xMSY88MOQJagAA+fX6juGJ3gy7ACVEgKGWJv2J+dFgSANYoMKmt//qv7Tbrt8w2ecAUDrWfez21De/sGleugDgrZ627pI1nPO3t+9TFBYO9DfMndty9mx/QJ3kWRCFeQ0zL1o2f8+Ot/XtnaIQDI+aNejWOz6T+PKB//lNq38me3T99NsxAGAuIABrVDSsIoQcPHJWEOgX7rhx9IdD6b/HZYWJovDR9WoYON/RffDQyYEQp4JaO+ecnDxz/uSZ83OqY1vLKRNB//zRrYTzeEXCN6vRV1bRfew9/L1mwW/H0h8BAMBiThImjBz78Ejim42XzUm3/dBQ4N233x31O8mYzyFJo4cCdA4IU6uchjkDUckQXSAtsXKm2oY2aNOiX4caAID1mMIEUdAb6I0WeYyJUi9RGFf78OhbUsYYpdTpcDDGFWVkqS+mbQCWFv3ncT2RBAawHtOaZERREEVB0BpzDBMA6mcCFQUq6tsJVBDVCX8EUXC5HIlfTHTJRQ2JL2vL1ZpBIrT/pCNWzhQrZyqD51H661ADALAeU2/rY25O3/6jVQ60/6vrPar/FwVCBMrVJwLhJBKNJu5K19ljapwwpEK5nwQBAMAa/rP7ymfFJvw5ePCwx+0aMwD0dBksApOeW/9P72Aw+RMl02piAOmgCQjAetGo9SVye3fsxj8cybTzaXVVlh8aShUCAIBlhloPx58HQ8bz+cR19gVyO0pqR6DaKl9uuwKbQxMQgJWosyz2q+V2Xn3NRzJsWT9jTjiirhPgcIhMYZxzQRQY4wKlXO1HpHBtoC+lVFYpibP9bFg97929o5Yt5KIHf5GQLQQAgLwoK/MmvZPUBWjG9Fq/X23NF0WRk1i5T6maGFaLflkRBCprgYEx1ts3GAyMNP139qLvP1gAAQAgL6ZUVRBCLp+X9sbc4XBUV1Uwrt7j0/j0P3rxr/3JCaeEqhsQ6vN6jh0/09YnzZiidvrs7s+x+QggEXIAAJapuHBd/HlNTeWY29PhEQBUoLEHHflTf1/dQBSS6hPB8Kg1JpNgaTAwCQEAIC8qK2LJgHxgjN94ZWPiO3Xl+F2GrOEfDUBeeD2xbvuGZi9YOp6dM861KSQAxgUBACAvRHVQbx6lWzgeS4OBeQgAANYTtNI/MQOcvA5Mrtr6YqPA+gZDhht0DBgHBoBUCAAA1vN6YvNA5E9Hr//jG5cnvoM0AGQLAQDAehXl1meAV60clTYY8BvXAHToCARmIAAAWK9mSmX+MsCZcwAA5iEAAFhvSrU6CkxRWD4WbdfTAFyNASOTQwDkAAEAwPpRYOVl3ksbYmmAPIUBQshgIGScBuBYPgxMQQAAsJ7bPWqVrh8/+lI+CuXOHuMZgarKMTEcmIIAAGA1qs7pZvC+1TGgd9B4RqBptWoDFMCYEAAALOZ05H2ORT0NEIka54F3nxhERyAwAwEAwGI+X/oWGM6uvPZaqw6kzRVtMCFEMIDJosEUBAAAi1VXlsczwLEEQIJTZ1qtOhDjnHGO4WCQMwQAAIvV1kzcqryhsPH6wCsXzZiwc4DihQVhACxWVZnHiaCT9AwEyn2Zph2F0vCNf/hi0jvPvPpB0jvNH+7PdrcIAAAWDwLwetPmAK7cdJ1VTUD66mDdfYGGGTXptpm7fN3ppuRiwj7mLl+b9I4iG1SYmrMvN0sGmoAALLZ+gTddAsByQ6EIIQRpAMgNAgCA9RNBT5hoVFYXDwbICZqAAKzAY13yPc5Mo3A7OrrOnD6b80EoSZr8x6lwHonKHteogceQ6L/efHnSBfnV1m2JL+cuW3368F57XjQEAABLf6Mc4pjbcE4MRwpnq70vPH2KZygYMQwAKxfN+NuxNgsOU7I4KQb/8r27DQOYKIz6l6awXGYGRAAAsNK371qf7qPFF6+yPAYQQvr9obrq8o9vXP7Ht5us2aMNaCPouD5tByHkgiUrctvPqosbTW65dvmFY27zyO//NJ4Alu6nyJDlRgAAyJd0GWBK1QBglUAomvrm7kPNaxsvQEegfNt34FDiyxXLlymMcUJcJiqC2Zf4iQzHgGcNAQDAAv6z+yoaRm7wJ5IsY/LnQrG/6XDiy4saYyv/cG3eDoHSR5/KucTPCwQAgMlgUftPhinhwLyOSNU094DlV+zgoSPx500JzwsHuoECWODn93838waJCQCVRU1A7X3hUFTSd5Y0GgCyMkCmlsAV62V1rqlZLDiKAABgmR9+/e/GTAA0LltobQ1AlhVZNugBsvtQM2YEMo9xNnPZFZdffxspWh2RKs6YJMkL1pqdcRYBAKC4cUJCEeMp4cA8j8dTVuYr9ivGOVdk1SXXfdLM9sgBAFhj09o5GT7dsX1X/LkiqfM35EAQDQd8eQYD4cqytAPQbD4jkBlitC/Q3bfzjWbq9BHiqfWEzX/3y7duzPm4vsran/zqD8QKHZEqQRBcLpfb4xZF8ZUXX9J/kMwzHSEAAORdj9+gp6ZV2rsGZk2tmj21On+HKDlpMzBcCj728EOZv3ziva1WnUdwsGc88SPui5/coD95+Jm3pM72k51Hat2mUk0IAADWZICvunSZye1FpzuHSkCa23+VPxDbG4aDmUMTi8ZNVzQmzqhqYfle+JADALBevicBTRIKS4bjgnYfap7I0ygepvpgMQtH6xUq1AAAJkFulYB0ZEWRFeY0Gn1amDMC3XnnTXrG8nhzd2vHAGNpx7KV+dyhsCRQ6vU4p1T6ptWWV5S5qblpNLb/7byZGkA6glWTdRQwBAAAC7y+44D5JqB8iERlwwBQyCilM+oqunqHkjox1VX5GhdME8XRPw7n5zoG9h5pNXljTglxuYq+V0++IQAAWCBD6d98LO0cbZ66hvjzcHfu00Sf7CCXXtSQIQ1QkB2BOCG03OuqrSpr6/YnrmowGIgMBaNVFSPr6qioOsGOIFCmmAoApd98YwUEAACLxwCbTwCMp9BPEpWMZwPefajZ4Sy41QKeeOLFO+/8OCGcCnTe7JrWzn4qCJwxh8OhMBaVlWNnu5cvmOZ2Od58/0TbiQ/jX7xgyfJ48w1nLPGjO+/4+BsfnCQJH6UuCQlJkAQGKAWDgSy6rhcGqo+HdjnF1Bv3YCg6GIgIQnIrvMxY6puxb+KeP3sIAACloN8fIsVHbQVKnBVDFGIlEuO8dyCozaycLF2qg6Iwyx6uGcB4vb7jQA4JAGu9v+2d+POkWeEKeEYgGv9Tf1ZT5dOfcEL6B0OGU1ykz3WXfqedbDUfTfsvU4cAAGBlBniCRwAkMrxfLpbRAD6PmqiYObUy3iIUjiq9AyGDvj2jmoxgXBAAAPI4C/REikiZFgaYu3wdKWDTaisIIVVl7hn1lfo7nKitQEmbUUrLvC7c6lsFAQCgRAwFLRtZNvGm1ZZr7fjCzOEAoPcHTdqMUrUGYHIgGIwJAQAgXyYsAaAbSMgDF93iMB53rK+qxz3SNz11nQPO1S3tMEbXEmMudo8AADAuTCmUFRlfe/XPpGgl3NSPFO6GOQ2v25EmDYB+oFnDQDAAy0xiBjgzWZIKcDhYbhyi4HCIJKWDkCynnVCo0ELFr59+Lf78C7dfN4lnggAAUAoZYJvgnDudomENYChkftEFU5PB5VagE0IkUZ+MP72EDR5+9u3M21qyYEA6CAAApYMTPmpgVRHMCGRAoDTDjG+iIJT7XD39waQtevoDpo/A81egT4pHt7zLuFYBSsmOXLBkRYZFwRAAAPKSAJjgDLDu3x9+8mtfvrPYF4dxO8VQVE47dJiSMo9LnRVudJNP6qABkzWAv2w/FCs9C6ZAz1lXtIoQUu8aMLk9ksAAJZ4A0MlS0SwcX13hzdjLh/q8TqcjuewKhovmB5yYMGAGagAAMDkLwsRd85FF8T8VxpSE5c3UkQGLltVqKwQ4tBUCOGPt3f5IVNY/iu9ESagROJ2Oj91w9dGzg8SuuqJVAouMubo9agAAUEBEgdZPKRNHT/k5FIwEQ7EbfCrQcp8781AAxng2OWFi/pa5kOXwUyAAAFjfBWhSEgC6Xzz8RPEMB4vd6L/+3rHEl9UVHu/wuDBdVGI9A0Ftxmc1C+B2iY6UJqBR++VcSrNAgqGaMqI4iz8GhLpGPUxAExBA0Q8BM69AOgI98cSLye8kLO1iqPUY2Z7NIVqPEZMLwmy6vLFhZk38pa9i5HnPQOCV7Ye721tIMaivcnUNjNR7WGSQeFyZv4IAAFD6GeDSGw6WP0F/b/y5VyD/ZcN8QuYnRgW9u+1PH3ueFLD6qjGKfh0CAADYG80uKiSOz0oMDD957DlSJOV+HAIAgMUmMQGQqnhHAxSFYEJgiA/ZLbSokAECAEDWfn7/dws5AfCLh5+IDweDAokKnrJRSeafPZ6cBZkUCAAAtkgAFN1wsHyahMngwoGBxEhw9+dvKoRggAAAYC8F0hFoUlk8GVxukaAQggECAEDJJgB0SAMU7MoBYUuDQYZJ39JBAADIWiEnAHRIAxRsDSDbYJD4vuUQAABKKgMMJSCcz0I/0f8HVPlauRfvHQEAAAAASUVORK5CYII="}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The selected axe is held by its matching retaining hook, visible adjacent to it. Release only item 1’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "With the hook released, the TOP view shows the axe clear toward the FRONT camera; its rearward path would meet the horizontal weapon behind it. Set Forward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 5. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The configured forward trajectory is the cleared route after releasing the hook. Attempt extraction now.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-bfe146465260", "created_at_unix_s": 1788846065.007774, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 6, "requested_group_seed": 6, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [6], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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Q7nAITdPmvOgfl5gt2NoAJg/3eiXA66vW3ubnu4c+ONNjejZJkrsH/FFJRgdQmCQEAKg0//i5xer/Jnw+Vp+gE4NzuSxFPzy6p7o6n9nZ8q4CvPzWodTHf52isMOnzGf44YQEw7HegVHMAAGThAAAFeXJb93242dfyqWzpNoViHPu8nhmz7pkZLC7oJc3NjV0+kqApMTf8Y+mXaCGcz7oD2b/uf39A4mvXK8ZKhi6gULpumvBkZyOf+l0W++pPfr2hAldzokoikx0OBwOp8slCGJXT/uXVh0ghTe2LCQhgrvGqYzLAGfJ2BcIID8IAFDGJX6Sf/zcYn/v6awPV6fI1zKplDPOmbJw0UpCDhRtNMDYCmEG0ugQqarXt8PBQIYTepzOAl0q2AeagKBsJHV74XzcHnWFr6EuJeK/55oZ2Z2PaYOyBEIp1xaD5MWbY23cJ0liVVbfI457XEMCACYPAQDKRlKrjjqBg2EPpSQWHuIsRljaiXSScMY4V9eE0b86T+4lhZc6JwQhZO6KNUX4aIAkCABQOZRYiCgSzTYAcMaYGgMURR0QrMja/NDFcjECJOownUPhzqFwov0n/Xeo0P4DlkAAgDJWe9sTxpeUS5TLjCm3rZy4UYVzUZuAR9C+RHVNyAKPBE4dDZCwpCX+m+h0iumO8bjHGv3R/gOWQACAMmv3z4SppX8spkSiylWz05ak8VMRUZ+WTZsKVP0q7tDaccHm9R1HH/jYhgc+tmH5ghn6wmQ1Pk/SN0xvqnGI+fzCXn/nA5O7VKhYCABQZu3+mTCZyUpUDQAsEpuol6R+rsSCMOrUCkWKAMbRAEkDAq5Y2Tp/VhMV6ODgoPGY9asWNNR4G2t9WA4MLIQAAJWDs3jpH42xqMTneTPGAG38lzYZtNqdSM0CFHdyHdNgVufzXLGqdc2yOalviaIwrbHa53EhAQBWQQCAyqE2/qhf6uN/TOKyTKYJamd/U+r8z+oSYPpzP+dcnRau0FeYIQ1w8bLIzObajZcvNH3T7XLMaK5FAgCsggAAlSPx+B+TuaRwWeEK47WyeacgURDVAQDatrYwAJXltNGiQFNDJ15yLQ2wenmrtlo9TdfWTymtTckNAOQNAQAqxw/3LY5ojT/q479a+hNFITLjrrCU/pvG2n2itNhlax41jqOnCjtbEdgKAgBUlG2nV2ilP1G/GJeZGgZkxpk/ZjYKLFH6q0mAqpr650c/NVURoEAJiM9/5VuFOTFUAswFBJU3lZCQzdQKBzoM1QJtVgnO+chgP9Em5C+ccZMCjZsXbkzSOjDrVy1IbOeaAJBiyZEPIAE1ALApbSV4QV8XXu8OKsWikVAOcyxb2xkUoPgQAKAyjTXupNmfaIHRBoKp+VWnx8OZ8mbVl0s2DZDrEmBo/4HMEACgMtE0JWzipSLHKBXVcVUXd4UDI7IU6ek8V7yrNFzQ337ulgmPRfsPWAsBAGxKYFEh3tsyPhiYiqKryis6XcUdDVDYwQehUKig54eyhgAANhWVFFFU5wuihFOqZoAj/mEpGPB63EW7BtOpoZMkMsB5LAGP0h8yQwAAm/rs0nepOhZsbEYgzhSP27PF+UyxL4Vm6gKUt7/44kOWnAcqGAIA2JfX4xZFJ2OKvryY10kfXLGvOB898ZwQKaJFHKgMNoEAAPbFOXNoLf56BaC+Lu1iLAVGs8kA5yqGEQAwEQQAsC+mREVR1BtgOOF/ueTPxb+GLEcD5JEAiEaiAU04Esnr0qDyYSQw2BeTooLgvbgWQHEngy6uU6dOTfUlQClCDQDs6wsr9wuCoOVgpyAAmKYBkjLATQ21ktb0H5XUiSvmta3L/vyjwfC41xdXvgFIQA0AbI1zLoouPQmc+u5Lp9umthVoOBD2uJ0zmmrzOO1bLz1986c+I2gLTI5NO4QYAAaoAYCtybGgw6E9BpVkExDjvG9oNO8poDsv9Bp/LMPspwAqBACwNSkW0Yb+TmXJeOkydR2YdCRZ0dt/CJ9oleMUvf1DoTAywJAWAgDY2n9bf0wQBEqFKYkBeYwGyCkNEI3Gzp67oOhrommzXeT6cVDZkAOAypFfk/1wf1dNfRPnvDgt/rnyOJ3xGkDuHKLoDwRFQVRLfzUHoP8PIA41ALA9ypwudymkR5O6AFFxss9n/Sd3c877BoYZ05qPCr/qPZQXBACwO4foEB1u015ApWDc43/uaQCHKMZikiQrnJXoDwhTCAEA7G5Bs6iNAyjFDPDk0wA6xpgsK6XZ0wmmEAIAAOm7cJYm+suXEo/TOckz9J/U1hDmRFYURUEtAMZBAAAghCpqprTEKIqlD+xaDOB6MgBAgwAAQETRIUw642otRtSAdN3ahU59nNok0gBj38q52hAEcBECAAChRBAdk21smSTTdWDSNUzlkQZQcaKgBgAGCAAAZEGzUJoJ0n/ffnSqL6GibLzj0xvv+PRUX0UJKa1qL8BU6ThzROsOVFSPfe/rRf7EUu3sWiTbX/mZHgYS2zaHAACgYqVaNDocgqRN5TCGM6LOXZGtaNdBoWGR5RdWvhAGEhAAADQlWv6TS2Y0nO4YkPT5fMDqMLD86tsIIcd2/caetxY5AADVohZHqXUB0h0xmwuaUjp/5friXhRUoBL6Rw/l5a4FR6b6EiqHaRcgKIJju36z/Orbll99mz0rAQgAkAMU+uVKXw4M0ltuyxiAJiDIB0/ZTvSiNH2p79FXJNTfSn1pNzl1AXKr85WO4QzjuSxzzH7lfgICAOSDpmwnZho2fanvodpL/a3Ul5BOOJrnegAJDoeIhQAmjAHLtYSwraAJCKC0ONUVYCyesScWHrX2hFAZUAMAKNcMMDoCWeiYLSsBCACQv6SG++zb8ZPa/TO/zPXkFcDtxC8mFAP+nUH+khrus2/HT2r3z/wy15OXhVwngQiGIwW7FrBvJQA5AIDy4Ha5o7Fo4iVnCi29NQzK3fmOTmInqAEAQIUQm5dO9SWUGQQAgBLSH4r/SiINkAel/7glfwvVc1cTe0AAACiPLkCmaQB0BLLW6Pk/EztBDgDK20un20i5Wbpu01RfAoAKNQCAYvvr+67N5jC0AkGhIQAAlJbuobQ9PjEjEFgLAQCgbGA0AFgLAQDK2M8f/T6poATA7XffXdxrAbtDEhjKTDlmfbPsAiSnrPvodgoTTgw3f+X6M4ffz3zMmsvX7d+3J9/LrFhVcy4Ldxwwfat67mo79AhCDQCgVDLAA0N+fePU4b1FvCIYxw7lfgJqAACl0gG0/XyPvjHEatMdIyt8z4HjlFI5FnO4nbLEOaFzfAW4VrABBACAUtFxoVffaBDiVQFTV12+zPiSCuKETUBgKpym/cc+EADApu3vpebya67bscukPMomDTAhWcESkjkIBjIF4EqCHABASWSA6+uqQ8FA4qXlaQCBMP3L2tOWNd9suz/QIAAAlEQCwOv1ZHkeWUleHAczAkF+EAAASqILENVWbW+ur5rwJJTYaXW0Qgp2HkndSesXqMstDJ8mNoAAAFBCqNnKZ8ZJgfIr/bt7ByZ3XVCZEAAASnQG0NQ0gGnpz9nECd6+E7ty/XSwAwQAgKnXfvxgfCv9872x9FcU5HLBAugGClAkkVAg13VgLGzsZ4Eu604GFQI1AICpt3LTnTFJynCAy5AGMIWOQJAHBACAIiUAvvqfbk53QP/AyPmO+DBgo9OYFKi4qNYFyD4QAACK3f6Tau6c6Uc+0PodmnQCgmyJzUstuVncHn1AEQAApl778YPLFrfq7f133bWFETGb7zLmgbPpCGQHSv/xqb6EMoMaAMDUq6n2LlvSKnD5uRdfS3eM05lVYIDJ4EqUK1H73EMEAIDCJwD4uF6bqV2AfvqTX+z441uEEL8/sGDerJUbbkm8hTRA8dTMJjaDAABQYJxFw8EMGeDlV9+U2Ha7nKfPXohJyUuDZQMdgawR6CS2gQAAMMWO7Xpj7IU2FcTwcKaMcQKGg1n/+B+wUemPAABQWmIxySmSYChi+i7SAGAt1AAACmjp2o2ZD/A2TDe+VBjztx/kuQ8BRkegSamx4+M/AgDAFMumpEceGAoENQCAwoqGg5m6ABkiwGjHYX0j+9FgSANYoMamj/+YDA7AYutvvjd152duzDTBwNa/+hrn/O2d+5UZCyLB4SWb7+k4dy46a636gCYKC1pnrVqxcO87b+sHez3OUGTcrEEfv9fwiZ+855H/8RXLfhg7qLFd108jzAYKYI2a1jWEkEPHzgkC/cz9W8a/OZr++7isMFEUPrJhLef8Qk//oSOnRsKcCmrtnHNy6uyFU2cvzK2PHy2nTAT9oydfJJwnKhLe2W1eX03/iXfx95qDgB0f/xEAACzmJBHCyIkPjhl3tl09N93xo6PB7W9vH/c7yZjXIUnjhwL0jgjT6pymOQNRyRBdIC2xdpbahua3adGvQw0AwHpMYYIo6A30Zos8xsVoFVEYV/vw6EdSxhil1OlwMMYVZWyGH6YdAJYW/RdwP5EEBrAe05pkRFEQRUHQGnNMM8DqewIVBSrqxwlUENUJfwRRcLkcxm80umJVq/FlU7VaMzBC+086Yu0ssXaW4r+A0l+HGgCA9Zj6WB93V/r2H61yoP2nrveo/icKhAiUqxsC4SQaixlPpesdyGqcMKRCuZ8EAQDAGoFz+6tnr9C3Dx066nG7JgwAA30mi8Ck59b/N+gPJb+jZFpNDCAdNAEBWC8Ws75E7u6PP/hHoplOPr25zvKPhkqFAABgmdHOo4ntUNh8Pp+E3qFxA8Syl9oRqKnOm9+pwObQBARgJer0xX+13M4bPnpVhiNbZs6NRGPqkQ6RKYxzLogCY1yglKv9iBSuDfSllMoqxTjbz8bLF2zfN27ZQi568BcJuUIAACgIn68qaU9SF6CZM5oCAbU1XxRFTuLlPqVqYlgt+mVFEKisBQbG2OCQPxQca/rvHUTff7AAAgBAQTTU1RBCrlmQ9sHc4XDU19Uwrj7j08T0P3rxr/3JCaeEqgcQ6q3ynDh5tmtImtmgdvrsH86z+QjACDkAAMvUzFuf2G5srJ3weHpxBAAVaPyLjv2p71cPEIWk+kQoorYdpYOlwSBLCAAABVFbE08GFAJjfMt1bcY9zdX4XYac4R8NQEFUeeLd9k3NWbR8MidnnGtTSABMCgIAQEGI6qDeAkq3cDyWBoPsIQAAWE/QSn9jBjh5HZh8dQ3FR4EN+cOmB/SMmAcGgFQIAADWq/LE54EonJ7BwO2bVhr3IA0AuUIAALBeTbX1GeA1q8elDUYC5jUAHToCQTYQAACs19hQW7gMcOYcAED2EAAArNdQr44CUxRWiEXb9TQAV2PA2OQQAHlAAACwfhRYta/qytZ4GqBAYYAQ4g+GTdMA6AgEWUIAALCe2z1ula7vPvkq4dbHgN4BzAgEk4IAAGA1qs7pZrLf6hgw6MeMQDApCAAAFnM6Cj7Hop4GiMbM88ABWR1/gI5AMCEEAACLeb3pp+bn7LrNm636IG2uaJMJIUJBNA1BVhAAACxWX1udyADHEwAGp892WvVBjHPGOYaDQd4QAAAs1tRYvFV5wxHz9YHREQiygQVhACxWV1vAiaCTDIwEq72Zph2FyvA3/+WzSXuee/39pD3tHxzI9bQIAAAWDwKoqkqbA7juxpusagLSVwfrHwq2zmxMd8z8levPHE4uJuwjm0KT5FVuVgw0AQFYbMOiqnQJAMuNhqOEEKQBID8IAADWTwRdNLGYrC4eDJAXNAEBWIHHu+R7nOn7gBLS09N39sy5vD+EkqTJf5wK59GY7HGNG3gMRvU13sT25+/ZRAj5yYs7jAfMX3H5maP77HnTEAAALP2NcogTHsM5MR0pnKvuociMBs9oKGoaADhTqDDxxdgYJ+Xg77/55aQ9egATx//lKiyfmQERAACs9LUHNqR7a+mlayyPAYSQ4UC4ub769k0r/9/bh605ow1oI+i4Pm0HIeSSZZfld541l7ZleeS6lfMmPOaJf/3NZAJYup8iQ5YbAQCgUNJlgClVA4BVguFY6k5FYfqixDbvCFRo+w8eMb68bOUKhTFOiCuLimDuJb6R6RjwnCEAAFggcG5/TevYA34xyXJB5pqGPBw4fNT4clVbfOUfrs3bIVD65C/zLvELAgEAYCpY1P6TYUo4yF5PtG66e8TyO3boyLHE9mHDdulAN1AAC/zoe9/IfIAxAaCyqAmoeygSjkn6yZJGA0BORsi0Crhjg6zZNS2HBUcRAAAs8w9f+g8TJgDaViy2tgYgy4osm/QAURTmwO931hhns1Zce82tnyJlqydaxxmTJHnRumxnnMU/EIDyxgkJR82nhMM0QdnzeDw+39iIgTLFOVdk1RU3fSKb45EDALDGjevmZnj3nZ27E9uKpM7fkAdBNB3w5fEHI7U+kwFoDbXe4dEoOgJNSIwNBfuHdr3ZTp1eQjxNnkj2fyn64LL8eGubvv+TXxEr9ETrBEFwuVxuj1sUxddeeVX/QTLPdIQAAFBwAwGTnppW6e4bmT2tbs60+tS3fFVjyxJANhkYLoWeevyHme/Vh+++aNXNDPkHJhM/Ej77iY36xuPP/VHq7T7Ve6zJnVWqCQEAwJoM8PVXrsjyeNHpzqMSkObxXxUIxs+WNBxMFNDGa4oai8Ybr20zzqhqYfle+vDvA8B6hZ4ENEk4IpmOC9p7tL2Yl1E+suqDxSwcrVeqUAMAmAL5VQLSkRVFVpjTbPTp6iUz/3yii5SYrVvv0DOWJ9v7O3tGGEs7ls3ndYcjkkBplcfZUOud3lRd43PTyU6jMa4GkI5g1WQdJQwBAMACf3jnYPZNQIUQjcmmAaCUUUpnNtf0DY4mdWJqrvO2LZouiuN/HM7P94zsO9aZ5YM5JeS+Wyuha39BIQAAWCBD6d9+Iu0cbZ7m1sR2pD//aaJP9ZArV8VPZTorXEl2BOKE0OoqV1Odr6s/YFzVwB+MjoZidTVj6+qoqDrBjiBQpmQVALhWw5h0XaHCIQAAWDwGOPsEwGQK/SQxyXw24D1H2h3Oklst4JlnXtm69XZCOBXogjmNnb3DVBA4Yw6HQ2EsJisnzvWvXDTd7XK89d6HXR9+kPjGS5atTDTfcMaMb229//Y33z9FDG/5N65JjiIwHpLAAJXAH8yh63ppoPp4aJfT2NQTf7oPhWP+YFQQkp/fZcZSd8a/M6VicOJcr5QmLoIOAQCgEgwHwqT8qK1AxlkxEv1WGeeDIyFtZuVk6VIdNKUwu9DnP98zbOHlVh4EAIDJ+sM7B/NIAFjrvR1/SmwnzQq3eslMUqJo4k99q7HOq29wQob9YdMpLtLnulOqC4ryYXsfsbH242n/ZeoQAACszAAXeQSAkenzsp4GICXP61ETFbOm1SZahCIxZXAkuVpDk5uMJjAasqyvbUVCAAAo4CzQxRSVMi0MMH/lelLCpjfVEELqfO6ZLbX6Hk7UVqCkwyilvipX9j17jJ2LIBUCAECFKOun3elN1Vo7vjDrYgDQ+4MmHUapWgNA506rIAAAFErREgC6EUMeuOwWh/G4431VPe6xvump6xxwrh5phzG6lphwsXsEAIBJYUqprMj4xuu/JWXL8FA/VribNt9UuR1p0gBo7ckZBoIBWGYKM8CZyZJUgsPB8uMQBYdDJCkdhGQ57YRCpRYqfrrtjcT2Z+69aQqvBAEAoBIywDbBOXc6RdMawGg4Zu1kcPkV6IQQSdQn40/PcMDjz7+d+VhLFgxIBwEAoHJwwscNrCqDGYFMCJRmmPFNFIRqr2tgOJR0xMBwMOtP4IUr0KfEky9sZ1yrAKVkRy5ZdlmGRcEQAAAKkgAocgZY98+PP/vFz2/NMCtcWXA7xXBMTjt0mBKfx6XOCje+ySd10ECWNYDf7TwSLz1LpkDPW1+sjhDS4hrJ8ngkgQEqPAGgkyXzheNLUH1NVcZePtRb5XQ6ksuuUKRsfsDihIFsoAYAAFOzIEzCR69akvhTYUwxLG+mjgxYsqJJWyHAoa0QwBnr7g9EY7L+VuIkiqFG4HQ6br7thlMdo7KSfWa4ovTF6gQWnXB1e9QAAKCEiAJtafCJ46f8HA1FQ+H4Az4VaLXXnXkoAGN8NByb0VSbISOS3yNzKcvjp0AAALC+C9CUJAB0jz3+TPkMB4s/6P/h3RPGl/U1nqqL48J0MYkNjIS0GZ/VLIDbJTpSmoDGnZdzSVKWzJvm87qyuY5GH1Gc5R8Dwn3jvrKAJiCAsh8Clr0S6Qj0zDOvJO8xLO1iqvME2ZnLR3SeIBsuX7FobsuER954TVvrrMbES2/N2PbASPC1nUf7uztIOWipc/WNjPWFZVE/8UwQ/xAAACo/A1x5w8GyQSm9ZEZDrt8VCgwmtqsE8vGNCwlZaIwKenfbf3rqZVLCWuqyqvogAABAxXJmM3c0zS0qGMdnGQPD9596iZRJuZ+AAABgsSlMAKQq39EAZSFkCAyJIbulFhUyQAAAyNmPvveNUk4APPb4M4nhYFAiUcHjG5dk/sHPkrMgUwIBAMAWCYCyGw5WSFMwGVwkOGKMBF/+9B2lEAwQAADspUQ6Ak0piyeDyy8SlEIwQAAAqNgEgA5pgJJdOSBiaTDIMOlbOggAADkr5QSADmmAkq0B5BoMjPsthwAAUFEZYKgAkUIW+kb/H+g7hMVh2mEmAAAAAElFTkSuQmCC"}], 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"}]], 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass nomination band is on the axe’s handle, identifying item 1 as the target.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The selected axe overlaps its own retaining hook in the SIDE and TOP projections, so that hook must be released before removal.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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gb5Fl8gMECfGncCTmBYpj64A1r/zl7CvvGs2znUBQCyAbN55dvemj95aeciEviNjQgGloutrbc/TtN978xTPtk0n6xywACGUDAisIrMgRiEgtMHkOY1VVouGArqkMgqLoYDnSMogkDtFmcJTygzGwPZOxa9XEu/a3WXehrqlppT8lF6gCGGLnPvuSGsb/+95dy2dHZ8xd7K9pJg0SdKIDAoGu7a1bf/9aP5hEQGwgCHSTLPOtNFAWMogTWJbjeIF3Oh0ImE5R9Hg8DDDdbidrZQFxHAkIUyjlvTgtSzT+EKFhXkcq/SeMC+gL99yW+PB3f3ocTAQuX8x8962PFi87t+vYPgQM3dTDhrZjp/S/+oIlJ5+wvKnz/dceOfNy0mpiRDatJvlqObL0nNvBaIFNg2WgRn5dZFkg8EI0GmEYweESsYkNXdd1jRU5VZWBiTVVIW2CIIiZCBRKWS9ODJiEXAMadJqQCiBJ+s+s94Bxxs59bfNnN6d96kdfv/G+nz558llXHtu/lTFM3dR1gA/s2Q4ZBOAJyxvbc3yLjb+7NZ8zGj3xahoGC6CkaAxwkKZJgiDwPMuyLMPKqmQYOi8IpmlqisywHNB1yDJE/k/MCPCXv/0gY2W7slbAQ3R6//PfvjLWJ0XJCAZ42dz0P0zy9fGsrJJvkzJ+FUBa6f/gfV/8zo9/OyF0wEAwct8XrvrBQ8+ee/Gn9u/8AAKsmqZh6Pt3bA4PBIzzLgITHI3IdGBijCyZ6PJ4EYJkHoBuMKQ+ADgEIRgMYAgMbGDL+cpy/Kt/GmF68Ljin+77j8G7w+SFHA1+4/7/tO///If/NvonRslO2lXGkfaeaU219KOb2FlA41AHJDEQHBp7e/enLv7D449det2du7asRaZukmyEcMfRPatfl+5/6PUffvUyMKGzgHiESdN/YnbI0Ug4GNA1BWPM8wLDsFb/n24AISZqwWRYrrZ+qOhmIgj9nIhrAqoMxg9wuNM/IsmkTN3BH2nvAQBMa6qlRsC4VgDZnT/jRAeQVe1wIyBR9Md57c/fAwD8iud3b1mvyhI2sWyYXZ0H33/71X/60RP/+928PDzjCMOaCYlQzKuv65osRQDGEEJFljxeP0JECcjRCGJZxDD+mno7XDxBhf6O1h2knlnVAAAnnbwsuzKgPqLxgy39E6GmwLhWAOPf9W+TKMzmzWxIK/3jfPnyxp+rJx3Zt10OBwDWdA339R/9aM2bn/3u7/74oy+ACUggrEANcxyLBGPZRbdpiqoruolNnuMABKqi6S4IEMsKTl4QVUUy5GhEkcD4c+vnIvfjYEC8Wxs/+sR+mEkTUB/R2LJ0blMm6W9zpL2nrqZqdE9qMjD2LqDxZgTYZJf+Nt+4dtp/PRnt6ToW6u+GWMMGjkQCW9avvPFzP5g5b8HPvnUzmCBc/7Uf9Ha2hfv7DE0juXYQ6FoQIQExDM8S5w8kMyN5KRLgeVEUndFomLwMm03N08etWz+b0Mf4iloELY2PgWCYWDfxa91aoibIxSygPqLRJFX0d/cF66q9o3oSkw52/Cz/x4kOyIt//dS8G7/82rwTTu7vPAJ0DRi6AvDhvdsVVfvBX1Y9cNewrkHjlq62I50HDrB2WieEdsody3E1jc0AYMVqs8UJIoSwu/1oY9MMXtdlmSjIaHSMq28+/8/357zSB1fUEIlPipkTtpNEJobUPl/fxBik2hmv6CCFRRDmpAy+/t0f/b8ffbckfwslC5kW/pRxrQAmivNnWL1JnlnGz/7qn2/4wk+OP/3CrsN7iMdcVTGLOw/teuelwPcffvf7954PRgVzfUfiQ3R6Y+6vVUmrHxIFQIiBEJqGjhAbCQZlKcJxgm5qEALe6STxYdMACCMEGAhU0+gb6ANjLf1lWRFFIa3Qjy/2R8xVtTQDYMlu8PopoqUIgGqYr3WRv50k76ZTBnZpUgXqgEtv/CLGoHvXSgaRliE8x2KMdRNi6zNx8lDWgMhiRFqIA1klTcYFnhRvaQaWdcAhLGnkisMAMORFpOUUgAzGoGXpJRCCN56dYAvBicv4quJ58L4vju0JFNbY/rnffXvz6tebZy8GJF8SkoCAoUT72t57+Znv//oFMCoElWG3vLDkHfnTGYaU9XK8SOS+Rga+G4bOspzT5RNFVzQSEgSHaegsLwgOF7bd5+Ng7b+jdYd9i/85l1bDK2rRlbXoqjom30oFCADpioqgg2NumCJe2yRc3ShcXk9kHCYXCN740Sf2Lf6Sr3/3R6AyqJ52wmlLF3W1ruzeudK2k+xFU1JLcAYBEwNVJx8XJGYl2UclPWUBmyhyMDZNu8eUtQ8A+z9+c99Hb84/bs6cZReM+h9XiaDxtvwfcx1QGC/86f7XHv/pjJmLEcsChHSSKIlDA71r337zWz8ZGiFQPnR22C0vIAROtwciJEkhVZF0XSXSH0DTIJlBmiZDiFRV1lRFkaOB7s5IoM80DVvUnnXdnWBMpX9Pd5fH4wYAXFYNr6hBV1hCny1dfRqCkEVQZJnrm8VrGoWrGvgr6lkMMEJo0ydbKk0HYFIwjlONp6R8MPtBQu+G2IuTUi3IBkv0J+9okul8+Z5bd+9Avi+hlEsBTDjnT0l46YmftMw6QRCdGCGDJNbrA/19K19/dcaJ15T9vZ3Db/ng8TjrGuvcPr9DcAm8A2GoSrIguiAm7nGH00daQ0PS/kE3sKKopoGJ18g0IUQkVXRMpT+ZZCBEr6xFZKlvB3bLA4SARZBjEG8pA1s+VZoOgFjHVr8Q0/pAYn1ADLLegVhjgM5A4iQEhgKAQQQ5tiZLkOlyxBoAWAemChFmoYGAAYFBLAXDJFaXdSjTDuWTSUMkEkOplCygMYwGX379rXb/SwxGvubuuYnEdQcGrDSYdDz31/+48vZv9fa0S9GICU1oGpIU7u1sa1hwUeeOt0HZQL7CX/vOE78DAPgaZtRWOQ0THdyzPdOeJ19xazQcFhwOYrzrmgE10hZiTKX/ucJoB6JtHXNtI/diB0kc2vTJlqXLTrCfmvzxgJR1OdG7mPzXtLw9xE020oQ4299ItDWZJ0ruDS/zGtonXxbOnZZ2+7a1z9ot5BaecUMBh53EsNt2vmUNgB229YSFF4/4yp//+iex68H6pi654MTjF1xcquX/OM8IqqoiPockWtc9F79/8R0/nDn/hHBwAENTFFkEjFB/R91x5zlcXgBeGJ+a3ClwU6td7QPZtOBHrz6RtOWi279SUdI/DgTg2gbuxc7K0wGD2M3ZbKMLAuLx97kZWR0SJTGDLO4essO+g09xLApFdZ4hwt+KAhAjIK47IISX3/iZkp9z67rnqA5IhJUU4swdBgYbNr1u3z1laay3wf/89qdxnWwJ/UHJP/iNvvnOxz/71U8uueDEX/70LxPI+XP5DWUp3H3rkfvPv/X7y046uaenF0PoEMVgKCyFeqFJ6k5zJ1GpZGdOoS+0WXjGDSyDPA6+K5jfGb792C9B5Ul/GwgtO6CdZApNAh1gL5Ozc+6n7ieS2jaDrIiugcgdhIlzjEXIivGSsK4V2iU72p1FrG4OiCTakl4igCGDRKH9X4yx1VYQkFSgwSPHgsKUMsO+v35buu0xuf4/v/1p0pa45Le3DVpzsajPm+9sLNWZjYIR8I9f+SoZ9YVx10BU1VIUYXG8+8T3z73l/vOWn3W4rcfhEAPBsGkYipTRd1QkpIFnEbSue+6qz9zHQMwLjnyVR14Utv4qufTvnL1sxvT03SU5lg299WIedkDj6NkBucjosmNg22FqGgCTbB8rFwwCjmEVFZgGQsiKE5uABI4gMLGlDMjryM6GCRgATQxVDXCkk24sBw0ZEJvEKUQObpIGJGU6fWoEjOg5SPW+kW/I53UEglLSTvb3ZJoYIvId7964tYTL/7LqgF//4j/tGNXGLXsbvVxnX+krTd576ofLb/7OxRddEI1GUXePabXWBOUhlglfBHY2HmLID7B8lFW7JPLmHzK+Uefs9FVdcTwXX5uHDoDD4gEPffcWe/u9V4wDYV0GSIoOyd+PhW3J1DhSPIhJnHewiIZBVu2FZRxwKJbRQ3w9xCYgM6RhLM0n5vkZ2gERdzQ5VoEp2cm88s5H8fvVPtdZpyykOiC7Ahj63ONhGPtOIChZ/w4t/4cP6MGllf5l5fcP/XjDlj3kniX1LEuGhLBG5KW3Pszrjb75+etJ0Fhd3N7eJStSvb9cCqBU66WUlLyJyiWfS29qPPpObKhsdjwXX3va9Dxy5EYOmk0WWI7BxFwm14mV4GNiZFVzkXQeKwiMsGmSFDGAiW6wPf8YWxYAMIn+IP1kyUtIpRg2iTFhj5u2dkUIMiw0MOBzSMrIIu7TsnZDq32H2gEjxQ4TFAFJEbGX/8NVwihQJiMgJv0J5C/R9Fz9P3YxUFwR2p+DdSkPSmDLKYaIZ5M8sK5qeO48dt++GTP8nfvb8hsjnLu+UW8/c8QXXnPxqVmOoBmGaWIjKR+AUvEkSlUMkMhCDiLLeCZjesnY6JgDmJgFZA8G2dk9pmH/CmL5/kSdQsZWBsR9ZD1lWq1H7HUYJKWI5F+EYAQWYonG4pSxVR1h8NRiMQVrnQcTNUGFw2aT+vadQedPStx32AvLt/wvhw74wx8e/tzn7rUvTJKcTBr7D5FFu9mp5jDbmnsw5cH2jMaMYvipC+G+g+KHG/aBmJc4JwyD9GceVDmxC3xQ35BDW08SpKLViVPgDdMUoZKvlZOvpqGUnBFXviWCXHJR1ZBJiS/QDJIIhA2TgSZJ3MdQNSBx+FgRYAtiKBgmCRJYGaLQxINGFcaqAVjSVsTUyYAhCBFjYMBa7SQ4BmKxEJs2ZkTEfnsJ24ftQHb53NcydpGqWAWQ1vmTKAzTOH/sx+V2/pRRB1g+TWs5U3p3Ss/mx+RQz9Sz/3nvoU94kW3dHbrva/eA3UO9JEckNgd1qL4pzVVt/5PU/SG+0hkWvI/dtVUISNliLdEA1HXDssdjg7itPO/BEMGgTRP/maWleBUy+TTNaMlom/iSIfav/UMe/DZxzAkf87yT+zHhaNm38SsnfgXF19H2ysYO/HIMyQJSTSzyDIegokGeGdpN4IBhQFWHgPT4IVt5ltR5qVrMI8Qy5B14llE0Q9HJ0TSD7EBsiISE0fwZ+edLpX9GFxDPsaqmpzp/hlQCmDwQHfDZe8gQxFKnHLjMw4e3rZFDPcBQ21b9pxdilsGfuqaMi46k5M2hJIpEg2Rwi30/aYtuFWQyCNpjcoe92P6PbcnHy/+Hp4MlHT5xSzx/eLDrCxy+hYQQ7ce2uWPrF9uTFnsXWDJNs31HTywU3J5+jLPLFSuk7tmbX0ZsFl5ava+mpqZ/YOCqM2cOfwb/7a3W6uqaS05sSFTWtoiMiWb7I7I/Ecv+zCKjk7H2jk3UsncatGATr4IhizZu3yZcH0NmrHWCqo4RgAILVIOkbwos4lnSRFDVY/4Be3UweAg8XHXYpx97ZF9vHIusP5JsERBUdKyV0w35uX+ia/80CiB27amanhj79XlJemGm2K/9eHRiv2UKBvzhj3+68847rSVHyfCgjkPb1kmhbmCoCOisJf15Bugf/cgtwBLk66TD6C72CNaPDjNM5rBQ6oc0JCOSn0vcklAIlGlLghgbFB2xfPAEmWgvUYc2xf6NXZ9DsivFY2a5IIgwsq0ce4+kWtOE/GY7I2BQC8VimGSVYN9JWD6TA9r3bWH9zFs7DF0l2lSOGrpqpbNgweu/+uZbNm/cGOrvA3hG7M8ddFFHw6H5Cxau3NR2ZP8BTnA4PVXXLJ+V8LnEP6lEfT3oSElnGGb6jnLbaaRDmMRS1A3TgCbEBsYMJlk7jJXST9KDTJ30lAVWoqf1ERq2fW2ZlAzZleT5kNfYFwHZmYwdMq1cItMwEQkLMNDONSo5VPonYf/aY581z7GKOmz5byf/ZI79jmrmT5l0wF//+lf7zvU3fWrEnddsOkg8M1b/gzMWT0m7z+Ftq6VABzAUBAwi/RHmEBBY6BJAnRsuaGDFU8V1z4VK+1eYRTfCspL5iKMWjBWZFUx2JZIckknvMUuMCMYE0rDnhvzG1lN268/Bl8VjP9arYq984e09hq4D02StOZocy1Y7BI51MwzDIdMwdFVTP9mz55zTztq/70AkGvVPB++17+zvjhiyaEKN4XU5JBkG29/fP2/R8X09XYKp6aHuF1/u0QBDEqsRg3jxxgvngXEDafRAujtj3cAMEfskeX/Y06lyO50/B5sYkpBxfB8rlGwC6+CQpJUX5Ihdu+WwpVSS07eqva4//WH8dhYYQ9hMzp+h5X/m2G+S9K801m07lvjwxadI18/7bj0u2t+ODRVhg4Emkf6MJf15UOtCc+sYcZkAltaesfTudQ/874hvcd8DP/7xD+7L5WTM/NKL0oAgWYpZmdqTm9wkS7xDcQrPvN7KmabT5WSxSSYny5KiSKoalVVZ10mtK8exDEQsyzEMw4vOffv2RyOhUH9/ROlDLHJ6RIdTRCxUZE2OKEqQYTjWNHTe5YHhUE2Nx3pn3sSY53nEoJXvH5BkVTcBK4qXn20ZEGMKQgARZyHx3iRut420pE/XDhultguNmXx2hdjgaxCEhnUd2tfgi88+Gd992Xe/Ucw5D4TGuGh83MLagt2W/qnOH6vyK6PzJ+nxKCT+j4ceQQxCIs9pukE6mQ/XjJfN0Td9SNLL5s5gEqQ/cAlE+s+pY9wnCOCUGgAuAuD+M35w/7oH6kd8u/se+HEuZ4UfvquAVyUyd96i2O+Rko7n39mjhoIOnkO61h3oa9/ZSfJVeI4j/+dZjnM5XCwnYIwD/d3BcMg0TcTxvCAEerunzpz9T3ddnulz/fnDL3W1HXF7vd3tx0SWIy9koGGYECGB590ul9vl87h8Dqdj07aBUDgcVrTLl7eU9lv69vcezGW3quYFZLXOQsMgKT7WWp7k9lvVW0mL+sG6MKscbLhHbygp0zQNbDmJDNsJSerL7F1LWZmflOVBicNmTQFNSAcCyYxV2dfY6gCGQfXV7tNOmNMbiPQHo6GIHJFUWdUNw7xszlCR155Dscv3jAXAycMaJ5pVy/gWCeDMagCWAxDrG3zGD94D4L51D7xf/InhYH+RR0DQasyiqCtW7Vj791zrYAsgR5tm/PDX5zY4WYZnUTQysP9gh6ZrM+Yu+t7tI9d+PfbKxn+eY7fV2gd2vZlpt/88O3bne+uWABNyPO9wee++/jQAwMPPrImGg+1d7YfUA7puIIZrqGtsmTrj/fVHeoLhtatHadzQEGQcAFmjY6upgy3ciRyPe4JigWBr5U9aAQ06gJB1s9tCD+WUDQ4jIjVipKzAljNWllGJW7NQMiqA2PIfJC//BzIv/3dvHGqDPvnIslpoqPZ43eKCWQ2GiTXNUFQtIqmhqPLUz76edv91ZEoV/s7VTPUCHpxbBcCZAPz34JMdANwPwL4zljvXrSrWRMXhYoMACCGOZVwiKle7opGsk8984Zvx+xzLnCC1FflG8sypRR7h8ec+FAUBq9KBtnZNU2Yct/g7t16YaWfhwJGkLfcurM+rmrVt39Ci6tsfv5N2n+bpLe9/tNrr9k2fMv3Ga+/s7g+8995oqwFrEgC0Ozaww0Z8DTIY4o5nfNluoPhvKx7stx8hxioZsLxAVpppLHkp7xOzzy4hr4ySHZbn2KTEf1v6Ww/iG8fe+TNOjACPiyTxQECmRLECEgXO53GOWBj94AoVrFB/fsGlADw0uE0B4J8B2A1WhvUj+rQ69kh3UV0icCQIigRCVTUgQlZbl7GEG8pDLQrx4NHUjd39Rp3fNeJr31252oGZvr7OUCT079ecA8CgyyVFyo8yuzetAgBEAx2KJu/cu2PGlBlXXn5rTyDoqPKvfLXss+diaVLWjbVEhxUvt1KBrDafFkmxgeGHGD4CzCB942IhX4ZBhj6s6XC++L0uh8j53I69R3rInOFBeHY8Dj4ZD5DPJSnx32YgEFv+jx/nz6jpgCx+8LSj73JsXfuNS579+Ztk9IrFZwHYAVaHzENaZ8iUtPSX+8XnnPLW6g25HBxHi08rwrqui6LdoX3ScsYpizq7+sIRqXcg3N3Vc8dnSMsg+d1Xtzgb7R2ikvrJug+hou04svuH158Pxiv9XYdI/cf0WavWrWyoa25m2XOWX7d6VbmtAYa46Rmoa4gbTPsxib8fYkzk/5dO2pXjgf5nw3GkjTSxI8gLyZhNAHVMckBJJ1FQyKChUxdPd4hcc73f73PuP9LTH5JUTRd5bvHcpqcKOFwFkLT8jzl/7EdJ/5bc+fPK6m1XnrMYTCiGKmyH88BDT/zgqyOPFvjGJbU/f7MHgHsA2AbeD4EDeocl/Q0T+D2oP5S89A6FchXrOFqs5wZjrBuawDtIu5ZJymshcQnDOBwChHCgn0RNohHJ6XKI519xKgB/+cuT0f4Qj+CBQwce+NH34Pr0Tphxxc5N7wEAwgNtfYFel8N16WU3yRxataKM4g4haBiYJWFq0j/RxCY7mDlW8MKBhIEts4DkTpDmcpZSyZ9aP5nU5BL5UxfPmDut7nBH/76jPdMb/Uvmpc/YpqDExP+49LeW/9amkT6hIpf/r6xOO40AjN/x8UULx29cUgvAJvBhAOzX2oNGVMWkEREGOmmNkrzz+k07cz2vaCjxVti5GabJEBfQGGiAxAAAmUlXdAAgie45y8zTLliyaDZx1vOcz+uSZeXqq4hDPxqR7JsyEAwE+o90tX/hq1/o7OjCp10AJg79XQeOHtq29oP1bEQ947wry/QuZL1vlWgNpvGQpLihp/MR2onrKGj3Ex3cGBshXAQcw9RVe5bOn3rRafOWzZ/mFPiiDjd5iX95yXWRSf+mXf4XI/3jor8YHdC69XkwupRkTsU3LjkM9mgdATOiAVv6GwaWFDMYNTt6C40ERMPDbvkTK3EiATRzEgQAEumeQwYAvPtezJnmdIjhUEhIEAp/evjRP/z2t3sP7m2aOf3uz95hb5yIM6nC/Uc3bNrY29511pkZs06Lw+7aQOK09m1IROT/cSUVdhPtYncGtd+oOCAALEI1PpfI0wDASAog0fkzEIhlpOCySf/VrR3emlpQNC8+/gYoD6s27E7/ROar/IGHkuflZuEbf5UiKimntLqoW9I/YoYipqwWeN3jaDjxVsgRANJNJOl2q/bxyOKbLy1Y+ttt7MigmDfee3/txxDCs8+ONZL7v8f/3NF28HNf/OK//+j7iS/s6OgCp08kI8Am2Hdk964NrXt2XX7JjSU/uN0hcHCcS9JT+V82w19i96uzqgAwstsgUkanFUSC6z/GaMqAYoIBrVufX3g8GboytjGAAvjVa+rnL+YME8vW2j8cNSUVKxreul89flbeFisuftik5cl1OIX+8BgrgEiwf13KjMtL8O8OPf7Imfc8+rM/r8n9UHMWzLf+xZu37Vs4bzrA4MihfdOm1h/cF+zpPnD0WOfRI+3VNc3/9r3v2fvffNNVTz/z8nnLz4gfYZ1W1LTNS8DY0N+9/70N2uKlZ7l91etXrSjHW6TmCuTVwCfJ52Anhtq2J6kbo3mcox8ETlz+l8/5s7q1w77jrakN9vYUqQPKZwRkIJv0f30vm1gONiK/f0u77WwUjJqhqBlVsKIBVQdKQU4gaBZbOEOS8AwzHJY0vbxTIbMEANL6fy7B8dQpsPVPnwHwC/lK/8GhdeTbu/cLd3d3ks55x82fa8UDsunajo6uM845Y93qdfbDDzftaD96SFEUt8dfU1PrdrkWzR/73gyZiPQf2dZ/ZNqcBacvP3/9qndLckwywN1y+turoKRVel7Ro8SdSb2wnf5vd4grLpG/rXugua6q4JdXFOywxP9Bkj77ckj/VB1QMCU3AjK3Hx7ZL5yvDnh8jXnePFNSsEqkP1Z1oBoFWRi4WAWgOmtrp9YYHCsdLbqzaIlIFP1xvnn32bkYAZ7qmvi1vLV1//y50+ztidI/CTv4kWoEnHjKiR9v+JhkGS5dAJYuAGVjytyTq3xV2z96u4THPLJ3x8BA4PzLL+Yd7jeei8XMTj/veofD+e5rjxd0SEuhWk0grGuODH80828jaNcAGwbpI0G8SrF211Y6UGJpMaWcoETpby//0yf/l5+Co8EvPv7G6EeDs/D63vyCToNrfyL9FQOqBnpra946wGq1PnQDBYA41l0FRTfnHLlOqtx87tZz00r/mBGQG4FAHsVxmULfHR1dglieFt4pHNvzUamk//xTLvE2zEbOOkftdHd1TW9Q/uiDT1oWLj3zogvPXH6hGg0WJv0xAC4ecwwWyA2ILHAKnMhzAscyDJNX3BxCyJI+eIzAcyLP8AwZH8+R/2IHV7qpTJSspBNVmZw/loLOvvzXTMxlLiNa3dphDcsdZt6VxBFUWmBh1kFu/PjPf8tj78duz3FHMl6pOCKhgUCvu8rn0VUZjCKnX/IPiQ9vver4Q4/fdujx9NI/RyNgcPkPAgGSERtf/ts0T2227yiyYui60xmbADOZ2Lkh1npIigKpB7RbOQ29x8CB4kbhIoRIloCVBRQbtkB8jxghkr+fl9uGlA9jSFoAWVUA2J4Waf2+DDJfgQaBR1cBDC3/Mzl/ipb+PMeIAisruqoN81cUrwNsI2A0osG5Sf8sjqCNvxu5XiyBXH8GQtqWLDlz+rX37tu1TZEkWOVjOf7i27/y1mO/BKPC3JaYOL7zuqWW6B/5JXlFAg4f607y/9iiP75DNBrFGDudQ6bwVVde9PIrbyd6gRIjAZWMaZJEYYQgQwQ4KWExAWBjg0vzy46Aloi3Xol0a24w6SVqzYMxTTJpuFx/AyWbBVDoOjK79LdazjE1PmeVR+wZiPYFosbwhIGSBAPGFbYjKK94QDHUVfmK+RrDA72IGORcaKBPN+S2A3vAKJK76I9zCf7dm1l1gDjot0ld/pumqahqUvg3GpWcTockpbF+Ojq6GhtHbtxdCYQ6tgeqF3Ica6q6PcAXY4bUsgCybM/rqlMNewa1XYI+mIuCEGYQZIRD20sTtabkpABiy/9CnT+vr9t54WnZ5hat3dlZW+Wc1uDzugWGQZJCmmhmMhjH3AjI0c1jWDWRVuccWKqYcMHgl7L5TEZEViSW41iOhQwDGAmOVhb22689mq/oj7NQz9gA9QioybT8j0YlWzfEdUDccxEIBPlBrZDWCAi8+1reZznpFrKH+8DsBlK1bo9G00kxMGAYYBgjNkUcBmcv/hHQdHI00isaA4yADoDB+st3/pREhn7nBTt/bOmf3fnjENimGk9DtbvKLTb4XTU+Z8Lk8RiJpWEFB4RLEg3ORfqbGG/f17F+y8HWfR27DnYeau/rHYiUJCY8Jrg9XmAa4cBAX2d7z+GjujTkISkfD99uHnr8toJfPpUZmhiVyJF60u8h7fLf5SGXsSwrcoILiNivFlneixSFUSxCHa1W32ZiANg/ejIYoKDQmP0S2xcAIYkH2Pr4UCtpcEQZBdih3P+CnD8jrv1XfkS6Awaj4OPeno83JzxRhkSjFx9/49rbCikWLYBgRP5g68FAWLJT1zmWcYr8mNsBBbPpraeb5i5r6+5AZOSHeXQHyXosHw/fXt4kP3uNn9b7b48fgQjaOsDM7GtONQIoNgc6pOPqucFpj4ZlC5DyAEx6O+cKcfgj+whWAwhoaxOsIZrCP3qwI8d+syb+X3gqkf7Zvf82iphD7we5PX634Iyg4h1BI7qAMMDHugaCYdkwTMNa/8iKForI4yckUADNjU2Hd3drhuGvrgt2TgzRP5V5kq36fNLGj7t7yfDCKs+C4ct/AxMnD5l7Q3xdLIQwFAw5nY54tMA2DjK9F4kEnH+5f0t+49syu6kmMBCxxAhgYuKbRH/tuY/55B+TFT8kjiSEyERJopOR9Xo06bxmE8AFlKABdn+Uq/SXNdK0NYv0f2Nbr+qoRVVN3uaZ05uqjptRO62pShTYTPKV9TclPiymT1xZkRV9y+62xIkTOTKe3UEbV79aV1vHc+yh3WUZ9/b2a4+We+Fvc0M1uZr7g0P6uMta/quqFgxFenoDkkRcQLKs6LoRjUrhcDQalWL+6+Fu7HPPOW3lKpr8k0ygfUcsS9OypYgCKKRwl3QCssu/7MFh0JoVc2z3BwV86ZTStIIoofNnd5Cf2zK1uc572VnzfC3zgNXd5aO3ntZ1o6svsvdwT1dfRCfL5+FvX1cX7O4u0gtUppTQI11WYREGvYFIV1/xA1jGHa3bP5m7cFlX+5HyRHoLd/dnQR/4fZCP9e9MwJG0/O/pDxmGEY5IRNhA4HY5dJ1YYyzLGsGI0ynKChne6PW4EUKIiKIMkYATzsT5zAnwgsnJng5tXhNrqLHieCK+C26gag8HNgHDgX4lfRHi93/08+9/9xvFnjQlBWLKJUKW/3Dk5f/r63ZeMJLzp7PtqNvJV4vKR5tU757d8SxhhkFVXkdDjSciqcGIYlWQD08JTdABY+IIGsn/A7btbU+YozCp2NP6yUQR/SQSk0b0E7A1ripx+R8MRQxdMwwTIShJSNN0wzLg7PmxkajEcRwEOByO+Hxep0NkWZKVPiFbQo8OkGEZVgIaazJWTa/dyS2v5DHrAwYMNknivw4Bj9iuQ5N52Pg4BBXg/Hl93c5zTpyb/aexemcXx6KaKmdjrcft4BNrRCCEIs/WVbtq/S6nyFldupKP5a2rK9gRVO72cKqmH2zrpVOnc3H4lFX0p0p/wyTVpaaJk5b/u/Yd1VTVjtaYJlYUJRQKSbIiyUpUkiVZCQSCAwOBYCgyMBBua+s81tYZjpBpghjjc88+NdEL1NHRBSdgj+iSE+jYub1NdwjEhcByEDHIbuifO9bKDyIGMgyxtxw80x4tqvEqpQBSXNIjNjtbt/OUxS28lcSbafm/emcXy6Aqj6Ox1mMn/iftgBD0OIWmOq9p4q6+SFTOWBNQDKV1BMX8PwBEJLV/IIyY8evNH1vKvurn0qz6Mcb9UTWq6gjBXY7GBcOX/3h4q1RLE2Bs6AnrUKBphq6bmqYzDFI1PSrJAs/V19cKfCHDaa1zmuQNbQLtO3YYc4+rYw0dI6sOIF9MAyAWmAbpMHo0wATaE9ME86auhqYP5Q1Ks/zHGZf/r6/buXjuVLeDz+78IT5WJz+lwVvnd8XKxFNgWVTjc05t8FVXOayagLIYAa373wL5k0UZkYbJUYUu/8dk1X/U+PQR49MhRYuomqTqsmaouqEZpm6Y3WF5V1dgf19of28wafm/cw8Znp79GybRSNKRBhsG0QHRqBwKRYPByNGjHXv2HDjpxBOSQsHw9As0w1R1wz4HVTfs8wkrWlTVJVUPyZp1npPTTxgn1LWH9PGBVgfPPAP81mdudRCF+PCAEWjfUcyZUOlfGOww6Q+zlX29vo7Mp63xOhKHgKayemcXzzF11e6mWo9T5DI1CIEQCgJbU+WUVT0SVQdCsj1rtMTBgEiBY1Iy6QBJ0SQlW8VQhS/8y3Tw97qvZCBUDWKEMRCSZgEIWneQVYkN2oPRgEy+F3H2QjB8+Z/CCMYmJhM6TcM0DcNQFIXjWH7P1tTOEEcGIpphAqs/PtnZaqKrGWSoMiR9DkwESV/7phIMvhvXfNK685Tjj7OyQDFpRJsz2CQvYYGpQDPSc6Cwd+/uC9ZVT9ZA+2iQqx/Dlv5nL5vDsiM4fxCCXrfQWON2OwWUVVcgCHmerfW7QhFFVvQo+QFn+3HmpQPiRWGt+99aOOtikA/JUelBMMYDIYku/5N4+7VHAQDlk/5P778AAskaEkVyTezBLlbKIMlFtFcYCukmQ0ha/u/YtR/mnFeOsWl3KLMeAMPUMYRV4d4DEDQAsHLVusSisNlXXvvWXx+1UoqAYa1krdY4xPwdXNtaZzrZFQAAYMPW3WedOJ9B+cUAoNX+gXxHewqU/nHo8r8oBTDM+ZO568PJi2YKPDOi88fl4BpqPH6vw54clB0EoUvkG2rcwYii6sQJmyUjqGDy1QGZLmNZ0eWil//3PjZ5+tyWW/Q/ue+C5K8l9t3YjcfI/xlroW1vTl3+e6MDQWdVgg7AOeoAbA3YcYV6h6oJuGQjQBs2Ez31HcbpaOVysPbjnWcunZ9XGIBjSHPpj7aNUtvBNa/85ewr7wITma999ycAgF/86NslPCY7LPMns/Nn8dypHucIg2pX7+xyCGxDjaep1iMKXI4RMIaBJFxc4yZZoWE5dRFRsCOomM4Qqct8jEFEUqzCBcooi/5scAxjxDrTgwVzpyeK3WOffJyoA+yMz1yOaUt/T7gvaXuSEUBJ5P1NO5efnIeTVhCYDzYTvwIlR7lfWtGf4gLKIK9fX7fzuJamWp/Tnqqa2fnTiRBJ8J9S7/O5xdgI1hwgwQCerfO7gpYjyMqvL5kjqAAjwK5ItPMFE/8KwzCDkdFokVbhov+Xh28kRj3oz2VnCIDIMiHLLBtc/pOvrKtzWO82WweQ1X0OuVumoULEpEp/bzQQdA713O7o6Lri7jte/fMjOf5dlcCqj7bdYY9hzgEq/cdQ7scZdEdYgi51+f/6up0zptQ3VrtJrm72AyHkcQqNNZ5qnyPtaO+sr4Vul9BQ467yiNYbZcsIyjsdaDAOnHNGkD2s1E4WjKGoeigqa4OO5srk7dcezSXF8/h7iIYogIcOXvvQwWuxmUfmDLKaB5g4vvxPDwSgRg5Yg5P1+C1xB2zq8VvatT9l3IJTkkcmOnd86b5REP02QyGvtNIfANBY446ncmaJ/TpErrHOU1/j5jmmgPRnlkHVXmd9jdsp8lq4Twv3J90Sdy68WXRuOsDqF4m1BI9mRFJDpGh5sl1q5cjuz31mb5x7H0NJoZFuLqeO8AyZUEi+leHe/664/8cGQcAi5JWC9s3eGNMEg0LfGw3Eb5nezhsNJBWFXXF3+lJkyqgRkUkPj0nDHV+675Ff/3jU3m4Ei3jp/OkuksqZTfq/s4HoiYEQGOjs2Ja2kLsU5TAOBytJegGOIBIJ+DzxKuSCvfa3/ycrut3h2cT4SGe/rI4c/r32ls+8+FSB69/xTL7Z/cff82iOaqDIkDiLoE46SWZb/tvRWCVBncd1AGWigzE41NbXMq0haXtb90Bz3cSrC7tjdKX/kAUwsyF5+f/2hn2L5kz1e8Riu6Hk/HKNr05z44ZurLcEY/lGNAIG5T+w6pOtFreGqap6apkCJRO5SP/UVf/QV5CzF0gzTEUzsi//7ZEjpfr2qBEwrjBNsz9ojTOZLJ6fUYbNLP2b/W4hHgXNtPx/Y0sXEmtqfc4m0vOHc4qcyLOiwAkcI3AMxzIci0hpjNUvFmNslxEgaDeRHaoSI2s0VW/rCR3uDLb3hDr6woqqk9+svcPgr5f11uvBrryNgN8/m2gEZAoI28v/WIah1eJfN0yOZTr7wmFpUpmZ+XLvYyjfTs5ZjIBMcn/TmhVLz746/rCb89dpI4SCdRMjCLMv/8uNKIpj+O4VjmHgyZGYd4cl/Ud5+Z/NBTS1zjOi0+PNLV1ep7DsuMZFs+o9Tp6I+UE6u3qwBlQN5CU4XSxYMMW1YAppCatqRltPaO/Rvr6gNGz17a8O9veVQwfYwj/+TlFZ1Q2TZZjsk14oaclX+sexLiK7w7xJmsVk3xlBbtbi5OX/xg1l/VK84b6VK9eed95Z9sODBw/f8I+ff+5Xvynrm1LSols125Pjw3lk1KU/WYunXf5fcOrcxEyeTMt/r4Cbq1i/A0jhYHd3b2dXT/xW/JmRZqI+Z321i3QcHP7+Xn91AQd88ffPjrBHLJs89o+k6KpGKr8GQlIBbzfJKMBZn5QOlMXnEwchxDAMz3Metyt9S/7hMGO9/I9zw5f/caxPoRIhDb4nvm/2jlF3/cdJ/4N08MQy8DjFLNJ/3c6Oao/YVOvxOId1ey4VEEKnwDb4XdVeB5fSUS6uA/LKCErUAanBAIRiBaUWWDeMqKT1BiK5hH8pWYyAXES/jcvp8HpcVV7SR2T27JawfzrD8jwvcBzPcjzDcojlIENugGEBwzaddk7SEY5+8glGbJE3kHAndmNid0glAcN6pfDKlWvjb3rw4GFbB9jnFj9PlhuhdpJSJJo24TOz7xg76Z/GBfT2hn0Xn37ciAJ9zfY2r0ucUuetq3Jm6vdZPAyDfG6xododlrRAOE23uELKgxN8QUmOIDudPI5umGFJsXp/FvE3gEqPBJx6wZ2577/69b9dd+sXDEOHEKpS+IxTjlfUBQ3Hthsm1k1TN4nDVzexYd3X5hxfU+WOv7a1dad8OLXx5zAgAE6eTANmIESIzCGxZpoiE2OBZUyMDRMLLGltb2BMRlVAcgdByJF8UxNCwDMMmToAsINlkhxBM2dOX9hYRXZmyM6kRZ1/JpAK6UdLyQWMyXCOCf1Z3TEWgd9sCuDMJS38SM6f1dvbBI6pq3I2VLudQsZ+n8Xz6AtrctyzJDpgUNDHesGZJg5G5Ggs/JupQRwlI2TV/1ge0n/wWzBJD0Gr79vevfsWLZwfnXdy3f6PMWbsJmv2f/fUzE6S/taXNMJ3xDGoySO6BY6zmogaGBNNYA2AtNWAiTFnzTYxMWZJMytSZmQ3HDUtTYAgJA2QSQkCbPaCFxN0AABg3nU3hf7+sp3ysMc/yw2tLnaU8oAxlid4w+22tva3X/jreFEAb2/Yd9mZ8+MCXZbTdz7gWKba62yscbsc9jyvQpgxNTl1N5V//cJ1uw73rt1yuLMvkrQ8J6cuAj1YYJ+4pJiwTUKdV0zcR6KqYU72uR5lMAKKye5/8f/+cO0/fC4+i3F7685FC+f3zDqx/oA9q5Js3+abmSr9lcMHIQA8S65IE8dVhTVudvCLhVbJYa1L4BnGzvYa7C5qPW3vFr+fdCfNhmHYRoDnwqvkla/u8LVAAELBjDVllOLRNIP2Zi+ZAnh7w77lJ822J6FmYU1re43XMa3eW+1zpI76yp1DRztz2c0rgMtOniap2rGu4LHuUF9IUjUDYBA1iJnCeuviOiDfHkFxHZAhK5R0pLCkP2W0253GdMCgoLV1QFfLskEdkAZb+te4hFqXiK3ZkKRHv4kNjMOKFpQ1+4vUTbM/qtS5RJ44LlMudZjufta9Pj3T97cUR9AOXwuoVOl/0heeyHXXx24v8r2ismp1Z5mo67OLrsvbPi6jAlgyb4pL5OKfZtrl/+ptbaLA1le76qvJb2h0PngIgcix9X63JZHN3qBkGtjJGMVkhRbQKs6e+1HAwSvHCChhp+uYDhgkUQekLv+VwwdJAFlgj6vzuXjW9hHZRoCBcVsgGlF13dLlJgYhWeuXFBfRAKW5flN1wLJlC95btR5UJBt/d2vO+xZ1tWAMorJG5i6UzQU9Coyt/2foO3h7w766Kld8fksm5w9p3ewWG/zE9Z+Y9V/2s0TQ7eAaql31freDj7WmKDIrdMSkoCRgieTFJCBV0Oee55M7L/7fHxIfbrf8PKnS33bcIQCm+Vw+kecYxDNIYBkHxzh51sVzPgfZGH+JYhgBiRR2g3Jy7vLTy3p8immaiqbTwFyRxH4YZy1tETgmu/Rf29pu9ft0+xIqhEcNhkEukeiAGp89QzgJWOD04BGLAwZJbA5KKavoz6QDuvr6U6W/eoRk/rgEttHrTF2TIAicHOsh65XYFhODiKqHVb2EyV2fnulLzQoFADzx8P+W7D0oqTXAld2dtySQn+7bG/ZVuUXbksok/Vdvb3MIXHOtp7Ha6vc5FjAM8rqEhmoXqToeroG8fn+5dUAltwLNJPdHYbpZXAfUNTYkB34xsKU/GVXtEsUMTcgdHFPjEvkEIyCi6n0RRSupEbBkydIkHUCNgLKiG8aEbgJx0TgIABAF8PaGfZeeOS+79LfbNdf4HM21Hvfwlg+jDM8xtT5XXZVL4FPLg4d0QL7kbgdQRp8kOyCOcSwm/TkEG9yOTFcli5DfwXsEPv68Zph9USWilMwIsKO+qfz2kZwjopQ80fTJ0wRiDGHPO3kOx4ywon9/R3uVW2ysJs6fJ18pf3SroJ9lRDHiGZwFRIOpDhjPpC7/JQX2sv46lcT/a91ilYPPNqeaZ30Ovi+qWMMjyfUVVrU+SXELbGqReb7Ec35mtbSsXLl2+XlnwYSMoCIPTskEadQ1wZP03h7rCDBRAO7BX06m5f9Kq91/VzjQ1dE5NAvDQnHkM6gLApfIu5y8PWxLUjSZLMFK9hWyTqD3dxSjAyjjk89+/f4akgMWo3U7uSB7e3sw5Lq46qnmwPQqd/aUHhYht8CKHBMZrBtSdbMvotQ4hSyaIxeSMj5ntbSsWrn2lFNP4nmSJbF//6F5xRydkqUGWLVHAVFKkQaaxfmTRspDkhPJsUwVzzpEzu3kvS5BN8z+oIQx4DiGZxHPsQLP8ByranpfQAIQ8CzDc4zAswLH8jwjyVpfQEIIxjbyjMCxHMeEI8pASGYZxPOxnQWeYRkUCMuaZuqG2dEbOtYVJCXgKd8+629M1AHFfTKU8Sv9u3uC1uAeFTI8mHpclRjKfhAIgVfgfCInaXY6KLk4QooWUjSvWHg+W2q+vx31bWye6nSKLMs4RKGwI1OyY5qmqtMUoBIpgCzS/43tvV63ML+lfslxTbV+V2dnYZW3aUfziGBaciNSgo8FgLSDTsLvdNh35k1194eq9h3pPdYZUFQjWdDX1Qa77V6k8JXV2688Z1FBJ0wZj9LfRpYNdbA9HwOMGdOaer1zs9SI2Tg4ttopDkhqdLB9mGoYIUXTDFPIc4R1dulPRkW2HRVcPoSQKPCn0nZwZcAwsaZP4ADAkjMv7+6KzTUZW1AW6b96Z5dXwFNrhCnVvCqHC5X+JQYhaE+f97lFq5wzdYJ8bfz+K6u3j/oJUspF6/adK578y1svPmrb/qSzm1M8euQoRKh79knxW9rXIgT9Tt4jcPHLBWMQkNSQouXuSDg69fj4LZP0BwCsf2eFEgmoqhYMRYr4cykZIT0BJ3IO6Ob3XwPjAzRC+ZWTb6whDZ+ZwRqx8QDHomqfo7HW4/OIVhAvmwlPdcCkcf6sePIv9v1Na1620oIZl9MBINi6dfvWrUOaPq0ygJYR4BbsLnBgMBSs90VVNWs2YZLQt9m/b3/iCi4133/9OysMJezzprFlKcWjafqEzgEdP2ScCLZmV5fLQaR/TZWzmJ4/5QBC6BC4hhqPXfQ/EEruFO0dcgQV/hbZdzB0jWG5vI75qa/+8MihQ/09vQCCHWufA6Xg8s/9aO+O7Q6Xa8r0aa/94QEwiV3/wxfqm9a8fNYlN3McaxqkSzOAMFEHHH98zPUX1wF1+zYyEPpE3sExYcWaNmotJPuiSg3pDSckfuGJsj7O/n37k7bIsiKKQpZqr5nTmz40v2caJsl6UE1FUZ/7yy/y/CQoKWAQkbXEPl3PvL3lpotOoJ9UyRTA6p1dAs801Lib671OsYwNnxN5fG3QJI09EGKQqesMYkhXXYN4qEjED0KW5RBi4udCmn2ZIoNcqArwLMfxgjXSEZKmDRC6p5jYNAEkknpduyJJUVWRraPbMCzLsRxrGAYvOhBCZF5xdRXDkj/W0HWMjbb2rv7+AdPQLC8Tedf5U5wj/hWzTr4Sm6YVoiTdhQWBr6ryqoqiYqG3q+vYvm3d3d06YPi6uRgyhk56TZMdMVnOmPrg5BmroTBikMtXxXI8xKbdicLufIJJY3rIcpzLU7V9w99DAwPRqNR5uAZMZukPVjyVnDO39s2nrb6hdlSX6Ad7miSZRZOiDGxNYGq6ue8w2rdtKB9U0fqjikfgeAalyv1UoZ9EFum//p0VAIDly8+yTw5jHAhndLdScocM6ogqicE/VZ6Qc+GXnHn5mPuC2IxZEy6xqc5b5XGUqmfWiNx2ljfd5sxGNCaXQld/eO/hnq7eiExMwmRvbqIdMDwgbAKgWDeykrM3vb+rV7eW/vZE2iYn00Rmog2N/I5lkGRl/0evpN2++NQL2vd1OgSe4zjGBJzAQcQaBgssgW4rAEOzlY19Apa4NzTT1O0vgGyzNCQRKBDqujIghRVFNQ2TYVldmSSN5z/79ftTW3CueCrm/MlSI3bNpz9L/rEzi60VAxyuDI4/fhHHsbNmTt2laR3t3fEU5H7NMeCY0lBbDXOW+889/rtc/hZbB5x33ln2t1rtc3zlO//xywe/l8trKZlQVF1VR04BauseaK5Lm35CyaoAVu/scghsfY27xuckw2HGbQ80SJrT+T2OhhpPRNKCYbnotGCYIEBwaftdbPvwHVBOJuQSKAO1Ccv/7Qmu/+y89Lc/DmkC206MfZe2Vh/UBJgoWp/PPTBAkkcxxuFw9MjRjr7uLnGkrM0c5X4WFs2f9e3/+O+ffO9bYJIyCt1BZFWbBDXAm99/bcmZl487BbB6ZxfPMTVVroYad2q7hfEGhFDg2Tq/KxhWZEVXSJlPxmDAiFmh1ro/Jv3LfOKU9Hz26/cnSf8CPqm4JhgyCyy5b/3PMqEgVKIhiFWiG6wnpUhQ5LxpewsXKfTjRoD98NChIy0zp/37z375/33zK8UctmLBGCia7cCjlFoBrN7ZySDk9zqmNHh9bnH0W34WnhVa6w5HlZ6BiEFywwrXAZRxJf0xxi+nuP6LUwaxCkGGeDahPRTMyis3DevSKdVKP4sOOHjwCG0RUTCmiVWtlB0EKpzhFgCEDpFrqvPWj13LzwJgWVTtddbXKBFZjUTVgq8NelGNt7V/kdI/kzK48ua7GTLwHcblCMb47ZceB2Vj/TsrEkcH233ivnhH7rNTKDFM07SmgBXCmlf+cvaVd42rj3LJWMeBhxx2q3d1iTxb6yfOn1HL/CkJEEJRZOurSdCCI1WdBZeGlWBZce0tnyn+IJWGHfhNIkfXfwG88vSfV7325Mb3Xvpkzcv27eP3yCK9rKxcudYwTAt84ADpY0p7hRZaAjYYAIhl501UNo91CtCQAli9s9Nq+OycUu91O/kJJP1tGEhGBTTWerxugWPTlIbR8uBJGfidQKx/Z8Xq1eu6egZ6+wL9A6FNm1upH6MAdKuoYqKL/nHnAiKJ805+SoO3tspF+itMKB596t2xPgVKiV3/k/IDXf/OCpe3GiLyP4ZBoaj6P3/4yz9/bnw5JcYvVsRG041JFQI21CWnXbj5g7+PpQJ4x2r43B0E3cfawCQmoUMojQaPX9d/0YHf8Uwk2Of111pBB9zd3fPqm+//y7//9Kf/37+M9XlNGDTdwBN8DEAimz/4+5LTLhxDHcDa0n98ojun2ndYBjlEvsrjaKj2TG2oqvLEOoPaTKuNFYthgA3DHAhLW/e0bd3TNhCSEteSjNW4wQh12g+pDhhv0t8mteJ3MrH+nRWnX3D1CUtPQBAaBgkKbNm+d6xPasJARkBrxuQR/wk6AIwRrCHWT62vWjJvypxpdW6nQApmJiwQQJZhan3uc0+as3BW4yc7j7bu7whF5cTgLuNpoDpgfJLJ9f/AT37xg29/DUwW1r+zYvl5Z1nXJFmfLF2ysK626rFf/2Ssz2ui5ICmdICfLDpg81gYAajK41g8t3lBS6Nngkv/RBiIGqq9F50+75ZLli2dN9U5fOoT42kYu1OjDPHH//dDSZLjt+uvv/JPf3s66QN64Ceke9pXvz3B+tx964EffuuBHz6c8ucAAL74L9+XZB0huy0VbDvWftZptJFZTuiGqQyOgphkbB4jO4A9dfHMOdPqSNFv2aT/zn1HwBiBAZ5a4zQ135GO/qhMSgTs2h/OVysHSHUYdQSNLb948Ptf+8737fubN29fsmTRv/3op/FnWZZo7v6+XjDRsHoRgR2tux7+29P79h1MfCocUVyOWB9ZCOHKd1ate/fVMTrNiQQGICqrBRcBjH82j4UdwLJAb+/s7uicJGv/JCCAosDV+d0RSTUxllUdDA4SFakOGE86wG7D8P66jfX1Q21NE0X/V7/9wEM/+QGYCJxx3mVWWxGiA1q37xKGtxiKS38q+vMCY6IAJlEAOD2jrAOQSGYkjb30txbm8e922JdMEibsfrpJ2+1nhr0w4anBjQxCHpfYWOuprSI1AsMmiI39300h/OLB73/80SemafZbDdpSeegnP5go0h8AsG7l6//9g/vfX72GNK5RSMfvVKj0zxfDNNOMgJ1cbLZE/2j6gtiCJ2KXluFKaNgpZapKIy/JcO5JT/AsU+NzQQg1Xe/uJ7kXtnoQvbVysAfgAhxB4+JDm0ysefslUq9XU5+4EWPwy/+aMHI/iXUrX1+38vV//FZykfPKd1aRZ6nbJ08Mw9QT+jVNbh1w0hkXblw3GnbABKv5KrxXBM9Ve111fo/TwScrFOvhtiP9uR+v5GdIsXVAsHfYpOyJK/3j/Oa/f5i0Zd27r1LpXwBWw77JvPxPJC79TzqjvNZARSiAeOPo2ioX6RfEsXETweGrs54Ghw7mWARHpX/Z7QAKJRVdJyMAK+2T2ZigCcqhDDLOBJ58IAhdDqHe7wlH1T4jokXDxAvkEKU8BDuV/qPKr/76hMCnWGy5oWl6jnuqqoYBEPjkCc8YY03Xrbli5P+dXX2d3f2BYDgUivb0DtjpKBAAMroUkMaidkiKUiY04rqt3M93Y3k8QhWkAOzG0VVeZ11U6ezosKU/8TtX+YMDuft/KGXkuts+39q6J/4w8X6cM886PcsRnI4RpnqlAcKPN+3a1rpvROmSGI6CKGY919fXXH7RGfFY2t49u5NeJUuTZFrn2KJpk6oJxDihshQABPC1dzeQi4nz8QyQTAwBEFH6PA3K6PPC47+/7rbPZ9/n/bXrszw7pXlYGDlH5HBAZI3CFvBYjezfm0ZRUUrfBYh+pqWmghTAM6+tJevHpS1z5szp6Ake7RwYCEldfUGAS2kEiMKwqmNKviRIYWy7VgbX3UMPEwa5kLRe08Sx/yJ4rK1zSnPeld7FeG/QoCkQy0mGI6SoUQpDI2MAqAqYCArgvQ1Zhq6Azu6+m684B4yF9P/W52+wH1Z7XS1TasOSsmX30T1Woaa3yj/Kp0QZyc1C7liPYMqz5F9b+lvzHYlKYBjLXV+Q3NX05GnSuROfnGqPnqeUiUlcAzyGsE+/uubmK84uybEOHYvl8M1oJqk1h9q6E5/t7B5aYj/96uqk15ZPJdii/5rLlh83fajElGWRhxU8LmHrLnpVjS8C/QNRKTribk3NzRBaNgEgfrwEbQGOtXXl5QjCGOtFCJe4xtq+bceIO7/xwv8V/EYVTgEKoK17oLmuqjynM4ksgKdfXWOJ4LOLF/2ZJL7NV+68kizSIPzfPydn+6WqhOK1QlrRn8iBIx2u4X3iKGPOu68+df4Vt4yoA9rbsqXtKjmokDgmxpGIhHMrMkoqRwcAhAL927cFc387SmGQQWCUUgMPb3wRAPDjXz9dgA54+tW1mQznr951lSXqAUndwtg0sTWFO5ZSZ7tcTZLVhRGEKINWsM8w8UHuZ/jMa2uzi377Tk9/5IPNu3SFdCAQ3EOjgy3HQrJ/LD6LNLYDm8aBJgr80incd3782xzPk5KWXHRAFqr9eaz7NN2IRoeNjshdAZDcYqcjpxGqELxJl/9FcO2Nt6T/XCGJwdx6VfrcsCQLYLwNhR9zYiLsvi/d/ONfP52LKWAJ/eQ5W/90z9VpW0pgMssleTuJ41l+UxNiwzTjPt5/uucaO5XaMGLa4pd/eTnl3ckZJpF0wknu/kyiP3b2ENjSv7Q8eN8XqQ4oBpN1nLBk9mAKfiwyDCE0se3wIQGAWNyVhH9NRJ4CmqZhjAWBZxmmoaEmcQpc4p0kjhzr2rXncC4KwA4yJFLl85x75tJ4GCAL//qVL+b4t1Moo8bQGva+L91smwJJaiBF4sf40h1XWGI6za8iTtrt8d2tluiMfd/++SGEdMO0fLvktf9099Uk1keKAEkGGATw14+8knrARK0AIZw6beqnLz91RNEf318RYlZC/gnklDISz64Zds0M3on/a/f7I/+1JsfF9xwWSh5+JwnWCh/HVEqekCuTvJAGfykTEnbvwbY5M5vjj21TAADw1MurIEp2cXztnqt0y6dj/wJJGaTt1ima+EHISHprKn18RRZL+7De8St3XYUslWOa2PItkd9eTCtg/LV7rzFM/OI7m7fuOnz8vOnZRX+RpPX/xIURNQKKZP6c2DTQwq6iRmv5nyOMrQByICEnNQYZ62iYjHXFZue/fvlbagRQxhtEiu0dbINja4Jbrj6/qc79P398wd745TuvgDE3PXnIsbE1e7lJ/KXxHJucrw1J/odJEgHxV+6KxZbtHRbNasxL9HucfCSqlirA5BA4AGIHo46gCQHDkMlcoKA8IHuub+nPiZInz7y95aaL6GC1vBm2jLU1AWJ4jMFX77nWIN6Y4Ub0WPeOjtsbDIQAxc7eJAXiMdfwS+9usRXAqg9bpzdV53LMuQ3Owx2aTEbNJXeDyReEoN/rACBS5HEqnOXX3DnaCqDQC9swsVEBPYrHP6pceMpAJZPGdO3pG0AIsgwSeJbnWI5l7CXSmEv/TNjjVW0joKnG3RcI9QVIo7ccaahx1/gcIhmKWSx+j6OGHSb9H7yPhv7GO/b1XdhrsWkWU0NAKR3jVDqNc9Jf91t2jtkU31JgB/RyjelVecTmOm9NlavodwVTGtJkH1IdMM6xFxCFvdbEOPe2o//1S5ocXC4K/QIrnVHqBfSzPycndFY7+Ls/fUm53i8fY0Xk2fpqF4SgvacoK1IUuOOm1x/dHyjmIJSCI8AFgxBCOURxM5G7AqCUD3a0YpOTjLIogP/3yGuaqjJMtq+kT1ITtUK1VZFbRpWQGQShU+Tq/MVaAM11vsZa79H9aZ6i0eBxGwCwFUA8d6sAVE0r6elQCsFdQBtwSqkUQNICH2UV/Wnpk9TE41QJsRO799bLRuFrQhBa2TvZSCoDToJhUMuUGpLDmgGqA8YtCMJc8jgzoZD0AcrYYJcBAwB8bjLbIylpkPxsE0L0bP5yadLDltaxM4xYb8TBYjHroWHl7ycWc6ZlQNFNK7vu4SdeHx01wORQzJmF2irXrKk1NBA1EYEIZrdWs6MqVAGMPW6nkCj301IJM+VHSQH89OEX40W8mXAIHOnzQyJskCE3oqtlVSfZpQgahikrIztPB6x9bDUwagZBvkAIpjX6HeIIfeWoETAOAwC2BcCyo2QB0HKw8kAmfpfp0JMbtlSr/sFlPlnpI4hMBOv8TgYhlkEME/svxjgcVQ3TRAjKit7eE4r1aME5qYHRNAjywu0Qjptel0vgmeqA8QiEpBtEoQMBNE2PTyagjAkIDnP7UHKHLYHDh0h8ooEFjuRTMwwknXuw3lTrtdf+xAiwiokxBi4Hb9fTy4oeDCuGieNtvklzUKu3FwGPbBCMHzUwpaGqtso91mcxGRj9CLCdv8tmaOyRSzcIKwuItgMaSxBCx7UM9bOh5A6br+iHkLTrUVSDY4lct5s5syzyuUW3k2cYklARjmqGrnvdgv1TGfo/xtygrS0KbGOtR9WNqBX+xQDIlt8fQqgbpm6YsX4K6RZmthr447NroBIuXg089nxsFIHgJXNs8oVjmVlTa3LpB2lDjYByk1cjIAIkSYSJkybzQtN10yTzyAp4LSVHbrn9HkXKWN2ZJfmCUqACiMoqgvBXT7wR32J781kGuZ1834Dp9zpscc8wkOcYn1t0iry92AdAGgiS7Orkg1rawrqHecQ013tVTZdkHSESHA5FFDI2AEJJ0aKKFghKDEMiB6Tdoj1tNQUsuFVNVzXDNLGqG4+/9P4pi2eC0aWhxjOjqTqv8C/VAeMKywJg0i41csHqBpRTPzgbGgYoOcWEcCqcjArA7r4Zuw+JkeUQWFHgBI7x+xyyojXXe23pjxBiEXEBsSzp1GkXZYzUXIU863Hyhsm5naTjkGlir0uwV2CSokUkLRSWHQInCryi6qpGmpBaPX8yzAmyGsEV9hEYjhrDOnIBicQIwhlNfoH6HydsBDixHZBZ0CVkGKZhmsW2kaJkJcvyPynvM5Eqj4N+roW6gOz52pZ/XxRYkWd9HofXJXAs43byvf2Rap/T7sxsz+wooFMQJME3iOpPrKlyAgCOHjlqb+cB8AHQPIfct6eJaZquqJokKft2fqLrJjEH4j9VcgbYNHDiQG6nkwjznkD6pmwtU2KunlCYlP7ed9e5YUmVFf2JVz7O90/weRxzptcVkP1JjYBxEgAYNhKgIIgCoNMKx5SCG3ldfX1sythnb8o2ffba274KKtACkFXNKXIcy/g9DpeT9zgFp8iRuC4DRZ7ls9Zen3z+tVKoL3X7tGlTEh/aMd+46E9zGqRIByLE8xzrEPmq087Z9vH7qmaQIIEFR0aOMQ4BqokV+XZMLvW/6Wjv6AF5wrGovtYbisiNtR6fu8BVBtUB464jdEGYxc2UpxSP20XLgEutAASexRjX+90Cz1Z5RYdA2oJqrlkmADoAMxa2yJkP6nGAtNKfjN87cix+f9q0KVlEfyLEwmAQTxJJQX21OyKp0mD1TdoCLmIeWCYCtE0F21ywtxQdqxMFtrnOd85Jczt7Q0zCCCrKxG4IOlhTWlBDUNoOaMyAEPo9xIVAKYBsF70kq011nvoal9clrNsla65ZqfuEoySHpzASlUEivJjx64QQNNZ6muo8tVmbd1ouqUEnln0rhei3Cg756Y3+Ko+j1u+e39IwN7f0/0zQRqHjBJKxUHA7IEiqGvN6BW0LWkIYRHp5lfKIlQSbyVG+eRfpCO10kv7GJgBnL3KCSJrpWj5Q+vEnRoTMd82Ew+FzOECNH7QknKfN8lPnZdRJwyfb2Nh/+4HOLMbMMDwuYXqjPx5xKqaBTBzqCIqDsNHVlbc7LpV4qnHuyIpqGhrEuXlyhoWKocMhSNFoZwdtCDE2MAxDm/wUzBilTyX4ZNJkXiRssfOC8IhbBg9lGLi9J+eGzJlOIB1etzi9yS/wLHX5TD6w1aKqkFdCUF3t5fn8VqA/++EDhbwXJR0cW3j8hjJMAST9BEjyfXyLLWQTd8DpbhlILLGxc/pjmf2D98kd++3izw6+e/y/cUGfZsvgoXoDkVBEyXgaKQ+HTmakn//0xpHTPT/YehjkD3UEjTmmVYZewAtZhqmu8lL5M4bwXK6N/M675t4yn8vEg7WT6+ORzKSHdg1WfN56oisFWvkPiXsONQCNPxxaLUPTjO2ctIKGme+kZfANhvaL768oemdvKMsvOenIg0Vp6euNC77OCoA6ggAA76547PrbvwDGAqsLSf4KAEKvxyWO1ASQUlYmYhegt1Y8jgGYuujsPeteGtszYRNH/RJJODypZdijlHwXlOnZJDlvjWiM71yazlkpRzBN3NEbHJYMmkL6XFB7UymMSIzBtiP9i6f5S3AsyihiFfPmrQAQhPW1VYVHjymlQBxpkkeZeGvF4/H7UxYtT3k+OZ6UKOujQZIkOebS31YAQw9gFnk5ktROv7hO97BM7rqBkNQfLGqmY6koTAdQI4B8iYFIla/Y0WwFQILA1vyJvGBZxu12Uvfz2OIQuKZ8uz/lyYuPP5S60RbiNnvWPQ8mJlmtp2G2ABjPWM2lgyMu4kryR2Rp/FikZUN1wJhgmGbYqgnPF5Yh9QP5vopGgMeqDPjq62767M1DS/Xu/vCLf/+kVKeR2iZ2QjAOrNd4Kk6BvXyI86ezNyjnMJip0HcYBgTg/NPmZ99n25H+UrxVxfHuisdG/01lWY1KuaYCJ4JIFeLE+81PJiCEYp4pWGUCTkDpX66h8LkTC7yRJBxyb6QWcumJympUzikLexS+okOH2mbMKLw1OTUCnn/sd6ctL6rFd9uRQ2CkcXWFkxArDijhtWu78nr1BytfLcM5VS48x4xVDGByMJYWAMZYVjRF1Un/Z1ktIApnH0TTzSIn+paKl197p3gjgGaFfrDqdVEsro+jOR6b81DpX3IcAseVMz1v0lNiCwDBnDpDmBgfONbX0RNO8soYrKfK4zhz6fTpTVUxMbp9hMx6jK1xELmZDngiGAEUAMCqN0hUbfml18uyVOAHYhpltAPyhIr+MiHyXF6/6D8+vSoxDEAZQQEsXjQ9zVZLaGu6MRCSQ1EFqomtuodyoiU5ozJYs90KoLNkjCKPyS/cIXILZ9dv3H4sGpTefo8821zvnTejdsRvKDaGODdGzUywdUDBKaHUEWSz6o3nx5cOKKhYjEr/8iEIGSUYHQZQrAJAUG1t3QtKh0Pk392wHwBw6Smkr5x930aSNUUdZraXw60fKxguW7zeW10T7OvN3Q54+b2tV517fNqnqA5I1AFWqFaacHYAFf3lxkEDAMXB1qakXfcFSErc1Eb/0c403d+KhEHQHr+ViGka9dXuHfu7DIOUcTEM63Pzs6dWYQB4Zlh6hmqIhb83JhX/EUntC0Zrkv7qkXxDJ8zLaVKVFk72+2c3AjJJf0qqOwgAUGBweIx0AJX+5We8pABNTguAQaaikVHAAIC9R/q7+4IAgLOWzdp9sNPnFknsBYPmhqoN2w4vnt3gcAiH2vrIuBgEq70Z+zmrmtbcULXqo30uB3EWGYbOMOQcqr2O3sCQrGcYZJAhX8nE9UF/UN5xMPjZG86KPQFhRFL/+PSqTDOBMQaKpgfDcn8wGpG1M5cMb21dOnvAwSNJjZUUUSOgtHyw6vUSH5EykYEA8Px4CfNMziwg1pqVCiF0DPrajnYFnCLv8zgcAudyEiHudQl2DyCvS7CnhkmKxpCxwKZ9w0TuD3VoCEUUjmWEhPINYsfBxDGPdml+DpWZVsf/EcS31egtHFU6e0KdvcFwVOnqCxvWwWt9LgTg/rb+tt5QX6hQR3MCmqNOc9Sxvob4lkOH2rKnA1117vEvv7c107M0I4hCyQSEkGcnXiOgiaQAbJEMAbBlPRmg2B2qq475T0h/N0AmtNjlMAJPWstBCBGCJDNnEGgN3Ig/lBW9qc7j8ww5c5wi1x8cJn8Nw8zNTT+SBsBAN82BYLStO9AXjKgaCTOYGLd1DbWMvvWyE244f+EVZx2X6Ri5N5t1Mrp981YP1aYfOtSW48spFEruEDmT8+yHeCvQPz69in7IcTLqz8a6qvbutrjki0/gMgxT4Fh7LLutHuJNkhGEDpG1e70llXQl6gPTxLVVrog0lCMUkVWNiOahfVgGcVlnDo+MdXqKqgfCUk8/eYfEJ3cd6prWGHPNTztp0I/0xOq0R/LkPHEUIdL0NDUg/Mqa7VeevSi7EUCjwRRKXjAIJQqWCufagibX52pAWc2QIcciW76RJX+6KngyfjGH5TLLoMTuypKsA4AT26q4HDzD5NN8Lt3CPyKp/cFoMCxrKTO7QxE5SSVkAiE4pT5WlDAiPMfIypCzK1EHZOfGS0979o0PqA6gUHIGigKZBVPuTnATQo4XTK4KAJKVPsPzbJQI6xIwvH4vOS+ooOq+oYPI1sK/PyhJpMtjmtxtDMD+o73T6r0AgI9efQEAcPIV16U9qNspNFR7eod7qDLhc4uaHjGMNO+Y3QhQpHHRx5RCmSgQv7RDyCt7Y8ULz1x93U1gMsrx0s8E/nDrfgDExMpexOQ9/DoLTDl7J3X0BoMR2XIrZeTAsZgCsLHVQDIQ1Fd7EuPV2WmodkdlLRxV4gVDuTuCqBFAoeQOw6B4Zkq5uXaCSPMCyOMTVFXDxBjlnFK9p4u1B63ObcBFVvDmS38wOmJnISXr6BgbnmXq/Hm0p6/3u4IRWVF1O9qclyPINgKyBAMoFEochkEFzIJf8cIz8f8CAK6+/pYK/0iJ2z1xIm7S/USMzHNTEzM445hEX2QbtptF/pMkUsvNrxpi2pvLWXXyoumbdh7etOPwmo/3PvXGxr+99pHb7dxxsGvHwa7C+sqlUlPlcjtzjQDbGbH1fneW6sRX1mzP8vIbLz3N1gFpn6UpoRRKHBahAoYxUJJAgwPWh2Q1xjjLiGyGQbKavHYORdK0/XGIPHHlD5fyidlBWSL4cRmaNBe+tLyxbueG1sPPvLvnuff2P7/6QNKzLIum1Fcxwwf+ZT8HjmX8XofHxSdljiZmhWbRASNGAqgOoFDiP89x0gN4QmONhBwc+25HR4eG96Z7gVPk2roCs6YOSTTNMI52BuprPEl7hvq7ZEXTDRM0DBVG8Tyr6bEDCzwryUr60xpM7x36hgd1QA7z2wshVeO5nUKaflLZU5MgcApctdfZH5Qjspp4ojk6guyUUOoIolCyAlmGgXkqgE/ddgep+7nilEnv2c8zBpBhYC9MJ2gRhF39kUQFEImqIcvxvXDhnMTmcQta0mRP+txiz0Aso4bLXMShaUasvMA6IVslxc9t2NdeDm1gvUl9tSfVRhnximNZVFvl7A9JqqZrevqYefZosE0mHUCbxFEo0EoULGx+VJw//vLBHEdCps4EnjTKI2s3UDTk2JFk9ayladrsSLJa5XGcdvx0bJqJ0j+1F/TChXMO7D98/Nwmk3Oven/L1AbfkuOa3t1AlAHDsMfPbdy6p8PuC0R6qO3rPvekFlBqIIQCz3IsS5bnWUEI1leTbtX5wiDodQmN1e5IVB0IyVYUPL+MoOzNIagOoFAggnzWQtFU2/2mT99uGGapwgYvphsTnyPjSnmMXieN1ta9DtHqJ6GGAAC9gehcMGRGqCm1WiVv3s8wyO0Q/F5HdZV7065j2XdmSZLZ0GyDvEbKCBxbW+UKRhRJIcPOEp/K0RF07mnHv/cBdQRRKOlBkDSBgBNzZu+LRSiPkuuPERQABjhe8JvpfsLOQ5uy7Gx/E/GKWQaReO/eQwP2nTg79vW1NNeURA3YC3+fW/R7nA6Rm9bo37y7LWOY29qfYVDaFkDZT+fxF9dmP5OINKQPshgB3sHMI+oIolAy5ICihlpf7h8OhKQJzSgOhRpL/ZG7ksimAExMlsCJYhJbnXz6Q1JHd9BupYAQtNKHMMcyXpdY7SMSVtXIsLD+YFRJyReysWX9exuPJAn9REqV8xNf+HtcIseS+jOOYaq9zt4MFXD2H1WAf/GDrYcVNs0VaSs8W9+wHqCHenLRAVeef9Ir727M8nY0GECpEG65/R5FShw7mHevsPOuufeWWz8z8YV/CZREkm7IzwWEMVA1IxCS7CoqhkEMQrputT6wPlyMsaLqUYkMeU/twJMXTpEvXl+LAlfldlR5SfPqRJE+v6Vh7aaheWQIIhPHA7aQGJgF+Qq/ctOpholDUeVIZ3Db/q7+oETyaYerMtZTm6gDMgGNWJSCZgRRKEkIPFvAL7Ri5H8euiG/D9E0cSgiRyQFk5JgJPKcNZA99izpvqkZgbAcjMiSrBVTjeUU+ZoqV+6L8FR/DoTQ7RQaazx11W6naCXmk5EDMer8btFq8AAhFAWuuX5o5Q4hUWwFeAwxBtFwSImGeaDNrBPPWlA3f4q7SgRO1nAygzcOOznsrfLnUhZw/SWn2ndoaRiFkvTTtn+heXaCg/EcUEreCoAM1VK1AGmuSZr1iwLrFLkhRzkmnaLDUSUYkYtc/iMEG2u9XG4aHgOgG0aSr4llkM8tNtf5/F4nb7l9bOJCHUE4e1odwyCvS2ys8S6e3Rh/LbSW/1kUwPmnzU+7fegVDMNwrNfrbKz3eV3CkOVh15SlDLrJpAO0hCm4VAdQKpYk/w/PMg6BK2A5P27CwBNTAZimGYwoEVnFGLMM8jgFfnASgI2i6WFJkRW9SOdPldvhdYu5GGymiWVF6wtEk96x1u9uqvO5nYK98I+TaCa0NFfXVrmb6rx2eMBlZyhZeZyJ42vyZrA/CccyNW6+3u8iLausmThauJ/cogEtGnDwOb2F3RyCQqHE4blC/D/WnBKqAZJh8/BvyFogLOm6ASF0OXi3U0hseWaa2DDJIEdQBCyDBJ5rqPWO2CsU2waHpPQHooGUaY71fjeJEWU9Bs+x9dVkN/utFsxq/GDzXjv/p7Dr5O9/XwkKJVM0OLE5BM0IolCsqSQkRS+vj+JTt96Rtl8Zha31JXe77AsQoXPC/GkAgO17dse3V3tBtZf0Tz7WLQ0EAgLniykA64NNjXbmC4LQ5RTqqz32iOCMYPJeylDHfy31fTNJ/6RtpJbE2rT3cGfL1LoPNpM1ArJuBaiAH3zz07YitM9mICRtaD32ya72UERJ+7mwItCD3SPqALtNtH2f6gBKhUMGwXNkUnler7ISFcm02rKd10SlkEIwe1iLpQ+EGY1pmmW294SPtPUuWzg1y0H2He6eNa023TMmALEV/YGjvdOnTE2z8I8q/cFoKEo6DaU/eoYvOjmtKOGB3fSNSP9CO0zt2XvEOhADjNhZXXbG3ItOnX2kM7Bm06G9R/sSDSYb1luXqAPSokjRqtr6gZ6uws6KQplMQESyNvL9icL0fW0oeWYBjT48IyfeBFZ2Cmq9H06t51VNn9nsv/eGsz5r3T59+cnhcHTBzPoFM+szHS3puqn1uYZymGIjCsj8+qJWCoPS31YJBw4ck4LBaX5ump/zcKYT6Um3XDKCLkzoikGjwZRKhkSAxbwVgNXpmKqANKAV6w+9/OGxVzaM0Bohb8psbMUbyf19fWuRb9lSy7XUcka0L14ClrRDWw/pXVEwLIP8HkdDtdvl4EbULGl1gCJF58weasSUvVMQhTJ5gQ6Ry6sEbAgq/9PB/vy/7MIwbGdUEg+MaWgdfRhjnmPnHjcbGOamXUfXbjoQiso+t2P5SXOum9usyurr7+/8eOfRUEQGAOha8oB1lqtds0OGEJw1XwQF0RfINoaXQWhm89DaOUfhn72yDEJAesyW2lEIIRB5ts7qDqSouqIZSdeit8ofHOjPfpAlcxr27juYfR9aG0yZ3EAIHAJfQJLGU088Gm8HTUkbA4B2fIXcRYBzSzFpaZptHf2bd7eFo4rAc/Nm1M+aUkN6KWioxuf0exxykBS1csM9SaSSFhmGYRqG+cG2jMvnnu09QxmTVa4an0vgrfkEFlddcpZh4m17jgbC0uGO/mBYVjRDUTWG4c5daCWJxsV5ztdD9h0RJA1Gsh/ho4+3fvRxmgX4v/zjzdmOjKDHyTdUu0NRtS8YTZ0an6gD0kaDFSl6/hknvLtui/2QRoMpFQiDEG9HIAviyccfefLxRxK3XJ3bSMi0rXWK7Ok2TmCf/vP/2fduvvsfhj9lTWSMKFv3tvcMkEIMB89u3rbj8OFDIs9GJLW9JxiOZhvnktgJeYSTYIZPd7Mk+3vrPtYT4r0k/RQAYk0Y8rqtw5TKH5+NtWCzR0ICAE5aSFKYRrQAFiyYJyma3SoOAsC+fzDL8j+t3I/z0988rSnZTJa8SKsDXNywz5O2iKBUGgyK/VTHA9eWrivn9ddcQ2aleHMdP+7xDHWqv+jsE0qTBRTXBEQZ3HgxAOBvT73RMxDp7A3ZyTZSkKvyki7bhokjspo6GDJ2RJKkizTNyDErlGReJhp1VpanrpuqZgTDUl8wquZ8qJHfa/jD1es/TnyYqQKgudbz0pvvg3HA9Zec+vybH2bXAdQRRJlkvPjsU0Tg3niLwLO2BZDdl5s0DOC8a+4F45vnX3opy7PXX3ONrRvcltxP+sPfXrOlGGWQPg306Wffsiu/BkKSLf3J0njwnVVNj0RVw0hT80X6KLCMVRSWa0UYxtiwugZBSO5rluMoqmgDQSkskYU/KB+DmQEQAsPAJPsngwVwzSVnAgCyq4GZ8+bbg6ohBIGwEpVVK6mUTC6FEIYlJRhRsvRHYh1AD3RmNwISm0NkgeoAymRVA0lccdmlmfb/1y/l5N4Z/4yoHgAAU6c0FKYM2Pja2urvNtTYRzeMQFiS5OHRXUwKnSKSKimkIUTq4TiWZFGmJrxngRTfktwb8kLdMGVFi8paRFICYblUC/84aRcOVrNm4qafN6txSr1/9cah2re0aqBlalVR54BJjdjmPR3b93eFo+RjjP+RrK9hRB2QWBdGHUEUSib+69dpFMbk4/kM6uGh38TuXH/NNXd9inh00kIcajGxCElnf+sO2RKR1FBEsRfyCA6VUciqFoyQfnCpx0IIcSyrG/k1hID2eAcWaTqR/hFJjUhqpkECWVQImT6fw3slPU6dM3zOScfx0F5oJ6c22XR2jlC6lQvHNbsavE0dPaGj3UEyOTJuGVST7oZZpoYpUtThq5ECQztQRxCFkpZXX3+DfjLPv/RSFhuC9CmLpdLEvCFEIWg66epsj3whs7EgtB0+umEqET1q9YNLPRYp0YZAzzAJPRMQkeYepomDYdJoWlI0TTdybyXNsoxL5HXD0KURxvzmgqob2MR8murmEsMg6HEJEAKVqD09mmRpZTUCLlzS/PJ7lgLImg9HHUGUSQ8V8UWSkHtjJ4Ja2TuhqBKKkGUpQ5r+s7ZrCGMsq1o4qqT1/rMM4lmGePDz7Adn9fWAoagyEJLCkqpquUp/CKFT5Ov87hqfM8fmUHik5/oCkWPdA2BU4KyOqo01br/XYedNxfFadkCW0rBPX3V2ovR/efW2Mp8shUKZhKB0Tf/1ASv3BkJSdyfwsVlaVu/l9K2e7UxK4jTJx/s/+FoyZiAUkSVFJcPFcvP7syxT5XE01nrr/G5ueFfqbO810nOdvaHD7SPUZJUQxppb0Fjjdjv4pNV8og5IJRwM5HL8B+/7YrGnSKFQKkUBYGCYZiAshyXi5OE5xu0U7KYLmDS5MfWERjeJsCxiWYb0Zstz+Q8hNE0sKVq+C/96v7up1utziznOjcnRAhgISZFSuJJyBJLhdkxtlauuyiVkVmNpjYCrzlmc+DCTEUB1AIVCycQw6YkBkJVY038EodshOEU+nhmZqbALQShwrJ25D/LH0iukmXQuO3ODC/9av1sUEuaRlcgCUDW95KlH2bE+Z76hxl3lERkG5uUIylEHUCgUSlqG1d8aBln+SwoJSAoC6/M4cum7RMbzQKgUJDoxxjkWDMc9/vGFfzmaexczx7hgWBb5vY56a3ZxXo4gUrJ3OclMzQ41AigUyggKgKR+ymowLBuGafmmHS4HP7TCziAYEYICx+hW9RYoA/Ya3174N1ke/9jCvyDpP6J0H+Xlvw1pccGz9dWuWp8zi8ZNawRI4WDiQ+oIolAo+SsATLwfAyFJVklvHKfI+9yOeGc0q5d2eskocCwklV+l95zYw9kdAlfnd9sLf69bZNmiFv4jvnQsDAACg6DXKTTUuH1ua45xPkZAkiOIQqFQQG78/8P0wxjdrSxIAAAAAElFTkSuQmCC"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "assistant": "The sword is the remaining interlocking weapon crossing the axe’s projected clearance, so rotate item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nROTATED ITEM 0 TO SLOT 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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gb5Fl8gMECfGncCTmBYpj64A1r/zl7CvvGs2znUBQCyAbN55dvemj95aeciEviNjQgGloutrbc/TtN978xTPtk0n6xywACGUDAisIrMgRiEgtMHkOY1VVouGArqkMgqLoYDnSMogkDtFmcJTygzGwPZOxa9XEu/a3WXehrqlppT8lF6gCGGLnPvuSGsb/+95dy2dHZ8xd7K9pJg0SdKIDAoGu7a1bf/9aP5hEQGwgCHSTLPOtNFAWMogTWJbjeIF3Oh0ImE5R9Hg8DDDdbidrZQFxHAkIUyjlvTgtSzT+EKFhXkcq/SeMC+gL99yW+PB3f3ocTAQuX8x8962PFi87t+vYPgQM3dTDhrZjp/S/+oIlJ5+wvKnz/dceOfNy0mpiRDatJvlqObL0nNvBaIFNg2WgRn5dZFkg8EI0GmEYweESsYkNXdd1jRU5VZWBiTVVIW2CIIiZCBRKWS9ODJiEXAMadJqQCiBJ+s+s94Bxxs59bfNnN6d96kdfv/G+nz558llXHtu/lTFM3dR1gA/s2Q4ZBOAJyxvbc3yLjb+7NZ8zGj3xahoGC6CkaAxwkKZJgiDwPMuyLMPKqmQYOi8IpmlqisywHNB1yDJE/k/MCPCXv/0gY2W7slbAQ3R6//PfvjLWJ0XJCAZ42dz0P0zy9fGsrJJvkzJ+FUBa6f/gfV/8zo9/OyF0wEAwct8XrvrBQ8+ee/Gn9u/8AAKsmqZh6Pt3bA4PBIzzLgITHI3IdGBijCyZ6PJ4EYJkHoBuMKQ+ADgEIRgMYAgMbGDL+cpy/Kt/GmF68Ljin+77j8G7w+SFHA1+4/7/tO///If/NvonRslO2lXGkfaeaU219KOb2FlA41AHJDEQHBp7e/enLv7D449det2du7asRaZukmyEcMfRPatfl+5/6PUffvUyMKGzgHiESdN/YnbI0Ug4GNA1BWPM8wLDsFb/n24AISZqwWRYrrZ+qOhmIgj9nIhrAqoMxg9wuNM/IsmkTN3BH2nvAQBMa6qlRsC4VgDZnT/jRAeQVe1wIyBR9Md57c/fAwD8iud3b1mvyhI2sWyYXZ0H33/71X/60RP/+928PDzjCMOaCYlQzKuv65osRQDGEEJFljxeP0JECcjRCGJZxDD+mno7XDxBhf6O1h2knlnVAAAnnbwsuzKgPqLxgy39E6GmwLhWAOPf9W+TKMzmzWxIK/3jfPnyxp+rJx3Zt10OBwDWdA339R/9aM2bn/3u7/74oy+ACUggrEANcxyLBGPZRbdpiqoruolNnuMABKqi6S4IEMsKTl4QVUUy5GhEkcD4c+vnIvfjYEC8Wxs/+sR+mEkTUB/R2LJ0blMm6W9zpL2nrqZqdE9qMjD2LqDxZgTYZJf+Nt+4dtp/PRnt6ToW6u+GWMMGjkQCW9avvPFzP5g5b8HPvnUzmCBc/7Uf9Ha2hfv7DE0juXYQ6FoQIQExDM8S5w8kMyN5KRLgeVEUndFomLwMm03N08etWz+b0Mf4iloELY2PgWCYWDfxa91aoibIxSygPqLRJFX0d/cF66q9o3oSkw52/Cz/x4kOyIt//dS8G7/82rwTTu7vPAJ0DRi6AvDhvdsVVfvBX1Y9cNewrkHjlq62I50HDrB2WieEdsody3E1jc0AYMVqs8UJIoSwu/1oY9MMXtdlmSjIaHSMq28+/8/357zSB1fUEIlPipkTtpNEJobUPl/fxBik2hmv6CCFRRDmpAy+/t0f/b8ffbckfwslC5kW/pRxrQAmivNnWL1JnlnGz/7qn2/4wk+OP/3CrsN7iMdcVTGLOw/teuelwPcffvf7954PRgVzfUfiQ3R6Y+6vVUmrHxIFQIiBEJqGjhAbCQZlKcJxgm5qEALe6STxYdMACCMEGAhU0+gb6ANjLf1lWRFFIa3Qjy/2R8xVtTQDYMlu8PopoqUIgGqYr3WRv50k76ZTBnZpUgXqgEtv/CLGoHvXSgaRliE8x2KMdRNi6zNx8lDWgMhiRFqIA1klTcYFnhRvaQaWdcAhLGnkisMAMORFpOUUgAzGoGXpJRCCN56dYAvBicv4quJ58L4vju0JFNbY/rnffXvz6tebZy8GJF8SkoCAoUT72t57+Znv//oFMCoElWG3vLDkHfnTGYaU9XK8SOS+Rga+G4bOspzT5RNFVzQSEgSHaegsLwgOF7bd5+Ng7b+jdYd9i/85l1bDK2rRlbXoqjom30oFCADpioqgg2NumCJe2yRc3ShcXk9kHCYXCN740Sf2Lf6Sr3/3R6AyqJ52wmlLF3W1ruzeudK2k+xFU1JLcAYBEwNVJx8XJGYl2UclPWUBmyhyMDZNu8eUtQ8A+z9+c99Hb84/bs6cZReM+h9XiaDxtvwfcx1QGC/86f7XHv/pjJmLEcsChHSSKIlDA71r337zWz8ZGiFQPnR22C0vIAROtwciJEkhVZF0XSXSH0DTIJlBmiZDiFRV1lRFkaOB7s5IoM80DVvUnnXdnWBMpX9Pd5fH4wYAXFYNr6hBV1hCny1dfRqCkEVQZJnrm8VrGoWrGvgr6lkMMEJo0ydbKk0HYFIwjlONp6R8MPtBQu+G2IuTUi3IBkv0J+9okul8+Z5bd+9Avi+hlEsBTDjnT0l46YmftMw6QRCdGCGDJNbrA/19K19/dcaJ15T9vZ3Db/ng8TjrGuvcPr9DcAm8A2GoSrIguiAm7nGH00daQ0PS/kE3sKKopoGJ18g0IUQkVXRMpT+ZZCBEr6xFZKlvB3bLA4SARZBjEG8pA1s+VZoOgFjHVr8Q0/pAYn1ADLLegVhjgM5A4iQEhgKAQQQ5tiZLkOlyxBoAWAemChFmoYGAAYFBLAXDJFaXdSjTDuWTSUMkEkOplCygMYwGX379rXb/SwxGvubuuYnEdQcGrDSYdDz31/+48vZv9fa0S9GICU1oGpIU7u1sa1hwUeeOt0HZQL7CX/vOE78DAPgaZtRWOQ0THdyzPdOeJ19xazQcFhwOYrzrmgE10hZiTKX/ucJoB6JtHXNtI/diB0kc2vTJlqXLTrCfmvzxgJR1OdG7mPzXtLw9xE020oQ4299ItDWZJ0ruDS/zGtonXxbOnZZ2+7a1z9ot5BaecUMBh53EsNt2vmUNgB229YSFF4/4yp//+iex68H6pi654MTjF1xcquX/OM8IqqoiPockWtc9F79/8R0/nDn/hHBwAENTFFkEjFB/R91x5zlcXgBeGJ+a3ClwU6td7QPZtOBHrz6RtOWi279SUdI/DgTg2gbuxc7K0wGD2M3ZbKMLAuLx97kZWR0SJTGDLO4essO+g09xLApFdZ4hwt+KAhAjIK47IISX3/iZkp9z67rnqA5IhJUU4swdBgYbNr1u3z1laay3wf/89qdxnWwJ/UHJP/iNvvnOxz/71U8uueDEX/70LxPI+XP5DWUp3H3rkfvPv/X7y046uaenF0PoEMVgKCyFeqFJ6k5zJ1GpZGdOoS+0WXjGDSyDPA6+K5jfGb792C9B5Ul/GwgtO6CdZApNAh1gL5Ozc+6n7ieS2jaDrIiugcgdhIlzjEXIivGSsK4V2iU72p1FrG4OiCTakl4igCGDRKH9X4yx1VYQkFSgwSPHgsKUMsO+v35buu0xuf4/v/1p0pa45Le3DVpzsajPm+9sLNWZjYIR8I9f+SoZ9YVx10BU1VIUYXG8+8T3z73l/vOWn3W4rcfhEAPBsGkYipTRd1QkpIFnEbSue+6qz9zHQMwLjnyVR14Utv4qufTvnL1sxvT03SU5lg299WIedkDj6NkBucjosmNg22FqGgCTbB8rFwwCjmEVFZgGQsiKE5uABI4gMLGlDMjryM6GCRgATQxVDXCkk24sBw0ZEJvEKUQObpIGJGU6fWoEjOg5SPW+kW/I53UEglLSTvb3ZJoYIvId7964tYTL/7LqgF//4j/tGNXGLXsbvVxnX+krTd576ofLb/7OxRddEI1GUXePabXWBOUhlglfBHY2HmLID7B8lFW7JPLmHzK+Uefs9FVdcTwXX5uHDoDD4gEPffcWe/u9V4wDYV0GSIoOyd+PhW3J1DhSPIhJnHewiIZBVu2FZRxwKJbRQ3w9xCYgM6RhLM0n5vkZ2gERdzQ5VoEp2cm88s5H8fvVPtdZpyykOiC7Ahj63ONhGPtOIChZ/w4t/4cP6MGllf5l5fcP/XjDlj3kniX1LEuGhLBG5KW3Pszrjb75+etJ0Fhd3N7eJStSvb9cCqBU66WUlLyJyiWfS29qPPpObKhsdjwXX3va9Dxy5EYOmk0WWI7BxFwm14mV4GNiZFVzkXQeKwiMsGmSFDGAiW6wPf8YWxYAMIn+IP1kyUtIpRg2iTFhj5u2dkUIMiw0MOBzSMrIIu7TsnZDq32H2gEjxQ4TFAFJEbGX/8NVwihQJiMgJv0J5C/R9Fz9P3YxUFwR2p+DdSkPSmDLKYaIZ5M8sK5qeO48dt++GTP8nfvb8hsjnLu+UW8/c8QXXnPxqVmOoBmGaWIjKR+AUvEkSlUMkMhCDiLLeCZjesnY6JgDmJgFZA8G2dk9pmH/CmL5/kSdQsZWBsR9ZD1lWq1H7HUYJKWI5F+EYAQWYonG4pSxVR1h8NRiMQVrnQcTNUGFw2aT+vadQedPStx32AvLt/wvhw74wx8e/tzn7rUvTJKcTBr7D5FFu9mp5jDbmnsw5cH2jMaMYvipC+G+g+KHG/aBmJc4JwyD9GceVDmxC3xQ35BDW08SpKLViVPgDdMUoZKvlZOvpqGUnBFXviWCXHJR1ZBJiS/QDJIIhA2TgSZJ3MdQNSBx+FgRYAtiKBgmCRJYGaLQxINGFcaqAVjSVsTUyYAhCBFjYMBa7SQ4BmKxEJs2ZkTEfnsJ24ftQHb53NcydpGqWAWQ1vmTKAzTOH/sx+V2/pRRB1g+TWs5U3p3Ss/mx+RQz9Sz/3nvoU94kW3dHbrva/eA3UO9JEckNgd1qL4pzVVt/5PU/SG+0hkWvI/dtVUISNliLdEA1HXDssdjg7itPO/BEMGgTRP/maWleBUy+TTNaMlom/iSIfav/UMe/DZxzAkf87yT+zHhaNm38SsnfgXF19H2ysYO/HIMyQJSTSzyDIegokGeGdpN4IBhQFWHgPT4IVt5ltR5qVrMI8Qy5B14llE0Q9HJ0TSD7EBsiISE0fwZ+edLpX9GFxDPsaqmpzp/hlQCmDwQHfDZe8gQxFKnHLjMw4e3rZFDPcBQ21b9pxdilsGfuqaMi46k5M2hJIpEg2Rwi30/aYtuFWQyCNpjcoe92P6PbcnHy/+Hp4MlHT5xSzx/eLDrCxy+hYQQ7ce2uWPrF9uTFnsXWDJNs31HTywU3J5+jLPLFSuk7tmbX0ZsFl5ava+mpqZ/YOCqM2cOfwb/7a3W6uqaS05sSFTWtoiMiWb7I7I/Ecv+zCKjk7H2jk3UsncatGATr4IhizZu3yZcH0NmrHWCqo4RgAILVIOkbwos4lnSRFDVY/4Be3UweAg8XHXYpx97ZF9vHIusP5JsERBUdKyV0w35uX+ia/80CiB27amanhj79XlJemGm2K/9eHRiv2UKBvzhj3+68847rSVHyfCgjkPb1kmhbmCoCOisJf15Bugf/cgtwBLk66TD6C72CNaPDjNM5rBQ6oc0JCOSn0vcklAIlGlLghgbFB2xfPAEmWgvUYc2xf6NXZ9DsivFY2a5IIgwsq0ce4+kWtOE/GY7I2BQC8VimGSVYN9JWD6TA9r3bWH9zFs7DF0l2lSOGrpqpbNgweu/+uZbNm/cGOrvA3hG7M8ddFFHw6H5Cxau3NR2ZP8BTnA4PVXXLJ+V8LnEP6lEfT3oSElnGGb6jnLbaaRDmMRS1A3TgCbEBsYMJlk7jJXST9KDTJ30lAVWoqf1ERq2fW2ZlAzZleT5kNfYFwHZmYwdMq1cItMwEQkLMNDONSo5VPonYf/aY581z7GKOmz5byf/ZI79jmrmT5l0wF//+lf7zvU3fWrEnddsOkg8M1b/gzMWT0m7z+Ftq6VABzAUBAwi/RHmEBBY6BJAnRsuaGDFU8V1z4VK+1eYRTfCspL5iKMWjBWZFUx2JZIckknvMUuMCMYE0rDnhvzG1lN268/Bl8VjP9arYq984e09hq4D02StOZocy1Y7BI51MwzDIdMwdFVTP9mz55zTztq/70AkGvVPB++17+zvjhiyaEKN4XU5JBkG29/fP2/R8X09XYKp6aHuF1/u0QBDEqsRg3jxxgvngXEDafRAujtj3cAMEfskeX/Y06lyO50/B5sYkpBxfB8rlGwC6+CQpJUX5Ihdu+WwpVSS07eqva4//WH8dhYYQ9hMzp+h5X/m2G+S9K801m07lvjwxadI18/7bj0u2t+ODRVhg4Emkf6MJf15UOtCc+sYcZkAltaesfTudQ/874hvcd8DP/7xD+7L5WTM/NKL0oAgWYpZmdqTm9wkS7xDcQrPvN7KmabT5WSxSSYny5KiSKoalVVZ10mtK8exDEQsyzEMw4vOffv2RyOhUH9/ROlDLHJ6RIdTRCxUZE2OKEqQYTjWNHTe5YHhUE2Nx3pn3sSY53nEoJXvH5BkVTcBK4qXn20ZEGMKQgARZyHx3iRut420pE/XDhultguNmXx2hdjgaxCEhnUd2tfgi88+Gd992Xe/Ucw5D4TGuGh83MLagt2W/qnOH6vyK6PzJ+nxKCT+j4ceQQxCIs9pukE6mQ/XjJfN0Td9SNLL5s5gEqQ/cAlE+s+pY9wnCOCUGgAuAuD+M35w/7oH6kd8u/se+HEuZ4UfvquAVyUyd96i2O+Rko7n39mjhoIOnkO61h3oa9/ZSfJVeI4j/+dZjnM5XCwnYIwD/d3BcMg0TcTxvCAEerunzpz9T3ddnulz/fnDL3W1HXF7vd3tx0SWIy9koGGYECGB590ul9vl87h8Dqdj07aBUDgcVrTLl7eU9lv69vcezGW3quYFZLXOQsMgKT7WWp7k9lvVW0mL+sG6MKscbLhHbygp0zQNbDmJDNsJSerL7F1LWZmflOVBicNmTQFNSAcCyYxV2dfY6gCGQfXV7tNOmNMbiPQHo6GIHJFUWdUNw7xszlCR155Dscv3jAXAycMaJ5pVy/gWCeDMagCWAxDrG3zGD94D4L51D7xf/InhYH+RR0DQasyiqCtW7Vj791zrYAsgR5tm/PDX5zY4WYZnUTQysP9gh6ZrM+Yu+t7tI9d+PfbKxn+eY7fV2gd2vZlpt/88O3bne+uWABNyPO9wee++/jQAwMPPrImGg+1d7YfUA7puIIZrqGtsmTrj/fVHeoLhtatHadzQEGQcAFmjY6upgy3ciRyPe4JigWBr5U9aAQ06gJB1s9tCD+WUDQ4jIjVipKzAljNWllGJW7NQMiqA2PIfJC//BzIv/3dvHGqDPvnIslpoqPZ43eKCWQ2GiTXNUFQtIqmhqPLUz76edv91ZEoV/s7VTPUCHpxbBcCZAPz34JMdANwPwL4zljvXrSrWRMXhYoMACCGOZVwiKle7opGsk8984Zvx+xzLnCC1FflG8sypRR7h8ec+FAUBq9KBtnZNU2Yct/g7t16YaWfhwJGkLfcurM+rmrVt39Ci6tsfv5N2n+bpLe9/tNrr9k2fMv3Ga+/s7g+8995oqwFrEgC0Ozaww0Z8DTIY4o5nfNluoPhvKx7stx8hxioZsLxAVpppLHkp7xOzzy4hr4ySHZbn2KTEf1v6Ww/iG8fe+TNOjACPiyTxQECmRLECEgXO53GOWBj94AoVrFB/fsGlADw0uE0B4J8B2A1WhvUj+rQ69kh3UV0icCQIigRCVTUgQlZbl7GEG8pDLQrx4NHUjd39Rp3fNeJr31252oGZvr7OUCT079ecA8CgyyVFyo8yuzetAgBEAx2KJu/cu2PGlBlXXn5rTyDoqPKvfLXss+diaVLWjbVEhxUvt1KBrDafFkmxgeGHGD4CzCB942IhX4ZBhj6s6XC++L0uh8j53I69R3rInOFBeHY8Dj4ZD5DPJSnx32YgEFv+jx/nz6jpgCx+8LSj73JsXfuNS579+Ztk9IrFZwHYAVaHzENaZ8iUtPSX+8XnnPLW6g25HBxHi08rwrqui6LdoX3ScsYpizq7+sIRqXcg3N3Vc8dnSMsg+d1Xtzgb7R2ikvrJug+hou04svuH158Pxiv9XYdI/cf0WavWrWyoa25m2XOWX7d6VbmtAYa46Rmoa4gbTPsxib8fYkzk/5dO2pXjgf5nw3GkjTSxI8gLyZhNAHVMckBJJ1FQyKChUxdPd4hcc73f73PuP9LTH5JUTRd5bvHcpqcKOFwFkLT8jzl/7EdJ/5bc+fPK6m1XnrMYTCiGKmyH88BDT/zgqyOPFvjGJbU/f7MHgHsA2AbeD4EDeocl/Q0T+D2oP5S89A6FchXrOFqs5wZjrBuawDtIu5ZJymshcQnDOBwChHCgn0RNohHJ6XKI519xKgB/+cuT0f4Qj+CBQwce+NH34Pr0Tphxxc5N7wEAwgNtfYFel8N16WU3yRxataKM4g4haBiYJWFq0j/RxCY7mDlW8MKBhIEts4DkTpDmcpZSyZ9aP5nU5BL5UxfPmDut7nBH/76jPdMb/Uvmpc/YpqDExP+49LeW/9amkT6hIpf/r6xOO40AjN/x8UULx29cUgvAJvBhAOzX2oNGVMWkEREGOmmNkrzz+k07cz2vaCjxVti5GabJEBfQGGiAxAAAmUlXdAAgie45y8zTLliyaDZx1vOcz+uSZeXqq4hDPxqR7JsyEAwE+o90tX/hq1/o7OjCp10AJg79XQeOHtq29oP1bEQ947wry/QuZL1vlWgNpvGQpLihp/MR2onrKGj3Ex3cGBshXAQcw9RVe5bOn3rRafOWzZ/mFPiiDjd5iX95yXWRSf+mXf4XI/3jor8YHdC69XkwupRkTsU3LjkM9mgdATOiAVv6GwaWFDMYNTt6C40ERMPDbvkTK3EiATRzEgQAEumeQwYAvPtezJnmdIjhUEhIEAp/evjRP/z2t3sP7m2aOf3uz95hb5yIM6nC/Uc3bNrY29511pkZs06Lw+7aQOK09m1IROT/cSUVdhPtYncGtd+oOCAALEI1PpfI0wDASAog0fkzEIhlpOCySf/VrR3emlpQNC8+/gYoD6s27E7/ROar/IGHkuflZuEbf5UiKimntLqoW9I/YoYipqwWeN3jaDjxVsgRANJNJOl2q/bxyOKbLy1Y+ttt7MigmDfee3/txxDCs8+ONZL7v8f/3NF28HNf/OK//+j7iS/s6OgCp08kI8Am2Hdk964NrXt2XX7JjSU/uN0hcHCcS9JT+V82w19i96uzqgAwstsgUkanFUSC6z/GaMqAYoIBrVufX3g8GboytjGAAvjVa+rnL+YME8vW2j8cNSUVKxreul89flbeFisuftik5cl1OIX+8BgrgEiwf13KjMtL8O8OPf7Imfc8+rM/r8n9UHMWzLf+xZu37Vs4bzrA4MihfdOm1h/cF+zpPnD0WOfRI+3VNc3/9r3v2fvffNNVTz/z8nnLz4gfYZ1W1LTNS8DY0N+9/70N2uKlZ7l91etXrSjHW6TmCuTVwCfJ52Anhtq2J6kbo3mcox8ETlz+l8/5s7q1w77jrakN9vYUqQPKZwRkIJv0f30vm1gONiK/f0u77WwUjJqhqBlVsKIBVQdKQU4gaBZbOEOS8AwzHJY0vbxTIbMEANL6fy7B8dQpsPVPnwHwC/lK/8GhdeTbu/cLd3d3ks55x82fa8UDsunajo6uM845Y93qdfbDDzftaD96SFEUt8dfU1PrdrkWzR/73gyZiPQf2dZ/ZNqcBacvP3/9qndLckwywN1y+turoKRVel7Ro8SdSb2wnf5vd4grLpG/rXugua6q4JdXFOywxP9Bkj77ckj/VB1QMCU3AjK3Hx7ZL5yvDnh8jXnePFNSsEqkP1Z1oBoFWRi4WAWgOmtrp9YYHCsdLbqzaIlIFP1xvnn32bkYAZ7qmvi1vLV1//y50+ztidI/CTv4kWoEnHjKiR9v+JhkGS5dAJYuAGVjytyTq3xV2z96u4THPLJ3x8BA4PzLL+Yd7jeei8XMTj/veofD+e5rjxd0SEuhWk0grGuODH80828jaNcAGwbpI0G8SrF211Y6UGJpMaWcoETpby//0yf/l5+Co8EvPv7G6EeDs/D63vyCToNrfyL9FQOqBnpra946wGq1PnQDBYA41l0FRTfnHLlOqtx87tZz00r/mBGQG4FAHsVxmULfHR1dglieFt4pHNvzUamk//xTLvE2zEbOOkftdHd1TW9Q/uiDT1oWLj3zogvPXH6hGg0WJv0xAC4ecwwWyA2ILHAKnMhzAscyDJNX3BxCyJI+eIzAcyLP8AwZH8+R/2IHV7qpTJSspBNVmZw/loLOvvzXTMxlLiNa3dphDcsdZt6VxBFUWmBh1kFu/PjPf8tj78duz3FHMl6pOCKhgUCvu8rn0VUZjCKnX/IPiQ9vver4Q4/fdujx9NI/RyNgcPkPAgGSERtf/ts0T2227yiyYui60xmbADOZ2Lkh1npIigKpB7RbOQ29x8CB4kbhIoRIloCVBRQbtkB8jxghkr+fl9uGlA9jSFoAWVUA2J4Waf2+DDJfgQaBR1cBDC3/Mzl/ipb+PMeIAisruqoN81cUrwNsI2A0osG5Sf8sjqCNvxu5XiyBXH8GQtqWLDlz+rX37tu1TZEkWOVjOf7i27/y1mO/BKPC3JaYOL7zuqWW6B/5JXlFAg4f607y/9iiP75DNBrFGDudQ6bwVVde9PIrbyd6gRIjAZWMaZJEYYQgQwQ4KWExAWBjg0vzy46Aloi3Xol0a24w6SVqzYMxTTJpuFx/AyWbBVDoOjK79LdazjE1PmeVR+wZiPYFosbwhIGSBAPGFbYjKK94QDHUVfmK+RrDA72IGORcaKBPN+S2A3vAKJK76I9zCf7dm1l1gDjot0ld/pumqahqUvg3GpWcTockpbF+Ojq6GhtHbtxdCYQ6tgeqF3Ica6q6PcAXY4bUsgCybM/rqlMNewa1XYI+mIuCEGYQZIRD20sTtabkpABiy/9CnT+vr9t54WnZ5hat3dlZW+Wc1uDzugWGQZJCmmhmMhjH3AjI0c1jWDWRVuccWKqYcMHgl7L5TEZEViSW41iOhQwDGAmOVhb22689mq/oj7NQz9gA9QioybT8j0YlWzfEdUDccxEIBPlBrZDWCAi8+1reZznpFrKH+8DsBlK1bo9G00kxMGAYYBgjNkUcBmcv/hHQdHI00isaA4yADoDB+st3/pREhn7nBTt/bOmf3fnjENimGk9DtbvKLTb4XTU+Z8Lk8RiJpWEFB4RLEg3ORfqbGG/f17F+y8HWfR27DnYeau/rHYiUJCY8Jrg9XmAa4cBAX2d7z+GjujTkISkfD99uHnr8toJfPpUZmhiVyJF60u8h7fLf5SGXsSwrcoILiNivFlneixSFUSxCHa1W32ZiANg/ejIYoKDQmP0S2xcAIYkH2Pr4UCtpcEQZBdih3P+CnD8jrv1XfkS6Awaj4OPeno83JzxRhkSjFx9/49rbCikWLYBgRP5g68FAWLJT1zmWcYr8mNsBBbPpraeb5i5r6+5AZOSHeXQHyXosHw/fXt4kP3uNn9b7b48fgQjaOsDM7GtONQIoNgc6pOPqucFpj4ZlC5DyAEx6O+cKcfgj+whWAwhoaxOsIZrCP3qwI8d+syb+X3gqkf7Zvf82iphD7we5PX634Iyg4h1BI7qAMMDHugaCYdkwTMNa/8iKForI4yckUADNjU2Hd3drhuGvrgt2TgzRP5V5kq36fNLGj7t7yfDCKs+C4ct/AxMnD5l7Q3xdLIQwFAw5nY54tMA2DjK9F4kEnH+5f0t+49syu6kmMBCxxAhgYuKbRH/tuY/55B+TFT8kjiSEyERJopOR9Xo06bxmE8AFlKABdn+Uq/SXNdK0NYv0f2Nbr+qoRVVN3uaZ05uqjptRO62pShTYTPKV9TclPiymT1xZkRV9y+62xIkTOTKe3UEbV79aV1vHc+yh3WUZ9/b2a4+We+Fvc0M1uZr7g0P6uMta/quqFgxFenoDkkRcQLKs6LoRjUrhcDQalWL+6+Fu7HPPOW3lKpr8k0ygfUcsS9OypYgCKKRwl3QCssu/7MFh0JoVc2z3BwV86ZTStIIoofNnd5Cf2zK1uc572VnzfC3zgNXd5aO3ntZ1o6svsvdwT1dfRCfL5+FvX1cX7O4u0gtUppTQI11WYREGvYFIV1/xA1jGHa3bP5m7cFlX+5HyRHoLd/dnQR/4fZCP9e9MwJG0/O/pDxmGEY5IRNhA4HY5dJ1YYyzLGsGI0ynKChne6PW4EUKIiKIMkYATzsT5zAnwgsnJng5tXhNrqLHieCK+C26gag8HNgHDgX4lfRHi93/08+9/9xvFnjQlBWLKJUKW/3Dk5f/r63ZeMJLzp7PtqNvJV4vKR5tU757d8SxhhkFVXkdDjSciqcGIYlWQD08JTdABY+IIGsn/A7btbU+YozCp2NP6yUQR/SQSk0b0E7A1ripx+R8MRQxdMwwTIShJSNN0wzLg7PmxkajEcRwEOByO+Hxep0NkWZKVPiFbQo8OkGEZVgIaazJWTa/dyS2v5DHrAwYMNknivw4Bj9iuQ5N52Pg4BBXg/Hl93c5zTpyb/aexemcXx6KaKmdjrcft4BNrRCCEIs/WVbtq/S6nyFldupKP5a2rK9gRVO72cKqmH2zrpVOnc3H4lFX0p0p/wyTVpaaJk5b/u/Yd1VTVjtaYJlYUJRQKSbIiyUpUkiVZCQSCAwOBYCgyMBBua+s81tYZjpBpghjjc88+NdEL1NHRBSdgj+iSE+jYub1NdwjEhcByEDHIbuifO9bKDyIGMgyxtxw80x4tqvEqpQBSXNIjNjtbt/OUxS28lcSbafm/emcXy6Aqj6Ox1mMn/iftgBD0OIWmOq9p4q6+SFTOWBNQDKV1BMX8PwBEJLV/IIyY8evNH1vKvurn0qz6Mcb9UTWq6gjBXY7GBcOX/3h4q1RLE2Bs6AnrUKBphq6bmqYzDFI1PSrJAs/V19cKfCHDaa1zmuQNbQLtO3YYc4+rYw0dI6sOIF9MAyAWmAbpMHo0wATaE9ME86auhqYP5Q1Ks/zHGZf/r6/buXjuVLeDz+78IT5WJz+lwVvnd8XKxFNgWVTjc05t8FVXOayagLIYAa373wL5k0UZkYbJUYUu/8dk1X/U+PQR49MhRYuomqTqsmaouqEZpm6Y3WF5V1dgf19of28wafm/cw8Znp79GybRSNKRBhsG0QHRqBwKRYPByNGjHXv2HDjpxBOSQsHw9As0w1R1wz4HVTfs8wkrWlTVJVUPyZp1npPTTxgn1LWH9PGBVgfPPAP81mdudRCF+PCAEWjfUcyZUOlfGOww6Q+zlX29vo7Mp63xOhKHgKayemcXzzF11e6mWo9T5DI1CIEQCgJbU+WUVT0SVQdCsj1rtMTBgEiBY1Iy6QBJ0SQlW8VQhS/8y3Tw97qvZCBUDWKEMRCSZgEIWneQVYkN2oPRgEy+F3H2QjB8+Z/CCMYmJhM6TcM0DcNQFIXjWH7P1tTOEEcGIpphAqs/PtnZaqKrGWSoMiR9DkwESV/7phIMvhvXfNK685Tjj7OyQDFpRJsz2CQvYYGpQDPSc6Cwd+/uC9ZVT9ZA+2iQqx/Dlv5nL5vDsiM4fxCCXrfQWON2OwWUVVcgCHmerfW7QhFFVvQo+QFn+3HmpQPiRWGt+99aOOtikA/JUelBMMYDIYku/5N4+7VHAQDlk/5P778AAskaEkVyTezBLlbKIMlFtFcYCukmQ0ha/u/YtR/mnFeOsWl3KLMeAMPUMYRV4d4DEDQAsHLVusSisNlXXvvWXx+1UoqAYa1krdY4xPwdXNtaZzrZFQAAYMPW3WedOJ9B+cUAoNX+gXxHewqU/nHo8r8oBTDM+ZO568PJi2YKPDOi88fl4BpqPH6vw54clB0EoUvkG2rcwYii6sQJmyUjqGDy1QGZLmNZ0eWil//3PjZ5+tyWW/Q/ue+C5K8l9t3YjcfI/xlroW1vTl3+e6MDQWdVgg7AOeoAbA3YcYV6h6oJuGQjQBs2Ez31HcbpaOVysPbjnWcunZ9XGIBjSHPpj7aNUtvBNa/85ewr7wITma999ycAgF/86NslPCY7LPMns/Nn8dypHucIg2pX7+xyCGxDjaep1iMKXI4RMIaBJFxc4yZZoWE5dRFRsCOomM4Qqct8jEFEUqzCBcooi/5scAxjxDrTgwVzpyeK3WOffJyoA+yMz1yOaUt/T7gvaXuSEUBJ5P1NO5efnIeTVhCYDzYTvwIlR7lfWtGf4gLKIK9fX7fzuJamWp/Tnqqa2fnTiRBJ8J9S7/O5xdgI1hwgwQCerfO7gpYjyMqvL5kjqAAjwK5ItPMFE/8KwzCDkdFokVbhov+Xh28kRj3oz2VnCIDIMiHLLBtc/pOvrKtzWO82WweQ1X0OuVumoULEpEp/bzQQdA713O7o6Lri7jte/fMjOf5dlcCqj7bdYY9hzgEq/cdQ7scZdEdYgi51+f/6up0zptQ3VrtJrm72AyHkcQqNNZ5qnyPtaO+sr4Vul9BQ467yiNYbZcsIyjsdaDAOnHNGkD2s1E4WjKGoeigqa4OO5srk7dcezSXF8/h7iIYogIcOXvvQwWuxmUfmDLKaB5g4vvxPDwSgRg5Yg5P1+C1xB2zq8VvatT9l3IJTkkcmOnd86b5REP02QyGvtNIfANBY446ncmaJ/TpErrHOU1/j5jmmgPRnlkHVXmd9jdsp8lq4Twv3J90Sdy68WXRuOsDqF4m1BI9mRFJDpGh5sl1q5cjuz31mb5x7H0NJoZFuLqeO8AyZUEi+leHe/664/8cGQcAi5JWC9s3eGNMEg0LfGw3Eb5nezhsNJBWFXXF3+lJkyqgRkUkPj0nDHV+675Ff/3jU3m4Ei3jp/OkuksqZTfq/s4HoiYEQGOjs2Ja2kLsU5TAOBytJegGOIBIJ+DzxKuSCvfa3/ycrut3h2cT4SGe/rI4c/r32ls+8+FSB69/xTL7Z/cff82iOaqDIkDiLoE46SWZb/tvRWCVBncd1AGWigzE41NbXMq0haXtb90Bz3cSrC7tjdKX/kAUwsyF5+f/2hn2L5kz1e8Riu6Hk/HKNr05z44ZurLcEY/lGNAIG5T+w6pOtFreGqap6apkCJRO5SP/UVf/QV5CzF0gzTEUzsi//7ZEjpfr2qBEwrjBNsz9ojTOZLJ6fUYbNLP2b/W4hHgXNtPx/Y0sXEmtqfc4m0vOHc4qcyLOiwAkcI3AMxzIci0hpjNUvFmNslxEgaDeRHaoSI2s0VW/rCR3uDLb3hDr6woqqk9+svcPgr5f11uvBrryNgN8/m2gEZAoI28v/WIah1eJfN0yOZTr7wmFpUpmZ+XLvYyjfTs5ZjIBMcn/TmhVLz746/rCb89dpI4SCdRMjCLMv/8uNKIpj+O4VjmHgyZGYd4cl/Ud5+Z/NBTS1zjOi0+PNLV1ep7DsuMZFs+o9Tp6I+UE6u3qwBlQN5CU4XSxYMMW1YAppCatqRltPaO/Rvr6gNGz17a8O9veVQwfYwj/+TlFZ1Q2TZZjsk14oaclX+sexLiK7w7xJmsVk3xlBbtbi5OX/xg1l/VK84b6VK9eed95Z9sODBw/f8I+ff+5Xvynrm1LSols125Pjw3lk1KU/WYunXf5fcOrcxEyeTMt/r4Cbq1i/A0jhYHd3b2dXT/xW/JmRZqI+Z321i3QcHP7+Xn91AQd88ffPjrBHLJs89o+k6KpGKr8GQlIBbzfJKMBZn5QOlMXnEwchxDAMz3Metyt9S/7hMGO9/I9zw5f/caxPoRIhDb4nvm/2jlF3/cdJ/4N08MQy8DjFLNJ/3c6Oao/YVOvxOId1ey4VEEKnwDb4XdVeB5fSUS6uA/LKCErUAanBAIRiBaUWWDeMqKT1BiK5hH8pWYyAXES/jcvp8HpcVV7SR2T27JawfzrD8jwvcBzPcjzDcojlIENugGEBwzaddk7SEY5+8glGbJE3kHAndmNid0glAcN6pfDKlWvjb3rw4GFbB9jnFj9PlhuhdpJSJJo24TOz7xg76Z/GBfT2hn0Xn37ciAJ9zfY2r0ucUuetq3Jm6vdZPAyDfG6xododlrRAOE23uELKgxN8QUmOIDudPI5umGFJsXp/FvE3gEqPBJx6wZ2577/69b9dd+sXDEOHEKpS+IxTjlfUBQ3Hthsm1k1TN4nDVzexYd3X5hxfU+WOv7a1dad8OLXx5zAgAE6eTANmIESIzCGxZpoiE2OBZUyMDRMLLGltb2BMRlVAcgdByJF8UxNCwDMMmToAsINlkhxBM2dOX9hYRXZmyM6kRZ1/JpAK6UdLyQWMyXCOCf1Z3TEWgd9sCuDMJS38SM6f1dvbBI6pq3I2VLudQsZ+n8Xz6AtrctyzJDpgUNDHesGZJg5G5Ggs/JupQRwlI2TV/1ge0n/wWzBJD0Gr79vevfsWLZwfnXdy3f6PMWbsJmv2f/fUzE6S/taXNMJ3xDGoySO6BY6zmogaGBNNYA2AtNWAiTFnzTYxMWZJMytSZmQ3HDUtTYAgJA2QSQkCbPaCFxN0AABg3nU3hf7+sp3ysMc/yw2tLnaU8oAxlid4w+22tva3X/jreFEAb2/Yd9mZ8+MCXZbTdz7gWKba62yscbsc9jyvQpgxNTl1N5V//cJ1uw73rt1yuLMvkrQ8J6cuAj1YYJ+4pJiwTUKdV0zcR6KqYU72uR5lMAKKye5/8f/+cO0/fC4+i3F7685FC+f3zDqx/oA9q5Js3+abmSr9lcMHIQA8S65IE8dVhTVudvCLhVbJYa1L4BnGzvYa7C5qPW3vFr+fdCfNhmHYRoDnwqvkla/u8LVAAELBjDVllOLRNIP2Zi+ZAnh7w77lJ822J6FmYU1re43XMa3eW+1zpI76yp1DRztz2c0rgMtOniap2rGu4LHuUF9IUjUDYBA1iJnCeuviOiDfHkFxHZAhK5R0pLCkP2W0253GdMCgoLV1QFfLskEdkAZb+te4hFqXiK3ZkKRHv4kNjMOKFpQ1+4vUTbM/qtS5RJ44LlMudZjufta9Pj3T97cUR9AOXwuoVOl/0heeyHXXx24v8r2ismp1Z5mo67OLrsvbPi6jAlgyb4pL5OKfZtrl/+ptbaLA1le76qvJb2h0PngIgcix9X63JZHN3qBkGtjJGMVkhRbQKs6e+1HAwSvHCChhp+uYDhgkUQekLv+VwwdJAFlgj6vzuXjW9hHZRoCBcVsgGlF13dLlJgYhWeuXFBfRAKW5flN1wLJlC95btR5UJBt/d2vO+xZ1tWAMorJG5i6UzQU9Coyt/2foO3h7w766Kld8fksm5w9p3ewWG/zE9Z+Y9V/2s0TQ7eAaql31freDj7WmKDIrdMSkoCRgieTFJCBV0Oee55M7L/7fHxIfbrf8PKnS33bcIQCm+Vw+kecYxDNIYBkHxzh51sVzPgfZGH+JYhgBiRR2g3Jy7vLTy3p8immaiqbTwFyRxH4YZy1tETgmu/Rf29pu9ft0+xIqhEcNhkEukeiAGp89QzgJWOD04BGLAwZJbA5KKavoz6QDuvr6U6W/eoRk/rgEttHrTF2TIAicHOsh65XYFhODiKqHVb2EyV2fnulLzQoFADzx8P+W7D0oqTXAld2dtySQn+7bG/ZVuUXbksok/Vdvb3MIXHOtp7Ha6vc5FjAM8rqEhmoXqToeroG8fn+5dUAltwLNJPdHYbpZXAfUNTYkB34xsKU/GVXtEsUMTcgdHFPjEvkEIyCi6n0RRSupEbBkydIkHUCNgLKiG8aEbgJx0TgIABAF8PaGfZeeOS+79LfbNdf4HM21Hvfwlg+jDM8xtT5XXZVL4FPLg4d0QL7kbgdQRp8kOyCOcSwm/TkEG9yOTFcli5DfwXsEPv68Zph9USWilMwIsKO+qfz2kZwjopQ80fTJ0wRiDGHPO3kOx4ywon9/R3uVW2ysJs6fJ18pf3SroJ9lRDHiGZwFRIOpDhjPpC7/JQX2sv46lcT/a91ilYPPNqeaZ30Ovi+qWMMjyfUVVrU+SXELbGqReb7Ec35mtbSsXLl2+XlnwYSMoCIPTskEadQ1wZP03h7rCDBRAO7BX06m5f9Kq91/VzjQ1dE5NAvDQnHkM6gLApfIu5y8PWxLUjSZLMFK9hWyTqD3dxSjAyjjk89+/f4akgMWo3U7uSB7e3sw5Lq46qnmwPQqd/aUHhYht8CKHBMZrBtSdbMvotQ4hSyaIxeSMj5ntbSsWrn2lFNP4nmSJbF//6F5xRydkqUGWLVHAVFKkQaaxfmTRspDkhPJsUwVzzpEzu3kvS5BN8z+oIQx4DiGZxHPsQLP8ByranpfQAIQ8CzDc4zAswLH8jwjyVpfQEIIxjbyjMCxHMeEI8pASGYZxPOxnQWeYRkUCMuaZuqG2dEbOtYVJCXgKd8+629M1AHFfTKU8Sv9u3uC1uAeFTI8mHpclRjKfhAIgVfgfCInaXY6KLk4QooWUjSvWHg+W2q+vx31bWye6nSKLMs4RKGwI1OyY5qmqtMUoBIpgCzS/43tvV63ML+lfslxTbV+V2dnYZW3aUfziGBaciNSgo8FgLSDTsLvdNh35k1194eq9h3pPdYZUFQjWdDX1Qa77V6k8JXV2688Z1FBJ0wZj9LfRpYNdbA9HwOMGdOaer1zs9SI2Tg4ttopDkhqdLB9mGoYIUXTDFPIc4R1dulPRkW2HRVcPoSQKPCn0nZwZcAwsaZP4ADAkjMv7+6KzTUZW1AW6b96Z5dXwFNrhCnVvCqHC5X+JQYhaE+f97lFq5wzdYJ8bfz+K6u3j/oJUspF6/adK578y1svPmrb/qSzm1M8euQoRKh79knxW9rXIgT9Tt4jcPHLBWMQkNSQouXuSDg69fj4LZP0BwCsf2eFEgmoqhYMRYr4cykZIT0BJ3IO6Ob3XwPjAzRC+ZWTb6whDZ+ZwRqx8QDHomqfo7HW4/OIVhAvmwlPdcCkcf6sePIv9v1Na1620oIZl9MBINi6dfvWrUOaPq0ygJYR4BbsLnBgMBSs90VVNWs2YZLQt9m/b3/iCi4133/9OysMJezzprFlKcWjafqEzgEdP2ScCLZmV5fLQaR/TZWzmJ4/5QBC6BC4hhqPXfQ/EEruFO0dcgQV/hbZdzB0jWG5vI75qa/+8MihQ/09vQCCHWufA6Xg8s/9aO+O7Q6Xa8r0aa/94QEwiV3/wxfqm9a8fNYlN3McaxqkSzOAMFEHHH98zPUX1wF1+zYyEPpE3sExYcWaNmotJPuiSg3pDSckfuGJsj7O/n37k7bIsiKKQpZqr5nTmz40v2caJsl6UE1FUZ/7yy/y/CQoKWAQkbXEPl3PvL3lpotOoJ9UyRTA6p1dAs801Lib671OsYwNnxN5fG3QJI09EGKQqesMYkhXXYN4qEjED0KW5RBi4udCmn2ZIoNcqArwLMfxgjXSEZKmDRC6p5jYNAEkknpduyJJUVWRraPbMCzLsRxrGAYvOhBCZF5xdRXDkj/W0HWMjbb2rv7+AdPQLC8Tedf5U5wj/hWzTr4Sm6YVoiTdhQWBr6ryqoqiYqG3q+vYvm3d3d06YPi6uRgyhk56TZMdMVnOmPrg5BmroTBikMtXxXI8xKbdicLufIJJY3rIcpzLU7V9w99DAwPRqNR5uAZMZukPVjyVnDO39s2nrb6hdlSX6Ad7miSZRZOiDGxNYGq6ue8w2rdtKB9U0fqjikfgeAalyv1UoZ9EFum//p0VAIDly8+yTw5jHAhndLdScocM6ogqicE/VZ6Qc+GXnHn5mPuC2IxZEy6xqc5b5XGUqmfWiNx2ljfd5sxGNCaXQld/eO/hnq7eiExMwmRvbqIdMDwgbAKgWDeykrM3vb+rV7eW/vZE2iYn00Rmog2N/I5lkGRl/0evpN2++NQL2vd1OgSe4zjGBJzAQcQaBgssgW4rAEOzlY19Apa4NzTT1O0vgGyzNCQRKBDqujIghRVFNQ2TYVldmSSN5z/79ftTW3CueCrm/MlSI3bNpz9L/rEzi60VAxyuDI4/fhHHsbNmTt2laR3t3fEU5H7NMeCY0lBbDXOW+889/rtc/hZbB5x33ln2t1rtc3zlO//xywe/l8trKZlQVF1VR04BauseaK5Lm35CyaoAVu/scghsfY27xuckw2HGbQ80SJrT+T2OhhpPRNKCYbnotGCYIEBwaftdbPvwHVBOJuQSKAO1Ccv/7Qmu/+y89Lc/DmkC206MfZe2Vh/UBJgoWp/PPTBAkkcxxuFw9MjRjr7uLnGkrM0c5X4WFs2f9e3/+O+ffO9bYJIyCt1BZFWbBDXAm99/bcmZl487BbB6ZxfPMTVVroYad2q7hfEGhFDg2Tq/KxhWZEVXSJlPxmDAiFmh1ro/Jv3LfOKU9Hz26/cnSf8CPqm4JhgyCyy5b/3PMqEgVKIhiFWiG6wnpUhQ5LxpewsXKfTjRoD98NChIy0zp/37z375/33zK8UctmLBGCia7cCjlFoBrN7ZySDk9zqmNHh9bnH0W34WnhVa6w5HlZ6BiEFywwrXAZRxJf0xxi+nuP6LUwaxCkGGeDahPRTMyis3DevSKdVKP4sOOHjwCG0RUTCmiVWtlB0EKpzhFgCEDpFrqvPWj13LzwJgWVTtddbXKBFZjUTVgq8NelGNt7V/kdI/kzK48ua7GTLwHcblCMb47ZceB2Vj/TsrEkcH233ivnhH7rNTKDFM07SmgBXCmlf+cvaVd42rj3LJWMeBhxx2q3d1iTxb6yfOn1HL/CkJEEJRZOurSdCCI1WdBZeGlWBZce0tnyn+IJWGHfhNIkfXfwG88vSfV7325Mb3Xvpkzcv27eP3yCK9rKxcudYwTAt84ADpY0p7hRZaAjYYAIhl501UNo91CtCQAli9s9Nq+OycUu91O/kJJP1tGEhGBTTWerxugWPTlIbR8uBJGfidQKx/Z8Xq1eu6egZ6+wL9A6FNm1upH6MAdKuoYqKL/nHnAiKJ805+SoO3tspF+itMKB596t2xPgVKiV3/k/IDXf/OCpe3GiLyP4ZBoaj6P3/4yz9/bnw5JcYvVsRG041JFQI21CWnXbj5g7+PpQJ4x2r43B0E3cfawCQmoUMojQaPX9d/0YHf8Uwk2Of111pBB9zd3fPqm+//y7//9Kf/37+M9XlNGDTdwBN8DEAimz/4+5LTLhxDHcDa0n98ojun2ndYBjlEvsrjaKj2TG2oqvLEOoPaTKuNFYthgA3DHAhLW/e0bd3TNhCSEteSjNW4wQh12g+pDhhv0t8mteJ3MrH+nRWnX3D1CUtPQBAaBgkKbNm+d6xPasJARkBrxuQR/wk6AIwRrCHWT62vWjJvypxpdW6nQApmJiwQQJZhan3uc0+as3BW4yc7j7bu7whF5cTgLuNpoDpgfJLJ9f/AT37xg29/DUwW1r+zYvl5Z1nXJFmfLF2ysK626rFf/2Ssz2ui5ICmdICfLDpg81gYAajK41g8t3lBS6Nngkv/RBiIGqq9F50+75ZLli2dN9U5fOoT42kYu1OjDPHH//dDSZLjt+uvv/JPf3s66QN64Ceke9pXvz3B+tx964EffuuBHz6c8ucAAL74L9+XZB0huy0VbDvWftZptJFZTuiGqQyOgphkbB4jO4A9dfHMOdPqSNFv2aT/zn1HwBiBAZ5a4zQ135GO/qhMSgTs2h/OVysHSHUYdQSNLb948Ptf+8737fubN29fsmTRv/3op/FnWZZo7v6+XjDRsHoRgR2tux7+29P79h1MfCocUVyOWB9ZCOHKd1ate/fVMTrNiQQGICqrBRcBjH82j4UdwLJAb+/s7uicJGv/JCCAosDV+d0RSTUxllUdDA4SFakOGE86wG7D8P66jfX1Q21NE0X/V7/9wEM/+QGYCJxx3mVWWxGiA1q37xKGtxiKS38q+vMCY6IAJlEAOD2jrAOQSGYkjb30txbm8e922JdMEibsfrpJ2+1nhr0w4anBjQxCHpfYWOuprSI1AsMmiI39300h/OLB73/80SemafZbDdpSeegnP5go0h8AsG7l6//9g/vfX72GNK5RSMfvVKj0zxfDNNOMgJ1cbLZE/2j6gtiCJ2KXluFKaNgpZapKIy/JcO5JT/AsU+NzQQg1Xe/uJ7kXtnoQvbVysAfgAhxB4+JDm0ysefslUq9XU5+4EWPwy/+aMHI/iXUrX1+38vV//FZykfPKd1aRZ6nbJ08Mw9QT+jVNbh1w0hkXblw3GnbABKv5KrxXBM9Ve111fo/TwScrFOvhtiP9uR+v5GdIsXVAsHfYpOyJK/3j/Oa/f5i0Zd27r1LpXwBWw77JvPxPJC79TzqjvNZARSiAeOPo2ioX6RfEsXETweGrs54Ghw7mWARHpX/Z7QAKJRVdJyMAK+2T2ZigCcqhDDLOBJ58IAhdDqHe7wlH1T4jokXDxAvkEKU8BDuV/qPKr/76hMCnWGy5oWl6jnuqqoYBEPjkCc8YY03Xrbli5P+dXX2d3f2BYDgUivb0DtjpKBAAMroUkMaidkiKUiY04rqt3M93Y3k8QhWkAOzG0VVeZ11U6ezosKU/8TtX+YMDuft/KGXkuts+39q6J/4w8X6cM886PcsRnI4RpnqlAcKPN+3a1rpvROmSGI6CKGY919fXXH7RGfFY2t49u5NeJUuTZFrn2KJpk6oJxDihshQABPC1dzeQi4nz8QyQTAwBEFH6PA3K6PPC47+/7rbPZ9/n/bXrszw7pXlYGDlH5HBAZI3CFvBYjezfm0ZRUUrfBYh+pqWmghTAM6+tJevHpS1z5szp6Ake7RwYCEldfUGAS2kEiMKwqmNKviRIYWy7VgbX3UMPEwa5kLRe08Sx/yJ4rK1zSnPeld7FeG/QoCkQy0mGI6SoUQpDI2MAqAqYCArgvQ1Zhq6Azu6+m684B4yF9P/W52+wH1Z7XS1TasOSsmX30T1Woaa3yj/Kp0QZyc1C7liPYMqz5F9b+lvzHYlKYBjLXV+Q3NX05GnSuROfnGqPnqeUiUlcAzyGsE+/uubmK84uybEOHYvl8M1oJqk1h9q6E5/t7B5aYj/96uqk15ZPJdii/5rLlh83fajElGWRhxU8LmHrLnpVjS8C/QNRKTribk3NzRBaNgEgfrwEbQGOtXXl5QjCGOtFCJe4xtq+bceIO7/xwv8V/EYVTgEKoK17oLmuqjynM4ksgKdfXWOJ4LOLF/2ZJL7NV+68kizSIPzfPydn+6WqhOK1QlrRn8iBIx2u4X3iKGPOu68+df4Vt4yoA9rbsqXtKjmokDgmxpGIhHMrMkoqRwcAhAL927cFc387SmGQQWCUUgMPb3wRAPDjXz9dgA54+tW1mQznr951lSXqAUndwtg0sTWFO5ZSZ7tcTZLVhRGEKINWsM8w8UHuZ/jMa2uzi377Tk9/5IPNu3SFdCAQ3EOjgy3HQrJ/LD6LNLYDm8aBJgr80incd3782xzPk5KWXHRAFqr9eaz7NN2IRoeNjshdAZDcYqcjpxGqELxJl/9FcO2Nt6T/XCGJwdx6VfrcsCQLYLwNhR9zYiLsvi/d/ONfP52LKWAJ/eQ5W/90z9VpW0pgMssleTuJ41l+UxNiwzTjPt5/uucaO5XaMGLa4pd/eTnl3ckZJpF0wknu/kyiP3b2ENjSv7Q8eN8XqQ4oBpN1nLBk9mAKfiwyDCE0se3wIQGAWNyVhH9NRJ4CmqZhjAWBZxmmoaEmcQpc4p0kjhzr2rXncC4KwA4yJFLl85x75tJ4GCAL//qVL+b4t1Moo8bQGva+L91smwJJaiBF4sf40h1XWGI6za8iTtrt8d2tluiMfd/++SGEdMO0fLvktf9099Uk1keKAEkGGATw14+8knrARK0AIZw6beqnLz91RNEf318RYlZC/gnklDISz64Zds0M3on/a/f7I/+1JsfF9xwWSh5+JwnWCh/HVEqekCuTvJAGfykTEnbvwbY5M5vjj21TAADw1MurIEp2cXztnqt0y6dj/wJJGaTt1ima+EHISHprKn18RRZL+7De8St3XYUslWOa2PItkd9eTCtg/LV7rzFM/OI7m7fuOnz8vOnZRX+RpPX/xIURNQKKZP6c2DTQwq6iRmv5nyOMrQByICEnNQYZ62iYjHXFZue/fvlbagRQxhtEiu0dbINja4Jbrj6/qc79P398wd745TuvgDE3PXnIsbE1e7lJ/KXxHJucrw1J/odJEgHxV+6KxZbtHRbNasxL9HucfCSqlirA5BA4AGIHo46gCQHDkMlcoKA8IHuub+nPiZInz7y95aaL6GC1vBm2jLU1AWJ4jMFX77nWIN6Y4Ub0WPeOjtsbDIQAxc7eJAXiMdfwS+9usRXAqg9bpzdV53LMuQ3Owx2aTEbNJXeDyReEoN/rACBS5HEqnOXX3DnaCqDQC9swsVEBPYrHP6pceMpAJZPGdO3pG0AIsgwSeJbnWI5l7CXSmEv/TNjjVW0joKnG3RcI9QVIo7ccaahx1/gcIhmKWSx+j6OGHSb9H7yPhv7GO/b1XdhrsWkWU0NAKR3jVDqNc9Jf91t2jtkU31JgB/RyjelVecTmOm9NlavodwVTGtJkH1IdMM6xFxCFvdbEOPe2o//1S5ocXC4K/QIrnVHqBfSzPycndFY7+Ls/fUm53i8fY0Xk2fpqF4SgvacoK1IUuOOm1x/dHyjmIJSCI8AFgxBCOURxM5G7AqCUD3a0YpOTjLIogP/3yGuaqjJMtq+kT1ITtUK1VZFbRpWQGQShU+Tq/MVaAM11vsZa79H9aZ6i0eBxGwCwFUA8d6sAVE0r6elQCsFdQBtwSqkUQNICH2UV/Wnpk9TE41QJsRO799bLRuFrQhBa2TvZSCoDToJhUMuUGpLDmgGqA8YtCMJc8jgzoZD0AcrYYJcBAwB8bjLbIylpkPxsE0L0bP5yadLDltaxM4xYb8TBYjHroWHl7ycWc6ZlQNFNK7vu4SdeHx01wORQzJmF2irXrKk1NBA1EYEIZrdWs6MqVAGMPW6nkCj301IJM+VHSQH89OEX40W8mXAIHOnzQyJskCE3oqtlVSfZpQgahikrIztPB6x9bDUwagZBvkAIpjX6HeIIfeWoETAOAwC2BcCyo2QB0HKw8kAmfpfp0JMbtlSr/sFlPlnpI4hMBOv8TgYhlkEME/svxjgcVQ3TRAjKit7eE4r1aME5qYHRNAjywu0Qjptel0vgmeqA8QiEpBtEoQMBNE2PTyagjAkIDnP7UHKHLYHDh0h8ooEFjuRTMwwknXuw3lTrtdf+xAiwiokxBi4Hb9fTy4oeDCuGieNtvklzUKu3FwGPbBCMHzUwpaGqtso91mcxGRj9CLCdv8tmaOyRSzcIKwuItgMaSxBCx7UM9bOh5A6br+iHkLTrUVSDY4lct5s5syzyuUW3k2cYklARjmqGrnvdgv1TGfo/xtygrS0KbGOtR9WNqBX+xQDIlt8fQqgbpm6YsX4K6RZmthr447NroBIuXg089nxsFIHgJXNs8oVjmVlTa3LpB2lDjYByk1cjIAIkSYSJkybzQtN10yTzyAp4LSVHbrn9HkXKWN2ZJfmCUqACiMoqgvBXT7wR32J781kGuZ1834Dp9zpscc8wkOcYn1t0iry92AdAGgiS7Orkg1rawrqHecQ013tVTZdkHSESHA5FFDI2AEJJ0aKKFghKDEMiB6Tdoj1tNQUsuFVNVzXDNLGqG4+/9P4pi2eC0aWhxjOjqTqv8C/VAeMKywJg0i41csHqBpRTPzgbGgYoOcWEcCqcjArA7r4Zuw+JkeUQWFHgBI7x+xyyojXXe23pjxBiEXEBsSzp1GkXZYzUXIU863Hyhsm5naTjkGlir0uwV2CSokUkLRSWHQInCryi6qpGmpBaPX8yzAmyGsEV9hEYjhrDOnIBicQIwhlNfoH6HydsBDixHZBZ0CVkGKZhmsW2kaJkJcvyPynvM5Eqj4N+roW6gOz52pZ/XxRYkWd9HofXJXAs43byvf2Rap/T7sxsz+wooFMQJME3iOpPrKlyAgCOHjlqb+cB8AHQPIfct6eJaZquqJokKft2fqLrJjEH4j9VcgbYNHDiQG6nkwjznkD6pmwtU2KunlCYlP7ed9e5YUmVFf2JVz7O90/weRxzptcVkP1JjYBxEgAYNhKgIIgCoNMKx5SCG3ldfX1sythnb8o2ffba274KKtACkFXNKXIcy/g9DpeT9zgFp8iRuC4DRZ7ls9Zen3z+tVKoL3X7tGlTEh/aMd+46E9zGqRIByLE8xzrEPmq087Z9vH7qmaQIIEFR0aOMQ4BqokV+XZMLvW/6Wjv6AF5wrGovtYbisiNtR6fu8BVBtUB464jdEGYxc2UpxSP20XLgEutAASexRjX+90Cz1Z5RYdA2oJqrlkmADoAMxa2yJkP6nGAtNKfjN87cix+f9q0KVlEfyLEwmAQTxJJQX21OyKp0mD1TdoCLmIeWCYCtE0F21ywtxQdqxMFtrnOd85Jczt7Q0zCCCrKxG4IOlhTWlBDUNoOaMyAEPo9xIVAKYBsF70kq011nvoal9clrNsla65ZqfuEoySHpzASlUEivJjx64QQNNZ6muo8tVmbd1ouqUEnln0rhei3Cg756Y3+Ko+j1u+e39IwN7f0/0zQRqHjBJKxUHA7IEiqGvN6BW0LWkIYRHp5lfKIlQSbyVG+eRfpCO10kv7GJgBnL3KCSJrpWj5Q+vEnRoTMd82Ew+FzOECNH7QknKfN8lPnZdRJwyfb2Nh/+4HOLMbMMDwuYXqjPx5xKqaBTBzqCIqDsNHVlbc7LpV4qnHuyIpqGhrEuXlyhoWKocMhSNFoZwdtCDE2MAxDm/wUzBilTyX4ZNJkXiRssfOC8IhbBg9lGLi9J+eGzJlOIB1etzi9yS/wLHX5TD6w1aKqkFdCUF3t5fn8VqA/++EDhbwXJR0cW3j8hjJMAST9BEjyfXyLLWQTd8DpbhlILLGxc/pjmf2D98kd++3izw6+e/y/cUGfZsvgoXoDkVBEyXgaKQ+HTmakn//0xpHTPT/YehjkD3UEjTmmVYZewAtZhqmu8lL5M4bwXK6N/M675t4yn8vEg7WT6+ORzKSHdg1WfN56oisFWvkPiXsONQCNPxxaLUPTjO2ctIKGme+kZfANhvaL768oemdvKMsvOenIg0Vp6euNC77OCoA6ggAA76547PrbvwDGAqsLSf4KAEKvxyWO1ASQUlYmYhegt1Y8jgGYuujsPeteGtszYRNH/RJJODypZdijlHwXlOnZJDlvjWiM71yazlkpRzBN3NEbHJYMmkL6XFB7UymMSIzBtiP9i6f5S3AsyihiFfPmrQAQhPW1VYVHjymlQBxpkkeZeGvF4/H7UxYtT3k+OZ6UKOujQZIkOebS31YAQw9gFnk5ktROv7hO97BM7rqBkNQfLGqmY6koTAdQI4B8iYFIla/Y0WwFQILA1vyJvGBZxu12Uvfz2OIQuKZ8uz/lyYuPP5S60RbiNnvWPQ8mJlmtp2G2ABjPWM2lgyMu4kryR2Rp/FikZUN1wJhgmGbYqgnPF5Yh9QP5vopGgMeqDPjq62767M1DS/Xu/vCLf/+kVKeR2iZ2QjAOrNd4Kk6BvXyI86ezNyjnMJip0HcYBgTg/NPmZ99n25H+UrxVxfHuisdG/01lWY1KuaYCJ4JIFeLE+81PJiCEYp4pWGUCTkDpX66h8LkTC7yRJBxyb6QWcumJympUzikLexS+okOH2mbMKLw1OTUCnn/sd6ctL6rFd9uRQ2CkcXWFkxArDijhtWu78nr1BytfLcM5VS48x4xVDGByMJYWAMZYVjRF1Un/Z1ktIApnH0TTzSIn+paKl197p3gjgGaFfrDqdVEsro+jOR6b81DpX3IcAseVMz1v0lNiCwDBnDpDmBgfONbX0RNO8soYrKfK4zhz6fTpTVUxMbp9hMx6jK1xELmZDngiGAEUAMCqN0hUbfml18uyVOAHYhpltAPyhIr+MiHyXF6/6D8+vSoxDEAZQQEsXjQ9zVZLaGu6MRCSQ1EFqomtuodyoiU5ozJYs90KoLNkjCKPyS/cIXILZ9dv3H4sGpTefo8821zvnTejdsRvKDaGODdGzUywdUDBKaHUEWSz6o3nx5cOKKhYjEr/8iEIGSUYHQZQrAJAUG1t3QtKh0Pk392wHwBw6Smkr5x930aSNUUdZraXw60fKxguW7zeW10T7OvN3Q54+b2tV517fNqnqA5I1AFWqFaacHYAFf3lxkEDAMXB1qakXfcFSErc1Eb/0c403d+KhEHQHr+ViGka9dXuHfu7DIOUcTEM63Pzs6dWYQB4Zlh6hmqIhb83JhX/EUntC0Zrkv7qkXxDJ8zLaVKVFk72+2c3AjJJf0qqOwgAUGBweIx0AJX+5We8pABNTguAQaaikVHAAIC9R/q7+4IAgLOWzdp9sNPnFknsBYPmhqoN2w4vnt3gcAiH2vrIuBgEq70Z+zmrmtbcULXqo30uB3EWGYbOMOQcqr2O3sCQrGcYZJAhX8nE9UF/UN5xMPjZG86KPQFhRFL/+PSqTDOBMQaKpgfDcn8wGpG1M5cMb21dOnvAwSNJjZUUUSOgtHyw6vUSH5EykYEA8Px4CfNMziwg1pqVCiF0DPrajnYFnCLv8zgcAudyEiHudQl2DyCvS7CnhkmKxpCxwKZ9w0TuD3VoCEUUjmWEhPINYsfBxDGPdml+DpWZVsf/EcS31egtHFU6e0KdvcFwVOnqCxvWwWt9LgTg/rb+tt5QX6hQR3MCmqNOc9Sxvob4lkOH2rKnA1117vEvv7c107M0I4hCyQSEkGcnXiOgiaQAbJEMAbBlPRmg2B2qq475T0h/N0AmtNjlMAJPWstBCBGCJDNnEGgN3Ig/lBW9qc7j8ww5c5wi1x8cJn8Nw8zNTT+SBsBAN82BYLStO9AXjKgaCTOYGLd1DbWMvvWyE244f+EVZx2X6Ri5N5t1Mrp981YP1aYfOtSW48spFEruEDmT8+yHeCvQPz69in7IcTLqz8a6qvbutrjki0/gMgxT4Fh7LLutHuJNkhGEDpG1e70llXQl6gPTxLVVrog0lCMUkVWNiOahfVgGcVlnDo+MdXqKqgfCUk8/eYfEJ3cd6prWGHPNTztp0I/0xOq0R/LkPHEUIdL0NDUg/Mqa7VeevSi7EUCjwRRKXjAIJQqWCufagibX52pAWc2QIcciW76RJX+6KngyfjGH5TLLoMTuypKsA4AT26q4HDzD5NN8Lt3CPyKp/cFoMCxrKTO7QxE5SSVkAiE4pT5WlDAiPMfIypCzK1EHZOfGS0979o0PqA6gUHIGigKZBVPuTnATQo4XTK4KAJKVPsPzbJQI6xIwvH4vOS+ooOq+oYPI1sK/PyhJpMtjmtxtDMD+o73T6r0AgI9efQEAcPIV16U9qNspNFR7eod7qDLhc4uaHjGMNO+Y3QhQpHHRx5RCmSgQv7RDyCt7Y8ULz1x93U1gMsrx0s8E/nDrfgDExMpexOQ9/DoLTDl7J3X0BoMR2XIrZeTAsZgCsLHVQDIQ1Fd7EuPV2WmodkdlLRxV4gVDuTuCqBFAoeQOw6B4Zkq5uXaCSPMCyOMTVFXDxBjlnFK9p4u1B63ObcBFVvDmS38wOmJnISXr6BgbnmXq/Hm0p6/3u4IRWVF1O9qclyPINgKyBAMoFEochkEFzIJf8cIz8f8CAK6+/pYK/0iJ2z1xIm7S/USMzHNTEzM445hEX2QbtptF/pMkUsvNrxpi2pvLWXXyoumbdh7etOPwmo/3PvXGxr+99pHb7dxxsGvHwa7C+sqlUlPlcjtzjQDbGbH1fneW6sRX1mzP8vIbLz3N1gFpn6UpoRRKHBahAoYxUJJAgwPWh2Q1xjjLiGyGQbKavHYORdK0/XGIPHHlD5fyidlBWSL4cRmaNBe+tLyxbueG1sPPvLvnuff2P7/6QNKzLIum1Fcxwwf+ZT8HjmX8XofHxSdljiZmhWbRASNGAqgOoFDiP89x0gN4QmONhBwc+25HR4eG96Z7gVPk2roCs6YOSTTNMI52BuprPEl7hvq7ZEXTDRM0DBVG8Tyr6bEDCzwryUr60xpM7x36hgd1QA7z2wshVeO5nUKaflLZU5MgcApctdfZH5Qjspp4ojk6guyUUOoIolCyAlmGgXkqgE/ddgep+7nilEnv2c8zBpBhYC9MJ2gRhF39kUQFEImqIcvxvXDhnMTmcQta0mRP+txiz0Aso4bLXMShaUasvMA6IVslxc9t2NdeDm1gvUl9tSfVRhnximNZVFvl7A9JqqZrevqYefZosE0mHUCbxFEo0EoULGx+VJw//vLBHEdCps4EnjTKI2s3UDTk2JFk9ayladrsSLJa5XGcdvx0bJqJ0j+1F/TChXMO7D98/Nwmk3Oven/L1AbfkuOa3t1AlAHDsMfPbdy6p8PuC0R6qO3rPvekFlBqIIQCz3IsS5bnWUEI1leTbtX5wiDodQmN1e5IVB0IyVYUPL+MoOzNIagOoFAggnzWQtFU2/2mT99uGGapwgYvphsTnyPjSnmMXieN1ta9DtHqJ6GGAAC9gehcMGRGqCm1WiVv3s8wyO0Q/F5HdZV7065j2XdmSZLZ0GyDvEbKCBxbW+UKRhRJIcPOEp/K0RF07mnHv/cBdQRRKOlBkDSBgBNzZu+LRSiPkuuPERQABjhe8JvpfsLOQ5uy7Gx/E/GKWQaReO/eQwP2nTg79vW1NNeURA3YC3+fW/R7nA6Rm9bo37y7LWOY29qfYVDaFkDZT+fxF9dmP5OINKQPshgB3sHMI+oIolAy5ICihlpf7h8OhKQJzSgOhRpL/ZG7ksimAExMlsCJYhJbnXz6Q1JHd9BupYAQtNKHMMcyXpdY7SMSVtXIsLD+YFRJyReysWX9exuPJAn9REqV8xNf+HtcIseS+jOOYaq9zt4MFXD2H1WAf/GDrYcVNs0VaSs8W9+wHqCHenLRAVeef9Ir727M8nY0GECpEG65/R5FShw7mHevsPOuufeWWz8z8YV/CZREkm7IzwWEMVA1IxCS7CoqhkEMQrputT6wPlyMsaLqUYkMeU/twJMXTpEvXl+LAlfldlR5SfPqRJE+v6Vh7aaheWQIIhPHA7aQGJgF+Qq/ctOpholDUeVIZ3Db/q7+oETyaYerMtZTm6gDMgGNWJSCZgRRKEkIPFvAL7Ri5H8euiG/D9E0cSgiRyQFk5JgJPKcNZA99izpvqkZgbAcjMiSrBVTjeUU+ZoqV+6L8FR/DoTQ7RQaazx11W6naCXmk5EDMer8btFq8AAhFAWuuX5o5Q4hUWwFeAwxBtFwSImGeaDNrBPPWlA3f4q7SgRO1nAygzcOOznsrfLnUhZw/SWn2ndoaRiFkvTTtn+heXaCg/EcUEreCoAM1VK1AGmuSZr1iwLrFLkhRzkmnaLDUSUYkYtc/iMEG2u9XG4aHgOgG0aSr4llkM8tNtf5/F4nb7l9bOJCHUE4e1odwyCvS2ys8S6e3Rh/LbSW/1kUwPmnzU+7fegVDMNwrNfrbKz3eV3CkOVh15SlDLrJpAO0hCm4VAdQKpYk/w/PMg6BK2A5P27CwBNTAZimGYwoEVnFGLMM8jgFfnASgI2i6WFJkRW9SOdPldvhdYu5GGymiWVF6wtEk96x1u9uqvO5nYK98I+TaCa0NFfXVrmb6rx2eMBlZyhZeZyJ42vyZrA/CccyNW6+3u8iLausmThauJ/cogEtGnDwOb2F3RyCQqHE4blC/D/WnBKqAZJh8/BvyFogLOm6ASF0OXi3U0hseWaa2DDJIEdQBCyDBJ5rqPWO2CsU2waHpPQHooGUaY71fjeJEWU9Bs+x9dVkN/utFsxq/GDzXjv/p7Dr5O9/XwkKJVM0OLE5BM0IolCsqSQkRS+vj+JTt96Rtl8Zha31JXe77AsQoXPC/GkAgO17dse3V3tBtZf0Tz7WLQ0EAgLniykA64NNjXbmC4LQ5RTqqz32iOCMYPJeylDHfy31fTNJ/6RtpJbE2rT3cGfL1LoPNpM1ArJuBaiAH3zz07YitM9mICRtaD32ya72UERJ+7mwItCD3SPqALtNtH2f6gBKhUMGwXNkUnler7ISFcm02rKd10SlkEIwe1iLpQ+EGY1pmmW294SPtPUuWzg1y0H2He6eNa023TMmALEV/YGjvdOnTE2z8I8q/cFoKEo6DaU/eoYvOjmtKOGB3fSNSP9CO0zt2XvEOhADjNhZXXbG3ItOnX2kM7Bm06G9R/sSDSYb1luXqAPSokjRqtr6gZ6uws6KQplMQESyNvL9icL0fW0oeWYBjT48IyfeBFZ2Cmq9H06t51VNn9nsv/eGsz5r3T59+cnhcHTBzPoFM+szHS3puqn1uYZymGIjCsj8+qJWCoPS31YJBw4ck4LBaX5ump/zcKYT6Um3XDKCLkzoikGjwZRKhkSAxbwVgNXpmKqANKAV6w+9/OGxVzaM0Bohb8psbMUbyf19fWuRb9lSy7XUcka0L14ClrRDWw/pXVEwLIP8HkdDtdvl4EbULGl1gCJF58weasSUvVMQhTJ5gQ6Ry6sEbAgq/9PB/vy/7MIwbGdUEg+MaWgdfRhjnmPnHjcbGOamXUfXbjoQiso+t2P5SXOum9usyurr7+/8eOfRUEQGAOha8oB1lqtds0OGEJw1XwQF0RfINoaXQWhm89DaOUfhn72yDEJAesyW2lEIIRB5ts7qDqSouqIZSdeit8ofHOjPfpAlcxr27juYfR9aG0yZ3EAIHAJfQJLGU088Gm8HTUkbA4B2fIXcRYBzSzFpaZptHf2bd7eFo4rAc/Nm1M+aUkN6KWioxuf0exxykBS1csM9SaSSFhmGYRqG+cG2jMvnnu09QxmTVa4an0vgrfkEFlddcpZh4m17jgbC0uGO/mBYVjRDUTWG4c5daCWJxsV5ztdD9h0RJA1Gsh/ho4+3fvRxmgX4v/zjzdmOjKDHyTdUu0NRtS8YTZ0an6gD0kaDFSl6/hknvLtui/2QRoMpFQiDEG9HIAviyccfefLxRxK3XJ3bSMi0rXWK7Ok2TmCf/vP/2fduvvsfhj9lTWSMKFv3tvcMkEIMB89u3rbj8OFDIs9GJLW9JxiOZhvnktgJeYSTYIZPd7Mk+3vrPtYT4r0k/RQAYk0Y8rqtw5TKH5+NtWCzR0ICAE5aSFKYRrQAFiyYJyma3SoOAsC+fzDL8j+t3I/z0988rSnZTJa8SKsDXNywz5O2iKBUGgyK/VTHA9eWrivn9ddcQ2aleHMdP+7xDHWqv+jsE0qTBRTXBEQZ3HgxAOBvT73RMxDp7A3ZyTZSkKvyki7bhokjspo6GDJ2RJKkizTNyDErlGReJhp1VpanrpuqZgTDUl8wquZ8qJHfa/jD1es/TnyYqQKgudbz0pvvg3HA9Zec+vybH2bXAdQRRJlkvPjsU0Tg3niLwLO2BZDdl5s0DOC8a+4F45vnX3opy7PXX3ONrRvcltxP+sPfXrOlGGWQPg306Wffsiu/BkKSLf3J0njwnVVNj0RVw0hT80X6KLCMVRSWa0UYxtiwugZBSO5rluMoqmgDQSkskYU/KB+DmQEQAsPAJPsngwVwzSVnAgCyq4GZ8+bbg6ohBIGwEpVVK6mUTC6FEIYlJRhRsvRHYh1AD3RmNwISm0NkgeoAymRVA0lccdmlmfb/1y/l5N4Z/4yoHgAAU6c0FKYM2Pja2urvNtTYRzeMQFiS5OHRXUwKnSKSKimkIUTq4TiWZFGmJrxngRTfktwb8kLdMGVFi8paRFICYblUC/84aRcOVrNm4qafN6txSr1/9cah2re0aqBlalVR54BJjdjmPR3b93eFo+RjjP+RrK9hRB2QWBdGHUEUSib+69dpFMbk4/kM6uGh38TuXH/NNXd9inh00kIcajGxCElnf+sO2RKR1FBEsRfyCA6VUciqFoyQfnCpx0IIcSyrG/k1hID2eAcWaTqR/hFJjUhqpkECWVQImT6fw3slPU6dM3zOScfx0F5oJ6c22XR2jlC6lQvHNbsavE0dPaGj3UEyOTJuGVST7oZZpoYpUtThq5ECQztQRxCFkpZXX3+DfjLPv/RSFhuC9CmLpdLEvCFEIWg66epsj3whs7EgtB0+umEqET1q9YNLPRYp0YZAzzAJPRMQkeYepomDYdJoWlI0TTdybyXNsoxL5HXD0KURxvzmgqob2MR8murmEsMg6HEJEAKVqD09mmRpZTUCLlzS/PJ7lgLImg9HHUGUSQ8V8UWSkHtjJ4Ja2TuhqBKKkGUpQ5r+s7ZrCGMsq1o4qqT1/rMM4lmGePDz7Adn9fWAoagyEJLCkqpquUp/CKFT5Ov87hqfM8fmUHik5/oCkWPdA2BU4KyOqo01br/XYedNxfFadkCW0rBPX3V2ovR/efW2Mp8shUKZhKB0Tf/1ASv3BkJSdyfwsVlaVu/l9K2e7UxK4jTJx/s/+FoyZiAUkSVFJcPFcvP7syxT5XE01nrr/G5ueFfqbO810nOdvaHD7SPUZJUQxppb0Fjjdjv4pNV8og5IJRwM5HL8B+/7YrGnSKFQKkUBYGCYZiAshyXi5OE5xu0U7KYLmDS5MfWERjeJsCxiWYb0Zstz+Q8hNE0sKVq+C/96v7up1utziznOjcnRAhgISZFSuJJyBJLhdkxtlauuyiVkVmNpjYCrzlmc+DCTEUB1AIVCycQw6YkBkJVY038EodshOEU+nhmZqbALQShwrJ25D/LH0iukmXQuO3ODC/9av1sUEuaRlcgCUDW95KlH2bE+Z76hxl3lERkG5uUIylEHUCgUSlqG1d8aBln+SwoJSAoC6/M4cum7RMbzQKgUJDoxxjkWDMc9/vGFfzmaexczx7hgWBb5vY56a3ZxXo4gUrJ3OclMzQ41AigUyggKgKR+ymowLBuGafmmHS4HP7TCziAYEYICx+hW9RYoA/Ya3174N1ke/9jCvyDpP6J0H+Xlvw1pccGz9dWuWp8zi8ZNawRI4WDiQ+oIolAo+SsATLwfAyFJVklvHKfI+9yOeGc0q5d2eskocCwklV+l95zYw9kdAlfnd9sLf69bZNmiFv4jvnQsDAACg6DXKTTUuH1ua45xPkZAkiOIQqFQQG78/8P0wxjdrSxIAAAAAElFTkSuQmCC"}], 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"}], 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"}], 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"}], 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s4V/JG0ucVrO/dLy9e3B3GLf7XAn4lxaPvv3hIX9wsBfrsSsWHrtiYerz8/TAtl0Htu06kPbSmm4c6fL1+sKCgGVJ1A3yp9/94sVnH33x2f/hJ5xwwU33//z3E/K2AQAoYC4dxyp+WmQCpjIElBb/qTFHxidpPdLNxcAbiL29cU9M0bkHwOF+ADbtOr99XkP5SasalVgiDbBkYWKm89adzcNdwDHLF1CK2rp9/9i4T9fJ8gW1i2ZXuV12WRIwxgOhIcvDz+0sdZfW1lb97N4vTMDHAADANGN9bsa98KNAheIBZJJ0Bcrd9poKVzA+JPTPoSlpmsPtvk0724NhPliGFYbyr0xvIMmWHc2btu7d29LjC8Y+d/kpp66e/+fXtv3fu7sOtPWpmi6JgukQGLdetOgz585qato5MW8UAIBpxm9fGJyBOK2dgCnzANKW//wgzQngVHscjzzzT39Ei5n9JMrsQmoUKHmMEKpwSWuW1JW6bCw3YKYBVq9cyIpGTeiAN2AQ2tUX7PdHDELM/d70M1ecnfaif35t29yG8oWNVQ6bzP0MHiy6/Nrb8/xBAAAwDVmfg3EvfA+ggJLAI3DJ2avebTq880B3ag7AbhFNqz4IITQUVa1muwieCm7a0ZyQAdMbWG26AjohWtO+UFSJDN/u4uPnHMMSPn/fsqCxcm59uaeUlZnKksgnR0uieOEVN0/8+wYAoED57Yibwgrf9BdQCIjX/4yAKOJVi2rd5tKeY7eIhq6nLv9Z/yItsVhP8ySadjRv2d7MxMCMCkmisGTRHIWwSD9CKHP5n+TK89cct6xx444W3RjyQrph/OXpR/70u1+M+a0CADAjeMYsB8rk0mu/NF2s/5QJQGb8h2tAMhaUpNrjePzPH/CKoCqPM/UulQwZwuZxiIqmt/TEKio9rUeYr5AKF4aEDCDkKbWvWzE7qGBvNGM7cgYfO3V5JJRlczJ3CLhPAABAsXHpUEM/vUx/AYWAUks/M7nwzBVm1EVYNr+6rTsQH9Fd6OkPHekKeBzS0kVLMUvz7s8qAxij5UvmHWzvn1XOKk3HQ3tHutgAAFBUXDrd7H4BCUDWxG9WWHBfluKqzuM/mWOYPQ7RFzXaugNyTfmW7fsxRqtXLjJDQFlkYNfeQ24b+mBfL58wM1ylEMs6+Njo+W27DmTuIwPrDwDFzKXT1vRPmQAMV/6fuRWAx3+4B0Ap7fGGI3F1uPhP8tjvC2wNxDDGNovc2Rf0lDpWLZuPENp/oDXtShw2S3VJoqc0LxAaQQbYObsOYHP3wNG/eQAAgGIuAx15/1cq1R5Hjy+xsUvVjNfebz7c4aNmBjimGlkFwBc1yh2iLA+mizmyJBqClWcaTlxWk1SPQDje2ulbtWiwyR8fLZBUAr7857R09Kc2F0pd/t/+5W8dxUcBAABQ1FVAmYnfrMTiWiiicOtvxn+GQE348QvPP5f5cI31ko7KSA1FlVc3t76yufVItz8SU+1W2emwpJ7JtWHLjv3DbSHmzYVSW0qA9QcAYDoiFML+r8QxRarKhsSk1f9wuvpCgYiSGv8pcVgz4z/8+Pk/PvX8H5/KegHlDlzuYA/f0xZ8b1fX9uYOXzC2efeRtNMEjJMbx5LMqc81XQEAAFD4TL0HwDdwlbpLD7T1v7Hp4PvbWls7/dGYahiER/95/KetJ6Drg8X4t1150uK5lXggD+yPEX+MpDYLSspAViXgMlDuYGWg3miWJhNJV2AE+v1sgMGshtrfPvLDsb5lAACAoq4C4hF5g9BNO9v6/JE+fzQSUzHCuw/1ul22huqS+mp3qdNqkcVAWOn1RiiiyfofjFF9VekOuVvRhjSJ+1u2+A/XgFtvuzUYjiuqTlP2D3NvwBulr2xuPW/t7LQHUjPun7rwn1NfgWVbLK51d/fwW1o6vR9sHVJlBAAAMC2QpjD+Qyn1BWMH2ny6YfgiBrfKFFFF00OR2M79oV0He+1W1tfBMEhgoMtbsv6ntqJkTr2ntzcxJyAtFpTJr375K4TQd+/9uq4bgXDcG4hG4ioxx8twGcgk81ZCaHNz69aD/bpOeNbBLrE5ZZ/45HXP//G3R//RAAAAFJUHMGd2rcMbOdAZ9gYTxZ2pMENPdMUcApzG7Z88iV26JBy7uPbVXi8v/8/xRb91/4MIoQfu/XplGdtXbBAajaveQNQfYpkAu9XiKbWXlThslkR5KEpxArq9oaY9bdG4FowMSk7mZGMAAIBpwaSWgaZ6AEn6fJF3thzu7Aul3phZ6Jns/6MSzAWA1fZoxvpXm3QjEf1/7+X1Y72kB+79Oj+IKdrhDm90YJ8BQljA2G6Ty90Ot8s+q7psy962Hm94oOiI+Q2+KE3zOcAJAABgGiFNYf0P3wGgK/HGmrIeL2vOzO8dbqNv5v6vvn4fb9M2cvBnBO4ZcAisFtntsscUbaCclBJKIzHWMfQI8u1s7kxsIDbBWKCUeIYGjgReHwoAADBNmNIqINPSOktc3f1s+Y+HN/SpnH/youbW/ubW/v0tfW98cJDb69xDQFm55/4Hv/2dB90uGx9Jn+1Khwv0MPnBGNVUuD5/zUfGcw0AAAAzNgQ07AQYirzBaGdv6MCR/s6+EKHUItBMAUjGf2RJHBQPC/sfxtgXNY4i/pOVaz99Y4ofMASME3rpKbWvXT5798FOXyAmSeLqpQ1VHpe7rIzfe/GVn8vLlQAAAMwEAUiz/oqqJ9faiVYQFEVi6vb9Xa2dvlA4mlUAMts/pNb/ZC0APToeuPfrhNK4qvuCUX8wlqwcxVgQBbx0Xs2iOdV4oDMEQmj+rKrSMnfqM4AGTC03ffGbabe43SXjfM6Hv/dv43wGACg0prIKaEgXIIycDsu6lbNWLKh64a296kDR5wgkrf9EhN4FjB1W2VHlbqhyU4R0nQQjcU0nxy5psA1I15z6Ck+5hw8YSKWnp++xn//7Z77wjQm4LiAncz8R/L9vfD95/JN/vxt+E8AMQJoqu585s8Wc/CW8vfngccsb3t3SkloAmjUt7HGIUQ2XuthOsWeefDKPV3jP/Q8mq4O4wMiSUOFmUyGT1p/TtKOZz5tMI2sECZhG5n5kQAymhBs+e2ttpfvBH/zH1Lz8TESaqvafsxtrsvZ/RggtaKzQDfL+tiOpGpC1/7PNKp20ajY1lGfQlLFlRzObNjwgAz09fZpuvNt0aOquaAZy/mU3N9TX5niya2h3vwR0lLGjo4IRpShLsRmIwUTz8I8e2Lq3DSFUV8UCrXffzRZn3/8+q98DpkEO4Cj6PxuEfrirvWlvB6/yzEwLJ+s+S0pLls8p+/Z3fpL3y051AkZoCZfMBPDp8z09fe9vP9zriyCEnnsa9gaPl/Mvu5kfDCcA2c39UBb07ec+GeWNYxFqq18+tusY6tJlVYJUIEaUL/7limvPOWnZ2hVzzN91os271cl+6cuPvzRvL1OsTEEIKBRRSpzZBzEmx7+IAp5TX7b7UI8eU4fr/8wHwLd2+f/wm9+gwqBpR3NdldsXjE31hcwY088sdmroLxdzP79vX7JLYBL+12L+x77N7dozposxCDlSt2zw2dBgPcLIbgEowfjh1h8hFI7GXQ4bt/5sqN+m9aABhS4AmdWf0XC4xGkdLv7DoZR294fVgbKfzOV/Msh+1FvAxpoJSJLZHi7VCSCE2q1yKKrA8n/cq/5B0z+XTY2mVmuWdcOCvn0ZdQD5LwsQBWFu915+TAhtrVuaoxhcd8tdv33kP/N+PUVFKJyYCoUQqqgqTb1r1yZW/A0yMP2qgLIGglL7P3f2hnSD/dPKTP/6okaZXaCU+mNk4gQgd1I1oLs/2FDt/v6DP57qi5rupj9hx03Tn0BRlGAgeJLgH/qgyd6ALQh4UAwoba0dRQyuu+UuhBDIwNFx21VnDHdXX1dOs6SAQtkJzPs/80GMrLh+aFw1dfyLPxTv7g/z+V9Zn4oPABiu/3O+4I0isvaIzjwzefKc+vKJu6SZyvmX3Xz+ZZ9JMf14bmNNqvUPBoPLQ60Z1n+KETATg7nde23NTZbmptS7MDJ0bbCamcsAMH76uvr5F3yYhZ4ETov/7DnU67TL/YFYLK6JFltDdWl9damn1G6RxZpyZzIBQAjdsqfjw13tOiEuu1xW4giyWWDU/D6B+7+GI2sgKBkFopR++rbs6WIgF9ad/QmPpzwj4DNIMBg8CU+q3e8KJbI4q+qOUsvfswxOmWYrniArCgA/4Ch48RnWxZ0TDkdrzUKgTM64+DNH8+xFz6SGgHa1pv4zVjp6g7b9Urnb0VBTunh2xbknL+F3xBW9qy/Ee8PpmrZoTuXSeVXNrf3vbz8SjIy+QSzvJBvGZWYCCKU3gPXPD3hY04/zYMrHZM2rXPbMG3vDY8jtL0IHX4/aZ81KyIC71Mk1AMJBYyWckgAAprEAPPdm+tgsQmlU0aI9gY7eYNOuI/W15asW1lR6nKGo0ueLmPEfCSPiKWX/Ghc0shX3e00tCBlJJ2ASlv9JkhGee7/5NVFkzaIRQq9vhFlg+SEX038U1jyrKR+rNT86znbEXm/rzNQAyAyPh67eQFYn4K0XHgMnoLAEIDX+09HRO8KZVlkwdP1wu7erL1Rd7ip32zVz/K8goJqKMi4AGKP5s8pjkciRLn8omogFoang/u+xCcCXf+q6g+3exx97dEquYSaxbPF8m22wvOdMav6psM499omw5sa6s+wOW7KYVH3z/1Be2aAlatXZRkWZHmnrbAQNAIrWA4jFtZiib9zRNvLcLl7oGVO0lk5fe0+A7//SNN3jZhkCfo4gYLfL6pxX3dLhC8SCaEr50x9gk1ceWHf2J1jQL84UXea/aCGfy3Nj3Vkjn2A582P8gJp/chztnb9nWvOjwy7Tvq6OuI65K+AuZXPokq4AMCqfvOnLf/zfh+CDmn4C0O+PBkLxvS294ahqmPU/siTqemL2bypphZ7c+vP9X4fbfSesbBxyxSJeOLtCttt/9bPB7BAwY8iX9e9ZsJr9z+uvKC9TFCYwSRRFsVjktPMpSfxhOhx2+bTzE7e+/tb4r2QVDSERfdCGsoaDgLECUaBpIAC333HbKxv2x1U9pmgeh8inaVV5nP5gLKpoqWdm3ejLUQnuDwymgLq7+2c31rQe6bbI4pyqwV1jwIxhVbQjl9NGXpgvWJoszGc2vd/rczlH+WtJWn+2UTGaUCDD0Nccv4a1e9q0ZchFkiHjS5M0mFOms8Fub0DGK/6wp8zFFchmncpGvADAmai/wt2HegyDkoEpj067JRrTuoYO/h15/hd3C2694sTkLZWVHt5FLnWkDDCtWbZ4fuqP4w+5lFZU9fb2V1UNSRGHI9FRNSCVeHyw2EyWpTXHr6nZtznl/uEM/SCBWHK49CAUG15fwGk2tHn0p9DL7OijQMM5AUBBCMCPf3RPT384pmjeQCwQiulqPBRRMMYkW5PkrON/k27B+9tbl86rdrtsgoDf335kydwqT6ldEPBX7/4RmnhuvvX26y85+b0trJVYnzf40MP/NQkvWmwJAE4iAZAnQqEwSySXsOX2qBqQuvxPs/48ERUMhmzZDPqYeB+xMjZm/SnrLDTOZwOyArVABSEAVR5npekOa5oRiauKqnf2hvYeaM88c+T4D0Jo696u1s7A4jmVLisy54X5F82uLHNOxgbmmz53Gz84Zc0ihND6f2yahBcF0hgu3jJc7OU9hGwDNT5pMpCqAaqqZaYB0kx/ODxKmD65ld2glBCqEaLqxKBU0Y2YZuiExDUS13VCqE7oESEqy5IkCrG4kiY5QH5SwfChFk4IiAf9LRbRYmGlezUVJccsrtV04gvG+v2Rt7ccziX+w6exe4PRD3a2ldkFj0PsD8U27WqrLJnw+Oldd92pG+TWa85N3vK5q88lhP7kJz+d6Jcu2vjPPF9LNpvuHDXSkhpvWRE78q6/tKTE6Ryw9akyEI5EEzOl2ZQ3c+VhGnG73Z5q/VNNv24Yjn3bOg0jquo6papOoqpuUGqY5p6YKpDVtU1DlwzdMPj2EWD88CgQIUTVjB5vqKM34AvGQpH4f3z/2/9693fgE56yVhCp5f9Z4Y3ZFdXwBqLtPcFNu9ioh0y3wGa38d4PqSX/vJA0X/PfxyQAnEee+sfDEAjKH1fd9EXW5NnpqK6uikVjc3v2HfVTRTU9ljDNVDWIr26BIAg8uMjFgJt+3lLUYpFFUeR/i9qAD6oqCSExCAkGI13dvWpL+rzPo6Zbciet/+Y3/5yvpy0evnPvv6maIYmCqukY492Hzf0iAw1EuARjzPp+z6kt+/GPH57q6y1WAfjefXcmU7WjQumgEnT0BDv6WHW/3SIqGjl2Sd2WPYmakLQ9XxO9Afiuu9hbMAxyS4YA/Pr3/0AIPfQQJAPyrAFs3lNNNdsS3LOPMscvca85wiURbtEJ1QwWYyGmlefLcHZAaFcoFohnCdNHympFQbBaLSUuh0WWZIvssNsEdotcUuKKx+LJKTHhUKSv39/vCxi8CtnXmb+3yKw/7xzHfwQByOSjH7+af0RWiyQI3KgnzLv5t5D4g6CUWfkUj8uMww24VaIgCBjXVZYKAhYE/BDIwFQ1g+MywMlFDEwl0L2BGFeC/kA0dR5kuUMk5gQYX5T1gZgcASCEfu7qczLvfeSpf2AMGpBPzr7oGlmWZFkSRCkYisTjSpXipQMx9gH7PyxOixTVjFHnMMfLWRk+Rli2yISwsA2l1DCMhLlByNqfUxEqRxIHFyWqnrnDkU+gQSLGXZKHUIIREZlVEiyy/M7LT6Mi4LKrb+A9nvgvZmAmG7uBUtYbeDB0NnAz0wBRsEgi+z0hLArmkj7jme/7/MUjvK4gskc8+szbBqE/eTj/swJnEhMeTLdapNT578OJAcbYZpXrq+W6qpL4opqOnuDOAz3eQJT/laxZWtfeG/IGogImk9b/J9OgeHu9CKErzlvz5gdHH6kAMnn9b0+xwU9nsgl/lLJ/vR1CabWeUwdQAWNREGgOI39t3sFFvZDVmotiNlOeCrNG2FxpcuNuk0UBY8luKbVZ2IGAy+xsBasTQ2arUbS3dE41QsQgSjwmyxJG6MlfPzytDToj2bQ74awNRGGGRmMIy3Un7k2O8BOZbWefnmCT44pmJDaGUlFM5OQxxkvm1118+opy97j2+nz2ytMRQrdedcbyky8fz/PMbCawHTR3AqwWpjE1NRWE0Lb2ntQTRvIMKIqrui8Ybenwd/f0X3reGkppMKwcbOn88UOPoInk61/7sm4WaRiE3jLgAXDTn+SND/ZihL953yjZDmCspGoAGxKXmwYwyzIQNhptYZ7l0dxaiZhhZeEHLGLsskqyyI5LbRbdMEQB22V+C5IEga1MU0I6WdldOpcfRCPhRHqswEz/5VffkDbpONWmJw75PcmVOrPj7ANjHrn5ybOPzpTDMS3SOXsO9/z+pc2xzNgdxscsrr/g1GUV7tG3XAznAXD++eHez3zh7qN4kiJhwj0ARdW5oRcEXFHp6e/z5eQZYGSzSnVVpdUVJZ2dvBkcdpfY6ocOhJsIHvzhQ1//2pfN9V263U+5OqjlmBA2v7l+7ZmXYsyjPrhHKsMY1ZmVoMNYc/aLsIhCg9vREYgqujGq0edBhRqXrdJlc8iSxQwbS+yLGfVs8YYxM4WmP7tNH3Bnk2tzfmCYC52kTWfnYCQxc5406HhMBn2sVJe7nHZLFgGgdNu+9vYu72cvP61iHH4AWP+pFIDMBb7TbunPOEFR9e7u/uHEQBSwLAmEUB6onZz9XxyoKp4SNr+5/oRzLmN19exXgC0WKWSbZbNaVhp9NllkYXRzhc4PIqrGAy8Oi0QJbfUnDC5G2CKxBTo/WTC/opqumPJAKe0Jx3VCF1SUuKzpuwEKyvRfdvUNyRh6wlgPlEcnz0nNiyZtOo+hU0oFRqJ7eRoTYdPHhN1qcbvsfb5whXuwz6tBiGB6FkvnVr2zeW/TnrZ7bruYu1y5888P2cxOWPtP8USwB+79f3xJlSYGrUe6rRYpORY4Ftd6zbU2jxclJ0cmb+FBpO7u/skRAO4B8MXTJWcdk/Wc1zfuFTBEgSaQK66/na9bFyyYZxqEhAmobdmaefJhb2hvT5CbxWqX/dh6T2ZcIhhXm/tCfZF4MvdoEYVZZc7ZZU6bLE2O6R8SRsej2PS02AuPz2DM6lsGP45CMuhjxSD0T682bdrZWllqoTgRu6OUarqxfF61NLBjo2lP221XnVVdUZJ8y39/OzF9k2fyMz+OslLHwjm1MCFgikNA99z/k6xikKYHdltiFaaoutUi8Xu5T8BvmeQlOY8CJYoVhgF29Ew0zz75i+TxFdffzg8WLpzfNefY5O1cDMKKdsgb5gbUIUvLa8qyRqVLbZY1DeW+mLqvN+DnO8jKZgUQam9pn+V2zvY4rQMWJ0eO1LMJpocO7GE/xI4Mjbfgy66+Ifn3k/w7MghJmPtEOCaxUjcvGE93gz5WBAFXlrmwwDwVjAyKWbFfQ3XZjZ866/m/vpf8t7966axfPv0GReikFbOSJVsIoVm1iRUk9AU6aiavJeFQMbiTK3ZSCZIHyS5vfMmf+gxTEHeHfZsFLwaEkM1bd3tRaQ3ySYKwss5jy2gr9PsjmtXK+0NgimzIbuPDZjAvTZmzpI2ivrbm+RUlVU7bO0rKPwrTpg/Y8cSSYHBtvn83q3VJXcITnhfFifJFXHQ2HWUs0om5SDedlvRzKEVHunwlFhSLq3ablU18Uo0bP8VGOHziklMQQn98/m2+fW/10llb9rT9c0cbwujZp34DveGmRwhoTDsGMnMGycQAjxddf/M3Ju2qcokC8WJQqAWaQi677rZgMOwLxuY1lGRdIqia0eVji315oPMPM+FJe45Qmdtl/h/LNJqWI02cbnp7pk0vxDD6pJEadeE5uVxWR1/5xgM5Pv+1n75JFIVrLzqhsZHtB+Q0H0i0Cdje3MVjQfxXc9Hpy5325FQ3BgyLPzqmvin5N+97eDgx4N3/+S2T3P85GQUC8s6V196YeePIu7iGjcUZMbdLdDtdfPtuoqYzeS9GPI58zpnr5sxuyPoEkuku/PXFN0RDKCqDnmrTDYPw1EIeDfpYSb76kSOsWLyxsTpp/VlPwIW1rCF8qe3Vjc2Uor+9vfOcExbzjpPA9BaAHMVgqgYADFiWIrfXiX+cI++2Tb93qDVm95uf5cDen7Q7B1uq8eqdoSZpJNt03+cvGeHeJ187iEbjkgvPOqlxMlrMTiijpkYn2aaPCYzx6sX1O/cnjP7O/W0L2RY61NkzZC/IR05YiBB6dWPzaxv3nXTM3Fk1ZdwzgwkB0zUElAvfu+/OVG2YzCgQZ4QokC8Y/dGPC2W7+ZXX3jSsoR6wBeyugQxkVsOaCJIMYzvM+szURzMrMxDhzcnijGyv8w4XgOE8gKQTUIACkDTo/GOfXgZ9rHz6pptXL65nHTvUIRMDR9gL9urG5rOOX1jlSbT7hijQtPcAhmPyrT+PAn3tq3eOXO1DEW3pTGxtGytXXnvToJ3OeBHeGWV4M87MdJbpyqzIhDFcg2Ieyx7IUQ4HnipjXUSpUdOg51JnME0N+lh54n//Z+n9/5Z5e38gMpwMcG8g2WQCnIAZ6wFMFR+/8hqH3Vrudlx5/lrCuk4SwyC/fn7D0NBFyno6A95pMvWWoTGRIdXfqafw//gfN2s3k8PaD4z15HsAqVGXxC9v5i7SJ4fvZ5OBkV0B1jDYzfwAcAJmpgcwEXz00k+x/5mWVRQFtgM+W6mHopGOvvDDv3tj4IaMuAlmMwV58xS245Q9TdpLZTcJYK8LkNSoS7IoaFTAoOeRu+/9QVYZGM4V4KYfODqK1AO46PJrdD0lu2tafrbQZp0nRElMXwyCsS5kXnrjw9QfqTkwgAW5TC2mlPZTzwgewN49rG0AqzwpSWxBx+XzEEJfvvXmCb5wYLzeQKb1z+oEwJbg4ShGAbjo8msIQVjI2PZpLvaqy11Xnrdmbn15XvqCASMb60RW2dxJNc4P/K67s8RVLrnmVvZHvnj24cNtZWXDdhJ0Omx8UhjbgVg7WIcOGlDgGpCjAIAGDEdxCcBFl19T5Snp7mfdJbMIwABV5SWfPB80IIux5p0MuKEej73OaqzzTtL6J2/5j/tG6gz8b/cP9vcGGSh8GaCELprL9gekAU5A7hSRADzyUxZbDEbiW/e29wUieKD5lNkSwKitLE3mbLu90arykivPXz2vvmIa+QHpkRDEWmpOI3uddy655rbcrX8SkIHpIgOGQZqP9F10xqq000AAcqeIBIDzyE9/EIkp2/Z39vijlFBMWX/g+uoyNh3Q7EObrIA//bj5leVl82dVTs7iWme7MVkshA+2LjZjnXcuueY2hFBSAHK0/klABgpfBhRVP9zhzVEAIAqUlaITAM7DP/zOKxtYE8fF8xIuZOqfUVtnLz840smaVDus6bVSfKIs78o7nssAYz051j8YCP7yIeb/5UUDtu06SCmNxZS+fn8gGNF0PVnotfWNZ/Jx+UAOGkBRry/c0RuglF5x/tq0E8AJyJEiFYBv3/1VfvDx89fxA5uT9Yfs6OjPFICkBvT4IsNtCX5j414M4wEKg4uvuRUjvHzA+gcGrP9NX/rm4Ek5C3dNuSurDNgdTk3Te/p8Tdv2dvf6FGVwrBXIwCTw3W//a1u33xuIsj2VH81JAMAJyKRIBSCpAbpBrrjgRG79OZs37zYGzENSABBCoWicUnTp2YPN6FOBzqAFAk/8Ll88J3nLg/d94+Yvf4sfrzuOdfDPBUppJBKzWi2yOSvm8MFDWWUAC1I4Em3r6N2z73AoHNOZN5AAZGCi+cLnb+v1hSnNIgDgBORI8QpAUgMoQotnV2U2vwlF4v3BWNotGGPoDl3IXHLNbanWv9cXYX0vEDrlxNX8Fk0b0mqGwzJAZo86TdMVRW070t7b5+vr989prK2vqzIHK2JKkZFi31NloGnHQU3TO7r6giG2WckwEnOJMcJNb/xxIt9usXPlp65LHOTmBMCGgDSKWgA43777q4tmV6XeEorEk8dpGhCOKsMOiXx/jyAIMB5gykP/y5fM4f01AoGAhuSk6UcI9fX0hMIRXddVVY/FlWg0Ho3FY3ElHlc1TeeFABhjd6nTHwjLsrRoQWM0GjcMIkki68khCrVVnqwaYLNZf/3EC6kXk5QBURA+fO3pyXj/RawBEAU6OkAAhmhAqulPJSkDoWj8X86CKFBhW3+TgD/d+h8+3NLR2XukrVtRVYywbJEEQXDYrGznNxubLrBptJructnjcdXptNdUeSxy9pHxqjoY8a+prbbZrIPDy/7ypmEYukEURTUMkpQBQcBbXgNvYEK48lPX5R4F2nuw43NfumdiLmT6AQKQ4L//8/6RPymuAaNGgfr8kYce/q+8/56AsVr/dScm0vuU0taW1n0HjvT3+xvqqxvqq1CiV/4w7bAR+tRFrbl84E88P7ALCaM5cxrT7m3acaD5YJs/EObxpeTtkBuYNA0YTgAQQqABHBCAMWvASFGgjXu37mt/9vdPjvw8QH65+c67e3q8SevPqnuXLObr9JaW1t17DwuCMHtWTW1NhYBZl6dLzjLHuOeJJ/48ZDNqmhK8888dBw+3q6pGU4JCVqtl48u/y+M1AGyA8P8+lPk5gAaMDAjA2DQAIdTS5U8KACGs75hukGhMVXXjjY17O3oDzz39W5Qbx5195YevQ+X4uPjsnay4s2JgKgi3/oSQvXv272luqakqn1Vf7bBb+Vo/v6Z/BCXIdAh+8/v/UzWWQ+YyIIpiaYnjjT8/NnHXU4RkasAIAoDADwABOAoZaOliM+ouPuuYtm7/6xv3xxRN0XRNN9iEFopETHMXgCXrPtbV3Rto3Zzj+UCm6c+0/n5/8IPN22qqPDXV5eJAx6ejNP2pwxoGRqENvZcilSR7R1OKfvvqrNRT0pTg0Sf/xudDcBmQJLG60vPSH34Bv9nJFADIBCQBD2BsGsCtPxOAM4957YN9H+4enFvNEVkvCZSjBixZ97G2tvZI5/ZcTgZGtf6z587ZvHkbpXTh/EZJ5KafuWiXnrEXGRTxBuApI3jiqqGobFM3t+Tmd2aeQxE9ppDEzDU2X5dNWPMFtbhKzNMSM37M0bvIG1RN9WfHmM3wYXeHonpl1RkpF9bIhh6zDeRUUTRN159ZzyZMGIYhCKLL5Xj7L+AKTGoUCOpBi30gzMh8/q57R/ADKKKhaLy5tW/8LyQm7BQw3oX/oYOH3nnng0ULGh0Om6pqPn/Q5w+tnHOAIvTSa1RRiT+k80mZyUkvikoicSMpCFhAorlpQNNp6rgIq4XNCxoQhMRjSxySRWZnCxhbZJZQxgIuL5WtcmIiEMZbEcJ/ft1GKWrasd/seI2wZInFWKWZu8SOEPYGIoRSvnsAGD+fvOnLWTUAGA4QgGH5/F33Xnn19WeuXZDs+BNTEnuICKEvvLk9Gh+sBUxiUMydgFyw2W2CUHCzyKed9feUlW7dulPX9cqKsgOH2gLBSDQWX9DYKyK0+zCSRFzilJilFpDId4VhZJWFslLZHELAWu/xA7tVLCuRzG58iP1WzDswQg6b6LAzneaP5c36rLIgyUJ6FZHExsIN/mhQt8v/z+0+h03s9C4SREEUBFHwNMyq5xsLJFEUzYGfX34dNgpMFFlnBb/1wmPgBIAAjMIzrJ7neqss2qyy02bp6g/ObajUdCMaV/2hmG6kzBQbymVXXZdLFMgcQpKrWhQ5WU1/bX39jh17Pty2X9M0SZI87pLamoo7rowgbMOoMTl13WkXE6Mdk8YZIyRlmG9Wrp/P7t89HdF3mrwdvXHKYoNNBqHLlpyHEIpGwvyEOXMSCYNf/Ib9tdx+Q2JfKwBMDuABjIKAsaIZMUXzBVnbqebWHneJIxxVFFVHw4+UyRFViVttdnvN0lj3BFanzFTrTygK+LyV5e5yT6nLabfIsiAIZrLXhgqDcMzQDSpLAqWs6xRCaPfeV/hdXAlaWtqSMkAIfehXj3X3evv6A48+9N2pvvbpCkSBxgQkgUfnyquv5y2g+Y8UYYOv/YcXABHTtz9sESS2jxQLoq7GKKV2u21WXbWAkaoqwaBPVZTGuQv27W2OK0qgNX08wFGwcNWJssVOkdDrC7vc5YKADm3+PzQjYz4Vle1t7bPqh3QxmdASz6PEoBG/2t4TD0X1A23Rls6YL6jyRILdtRZjrMbCbLcwMVg9McsRi4SSEpej3OMGDThqIBWcO0XqAXz06i8JLPgqYBaJZUFYSlhZHgsRm3uFsCBiQiRRisdjXX5/V1dPOBQ65Rg2WDxh/ZkmGCNowOnHzXn5rSbKZELiYR4lGtbjYY+njBAjHo0Qg5S63Xanw0CiZ/7JajwiyFZNVymhxNDMhCMmhoYHHk4pFSXJVVEpyRYeqBBFkRqGrdQtsG5lgtPlLi0t7zjSgiJi46KV1XW1010Aslp/iz1x3NbRzTWgEE0/R8TOCuvictYoYu0xnpvv7uVRqaqKniWL5iSDTX39/RYR8/ESGOM5cxq/cBNbc0wj5i49LXlMMFv3tO5+faouBpyAmSwAl33+u8gc4yWw5lwipVQSscXs2YsxJoahxGMWq8W06URhPVsopZiY9XtsUz5r6EjVWISoCuFVf6atNSv0iHmKWdAnyUTTiKEbuoaoUe6SrILt7c3744qKKD37pJy6CjPrL0hsMKP57BjjaExxODWb1WK1OaKhAMbYIkmIKrqmUtFCsEQFiiVRMCyEsOukhs5khmkBpqygkU0yFjChuoYQNTRs6LoWi1B2MgpZrFFPJVFVLebd07Qh4E3fizTdTf/Lb2ypKHdjjFcum88/z7aO7tuv8aECx7T0N9/d+9kbL0/e1taRmDnB88pWS6LpkD8YKXzrP3v52QOpK9ogd1CEdaOHEDPYhVjnVFVHNauWxExvh1IaxHXsLe99awqvGVLB01IAPvvN/8aiSAjVNCUaCSmxaDwaokY8UYtBid3h0gwcCyuUEmIYhBJd0wRRIOwng+iGoev/ct2XzEW+gpCOLSWIEqcFueyWB+/7Jlv/C6wVmLnwp7IsSyyUzKysJIlIFIkkIWJQSqw2q2yRW490qlqW4p+stUCUMGdBlKxUINj8l6BrasAfkCvcmhKTZJk/rab2YTVg+hWsaQxb/pt6xVaLhkExopFu/rQ6Qv09I612u1KO+/ah6W79K1Osv93l/sTFZyGE9u/bv7lp7+x6T7mHjXGeLpx64srdu/ey6P+yJWl3Oe3M+vuDiWLQ27/6rV/8qOByALOWsG0NVWK3udmhQ5SwLCJiGFgUDJVIAtYI++sVRYGte5DO2u2JmG+e85BOSRTcSxdFRDalByN0aOc/pvoNAYWaAzj2rEvrZ88vKfXIkkWSZIMQ3Yy/hwJeVWHdeDDVRFFiRXUYO11uLCBi6IkdOJSoqmqxWo476YLursMIoc7OiM1la93bjDHWlDjGVJBkV0kJRqLdaVuycil7SEzdueXNgaIRcyywJBFV5St3SnRDNxBbqOv79h2Mx5XTj1sweLnDRIF4Meibmw6x4eyJJjCULecxrqkqt9klXdXKKyoVjezcvj3StWuyPt3pYf1TTf+ixQtT18sHmpv3NrdXV7rnzqln5nISPIBk/b+p7QM3IqQRlKwvyhFCX32z+7UP+pYvPT95m6OkrP1I+o7CwtGAuSvONQyjWuwQBUHTDUFgBbWUIlnCRNcxq2MWMMWawUqoMDZ3TGOW1hAEgVIsijhuKoROqGQRNQ1RgmNW1jZDlOR9H/59ancFn3HxZ1ARU4gewNY31m9N+fHTX/tPh9UuWy0l7jJWOi3JxFC4uSdEp4TIkshCQGYs31Eya+/OrYZGn/3NE4RZW6Kz7+x0kQmHjs2uvz5JQAYhhrZv6yZBlKyO0rpZs2bNnV1TX7Njyzs66+xASj0VdocrGPD7fYG4HiPECISiFFvOPXEOIURPGoIR6T/wbuaN/hb2fekJ59nszki0z27FE70R6LIv3R/o6zUMPeL3lYcPtQsVksVuc7kFQTAzDhrTTmL+Y5UlQZIDvT02l9Nud86qr/vjf43eHym/pFn/tHvnzp09dy5bSG5p2vXgNwZHueUNjcTCGiEoYH6nlHqDmqqZG4Yp8oe0GNs8zDYECwK+4NQqi9syhicXcGOtXZaGbP5I1hcUGvNWnkMprhA6rFYaj1NMqSxig1CrLGoaC08iJBADiSIyCBJNVTCDPuYyCglm1BXrOpJYfoNpADKIJAo6olalU5KwIk7q6hOiQNNDANJ44od3jXrO13/8h2g00tvdG9x72NvvjYXD8VjEMHS2zcqMFouiaCCDBdFZNB0zbUBYxKKhRg1CYrFI0NdzcP8ee0nlgiULl6ya/+3PDi7Q0jnmeva0PDQ/PKPuCDvP+TfkZf7wqfMRmo9++rpljMVJ97ONrcxfwRTjurWsP5333y7Neu7B7dt1VbOpvfPKROy04mj/fq9NicWtdrsgiZFgSBBF2WLDomC32aw2e5mnRBCEErushAIXf/qOF574KZosHn34ewihO+9OqM727btWrVqeesKhgy3z5s8J+Hzz59T96il06zWJ5hz5oq83/uyrnYpKogpv2EM1tsxI/CpJshGE2eK/vsq6bl3lmJ7fZu4fTiUeCaJCYvkJFynxaBnppkYHMjdLELbeFxBh+TYBU76KZ6eywCk2mFtLtZQlkcgyWaKqJ/ZdE1MsRYEahFUrsGArK6agFq1r1Qnnbt8I4aApYxoIQC48+JVPZd540oU3Wh1OQ9HVuKrGQkpMxWzvD9X5P2tsaFhj85pEyTAUbCDWpiXm29h9ePe2mk/e/sPFK5bardI3P3dB2tM+8/snr7z6eoxwoqnMiLVAd5w9esKA85VL5v34r4ODZ0fFb3NTlrpg1p9XLo1A/cIF/Z2dwV51T1yyh1r6sVu22kRJYvkSw3CXV5RW1UoWa7SvXdfjMV/Y0HVCiBKJOJnXJXz003e+/MTDaBJ5+Pv3Jo9v/MJX582fn/yRIuR0uXw+/wTFfwJhvatPUbTRV+WGQd/60Luw0empHYMjwlwJjPwhhQXWGxINRH3+xNawQmDXxr+deNxyQ6MaxgKiIovoIBFjjS2hWPbN0Clh6ye2wBcFbBBUYpdCEd3sjWv2Q6LYbhU1wxAFpOu6xDwe5l4iTBXNEFkFHhMGgWBFZ5/DpNUCZXUCipkZIgBZ+eeLj2feePa/fIYSwR8Ix2Nhnbm1GrVYECujRJpBMMWCQILeQ9v8bc17dtY1zPnyA086bLbvffXKTA1QtcEpH5OPUtHA7L7ZyoD9ozMp/8H6rE6AgMXqWY2yxRrp74i7aq26bua5DUmSBZblVkPdRyxWGxMzg8iiaJetJSUliqbEFFVXNVmYyr+Tx3/+o7sfeDD54+zGIf33847E7FSuJ/f51X9s7LviYw3IkmtLD945rsDxk0qP3G8YBBMzx2H2PDUIFhEtscsxzDwis6jOwJh5NJqBRBkjQ0i4SgJSdMI+SeYnsX1wTDYMYrNKuq6Z7gKr4iMIybHeY06+cNuGF6fwzb5VxG0hiq4Rzet/eeyNFx5tevsPeza9MGv+0rkLjvGUVUtYMFTFMFTdiGs68/wNQwmHew7s3/ryiy++9cY7n7jhO3c+8NuMLhGjwCp4xsJXLpmX+8nE6SFON3GwL2ofpR7mhUe+H+hpD/W1K6qCBEm22rEgUkI0JR4Ph6KhQDQUCXh9kmTRdSJg0eawRWKRYCgYCQaVeDwY8J9zxc1o6vj+PV+ftNcyF+hj+MXtaA7t2pdbDEenvW3RnQdChS8Ae5veQga1iNjCnGYc05njZa7bWRZcFlmNNduzIAhmQ1MUUwy7TWLZYQEblIUlWYLcLrKYj1nFZ7o+WNV0c+1PRRERSjQ2JkdQlexzWMfPJ2/68gQ984xhJnsAo/LW+p8lj08+7ybdUA2qh/xeQjSBiFQQdUPFutbVFenubj3SfuSCa7+1eOUSWZJ+9LWhEacRo0ATQcf9bw/2tUks/0MjP6S0osowjFgkbA5EFDRNlUSJu+tmtpzVcoTCIaezRJBEfyho7l5AktWG2KR0PZfi10nA7akI+Pon8AUo6wU9QpenTBSNvPZB37x6u93c8DUccZ/atC+4aZe/s0+Jxhf279izbOVSVMB4cWWN3BdW2CqGGX4WLcU6QYqiWa0i25fCbmOFnixGhLGmm4M2BVFIBIKwOQaNKarAWt6yvzrdIKLAKoQollgMFbPKAynaftypF3/47guT874gFZxKUQtAKhte+V9+cPKFN1mtzv6ejlgogCmLBOssfCkGvPGQv6u99WBFRe2NX3ukcf7Cbf94dKqulkqmrRkYartKSEw4GoEXfv1DfnDqaWc6hVjMwO192txla4O+PoqoJMvm/gM1EvbWz17Y29eja2zVJ4mS1cZ2qTpKprjoXjUS3qrbUzGBL4NZ82ezIHgMq/SO3vgbm/svOKcOifiGL+4TLa64qj/1M1aqxIgbzS3hDdv8B9sicZUcOGjXlP3xaHj7ls0y29SNMVNi0WKRJUmQRNHGWgSitSedx/uRbtrwMpoK9jW9Vbl6OVvyE1Z5zXZJmnsvCTFMy45ZEJ8VfrKsBkY0HtXsThvrpE1YVsCcf4AsElYUnSWOB5Yr/GPFhJibb5i3hZAQiwbQ1EHGovczDBCAdDa8mFCC86++x9vd6us5QqnKYuOsaEjSVCUe8Xa2Ne9uqqmdvbRu3oLOpr/l8feRaypYlhMeAP93NZZkxLvvvHnmKess2ECSEAkHNULisRhWFFGUzE1xYiAcZuV6Attmwbx+85+6yvZUFwVayiSAUWk7wNbHXb39G9/p/dFPdhFDV9S421PTMLvx03fsJyguSAIxDKoTJKCePp8VO+ctbAz0dcb6g06LSCnbMCUiJFlkSWTzYQxixAxDEpjoioJAEFq+6jSdjZzTeNdqVdcdTofTbt3ywWtogvGiKpfYiwVKCCsXlthqHlOKDYNKksBKoSkL6JgCwUp9dNXMDhMkY2biDQNTMz/F99ezk1jNKxIFge3sYVuGzZ1i7G9YnJJUMCli088BARiWv//+AYTQx675ZijQ3d12QFdVRHQDibouYqyoerSv//ChvdsrquvLKmrmLF166P3nJ/H3Jg/uP2IbhgfvGS4PzLni6w/1th1S9r+PRGQlpK/lgKtuFsvHCeyfMCtsojTY3+10OTVFiUXDgihiHZeWlbOWFVPHV+9PuC/psALDfDZw5sWdKUPDBmlrFsIxJRgMC8yisdxoLBZ22O2i2e6DUk0Q2TLeaZOr6qqPPemkpvfesdcQh8euK3osHLc4LVWLXW2b/a6KCpfDarFIdqJiLNqsNkm2Kjo2WELV3JjOLL3Ek6yImUm+odZAmLKXRjgSV8Kx2Jq15+hEjyssbhmLq52HtuX3c0AI7Wl6c90xzAnAAhaoyLpUsXIggaW3+No9cZFmQaiA2WBUXpNmFgixxC/vqmWwhAGhbJOwOXqBCmz/GIv/sIdjLMS7jznho9s2Tp6vA6afAwIwCv/31PcSkZOLb+nv6YhHgphorLmQpiNRDIX6IxF/V/vhfbu2Ni5ctOq4dfvefRZNghNgsSQSlTwKlHMWzdvd1tm8341lmxC3YBb0KS1xWe1WNR6zWm2GruuqgqnhtDu6Aj4Wz5VkQqmhqpgYF1x7+0u/m8rptZUVZZTSPfsSH04kFNqwsf+EFWWiU8qjDJhqmAgBte6jbZ09IsZ2q83hsDst1pJqZvGZ+eJWjFlsVvNuwUTRVN1Q272B2oZGX1eXKOBqO4kFgpQsKpXFj51xKnv209ATj/9h7alnGZq6d8+OBXWzdIri0dii444d007g448/XxatNovDZdMdTossW1bMnx8JR3xBf0zXSktd2ze/k5dPIyJWWWmP2cjE3O2V2CzPdlMKAtYNlvI11yAUm2X+VllgC//Eo3nQSFQ1Q5Yx0qmYWLWwh+ts9wB7QtbRhSJNm6hUsGw2CgOyAh9Nrrz7wiOsx+elXwx4u0O+LmoYZqMeTAVBZzvOwod3hntbDjbOnVfT2Ni1N8sG4DyCLdYBD8AUglESwCmEfRVWokjuPuyxx47Q0npRttglKRrwKZEwiwAJoixbdd2wO0spQrLFqqkxg+2G9dlsjuM+8okPX51ER2eAH937tR/87Nf8eOliViu1Z98hZ0nJ2ztKtu7btnJhyYkrPbIrPzLwlft2BQIhSRRcDqfL7phbVydLsiRZBLPzIOsxRQzK2kOxzcFYkDyzE3PiYkdkRSUVFZV1jXP7ejora0Ph+GK2ohfRxz5iWn+TM844obLSNqdm3elnrEt93cOtnblf5KZN2TsorDnuLLtgyBbhjHPPEy3Y7pADvkgwGCOE2qQSqyy99+7YVtm7trx5/LErDIOY0X4uAMg08WyPgCiygI/ZLoU5RbIsxhXDYmG+wsAYPWQYiLXVwkgQTQ1hookEicWImJCw/zP3QVYSDa+AYu8FVPicfuFn45rm72vXlMQ2FtaXSJSxKJl7j5E3GLTYXRab62Ln73J8Ttk1pEfYyE5A5xNm27cBCVjR9n7qvSOEgC743DcjXQcD4ajVZueNUUVR1FVFsliUWJR1hiBEttpsdlco4GUl24LIcwOyJNocToTw289n2V0xOVx54+fXHr8m9ZakN1Aib1uxoOSkVWUWl5ywUmPknEtfR0h32dx2m03AgiRKMitgN2oXjW23h2D2kmImEbMin1TT37xvvySJlZXlwz02VQPSPIDS2qWabtgddvb70jRf2w40WaxevtTGRmqyPzfdoMyGYxYDEgUqWsySH3NnA8sJS1jTDJtFZrsEEo9mwSBJwpGYapHFRE+sxOQ1auhsPxkLyCBhx+4JbIf1/G/Tt7J72EzmdM669LOoyAAP4Gh4+8VE/c8p538mEOiLRoJmiQrzh0UqUaopUV807JUsDjS4gzWfYNug9R8TL/36e+suujYejepanMV1mZMuSJIlEgyyJAdGst1mk2QssRi0IFq2v/kXVDA88/h/P/P4EBlIegMh7Zh/7kE7mrctnO1Yu9RdUWPPUQY+ecMHvv5AaUlpY3W1JIrxWOwbdzX+4e8dhtnp5igglHb31VSUl1740UHTf++3f6DrxOl0OZ1OVVVvv+3qrI+dO7tuOD8g2MW6wLJWiJNO0649p65eqhJRZ8WbBtt3KCKRpSUI3ybMw2Vm5B9ZLJJuEMli0VmKmMsARqwBo8g6skhmlzjCPiarVYzpZnZBwNu2T3YzRF8ollUDig0QgHHx3t8fY+mBCz8XivhCAR9rAE1VWUBOpyPgD+rxyARlArCDZ9P4D2N7ZpfL4bA3xqPhWDiMCNFDvm1NG9D0obvXf/jQ4f5+X6YMhPVjmg6ibfu3rFpQctxSd1XdsDJw9ec29/b4nA670+qoqaj67W8GQ/AH9gUlgRW6JG95+43OaFyRZYvb7dY07aSTy4a7tpZ2j9kfltbX1/GJBYcPHkQI3XTjJ0dY+OeoAVPIu02JJuTHrVpu9jnHBAuYCJQg1tNNJywFwJ0EAauqKptZADMwxFIEBqUWixyOKnarLJv7gyml72+BDrhTDwhAHqh0RCodFlRV0xlEsWCfoul2uy0aiajqRPWKEHhRPteAMQrA608ngumnnHp6mayFHGjZsSfs3roRTSsqKjyHDx329vuPO351mgwQcc3Ww2hb85aVC1xrlrpr6x1IxDfeusfjcVjsrOCSIs1VIvz+1x/J+sx2KyvESb3l9LPYPJOR4abfXAhLLFO9v6WhvioS8LI/j9xMf4FrAOfDYZbqJ69exlLjWNAItVosqmpYZIlt9GVbhpHIkr8smKgTumX77km/amBYQADySV0pQmU1uk76fCFVUb3ewVZlbsk58mPZyPmcnQDBkzD9Zmn4UV7te+++ffZpJ9sEzSEeZbhjyimvKOMygDA6bu3qVBmg0prtLWh7C1rRuH/NUvfjP1+SaGvJ0Rv0oLlR1SxjVDQSjZlrfooiMcNhF2NKruKdavoHb9Vje3btXnPssqN7X3Nnjy45BcWGpuxmfe0xywVJ+GDr5GUsgDEBApAH1j/71KVXXMOP53c/HtOoGKT2xbfs29c8qt1P4oi3RW2zcjxZNJ91/CUvCpFsslHmsC5fuWbXji1oelJewWIyTAm8/jQZQAjtPLJo5xG0bNY+h00MhnUzME0VlQTCvFsxewbdoJFY4keKmAaM+qJHOsoTrT2Hmn6XLfFriSmGpulHV4PY18dchxnA5m0Q5yloQADyyfxe1iFOFrDbhg62brIZmcv6sTGcE5D8tY1TA9577+0zTjnJZcUua563U00J5eVlmTLAlWB322JKacA3GOni5j6msjT9QPja3ABgVtbquuH3h3Q2CS4dlgMd+OhTp4ElTT9PH3h9wd5+f31tZXGa/sLHB3lgKAPNF9wD4ALA97v/vXepIImlHse5tu25P0+qE8C3TWYVgPjB1Om/7LS6P+ZaCZrGKaecVmM34hrpCSmbtzShgue2uxITg0cmKQNJ3np3c+qPF5x/+sjPsP/AkQ0bt2fVgEy49U8bE1dVUXb8mmVsXN0YTf8Pv3NPLg8BxlMJirIVgxZbJSh4APlh/bNPXbK2HA2MMhQEZLPLK1fUUV1FR9u8Ek9KF+/33nvn1BPWzqmwx3KzdNPLG+jr8x2/bk2OFj+NOY21rW3d/d255mMzh4R6faHePn9D3ehOAFh/YEoAAcgPHz+xhsSGOO9nlW5HR7YrOt0eoKvqHEeRCUi0ycqGZX6tOtQJGA99ilQZV0pd1oVLVzbvmVH5uspKVizEPjEpe7sx/iEnTXfaB+600B6aW2dQs6dN2m0GMVqOdFZVlo3gBIDpB6YQEIA8cPlps5Vgr11ItARIxSphz1FtN3lxn+PCxSOlEJJtDMc/XKSstrFTD5FwpHLhSmtFvQ0Zm98t3DGtv/zP7131mS96PGMb7Kea/k1SBsrKcnq4KMvdvf5o7Ojb1LBMQJ+voa4q8y4w/cCUU3QTwSaCeNhnxXGrmL21rNuO9/aMIRvsiLex8HGZbWTLbphf+sDBeCCGFqc2z6IVAsZ2l1Nn4zsKmqcf+5nPF/D5xtxEnnVr040crT+L7DvsVZW85DYHsg36MghpOdKlaumFtmD9gUIAPIDxcsnackkPWERis2S32AJGFc4x19isqx6lGoQb/bzMFvzg1fUnX3SVEldYR2hkbH0re6OxguLpx9g0t6s+80WWyhujN5A7GOPamvLePl8enQAw/YWDr+gLgcADGC9YC1kE3SoRlw2XubN/nnYZd4Xy3MeFL/zJwJc5luPo2fC3p0VRtNhtH7xcQM1/RuXpx3521N7AZDoBh1uZE9DX501a/x9+555iKPW542w1+TW1V/KJ6+7I5bQ31k/ZmL8pATyA8WIVNKtInTZU6sSyhCorhL7+LLa4ZCyF9rlsChtiaXhz3vHx+lP/g6Ynqd5A3h2C8TgBa+O/SRx1oJ6hUzvvOJt9/+nrFjSdmXKzDowT8ADGi1UkdistdWCrzIzywICkLETUOP9C+YAqiMbNrxj7DnBvgPn1+XYIxuQErI3/Jvk16slfuSSxVa0YKKo3O10AD2C82Cy0xIEdVrYE59a/3INSmgBlIVUDnBZb1nMc8baIbdYIJofyTqNmH3aE0N6zT1zy+pC9YMUJ14CR0wM9fd7qsTRoy0smAAAKEPAAxsujG8QSOxbFxORr3gI9d/OSdAsiajygR1K/Rl5wVh5fS8OIhhGKIBQ2v8a+DXgGk9/0wNgyAQAwTQAByAOyxIM/pvU39wMJGNVXjdlYSGRI/eWoFT40mvIVQ6zxLjC8DIxHCbgT4LBb024XcfrXWCmqwEgBvllfvqszphcgAHng7uewufpHSesvCuxrfv24FoyjPzhGUJygmIHiOorpbFI5MO70gD8QyPqlq2qJ0zZOcw8UPm8UUyEQ5ADywz3PC9/9uJGw/kwAsCQiScQr5+Edh8awMJeIqAu57usSoiEzA2DmASjdV7twcVfz0b6D4koP+HyBmuqxzWlh2p5h8d9+6Q9pt6w2y3vyOP1thjGFb/YT192RtSVcMQMCkE+wue1LFLDpATANsIj45KWiVcJv7NCzdYsZlwZUXr/E/6sP2OsmZQAYS9nocBrwXz/4DnyQQDEAApA3vvVn8d8vM0TMrL8kIElEcuILSwK6YLX0UhPrBzCqDOTuASCESmNBAVEBUZGy75ABPgqHoBAAJwCYEkAA8sk3nhN//EliWn9sMZf/3PpLAqKEnr1UiMbJ+6w35bBogjGmwLJw57m8MRwPMynjfAMAABQTkDbMM1/5oyAxu5+w/rLpCmCKNJ3GVaqodEU1WVxOFrizLPNLqhZBWrFoKcAKmSJ5s74iLgQCDyD/vE5uUML6tRVPWVgeGIkIGQaNK8z66wYyDEQIJRTNdhGDIJ0KBhJ1ZDVslb7+PRNwOQAAANkBDyD//PGpJ1YsrPsHvo0FfzAz93GVKBrVdGoYVDeoQZBBMKHIoAJBgoEkQy7xBYqlDiR3vnCOzr/uPVe799ws4xZmGAW1Lp5oiurNFizgAUwIFkmsqyzdiL5GCFnX/0NFpZrGrT8iFBHKrT82uPUXnb7w0FZhRcwXzklvnZ+Ea8D9/5An94qAXHFLzhHuHcNMjCmtBD2rmMYCgwBMIBghURCcFzzqROjQ725k1p+w7bpcAxLLf2z1RntRcTOC0Z92MjCyESxAmzhpb7bwa5/OKibTz8HL5tVM9TXMQO6751+Tx6cfvyj1rm2/vjE19N+nhIZ7Etm1BCFUUP9CpsruV+LsWxwKUwbuOW8MV5XZ93t6/caL6s3OPMADmBDue+A/UjUglYNRmjLQpejaqY9psT8q956rFaYGAMC0AJLAE87bm/aj4iaZy82v9S9YHnhFG+sI6OmbHR3nmwWmFvAAChc6zbsPF4m5B4DpCwhA4TIdrT8Yfc4Dr2i5B8dzmQBayBTVm51hgAAA4wWMPgBMUyAHMFHc98B/8AM6Q9MARRXWn+jgeNG+2emV8Jh5gAcw4UzHSM7IgNHPO0UVGCmqN1vggAcAABMFOAFAgQMCAAAFQVGVSBbVmy1kQAAmIw0AjAePaIcPcFpQVB7PzAAEYJKYGXngScv6ekR78ottmxYc/AtNN2CfVJKAHkn90sJ7p/IXA5hAErhwKaiNYJNm90c+gWuASKZfF7WAHsnltOne1yLHtwkUCCAAhUvhWP9Ci/BMLxkAmwgULCAAwHQN6xuCA4LOADAeIAcweXng3NMAbsk5zk7rhUlqWH88PPCKxr/ydF0AUKSABzDZ5G7ZZ8Z+mXzV8IC5B4C8AwJQ1LT/5zahulqoYBnnmotq8/W0YPQBYFoAIaDJ5uwv/HeB7JfpvfXXqT/6Hn5nnE8IER4AmF6ABzB5aYB77/nXtzftT5sQOZPgZTlHV60PER4AmHxAACaP+1MSwmOapDq9SFZnjqoEYPQBYGoBAZgaisH2DecQFMN7B4BpAQgAMBkyAKPbAaAAgSQwAABAkQICAAAAUKSAAAAAABQpIAAAAABFCggAAABAkQICAAAAUKSAAAAAABQpIAAAAABFCggAAABAkQICAAAAUKSAAAAAABQpIAAAAABFCggAAABAkQICAAAAUKSAAAAAABQpIAAAAEwBbslJ4YOfamAgDABMAW7JmfX2xDjNGfe+soIn8kqAXAAPAAAmlp++bnFLTrfkLDW/86/hTnbE26b172NM8z6n+5udAYAAAMAkAQteoNAAAQCAwloXT3eK6s1Od0AAAKCwKKrASFG92QIEksAAMBk88Ip2z3nyCPfOpF/DDHs7MxgQAACYJMAsAoUGCAAwXjyinR8Y5neRTPdSRgAoFkAAgHEZ/UwMwQEyAADTAhAAID92Pw2QAQAofKAKCMiJn78m/fy1MS8XDMHBlQAAgAIEPABgzGnMEapZsp6PUK7nAwAwmYAAAPmXASh3AYBpAQgAkB8ZAKMPANMOEABgXIDdB4DpCySBAQAAihQQAAAAgCIFBAAAAKBIAQEAAAAoUkAAAAAAihQQAAAAgCIFBAAAAKBIAQEAAAAoUmAjWBFjkLoNz8UkK5JkO9X9606b6gsCAGBSwcvm1UzuKwIFQeSy+yNVc4i7klodSBIxNcSY3xnqc/z3LVN9aQAATBIgAMWI/6oH443LiMNNLTZksWDZgiRJwASH+xz9bSU/vHqqLxAAgMkAcgBFR/QT98ZnLTFcHupwIVcJLnGjUjcuddNSD62dG6udo33j91N9jQAATAaQAyg6IhUNpLQC2V3I4cTOEsGFsQ0h0Rzpa8hUaAgZ8fKpvkgAACYB8ACKC/s13zU8VcjuwE6XUFoqVmN5EbKuQJalSGxA2INwmdVwl1c98OxUXykAABMOeADFRZAKyGZHNjt2uHApkhpRiQuVIxRFyF+GVAkRAyG7s1/OdfYvAADTF/AAigxZQhhhqxXZsOBEomn9z0Zojjm2UXAhLCEkCliGIY4AMPMBASgu6iWKDQULFFsSk3qjCG1HqAchghCiCOkIU2OhhU71lQIAMOGAABQXvY/9m6RGUTyEzR8NBfkR2o1QN0IqQiSAUAzJanTvFz861VcKAMCEAwJQdNTGfUKoFwVDKMosvqagGEKKgQwvon1ICMfq4/6pvkYAACYD2AhWjGhff9JfPZtU1GK3GznNuD9BKI6ESLTC34VuPWWqLxAAgMkABKBIKf3W746UVmul5cjuQKIkUEPS4gsVX89tZ0/1pQEAMEmAABQ7tT/+KxKlrv93wVRfCAAAkw0IAAAAQJECSWAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAACgSAEBAAAAKFJAAAAAAIoUEAAAAIAiBQQAAAAAFSf/H1Pc/tER3NpkAAAAAElFTkSuQmCC"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "With the hook released and sword rotated, the clear axis is toward the FRONT camera; the other depth direction meets the rack frame.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 6. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The forward direction indicator projects into the open space in both SIDE and TOP views after the clearance edits. Extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-a5feed00e148", "created_at_unix_s": 1788846046.042689, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 7, "requested_group_seed": 7, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [7], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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hjt1FjLHgwx/92Hkr5hw/1glME5hmy/vPj/iW5GyAkRH20x3kX7/6JeIWh/Ce21a+/sIji5aeyUPetkzT1mxsmqZy4sjOF55a9dK+6rSLODV0gGWaiEOWaVommQE4mV9E+8XjcUXVMORsgHRNQza2dSMeicZjUdPSdEOxIRzvc2dMfT78UZLNQyE6wKlg9ewjP33h8V8n10dj8d72nuRjfE50UsFMQJn5x0/cunA292R0TjTS3x/qMC0bIA4D3Nt16JVVL6iXrLz5VA0A0N8TAlMFy7I4iEwLQp74AEzTJHWBHE2gqQr5BxPtCDkSKWXquiBAhDiPN/DGX3453ufOqAjpv2JB09aWQ3SNqpIfIEjxP0VjCStQEqoD1j3/hwuuu2csz3YSwWYAubjtguot77654szLREnGlgFsyzD1nu7jr738yo+faJtK0j8xA4BQtSBwnMCaGoOI5AKT1zDWdS0eDZmGziEoyy5eICWDSOAQKwbHGH0wBtQymbhXbbznYKuzCE1Dzyj9GfnAFMAguw/QW2oI//3Ne1bOi89asCxY00wKJJhEB4RCnTtbtv/6xT4whYDYQhCYNhnmO2GgPOSQIPG8IIiS6Ha7ELDdsuzz+Thge71u3okCEgTiEGYwRvfmdGaiyacIDbE6Muk/aUxAn/74h1Of/up3D4PJwDXLuK+/+u6yUy/qPHEAAcu0zahl7Nqt/K+5ePkZp6xs6njrxT+ddw0pNTEiW9aSeLU8WXHhXWCswLbFc9Agvy4yLJBEKR6PcZzk8sjYxpZpmqbBy4Kuq8DGhq6RMkEQJKYIDMao3pwYcCmxBszpNCkVQJr0n13vAxOM3QdaF81rzvjSd790230/fPSM8687cXA7Z9mmbZoAH9q3E3IIwFNWNrbleYjNv7qzkDMaO/FqWxYPoKIZHHCRokmSJIkiz/M8x6u6YlmmKEm2bRuayvECME3Ic0T+T04P8Oe++gDnRLvyjsNDdvu/96+fH++TYmQFA3zqgsw/TPL1ibyqk2+TMXEVQEbp/8B9n/na9385KXRAfzh236ev//ZPnrzoig8e3P02BFi3bcsyD+7aGu0PWRdfDiY5BpHpwMYYOTLR4/MjBEk/ANPiSH4AcElSOBzCEFjYwo7xlRfEF343QvfgCcU/3PfvA4tD5IUaD3/5G9+jyz/6zr+O/YkxcpNxlHGsrXtGUy27dJM7CmgC6oA0+sODbW8/9sErfvPwQ1fd/NE929Yj27RJNEK0/fi+tS8p3/jJS9/5wtVgUkcBiQiTov9k2qHGY9FwyDQ0jLEoShzHO/V/ugCEmKgFm+OF2vrBpJvJIPTzIqkJmDKYOMChRv+YopI0dZd4rK0bADCjqZZNAia0Asht/JkgOoCMaodOAlJFf5IXf/9NAMDPRHHvto26qmAbq5bd2XH4rdde+IfvPvK/Xy/IwjOBsJyekAglrPqmaahKDGAMIdRUxecPIkSUgBqPIZ5HHBesqafu4kkq9He17CL5zLoBADj9jFNzKwNmI5o4UOmfCpsKTGgFMPFN/5RUYbZwdkNG6Z/kc9c0/kg//diBnWo0BLBhGri37/i76175xNd/9dvvfhpMQkJRDRpYEHgkWade/mFD003NtLEtCgKAQNcM0wMB4nnJLUqyrimWGo9pCph4Zv185H4SDIh1a/O779On2TQBsxGNLysWNGWT/pRjbd11NVVje1JTgfE3AU20SQAlt/SnfPmmGf/xaLy780SkrwtiA1s4Fgtt27j6tk9+e/bCxf/1lQ+AScItX/x2T0drtK/XMgwSaweBaYQRkhDHiTwx/kDSM1JUYiFRlGXZHY9Hyduw3dQ8c8Ka9XMJfYyvrUXQ0fgYSJaNTRu/2GWkaoJ8pgXMRjSWDBf9Xb3humr/mJ7ElIOfOMP/CaIDCuJfPrjwts+9uPCUM/o6jgHTAJapAXx0/05NN779hzX33zOkatCEpbP1WMehQzwN64SQhtzxglDT2AwA1pwyW4IkQwi72o43Ns0STVNViYKMx8c5++ZT//iNvEf64NoaIvFJMnPKehLIxJHc51uaOItkO+NV7SSxCMK8lMGXvv7d//7u18vyWRg5yDbwZ0xoBTBZjD9D8k0KjDJ+8mf/eOunf3DyOZd1Ht1HLOa6jnnccWTP68+GvvXgG9+69xIwJtgb21OfonMa83+vTkr9EC8AQhyE0LZMhPhYOKwqMUGQTNuAEIhuN/EP2xZAGCHAQaDbVm9/Lxhv6a+qmixLGYV+crA/YqyqoxkATzaDt0yTHUUAdMt+sZN8dhK8m0kZ0NSkCtQBV932GYxB157VHCIlQ0SBxxibNsTONXGLUDWAzGNESogDVSdFxiWRJG8ZFlZNICCsGOSOwwBw5E2k5BSAHMZgzoorIQQvPznJBoKTl4mVxfPAfZ8Z3xMorrD9U7/66ta1LzXPWwZIvCQkDgFLi/e2vvncE9/6+V/BmBDWhjwKwpF35KNzHEnrFUSZyH2DNHy3LJPnBbcnIMueeCwiSS7bMnlRklweTM3nE2Dsv6tlF30kP85V1fDaWnRdLbq+jis0UwECQKqiIugSuFunyTc1STc0StfUExmHyQ2CN7/7Pn0k3/Klr38XVAbVM045e8XSzpbVXbtX03kSHTSllQTnELAx0E1yuSCZVpJtdFJTFvCpIgdj26Y1ppxtADj43isH3n1l0Unz55966Zh/uEoETbTh/7jrgOL46+++8eLDP5w1exnieYCQSQIlcaS/Z/1rr3zlB4MtBEYPkx/yKAgIgdvrgwgpSkTXFNPUifQH0LZIZJBhqBAiXVcNXdPUeKirIxbqtW2Litrzb/4oGFfp393V6fN5AQBXV8Nra9C1jtDny5efhiDkEZR57pZm+cZG6foG8dp6HgOMENry/rZK0wGYJIzj4ZOntHgw+iSldkPizWmhFmSFI/rTN7RJd75Cz62rp7/QtzBGSwFMOuNPWXj2kR/MmXuKJLsxQhYJrDf7+3pXv/TCrNNuHPVju4c+CsHnc9c11nkDQZfkkUQXwlBXVEn2QEzM4y53gJSGhqT8g2lhTdNtCxOrkW1DiEio6LhKf9LJQIpfV4vIUJ86dkcHCAGPoMAh0VEGVD5Vmg6A2MROvRDbuSCJOiAWGe9AbHDA5CAxEgJLA8Aighw7nSVIdzkyGwDYBLYOEeahhYAFgUVmCpZNZl3OrmzqyiedhognhlEpUUDj6A2+5pY7af1LDEa+5z5+O/Hr9vc7YTCZeOqP/37dXV/p6W5T4jEb2tC2FCXa09HasPjyjl2vgVEDBYp/7+uP/AoAEGiYVVvltmx0eN/ObFuece2d8WhUcrnI5N00LGiQshDjKv0vksbaEU11zE2NwjPtJHBoy/vbVpx6Cn1p6vsDho3Lid7F5K/tWHuImWykDnHU3ki0NeknSpaGpnkNblMoSxbMyLh+x/onaQm5JefeWsRupzD8jt2vOg1gh6w9ZckVI77zRz//QeJ+cL6pKy897eTFV5Rr+D/BI4KqqojNIY2WDU8ll6+4+zuzF50SDfdjaMsyj4AV6WuvO+lil8cPwF8npiZ3S8L0ak9bfy4t+O4Lj6Stufyuz1eU9E8CAbipQXimo/J0wAC0OBuddEFALP4BL6fqg6IkMSFLmoeo23fgJYFHkbgpckT4O14AMglI6g4I4TW3faTs59yy4SmmA1LhFY0Yc4eAwaYtL9HFM1ckahv8zy9/mNTJjtAfkPwD3+grr7/3Xz/7wZWXnvbTH/5hEhl/rrl1VBJ3X/3TNy6581unnn5Gd3cPhtAly+FIVIn0QJvkneZPqlLJzfxi30hZcu6tPId8LrEzXNgZvvbQT0HlSX8KhM48oI1ECk0BHUCHybm56IPfIJKaToMcj66FyALCxDjGI+T4eIlb13Htkg1pZRGnmgMigbaklgjgSCNRSP9ijJ2ygoCEAg3sOeEUZowy/Fsbd2Ran5Dr//PLH6atSUp+um5gNpfw+rzy+uZyndkYTAL+/vNfIK2+MO7sj+vGMEVYGm888q2L7vjGxSvPP9ra7XLJoXDUtixNyWo7KhFSwLMEWjY8df1H7uMgFiVXocqjIIobf5Vd+nfMO3XWzMzVJQWej7z6TAHzgMaxmwfkI6NHHQtTg6ltAUyifZxYMAgEjtd0YFsIIcdPbAPiOILAxo4yIO8jG1s24AC0MdQNIJBKuokYNGRBbBOjENm5TQqQjNLps0nAiJaD4dY38g0F/K5QWEnbiH5Pto0hIt/x3s3byzj8H1Ud8PMff4/6qDZv29/oFzp6y59p8uZj31n5ga9dcfml8XgcdXXbTmlNMDokIuFLgEbjIY78AEePUdUuqbzym6wH6piXOasrie+KmwrQAXCIP+AnX7+Drr/32gkgrEcBEqJD4vcTblvSNY4kD2Li5x1IouGQk3vhTA4ElIjoIbYeMicgPaRhIswnYfkZ3AARczTZV5Eh2ek8//q7yeXqgOf8M5cwHZBbAQxe96Qbhi6Eworz7+Dwf2iDHlxe6T+q/Pon39+0bR9ZcqSeM5MhLqwRefbVdwo60D996hbiNNaXtbV1qppSH8ysAJyQ+pKGPOUaLw0LyZusXPnJzFONP7+eaCqbG98VN509s4AYuZGdZlMFXuAwmS6T+8QJ8LExcrK5SDiP4wRG2LZJiBjARDdQyz/GzgwA2ER/kHqy5C0kUwzbZDJB2007myIEOR5aGIh5BGXkEPcZWb+phS6wecBIvsMURUBCROjwf6hKGANGaRKQkP4E8kkMM1/7D00GSipCeh2cW3lAAjtGMUQsm+SJc1fDixbyBw7MmhXsONhK2gg7OyA3u006nQLdIr8El1CSvtHvOm/EN954xVk59mBYlm1jKy0egFHxpEpVDJDMQwEiZ/JM2vSSttEJAzCZFpAtOESje2yL/goS8f5EnUKOKgNiPnJesp3SI3QcBkkqIvkXIRiDxcxEE37KxKiOMHBqCZ+CM86DqZqgwuFzSX26MGD8Geb3HfLG0Rv+j4YO+M1vHvzkJ++lNyYJTiaF/QfJod1oqDnMNeYeCHmgltHEpBh+8DJ44LD8zqYD8cWOxRNi3cKKAQwLxA1c7YYuIcMOLYvUZx5QOYkbfEDfkF07LxLyKcuZW524JdGybRlqhc5yCtU0jLIz4si3TJBbLq5bKknxJbcuh0jQJwdtEriPoW5BYvBxPMAOZKJg2cRJ4ESIQhsPTKow1i3Ak7IitkkaDEGIOAsD3iknIXAQy8XMaROTiMRvL2X9kA3IJp/8YtYqUhWrADIaf1KFYQbjD30+2safUdQBzjjcGc6U35zSvfUhNdI9/YJ/3H/kfVHmW/ZG7vvix0+89e+0EKVqAs1MlBeo9WQ+ZqIP6qB5KMNdTf9Jq/6QHOkMcd4nFqkKAcPWOEM0AE3TcubjiUbcTpz3gItgYE6T/JllpHQVMvU0zVjJaEpyyJD4l/6QB75NYm8cCOBIrEkIR2d+m7xzkndQchxNRzbU8StwJApIt7EscgKCmgFFbnAzSQCWBXUTAlLjh6wVeZLnpRsJixDPkSOIPKcZlmaSvRkW2YDMIVICRgtn5J8vk/5ZTUCiwOuGOdz4M6gSwNSB6IBPfJw0QSx3yIFgHG3ftU6NdANLb13zPT/EPIc/eCMZdOhm4idEZx02BrIAvWKpJ5AWvDkYRJE6IRlYQ5fT1phOQiaHIG2TO+TN9A+dySfT/4eGg6XtPnVNMn54oOoLHLqGuBDpczrdofqFWtISR4Fl0zQ7d3UnXMFtmds4ezyJROru/YVFxObg2bUHampq+vr7rz9v9tBX8F9ebamurrnytIZUZU1FZEI000s0YDiksjubjE7H2TrRUYtuNDCDTb0LBme0yfltyv0xOI11TlA3MQJQ4oFukfBNiUciT4oI6mbCPkBHBwO7SPEbkvuenn7iGb3fBB45H5KskRDUTGyMphnyk//Axv4ZFEDi3tMNM9X3G/CT8MJsvl/6fGx8v6PkDPjNb3/30Y9+1BlylA0faj+yZ4MS6QKWjoDJO9Jf5ID57ne9EmzyQ2L6x9Ai1n8yEa6SoUhK7peE1VXqaTs/Osxx2d1Cw89xUEakv5a6JiURKNuaFDE2IDoS8eApMpEOUQdXJf5N3J+DsmuYxcwxQRBhRGc5dIu0XNOU+GYaETCghRI+TDJKoAspw2eyQ7pMhfUTr+6yTJ1oUzVumboTzoIlf/CGD9yxdfPmSF8vwLMSH3fARB2PRhYtXrJ6S+uxg4cEyeX2Vd24cm7KdUleqVR9PWBIyTQxzPYd5bfRSLuwyUzRtGwL2hBbGHOYRO1wTkg/CQ+yTVJTFjiBns4ltOj82plScmRTEudD3kNvArIxaTtkO7FEtmUj4hbgII01KjtM+qdBf+2Jay0KvKYPGf7T4J/svt8xjfwZJR3wxz/+kS7ccvsHR9x43ZbDxDLj1D84d9m0jNsc3bFWCbUDS0PAItIfYQEBiYceCdR54eIGST5LXvdEGANg2kQNyKTjVqnYJRfCcoL5iKEWjBfZFUxuJZLukslsMUv1CCYE0pDXBu3GzkvUNjfwtqTvx3lX4p1/fW2fZZrAtnmnj6bA89UuSeC9HMcJyLYsUzf09/ftu/Ds8w8eOBSLx4MzwZttu/u6YpYq29DgRFONKJbF9/X1LVx6cm93p2QbZqTrmee6DcCRwGrEIVG+7bKFYMJACj2Q6s7YtDBHxD4J3h/y8nC5ncmeg20Mics4uY3jSraBs3NIwsqL+k2s33bUUSrp4VvVfs/vfjNxKwuMI3w248/g8D+77zdN+lcaG3acSH36zGOk6ud9d54U72vDlo6wxUGbSH/Okf4iqPWgBXWcfKoEVtResOJjG+7/X554zHLd6ffd//3vf/u+fE7GJuFFJYEgGYo5kdpTm/wkS7JC8TCeeKlFsG23x81jm3ROVhVNU3Q9ruqqaZJcV0HgOYh4XuA4TpTdBw4cjMcikb6+mNaLeOT2yS63jHioqYYa07Qwxwm8bZmixwejkZoan3Nk0cZYFEXEodVvHVJU3bQBL8vXXOBMIMYVhAAixkJivUldTydpaVeXuo2GlwtNTPlohtjAexCElnMf0nvwmScfTW5+6te/XMo590fGOWl8wsJTwU6l/3Djj5P5ldX4k/Z8DAL/J0KNIA4hWRQM0yKVzIdqxqvnm1veIeFlC2ZxKdIfeCQi/efXcd5TJHBmDQCXA/CNc7/9jQ33148oje67//v5nBV+8J4i3pXKgoVLE79HRiaefn2fHgm7RAGZRleot213B4lXEQWB/C/yguBxeXhBwhiH+rrC0Yht20gQRUkK9XRNnz3vH+65Jtt1/dGDz3a2HvP6/V1tJ2ReIG/koGXZECFJFL0ej9cT8HkCLrdry47+SDQa1YxrVs4p77f01W8+kM9mVc2LyWidh5ZFQnycsTyJ7Xeyt9IG9QN5YU462FCL3mBQpm1b2DESWdQISfLL6KblzMxPi/JgJOFzhoCmhAOBdMYr7Wt8dQDHofpq79mnzO8JxfrC8UhMjSm6qpuWZV89fzDJa9+RxO177mLgFmGNG82t5QJLJXBeNQArAUjUDT73228CcN+G+98q/cRwuK/EPSDoFGbR9FVrdq3/W755sEWQ55xm4vDHpza5eU7kUTzWf/Bwu2EasxYs/eZdI+d+PfT85n+cT8tqHQB7Xsm22fcuSCx8c8NyYENBFF0e/8duORsA8OAT6+LRcFtn2xH9kGlaiBMa6hrnTJ/11sZj3eHo+rVj1G5oENIOgIzRsVPUgQp3IseTlqCEI9gZ+ZNSQAMGIOQ8aFnowZiygWZEJEeMpBVQOeNEGZW5NAsjqwJIDP9B+vC/P/vwf+/mwTLoU48co4WGap/fKy+e22DZ2DAsTTdiih6Ja4/915cybr+BdKnCX7uBq14sgouqADgPgP8ceLEdgG8AcODcle4Na0qdouJoqU4AhJDAcx4Zlb1c0YN3DY3q2DvYVD2N1Z5/pgsCz52itJZ4XHX29BL38PBT78iShHXlUGubYWizTlr2tTsvy7axdOhY2pp7l9QXlM3aemBwUPXV917PuE3zzDlvvbvW7w3MnDbztps+2tUXevPNsVYDTicASCs28ENafA0w4OJORnxRM1Dyt5V09tNniHNSBhwrkBMjlwheKvjE6NmlxJUxcsOLAp8W+E+lv/MkuXL8jT8TZBLg85CiOxCQLlG8hGRJCPjcIyZGP7BKB6v0H116FQA/GVinAfCPAOwFq6PmMXNGHX+sq6QyQTgWBiUCoa5bECGnrMt4IgzGoZaEfPj48JVdfVZd0DPie99YvdaFud7ejkgs8m83XgjAgMllmJQfY/ZuWQMAiIfaNUPdvX/XrGmzrrvmzu5Q2FUVXP3CqPeeS4RJOQ/eER2Ov9wJBXLKfDqk+QaG7mJoCzCL1I1LuHw5DllOZkzKoQoj6He7ZDHgde0/1k36DA8g8hOx8clEgFyXtMB/Sn8oMfyfOMafMdMBOezgGVvf5Vm69stXPvmjV0jrFYdPALALrI3YR4yOiK0YmW/3Ky4889W1m/LZOY5H8tks9z5M05RlWqF9ynLumUs7OnujMaWnP9rV2X33R0jJIPWNF7a5G+kGcUV/f8M7UDN2Hdv7nVsuAROVvs4jJP9j5tw1G1Y31DU38/yFK29eu2a0ZwMcMdNz0DSQMBD2YxN7P8SYyP/Pnr4nzx39z6aTSBlpMo8gbyRtNgE0MYkBJZVEQTGNhs5aNsslC831wWDAffBYd19E0Q1TFoVlC5oeK2J3FUDa8D9h/KHP0v4tu/Hn+bU7rrtwGZhUDGbYDuX+nzzy7S+M3Frgy1fW/uiVbgA+DsAO8FYEHDLbHelv2SDoQ32R9KF3JJKvWMfxUi03GGPTMiTRRcq1TFFejMjLOc7lkiCE/X3EaxKPKW6PS77k2rMA+MMfHo33RUQEDx05dP93vwk3ZjbCTCh2b3kTABDtb+0N9Xhcnquuvl0V0JpVoyjuEIKWhXnipib1E21s8wORY0UPHIgb2JkWkNgJUlzOUSqFUxsknZo8snjWslkLZtQdbe87cLx7ZmNw+cLMEdsMlBr4n5T+zvDfWTXSFSpx+P/82ozdCMDEbR9fsnD88pW1AGwB74TAQaMtbMV1JyUYA5OURknfeOOW3fmeVzyS+iju3Czb5ogJaPw1QOkOgDS65p9qn33p8qXziLFeFAJ+j6pqN1xPDPrxmEIfWn84FOo71tn26S98uqO9E599KZg89HUeOn5kx/q3N/Ix/dyLrxulo5DxvpOiNRDGQ4LiBl8uRGinjqMgrSc6sDLRQrgEBI6rq/atWDT98rMXnrpohlsSS9rd1CX55aXnRab9m3H4X4r0T4r+UnRAy/anwdhSlj4VX77yKNhntIfsmAGo9LcsrGh2OG639xTrCYhHhzwKJ5HiRBxo9hRwAKTSNZ80AHjjzYQxze2So5GIlCIUfvfgn3/zy1/uP7y/afbMj33ibrpyMvakivYd37Rlc09b5/nnZY06LQ1atYH4aeljUEQUfrnSEruJdqGVQemBSgMCwCNUE/DIInMAjKQAUo0//aFERAoeNem/tqXdX1MLSuaZh18Go8OaTXszv5D9Lr//J+n9cnPw5T8qMZ2kUzpV1B3pH7MjMVvVi7zvcTya+ihmDwCZNlJMWqp96kClPy1jRxrFvPzmW+vfgxBecEGikNz/Pfz79tbDn/zMZ/7tu99KfWN7eyc4ZzJNAijh3mN792xq2bfnmitvK/vOaYXAgXYuaS8VftsMfQutV+dkAWBEyyAyxqYURIrpP8FYyoBSnAEt259ecjJpujK+PoAi+NmL+qeuECwbq87YPxq3FR1rBt5+UD95bsEzVlx6s0nHkutyS33RcVYAsXDfhlJ7XCaYv3iR8y/euuPAkoUzAQbHjhyYMb3+8IFwd9eh4yc6jh9rq65p/tdvfpNu/4Hbr3/8iecuXnlucg8bjJLO5EowPvR1HXxzk7FsxfneQPXGNatG4xDDYwUKKuCTZnOggaF07knyxiaAHbLinMCpw//RM/6sbWmnC/6a2nBPd4k6YPQmAVnIJf1f2s+npoONyK9fNT58AQrH7UjcjmtYM0itUK0oIxC0S02cIUF4lh2NKoY5ul0hx8z+k5T+A03ryLd376c/1tVBKuedtGiB4w/IpWvb2zvPvfDcDWs30KfvbNnVdvyIpmleX7Cmptbr8SxdNP61GbIR6zu2o+/YjPmLz1l5ycY1b5Rln6SBu2P0p6OgtFF6Qd6j1I1JvjAN/6cV4koL5G/t6m+uqyr67RUFPyTwf4C0az8a0n+4Diiask8CctTnGXH0X6gOeHidffFCW9GwTqQ/1k2gW0XNMHCpCkB319ZOr7EEXjlecmXRCYCvuiZ5L29vObhowQy6PlX6p0GdH8MnAaededp7m94jUYYrFoMVi0fvnKctOKMqULXz3dfKuM9j+3f194cuueYK0eV9+amEz+yci29xudxvvPhwUbt0FKpTBMK550jzR7vwMoI0B9iySB0JYlVKlLt2woFSU4sZowmfKv3p8D9z8P/oU8okYIwNQeXVAQNjfyL9NQvqFnp1O7zi5MK+AxI4VyJI4L1VEALBPUUqZ4VC4UAg38FKNtd3e3tnY2M9GBNO7Ht3SH3BElh05pWtRw9EI2HJ7fJWB3rC6olt++YsWdHUXAMMoMbDG1cXE0CBAfCIpIY5bexOUoI5gRZ3MCwSRZb/rkh5aNI7HvCAAxhbpqNFnJ40HAKxSeiBn4xk8o9nM/44Cjr38N+wsZA9jWhtS7vTLHfI9K4shqDykuvey129Mw++//u/FLD1Q3fluSFpr1QasUh/qMdbFfCZulrirko6jZKLGqUM/0EoRCJik8N/SvP0ZrqgqZplmm53ogPMVGL3pkTpISUOlG7Q5sQ09JwAh0prhYsQIlECThRQotkCsT1ihEj8fkFmG5I+jCEpAeRkAWDaLdL5fVmkvwJzAo+tAhgc/mcz/pQs/UWBkyVe1UzdGGKvKF0HjN0kAJY6Cdj8q5HzxVLI92cgZSzJkjfn3HTvgT07NEWBVQFeEK+46/OvPvRTMCU4eqIrzf5DRX9yg3g8jjF2uwenwtdfd/lzz7+WagVK9QRUMrZNAoURgmTQbpMUFhsAPtG4tLDoCOiIeOedyHT6BpNaok4/GNsmnYZH6zMwcs0Aih1H5pb+Tsk5ribgrvLJ3f3x3lDcGhowUBZnwITipf3k2hZkCyqFuqpAKV9jtL8H8RwnCJH+XtNSWw/tA5McWSZVmzIO/23b1nQ9zf0bjytut0tR1PG1Ak1wIu07Q9VLBIG3dZM28MWYo81NnZDmAtAt2oOapqAPxKIghDkEOenIzvJ4rRl5KYDE8L9Y489LG3ZfdnauvkXrd3fUVrlnNAT8XonjkKKRIprZJozjPgnI08xjOTmRTuUcWC5/QNHgZ5NVhopB1RReEHiBhxwHOAWOUxR2JBJdZpbc2wyAY6Am2/A/HleobkjqgKTlIhQKiwNaIeMkIPTGiwWfypQbyB7tBfMaSNY6bY1mkmRgwHHAskYsijgEgQ7+ETBMsjdSKxoDjIAJgMUHR+/8GakM/s6LNv5Q6Z/b+OOS+KYaX0O1t8orNwQ9NQF3SufxBKmpYUWnB5clNzgf6W9jvPNA+8Zth1sOtO853HGkrbenP5ZjKjDB8fr8wLaiof7ejrbuo8dNZdBCMpYsM8oQgHSsntR7yDj89/jIbayqmppiAiLzV4cc+yRJYQyHSHuLU7eZTADoj540BijKNUbfQm0BEBJ/ANXHR1pIgSPGGMAPxv4XZfwZcey/+l1SHTAcB+/1dL+3NeWFUQg0eubhl2/68FVgTAjH1Le3Hw5FFRq6LvCcWxbHfR5QNFtefbxpwamtXe2ItPywj+8iUY+TFzrGz2j9p+1HIIJUB9jZbc3DJwEMyqF25aR6YaDbo+XMBUh6ACa1nfOFGPwR3YNTAAJSbYINxEL4xw5+ZN9vzsD/y84i0j+39Z+iyXnUflDbkotFRwSVbgga0QSEAT7R2R+OqpZlW874R9WMSEydOC6BImhubDq6t8uwrGB1XbhjfM5hds0QT0ZxvNfVQ5oXVvkWDx3+W5gYeUjfG2Lr4iGEkXDE7XYlvQV0cpBtt8QTcMk1wW2FtW8rh0FrwgERTyYBXEJ8E+8v7ftYSCwyGfFDYkhCiHSUJDoZOe9HU85qNglMQCkaYO+7+Up/1SBFW3NI/5d39OiuWlTV5G+ePbOp6qRZtTOaqmSJzyZf+WBT6tNS6sSNKqpmbtvbmtpxIk8msjlo89oX6mrrRIE/sndyt3u7tZrczX3hQX3c6Qz/dd0IR2LdPSFFISYgVdVM04rHlWg0Ho8rCfv1UDP2RReevXoNC/5JJ9S2KxGl6cyliAIoJnGXVAKi6V+0cRjJLYDgxN63i/jSGeUpBVFG48/esLhgzvTmOv/V5y8MzFkInOou7776uGlanb2x/Ue7O3tjJhk+Dz18XV24q6tEK9AohYQe63S6bmHQE4p19pbegGXC0bLz/QVLTu1sG7emV1v8s2e251sBOyeutOF/d1/EsqxoTCHCBgKvx2WaZDbG87wVjrndsqqR5o1+nxeR7CTaoiqTJ+CU83AhfQL8YGqyr91Y2MRbeiI5nojvoguo0ubANuAE0Kdlbtb2re/+6Ftf/3KpJ80YBpnKpUKG/3Dk4f9LG3ZfOpLxp6P1uNctVsvau1t0/769yShhjkNVfldDjS+m6OGY5mSQDw0JTdEB42IIGsn+A3bsb0vpozCl2NfyPpj8YKddVerwPxyJWaZhWTZCUFGQYZiWM4Gj/WNjcUUQBAhwNBoLBEhfQZ4nUemTsiT02AA5nuMVYPA2ByF0rhZtEl/ALpz/OWyTwH8TAhHxnUcm9+xz0oGKMP68tGH3hactyP3TWLu7U+BRTZW7sdbndYmpOSIQQlnk66o9tUGPWxacKl3p+/LX1RVtCBrt8nC6YR5u7ZkIXVOmHiVeVMsm2aW2jdOG/3sOHDd0nXprbBtrmhaJRBRVU1QtrqiKqoVC4f7+UDgS6++PtrZ2nGjtiMZIN0GM8UUXnJVqBWpv74STsEZ02Qm1797ZarokYkLgBYg4RAv6548z8oOIgxxH5lsukWuLl6cELCN/hmnsEYudbdh95rI5ohPEm234v3Z3J8+hKp+rsdZHA//Tj4qgzy011fnrq70uSRilYVZ528Uk7D8AxBS9r38K2n8mAn394QO1C5JynJLoQU6hy8MEDca4N6a1heLtEWWNGUgb/mPbSlXYlkV2bVum8yB+fGxjw7A0zYjFlUg0HgpHjx1vO3a8jaqBIj/MVB8hhNp27TqhkQ6RJvmohXvEnCoS5C+pMHo8xPUcTQ0TLJi6GhY+VDAow/AfZx3+v7Rh97IF070uMbfxh9hY3eK0Bn9d0JNIEx8Gz6OagHt6Q6C6yuXkBIzKJKDl4KugrL9cUjA5rrHh/yix/9CxY8fb28LxjojSHVN7YlqfoodUPaIZMc0IJ5d1Q9FN1bB00zIs27Tsrqi6pzN0sDdysCecNvzfvY80T8/9DROdQirSYMuyDcOMx9VIJB4Ox44fb9+379Dpp52S5gqG51xqWLZuWvQcdNOi5xPVjLhuKroZUQ3nPKemnTBJpHMfqeMDnQqeBVYjdK65U0EU4qP9VqhtVylnwqR/cfBDpD/Mlfb10gbinavxu1KbgA5n7e5OUeDqqr1NtT63LGQrEAIhlCS+psqt6mYsrvdHVNprtMzOgFiRbVKy6QBFMxQtV8YQoxTicfWE1snZEVJwBgLOKTuDSNlIyEGoOzYcjj511nAIOZnYoC0cD6nke5HnLUkb/hc6MsekQ6dt2bZlWZqmCQIv7ts+vDLEsf6YYdnAqY9PNnaK6BoWaaoMSZ0DG0FS176pDI3vJjTvt+w+8+STnChQDEiZuHzBNnkLD2wN2rHuQ8Udvas3XFc9VR3tY0G+UYlU+l9w6nyeH8H4gxD0e6XGGq/XLaGcugJBKIp8bdATiWmqZsbJDzjXj7MgHZBMCms5+OqSuVeAQkj3Sg+AMe6PKGz4P3pg27QAH0uxulBHIW3mQq78wLITMkhiEekIQyPVZAhpw/9dew7CvOPKMbZphTLnCbBsE0NYFe05BEEDAKvXbEhNCpt33U2v/vHPTkgRsJyRrFMah0x/B8a2zplOdQUAANi0fe/5py3iUGE+AOiUfyDf0b4ipX8SNvwvSQEMMf5kr/pwxtLZksiNaPzxuISGGl/Q76Kdg3KDIPTIYkONNxzTdNMyaYv0LJOAoilUB2S7jVXNVEse/t/7EKtzOwKdQrDOSBSFTulOQcvODywPvMA5A226evjw3x/vD7urUnQAzlMHYKfBjifSM5hNIKRPAowhPdGHH2GqOwFSWP/e7vNWLCrIDSBwpLj0uzvGqOzguuf/cMF194DJzBe//gMAwI+/+9Uy7hMNifzJbvxZtmC6zz1Co9q1uztlkW+o8TXV+mRJyLM4CMdB4i6u8XpIsFCGDYp2BpQSDjR8mI8xiCmak7jAmEAIHJcM1ly8YGbqSyfeJwUt/PF+7Hgb8y9XTKW/L9qbtp4lheXgrS27SY5n3kgS917LpC86OwZ88es/SIr+8kr/oVFAWX4dL23YfdKcptqAm3ZVzW786UAIVvld0+oDAa+caMGaB8QZIPJ1QU9d0CMK/IhxSMWlB+fvDU5K/jSfhGXZ4dj4lEhjZAMCIPMc/aYGhv8kuDDN+k91gG2SVK8RsUl2Uwbp74+HUp+2t3de+7G72VeTypp3C/htvr21LBl/U5YvjqbcH6YAHKk7fPj/0obds6bVN1Z7Saxu7h0h5HNLjTW+6oCr0NbeCEGvR2qo8Vb5ZOdAuSKCCoJMAgb8wHnrgETwIQ0bp2i6GYmrxoChmTF6YLuAyBnkFA+wHaWdNvxPBQJQoxLxjS0z+Ug7aPKRUfozJix4WPDIZOfuz9432nI/yaDLK6P0BwA01niToZw5fL8uWWis89XXeEWBK6IyLM+har+7vsbrlkUj2mtE+9IeqRsXXyw6Px3ghJ5jI8WiGVP0CElanmq32oSlS8irIjxHOhSSb2Wo9b8zaf+hkMazCPmVMH3QlQlNMCD0/fFQ8pHtcP54KC0pjE0Cxp2YmtfEbrJw92fv+9PPvz9mhxvBZrdi0UwPCeXMJf1f30T0RH8E9He078iYyF2OPC+Xi1cUs4iIIBIO9Knb8jzKQOIR+U/VTFrh2cb4WEefqo/s/r3pjo8889if8zwWo3R4BE2nEXmO4T/1xmop6jypAxiTHYzBkdbeOTMa0ta3dvU3102+vLC7x1b6D84AZjekD/9f23Rg6fzpQZ9cappu3m83xOoMD2HwwfvL0JZvxEnAgPwHcVWnQ37bsnXdHJ6mwBh3K5Bh2Zph5R7+05Yj5fr22CRgQmHbdl/YaWcyVSw/YwyfXfo3B71S0pebbfj/8rZOJNfUBtxNpOaP4JYFWeRlSZAEThI4gecEHpHUGKdeLMaYphEgkuZDSRmj6WZrd+RoR7itO9LeG9V0J8GcbjDw6+X99Wa4s+BJwK+fTJ0EZIsKpcP/RIShU+LftGyB5zp6o1FlSk0zJwVdKcGg2TBtjCDMPfwfbWRZHsejVziWhadGYN7djvQf4+F/LhPQ9DrfiEaPV7Z1+t3SqSc1Lp1b73OLRMwP0NHZjQ2gG6AgwenhweJpnsXTSElY3bBauyP7j/f2hpUho+9gdbivdzR0ABX+ySPFVd20bJ7jcnd6YZQX5yaiFebtRLGYHBsjKMxdlj7837xpVL8Uf7R39er1F198Pn16+PDRW//+U0/97BejelBGRkwnZ3tqXJw/jbn0J2PxjMP/S89akBrJk23475dwcxUfdAElGu7q6uno7E4+Sj8zUkw04K6v9pCKg0OP7w9WF7HDZ3795AhbJNJ6Ev8omqkbJPOrP6IUcThGESCEOI4TRcHn9WQuyT8UbryH/0lu/dzfj/cpVCKkwPfkt83ePeam/ySZs1JdIpkZ+NxyDum/YXd7tU9uqvX53EOqPZcLCKFb4huCnmq/SxhWUS6pAwpLDUvRAcOdAQglEkodsGlZccXoCcXycf8yyoLH7fL7PFV+Ukdk3rw50eBMjhdFURIEkRdEjhcQL0COPADHA45vOvvCtD0cf/99jPgSHyBlIfHgEgvQOa5fia5evT550MOHj1IdQM8teZ68MELuJKNEDGPSR2bfPX7SP4MJ6LVNB64456QRBfq6na1+jzytzl9X5c5W77N0OA4FvHJDtTeqGKFohmpxxdQISrEFpRmCaDh5EtOyo4rm1P4s4TMwCmHtS3+5+c5PW5YJIdSV6LlnnqzpixtO7LRsbNq2aRODr2ljy1k25p9cU+VNvrelZbd6dHjhzyFAANwi6QZMy8yRO4r0NEU2xhLP2RhbNpZ4Utrewpi0qoBkAUEokHhTG0IgchypVg2wi+fSDEGzZ89c0lhFNubIxqREXXA2UIqpR8vIB4xJc45Jfa3uHg/Hby4FcN7yOeJIxp+1O1slgaurcjdUe92kmv9odU3681/X5bllWXTAgKBP1IKzbRyOqfGE+zdbgTjGyJz+6UfyvUwP3YWxTWoIOnXf9u8/sHTJovjCM+oOvocxR4us0b/7aualSX/nSxrhOxI41OSTvZIgOEVELYyJJnAaQFI1YGMsOL1NbIx5UsyKpBnRgqO2owkQhKQAMklBgM1+8EyKDgAALLz59sjfnqMhD/uCc70QAmY+HDUwxuokL7jd2tr22l//OFEUwGubDlx93qKkQFfVzJUPBJ6r9rud6j20n1cxzJqeHro7nH/59M17jvas33a0ozeWNjwnpy4DM1xknbg0nzAlJc8rIe5jcd0i0w7WF7AkNv/qzry3Rc/8329u+rtPJsv77GzZvXTJou65p9Ufor0qyfodgdnDpb929DAEQOTJHWnjpKpw2s0OfLHQSTms9Ugix9For4HqovQOSB4hURhxyEKGFUOgkwDfZderq1/YFZgDAYiEs+aUMUrHMCxWm71sCuC1TQdWnj6PdkLNwbqWthq/a0a9vzrgGt7qK3+OHO/IZzO/BK4+Y4aiGyc6wye6Ir0RRTcsgEHcItMU3l+X1AGFNgxI6oAsUaGkIoUj/RljTUIHDAhaqgM655w6oAMyQKV/jUeq9cjY6Q1JavTb2MI4qhlh1aBfpGnbfXGtziOLxHA57FaHmZZzbvWh2YG/DDME7QrMAZUq/Qua7ZV4rLiqO9VZJuv47PKbPzqBFMDyhdM8spC8mhmH/2t3tMoSX1/tqa8mv6GxufAQAlng64NeRyLbPWHFtrCbs0qJCi2iXjTt+1HEzhnF64ABUnXA8OG/dvQwcSBL/El1AY/IUxsRnQRYGLeG4jHdNB1dbmMQUY0+RfMQDVCe+3e4Djj11MVvrtkIKpKCZnulHAhjEFcN0ndh1EzQY8D42n8Gv4PXNh2oq/Ik+7dkM/6Q0s1euSFITP+pUf+jfpYIel1CQ7WnPuh1iek9hIuLCh0xKCgN0qGKMYY883+/SX2607HzDJf+1HCHAJgR8ARkUeCQyCGJ51wC5xZ5jygEXGRl8i2aZYUUktg9qid/0cpzRnX/DNu2NcNkjrkSSfwwzl8xRxK43NJ/fUubU+/TG0jJEB4zOA55ZKIDagK0h3AasMjuwSMmBwyQWhyUMS46oLO3b7j014+RyB+PxDf63cPHJAgCt8D7yHglscbGIKabUd0sY3DXh2YHhkeFAgAeefB/y3YMxvAcYFadtywK4LVNB6q8Mp1JZZP+a3e2uiShudbXWO3U+xwPOA75PVJDtYdkHQ/VQP5gcLR1ACsFOr46oK6xId3xiwGV/qRVtUeWsxQhdwlcjUcWUyYBMd3sjWlGWScBy5evSNMBbBIwqpiWNamLQFw+ARwARAG8tunAVectzC39abnmmoCrudbnHVryYYwRBa424Kmr8kji8PTgQR1QKPnPAxjjPg9IYp9ISH8BwQavK9tdySMUdIk+SUy+blh2b1yLaWWbBFCv73B++ae8PaKMAjHMqVMEYhzhLz5jPmmql5O3drVVeeXGamL8efT50fduFfWzjGlWMoKzCG8w0wETmeHDf0WDPXywTif+/1qvXOUSc/WpFvmAS+yNa5Yj8jEAUd3oVTSvxA9PMi+UZMzP3DlzVq9ev/Li82FKRFCJO2dkgxTqmuRBeq+NtweYKADvwC8n2/B/tVPuvzMa6mzvGOyF4aC5CmnUBYFHFj1ukTbbUjRDJUOwsn2FvBuYfe2l6ADGxOQTX/pGDYkBS9Cyk9yQPT3dGAqdQvV0u39mlTd3SA+PkFfiZYGLDeQN6abdG9Nq3FIOzZEPaRGfc+fMWbN6/ZlnnS6KJEri4MEjC0vZOyNHDrBOWwExyhEGmsP4k0HKQxITKfBclci7ZMHrFv0eybTsvrCCMRAETuSRKPCSyIkCrxtmb0gBEIg8JwqcJPKSwIsip6hGb0hBCCZWipwk8ILARWNaf0TlOSSKiY0lkeM5FIqqhmGblt3eEznRGSYp4MO+fT7YmKoDSrsyjIkr/bu6w07jHh1yIph+UpUcyb0TCIFfEgKyoBg0HJTcHBHNiGiGXy4+nm14vD/1+jY2T3e7ZZ7nXLJU3J4ZubFtWzdZCFCZFEAO6f/yzh6/V1o0p375SU21QU/H0F7beZOxNY8MZqQXIiUEeABIOeg0gm4XXVg43dsXqTpwrOdER0jTrXRBX1cb7qK1SOHza3ded+HSok6YMRGlP0VVLX2gPB8HrFkzmnr8C3LkiFFcAl/tlvsVPT5QPky3rIhmGJYtFdjCOrf0J60iW49LngBCSJbEs1g5uFHAsrFhTmIHwPLzrunqTPQ1GV9QDum/dnenX8LTa6Rp1aKuRouV/mUGIUi7zwe8spPOObyDfG1y+fm1O8f8BBmjRcvO3ase/cOrz/yZzv1JZTe3fPzYcYhQ17zTk4+M70UIBt2iTxKStwvGIKToEc3I35BwfPrJyUc26Q8A2Pj6Ki0W0nUjHImV8HEZWSE1ASdzDOjWt14EEwM0QvqVW2ysIQWfuYEcsYmAwKPqgKux1hfwyY4TL9cUnumAKWP8WfXoH+jylnXPOWHBnMftAhBs375z+/ZBTZ9RGUBnEuCVaBU4MOAKNnvjup4zmjBN6FMOHjiYOoIbHu+/8fVVlhYN+DPMZRmlYxjmpI4BnThk7Qi2bk+nx0Wkf02Vu5SaP6MBhNAlCQ01Ppr03x9JrxTtHzQEFX+I3BtYpsHxQimHYBRm+h86UN+y7rnzr/yAIPC2Rao0AwhTdcDJJydMf0kdUHdgMwdhQBZdAhfVnG6jzkCyN67VkNpwUuoXnirrkxw8cDBtjapqsizlyPaaPbPpHfubtmWTqAfd1jT9qT/8mH3vpYJBTDVS63Q98dq22y8/hV3YsimAtbs7JZFrqPE21/vd8igWfC4aDiG/l/xodd1SNFNRjTRnQKoOKNQZkPHz8ggmHIiMArn3IVSy9AerHkuPmVv/yuNO3VDq1SX6gXaTBIDMCdKUAdUEtmHaB46iAzsG40E1oy+u+SRB5NBwuT9c6KeRQ/pvfH0VAGDlyvPpyWGMQ9Gs5lZG/pBGHXEt9feuq5OyL/zy864Zd1sQnzVqwiM31fmrfK5y1cwqMxAIHOf3yA213khcNQyaFli6DiDCfxwz3Rif+NI3hpfgXPVYwviTI0fsxg99gvxDI4tJOwFCqjI4+eSlgsDPnT19j2G0t3UlQ5D7DFe/a1pDbTXMW+4/9fCv8vmmqA64+OLzaaPj6oDr81/7958+8E32LZeCppu6PnIIUGtXf3NdxvATRk4FsHZ3p0vi62u8NQE3aQ4zYYUhJMXpgj5XQ40vphjhqFpyWHBC+id6RTHGidqU4f/OFNN/bp79y28HNQFV4Qll4HyvjqvAWQkswwgEvP39JHgUYxyNxY8db+/t6pRHitrMU+7nYOmiuV/99//8wTe/AqYohc72ikDVjSmQA7z1rReXn3fNhFMAa3d3igJXU+VpqPEOL7cw0YAQSiJfF/SEo5qqmRpJ8yl+EkClP3V4YOxYlhljyye+9I006V/ETpKaYHBa4Mh95z+nejCEWjwCsU50g/OSEgvLgj9jbeEShX5yEkCfHjlybM7sGf/2Xz/9f//0+VJ2W7FgDDSDGvAY5VYAa3d3cAgF/a5pDf6AVx77kp/FR4XWeqNxrbs/ZpHYsCJ1AIKQ5xBCpA8MawM2EaQ/xvi5Yab/0pRBIkOQI5ZNSJuCOXHltuXcOuUa6efQAYcPH2MlIorGtrFulLOCQIUzdAYAoUsWmur89eNX8rMIeB5V+931NVpM1WNxveh7g+MQLTRtmOwOmxBj/xKlfzZlcN0HPsaRhu8wKUcwxq89+zAYNTa+viq1dTCtE/eZu/PvncJIYNu20wWsGNY9/4cLrrtnQl3K5ePtBx402K3d0ymLfG2QGH8mZuRPNiCEsszXVxOnhUAkeJGpYQLvjAsB6SZY9MncdMdHin5vhTt+08jT9F8Ezz/++zUvPrr5zWffX/ccfbz3JhmkjyqrV6+3LNsBHzpE6piyWqHFpoAN/DxJ+C+YvGwd7xCgQQWwdneHU/DZPa3e73WLk0j6UzhIWgU01vr8XkngUXE6gDQJhNAkBiA2wZwcjt9JxMbXV61du6Gzu7+nN9TXH9mytYXdZkVgOkkVk130TzgTEIQk6Xdag7+2ykPqK0wq/vzYG2XZj2P9J5HiZdkboxTT/5S8ehtfX+XxV0NE/uM4FInr//ObP/zjJyeWUWLi4nhsDNOaUi5gS19+9mVb3/7beCqA152Cz11h0HWiFUxhUiqEZvMGGyaZoY/1iVUwGUz/JTt+JzKxcK8/WOs4HXBXV/cLr7z1z//2wx/+v38e7/OaNBimhafQL3Tr239bfvZl46gDeCr9Jyamezpd4DnkksUqn6uh2je9oarKl6gMSplRm6i4ggG2LLs/qmzf17p9X2t/REkdS3JO4QYr0pFNB5Ai40Z6ICljzKQ/ZXjG71Ri4+urzrn0hlNWnIIgtCziFNi2c/94n9SkgbSANqwp9vvc6uiA8To6b8n10+urli+cNn9GndctkYSZSQsEkOe42oD3otPnL5nb+P7u4y0H2yNxNVWkc76GbDqAzC6n2t01mchm+r//Bz/+9le/CKYKG19ftfLi8517koxPVixfUldb9dDPfzDe5zVZYkCHVYCfKjpg63hMAlCVz7VsQfPiOY2+SS79U+Egaqj2X37OwjuuPHXFwunuoV2fOF9DxndppMr8VLu3JjK//e/vKIqafNxyy3W/+8vjadvc/wNSPe0LX70fTCq+cv93vnL/dx4c9nEAAJ/5528pqokQRATYeqLt/LNZIbO8MC3b+ZFOQbaO0zyAP2vZ7Pkz6kjS76hJ/90HjoFxAgM8vcZtG4Fj7X1xlaQI0NwfIVCrhrrTJgEmG/6POT9+4Ftf/Nq36PLWrTuXL1/6r9/9YfJVnieau6+3B0w2nFpEYFfLngf/8viBA4dTX4rGNI8rUUcWQrj69TUb3nhhnE5zMoEBiKt60UkAE5+t4zEP4HlgtnV0tXdMkbF/GhBAWRLqgt6YotsYq7oJBhqJyhl0ABv+j5sOoGUY3tqwub6+JvlSquj/wlfv/8kPvg0mA+defDWECR3QsnOPNLTEUFL6M9FfEBgTBTCFHMCZGWMdgGTSI2n8pb8zME9+t0O+ZBIwQevppq2nrwx5Y8pLAys5hHweubHWV1tFcgSGdBAb/8/NIPz4gW+99+77tm33OQXahvOTH3x7skh/AMCG1S/957e/8dbadaRwjaZn3IZJ/0KxbDtDC9ipxVZH9I+lLYifIKWPhyqhIaeULSuNvCXLuae9IPJcTcADITRMs6uPxF5Q9SD7a9VwN8AFNwxgqqPsrHvtWZKvV1OfuhJj8NP/mDRyP40Nq1/asPqlv/9KepLz6tfXkFeZ2adALMs2U+o1TW0dcPq5l23eMBbzgEmW81V8rQhRqPZ76oI+t0tMVyjO0x3H+vLfX9nPkEF1QLhnSKfsySv9k/ziP7+TtmbDGy8w6V8ETsG+qTz8TyUp/U8/d3RnAxWhAJKFo2urPKRekMAnpwiuQJ3zMjhyOM8kOCb9R30ewGAMxzTtKdAGoBRNMBrKIGtP4KkHgtDjkuqDvmhc77ViRjxKrEAuWSlAsDPpP6b87I+PSOKwGVt+GCSnLy903cAASGJ6h2eMsWGaTl8x8n9HZ29HV18oHI1E4t09/TQcBQKAOFKYJuGpqpQR6jhgENNt5V7fzaNjEaogBUALR1f53XVxraO9nUp/YneuCob787f/MEaRmz/8qZaWfcmnqctJzjv/nBx7cLtG6OqVAQjf27JnR8uBEaVLqjsKosTsub6+5prLz0360vbv25v2LlVJjDEYpWAYU6oIxAShshQABPDFNzaRm0kIiBxQbAwBkFHmOA3G2PPXh39984c/lXubt9ZvzPHqtOYhbuQ8UaMhmbeKG8BjPXZwfwZFxSh/FSB2TctNBSmAJ15cT8aPK+bMnz+/vTt8vKO/P6J09oYBLuckQJaGZB0zCiVFCpO2bCltGgefpjRyIWG9to0TfxE80doxrbmhiIMWbV1AA1OBREwyHCFEjVEcTplepgImgwJ4c1PWpisAgI6u3g9ceyEYD+n/lU/dSp9W+z1zptVGFW3b3uP7nERNf1VwjE+JMZKZhSw4z+CwV8m/VPo7rdyISuA4x1xflNw1zOKLACY7p9LW84xRYgrnAI8j/OMvrPvAtReUZV9HTiRi+GY1k9CaI61dqa92dA0OsR9/YW3ae0dPJVDRf+PVK0+aOZhiyvPIx0s+j7R9D7urJhahvv64Eh9xs6bmZgidOQEgdrwUbQFOtHYWZAjCGJslCJekxtq5Y9eIG7/81/8r+kAVThEKoLWrv7muanROZwrNAB5/YZ0jgi8oXfRnk/iUz3/0OjJIg/B/f58e7TdcJZSuFTKK/lQOHWv3DK0Txxh33njhsUuuvWNEHdDWmitsV8tDhSSxMY7FFJxfklFaOjoAIBLq27kjnP/hGMVBGoExyg08uvkZAMD3f/54ETrg8RfWZ5s4f+Ge6x1RD5wGi9i2sdOFOxFSR02uNonqwghClEUr0DNMfZL/GT7x4vrcop8udPfF3t66x9RIBQLJO9g20jEspNvH0nqFcXwGA5osiSumCV/7/i/zPE9GRvLRATmoDhYw7jNMKx4f0joifwVAYovdrrxaqELwChv+l8BNt92R+bpC4oO58/rMsWFpM4CJ1hR+3EmIsPs++4Hv//zxfKYCjtBP77P1Dx+/IWNJCUx6uaSvJ348x25qQ9J+PWnj/YeP30hDqS0roS1++ofnhh2dnGEaaSecZu7PJvoTZw8Blf7l5YH7PsN0QCnYvOuU5fMGQvATnmEIoY2pwYc4ABJ+V+L+tRF5CRiGgTGWJJHnuIaGmtQucKkLaRw70bln39F8FAB1MqRSFfBddN6KpBsgB//y+c/k+dkZjDFjcAx732c/QKcCaWpgmMRP8Nm7r3XEdIZfRZKM65ObOyXRObpMf34IIdOyHdsuee8/fOwG4usjSYAkAgwC+PM/PT98h6laAUI4fcb0D11z1oiiP7m9JiVmCYUHkDNGkWR0zZB7ZmAh+S+t90f+Op3jklsOcSUPXUiDd9zHCZVSIOTOJG9kzl/GpITff7h1/uzm5HM6FQAAPPbcGojSTRxf/Pj1pmPTob9AkgZJzTolk9wJaUnvdKVPjsgSYR/OET9/z/XIUTm2jR3bEvntJbQCxl+890bLxs+8vnX7nqMnL5yZW/SXSEb7T1IYsUlAiSyan+gGWtxd1OgM//OEowogD1JiUhOQto6WzTl3bG7+46e/ZJMAxkSDSLH9A2VwqCa444ZLmuq8//Pbv9KVn/votTBhpidPBT4xZh9tUn9posCnx2tDEv9hk0BA/Pl7Er5lusHSuY0FiX6fW4zF9XI5mFySAEBiZ8wQNCngONKZCxQVB0T7+pb/nBgF8sRr226/nDVWK5ghw1iqCRAnYgy+8PGbLGKNGTqJHu/a0cn5BgchQImzt0mCeMI0/Owb26gCWPNOy8ym6nz2uaDBfbTdUEmrufRqMIWCEAz6XQDEStxPhbPyxo+OtQIo9sa2bGxVQI3iiY+uFh8yUMlkmLp29/YjBHkOSSIvCrzAc3SINO7SPxu0vSqdBDTVeHtDkd4QKfSWJw013pqASyZNMUsl6HPV8EOk/wP3MdffRIfe38W9F9t2KTkEjPIxQaXTBCfzfb9t97h18S0H1KGXr0+vyic31/lrqjwlHxVMa8gQfch0wASHDiCKe6+Ncf5lR//jpyw4eLQo9gusdMaoFtB//T49oLPaJX7sQ1eO1vEKmazIIl9f7YEQtHWXNIuUJeGkmfXHD4ZK2QmjaA9w0SCEUB5e3GzkrwAYowc/Vr7JKcaoKID//tOLhq5zXK6vpFfRU7VCtZORO4oqITsIQrcs1AVLnQE01wUaa/3HD2Z4iXmDJ6wDgCqAZOxWEeiGUdbTYRSDt4gy4IxyKYC0AT7KKfoz0qvoqfupkhIndu+dV4/B14QgdKJ3cpGWBpwGx6E502pIDGsWmA6YsCAI84njzIZGwgcY4wNNAwYABLykt0da0CD52aa46PnC5dKUhy+vYWcIidqIA8lizlPLid9PTebMSL9m2k503YOPvDQ2aoDLI5kzB7VVnrnTa5gjajICEcw9W82NrjEFMP543VKq3M9IJfSUHyMF8MMHn0km8WbDJQmkzg/xsEGOPIiuVnWTRJciaFm2qo1sPO13tqFqYMwmBIUCIZjRGHTJI9SVY5OACegAoDMAnh+jGQBLBxsdSMfvUdr11IYv16h/YJhPRvoIIhvBuqCbQ4jnEMcl/mKMo3Hdsm2EoKqZbd2RRI0WnJcaGMsJQUF4XdJJM+vycTwzHTARgZBUgyi2IYBhmMnOBIxxAcEhZh9G/vBlMPgQiU80sCSQeGqOg6RyDzabav107E8mAU4yMcbA4xJpPr2qmeGoZtk4WeabFAd1ansR8MgTgomjBqY1VNVWecf7LKYCY+8BpvG7fJbCHvlUg3CigFg5oPEEIXTSnMF6Noz84QsV/RCScj2abgk8keu0mDPPo4BX9rpFjiMBFdG4YZmm3yvRn8rg/xgLA3NtWeIba326acUd9y8GQHXs/hBC07JNy07UU8g0MKNq4LdProNatHQ18NDTiVYEkp/0sSkUgefmTq/Jpx4khU0CRpuCCgERIAkiTO00WRCGado26UdWxHsZeXLHXR/XlKzZnTmCLxhFKoC4qiMIf/bIy8k11JrPc8jrFnv77aDfRcU9x0FR4AJe2S2LdLAPgNIfJtHV6Tt1tIWzhEXENdf7dcNUVBMh4hyOxDTSNgBCRTPimhEKKxxHPAek3CLttjoMLHl1w9QNy7axbloPP/vWmctmg7GlocY3q6m6IPcv0wETCmcGwGUcauSDUw0or3pwFOYGKDuluHAqnKwKgFbfTCxDMslySbwsCZLABQMuVTOa6/1U+iOEeERMQDxPKnXSpIyRiquQV31u0bIFr5tUHLJt7PdIdASmaEZMMSJR1SUJsiRquqkbpAipU/MnS58gpxBccZfActVYzp6LCCRGEM5qCkrM/jhpPcCp5YDsom4hy7It2y61jBQjJzmG/2lxn6lU+VzsuhZrAqL9tR37vizxssgHfC6/RxJ4zusWe/pi1QE3rcxMe3YUUSkIEucbRPWn1VS5AQDHjx2n60UAAgA0zyfLtJuYYZiabiiKdmD3+6Zpk+lA8qdKzgDbFk5tyO12E2HeHcpclG3OtISpJxIlqb/33XNRVNFVzXzk+fcK/QgBn2v+zLoioj/ZJGCCOACGtAQoCqIAWLfCcaXoQl433JLoMvaJ23N1n73pw18AFTgDUHXDLQsCzwV9Lo9b9LkltywQvy4HZZEXc+Zen3HJTUqkd/j6GTOmpT6lPt+k6M9wGiRJByIkigLvksWqsy/c8d5bumERJ4GDQFqOcS4J6qkZ+dQnN/xvJtrau0GBCDyqr/VHYmpjrS/gLXKUwXTAhKsIXRR2aT3lGaXj9bA04HIrAEnkMcb1Qa8k8lV+2SWRsqCGZ64NgAnArCVz1Ow79blARulP2u8dO5FcnjFjWg7RnwqZYXBIJIGkoL7aG1N0ZSD7JmMCF5keOFMESKcKdLpA15Tsq5MlvrkucOHpCzp6IlxKCyrG5C4IOpBTWlRBUFYOaNyAEAZ9xITAKIJcN72i6k11vvoaj98jbdijGp65w7eJxkkMT3GkKoNURDnr1wkhaKz1NdX5anMW73RMUgNGLPooh+h3Eg7FmY3BKp+rNuhdNKdhQX7h/9lghUInCCRioehyQJBkNRb0DlYWtIxwiNTyKuceKwk+m6F86x5SEdrtJvWNbQAuWOoGsQzdtQKg/O1PrBjp75oNlyvgcoGaIJiTcp6UlWctzKqThna2odDPfqgjx2RmCD6PNLMxmPQ4lVJAJgkzBCVB2OrsLNgcN5xkqHH+qJpuWwbE+VlyhriKocslKfF4RzsrCDE+cBzHivwUzTiFT6XYZDJEXqSsoXFBeMQ1A7uyLNzWnXdB5mwnkAm/V57ZFJREnpl8ph7YKVFVzDshqK72i2JhI9D/+s79xRyLkQmBL95/wxiiANJ+AiT4PrmGCtnUDXCmRxZSU2xoTH8isn9gmSzQwyVfHTh68m9S0GdYM7CrnlAsEtOynsawp4MnM9LPf2bjyOGeb28/CgqHGYLGHdtJQy/ijTzHVVf5mfwZR0Qh30J+F9947yify+SDp8H1SU9m2lOag5Xst55qSoFO/EPqloMFQJNPB0fL0LYTG6eNoGH2hYwMHGBwu+T2mmZ29ERy/JLT9jyQlJY537jo+6wImCEIAPDGqoduuevTYDxwqpAUrgAg9Ps88khFABmjymSsAvTqqocxANOXXrBvw7PjeyZ8aqtfIgmHBrUMeTYs3gVlezVNzjstGpMbl6dy1rA92DZu7wkPCQYdRuZYULqqHJNIjMGOY33LZgTLsC/GGOIk8xasABCE9bVVxXuPGeVAHqmTxyjx6qqHk8vTlq4c9nq6PylV1sfDJEhy3KU/VQCDT2AOeTmS1M48uM70dJTMdf0RpS9cUk/HclGcDmCTAPIlhmJVgVJbsxUBcQI7/ScKguc5r9fNzM/ji0sSmgqt/lQgzzz8k+ErqRCn7NvwNJic5Jw9DZkLgImMU1w6POIgriwfIkfhxxJnNkwHjAuWbUednPBC4TmSP1Dou5gHeLzSgG+4+fZPfGBwqN7VF33mb++X6zSGl4mdFEyA2WsyFKfIWj7E+NPRE1bzaMxU7BGGAAG45OxFubfZcayvHIeqON5Y9dDYH1RV9biSbyhwKohkIU6+3/xUAkIoFxiCNUrASSj9R6spfP4kHG8kCIcsjVRCLjNxVY+reUVhj8FXdORI66xZxZcmZ5OApx/61dkrSyrx3XrsCBipXV3xpPiKQ1p0/frOgt799uoXRuGcKhdR4MbLBzA1GM8ZAMZY1QxNN0n9Z1UvwgtHd2KYdokdfcvFcy++XvokgEWFvr3mJVkurY6jPRGL8zDpX3ZckiCMZnjelKfMMwAE86oMYWN86ERve3c0zSpj8b4qn+u8FTNnNlUlxOjOESLrMXbaQeQ3dcCTYRLAAACseZl41VZedYuqKkVeENsaxXlAgTDRP0rIolDQL/q3j69JdQMwRlAAy5bOzLDWEdqGafVH1Ehcg3pqqe7BmGhFzaoM1u10HOg8aaMoYvILd8nCknn1m3eeiIeV194krzbX+xfOqh3xG0q0Ic6PMZsmUB1QdEgoMwRR1rz89MTSAUUlizHpP3pIUlYJxpoBlKoAENRbWvaD8uGSxTc2HQQAXHUmqStHlymKamj6kGn7aJj1EwnDo+av91fXhHt78p8HPPfm9usvOjnjS0wHpOoAx1WrTLp5ABP9o42LOQBKg68dFnbdGyIhcdMbg8c7MlR/KxEOQdp+KxXbtuqrvbsOdloWSePiOD7gFedNr8IAiNyQ8Azdkos/NiYZ/zFF7w3Ha9I+9Ui2oVMW5tWpyoim2/1zTwKySX/GcHMQAKBI5/A46QAm/UefiRICNDVnAByyNYO0AgYA7D/W19UbBgCcf+rcvYc7Al6Z+F4waG6o2rTj6LJ5DS6XdKS1l7SLQbDan7Wes24YzQ1Va9494HERY5FlmRxHzqHa7+oJDcp6jkMWafKVTlIf9IXVXYfDn7j1/MQLEMYU/bePr8nWExhjoBlmOKr2heMx1TxvOa0lOkD55gMuESl6IqWITQLKy9trXirzHhmTGQiAKE4UN8/UjALinV6pEELXgK3teGfILYsBn8slCR43EeJ+j0RrAPk9Eu0apmgGR9oC2/SBidwfrNAQiWkCz0kp6RtkHgdT2zzS1Pw8MjOdiv8jiG+n0Fs0rnV0Rzp6wtG41tkbsZyd1wY8CMCDrX2tPZHeSLGG5hQMV53hquMDDck1R4605g4Huv6ik597c3u2V1lEEIORDQihyE++QkCTSQFQkQwBoLKeNFDsitRVJ+wnpL4bIB1aaDqMJJLSchBChCCJzBkAOg03kk9VzWyq8wV8g8Yctyz0hYfIX8uy8zPTj6QBMDBtuz8cb+0K9YZjukHcDDbGrZ2DJaPvvPqUWy9Zcu35J2XbR/7FZt2cSR/+6sHc9CNHWvN8O4PByB8iZ/Lu/ZAsBfrbx9ewi5wkq/5srKtq62pNSr5kBy7LsiWBp23ZqXpIFklGELpkntZ6S0vpStUHto1rqzwxZTBGKKbqBhHNg9vwHBJy9hweGef0NN0MRZXuPnKE1Bf3HOmc0Zgwzc84fcCO9MjajHvy5d1xFCFS9HS4Q/j5dTuvu2Bp7kkA8wYzGAXBIZQqWCqcm4rqXJ/vBMophgwFHlH5Rob8mbLgSfvFPIbLPIdSqysrqgkATi2r4nGJHFdI8blMA/+YoveF4+Goagzr2R2JqWkqIRsIwWn1iaSEEREFTtUGjV2pOiA3t1119pMvv810AIORN1CWSC+Y0a4ENynkeNHkqwAgGelzosjHibAuA0Pz99LjgorK7hvcieoM/PvCikKqPGaI3cYAHDzeM6PeDwB494W/AgDOuPbmjDv1uqWGal/PUAtVNgJe2TBjlpXhiLknAZoyIeqYMhiTBWKXdkkFRW+s+usTN9x8O5iKcrz8PYHf2X4QADk1sxdxBTe/zgE3mrWT2nvC4ZjqmJWycuhEQgFQqBpIB4L6al+qvzo3DdXeuGpE41oyYSh/QxCbBDAY+cNxKBmZMtrcNEmkeREUcAV13bIxRnmHVO/r5Gmj1QUNuMQM3kLpC8dHrCyk5WwdQxF5ri5YQHn6+qAnHFM13aTe5oIMQXQSkMMZwGAwknAcKqIX/Kq/PpH8CwC44ZY7KvySErN7akfctOVUrOx9U1MjOJPYRF/karabQ/6TIFLHzK9bcsaHx111xtKZW3Yf3bLr6Lr39j/28ua/vPiu1+vedbhz1+HO4urKDaemyuN15+sBphGx9UFvjuzE59ftzPH22646m+qAjK+ykFAGIwmPUBHNGBhpoIEG64OyGmOco0U2xyFVTx87R2IZyv64ZJGY8odK+dTooBwe/KQMTesLX15e3rB7U8vRJ97Y99SbB59eeyjtVZ5H0+qruKEN/3Kfg8BzQb/L5xHTIkdTo0Jz6IARPQFMBzAYyZ/nBKkBPKlxWkIOtH2n3tHB5r2Z3uCWhdbO0NzpgxLNsKzjHaH6Gl/alpG+TlUzTMsGDYOJUaLIG2Zix5LIK6qW+bQGwnsHv+EBHZBH//ZiGK7xvG4pQz2p3KFJELglodrv7gurMVVPPdE8DUE0JJQZghiMnECe42CBCuCDH76b5P1ce+aUt+wX6API0rAXZhK0CMLOvliqAojF9Yhj+F6yZH5q8bjFczJETwa8cnd/IqJGyJ7EYRhWIr3AOSGqkpLnNuRrHw1t4Bykvto3fI4y4h3H86i2yt0XUXTDNMzMPvPc3mBKNh3AisQxGNAJFCyuf1SS3/70gTxbQg7vCTxllEfOaqBo0LCjqPr5KzKU2VFUvcrnOvvkmdi2U6X/8FrQS5bMP3Tw6MkLmmzBu+atbdMbAstPanpjE1EGHMefvKBx+752WheI1FA70HXR6UPL9ZQDCKEk8gLPk+F5ThCC9dWkWnWhcAj6PVJjtTcW1/sjquMFLywiKHdxCKYDGAyIoJgzUXT43P32D91lWXa53AbPZGoTnycTSnmMXSWNlpb9LtmpJ6FHAAA9ofgCMDiN0IflapW9eD/HIa9LCvpd1VXeLXtO5N6YJ0Fmg70NCmopIwl8bZUnHNMUjTQ7S30pT0PQRWef/ObbzBDEYGQGQVIEAk7Onr3PlKA8yq4/RlAAGOBkwm+25ZSNB1fl2Jh+E8mMWQ4Rf+/+I/10IcmuA71zmmvKogbowD/glYM+t0sWZjQGt+5tzermdrbnOJSxBFDu03n4mfW5zySmDOqDHJMA/0DkETMEMRhZYkBRQ20g/4sDISlCM4ZNocZTf+SvJHIpABuTIXCqmMROJZ++iNLeFaalFBCCTvgQFnjO75GrA0TC6gZpFtYXjmvD4oUoVNa/uflYmtBPpVwxP8mBv88jCzzJPxM4rtrv7smSAUc/VBH2xbe3H9X4DHckVXhU3/A+YEa689EB111y+vNvbM5xOOYMYFQId9z1cU1JbTtYcK2wi2+89447PzL5hX8ZlESabijMBIQx0A0rFFFoFhXHIQ4h03RKHzgXF2Os6WZcIU3eh1fgKQi3LJaur2VJqPK6qvykeHWqSF80p2H9lsF+ZAgiGycdtpBMMIuyFX7+9rMsG0fi2rGO8I6DnX1hhcTTDlVlvK82VQdkA1oJLwWLCGIw0pBEvohfaMXI/wJ0Q2EX0bZxJKbGFA2TlGAki4LTkD3xKqm+aVihqBqOqYpqlJKN5ZbFmipP/oPw4fYcCKHXLTXW+OqqvW7ZCcwnLQcS1AW9slPgAUIoS0Jz/eDIHUKi2IqwGGIM4tGIFo+KwJhdJ5+/uG7RNG+VDNy85eYGHgJ2C9hfFcwnLeCWK8+iCyw1jMFI+2nTX2iBleBgMgaUUbACIE21dCNEimuSYv2yxLtlYdBQjkml6GhcC8fUEof/CMHGWr+Qn4bHAJiWlWZr4jkU8MrNdYGg3y06Zh9KUqgjCOfNqOM45PfIjTX+ZfMak++FzvA/hwK45OxFGdcPvoPjOIH3+92N9QG/RxqcedCcsmGNbrLpACOlCy7TAYyKJc3+I/KcSxKKGM5PGDfw5FQAtm2HY1pM1THGPId8bkkc6ARA0QwzqmiqZpZo/KnyuvxeOZ8Jm21jVTN6Q/G0I9YGvU11Aa9bogP/JKnThDnN1bVV3qY6P3UPuGmEkhPHmdq+pmAG6pMIPFfjFeuDHlKyyumJY0T7yCMeMuIhl5jXIWhxCAaDkUQUirH/OH1KmAZIhy/AvqEaoahimhaE0OMSvW4pteSZbWPLJo0cQQnwHJJEoaHWP2KtUEwnHIrWF4qHhnVzrA96iY8o5z5Ega+vJpvRQy2Z2/j21v00/qe4++Rvf1sNiiWbNzi1OASLCGIwnK4kJESvoEvxwTvvzlivjMHXBtKrXfaGiNA5ZdEMAMDOfXuT66v9oNpP6ief6FL6QyFJCCQUgHNhh3s7CwVB6HFL9dU+2iI4K5gcSxus+G8MP2426Z+2juSSOKv2H+2YM73u7a1kjICcRxEq4Nv/9CGqCOnZ9EeUTS0n3t/TFolpGa8LLwMz3DWiDqBlouky0wGMCoc0ghdIp/KC3uUEKpJutaN2XpOVYhLBaLMWRx9IsxozFMts644ea+05dcn0HDs5cLRr7ozaTK/YACRG9IeO98ycNj3DwD+u9YXjkTipNJR571m+6PSwopQntOgbkf7FVpjat/+YsyMOWImzuvrcBZefNe9YR2jdliP7j/emTpgovL8uVQdkRFPiVbX1/d2dxZ0VgzGVgIhEbRT6E4WZ69owCowCGntETk19SLzqlvT6IJxeL+qGObs5eO+t53/CeXzomjOi0fji2fWLZ9dn21vafVMb8AzGMCVaFJD+9SWNFAakP1UJhw6dUMLhGUFhRlDwCbYbmWmPfCKCLkupisG8wYxKhniA5YIVgFPpmKmADKBVG488986J5zeNUBqhYEZ5spUsJPe3jS0lHnJOrTCnVrDivckUsLQNWrtJ7Yqi4TkU9Lkaqr0elzCiZsmoAzQlPn/eYCGm3JWCGIypC3TJQkEpYIMw+Z8J/kf/QRPDMI2oJBYY2zLaezHGosAvOGkesOwte46v33IoElcDXtfK0+ffvKBZV/WX3tr93u7jkZgKADCN9AbrvFC7bpcKITh/kQyKojeUqw0vh9Ds5sGxc57CP3dmGYSA1Jgtt6EQQiCLfJ1THUjTTc2w0u5Ff1Uw3N+XeyfL5zfsP3A49zYsN5gxtYEQuCSxiCCNxx75c7IcNCOjDwBS/wpZREDwKglpadut7X1b97ZG45okCgtn1c+dVkNqKRioJuAO+lxqmCS1CkMtSSSTFlmWZVuW/faOrMPn7p3dgxGTVZ6agEcSnf4EDtdfeb5l4x37joeiytH2vnBU1QxL0w2OEy5a4gSJJsV53vdD7g0RJAVGcu/h3fe2v/tehgH4P//9B3LtGUGfW2yo9kbiem84PrxrfKoOyOgN1pT4Jeee8saGbfQp8wYzKhAOIZF6IIvi0Yf/9OjDf0pdc0N+LSEzltYpsabbBIF//Pf/R5c+8LG/G/qS05Expm3f39bdTxIxXCK/dceuo0ePyCIfU/S27nA0nqudS2ol5BFOghva3c2R7G9ueM9M8feS8FMAyGzCUjdsH6JUfvtkogQbbQkJADh9CQlhGnEGsHjxQkUzaKk4CAD/1uEcw/+Mcj/JD3/xuKHlmrIUREYd4BGGXE9WIoJRaXAo8VOdCNxUvqqct9x4I+mV4s+3/bjPN1ip/vILTilPFFBSExBlcNsVAIC/PPZyd3+soydCg22UsFDlJ1W2LRvHVH14Y8jEHkmQLjIMK8+oUBJ5mTqpc6I8TdPWDSscVXrDcT3vXY18rKFP1258L/VptgyA5lrfs6+8BSYAt1x51tOvvJNbBzBDEGOK8cyTjxGBe9sdksjTGUBuW25aM4CLb7wXTGyefvbZHK/ecuONVDd4Hbmf9sFfW7etFGWQOQz08SdfpZlf/RGFSn8yNB44sm6YsbhuWRlyvkgdBZ5zksLyzQjDGFtO1SAIybLhGI7imtEfVqIKGfiD0WMgMgBCYFmYRP9kmQHceOV5AIDcamD2wkW0UTWEIBTV4qruBJWSzqUQwqiihWNajvpIvAuYoY7ck4DU4hA5YDqAMVXVQBrXXn1Vtu3/5bN5mXcmPiOqBwDA9GkNxSkDPjm2duq7DRb2MS0rFFUUdah3F5NEp5iiKxopCDF8dwJPoiiHB7zngCTfktgb8kbTslXNiKtGTNFCUbVcA/8kGQcOTrFmYqZfOLdxWn1w7ebB3LeMamDO9KqSzgGTHLGt+9p3HuyMxsllTH5IPtAwog5IzQtjhiAGIxv/8fMMCmPq8XQW9fCTXyQWbrnxxns+SCw6GSEGtYRYhKSyv7NA1sQUPRLT6EAewcE0ClU3wjFSD274vhBCAs+bVmEFISBt78AjwyTSP6boMUXP1kgghwoh3efzOFba8+F9hi88/SQR0oF2emgTpaNjhNStfDip2dPgb2rvjhzvCpPOkcmZQTWpbpija5imxF2BGiU0uAEzBDEYGXnhpZfZlXn62WdzzCFInbJEKE3CGkIUgmGSqs605QvpjQUhNfiYlq3FzLhTD274vkiKNgRmlk7o2YCIFPewbRyOkkLTimYYppV/KWme5zyyaFqWqYzQ5jcfdNPCNhYzZDeXGQ5Bn0eCEOhE7ZnxtJlWzknAZcubn3vTUQA54+GYIYgx5WEivkRSYm9oIKgTvROJa5EYGZZypOg/T01DGGNVN6JxLaP1n+eQyHPEgl9gPTinrgeMxLX+iBJVdN3IV/pDCN2yWBf01gTceRaHwiO91huKnejqB2OC4FRUbazxBv0uGjeVxO/MA3Kkhn3o+gtSpf9za3eM8skyGIwpCMpU9N/sd2JvICR5d5KY6KXl1F7OXOqZRlISo0kh1v+B95I2A5GYqmg6aS6Wn92f57kqn6ux1l8X9ApDq1LnOtZIr3X0RI62jZCTVUY4p29BY43X6xLTRvOpOmA40XAon/0/cN9nSj1FBoNRKQoAA8u2Q1E1qhAjjyhwXrdEiy5gUuTGNlMK3aTC84jnOVKbrcDhP4TQtrGiGYUO/OuD3qZaf8Ar59k3Js8ZQH9EiZXDlJQnkDS342qrPHVVHim7Gss4Cbj+wmWpT7NNApgOYDAY2RgiPTEAqpYo+o8g9LoktywmIyOzJXYhCCWBp5H7oHAcvUKKSeezsTAw8K8NemUppR9ZmWYAumGWPfQoN851FhtqvFU+meNgQYagPHUAg8FgZGRI/q1lkeG/ohGHpCTxAZ8rn7pLpD0PhFpRohNjnGfCcNLinxz4j0Zx71L6GBcNz6Og31Xv9C4uyBBEUvauIZGpuWGTAAaDMYICIKGfqh6OqpZlO7Zpl8clDo6wswhGhKAkcKaTvQVGATrGpwP/Jsfinxj4FyX9R5TuYzz8p5ASFyJfX+2pDbhzaNyMkwAlGk59ygxBDAajcAWAifWjP6KoOqmN45bFgNeVrIzm1NLOLBklgYck86v8lhPanN0lCXVBLx34+70yz5c08B/xreMxASBwCPrdUkONN+B1+hgXMglIMwQxGAwGyI//D3HfvGbUdDPbAAAAAElFTkSuQmCC"}], 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"}], 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hjt1FjLHgwx/92Hkr5hw/1glME5hmy/vPj/iW5GyAkRH20x3kX7/6JeIWh/Ce21a+/sIji5aeyUPetkzT1mxsmqZy4sjOF55a9dK+6rSLODV0gGWaiEOWaVommQE4mV9E+8XjcUXVMORsgHRNQza2dSMeicZjUdPSdEOxIRzvc2dMfT78UZLNQyE6wKlg9ewjP33h8V8n10dj8d72nuRjfE50UsFMQJn5x0/cunA292R0TjTS3x/qMC0bIA4D3Nt16JVVL6iXrLz5VA0A0N8TAlMFy7I4iEwLQp74AEzTJHWBHE2gqQr5BxPtCDkSKWXquiBAhDiPN/DGX3453ufOqAjpv2JB09aWQ3SNqpIfIEjxP0VjCStQEqoD1j3/hwuuu2csz3YSwWYAubjtguot77654szLREnGlgFsyzD1nu7jr738yo+faJtK0j8xA4BQtSBwnMCaGoOI5AKT1zDWdS0eDZmGziEoyy5eICWDSOAQKwbHGH0wBtQymbhXbbznYKuzCE1Dzyj9GfnAFMAguw/QW2oI//3Ne1bOi89asCxY00wKJJhEB4RCnTtbtv/6xT4whYDYQhCYNhnmO2GgPOSQIPG8IIiS6Ha7ELDdsuzz+Thge71u3okCEgTiEGYwRvfmdGaiyacIDbE6Muk/aUxAn/74h1Of/up3D4PJwDXLuK+/+u6yUy/qPHEAAcu0zahl7Nqt/K+5ePkZp6xs6njrxT+ddw0pNTEiW9aSeLU8WXHhXWCswLbFc9Agvy4yLJBEKR6PcZzk8sjYxpZpmqbBy4Kuq8DGhq6RMkEQJKYIDMao3pwYcCmxBszpNCkVQJr0n13vAxOM3QdaF81rzvjSd790230/fPSM8687cXA7Z9mmbZoAH9q3E3IIwFNWNrbleYjNv7qzkDMaO/FqWxYPoKIZHHCRokmSJIkiz/M8x6u6YlmmKEm2bRuayvECME3Ic0T+T04P8Oe++gDnRLvyjsNDdvu/96+fH++TYmQFA3zqgsw/TPL1ibyqk2+TMXEVQEbp/8B9n/na9385KXRAfzh236ev//ZPnrzoig8e3P02BFi3bcsyD+7aGu0PWRdfDiY5BpHpwMYYOTLR4/MjBEk/ANPiSH4AcElSOBzCEFjYwo7xlRfEF343QvfgCcU/3PfvA4tD5IUaD3/5G9+jyz/6zr+O/YkxcpNxlHGsrXtGUy27dJM7CmgC6oA0+sODbW8/9sErfvPwQ1fd/NE929Yj27RJNEK0/fi+tS8p3/jJS9/5wtVgUkcBiQiTov9k2qHGY9FwyDQ0jLEoShzHO/V/ugCEmKgFm+OF2vrBpJvJIPTzIqkJmDKYOMChRv+YopI0dZd4rK0bADCjqZZNAia0Asht/JkgOoCMaodOAlJFf5IXf/9NAMDPRHHvto26qmAbq5bd2XH4rdde+IfvPvK/Xy/IwjOBsJyekAglrPqmaahKDGAMIdRUxecPIkSUgBqPIZ5HHBesqafu4kkq9He17CL5zLoBADj9jFNzKwNmI5o4UOmfCpsKTGgFMPFN/5RUYbZwdkNG6Z/kc9c0/kg//diBnWo0BLBhGri37/i76175xNd/9dvvfhpMQkJRDRpYEHgkWade/mFD003NtLEtCgKAQNcM0wMB4nnJLUqyrimWGo9pCph4Zv185H4SDIh1a/O779On2TQBsxGNLysWNGWT/pRjbd11NVVje1JTgfE3AU20SQAlt/SnfPmmGf/xaLy780SkrwtiA1s4Fgtt27j6tk9+e/bCxf/1lQ+AScItX/x2T0drtK/XMgwSaweBaYQRkhDHiTwx/kDSM1JUYiFRlGXZHY9Hyduw3dQ8c8Ka9XMJfYyvrUXQ0fgYSJaNTRu/2GWkaoJ8pgXMRjSWDBf9Xb3humr/mJ7ElIOfOMP/CaIDCuJfPrjwts+9uPCUM/o6jgHTAJapAXx0/05NN779hzX33zOkatCEpbP1WMehQzwN64SQhtzxglDT2AwA1pwyW4IkQwi72o43Ns0STVNViYKMx8c5++ZT//iNvEf64NoaIvFJMnPKehLIxJHc51uaOItkO+NV7SSxCMK8lMGXvv7d//7u18vyWRg5yDbwZ0xoBTBZjD9D8k0KjDJ+8mf/eOunf3DyOZd1Ht1HLOa6jnnccWTP68+GvvXgG9+69xIwJtgb21OfonMa83+vTkr9EC8AQhyE0LZMhPhYOKwqMUGQTNuAEIhuN/EP2xZAGCHAQaDbVm9/Lxhv6a+qmixLGYV+crA/YqyqoxkATzaDt0yTHUUAdMt+sZN8dhK8m0kZ0NSkCtQBV932GYxB157VHCIlQ0SBxxibNsTONXGLUDWAzGNESogDVSdFxiWRJG8ZFlZNICCsGOSOwwBw5E2k5BSAHMZgzoorIQQvPznJBoKTl4mVxfPAfZ8Z3xMorrD9U7/66ta1LzXPWwZIvCQkDgFLi/e2vvncE9/6+V/BmBDWhjwKwpF35KNzHEnrFUSZyH2DNHy3LJPnBbcnIMueeCwiSS7bMnlRklweTM3nE2Dsv6tlF30kP85V1fDaWnRdLbq+jis0UwECQKqiIugSuFunyTc1STc0StfUExmHyQ2CN7/7Pn0k3/Klr38XVAbVM045e8XSzpbVXbtX03kSHTSllQTnELAx0E1yuSCZVpJtdFJTFvCpIgdj26Y1ppxtADj43isH3n1l0Unz55966Zh/uEoETbTh/7jrgOL46+++8eLDP5w1exnieYCQSQIlcaS/Z/1rr3zlB4MtBEYPkx/yKAgIgdvrgwgpSkTXFNPUifQH0LZIZJBhqBAiXVcNXdPUeKirIxbqtW2Litrzb/4oGFfp393V6fN5AQBXV8Nra9C1jtDny5efhiDkEZR57pZm+cZG6foG8dp6HgOMENry/rZK0wGYJIzj4ZOntHgw+iSldkPizWmhFmSFI/rTN7RJd75Cz62rp7/QtzBGSwFMOuNPWXj2kR/MmXuKJLsxQhYJrDf7+3pXv/TCrNNuHPVju4c+CsHnc9c11nkDQZfkkUQXwlBXVEn2QEzM4y53gJSGhqT8g2lhTdNtCxOrkW1DiEio6LhKf9LJQIpfV4vIUJ86dkcHCAGPoMAh0VEGVD5Vmg6A2MROvRDbuSCJOiAWGe9AbHDA5CAxEgJLA8Aighw7nSVIdzkyGwDYBLYOEeahhYAFgUVmCpZNZl3OrmzqyiedhognhlEpUUDj6A2+5pY7af1LDEa+5z5+O/Hr9vc7YTCZeOqP/37dXV/p6W5T4jEb2tC2FCXa09HasPjyjl2vgVEDBYp/7+uP/AoAEGiYVVvltmx0eN/ObFuece2d8WhUcrnI5N00LGiQshDjKv0vksbaEU11zE2NwjPtJHBoy/vbVpx6Cn1p6vsDho3Lid7F5K/tWHuImWykDnHU3ki0NeknSpaGpnkNblMoSxbMyLh+x/onaQm5JefeWsRupzD8jt2vOg1gh6w9ZckVI77zRz//QeJ+cL6pKy897eTFV5Rr+D/BI4KqqojNIY2WDU8ll6+4+zuzF50SDfdjaMsyj4AV6WuvO+lil8cPwF8npiZ3S8L0ak9bfy4t+O4Lj6Stufyuz1eU9E8CAbipQXimo/J0wAC0OBuddEFALP4BL6fqg6IkMSFLmoeo23fgJYFHkbgpckT4O14AMglI6g4I4TW3faTs59yy4SmmA1LhFY0Yc4eAwaYtL9HFM1ckahv8zy9/mNTJjtAfkPwD3+grr7/3Xz/7wZWXnvbTH/5hEhl/rrl1VBJ3X/3TNy6581unnn5Gd3cPhtAly+FIVIn0QJvkneZPqlLJzfxi30hZcu6tPId8LrEzXNgZvvbQT0HlSX8KhM48oI1ECk0BHUCHybm56IPfIJKaToMcj66FyALCxDjGI+T4eIlb13Htkg1pZRGnmgMigbaklgjgSCNRSP9ijJ2ygoCEAg3sOeEUZowy/Fsbd2Ran5Dr//PLH6atSUp+um5gNpfw+rzy+uZyndkYTAL+/vNfIK2+MO7sj+vGMEVYGm888q2L7vjGxSvPP9ra7XLJoXDUtixNyWo7KhFSwLMEWjY8df1H7uMgFiVXocqjIIobf5Vd+nfMO3XWzMzVJQWej7z6TAHzgMaxmwfkI6NHHQtTg6ltAUyifZxYMAgEjtd0YFsIIcdPbAPiOILAxo4yIO8jG1s24AC0MdQNIJBKuokYNGRBbBOjENm5TQqQjNLps0nAiJaD4dY38g0F/K5QWEnbiH5Pto0hIt/x3s3byzj8H1Ud8PMff4/6qDZv29/oFzp6y59p8uZj31n5ga9dcfml8XgcdXXbTmlNMDokIuFLgEbjIY78AEePUdUuqbzym6wH6piXOasrie+KmwrQAXCIP+AnX7+Drr/32gkgrEcBEqJD4vcTblvSNY4kD2Li5x1IouGQk3vhTA4ElIjoIbYeMicgPaRhIswnYfkZ3AARczTZV5Eh2ek8//q7yeXqgOf8M5cwHZBbAQxe96Qbhi6Eworz7+Dwf2iDHlxe6T+q/Pon39+0bR9ZcqSeM5MhLqwRefbVdwo60D996hbiNNaXtbV1qppSH8ysAJyQ+pKGPOUaLw0LyZusXPnJzFONP7+eaCqbG98VN509s4AYuZGdZlMFXuAwmS6T+8QJ8LExcrK5SDiP4wRG2LZJiBjARDdQyz/GzgwA2ER/kHqy5C0kUwzbZDJB2007myIEOR5aGIh5BGXkEPcZWb+phS6wecBIvsMURUBCROjwf6hKGANGaRKQkP4E8kkMM1/7D00GSipCeh2cW3lAAjtGMUQsm+SJc1fDixbyBw7MmhXsONhK2gg7OyA3u006nQLdIr8El1CSvtHvOm/EN954xVk59mBYlm1jKy0egFHxpEpVDJDMQwEiZ/JM2vSSttEJAzCZFpAtOESje2yL/goS8f5EnUKOKgNiPnJesp3SI3QcBkkqIvkXIRiDxcxEE37KxKiOMHBqCZ+CM86DqZqgwuFzSX26MGD8Geb3HfLG0Rv+j4YO+M1vHvzkJ++lNyYJTiaF/QfJod1oqDnMNeYeCHmgltHEpBh+8DJ44LD8zqYD8cWOxRNi3cKKAQwLxA1c7YYuIcMOLYvUZx5QOYkbfEDfkF07LxLyKcuZW524JdGybRlqhc5yCtU0jLIz4si3TJBbLq5bKknxJbcuh0jQJwdtEriPoW5BYvBxPMAOZKJg2cRJ4ESIQhsPTKow1i3Ak7IitkkaDEGIOAsD3iknIXAQy8XMaROTiMRvL2X9kA3IJp/8YtYqUhWrADIaf1KFYQbjD30+2safUdQBzjjcGc6U35zSvfUhNdI9/YJ/3H/kfVHmW/ZG7vvix0+89e+0EKVqAs1MlBeo9WQ+ZqIP6qB5KMNdTf9Jq/6QHOkMcd4nFqkKAcPWOEM0AE3TcubjiUbcTpz3gItgYE6T/JllpHQVMvU0zVjJaEpyyJD4l/6QB75NYm8cCOBIrEkIR2d+m7xzkndQchxNRzbU8StwJApIt7EscgKCmgFFbnAzSQCWBXUTAlLjh6wVeZLnpRsJixDPkSOIPKcZlmaSvRkW2YDMIVICRgtn5J8vk/5ZTUCiwOuGOdz4M6gSwNSB6IBPfJw0QSx3yIFgHG3ftU6NdANLb13zPT/EPIc/eCMZdOhm4idEZx02BrIAvWKpJ5AWvDkYRJE6IRlYQ5fT1phOQiaHIG2TO+TN9A+dySfT/4eGg6XtPnVNMn54oOoLHLqGuBDpczrdofqFWtISR4Fl0zQ7d3UnXMFtmds4ezyJROru/YVFxObg2bUHampq+vr7rz9v9tBX8F9ebamurrnytIZUZU1FZEI000s0YDiksjubjE7H2TrRUYtuNDCDTb0LBme0yfltyv0xOI11TlA3MQJQ4oFukfBNiUciT4oI6mbCPkBHBwO7SPEbkvuenn7iGb3fBB45H5KskRDUTGyMphnyk//Axv4ZFEDi3tMNM9X3G/CT8MJsvl/6fGx8v6PkDPjNb3/30Y9+1BlylA0faj+yZ4MS6QKWjoDJO9Jf5ID57ne9EmzyQ2L6x9Ai1n8yEa6SoUhK7peE1VXqaTs/Osxx2d1Cw89xUEakv5a6JiURKNuaFDE2IDoS8eApMpEOUQdXJf5N3J+DsmuYxcwxQRBhRGc5dIu0XNOU+GYaETCghRI+TDJKoAspw2eyQ7pMhfUTr+6yTJ1oUzVumboTzoIlf/CGD9yxdfPmSF8vwLMSH3fARB2PRhYtXrJ6S+uxg4cEyeX2Vd24cm7KdUleqVR9PWBIyTQxzPYd5bfRSLuwyUzRtGwL2hBbGHOYRO1wTkg/CQ+yTVJTFjiBns4ltOj82plScmRTEudD3kNvArIxaTtkO7FEtmUj4hbgII01KjtM+qdBf+2Jay0KvKYPGf7T4J/svt8xjfwZJR3wxz/+kS7ccvsHR9x43ZbDxDLj1D84d9m0jNsc3bFWCbUDS0PAItIfYQEBiYceCdR54eIGST5LXvdEGANg2kQNyKTjVqnYJRfCcoL5iKEWjBfZFUxuJZLukslsMUv1CCYE0pDXBu3GzkvUNjfwtqTvx3lX4p1/fW2fZZrAtnmnj6bA89UuSeC9HMcJyLYsUzf09/ftu/Ds8w8eOBSLx4MzwZttu/u6YpYq29DgRFONKJbF9/X1LVx6cm93p2QbZqTrmee6DcCRwGrEIVG+7bKFYMJACj2Q6s7YtDBHxD4J3h/y8nC5ncmeg20Mics4uY3jSraBs3NIwsqL+k2s33bUUSrp4VvVfs/vfjNxKwuMI3w248/g8D+77zdN+lcaG3acSH36zGOk6ud9d54U72vDlo6wxUGbSH/Okf4iqPWgBXWcfKoEVtResOJjG+7/X554zHLd6ffd//3vf/u+fE7GJuFFJYEgGYo5kdpTm/wkS7JC8TCeeKlFsG23x81jm3ROVhVNU3Q9ruqqaZJcV0HgOYh4XuA4TpTdBw4cjMcikb6+mNaLeOT2yS63jHioqYYa07Qwxwm8bZmixwejkZoan3Nk0cZYFEXEodVvHVJU3bQBL8vXXOBMIMYVhAAixkJivUldTydpaVeXuo2GlwtNTPlohtjAexCElnMf0nvwmScfTW5+6te/XMo590fGOWl8wsJTwU6l/3Djj5P5ldX4k/Z8DAL/J0KNIA4hWRQM0yKVzIdqxqvnm1veIeFlC2ZxKdIfeCQi/efXcd5TJHBmDQCXA/CNc7/9jQ33148oje67//v5nBV+8J4i3pXKgoVLE79HRiaefn2fHgm7RAGZRleot213B4lXEQWB/C/yguBxeXhBwhiH+rrC0Yht20gQRUkK9XRNnz3vH+65Jtt1/dGDz3a2HvP6/V1tJ2ReIG/koGXZECFJFL0ej9cT8HkCLrdry47+SDQa1YxrVs4p77f01W8+kM9mVc2LyWidh5ZFQnycsTyJ7Xeyt9IG9QN5YU462FCL3mBQpm1b2DESWdQISfLL6KblzMxPi/JgJOFzhoCmhAOBdMYr7Wt8dQDHofpq79mnzO8JxfrC8UhMjSm6qpuWZV89fzDJa9+RxO177mLgFmGNG82t5QJLJXBeNQArAUjUDT73228CcN+G+98q/cRwuK/EPSDoFGbR9FVrdq3/W755sEWQ55xm4vDHpza5eU7kUTzWf/Bwu2EasxYs/eZdI+d+PfT85n+cT8tqHQB7Xsm22fcuSCx8c8NyYENBFF0e/8duORsA8OAT6+LRcFtn2xH9kGlaiBMa6hrnTJ/11sZj3eHo+rVj1G5oENIOgIzRsVPUgQp3IseTlqCEI9gZ+ZNSQAMGIOQ8aFnowZiygWZEJEeMpBVQOeNEGZW5NAsjqwJIDP9B+vC/P/vwf+/mwTLoU48co4WGap/fKy+e22DZ2DAsTTdiih6Ja4/915cybr+BdKnCX7uBq14sgouqADgPgP8ceLEdgG8AcODcle4Na0qdouJoqU4AhJDAcx4Zlb1c0YN3DY3q2DvYVD2N1Z5/pgsCz52itJZ4XHX29BL38PBT78iShHXlUGubYWizTlr2tTsvy7axdOhY2pp7l9QXlM3aemBwUPXV917PuE3zzDlvvbvW7w3MnDbztps+2tUXevPNsVYDTicASCs28ENafA0w4OJORnxRM1Dyt5V09tNniHNSBhwrkBMjlwheKvjE6NmlxJUxcsOLAp8W+E+lv/MkuXL8jT8TZBLg85CiOxCQLlG8hGRJCPjcIyZGP7BKB6v0H116FQA/GVinAfCPAOwFq6PmMXNGHX+sq6QyQTgWBiUCoa5bECGnrMt4IgzGoZaEfPj48JVdfVZd0DPie99YvdaFud7ejkgs8m83XgjAgMllmJQfY/ZuWQMAiIfaNUPdvX/XrGmzrrvmzu5Q2FUVXP3CqPeeS4RJOQ/eER2Ov9wJBXLKfDqk+QaG7mJoCzCL1I1LuHw5DllOZkzKoQoj6He7ZDHgde0/1k36DA8g8hOx8clEgFyXtMB/Sn8oMfyfOMafMdMBOezgGVvf5Vm69stXPvmjV0jrFYdPALALrI3YR4yOiK0YmW/3Ky4889W1m/LZOY5H8tks9z5M05RlWqF9ynLumUs7OnujMaWnP9rV2X33R0jJIPWNF7a5G+kGcUV/f8M7UDN2Hdv7nVsuAROVvs4jJP9j5tw1G1Y31DU38/yFK29eu2a0ZwMcMdNz0DSQMBD2YxN7P8SYyP/Pnr4nzx39z6aTSBlpMo8gbyRtNgE0MYkBJZVEQTGNhs5aNsslC831wWDAffBYd19E0Q1TFoVlC5oeK2J3FUDa8D9h/KHP0v4tu/Hn+bU7rrtwGZhUDGbYDuX+nzzy7S+M3Frgy1fW/uiVbgA+DsAO8FYEHDLbHelv2SDoQ32R9KF3JJKvWMfxUi03GGPTMiTRRcq1TFFejMjLOc7lkiCE/X3EaxKPKW6PS77k2rMA+MMfHo33RUQEDx05dP93vwk3ZjbCTCh2b3kTABDtb+0N9Xhcnquuvl0V0JpVoyjuEIKWhXnipib1E21s8wORY0UPHIgb2JkWkNgJUlzOUSqFUxsknZo8snjWslkLZtQdbe87cLx7ZmNw+cLMEdsMlBr4n5T+zvDfWTXSFSpx+P/82ozdCMDEbR9fsnD88pW1AGwB74TAQaMtbMV1JyUYA5OURknfeOOW3fmeVzyS+iju3Czb5ogJaPw1QOkOgDS65p9qn33p8qXziLFeFAJ+j6pqN1xPDPrxmEIfWn84FOo71tn26S98uqO9E599KZg89HUeOn5kx/q3N/Ix/dyLrxulo5DxvpOiNRDGQ4LiBl8uRGinjqMgrSc6sDLRQrgEBI6rq/atWDT98rMXnrpohlsSS9rd1CX55aXnRab9m3H4X4r0T4r+UnRAy/anwdhSlj4VX77yKNhntIfsmAGo9LcsrGh2OG639xTrCYhHhzwKJ5HiRBxo9hRwAKTSNZ80AHjjzYQxze2So5GIlCIUfvfgn3/zy1/uP7y/afbMj33ibrpyMvakivYd37Rlc09b5/nnZY06LQ1atYH4aeljUEQUfrnSEruJdqGVQemBSgMCwCNUE/DIInMAjKQAUo0//aFERAoeNem/tqXdX1MLSuaZh18Go8OaTXszv5D9Lr//J+n9cnPw5T8qMZ2kUzpV1B3pH7MjMVvVi7zvcTya+ihmDwCZNlJMWqp96kClPy1jRxrFvPzmW+vfgxBecEGikNz/Pfz79tbDn/zMZ/7tu99KfWN7eyc4ZzJNAijh3mN792xq2bfnmitvK/vOaYXAgXYuaS8VftsMfQutV+dkAWBEyyAyxqYURIrpP8FYyoBSnAEt259ecjJpujK+PoAi+NmL+qeuECwbq87YPxq3FR1rBt5+UD95bsEzVlx6s0nHkutyS33RcVYAsXDfhlJ7XCaYv3iR8y/euuPAkoUzAQbHjhyYMb3+8IFwd9eh4yc6jh9rq65p/tdvfpNu/4Hbr3/8iecuXnlucg8bjJLO5EowPvR1HXxzk7FsxfneQPXGNatG4xDDYwUKKuCTZnOggaF07knyxiaAHbLinMCpw//RM/6sbWmnC/6a2nBPd4k6YPQmAVnIJf1f2s+npoONyK9fNT58AQrH7UjcjmtYM0itUK0oIxC0S02cIUF4lh2NKoY5ul0hx8z+k5T+A03ryLd376c/1tVBKuedtGiB4w/IpWvb2zvPvfDcDWs30KfvbNnVdvyIpmleX7Cmptbr8SxdNP61GbIR6zu2o+/YjPmLz1l5ycY1b5Rln6SBu2P0p6OgtFF6Qd6j1I1JvjAN/6cV4koL5G/t6m+uqyr67RUFPyTwf4C0az8a0n+4Diiask8CctTnGXH0X6gOeHidffFCW9GwTqQ/1k2gW0XNMHCpCkB319ZOr7EEXjlecmXRCYCvuiZ5L29vObhowQy6PlX6p0GdH8MnAaededp7m94jUYYrFoMVi0fvnKctOKMqULXz3dfKuM9j+3f194cuueYK0eV9+amEz+yci29xudxvvPhwUbt0FKpTBMK550jzR7vwMoI0B9iySB0JYlVKlLt2woFSU4sZowmfKv3p8D9z8P/oU8okYIwNQeXVAQNjfyL9NQvqFnp1O7zi5MK+AxI4VyJI4L1VEALBPUUqZ4VC4UAg38FKNtd3e3tnY2M9GBNO7Ht3SH3BElh05pWtRw9EI2HJ7fJWB3rC6olt++YsWdHUXAMMoMbDG1cXE0CBAfCIpIY5bexOUoI5gRZ3MCwSRZb/rkh5aNI7HvCAAxhbpqNFnJ40HAKxSeiBn4xk8o9nM/44Cjr38N+wsZA9jWhtS7vTLHfI9K4shqDykuvey129Mw++//u/FLD1Q3fluSFpr1QasUh/qMdbFfCZulrirko6jZKLGqUM/0EoRCJik8N/SvP0ZrqgqZplmm53ogPMVGL3pkTpISUOlG7Q5sQ09JwAh0prhYsQIlECThRQotkCsT1ihEj8fkFmG5I+jCEpAeRkAWDaLdL5fVmkvwJzAo+tAhgc/mcz/pQs/UWBkyVe1UzdGGKvKF0HjN0kAJY6Cdj8q5HzxVLI92cgZSzJkjfn3HTvgT07NEWBVQFeEK+46/OvPvRTMCU4eqIrzf5DRX9yg3g8jjF2uwenwtdfd/lzz7+WagVK9QRUMrZNAoURgmTQbpMUFhsAPtG4tLDoCOiIeOedyHT6BpNaok4/GNsmnYZH6zMwcs0Aih1H5pb+Tsk5ribgrvLJ3f3x3lDcGhowUBZnwITipf3k2hZkCyqFuqpAKV9jtL8H8RwnCJH+XtNSWw/tA5McWSZVmzIO/23b1nQ9zf0bjytut0tR1PG1Ak1wIu07Q9VLBIG3dZM28MWYo81NnZDmAtAt2oOapqAPxKIghDkEOenIzvJ4rRl5KYDE8L9Y489LG3ZfdnauvkXrd3fUVrlnNAT8XonjkKKRIprZJozjPgnI08xjOTmRTuUcWC5/QNHgZ5NVhopB1RReEHiBhxwHOAWOUxR2JBJdZpbc2wyAY6Am2/A/HleobkjqgKTlIhQKiwNaIeMkIPTGiwWfypQbyB7tBfMaSNY6bY1mkmRgwHHAskYsijgEgQ7+ETBMsjdSKxoDjIAJgMUHR+/8GakM/s6LNv5Q6Z/b+OOS+KYaX0O1t8orNwQ9NQF3SufxBKmpYUWnB5clNzgf6W9jvPNA+8Zth1sOtO853HGkrbenP5ZjKjDB8fr8wLaiof7ejrbuo8dNZdBCMpYsM8oQgHSsntR7yDj89/jIbayqmppiAiLzV4cc+yRJYQyHSHuLU7eZTADoj540BijKNUbfQm0BEBJ/ANXHR1pIgSPGGMAPxv4XZfwZcey/+l1SHTAcB+/1dL+3NeWFUQg0eubhl2/68FVgTAjH1Le3Hw5FFRq6LvCcWxbHfR5QNFtefbxpwamtXe2ItPywj+8iUY+TFzrGz2j9p+1HIIJUB9jZbc3DJwEMyqF25aR6YaDbo+XMBUh6ACa1nfOFGPwR3YNTAAJSbYINxEL4xw5+ZN9vzsD/y84i0j+39Z+iyXnUflDbkotFRwSVbgga0QSEAT7R2R+OqpZlW874R9WMSEydOC6BImhubDq6t8uwrGB1XbhjfM5hds0QT0ZxvNfVQ5oXVvkWDx3+W5gYeUjfG2Lr4iGEkXDE7XYlvQV0cpBtt8QTcMk1wW2FtW8rh0FrwgERTyYBXEJ8E+8v7ftYSCwyGfFDYkhCiHSUJDoZOe9HU85qNglMQCkaYO+7+Up/1SBFW3NI/5d39OiuWlTV5G+ePbOp6qRZtTOaqmSJzyZf+WBT6tNS6sSNKqpmbtvbmtpxIk8msjlo89oX6mrrRIE/sndyt3u7tZrczX3hQX3c6Qz/dd0IR2LdPSFFISYgVdVM04rHlWg0Ho8rCfv1UDP2RReevXoNC/5JJ9S2KxGl6cyliAIoJnGXVAKi6V+0cRjJLYDgxN63i/jSGeUpBVFG48/esLhgzvTmOv/V5y8MzFkInOou7776uGlanb2x/Ue7O3tjJhk+Dz18XV24q6tEK9AohYQe63S6bmHQE4p19pbegGXC0bLz/QVLTu1sG7emV1v8s2e251sBOyeutOF/d1/EsqxoTCHCBgKvx2WaZDbG87wVjrndsqqR5o1+nxeR7CTaoiqTJ+CU83AhfQL8YGqyr91Y2MRbeiI5nojvoguo0ubANuAE0Kdlbtb2re/+6Ftf/3KpJ80YBpnKpUKG/3Dk4f9LG3ZfOpLxp6P1uNctVsvau1t0/769yShhjkNVfldDjS+m6OGY5mSQDw0JTdEB42IIGsn+A3bsb0vpozCl2NfyPpj8YKddVerwPxyJWaZhWTZCUFGQYZiWM4Gj/WNjcUUQBAhwNBoLBEhfQZ4nUemTsiT02AA5nuMVYPA2ByF0rhZtEl/ALpz/OWyTwH8TAhHxnUcm9+xz0oGKMP68tGH3hactyP3TWLu7U+BRTZW7sdbndYmpOSIQQlnk66o9tUGPWxacKl3p+/LX1RVtCBrt8nC6YR5u7ZkIXVOmHiVeVMsm2aW2jdOG/3sOHDd0nXprbBtrmhaJRBRVU1QtrqiKqoVC4f7+UDgS6++PtrZ2nGjtiMZIN0GM8UUXnJVqBWpv74STsEZ02Qm1797ZarokYkLgBYg4RAv6548z8oOIgxxH5lsukWuLl6cELCN/hmnsEYudbdh95rI5ohPEm234v3Z3J8+hKp+rsdZHA//Tj4qgzy011fnrq70uSRilYVZ528Uk7D8AxBS9r38K2n8mAn394QO1C5JynJLoQU6hy8MEDca4N6a1heLtEWWNGUgb/mPbSlXYlkV2bVum8yB+fGxjw7A0zYjFlUg0HgpHjx1vO3a8jaqBIj/MVB8hhNp27TqhkQ6RJvmohXvEnCoS5C+pMHo8xPUcTQ0TLJi6GhY+VDAow/AfZx3+v7Rh97IF070uMbfxh9hY3eK0Bn9d0JNIEx8Gz6OagHt6Q6C6yuXkBIzKJKDl4KugrL9cUjA5rrHh/yix/9CxY8fb28LxjojSHVN7YlqfoodUPaIZMc0IJ5d1Q9FN1bB00zIs27Tsrqi6pzN0sDdysCecNvzfvY80T8/9DROdQirSYMuyDcOMx9VIJB4Ox44fb9+379Dpp52S5gqG51xqWLZuWvQcdNOi5xPVjLhuKroZUQ3nPKemnTBJpHMfqeMDnQqeBVYjdK65U0EU4qP9VqhtVylnwqR/cfBDpD/Mlfb10gbinavxu1KbgA5n7e5OUeDqqr1NtT63LGQrEAIhlCS+psqt6mYsrvdHVNprtMzOgFiRbVKy6QBFMxQtV8YQoxTicfWE1snZEVJwBgLOKTuDSNlIyEGoOzYcjj511nAIOZnYoC0cD6nke5HnLUkb/hc6MsekQ6dt2bZlWZqmCQIv7ts+vDLEsf6YYdnAqY9PNnaK6BoWaaoMSZ0DG0FS176pDI3vJjTvt+w+8+STnChQDEiZuHzBNnkLD2wN2rHuQ8Udvas3XFc9VR3tY0G+UYlU+l9w6nyeH8H4gxD0e6XGGq/XLaGcugJBKIp8bdATiWmqZsbJDzjXj7MgHZBMCms5+OqSuVeAQkj3Sg+AMe6PKGz4P3pg27QAH0uxulBHIW3mQq78wLITMkhiEekIQyPVZAhpw/9dew7CvOPKMbZphTLnCbBsE0NYFe05BEEDAKvXbEhNCpt33U2v/vHPTkgRsJyRrFMah0x/B8a2zplOdQUAANi0fe/5py3iUGE+AOiUfyDf0b4ipX8SNvwvSQEMMf5kr/pwxtLZksiNaPzxuISGGl/Q76Kdg3KDIPTIYkONNxzTdNMyaYv0LJOAoilUB2S7jVXNVEse/t/7EKtzOwKdQrDOSBSFTulOQcvODywPvMA5A226evjw3x/vD7urUnQAzlMHYKfBjifSM5hNIKRPAowhPdGHH2GqOwFSWP/e7vNWLCrIDSBwpLj0uzvGqOzguuf/cMF194DJzBe//gMAwI+/+9Uy7hMNifzJbvxZtmC6zz1Co9q1uztlkW+o8TXV+mRJyLM4CMdB4i6u8XpIsFCGDYp2BpQSDjR8mI8xiCmak7jAmEAIHJcM1ly8YGbqSyfeJwUt/PF+7Hgb8y9XTKW/L9qbtp4lheXgrS27SY5n3kgS917LpC86OwZ88es/SIr+8kr/oVFAWX4dL23YfdKcptqAm3ZVzW786UAIVvld0+oDAa+caMGaB8QZIPJ1QU9d0CMK/IhxSMWlB+fvDU5K/jSfhGXZ4dj4lEhjZAMCIPMc/aYGhv8kuDDN+k91gG2SVK8RsUl2Uwbp74+HUp+2t3de+7G72VeTypp3C/htvr21LBl/U5YvjqbcH6YAHKk7fPj/0obds6bVN1Z7Saxu7h0h5HNLjTW+6oCr0NbeCEGvR2qo8Vb5ZOdAuSKCCoJMAgb8wHnrgETwIQ0bp2i6GYmrxoChmTF6YLuAyBnkFA+wHaWdNvxPBQJQoxLxjS0z+Ug7aPKRUfozJix4WPDIZOfuz9432nI/yaDLK6P0BwA01niToZw5fL8uWWis89XXeEWBK6IyLM+har+7vsbrlkUj2mtE+9IeqRsXXyw6Px3ghJ5jI8WiGVP0CElanmq32oSlS8irIjxHOhSSb2Wo9b8zaf+hkMazCPmVMH3QlQlNMCD0/fFQ8pHtcP54KC0pjE0Cxp2YmtfEbrJw92fv+9PPvz9mhxvBZrdi0UwPCeXMJf1f30T0RH8E9He078iYyF2OPC+Xi1cUs4iIIBIO9Knb8jzKQOIR+U/VTFrh2cb4WEefqo/s/r3pjo8889if8zwWo3R4BE2nEXmO4T/1xmop6jypAxiTHYzBkdbeOTMa0ta3dvU3102+vLC7x1b6D84AZjekD/9f23Rg6fzpQZ9cappu3m83xOoMD2HwwfvL0JZvxEnAgPwHcVWnQ37bsnXdHJ6mwBh3K5Bh2Zph5R7+05Yj5fr22CRgQmHbdl/YaWcyVSw/YwyfXfo3B71S0pebbfj/8rZOJNfUBtxNpOaP4JYFWeRlSZAEThI4gecEHpHUGKdeLMaYphEgkuZDSRmj6WZrd+RoR7itO9LeG9V0J8GcbjDw6+X99Wa4s+BJwK+fTJ0EZIsKpcP/RIShU+LftGyB5zp6o1FlSk0zJwVdKcGg2TBtjCDMPfwfbWRZHsejVziWhadGYN7djvQf4+F/LhPQ9DrfiEaPV7Z1+t3SqSc1Lp1b73OLRMwP0NHZjQ2gG6AgwenhweJpnsXTSElY3bBauyP7j/f2hpUho+9gdbivdzR0ABX+ySPFVd20bJ7jcnd6YZQX5yaiFebtRLGYHBsjKMxdlj7837xpVL8Uf7R39er1F198Pn16+PDRW//+U0/97BejelBGRkwnZ3tqXJw/jbn0J2PxjMP/S89akBrJk23475dwcxUfdAElGu7q6uno7E4+Sj8zUkw04K6v9pCKg0OP7w9WF7HDZ3795AhbJNJ6Ev8omqkbJPOrP6IUcThGESCEOI4TRcHn9WQuyT8UbryH/0lu/dzfj/cpVCKkwPfkt83ePeam/ySZs1JdIpkZ+NxyDum/YXd7tU9uqvX53EOqPZcLCKFb4huCnmq/SxhWUS6pAwpLDUvRAcOdAQglEkodsGlZccXoCcXycf8yyoLH7fL7PFV+Ukdk3rw50eBMjhdFURIEkRdEjhcQL0COPADHA45vOvvCtD0cf/99jPgSHyBlIfHgEgvQOa5fia5evT550MOHj1IdQM8teZ68MELuJKNEDGPSR2bfPX7SP4MJ6LVNB64456QRBfq6na1+jzytzl9X5c5W77N0OA4FvHJDtTeqGKFohmpxxdQISrEFpRmCaDh5EtOyo4rm1P4s4TMwCmHtS3+5+c5PW5YJIdSV6LlnnqzpixtO7LRsbNq2aRODr2ljy1k25p9cU+VNvrelZbd6dHjhzyFAANwi6QZMy8yRO4r0NEU2xhLP2RhbNpZ4Utrewpi0qoBkAUEokHhTG0IgchypVg2wi+fSDEGzZ89c0lhFNubIxqREXXA2UIqpR8vIB4xJc45Jfa3uHg/Hby4FcN7yOeJIxp+1O1slgaurcjdUe92kmv9odU3681/X5bllWXTAgKBP1IKzbRyOqfGE+zdbgTjGyJz+6UfyvUwP3YWxTWoIOnXf9u8/sHTJovjCM+oOvocxR4us0b/7aualSX/nSxrhOxI41OSTvZIgOEVELYyJJnAaQFI1YGMsOL1NbIx5UsyKpBnRgqO2owkQhKQAMklBgM1+8EyKDgAALLz59sjfnqMhD/uCc70QAmY+HDUwxuokL7jd2tr22l//OFEUwGubDlx93qKkQFfVzJUPBJ6r9rud6j20n1cxzJqeHro7nH/59M17jvas33a0ozeWNjwnpy4DM1xknbg0nzAlJc8rIe5jcd0i0w7WF7AkNv/qzry3Rc/8329u+rtPJsv77GzZvXTJou65p9Ufor0qyfodgdnDpb929DAEQOTJHWnjpKpw2s0OfLHQSTms9Ugix9For4HqovQOSB4hURhxyEKGFUOgkwDfZderq1/YFZgDAYiEs+aUMUrHMCxWm71sCuC1TQdWnj6PdkLNwbqWthq/a0a9vzrgGt7qK3+OHO/IZzO/BK4+Y4aiGyc6wye6Ir0RRTcsgEHcItMU3l+X1AGFNgxI6oAsUaGkIoUj/RljTUIHDAhaqgM655w6oAMyQKV/jUeq9cjY6Q1JavTb2MI4qhlh1aBfpGnbfXGtziOLxHA57FaHmZZzbvWh2YG/DDME7QrMAZUq/Qua7ZV4rLiqO9VZJuv47PKbPzqBFMDyhdM8spC8mhmH/2t3tMoSX1/tqa8mv6GxufAQAlng64NeRyLbPWHFtrCbs0qJCi2iXjTt+1HEzhnF64ABUnXA8OG/dvQwcSBL/El1AY/IUxsRnQRYGLeG4jHdNB1dbmMQUY0+RfMQDVCe+3e4Djj11MVvrtkIKpKCZnulHAhjEFcN0ndh1EzQY8D42n8Gv4PXNh2oq/Ik+7dkM/6Q0s1euSFITP+pUf+jfpYIel1CQ7WnPuh1iek9hIuLCh0xKCgN0qGKMYY883+/SX2607HzDJf+1HCHAJgR8ARkUeCQyCGJ51wC5xZ5jygEXGRl8i2aZYUUktg9qid/0cpzRnX/DNu2NcNkjrkSSfwwzl8xRxK43NJ/fUubU+/TG0jJEB4zOA55ZKIDagK0h3AasMjuwSMmBwyQWhyUMS46oLO3b7j014+RyB+PxDf63cPHJAgCt8D7yHglscbGIKabUd0sY3DXh2YHhkeFAgAeefB/y3YMxvAcYFadtywK4LVNB6q8Mp1JZZP+a3e2uiShudbXWO3U+xwPOA75PVJDtYdkHQ/VQP5gcLR1ACsFOr46oK6xId3xiwGV/qRVtUeWsxQhdwlcjUcWUyYBMd3sjWlGWScBy5evSNMBbBIwqpiWNamLQFw+ARwARAG8tunAVectzC39abnmmoCrudbnHVryYYwRBa424Kmr8kji8PTgQR1QKPnPAxjjPg9IYp9ISH8BwQavK9tdySMUdIk+SUy+blh2b1yLaWWbBFCv73B++ae8PaKMAjHMqVMEYhzhLz5jPmmql5O3drVVeeXGamL8efT50fduFfWzjGlWMoKzCG8w0wETmeHDf0WDPXywTif+/1qvXOUSc/WpFvmAS+yNa5Yj8jEAUd3oVTSvxA9PMi+UZMzP3DlzVq9ev/Li82FKRFCJO2dkgxTqmuRBeq+NtweYKADvwC8n2/B/tVPuvzMa6mzvGOyF4aC5CmnUBYFHFj1ukTbbUjRDJUOwsn2FvBuYfe2l6ADGxOQTX/pGDYkBS9Cyk9yQPT3dGAqdQvV0u39mlTd3SA+PkFfiZYGLDeQN6abdG9Nq3FIOzZEPaRGfc+fMWbN6/ZlnnS6KJEri4MEjC0vZOyNHDrBOWwExyhEGmsP4k0HKQxITKfBclci7ZMHrFv0eybTsvrCCMRAETuSRKPCSyIkCrxtmb0gBEIg8JwqcJPKSwIsip6hGb0hBCCZWipwk8ILARWNaf0TlOSSKiY0lkeM5FIqqhmGblt3eEznRGSYp4MO+fT7YmKoDSrsyjIkr/bu6w07jHh1yIph+UpUcyb0TCIFfEgKyoBg0HJTcHBHNiGiGXy4+nm14vD/1+jY2T3e7ZZ7nXLJU3J4ZubFtWzdZCFCZFEAO6f/yzh6/V1o0p375SU21QU/H0F7beZOxNY8MZqQXIiUEeABIOeg0gm4XXVg43dsXqTpwrOdER0jTrXRBX1cb7qK1SOHza3ded+HSok6YMRGlP0VVLX2gPB8HrFkzmnr8C3LkiFFcAl/tlvsVPT5QPky3rIhmGJYtFdjCOrf0J60iW49LngBCSJbEs1g5uFHAsrFhTmIHwPLzrunqTPQ1GV9QDum/dnenX8LTa6Rp1aKuRouV/mUGIUi7zwe8spPOObyDfG1y+fm1O8f8BBmjRcvO3ase/cOrz/yZzv1JZTe3fPzYcYhQ17zTk4+M70UIBt2iTxKStwvGIKToEc3I35BwfPrJyUc26Q8A2Pj6Ki0W0nUjHImV8HEZWSE1ASdzDOjWt14EEwM0QvqVW2ysIQWfuYEcsYmAwKPqgKux1hfwyY4TL9cUnumAKWP8WfXoH+jylnXPOWHBnMftAhBs375z+/ZBTZ9RGUBnEuCVaBU4MOAKNnvjup4zmjBN6FMOHjiYOoIbHu+/8fVVlhYN+DPMZRmlYxjmpI4BnThk7Qi2bk+nx0Wkf02Vu5SaP6MBhNAlCQ01Ppr03x9JrxTtHzQEFX+I3BtYpsHxQimHYBRm+h86UN+y7rnzr/yAIPC2Rao0AwhTdcDJJydMf0kdUHdgMwdhQBZdAhfVnG6jzkCyN67VkNpwUuoXnirrkxw8cDBtjapqsizlyPaaPbPpHfubtmWTqAfd1jT9qT/8mH3vpYJBTDVS63Q98dq22y8/hV3YsimAtbs7JZFrqPE21/vd8igWfC4aDiG/l/xodd1SNFNRjTRnQKoOKNQZkPHz8ggmHIiMArn3IVSy9AerHkuPmVv/yuNO3VDq1SX6gXaTBIDMCdKUAdUEtmHaB46iAzsG40E1oy+u+SRB5NBwuT9c6KeRQ/pvfH0VAGDlyvPpyWGMQ9Gs5lZG/pBGHXEt9feuq5OyL/zy864Zd1sQnzVqwiM31fmrfK5y1cwqMxAIHOf3yA213khcNQyaFli6DiDCfxwz3Rif+NI3hpfgXPVYwviTI0fsxg99gvxDI4tJOwFCqjI4+eSlgsDPnT19j2G0t3UlQ5D7DFe/a1pDbTXMW+4/9fCv8vmmqA64+OLzaaPj6oDr81/7958+8E32LZeCppu6PnIIUGtXf3NdxvATRk4FsHZ3p0vi62u8NQE3aQ4zYYUhJMXpgj5XQ40vphjhqFpyWHBC+id6RTHGidqU4f/OFNN/bp79y28HNQFV4Qll4HyvjqvAWQkswwgEvP39JHgUYxyNxY8db+/t6pRHitrMU+7nYOmiuV/99//8wTe/AqYohc72ikDVjSmQA7z1rReXn3fNhFMAa3d3igJXU+VpqPEOL7cw0YAQSiJfF/SEo5qqmRpJ8yl+EkClP3V4YOxYlhljyye+9I006V/ETpKaYHBa4Mh95z+nejCEWjwCsU50g/OSEgvLgj9jbeEShX5yEkCfHjlybM7sGf/2Xz/9f//0+VJ2W7FgDDSDGvAY5VYAa3d3cAgF/a5pDf6AVx77kp/FR4XWeqNxrbs/ZpHYsCJ1AIKQ5xBCpA8MawM2EaQ/xvi5Yab/0pRBIkOQI5ZNSJuCOXHltuXcOuUa6efQAYcPH2MlIorGtrFulLOCQIUzdAYAoUsWmur89eNX8rMIeB5V+931NVpM1WNxveh7g+MQLTRtmOwOmxBj/xKlfzZlcN0HPsaRhu8wKUcwxq89+zAYNTa+viq1dTCtE/eZu/PvncJIYNu20wWsGNY9/4cLrrtnQl3K5ePtBx402K3d0ymLfG2QGH8mZuRPNiCEsszXVxOnhUAkeJGpYQLvjAsB6SZY9MncdMdHin5vhTt+08jT9F8Ezz/++zUvPrr5zWffX/ccfbz3JhmkjyqrV6+3LNsBHzpE6piyWqHFpoAN/DxJ+C+YvGwd7xCgQQWwdneHU/DZPa3e73WLk0j6UzhIWgU01vr8XkngUXE6gDQJhNAkBiA2wZwcjt9JxMbXV61du6Gzu7+nN9TXH9mytYXdZkVgOkkVk130TzgTEIQk6Xdag7+2ykPqK0wq/vzYG2XZj2P9J5HiZdkboxTT/5S8ehtfX+XxV0NE/uM4FInr//ObP/zjJyeWUWLi4nhsDNOaUi5gS19+9mVb3/7beCqA152Cz11h0HWiFUxhUiqEZvMGGyaZoY/1iVUwGUz/JTt+JzKxcK8/WOs4HXBXV/cLr7z1z//2wx/+v38e7/OaNBimhafQL3Tr239bfvZl46gDeCr9Jyamezpd4DnkksUqn6uh2je9oarKl6gMSplRm6i4ggG2LLs/qmzf17p9X2t/REkdS3JO4QYr0pFNB5Ai40Z6ICljzKQ/ZXjG71Ri4+urzrn0hlNWnIIgtCziFNi2c/94n9SkgbSANqwp9vvc6uiA8To6b8n10+urli+cNn9GndctkYSZSQsEkOe42oD3otPnL5nb+P7u4y0H2yNxNVWkc76GbDqAzC6n2t01mchm+r//Bz/+9le/CKYKG19ftfLi8517koxPVixfUldb9dDPfzDe5zVZYkCHVYCfKjpg63hMAlCVz7VsQfPiOY2+SS79U+Egaqj2X37OwjuuPHXFwunuoV2fOF9DxndppMr8VLu3JjK//e/vKIqafNxyy3W/+8vjadvc/wNSPe0LX70fTCq+cv93vnL/dx4c9nEAAJ/5528pqokQRATYeqLt/LNZIbO8MC3b+ZFOQbaO0zyAP2vZ7Pkz6kjS76hJ/90HjoFxAgM8vcZtG4Fj7X1xlaQI0NwfIVCrhrrTJgEmG/6POT9+4Ftf/Nq36PLWrTuXL1/6r9/9YfJVnieau6+3B0w2nFpEYFfLngf/8viBA4dTX4rGNI8rUUcWQrj69TUb3nhhnE5zMoEBiKt60UkAE5+t4zEP4HlgtnV0tXdMkbF/GhBAWRLqgt6YotsYq7oJBhqJyhl0ABv+j5sOoGUY3tqwub6+JvlSquj/wlfv/8kPvg0mA+defDWECR3QsnOPNLTEUFL6M9FfEBgTBTCFHMCZGWMdgGTSI2n8pb8zME9+t0O+ZBIwQevppq2nrwx5Y8pLAys5hHweubHWV1tFcgSGdBAb/8/NIPz4gW+99+77tm33OQXahvOTH3x7skh/AMCG1S/957e/8dbadaRwjaZn3IZJ/0KxbDtDC9ipxVZH9I+lLYifIKWPhyqhIaeULSuNvCXLuae9IPJcTcADITRMs6uPxF5Q9SD7a9VwN8AFNwxgqqPsrHvtWZKvV1OfuhJj8NP/mDRyP40Nq1/asPqlv/9KepLz6tfXkFeZ2adALMs2U+o1TW0dcPq5l23eMBbzgEmW81V8rQhRqPZ76oI+t0tMVyjO0x3H+vLfX9nPkEF1QLhnSKfsySv9k/ziP7+TtmbDGy8w6V8ETsG+qTz8TyUp/U8/d3RnAxWhAJKFo2urPKRekMAnpwiuQJ3zMjhyOM8kOCb9R30ewGAMxzTtKdAGoBRNMBrKIGtP4KkHgtDjkuqDvmhc77ViRjxKrEAuWSlAsDPpP6b87I+PSOKwGVt+GCSnLy903cAASGJ6h2eMsWGaTl8x8n9HZ29HV18oHI1E4t09/TQcBQKAOFKYJuGpqpQR6jhgENNt5V7fzaNjEaogBUALR1f53XVxraO9nUp/YneuCob787f/MEaRmz/8qZaWfcmnqctJzjv/nBx7cLtG6OqVAQjf27JnR8uBEaVLqjsKosTsub6+5prLz0360vbv25v2LlVJjDEYpWAYU6oIxAShshQABPDFNzaRm0kIiBxQbAwBkFHmOA3G2PPXh39984c/lXubt9ZvzPHqtOYhbuQ8UaMhmbeKG8BjPXZwfwZFxSh/FSB2TctNBSmAJ15cT8aPK+bMnz+/vTt8vKO/P6J09oYBLuckQJaGZB0zCiVFCpO2bCltGgefpjRyIWG9to0TfxE80doxrbmhiIMWbV1AA1OBREwyHCFEjVEcTplepgImgwJ4c1PWpisAgI6u3g9ceyEYD+n/lU/dSp9W+z1zptVGFW3b3uP7nERNf1VwjE+JMZKZhSw4z+CwV8m/VPo7rdyISuA4x1xflNw1zOKLACY7p9LW84xRYgrnAI8j/OMvrPvAtReUZV9HTiRi+GY1k9CaI61dqa92dA0OsR9/YW3ae0dPJVDRf+PVK0+aOZhiyvPIx0s+j7R9D7urJhahvv64Eh9xs6bmZgidOQEgdrwUbQFOtHYWZAjCGJslCJekxtq5Y9eIG7/81/8r+kAVThEKoLWrv7muanROZwrNAB5/YZ0jgi8oXfRnk/iUz3/0OjJIg/B/f58e7TdcJZSuFTKK/lQOHWv3DK0Txxh33njhsUuuvWNEHdDWmitsV8tDhSSxMY7FFJxfklFaOjoAIBLq27kjnP/hGMVBGoExyg08uvkZAMD3f/54ETrg8RfWZ5s4f+Ge6x1RD5wGi9i2sdOFOxFSR02uNonqwghClEUr0DNMfZL/GT7x4vrcop8udPfF3t66x9RIBQLJO9g20jEspNvH0nqFcXwGA5osiSumCV/7/i/zPE9GRvLRATmoDhYw7jNMKx4f0joifwVAYovdrrxaqELwChv+l8BNt92R+bpC4oO58/rMsWFpM4CJ1hR+3EmIsPs++4Hv//zxfKYCjtBP77P1Dx+/IWNJCUx6uaSvJ348x25qQ9J+PWnj/YeP30hDqS0roS1++ofnhh2dnGEaaSecZu7PJvoTZw8Blf7l5YH7PsN0QCnYvOuU5fMGQvATnmEIoY2pwYc4ABJ+V+L+tRF5CRiGgTGWJJHnuIaGmtQucKkLaRw70bln39F8FAB1MqRSFfBddN6KpBsgB//y+c/k+dkZjDFjcAx732c/QKcCaWpgmMRP8Nm7r3XEdIZfRZKM65ObOyXRObpMf34IIdOyHdsuee8/fOwG4usjSYAkAgwC+PM/PT98h6laAUI4fcb0D11z1oiiP7m9JiVmCYUHkDNGkWR0zZB7ZmAh+S+t90f+Op3jklsOcSUPXUiDd9zHCZVSIOTOJG9kzl/GpITff7h1/uzm5HM6FQAAPPbcGojSTRxf/Pj1pmPTob9AkgZJzTolk9wJaUnvdKVPjsgSYR/OET9/z/XIUTm2jR3bEvntJbQCxl+890bLxs+8vnX7nqMnL5yZW/SXSEb7T1IYsUlAiSyan+gGWtxd1OgM//OEowogD1JiUhOQto6WzTl3bG7+46e/ZJMAxkSDSLH9A2VwqCa444ZLmuq8//Pbv9KVn/votTBhpidPBT4xZh9tUn9posCnx2tDEv9hk0BA/Pl7Er5lusHSuY0FiX6fW4zF9XI5mFySAEBiZ8wQNCngONKZCxQVB0T7+pb/nBgF8sRr226/nDVWK5ghw1iqCRAnYgy+8PGbLGKNGTqJHu/a0cn5BgchQImzt0mCeMI0/Owb26gCWPNOy8ym6nz2uaDBfbTdUEmrufRqMIWCEAz6XQDEStxPhbPyxo+OtQIo9sa2bGxVQI3iiY+uFh8yUMlkmLp29/YjBHkOSSIvCrzAc3SINO7SPxu0vSqdBDTVeHtDkd4QKfSWJw013pqASyZNMUsl6HPV8EOk/wP3MdffRIfe38W9F9t2KTkEjPIxQaXTBCfzfb9t97h18S0H1KGXr0+vyic31/lrqjwlHxVMa8gQfch0wASHDiCKe6+Ncf5lR//jpyw4eLQo9gusdMaoFtB//T49oLPaJX7sQ1eO1vEKmazIIl9f7YEQtHWXNIuUJeGkmfXHD4ZK2QmjaA9w0SCEUB5e3GzkrwAYowc/Vr7JKcaoKID//tOLhq5zXK6vpFfRU7VCtZORO4oqITsIQrcs1AVLnQE01wUaa/3HD2Z4iXmDJ6wDgCqAZOxWEeiGUdbTYRSDt4gy4IxyKYC0AT7KKfoz0qvoqfupkhIndu+dV4/B14QgdKJ3cpGWBpwGx6E502pIDGsWmA6YsCAI84njzIZGwgcY4wNNAwYABLykt0da0CD52aa46PnC5dKUhy+vYWcIidqIA8lizlPLid9PTebMSL9m2k503YOPvDQ2aoDLI5kzB7VVnrnTa5gjajICEcw9W82NrjEFMP543VKq3M9IJfSUHyMF8MMHn0km8WbDJQmkzg/xsEGOPIiuVnWTRJciaFm2qo1sPO13tqFqYMwmBIUCIZjRGHTJI9SVY5OACegAoDMAnh+jGQBLBxsdSMfvUdr11IYv16h/YJhPRvoIIhvBuqCbQ4jnEMcl/mKMo3Hdsm2EoKqZbd2RRI0WnJcaGMsJQUF4XdJJM+vycTwzHTARgZBUgyi2IYBhmMnOBIxxAcEhZh9G/vBlMPgQiU80sCSQeGqOg6RyDzabav107E8mAU4yMcbA4xJpPr2qmeGoZtk4WeabFAd1ansR8MgTgomjBqY1VNVWecf7LKYCY+8BpvG7fJbCHvlUg3CigFg5oPEEIXTSnMF6Noz84QsV/RCScj2abgk8keu0mDPPo4BX9rpFjiMBFdG4YZmm3yvRn8rg/xgLA3NtWeIba326acUd9y8GQHXs/hBC07JNy07UU8g0MKNq4LdProNatHQ18NDTiVYEkp/0sSkUgefmTq/Jpx4khU0CRpuCCgERIAkiTO00WRCGado26UdWxHsZeXLHXR/XlKzZnTmCLxhFKoC4qiMIf/bIy8k11JrPc8jrFnv77aDfRcU9x0FR4AJe2S2LdLAPgNIfJtHV6Tt1tIWzhEXENdf7dcNUVBMh4hyOxDTSNgBCRTPimhEKKxxHPAek3CLttjoMLHl1w9QNy7axbloPP/vWmctmg7GlocY3q6m6IPcv0wETCmcGwGUcauSDUw0or3pwFOYGKDuluHAqnKwKgFbfTCxDMslySbwsCZLABQMuVTOa6/1U+iOEeERMQDxPKnXSpIyRiquQV31u0bIFr5tUHLJt7PdIdASmaEZMMSJR1SUJsiRquqkbpAipU/MnS58gpxBccZfActVYzp6LCCRGEM5qCkrM/jhpPcCp5YDsom4hy7It2y61jBQjJzmG/2lxn6lU+VzsuhZrAqL9tR37vizxssgHfC6/RxJ4zusWe/pi1QE3rcxMe3YUUSkIEucbRPWn1VS5AQDHjx2n60UAAgA0zyfLtJuYYZiabiiKdmD3+6Zpk+lA8qdKzgDbFk5tyO12E2HeHcpclG3OtISpJxIlqb/33XNRVNFVzXzk+fcK/QgBn2v+zLoioj/ZJGCCOACGtAQoCqIAWLfCcaXoQl433JLoMvaJ23N1n73pw18AFTgDUHXDLQsCzwV9Lo9b9LkltywQvy4HZZEXc+Zen3HJTUqkd/j6GTOmpT6lPt+k6M9wGiRJByIkigLvksWqsy/c8d5bumERJ4GDQFqOcS4J6qkZ+dQnN/xvJtrau0GBCDyqr/VHYmpjrS/gLXKUwXTAhKsIXRR2aT3lGaXj9bA04HIrAEnkMcb1Qa8k8lV+2SWRsqCGZ64NgAnArCVz1Ow79blARulP2u8dO5FcnjFjWg7RnwqZYXBIJIGkoL7aG1N0ZSD7JmMCF5keOFMESKcKdLpA15Tsq5MlvrkucOHpCzp6IlxKCyrG5C4IOpBTWlRBUFYOaNyAEAZ9xITAKIJcN72i6k11vvoaj98jbdijGp65w7eJxkkMT3GkKoNURDnr1wkhaKz1NdX5anMW73RMUgNGLPooh+h3Eg7FmY3BKp+rNuhdNKdhQX7h/9lghUInCCRioehyQJBkNRb0DlYWtIxwiNTyKuceKwk+m6F86x5SEdrtJvWNbQAuWOoGsQzdtQKg/O1PrBjp75oNlyvgcoGaIJiTcp6UlWctzKqThna2odDPfqgjx2RmCD6PNLMxmPQ4lVJAJgkzBCVB2OrsLNgcN5xkqHH+qJpuWwbE+VlyhriKocslKfF4RzsrCDE+cBzHivwUzTiFT6XYZDJEXqSsoXFBeMQ1A7uyLNzWnXdB5mwnkAm/V57ZFJREnpl8ph7YKVFVzDshqK72i2JhI9D/+s79xRyLkQmBL95/wxiiANJ+AiT4PrmGCtnUDXCmRxZSU2xoTH8isn9gmSzQwyVfHTh68m9S0GdYM7CrnlAsEtOynsawp4MnM9LPf2bjyOGeb28/CgqHGYLGHdtJQy/ijTzHVVf5mfwZR0Qh30J+F9947yify+SDp8H1SU9m2lOag5Xst55qSoFO/EPqloMFQJNPB0fL0LYTG6eNoGH2hYwMHGBwu+T2mmZ29ERy/JLT9jyQlJY537jo+6wImCEIAPDGqoduuevTYDxwqpAUrgAg9Ps88khFABmjymSsAvTqqocxANOXXrBvw7PjeyZ8aqtfIgmHBrUMeTYs3gVlezVNzjstGpMbl6dy1rA92DZu7wkPCQYdRuZYULqqHJNIjMGOY33LZgTLsC/GGOIk8xasABCE9bVVxXuPGeVAHqmTxyjx6qqHk8vTlq4c9nq6PylV1sfDJEhy3KU/VQCDT2AOeTmS1M48uM70dJTMdf0RpS9cUk/HclGcDmCTAPIlhmJVgVJbsxUBcQI7/ScKguc5r9fNzM/ji0sSmgqt/lQgzzz8k+ErqRCn7NvwNJic5Jw9DZkLgImMU1w6POIgriwfIkfhxxJnNkwHjAuWbUednPBC4TmSP1Dou5gHeLzSgG+4+fZPfGBwqN7VF33mb++X6zSGl4mdFEyA2WsyFKfIWj7E+NPRE1bzaMxU7BGGAAG45OxFubfZcayvHIeqON5Y9dDYH1RV9biSbyhwKohkIU6+3/xUAkIoFxiCNUrASSj9R6spfP4kHG8kCIcsjVRCLjNxVY+reUVhj8FXdORI66xZxZcmZ5OApx/61dkrSyrx3XrsCBipXV3xpPiKQ1p0/frOgt799uoXRuGcKhdR4MbLBzA1GM8ZAMZY1QxNN0n9Z1UvwgtHd2KYdokdfcvFcy++XvokgEWFvr3mJVkurY6jPRGL8zDpX3ZckiCMZnjelKfMMwAE86oMYWN86ERve3c0zSpj8b4qn+u8FTNnNlUlxOjOESLrMXbaQeQ3dcCTYRLAAACseZl41VZedYuqKkVeENsaxXlAgTDRP0rIolDQL/q3j69JdQMwRlAAy5bOzLDWEdqGafVH1Ehcg3pqqe7BmGhFzaoM1u10HOg8aaMoYvILd8nCknn1m3eeiIeV194krzbX+xfOqh3xG0q0Ic6PMZsmUB1QdEgoMwRR1rz89MTSAUUlizHpP3pIUlYJxpoBlKoAENRbWvaD8uGSxTc2HQQAXHUmqStHlymKamj6kGn7aJj1EwnDo+av91fXhHt78p8HPPfm9usvOjnjS0wHpOoAx1WrTLp5ABP9o42LOQBKg68dFnbdGyIhcdMbg8c7MlR/KxEOQdp+KxXbtuqrvbsOdloWSePiOD7gFedNr8IAiNyQ8Azdkos/NiYZ/zFF7w3Ha9I+9Ui2oVMW5tWpyoim2/1zTwKySX/GcHMQAKBI5/A46QAm/UefiRICNDVnAByyNYO0AgYA7D/W19UbBgCcf+rcvYc7Al6Z+F4waG6o2rTj6LJ5DS6XdKS1l7SLQbDan7Wes24YzQ1Va9494HERY5FlmRxHzqHa7+oJDcp6jkMWafKVTlIf9IXVXYfDn7j1/MQLEMYU/bePr8nWExhjoBlmOKr2heMx1TxvOa0lOkD55gMuESl6IqWITQLKy9trXirzHhmTGQiAKE4UN8/UjALinV6pEELXgK3teGfILYsBn8slCR43EeJ+j0RrAPk9Eu0apmgGR9oC2/SBidwfrNAQiWkCz0kp6RtkHgdT2zzS1Pw8MjOdiv8jiG+n0Fs0rnV0Rzp6wtG41tkbsZyd1wY8CMCDrX2tPZHeSLGG5hQMV53hquMDDck1R4605g4Huv6ik597c3u2V1lEEIORDQihyE++QkCTSQFQkQwBoLKeNFDsitRVJ+wnpL4bIB1aaDqMJJLSchBChCCJzBkAOg03kk9VzWyq8wV8g8Yctyz0hYfIX8uy8zPTj6QBMDBtuz8cb+0K9YZjukHcDDbGrZ2DJaPvvPqUWy9Zcu35J2XbR/7FZt2cSR/+6sHc9CNHWvN8O4PByB8iZ/Lu/ZAsBfrbx9ewi5wkq/5srKtq62pNSr5kBy7LsiWBp23ZqXpIFklGELpkntZ6S0vpStUHto1rqzwxZTBGKKbqBhHNg9vwHBJy9hweGef0NN0MRZXuPnKE1Bf3HOmc0Zgwzc84fcCO9MjajHvy5d1xFCFS9HS4Q/j5dTuvu2Bp7kkA8wYzGAXBIZQqWCqcm4rqXJ/vBMophgwFHlH5Rob8mbLgSfvFPIbLPIdSqysrqgkATi2r4nGJHFdI8blMA/+YoveF4+Goagzr2R2JqWkqIRsIwWn1iaSEEREFTtUGjV2pOiA3t1119pMvv810AIORN1CWSC+Y0a4ENynkeNHkqwAgGelzosjHibAuA0Pz99LjgorK7hvcieoM/PvCikKqPGaI3cYAHDzeM6PeDwB494W/AgDOuPbmjDv1uqWGal/PUAtVNgJe2TBjlpXhiLknAZoyIeqYMhiTBWKXdkkFRW+s+usTN9x8O5iKcrz8PYHf2X4QADk1sxdxBTe/zgE3mrWT2nvC4ZjqmJWycuhEQgFQqBpIB4L6al+qvzo3DdXeuGpE41oyYSh/QxCbBDAY+cNxKBmZMtrcNEmkeREUcAV13bIxRnmHVO/r5Gmj1QUNuMQM3kLpC8dHrCyk5WwdQxF5ri5YQHn6+qAnHFM13aTe5oIMQXQSkMMZwGAwknAcKqIX/Kq/PpH8CwC44ZY7KvySErN7akfctOVUrOx9U1MjOJPYRF/karabQ/6TIFLHzK9bcsaHx111xtKZW3Yf3bLr6Lr39j/28ua/vPiu1+vedbhz1+HO4urKDaemyuN15+sBphGx9UFvjuzE59ftzPH22646m+qAjK+ykFAGIwmPUBHNGBhpoIEG64OyGmOco0U2xyFVTx87R2IZyv64ZJGY8odK+dTooBwe/KQMTesLX15e3rB7U8vRJ97Y99SbB59eeyjtVZ5H0+qruKEN/3Kfg8BzQb/L5xHTIkdTo0Jz6IARPQFMBzAYyZ/nBKkBPKlxWkIOtH2n3tHB5r2Z3uCWhdbO0NzpgxLNsKzjHaH6Gl/alpG+TlUzTMsGDYOJUaLIG2Zix5LIK6qW+bQGwnsHv+EBHZBH//ZiGK7xvG4pQz2p3KFJELglodrv7gurMVVPPdE8DUE0JJQZghiMnECe42CBCuCDH76b5P1ce+aUt+wX6API0rAXZhK0CMLOvliqAojF9Yhj+F6yZH5q8bjFczJETwa8cnd/IqJGyJ7EYRhWIr3AOSGqkpLnNuRrHw1t4Bykvto3fI4y4h3H86i2yt0XUXTDNMzMPvPc3mBKNh3AisQxGNAJFCyuf1SS3/70gTxbQg7vCTxllEfOaqBo0LCjqPr5KzKU2VFUvcrnOvvkmdi2U6X/8FrQS5bMP3Tw6MkLmmzBu+atbdMbAstPanpjE1EGHMefvKBx+752WheI1FA70HXR6UPL9ZQDCKEk8gLPk+F5ThCC9dWkWnWhcAj6PVJjtTcW1/sjquMFLywiKHdxCKYDGAyIoJgzUXT43P32D91lWXa53AbPZGoTnycTSnmMXSWNlpb9LtmpJ6FHAAA9ofgCMDiN0IflapW9eD/HIa9LCvpd1VXeLXtO5N6YJ0Fmg70NCmopIwl8bZUnHNMUjTQ7S30pT0PQRWef/ObbzBDEYGQGQVIEAk7Onr3PlKA8yq4/RlAAGOBkwm+25ZSNB1fl2Jh+E8mMWQ4Rf+/+I/10IcmuA71zmmvKogbowD/glYM+t0sWZjQGt+5tzermdrbnOJSxBFDu03n4mfW5zySmDOqDHJMA/0DkETMEMRhZYkBRQ20g/4sDISlCM4ZNocZTf+SvJHIpABuTIXCqmMROJZ++iNLeFaalFBCCTvgQFnjO75GrA0TC6gZpFtYXjmvD4oUoVNa/uflYmtBPpVwxP8mBv88jCzzJPxM4rtrv7smSAUc/VBH2xbe3H9X4DHckVXhU3/A+YEa689EB111y+vNvbM5xOOYMYFQId9z1cU1JbTtYcK2wi2+89447PzL5hX8ZlESabijMBIQx0A0rFFFoFhXHIQ4h03RKHzgXF2Os6WZcIU3eh1fgKQi3LJaur2VJqPK6qvykeHWqSF80p2H9lsF+ZAgiGycdtpBMMIuyFX7+9rMsG0fi2rGO8I6DnX1hhcTTDlVlvK82VQdkA1oJLwWLCGIw0pBEvohfaMXI/wJ0Q2EX0bZxJKbGFA2TlGAki4LTkD3xKqm+aVihqBqOqYpqlJKN5ZbFmipP/oPw4fYcCKHXLTXW+OqqvW7ZCcwnLQcS1AW9slPgAUIoS0Jz/eDIHUKi2IqwGGIM4tGIFo+KwJhdJ5+/uG7RNG+VDNy85eYGHgJ2C9hfFcwnLeCWK8+iCyw1jMFI+2nTX2iBleBgMgaUUbACIE21dCNEimuSYv2yxLtlYdBQjkml6GhcC8fUEof/CMHGWr+Qn4bHAJiWlWZr4jkU8MrNdYGg3y06Zh9KUqgjCOfNqOM45PfIjTX+ZfMak++FzvA/hwK45OxFGdcPvoPjOIH3+92N9QG/RxqcedCcsmGNbrLpACOlCy7TAYyKJc3+I/KcSxKKGM5PGDfw5FQAtm2HY1pM1THGPId8bkkc6ARA0QwzqmiqZpZo/KnyuvxeOZ8Jm21jVTN6Q/G0I9YGvU11Aa9bogP/JKnThDnN1bVV3qY6P3UPuGmEkhPHmdq+pmAG6pMIPFfjFeuDHlKyyumJY0T7yCMeMuIhl5jXIWhxCAaDkUQUirH/OH1KmAZIhy/AvqEaoahimhaE0OMSvW4pteSZbWPLJo0cQQnwHJJEoaHWP2KtUEwnHIrWF4qHhnVzrA96iY8o5z5Ega+vJpvRQy2Z2/j21v00/qe4++Rvf1sNiiWbNzi1OASLCGIwnK4kJESvoEvxwTvvzlivjMHXBtKrXfaGiNA5ZdEMAMDOfXuT66v9oNpP6ief6FL6QyFJCCQUgHNhh3s7CwVB6HFL9dU+2iI4K5gcSxus+G8MP2426Z+2juSSOKv2H+2YM73u7a1kjICcRxEq4Nv/9CGqCOnZ9EeUTS0n3t/TFolpGa8LLwMz3DWiDqBlouky0wGMCoc0ghdIp/KC3uUEKpJutaN2XpOVYhLBaLMWRx9IsxozFMts644ea+05dcn0HDs5cLRr7ozaTK/YACRG9IeO98ycNj3DwD+u9YXjkTipNJR571m+6PSwopQntOgbkf7FVpjat/+YsyMOWImzuvrcBZefNe9YR2jdliP7j/emTpgovL8uVQdkRFPiVbX1/d2dxZ0VgzGVgIhEbRT6E4WZ69owCowCGntETk19SLzqlvT6IJxeL+qGObs5eO+t53/CeXzomjOi0fji2fWLZ9dn21vafVMb8AzGMCVaFJD+9SWNFAakP1UJhw6dUMLhGUFhRlDwCbYbmWmPfCKCLkupisG8wYxKhniA5YIVgFPpmKmADKBVG488986J5zeNUBqhYEZ5spUsJPe3jS0lHnJOrTCnVrDivckUsLQNWrtJ7Yqi4TkU9Lkaqr0elzCiZsmoAzQlPn/eYCGm3JWCGIypC3TJQkEpYIMw+Z8J/kf/QRPDMI2oJBYY2zLaezHGosAvOGkesOwte46v33IoElcDXtfK0+ffvKBZV/WX3tr93u7jkZgKADCN9AbrvFC7bpcKITh/kQyKojeUqw0vh9Ds5sGxc57CP3dmGYSA1Jgtt6EQQiCLfJ1THUjTTc2w0u5Ff1Uw3N+XeyfL5zfsP3A49zYsN5gxtYEQuCSxiCCNxx75c7IcNCOjDwBS/wpZREDwKglpadut7X1b97ZG45okCgtn1c+dVkNqKRioJuAO+lxqmCS1CkMtSSSTFlmWZVuW/faOrMPn7p3dgxGTVZ6agEcSnf4EDtdfeb5l4x37joeiytH2vnBU1QxL0w2OEy5a4gSJJsV53vdD7g0RJAVGcu/h3fe2v/tehgH4P//9B3LtGUGfW2yo9kbiem84PrxrfKoOyOgN1pT4Jeee8saGbfQp8wYzKhAOIZF6IIvi0Yf/9OjDf0pdc0N+LSEzltYpsabbBIF//Pf/R5c+8LG/G/qS05Expm3f39bdTxIxXCK/dceuo0ePyCIfU/S27nA0nqudS2ol5BFOghva3c2R7G9ueM9M8feS8FMAyGzCUjdsH6JUfvtkogQbbQkJADh9CQlhGnEGsHjxQkUzaKk4CAD/1uEcw/+Mcj/JD3/xuKHlmrIUREYd4BGGXE9WIoJRaXAo8VOdCNxUvqqct9x4I+mV4s+3/bjPN1ip/vILTilPFFBSExBlcNsVAIC/PPZyd3+soydCg22UsFDlJ1W2LRvHVH14Y8jEHkmQLjIMK8+oUBJ5mTqpc6I8TdPWDSscVXrDcT3vXY18rKFP1258L/VptgyA5lrfs6+8BSYAt1x51tOvvJNbBzBDEGOK8cyTjxGBe9sdksjTGUBuW25aM4CLb7wXTGyefvbZHK/ecuONVDd4Hbmf9sFfW7etFGWQOQz08SdfpZlf/RGFSn8yNB44sm6YsbhuWRlyvkgdBZ5zksLyzQjDGFtO1SAIybLhGI7imtEfVqIKGfiD0WMgMgBCYFmYRP9kmQHceOV5AIDcamD2wkW0UTWEIBTV4qruBJWSzqUQwqiihWNajvpIvAuYoY7ck4DU4hA5YDqAMVXVQBrXXn1Vtu3/5bN5mXcmPiOqBwDA9GkNxSkDPjm2duq7DRb2MS0rFFUUdah3F5NEp5iiKxopCDF8dwJPoiiHB7zngCTfktgb8kbTslXNiKtGTNFCUbVcA/8kGQcOTrFmYqZfOLdxWn1w7ebB3LeMamDO9KqSzgGTHLGt+9p3HuyMxsllTH5IPtAwog5IzQtjhiAGIxv/8fMMCmPq8XQW9fCTXyQWbrnxxns+SCw6GSEGtYRYhKSyv7NA1sQUPRLT6EAewcE0ClU3wjFSD274vhBCAs+bVmEFISBt78AjwyTSP6boMUXP1kgghwoh3efzOFba8+F9hi88/SQR0oF2emgTpaNjhNStfDip2dPgb2rvjhzvCpPOkcmZQTWpbpija5imxF2BGiU0uAEzBDEYGXnhpZfZlXn62WdzzCFInbJEKE3CGkIUgmGSqs605QvpjQUhNfiYlq3FzLhTD274vkiKNgRmlk7o2YCIFPewbRyOkkLTimYYppV/KWme5zyyaFqWqYzQ5jcfdNPCNhYzZDeXGQ5Bn0eCEOhE7ZnxtJlWzknAZcubn3vTUQA54+GYIYgx5WEivkRSYm9oIKgTvROJa5EYGZZypOg/T01DGGNVN6JxLaP1n+eQyHPEgl9gPTinrgeMxLX+iBJVdN3IV/pDCN2yWBf01gTceRaHwiO91huKnejqB2OC4FRUbazxBv0uGjeVxO/MA3Kkhn3o+gtSpf9za3eM8skyGIwpCMpU9N/sd2JvICR5d5KY6KXl1F7OXOqZRlISo0kh1v+B95I2A5GYqmg6aS6Wn92f57kqn6ux1l8X9ApDq1LnOtZIr3X0RI62jZCTVUY4p29BY43X6xLTRvOpOmA40XAon/0/cN9nSj1FBoNRKQoAA8u2Q1E1qhAjjyhwXrdEiy5gUuTGNlMK3aTC84jnOVKbrcDhP4TQtrGiGYUO/OuD3qZaf8Ar59k3Js8ZQH9EiZXDlJQnkDS342qrPHVVHim7Gss4Cbj+wmWpT7NNApgOYDAY2RgiPTEAqpYo+o8g9LoktywmIyOzJXYhCCWBp5H7oHAcvUKKSeezsTAw8K8NemUppR9ZmWYAumGWPfQoN851FhtqvFU+meNgQYagPHUAg8FgZGRI/q1lkeG/ohGHpCTxAZ8rn7pLpD0PhFpRohNjnGfCcNLinxz4j0Zx71L6GBcNz6Og31Xv9C4uyBBEUvauIZGpuWGTAAaDMYICIKGfqh6OqpZlO7Zpl8clDo6wswhGhKAkcKaTvQVGATrGpwP/Jsfinxj4FyX9R5TuYzz8p5ASFyJfX+2pDbhzaNyMkwAlGk59ygxBDAajcAWAifWjP6KoOqmN45bFgNeVrIzm1NLOLBklgYck86v8lhPanN0lCXVBLx34+70yz5c08B/xreMxASBwCPrdUkONN+B1+hgXMglIMwQxGAwGyI//D3HfvGbUdDPbAAAAAElFTkSuQmCC"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The axe’s retaining hook lies in its forward removal corridor, so release that hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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nL205drQL6DrQ9dYPXhhxl/hsgJIW+tMd4ptf/zJxi0N4103L33jx4fmLTmMhaxq6biom1nVdOn54x4tPrXx5b3XShzg5dICh64hBhq4bOpkBWJlfRPtFo1FJVjBkTIBURUEmNlUtGgpHI2HdUFRNMiEc62unTH5u/yjJ5rEhOsCqYPXcw7968fE/xNeHI9G+jt74Y2wudEJBTUDp+dInbpw3k3ky3BIODQwEOnXDBIjBAPd1H3x15YvyhcuvX6YAAAZ6A2CyYBgGA5FuQMgSH4Cu66QukKUJFFki/2CiHSFDIqV0VeU4iBDjcvvefOR3Y33tlIqQ/kvnNm1pPWivkWXyAwQJ/qdwJGYFimPrgLUv/PXcq+8azaudQNAZQDZuOrd683tvLT3tYl4QsaEB09B0tbfn2OuvvPqLJ9onk/SPzQAglA0ILCewIkcgIrnA5DWMVVWJhgO6pjIIiqKD5UjJIBI4RIvBUcoPxsC2TMbuVRPvPtBmLUJdU9NKf0ouUAUwxK799i01jP/5zl3LZ0dnzF3sr2kmBRJ0ogMCga4drdv+8FI/mERAbCAIdJMM860wUBYyiBNYluN4gXc6HQiYTlH0eDwMMN1uJ2tFAXEccQhTKOW9Oa2ZaPwpQsOsjlT6TxgT0Kc/fnvi09//+SEwEbhyMfOt195bvOz8ruP7ETB0Uw8b2s5d0v/pC5acetLyps63X/r72VeSUhMjsnkNiVfLkaXn3QFGC2waLAM18usiwwKBF6LRCMMIDpeITWzouq5rrMipqgxMrKkKKRMEQWyKQKGU9ebEgEmINaBOpwmpAJKk/8x6Dxhn7NrfNn92c9qXfvDlm+756aOnnnP18QPbGMPUTV0H+ODeHZBBAJ60vLE9x1Ns+v1t+VzR6IlX0zBYACVFY4CDFE0SBIHnWZZlGVZWJcPQeUEwTVNTZIblgK5DliHyf2J6gD/39fsYK9qVtRweotP7w29+fqwvipIRDPCyuel/mOTr41lZJd8mZfwqgLTS/757PvONH/1uQuiAgWDknk9f871fPnn+pR86sOsdCLBqmoahH9i5JTwQMC64BExwNCLTgYkxsmSiy+NFCJJ+ALrBkPwA4BCEYDCAITCwgS3jK8vxL/55hO7B44p/u+e/BheHyQs5GvzKt39oL//8+98c/QujZCftKONoe8+0plr60U3sKKBxqAOSGAgOtb392Icu/eNDD15+/Ud3b12HTN0k0QjhjmN717wsffuXL3//C1eACR0FxCNMiv6TaYccjYSDAV1TMMY8LzAMa9X/6QYQYqIWTIblauuHkm4mgtDPibgmoMpg/ACHG/0jkkzS1B380fYeAMC0plo6CRjXCiC78Wec6AAyqh0+CUgU/XFe+st3AAC/5vk9WzeosoRNLBtmV+eht19/8d9+8PD/fSsvC884wrB6QiIUs+rruiZLEYAxhFCRJY/XjxBRAnI0glgWMYy/pt52F09Qob+zdSfJZ1Y1AMAppy7LrgyojWj8YEv/ROhUYFwrgPFv+rdJFGbzZjaklf5xPndl48/VU47u3yGHAwBruob7+o+9t/bVT3zr93/6wafBBCQQVqCGOY5FgrHskts1RdUV3cQmz3EAAlXRdBcEiGUFJy+IqiIZcjSiSGD8mfVzkftxMCDWrU3vfWA/zaQJqI1obFk6tymT9Lc52t5TV1M1uhc1GRh7E9B4mwTYZJf+Nl9ZMe0nj0Z7uo6H+rsh1rCBI5HA1g2rbvrk92bOW/Czr90CJgg3fPF7vZ1t4f4+Q9NIrB0EuhZESEAMw7PE+ANJz0heigR4XhRFZzQaJrths6l5+rg162cT+hhfVYugpfExEAwT6yZ+qVtL1AS5TAuojWg0SRX93X3BumrvqF7EpIMdP8P/caID8uI/PjTvps+9NO+kU/s7jwJdA4auAHxk3w5F1b7319X33jWsatC4pavtaOfBg6wd1gmhHXLHclxNYzMAWLHKbHGCCCHsbj/W2DSD13VZJgoyGh3j7JtPfenbOY/0wVU1ROKTZOaE9SSQiSG5zzc0MQbJdsYrO0hiEYQ5KYMvf+sH//ODb5XkvVCykGngTxnXCmCiGH+G5ZvkGWX85K+/dOOnf3zimRd3HdlLLOaqilnceXj3G88Fvnv/m9+9+0IwKpgbOhKfojMbc99XJaV+iBcAIQZCaBo6QmwkGJSlCMcJuqlBCHink/iHTQMgjBBgIFBNo2+gD4y19JdlRRSFtEI/PtgfMVbV0gyAJZvBG6aIliIAqmG+1EXeOwneTacM7NSkCtQBl9/0GYxB9+5VDCIlQ3iOxRjrJsTWZ+LkoawBkcWIlBAHskqKjAs8Sd7SDCzrgENY0sgdhwFgyE6k5BSADMagZellEIJXnpxgA8GJy/jK4rnvns+M7QUUVtj+qd9/fcual5tnLwYkXhISh4ChRPva3nr+ie/+5hkwKgSVYY+8sOQdeesMQ9J6OV4kcl8jDd8NQ2dZzunyiaIrGgkJgsM0dJYXBIcL2+bzcTD239m6037E387l1fCqWnR1Lbqmjsk3UwECQKqiIujgmBuniCuahGsbhSvriYzD5AbBm977wH7Ed/nyt34AKoPqaSedsXRRV+uq7l2r7HmSPWhKKgnOIGBioOrk44JkWkm2UUlNWcAmihyMTdOuMWVtA8CB91/d/96r80+YM2fZRaP+5ioRNN6G/2OuAwrjmT9/+6WHfjpj5mLEsgAhnQRK4tBA77rXX/3aj4daCJQPnR32yAsIgdPtgQhJUkhVJF1XifQH0DRIZJCmyRAiVZU1VVHkaKC7MxLoM03DFrXnXP9RMKbSv6e7y+NxAwCuqIZX1aCrLKHPli4/DUHIIiiyzA3N4nWNwjUN/FX1LAYYIbT5g62VpgMwSRjHqZOnpHgw+0lC7YbYzkmhFmSFJfqTNzRJd758r627dyDfXSjlUgATzvhTEp57+Mcts04SRCdGyCCB9fpAf9+ql1+ccfJ1ZT+3c/gjHzweZ11jndvndwgugXcgDFVJFkQXxMQ87nD6SGloSMo/6AZWFNU0MLEamSaEiISKjqn0J50MhOjVtYgM9W3HbnmAELAIcgziLWVgy6dK0wEQ69iqF2JaH0isDohBxjsQawzQGUiMhMBQADCIIMdWZwnSXY7MBgDWgalChFloIGBAYJCZgmGSWZd1KNN25ZNOQ8QTQ6mUKKAx9AZfecNtdv1LDEa+5z5+M/HrDgxYYTDpeOpv/3X1HV/r7WmXohETmtA0JCnc29nWsOCSzp2vg7KBfIXv+8bDvwcA+Bpm1FY5DRMd2rsj05anXnVbNBwWHA4yedc1A2qkLMSYSv/zhdF2RNs6ZkUj92wHCRza/MHWpctOsl+a/P6AlHE50buY/DUtaw8xk43UIc62NxJtTfqJkqXhaV5D2+TLwrnT0q7fvu5Ju4TcwrNuLOCwkxh2+67XrAaww9aetPDSEff8+W9+HLsfrG/qsotOPnHBpaUa/o/ziKCqKmJzSKJ1/VPx5Uvv/P7M+SeFgwMYmqLIImCE+jvqTrjA4fIC8Mz41OROgZta7WofyKYF33vx4aQ1l9zx+YqS/nEgACsauGc7K08HDGIXZ7MnXRAQi7/PzcjqkCiJTcji5iHb7Tv4EseiUFTnGSL8LS8AmQTEdQeE8MqbPlLya25d/xTVAYmwkkKMucPAYOPml+3F05bGahv87+9+GtfJltAflPyD3+irb7z/s1//+LKLTv7VT/86gYw/V95YlsTd1/7+7Qtv++6yU07t6enFEDpEMRgKS6FeaJK809xJVCrZmVPojjYLz7qRZZDHwXcF87vC1x/8Fag86W8DoTUPaCeRQpNAB9jD5Oyc/6FvE0ltT4Msj66ByALCxDjGImT5eIlb13Ltkg3tyiJWNQdEAm1JLRHAkEai0P6LMbbKCgISCjR45JhTmFJm2Lc3bE+3PibX//d3P01aE5f89rrB2VzM6/PqG5tKdWWjMAn4189/gbT6wrhrIKpqKYqwON58+Lvn3/rtC5afc6Stx+EQA8GwaRiKlNF2VCSkgGcRtK5/6pqP3MNAzAuOfJVHXhQ2/iq59O+cvWzG9PTVJTmWDb32bB7zgMbRmwfkIqPLjoFtg6lpAEyifaxYMAg4hlVUYBoIIctPbALiOILAxJYyIPuRjQ0TMACaGKoa4Egl3VgMGjIgNolRiBzcJAVIynT5dBIwouUg1fpGviGf1xEISkkb2d+TaWKIyHe8Z9O2Eg7/y6oDfvOLH9o+qk1b9zV6uc6+0meavPXY95ff8o1LL7koGo2i7h7TKq0JykMsEr4I7Gg8xJAfYPkoq3ZJ5NU/ZjxR5+z0WV1xPJeuyEMHwGH+gF9+61Z7/d1XjQNhXQZIiA6J34+5bUnXOJI8iImfdzCJhkFW7oU1OeBQLKKH2HrInID0kIaxMJ+Y5WdoA0TM0eRYBYZkJ/PCG+/Fl6t9rnNOW0h1QHYFMPS5x90w9kIgKFn/Dg3/hzfowaWV/mXlD7/80cate8mSJfWsmQxxYY3Ic6+9m9eJvvqpG4jTWF3c3t4lK1K9P70CsELqixrylGq8lBKSN1G57JPppxoPvBFrKpsdz6UrzpieR4zcyE6zyQLLMZhMl8l9YgX4mBhZ2VwknMdyAiNsmiREDGCiG2zLP8bWDACYRH+QerJkF5Iphk0ymbDbTVubIgQZFhoY8DkEZWQR92lZt7HVXqDzgJF8hwmKgISI2MP/4SphFCjTJCAm/QnknWh6rvYfOxkorgjtz8G6lQclsGUUQ8SySZ5YdzU8fx67f/+MGf7OA22kjbB1AHKzm6TTKVAN8ktwcEXpG/WOs0fc8bpLT89yBM0wTBMbSfEAlIonUapigEQWchBZk2fSppe0jY4ZgMm0gGzBIDu6xzTsX0Es3p+oU8jYyoCYj6yXTKv0iD0OgyQVkfyLEIzAQmaiMT9lbFRHGLy0mE/BGufBRE1Q4bDZpL69MGj8SfH7DtuxfMP/cuiAP/7x/k9+8m77xiTByaSw/xBZtJsdag6zjbkHQx5sy2hsUgw/dDHcf0h8d+P+6ALL4gmxamBJA5oBohqudkIHl+aAhkHqMw+qnNgNPqhvyKGtFwm5lOXMrk6cAm+YpgiVfGc5+WoaSskZceRbIsgtF1UNmaT4kluXQSTok4EmCdzHUDUgMfhYHmALMlEwTOIksCJEoYkHJ1UYqwZgSVkRUycNhiBEjIEBa5WT4BiIxULmtLFJROy3l7B+2AZkk09+MWMVqYpVAGmNP4nCMI3xx35ebuNPGXWANQ63hjOlN6f0bHlQDvVMPfdL+w5/wIts657QPV/8+PG3/8suRCnrQNFj5QVqXenPGeuDOmQeSnNX2/8kVX+Ij3SGOe9ji7YKGabtf3LJl9vP/tL7HGBE8IEH/L9Hf27/lixdZU/w7d9VbE4T/5mlpXgVMvk0zWjJaJv4kCH2r/1DHvw2ib1xMIAjtiYmHK35bfzOid9B8XG0PbKxHb8cQ6KAVBOLPMMhqGiQZ4Y2EzhgGFDVISA1fshaniV5XqoWswixDDkDzzKKZig6OZpmkA3IHCIhYDR/Rv75Uumf0QTEc6yq6anGnyGVACYPRAd84uOkCWKpQw447UjHzrVyqAcYatvqH3ohZhn8oevIoEPVYz8he9ZhYiBy0M0XewFJwZtDQRSJE5LBNfay/fcfy7+06hDQ2gHwAeQEyAMkDD5/w1fqRPC9x/434Ri2xohPfhK1S4K8GTx84pp4/PBg1Rc4fA1xIdrP7emOrV9sS1rsLLBkmmbHzp6YK7g9fRtnlyuWSN2zL7+I2Cw8t2Z/TU1N/8DANWfPHP4KfuS11urqmstObkhU1raIjIlm+yMaNBzasjuTjE7G2jrWUcveaHAGm3gXDM1o4/PbBEE6NI21LlDVMQJQYIFqkPBNgUU8S4oIqnrMPmCPDgYPkeA3JPe9ffmxZ8TmgyDHIutNkjUCgoqOtXKaIT/5b3Tsn0YBxO49VdMTfb8+LwkvzOT7tZ+Pju+3TM6AP/7pzx/96EetIUfJ8KCOw7vXS6FuYKgI6Kwl/XkG6O/9wC3AJi8kpn8MDWL9JxPhKhHypOR+URjdhez11wVfWvNBj+72QEZgq4BYA1yWLokKoDMIvrniSz98NqYDCKnXOCQjkl9LXJOQCJRpTYIYGxQdsXjwBJloD1GHVsX+jd2fQ7IrxWJmmSCIMEIkCiW2RVKuaUJ8sx0RMKiFYj5MMkqwFxKGz+SA9rItrJ94baehqxAAXY4aumqFs2DB67/2llu3bNoU6u8DeEbs7Q6aqKPh0PwFC1dtbjt64CAnOJyequuWz0r4XOKfVKIGHzSkpJsYZvqOcttopEOYAEGkG6YBTYgNjBlMonYYK6SfhAeZOqkpC6xAT+sjNOz5teXjZcimJM6H7GPfBGRj0nbItGKJTMNExC3AQDvWqORQ6Z9WAcQ+a55jFXXY8N8O/sns+x3VyJ8y6YC//e1v9sINN39oxI3Xbj5ELDNW/YOzFk9Ju82R7WukQAcwFAQMIv0R5hAQWOgSQJ0bLmgQxNPFtU8EMQC6SdSASDpuFYtZUCGst7fv0tweYLpIzJ4X+ABYDkA3AJsACLtAbzd4/I4v3fJggg4oK5kVTHYlkuySSW8xS/QIxgTSsNeG7MbWS7ZtbnC3uO/H2iu25zOv7zV0HZgma/XR5Fi22iFwrJthGA6ZhqGrmvrB3r3nnXHOgf0HI9Gofzp4q31Xf3fEkEUTagyvyyHJMNj+/v55i07s6+kSTE0PdT/7fI8GGBJYjRjEizddPA+MG0ihB1LdGesGZojYJ8H7w15Oldvp7DnYxJC4jOPbWK5kE1gHhySsvKDfxLqtRyylkhy+Ve11/fmP47eywLgwASUZf4aG/5l9v0nSv9JYv/144tNnHyNVP++57YRofzs2VIQNBppE+jOW9OdBrQvNrWPEZQJYWnvu0o+tv/f/WOIxy3an33Pvj370vXtyuRiThBflx38q5ytGFAs81FWgOXAUqDw4ZlXjsi/JMMC6g2DCNDbLldwkS7xCcQpPvNzKmabT5WSxSTony5KiSKoalVVZ10muK8exDEQsyzEMw4vO/fsPRCOhUH9/ROlDLHJ6RIdTRCxUZE2OKEqQYTjWNHTe5YHhUE2Nxzozb2LM8zxi0Kq3D0qyqpuAFcUrz7UmEGMKQgCR6m3EepO43p6kJX26ttsotVxobMpnZ4gN7oMgNCzvlD1Pe/bJR+ObL/vWV4q55oHQGCeNj1tYW7Db0j/V+GNlfmU0/iQ9H4XA//FQI4hBSOQ5TTdIJfPhmvGKOfrmd0l42dwZTIL0By6BSP85dYz7JAGcVgPAJQB8+6zvfXv9vfUjSqN77v1RLleF778r373Cn56PHW4gCECOgqjT6OBCPrDF+gkrlkbBMlDoD2eQp9/Yq4aCDp5DutYd6Gvf1UniVXiOI//zLMe5HC6WEzDGgf7uYDhkmibieF4QAr3dU2fO/re7rsz0Rfz8/ue62o66vd7u9uMiy5EdGWgYJkRI4Hm3y+V2+Twun8Pp2Lx9IBQOhxXtyuUtoKR8/Tv35bJZVfMCMlpnoWGQEB9rLE9i+63sraRB/WBemJUONtyiNxSUaZoGtoxEhlUKlJi3LL1irSgZSVEelHROYJw1HAgkM1ZpX2OrAxgG1Ve7zzhpTm8g0h+MhiJyRFJlVTcM84o5Q0leew/Hbt+zFgAnD2ucaFYt41skgLOrLStLrG7wWd97C4B71t/7dvEXhoP9+e6ihwewoWEWkXA8DpmwStUF3QcgD7AKzCgAUWBGlRw1UO7kOKcZP/ztqY1OluFZFI0MHDjUoenajLmLvnPHyLlfD76w6Utz7LJa+8HuVzNt9sNzYwvfWb8EmJDjeYfL+7EbzgAA3P/E2mg42N7Vflg9qOsGYriGusaWqTPe3nC0Jxhet2aU2g0NQdoBkDE6too62MKdyPG4JSjmCLZG/qQU0KABCFkPuyz0UEzZYDMikiNG0gpsOWNFGZW4NAslowKIDf9B8vB/IPPwf8+moTLok48so4WGao/XLS6Y1WCYWNMMRdUikhqKKo/97Mtpt19PulThb1zLVC/gwflVAJwNwH8PvtgBwLcB2H/Wcuf61cWOtHE4fyeAqZq9QaDLQA2bchCG3XjAYzocgOMtd58BVAWrpa+QkUmjfOTTX40vcyxzktRW5InkmVOLPMJDT70rCgJWpYNt7ZqmzDhh8TduuzjTxsLBo0lr7l5Yn1c2a9v+oUHV199/I+02zdNb3n5vjdftmz5l+k0rPtrdH3jrrdFWA1YnAGhXbGCHtfgaZNDFHY/4ss1A8d9W3NlvP0OMlTJgWYGsGLlY8FLeF2ZfXUJcGSU7LM+xSYH/tvS3nsRXjr3xZ5xMAjwuUnQHAtIlihWQKHA+j3PExOj7Vqpgpfrziy4H4JeD6xQAvgTAHrAqrB/Vp9WxR7uLKhOEI8F8d3EyICwFDDkIJR+MeKDTBUQnFETAsuQXCQDUddIEeNTh2NLUIxIPHUtd2d1v1PldI+775qo1Dsz09XWGIqH/vO48AAZNLilSfpTZs3k1ACAa6FA0ede+nTOmzLj6ytt6AkFHlX/Vi2XvPRcLk7IerCU6LH+5FQpklfm0SPINDD/E8BZgBqkbF3P5MgwyrMyYhFPlh9/rdIi8z+3Yd7SH9BkehGfHY+OT8QD5XJIC/20GArHh//gx/oyaDshil0/b+i7H0rVfuezJn79KWq9YfAKAnWBNyDysdYZMSUt/u1963mmvrdmYy8FxNJTLZsMOfubpjzz3lKGpWArhiBs6XVB0YE4ALEeCWgBgDMMrFF9lbnxx1mmLOrv6whGpdyDc3dVz50dIySD5zRe3OhvtDaKS+sH6d6Gi7Ty65/s3XAjGK/1dh0n+x/RZq9evaqhrbmbZ85Zfv2Z1uWcDDDHTM1DXEDcY9mMSez/EmMj/z56yO8cD/e/GE0gZaTKPIDuSNpsA6pjEgJJKoqCQRkOnL57hELnmer/f5zxwtKc/JKmaLvLc4rlNjxVwuAogafgfM/7Yz5L+Lbnx54U1268+bzGYUAxl2A7n3l8+/L0vjNxa4CuX1f781R4APg7AdvB2CBzUOyzpb5jA70H9oeQEmFAoV7GOo3kXml79+Qs81311QI0YpoEDCg72keE/L0CWxZYG4ABAz/wETCJeColLGMbhECCEA/3EaxKNSE6XQ7zwqtMB+OtfH432h3gEDx4+eO8PvgM3pDfCjCt2bX6L+PMH2voCvS6H6/IrbpY5tHplGcUdQtAwMEvc1KR+oolNNpZdUXg0M3EDW9MCEjtBistZSiV/av2kU5NL5E9fPGPutLojHf37j/VMb/QvmZc+YpuCEgP/49LfGv5bq0b6hIoc/r+wJm03AjB+28cXbVr8ymW1AGwG7wbAAa09aERVKyUYA52URkneeMPmXbleVzSU+MhxL/dzP/OIHIN1ZKrQUEA0AAa6cW8H7OsQwv21oyj9Ex0ApCdd0Q6AJLrnLDPPuGjJotnEWM9zPq9LlpVrryEG/WhEsh/KQDAQ6D/a1f7pL3y6s6MLn3ERmDj0dx08dnj7unc2sBH1rAuuLtNZyHjfStEaDOMhQXFDL+cjtBPHUdCuJzq4MtZCuAg4hqmr9iydP/WSM+Ytmz/NKfBFHW7yEv/ykvMik/5NO/wvRvrHRX8xOqB129NgdClJn4qvXHYE7NU6AmZEA7b0NwwsKWYwanb0FuoJiIaHPXLG9/LvWhrrHSzggM5inQW6iMwal1j3+h/BWFAqB0Ai3XNIA4A334oZ05wOMRwKCQlC4c/3P/DH3/1u36F9TTOnf+wTd9orJ2JPqnD/sY2bN/W2d51zdsao0+KwqzYQP639GBIR+X9cSYndRLvYlUHtExUHBIBFqMbnEnnqABhJASQafwYC0bTemxJK/zWtHd6aWlA0zz70CigPqzfuSf9C5rv83l8m98vNwlf+JkVUkk5pVVG3pH/EDEVMWS3wvsfRcOIjr321x39et/rhiwb2fu7OT/yr89jN/IDzpT+A8cfiWy4vWPrbZexIo5hX3np73fsQwnPPjRWS+8dDf+loO/TJz3zmP3/w3cQdOzq6wJkTaRJgE+w7umf3xta9u6+87KaSH9yuEDjYziXppfxv3eG72PXqrCwAjOwyiJTRKQWRYPqPMZpRVMU4A1q3Pb3wRNJ0ZWx9AAXw65fUT13KGSaWrbF/OGpKKlY0vO2AeuKsvGesuPhmkxB1/v6r06fX9YfHOIQuEuxfn9Lj8jL8+8MP/f3sjz/ws7+szf1QcxbMt/7FW7bvXzhvOsDg6OH906bWH9of7Ok+eOx457Gj7dU1zd/8znfs7W+5+ZrHn3j+guVnxY+wXiuq2+ZlYGzo7z7w1kZt8dJz3L7qDatXluMUqQW08irgk2RzsANDbScYyRujcZyj7wROHP6Xz/izprXDXvDW1AZ7e4rUAeWbBGQgm/R/eR+bmA42In94Tbv9XBSMmqGoGVWwopFaoUpBRiBYdLwmCcIzzHBY0vTydoXM4gBIa/+5DMdDp8C2P38EwE/nK/0Hm9aRb+/uT3+su5NUzjth/lzLH5BN13Z0dJ113lnr16y3n767eWf7scOKorg9/pqaWrfLtWj+2NdmyESk/+j2/qPT5iw4c/mFG1a/WZJjkgbultHfHgUljdLzir5P3JjkC9vh/3aFuOIC+du6B5rrqgrevaJghwX+D5L02ZdD+qfqgIIp+SQgS32eEUf/+eqAh9aaF8wzJQWrRPpjVQeqUdAMAxerAFRnbe3UGoNjpWMFVRYtA4miP85XP3ZuLpMAT3VN/F7e1npg/txp9vpE6Z+E1dc2zSTg5NNOfn/j+yTKcOkCsHQBKBtT5p5a5ava8d7rJTzm0X07BwYCF155Ke9wv/JUzGd25gU3OBzON196qKBDWgrVKgJh3XOk+aNJjEL53bd2DrBhkDoSxKoUK3dthQMlphZTyglKlP728D998H/5Kdgb/OxDr4y+NzgLL+/Lz+k0OPYn0l8xoGqg17blrQOsUutDD1AAiGPdVVB0c86R86TKzSdvOz+t9I9NAnIjEMgjOc6W/ql0dHQJ4iglQxzf+16ppP/80y7zNsxGzjpH7XR3dU1vUH7vnQ9aFi49+5KLz15+sRoNFib9MQAuHnMMFsgDiCxwCpzIcwLHMgyTl98cQsiSOniMwHMiz/AMaR/Pkb+YtEedgB74iUg6UZXJ+GMp6OzDf83EXOba+mtaO6xmucOmdyUxBJWWbPde9uqdOfCjvzySx9YP3pHjhqS9UnFEQgOBXneVz6OXofxDFs687F8Sn952zYmHH7r98EPppX+Ok4DB4T8IBEhEbHz4b9M8tdleUGTF0HWnM9YBZjKxa2Os9JAUBVIPaLdiGnqPg4PFtcJFCOkmqQVqDfmtnwOxPWKESPx+XmYbkj6MISkBZGUBYLtbpPX7Mkh/BeoEHl0FMDT8z2T8KVr68xwjCqys6Ko2zF5RvA6wJwGj4Q3OTfpnMQRt+v3I+WIJ5PozENKWZMmZM1fcvX/3dkWSYJWP5fhL7/j8aw/+CowKc1ti4vij1y+1RP/Iu+TlCThyvDvJ/mOL/vgG0WgUY+x0Dk2Fr7n6kudfeD3RCpToCahkTJNU2kEIMkSAkxQWEwA21rg0v+gIaIl4a0+kW32DSS1Rqx+MaZJOw+V6D5RsM4BCx5HZpb9Vco6p8TmrPGLPQLQvEDWGBwyUxBkwrrANQXn5A4qhrspXzNcYHuhFZELOhQb6dENuO7gXjCK5i/44l+Hfv5pVB4iDdpvU4b9pmoqqJrl/o1HJ6XRIUprZT0dHV2NjfR4XN3kJdewIVC/kONZUdbuBL8aM3dzUCmnOA9WwnMnWIXQyi7CLhiLMIMgIh3eUxmtNyUkBxIb/hRp/Xl6/6+IzsvUtWrers7bKOa3B53ULDIMkhRTRzDRhHPNJQI5mHsPKibTaYsBS+YQLBj+XzWYyIrIisRzHcixkGMBIcLSisF9/6YF8RX+chXrGAqhHQU2m4X80Ktm6Ia4D4paLQCDID2qFtJOAwJsv5X2Vk24ge6QPzG4gWet2azSdJAMDhiHtg/IyAXH24B8BTSdHI7WiMcCI9CMyWH/5rp+SyNDvvGDjjy39sxt/HALbVONpqHZXucUGv6vG52RTAv4SU8MKdgiXxBuci/Q3Md6xv2PD1kOt+zt2H+o83N7XOxApiU94THB7vMA0woGBvs72niPHdGnIQlI+7r/DPPzQ7QXvPpUZ6hiVyNF6Uu8h7fDf5SG3sSwrcoIJiMxfLbKciySFUSxCHa1W3WYyAbB/9KQxQEGuMXsX2xYAIfEH2Pr4cCspcEQZBdih2P+CjD8jjv1XvUeqAwaj4P3enve3JLxQhkCjZx96ZcXthSSLFkAwIr+z7VAgLNmh6xzLOEV+zOcBBbP5tceb5i5r6+5ApOWHeWwniXosH/ffUd4gP3uMn9b6b7cfgQjaOsDMbGtOnQRQbA52SCfUc4PdHg1rLkDSAzCp7ZwrxOCP7CNYBSCgrU2whmgI/+jBjuz7zRr4f/HpRPpnt/7bKGIOtR/k9vhiwRFBxRuCRjQBYYCPdw0Ew7JhmIY1/pEVLRSRx49LoACaG5uO7OnWDMNfXRfsnBiifyrzKFv1qaSV73f3kuaFVZ4Fw4f/BiZGHoSQZetiIYShYMjpdMS9BfbkINO5iCfgwiv9W/Nr35bZTDWBgYglkwAmJr6J99fu+5hP/DEZ8UNiSEKIdJQkOhlZ+1u9KCijawJK0AB73stV+ssaKdqaRfq/sr1XddSiqiZv88zpTVUnzKid1lQlCmwm+cr6mxKfFlMnrqzIir51T1tix4kcGc/moE1rXqyrreM59vCesrR7e/2lB8o98Le5sZrczf3BIX3cZQ3/VVULhiI9vQFJIiYgWVZ03YhGpXA4Go1KMfv1cDP2+eedsWo1Df5JJtC+Mxalac2liAIoJHGXVAKy07/sxmHQ6hVzfM87BXzplNKUgiih8WdPkJ/bMrW5znvFOfN8LfOAVd3lvdce13Wjqy+y70hPV19EJ8Pn4aevqwt2dxdpBSpTSOjRLiuxCIPeQKSrL+8GLOOf1h0fzF24rKv9aHk8vYWb+7OgD/whyMfqdybgSBr+9/SHDMMIRyQibCBwuxy6TmZjLMsawYjTKcoKad7o9bgRQoiIogyegJPOxvn0CfCCycneDm1eE2uoseR4Ir4LLqBqNwc2AcOBfiV9EuJ3f/Dz737rK8VeNCUFMpVLhAz/4cjD/5fX77poJONPZ9sxt5OvFpX3NqvevXviUcIMg6q8joYaT0RSgxHFyiAfHhKaoAPGxBA0kv0HbN/XntBHYVKxt/WDiSL6iScmjegnYKtdVeLwPxiKGLpmGCZCUJKQpumGNYGz+8dGohLHcRDgcDji85G+gixLotInZEno0QEyLMNKQGNNxsrptSu55RU8Zn3AgMEmCfzXIeAR23V4MjcbH4egAow/L6/fdd7Jc7P/NNbs6uJYVFPlbKz1uB18Yo4IhFDk2bpqV63f5RQ5q0pX8rG8dXUFG4LKXR5O1fRDbb2063QuBp+yiv5U6W+YJLvUNHHS8H/3/mOaqtreGtPEiqKEQiFJViRZiUqyJCuBQHBgIBAMRQYGwm1tncfbOsMR0k0QY3z+uacnWoE6OrrgBKwRXXICHbt2tOkOgZgQSAtRBtkF/XPHGvlBxECGIfMtB8+0R4sqvEopgBST9IjFztbvOm1xC28F8WYa/q/Z1cUyqMrjaKz12IH/SRsgBD1OoanOa5q4qy8SlTPmBBRDaQ1BMfsPABFJ7R8II2b8WvPHlrKP+rk0o36McX9Ujao6QnC3o3HB8OE/Hl4q1dIEGBt6wjgUaJqh66am6QyDVE2PSrLAc/X1tQJfSHNa65omeUGbQPvOncbcE+pYQ8fIygPIF9MAiAWmQSqMHgswgfbEMMG8qauh4UN5g9IM/3HG4f/L63ctnjvV7eCzG3+IjdXJT2nw1vldsTTxFFgW1ficUxt81VUOKyegLJOA1gOvgfzJooxIweSoQof/YzLqP2Z8+Kjx4ZCiRVRNUnVZM1Td0AxTN8zusLy7K3CgL3SgN5g0/N+1lzRPz/4NE28kqUiDDYPogGhUDoWiwWDk2LGOvXsPnnLySUmuYHjmRZphqrphX4OqG/b1hBUtquqSqodkzbrOyWknjBPq2kvq+ECrgmeeDn7rM7cqiEJ8ZMAItO8s5kqo9C8Mdpj0h9nSvl5eT/rT1ngdiU1AU1mzq4vnmLpqd1OtxylymQqEQAgFga2pcsqqHomqAyHZ7jVaYmdApMA2KZl0gKRokpItY6jCB/5lOvhb3VczEKoGmYQxEJJiAQhaC8jKxAbtwWhAJt+LOHshGD78T2GEySYmHTpNwzQNw1AUheNYfu+21MoQRwcimmECqz4+2dgqoqsZJmPZNFXDRJDUtW8qQeO7cc0HrbtOO/EEKwoUk0K0OYNNsgsLTAWakZ6DhZ29uy9YVz1ZHe2jQa52DFv6n7tsDsuOYPxBCHrdQmON2+0UUFZdgSDkebbW7wpFFFnRo+QHnO3HmZcOiCeFtR54beGsS0E+JHulB8EYD4QkOvxP4vWXHgAAlE/6P37gIggkq0kUiTWxG7tYIYMkFtEeYSikmgwhafi/c/cBmHNcOcamXaHMegIMU8cQVoV7D0LQAMCq1esTk8JmX73itb89YIUUAcMayVqlccj0d3Bsa13pZFcAAICN2/acc/J8BuXnA4BW+QfyHe0tUPrHocP/ohTAMONP5qoPpy6aKfDMiMYfl4NrqPH4vQ67c1B2EIQukW+ocQcjiqoTI2yWiKCCyVcHZLqNZUWXix7+3/3g5KlzW27R/+j+i5K/lth3YxceI/8z1kDbXp06/PdGB4LOqgQdgHPUAdhqsOMK9Q5lE3DJkwBtWE/01DOMcWfN0WTd+7vOXjo/LzcAx5Di0u9tH6Wyg2tf+Ou5V98FJjJf/NaPAQC/+MHXS3hMdljkT2bjz+K5Uz3OERrVrtnV5RDYhhpPU61HFLgcPWAMA4m7uMZNokLDcuogomBDUDGVIVKH+RiDiKRYiQuUURb92eAYxohVpgcL5k5PFLvHP3g/UQfYEZ+5HNOW/p5wX9L6pEkAJZG3N+9afmoeRlpBYN7ZQuwKlBzlfmlFf4oJKIO8fnn9rhNammp9TrurambjTydCJMB/Sr3P5xZjLVhzgDgDeLbO7wpahiArvr5khqACJgF2RqIdL5j4LgzDDEZGo0RahYv+Xx25iUzqQX8uG0MARJYJWdOyweE/+cq6OofVbrN1ABnd5xC7ZRoqREyq9PdGA0HnUM3tjo6uqz5254t/+XuO76sSWP3e9jvtNsw5QKX/GMr9OIPmCEvQpQ7/X16/a8aU+sZqN4nVzX4ghDxOobHGU+1zpG3tnXVf6HYJDTXuKo9onShbRFDe4UCDfuCcI4LsZqV2sGAMRdVDUVkbNDRXJq+/9EAuIZ4nfpxoiAL45aEVvzy0Apt5RM4gq3iAiePD//RAAGrkgNU4WY8/EjfAph5/pB37U8YtOCV4ZKJz52fvGQXRbzPk8kor/QEAjTXueChnFt+vQ+Qa6zz1NW6eYwoIf2YZVO111te4nSKvhfu0cH/SI3HjwotF56YDrHqRWEuwaEYkNUSSlifbrVaO6P7ce/bGuftBlOQa6eZyqgjPkA6F5FsZbv3vitt/bBAELEJeKWg/7JUxTTAo9L3RQPyR6XTeaCApKeyqj6VPRaaMGhGZ1PCYNNz52Xv+/psfjdrpRpgRL50/3UVCObNJ/zc2Ej0xEAIDnR3b0yZylyIdxuFgJUkvwBBEPAGfIlaFXLDH/vZ/sqLbFZ5NjI929svqyO7fFbd+5NnHChz/jmfyje4/8eMP5KgGinSJswjqpJJktuG/7Y1VEtR5XAdQJjoYg8NtfS3TGpLWt3UPNNdNvLywO0dX+g/NAGY2JA//X9+4f9GcqX6PWGw1lJx31/jqNA9u6MF6S9CWb8RJwKD8B1Z+slXi1jBVVU9NU6BkIhfpnzrqH/oKcrYCaYapaEb24b/dcqRU3x6dBIwrTNPsD1rtTCaL5WeUYTNL/2a/W4h7QTMN/1/Z2oXEmlqfs4nU/OGcIifyrChwAscIHMOxDMcikhpj1YvFGNtpBAjaRWSHssTIGE3V23pCRzqD7T2hjr6wourkN2tvMPjrZb31erAr70nAH55MnARkcgjbw/9YhKFV4l83TI5lOvvCYWlSTTPz5e4HUb6VnLNMAjLJ/c1rVy4999r4027OX6eN4ArWTYwgzD78LzeiKI7h2Sscw8CTIzDvTkv6j/LwP5sJaGqdZ0Sjx6tbu7xOYdkJjYtm1XucPBHzg3R29WANqBrIS3C6WLBgimvBFFISVtWMtp7QvmN9fUFp2OjbXx3s7yuHDrCFf/xMUVnVDZNlmOydXihpyVf6x7FuIrvCvEmKxWTfGEFu1uLk4f+mjWX9UrzhvlWr1l1wwTn200OHjtz4r5966te/LetJKWnRrZztyfHh/H3UpT8Zi6cd/l90+tzESJ5Mw3+vgJurWL8DSOFgd3dvZ1dP/FH8lZFioj5nfbWLVBwcfn6vv7qAAz77hydH2CIWTR77R1J0VSOZXwMhqYDTTTIKMNYnhQNlsfnEQQgxDMPznMftSl+SfzjMWA//49z4uX8d60uoREiB74lvm71z1E3/cdL/IB08mRl4nGIW6b9+V0e1R2yq9Xicw6o9lwoIoVNgG/yuaq+DS6koF9cBeUUEJeqAVGcAQrGEUgusG0ZU0noDkVzcv5Qsk4BcRL+Ny+nwelxVXlJHZPbslrB/OsPyPC9wHM9yPMNyiOUgQx6AYQHDNp1xXtIRjn3wAUZskQ+QsBB7MLEFkknAsF4pvGrVuvhJDx06YusA+9ri18lyI+ROUopE0yZ8ZPadYyf905iAXt+4/9IzTxhRoK/d0eZ1iVPqvHVVzkz1PouHYZDPLTZUu8OSFginqRZXSHpwgi0oyRBkh5PH0Q0zLClW7c8i3gOodE/A6Rd9NPft17z8yPW3fdowdAihKoXPOu1ERV3QcHyHYWLdNHWTGHx1ExvWsjbnxJoqd3zf1tZd8pHUwp/DgAA4edINmIEQIdKHxOppikyMBZYxMTZMLLCktL2BMWlVAckCgpAj8aYmhIBnGNJ1AGAHyyQZgmbOnL6wsYpszJCNSYk6/0wgFVKPlpILGJPmHBP6s7pzLBy/2RTA2Uta+JGMP2t2tAkcU1flbKh2O4WM9T6L54Fn1ua4ZUl0wKCgj9WCM00cjMjRmPs3U4E4SkbIqP/BPKT/4LdgkhqCVt23ffv2L1o4Pzrv1LoD72PM2EXW7L97a2YnSX/rSxrhO+IY1OQR3QLHWUVEDYyJJrAaQNpqwMSYs3qbmBizpJgVSTOyC46aliZAEJICyCQFATZ7wbMJOgAAMO/6m0P/fN4Oedjrn+WGVhU7SnnAGMsTvOB2W1v768/8bbwogNc37r/i7PlxgS7L6SsfcCxT7XU21rhdDrufVyHMmJocupvKf3z6+t1HetdtPdLZF0kanpNLF4EeLLBOXJJP2CYhzysm7iNR1TAne1+PMkwCionuf/Yff1zxL5+M92Lc0bpr0cL5PbNOrj9o96ok67f7ZqZKf+XIIQgAz5I70sRxVWG1mx38YqGVcljrEniGsaO9BquLWi/bm8WXkxbSrBiGPQnwXHyNvOrFnb4WCEAomDGnjFI8mmbQ2uwlUwCvb9y//JTZdifULKxtba/xOqbVe6t9jtRWX7lz+FhnLpt5BXDFqdMkVTveFTzeHeoLSapmAAyiBpmmsN66uA7It0ZQXAdkiAolFSks6U8Z7XKnMR0wKGhtHdDVsmxQB6TBlv41LqHWJWKrNySp0W9iA+OwogVlzf4iddPsjyp1LpEnhsuUWx2mW8661Ydn+h5JMQTt9LWASpX+p3z64Vw3ffCOIs8VlVWrOstEHZ9dcn3e8+MyKoAl86a4RC7+aaYd/q/Z3iYKbH21q76a/IZG54OHEIgcW+93WxLZ7A1KpoGdjFFMVGgBpeLsvh8FHLxyJgElrHQd0wGDJOqA1OG/cuQQcSAL7Al1PhfP2jYiexJgYNwWiEZUXbd0uYlBSNb6JcVFNEBp7t9UHbBs2YK3Vm8AFcmm39+W87ZF3S0Yg6iskb4LZTNBjwJja/8Z+g5e37i/rsoV79+SyfhDSje7xQY/Mf0nRv2X/SoRdDu4hmpXvd/t4GOlKYqMCh0xKCgJWCJ5MQlIFfS5x/nkzrP/+GPi0x2WnSdV+tuGOwTANJ/LJ/Icg3gGCSzj4Bgnz7p4zucgK+O7KIYRkEhiNygn5y8/s6zHp5imqWg6dcwVSeyHcc7SFoFjskv/da3tVr1Pty8hQ3jUYBjkEokOqPHZPYSTgAV2Dx4xOWCQxOKglLKK/kw6oKuvP1X6q0dJ5I9LYBu9ztQxCYLAybEeMl6JrTExiKh6WNVLGNz14Zm+1KhQAMDD9/9fyc5BSc0BruzqvCWB/HRf37i/yi3aM6lM0n/NjjaHwDXXehqrrXqfYwHDIK9LaKh2kazj4RrI6/eXWwdUcinQTHJ/FLqbxXVAXWNDsuMXA1v6k1bVLlHMUITcwTE1LpFPmAREVL0vomglnQQsWbI0SQfQSUBZ0Q1jQheBuGQcOACIAnh94/7Lz56XXfrb5ZprfI7mWo97eMmHUYbnmFqfq67KJfCp6cFDOiBfcp8HUEafpHlAHPN4TPpzCDa4HZnuShYhv4P3CHz8dc0w+6JKRCnZJMD2+qbyu7/n7BGl5ImmT54iEGMIe8GpczhmhBH92zvbq9xiYzUx/jz6Qvm9WwX9LCOKEY/gLMAbTHXAeCZ1+C8psJf116nE/1/rFqscfLY+1Tzrc/B9UcVqHknur7Cq9UmKW2BTk8zzJR7zM6ulZdWqdcsvOAcmRAQVeXBKJkihrgkepPf6WHuAiQJwD/5yMg3/V1nl/rvCga6OzqFeGBaKI59GXRC4RN7l5O1mW5KiyWQIVrKvkHUCvb+jGB1AGZ984svfriExYDFad5Absre3B0Oui6ueag5Mr3JnD+lhEXILrMgxkcG8IVU3+yJKjVPIojlyISnic1ZLy+pV6047/RSeJ1ESBw4cnlfM0SlZcoBVuxUQpRRhoFmMP2mkPCQxkRzLVPGsQ+TcTt7rEnTD7A9KGAOOY3gW8Rwr8AzPsaqm9wUkAAHPMjzHCDwrcCzPM5Ks9QUkhGBsJc8IHMtxTDiiDIRklkE8H9tY4BmWQYGwrGmmbpgdvaHjXUGSAp7y7bP+xkQdUNwnQxm/0r+7J2g17lEhw4OpJ1SJoewHgRB4Bc4ncpJmh4OSmyOkaCFF84qFx7OlxvvbXt/G5qlOp8iyjEMUCjsyJTumaao6DQEqkQLIIv1f2dHrdQvzW+qXnNBU63d1dhaWeZu2NY8IpiUXIiX4WABIOegk/E6HvTBvqrs/VLX/aO/xzoCiGsmCvq422G3XIoUvrNlx9XmLCrpgyniU/jaybKiD5fkYYMyY1tTrnZslR8zGwbHVTnFAUqOD5cNUwwgpmmaYQp4trLNLf9Iqsu2Y4PIhhESBP52WgysDhok1fQI7AJacfWV3V6yvydiCskj/Nbu6vAKeWiNMqeZVOVyo9C8xCEG7+7zPLVrpnKkd5Gvjyy+s2THqF0gpF607dq189K+vPfuAPfcnld2c4rGjxyBC3bNPiT/S7osQ9Dt5j8DFbxeMQUBSQ4qWuyHh2NQT449M0h8AsOGNlUokoKpaMBQp4u1SMkJqAk7kGNAtb78ExgdohPQrJ99YQwo+M4M5YuMBjkXVPkdjrcfnES0nXrYpPNUBk8b4s/LRv9rLm9c+b4UFMy6nA0CwbduObduGNH1aZQCtSYBbsKvAgUFXsN4XVdWs0YRJQt/mwP4DiSO41Hj/DW+sNJSwz5tmLkspHk3TJ3QM6PghY0ewtbu7XA4i/WuqnMXU/CkHEEKHwDXUeOyk/4FQcqVo75AhqPBTZN/A0DWG5Yo5BSU/0//wgfrmtc+fc9ktHMeaBqnSDCBM1AEnnhgz/cV1QN3+TQyEPpF3cExYsbqNWgPJvqhSQ2rDCYlfeKKsj3Ng/4GkNbKsiKKQJdtr5vSmd83vmIZJoh5UU1HUp/76C/q9FwsGEVlLrNP1xOtbb77kJPrBlkwBrNnVJfBMQ427ud7rFMtY8LlgGIS8bvKjVVVDUnRJ1pKcAYk6IF9nQNr3yyIYcyBSxkD6g5WPJcfMrXv1catuqO3VJfrB7iZJetGkKANbE5iabu4/gvZvH4oHVbT+qOIROJ5BqXI/VegnkUX6b3hjJQBg+fJz7IvDGAfCGc2tlNwhjTqiSuLvXZUnZF/4JWdfOea2IDZj1IRLbKrzVnkcpaqZVWIg4BjG6xIbat2hqKxpdlpg8TqACP8xzHSjfOLL304twbnysZjxJ0uO2HUf/gT5x44sJu0ECInK4MQTF3EcO2vm1N2a1tHeHQ9B7tccA44pDbXVMGe5/9RDv8/lm7J1wAUXnGM3Oq72OT7/jf/61X3fod9yMSiqrqojhwC1dQ8016UNP6FkVQBrdnU5BLa+xl3jc5LmMONWGEJSnM7vcTTUeCKSFgzLRYcFx6R/rFcUZYyoTRj+70gw/WfnuUf+NKQJbBUeUwbW92q5CqyVwNA0n889MECCRzHG4Uj06LGOvu4ucaSozRzlfhYWzZ/19f/67x9/52tgkjIK1UFkVZsEOcBb3n5pydlXjjsFsGZXF88xNVWuhhp3armF8QaEUODZOr8rGFZkRVdImk/hkwBb+tsOD4wtyzJldPnEl7+dJP0LOEhcEwxNCyy5b/1nVQ+GUImGIFaJbrBekiJBkfOmrS1cpNCPTwLsp4cPH22ZOe0/f/ar//fVzxdz2IoFY6BotgGPUmoFsGZXJ4OQ3+uY0uD1ucXRL/lZeFRorTscVXoGIgaJDStQByAIWQYhRPrA0DZg40H6Y4yfTzH9F6cMYhmCDLFsQrspmBVXbhrWrVOqkX4WHXDo0FFaIqJgTBOrWikrCFQ4w2cAEDpErqnOWz92JT8LgGVRtddZX6NEZDUSVQu+NxgG2YWmNZ3eYeNi7F+k9M+kDK6+5WMMafgO43IEY/z6cw+BsrHhjZWJrYPtOnGfuTP33imUGKZpWl3ACmHtC3899+q7xtVHuWSs/cBDBrs1u7tEnq31E+PP+Iz8yQSEUBTZ+mritOCIBC8wNYxjrXEhIN0EC76YFbd+pOB9K9zxm0SOpv8CeOHxv6x+6dFNbz33wdrn7cf7b5FBellZtWqdYZgW+OBBUseU1gotNAVs8OdJwn/BxGXLWIcADSmANbs6rYLPzin1XreTn0DS34aBpFVAY63H6xY4FhWmA0iTQAh1YgCiE8yJ4fidQGx4Y+WaNeu7egZ6+wL9A6HNW1rpbVYAupVUMdFF/7gzAUFIkn6nNHhrq1ykvsKE4oHH3izJcSzrP4kUL8nRKMWY/iflp7fhjZUubzVE5D+GQaGo+r9//OuXPjm+jBLjF8tjo+nGpHIBG+qSMy7e8s4/x1IBvGEVfO4Ogu7jbWASk1AhNJM3WNPJDH20L6yCSWP6L9rxO56JBPu8/lrL6YC7u3tefPXtf//Pn/70//37WF/XhEHTDTyJfqFb3vnnkjMuHkMdwNrSf3yiO6faCyyDHCJf5XE0VHumNlRVeWKVQW2m1cYqrmCADcMcCEvb9rZt29s2EJISx5KMVbjBCHVm0gGkyLiWHEhKGTXpb5Oa8TuZ2PDGyjMvuvakpSchCA2DOAW27tg31hc1YSAtoDVjkv0+t1g6YKzOzhpi/dT6qiXzpsyZVud2CiRhZsICAWQZptbnPv+UOQtnNX6w61jrgY5QVE4U6YynIZMOILPLyXZ3TSQymf7v/fEvvvf1L4LJwoY3Vi6/4BzrniTjk6VLFtbVVj34mx+P9XVNlBjQlArwk0UHbBmLSQCq8jgWz21e0NLomeDSPxEGooZq7yVnzrv1smVL5011Du/6xHga0u6lkCrzk+3eGs/86X++L0ly/HHDDVf/+ZHHk7a598eketoXvn4vmFB87d7vf+3e79+f8nYAAJ/59+9Kso4QRATYdrz9nDNoIbOc0A3T+pFOQraM0TyAPX3xzDnT6kjSb9mk/679R8EYgQGeWuM0Nd/Rjv6oTFIE7NwfzlcrB3qSJgE6Hf6POr+477tf/MZ37eUtW3YsWbLomz/4afxVliWau7+vF0w0rFpEYGfr7vsfeXz//kOJL4UjissRqyMLIVz1xur1b744Rpc5kcAARGW14CSA8c+WsZgHsCzQ2zu7Ozonydg/CQigKHB1fndEUk2MZVUHg41ExTQ6gA7/x0wH2GUY3l6/qb6+Jv5Souj/wtfv/eWPvwcmAmddcAWEMR3QumO3MLzEUFz6U9GfFxgTBTCJHMDpGWUdgETSI2nspb81MI9/t8O+ZBIwYdfTTVpvvzJsx4SXBlcyCHlcYmOtp7aK5AgM6yA29u+bQvjFfd99/70PTNPstwq0pfLLH39vokh/AMD6VS//9/e+/faataRwjaKm3YZK/3wxTDNNC9jJxRZL9I+mLYgdJ6WPhyuhYZeUKSuN7JLh2pNe4FmmxueCEGq63t1PYi9s9SB6a+VgD8B5NwygqqPkrH39OZKvV1OfuBJj8KufTBi5n8T6VS+vX/Xyv34tOcl51RuryavU7JMnhmHqCfWaJrcOOOWsizetH415wATL+Sq8VgTPVXtddX6P08EnKxTr6faj/bkfr+RXSLF1QLB3WKfsiSv94/z2v7+ftGb9my9S6V8AVsG+yTz8TyQu/U85q7yzgYpQAPHC0bVVLlIviGPjUwSHr856GRw+lGMSHJX+ZZ8HUCip6Lo5CdoAFKMJyqEMMvYEnnwgCF0Ood7vCUfVPiOiRcPECuQQpTwEO5X+o8qv//awwKfM2HJDIzl9OaGqGgZA4JM7PGOMNV23+oqR/zu7+jq7+wPBcCgU7ekdsMNRIACIIYVpYp6qShmhjgEaMd1W7ue7qTwWoQpSAHbh6Cqvsy6qdHZ02NKf2J2r/MGB3O0/lDJy/e2fam3dG3+auBzn7HPOzHIEp2OErl5pgPD9zbu3t+4fUbokuqMgis2e6+trrrzkrLgvbd/ePUl7yVJsjEEpBk2bVEUgxgmVpQAggC+9uZHcTJyPZ4BkYgiAiNLHaVBGn2ce+sP1t38q+zZvr9uQ5dUpzcPcyDkihwMiaxQ2gMdq5MC+NIqKUvoqQPQzLTUVpACeeGkdGT8ubZkzZ05HT/BY58BASOrqCwJcykmAKAzLOqbkS4IUJm3ZEto0Dj1NaORCwnpNE8f+Ini8rXNKc0MBJy3YuoAGpwKxmGQ4QogapTCsMr1UBUwEBfDWxoxNVwAAnd19t1x1HhgL6f+1T91oP632ulqm1IYlZeueY3utRE1vlX+UL4kykpmFLFjPYMqr5F9b+lut3IhKYBjLXF+Q3NX0wosAxjun2q3nKWViEucAjyHs4y+uveWqc0tyrMPHYzF8M5pJaM3htu7EVzu7h4bYj7+4Jmnf8qkEW/Rfd8XyE6YPpZiyLPKwgsclbNtN76rxRaB/ICpFR9ysqbkZQmtOAIgdL0FbgONtXXkZgjDGehHCJa6xdmzfOeLGrzzzj4JPVOEUoADaugea66rKczmTaAbw+ItrLRF8bvGiP5PEt/n8R68mgzQI/+8vydF+qSqheK2QVvQncvBoh2t4nTjKmPPmi49deNWtI+qA9rZsYbtKDiokjolxJCLh3JKMktLRAQChQP+O7cHcT0cpDNIIjFJq4JFNzwIAfvSbxwvQAY+/uC7TxPkLd11jiXpgNVjEpomtLtyxkDrb5GqSqC6MIEQZtIJ9hYlPcr/CJ15al1302ws9/ZF3tuzWFVKBQHAPtY20DAvJ9rGkXmEMm8aAJgr80incN370uxyvk5KWXHRAFqr9eYz7NN2IRoe1jshdAZDYYqcjpxaqELxKh/9FsOKmW9N/rpD4YG67Jn1sWNIMYLw1hR9zYiLsns/e8qPfPJ7LVMAS+sl9tv7t49emLSmBSS+X5PXEj2fZTU1I2q/Hbbz/9vHr7FBqw4hpi1/99fmUs5MrTCLpgpPM/ZlEf+zqIbClf2m5757PUB1QDCbrOGnJ7MEQ/JhnGEJoYtvgQxwAMb8rcf+aiLwENE3DGAsCzzJMQ0NNYhe4xIUkjh7v2r33SC4KwHYyJFLl85x/9tK4GyAL//H5z+T43imUUWNoDHvPZ2+xpwJJaiBF4sf47J1XWWI6za8iTtr18c2tkuiMvWz//BBCumFatl2y77997Fri6yNJgCQCDAL4m7+/kHrARK0AIZw6beqHrzx9RNEf314RYrOE/APIKWUkHl0z7J4ZXIj/a9f7I3+tznHxLYe5kocvJMFa7uOYSskTcmeSHanzlzIhYfcdapszszn+3J4KAAAee341RMkmji9+/BrdsunYv0CSBmmbdYomfhDSkt7qSh8fkcXCPqwzfv6ua5ClckwTW7Yl8tuLaQWMv3j3dYaJn31jy7bdR06cNz276C+StPafuDCik4AimT8n1g20sLuo0Rr+5whjK4AcSIhJjUHaOhomY92x2fnJr35HJwGU8QaRYvsGy+DYmuDWay9sqnP/75+esVd+7qNXwZiZnjzl2NiYvdwk/tJ4jk2O14Yk/sMkgYD483fFfMv2BotmNeYl+j1OPhJVS+VgcggcALGDUUPQhIBhSGcuUFAckN3Xt/TXRMmTJ17fevMltLFa3gwbxtqaADE8xuALH19hEGvM8En0WNeOjs83GAgBil29SRLEY6bh597caiuA1e+2Tm+qzuWYcxucRzo0mbSaS64Gky8IQb/XAUCkyONUOMuv++hoK4BCb2zDxEYF1Cge/6hy4SEDlUyaqWtP3wBCkGWQwLM8x3IsYw+Rxlz6Z8Jur2pPAppq3H2BUF+AFHrLkYYad43PIZKmmMXi9zhq2GHS/757qOtvvGPf34Xti02zmBwCSukYp9JpnJP+vt+6a8y6+JYC26GXq0+vyiM213lrqlxFnxVMaUgTfUh1wDjHHkAUtq+Jce5lR3/yKxocXC4K/QIrnVGqBfSzvyQHdFY7+I99+LJynS+fyYrIs/XVLghBe09Rs0hR4E6YXn/sQKCYg1AK9gAXDEII5eDFzUTuCoBSPtjR8k1OMsqiAP7n7y9pqsow2b6SPklN1ArVVkZuGVVCZhCETpGr8xc7A2iu8zXWeo8dSPMS9QaPWweArQDisVsFoGpaSS+HUgjuAsqAU0qlAJIG+Cir6E9Ln6QmHqdKiF3Y3bddMQpfE4LQit7JRlIacBIMg1qm1JAY1gxQHTBuQRDmEseZCYWED1DGBjsNGADgc5PeHklBg+Rnm+CiZ/OXS5MetrSGnWHEaiMOJotZTw0rfj8xmTMtA4puWtF19z/88uioASaHZM4s1Fa5Zk2toY6oiQhEMPtsNTuqQhXA2ON2ColyPy2V0FN+lBTAT+9/Np7EmwmHwJE6P8TDBhnyILpaVnUSXYqgYZiyMrLxdMDaxlYDozYhyBcIwbRGv0Mcoa4cnQSMQweAPQNg2VGaAdB0sPJAOn6X6dCTG7ZUo/7BYT4Z6SOITATr/E4GIZZBDBP7izEOR1XDNBGCsqK394RiNVpwTmpgNCcEeeF2CCdMr8vF8Ux1wHgEQlINotCGAJqmxzsTUMYEBIeZfSi5w5bA4EMkPtHAAkfiqRkGkso9WG+q9dpjfzIJsJKJMQYuB2/n08uKHgwrhonjZb5JcVCrthcBjzwhGD9qYEpDVW2Ve6yvYjIw+h5gO36XzVDYI5dqEFYUEC0HNJYghE5oGapnQ8kdNl/RDyEp16OoBscSuW4Xc2ZZ5HOLbifPMCSgIhzVDF33ugX7pzL0P8bc4FxbFNjGWo+qG1HL/YsBkC27P4RQN0zdMGP1FNINzGw18Kcn10IlXLwaePDpWCsCwUv62OQLxzKzptbkUg/Shk4Cyk1ehYAIkAQRJnaazAtN102T9CMrYF9Kjtx6x8cVKWN2Z5bgC0qBCiAqqwjCXz/8SnyNbc1nGeR28n0Dpt/rsMU9w0CeY3xu0Sny9mAfAGkgSKKrkw9qaQtrCfOIaa73qpouyTpCxDkciiikbQCEkqJFFS0QlBiGeA5IuUW722oKWHCrmq5qhmliVTceeu7t0xbPBKNLQ41nRlN1Xu5fqgPGFdYMgEk71MgFqxpQTvXgbKgboOQU48KpcDIqALv6ZmwZkkmWQ2BFgRM4xu9zyIrWXO+1pT9CiEXEBMSypFKnnZQxUnEV8qrHyRsm53aSikOmib0uwR6BSYoWkbRQWHYInCjwiqqrGilCatX8ydAnyCoEV9hHYDhqDOvIBQQSIwhnNPkFan+csB7gxHJAZkG3kGGYhmkWW0aKkpUsw/+kuM9EqjwO+rkWagKy+2tb9n1RYEWe9XkcXpfAsYzbyff2R6p9Trsys92zo4BKQZA43yCqP7mmygkAOHb0mL2eB8AHQPMcsmx3E9M0XVE1SVL27/pA100yHYj/VMkVYNPAiQ25nU4izHsC6YuytUyJmXpCYZL6e89d54clVVb0h194P9+34PM45kyvKyD6k04CxokDYFhLgIIgCoB2KxxTCi7kde0NsS5jn7g5W/fZFbd/AVTgDEBWNafIcSzj9zhcTt7jFJwiR/y6DBR5ls+ae33qhSukUF/q+mnTpiQ+tX2+cdGf5jJIkg5EiOc51iHyVWect/39t1XNIE4CC460HGMcAlQTM/Jtn1zq33S0d/SAPOFYVF/rDUXkxlqPz13gKIPqgHFXEbogzOJ6ylOKx+2iacClVgACz2KM6/1ugWervKJDIGVBNdcsEwAdgBkLW+TMB/U4QFrpT9rvHT0eX542bUoW0Z8ImWEwiCeBpKC+2h2RVGkw+yZtAheZHlhTBGhPFezpgr2maF+dKLDNdb7zTpnb2RtiElpQUSZ2QdDBnNKCCoLSckBjBoTQ7yEmBEoBZLvpJVltqvPU17i8LmH9bllzzUrdJhwlMTyFkagMEuHFjF8nhKCx1tNU56nNWrzTMkkNGrHsRylEv5VwyE9v9Fd5HLV+9/yWhrm5hf9nghYKHSeQiIWCywFBktWY1x60LGgJYRCp5VXKI1YSbCZD+ZbdpCK000nqG5sAnLvICSJpumv5QOnbnxgR0t81Ew6Hz+EANX7QknCdNstPn5dRJw3vbGNjv/eDnVkmM8PwuITpjf64x6mYAjJxqCEoDsJGV1fe5rhU4qHGuSMrqmloEOdmyRnmKoYOhyBFo50dtCDE2MAwDC3yUzBjFD6VYJNJE3mRsMaOC8Ijrhk8lGHg9p6cCzJnuoB0eN3i9Ca/wLPU5DP5wFaJqkL2hKC62svz+Y1Af/b9ews5FyUdHFu4/4YyTAEk/QRI8H18jS1kEzfA6R4ZSEyxsWP6Y5H9g8tkwT5d/NXBs8f/xgV9mjWDh+oNREIRJeNlpDwdupiRfv7TG0cO93xn2xGQP9QQNOaYVhp6ATuyDFNd5aXyZwzhuVwL+V1w3d1lvpaJB2sH18c9mUlP7RyseL/1RFMKtOIfErccKgAafzo0WoamGds4aQQNMy+kZfAEQ9vFt1cUvbM3lOWXnHTkwaS09PnGBd9nBUANQQCAN1c+eMMdnwZjgVWFJH8FAKHX4xJHKgJIKSsTsQrQaysfwgBMXXTu3vXPje2VsImtfokkHB7UMuxZSrwLyvRqkpy3WjTGNy5N5ayUI5gm7ugNDgsGTSF9LKi9qhSTSIzB9qP9i6f5S3AsyihiJfPmrQAQhPW1VYV7jymlQBypk0eZeG3lQ/HlKYuWp7ye7E9KlPXRIAmSHHPpbyuAoScwi7wcSWqnH1yne1omc91ASOoPFtXTsVQUpgPoJIB8iYFIla/Y1mwFQJzAVv+JvGBZxu12UvPz2OIQuKZ8qz/lybMP/TJ1pS3EbfaufxpMTLLOnobNBcB4xiouHRxxEFeSN5Gl8GORMxuqA8YEwzTDVk54vrAMyR/Idy/qAR6rNOBrr7/5E7cMDdW7+8PP/vODUl1GapnYCcE4mL3GQ3EKrOVDjD+dvUE5h8ZMhZ5hGBCAC8+Yn32b7Uf7S3GqiuPNlQ+O/kllWY1KuYYCJ4JIFuLE+81PJiCEYp4hWGUCTkDpX66m8LkTc7yRIByyNFIJufREZTUq5xSFPQpf0eHDbTNmFF6anE4Cnn7w92csL6rEd9vRw2CkdnWFk+ArDijhdeu68tr7nVUvluGaKheeY8bKBzA5GMsZAMZYVjRF1Un9Z1ktwAtnH0TTzSI7+paK5196o/hJAI0KfWf1y6JYXB1HczwW56HSv+Q4BI4rZ3jepKfEMwAEc6oMYWJ88HhfR084ySpjsJ4qj+PspdOnN1XFxOiOESLrMbbaQeQ2dcATYRJAAQCsfoV41ZZffoMsSwV+IKZRxnlAnlDRXyZEnsvrF/2nx1cnugEoIyiAxYump1lrCW1NNwZCciiqQDWxVPdQTLQkZ1QGa3dYDnSWtFHkMfmFO0Ru4ez6TTuOR4PS62+RV5vrvfNm1I74DcXaEOfGqE0TbB1QcEgoNQTZrH7l6fGlAwpKFqPSv3wIQkYJRpsBFKsAEFRbW/eB0uEQ+Tc3HgAAXH4aqStnL9tIsqaow6bt5TDrxxKGy+av91bXBPt6c58HPP/WtmvOPzHtS1QHJOoAy1UrTbh5ABX95cZBHQDFwdamhF33BUhI3NRG/7HONNXfioRB0G6/lYhpGvXV7p0HugyDpHExDOtz87OnVmEAeGZYeIZqiIWfG5OM/4ik9gWjNUnveiTb0EnzcupUpYWT7f7ZJwGZpD8l1RwEACjQOTxGOoBK//IzXkKAJucMgEGmopFWwACAfUf7u/uCAIBzls3ac6jT5xaJ7wWD5oaqjduPLJ7d4HAIh9v6SLsYBKu9Ges5q5rW3FC1+r39LgcxFhmGzjDkGqq9jt7AkKxnGGSQJl/JxPVBf1DeeSj4iRvPib0AYURS//T46kw9gTEGiqYHw3J/MBqR9bOX2LVEByndfMDBI0mNpRTRSUBpeWf1yyU+ImUiAwHg+fHi5pmcUUCs1SsVQugYtLUd6wo4Rd7ncTgEzuUkQtzrEuwaQF6XYHcNkxSNIW2BTfuBidwfqtAQiigcywgJ6RtkHgcT2zzaqfk5ZGZaFf9HEN9WobdwVOnsCXX2BsNRpasvZFgHr/W5EIAH2vrbekN9oUINzQlojjrNUcf6GuJrDh9uyx4OdM35Jz7/1rZMr9KIIAolExBCnp14hYAmkgKwRTIEwJb1pIFid6iuOmY/IfXdAOnQYqfDCDwpLQchRAiSyJxBoNVwI/5UVvSmOo/PM2TMcYpcf3CY/DUMMzcz/UgaAAPdNAeC0bbuQF8womrEzWBi3NY1VDL6titOuvHChVedc0KmY+RebNbJ6PbDWz2Um374cFuOu1MolNwhcibn3g/xUqB/enw1/ZDjZNSfjXVV7d1tcckX78BlGKbAsXZbdls9xIskIwgdImvXektK6UrUB6aJa6tcEWkoRigiqxoRzUPbsAzisvYcHhnr8hRVD4Slnn5yhsQXdx/umtYYM81PO2XQjvTwmrRH8uTccRQhUvQ01SH8wtodV5+7KPskgHqDKZS8YBBKFCwVzoqCOtfnOoGyiiFDjkW2fCND/nRZ8KT9Yg7DZZZBidWVJVkHACeWVXE5eIbJp/hcuoF/RFL7g9FgWNZSenaHInKSSsgEQnBKfSwpYUR4jpGVIWNXog7Izk2Xn/HkK+9QHUCh5AwUBdILptyV4CaEHC+YXBUAJCN9hufZKBHWJWB4/l5yXFBB2X1DB5GtgX9/UJJIlcc0sdsYgAPHeqfVewEA7734DADg1KuuT3tQt1NoqPb0DrdQZcLnFjU9Yhhpzph9EqBI46KOKYUyUSB2aYeQV/TGymeeuPb6m8FklOOl7wn87rYDAIiJmb2Iybv5dRaYctZO6ugNBiOyZVbKyMHjMQVgY6uBZCCor/Yk+quz01DtjspaOKrEE4ZyNwTRSQCFkjsMg+KRKeVmxQSR5gWQxyeoqoaJMco5pHpvF2s3Wp3bgIvM4M2X/mB0xMpCStbWMTY8y9T58yhPX+93BSOyouq2tzkvQ5A9CcjiDKBQKHEYBhXQC37lM0/E/wIArr3h1gr/SInZPbEjbtJyIkbmvqmJEZxxTKIvsjXbzSL/SRCpZeZXDTHtw+WsOnXR9M27jmzeeWTt+/see2XTIy+953Y7dx7q2nmoq7C6cqnUVLnczlw9wHZEbL3fnSU78YW1O7LsftPlZ9g6IO2rNCSUQonDIlRAMwZKEmiwwfqQrMYYZ2mRzTBIVpPHzqFImrI/DpEnpvzhUj4xOiiLBz8uQ5P6wpeWV9bv2th65Ik39z711oGn1xxMepVl0ZT6KmZ4w7/s18CxjN/r8Lj4pMjRxKjQLDpgRE8A1QEUSvznOU5qAE9orJaQg23fbe/oUPPedDs4Ra6tKzBr6pBE0wzjWGegvsaTtGWov0tWNN0wQcNQYhTPs5oeO7DAs5KspL+swfDeoW94UAfk0L+9EFI1ntsppKknlT00CQKnwFV7nf1BOSKriReaoyHIDgmlhiAKJSuQZRiYpwL40O13kryfq06b9Jb9PH0AGRr2wnSCFkHY1R9JVACRqBqyDN8LF85JLB63oCVN9KTPLfYMxCJquMxJHJpmxNILrAuyVVL82oZ97eXQBtZJ6qs9qXOUEe84lkW1Vc7+kKRquqan95ln9wbbZNIBtEgchQKtQMHC+kfF+dOv7suxJWRqT+BJozyyVgNFQ4YdSVbPWZqmzI4kq1UexxknTsemmSj9U2tBL1w45+CBIyfObTI59+q3t05t8C05oenNjUQZMAx74tzGbXs77LpApIba/u7zTxlerqcUQAgFnuVYlgzPs4IQrK8m1arzhUHQ6xIaq92RqDoQki0veH4RQdmLQ1AdQKFABPmsiaKpc/ebP3yHYZilchs8m65NfI6MK+UxepU0Wlv3OUSrnoQaAgD0BqJzwdA0Qk3J1Sp58X6GQW6H4Pc6qqvcm3cfz74xS4LMhnob5NVSRuDY2ipXMKJICml2lvhSjoag88848a13qCGIQkkPgqQIBJyYPXufLUJ5lFx/jKAAMMDxhN9MywkbD63KsrH9TcQzZhlE/L37Dg/YC3F27u9raa4piRqwB/4+t+j3OB0iN63Rv2VPW0Y3t7U9w6C0JYCyX85Dz67LfiURaUgfZJkEeAcjj6ghiELJEAOKGmp9uX84EJIiNKPYFGos9UfuSiKbAjAxGQIniklsVfLpD0kd3UG7lAJC0AofwhzLeF1itY9IWFUjzcL6g1ElJV7Ixpb1b206miT0EylVzE984O9xiRxL8s84hqn2OnszZMDZb6oA++I7244obJo70lZ4tr5hPUAP9eSiA66+8JQX3tyU5XTUGUCpEG694+OKlNh2MO9aYRdcd/ett31k4gv/EiiJJN2QnwkIY6BqRiAk2VlUDIMYhHTdKn1gfbgYY0XVoxJp8p5agScvnCJfvL4WBa7K7ajykuLViSJ9fkvDus1D/cgQRCaOO2whmWAWZCv8/M2nGyYORZWjncHtB7r6gxKJpx2uylhPbaIOyAQ0Yl4KGhFEoSQh8GwBv9CKkf956Ib8PkTTxKGIHJEUTFKCkchzVkP22Kuk+qZmBMJyMCJLslZMNpZT5GuqXLkPwlPtORBCt1NorPHUVbudohWYT1oOxKjzu0WrwAOEUBS45vqhkTuERLEVYDHEGETDISUa5oE2s048Z0Hd/CnuKhE4WcPJDD447OSwt8qfS1rADZedbi/Q1DAKJemnbf9C86wEB+MxoJS8FQBpqqVqAVJckxTrFwXWKXJDhnJMKkWHo0owIhc5/EcINtZ6udw0PAZAN4wkWxPLIJ9bbK7z+b1O3jL72MSFOoJw9rQ6hkFel9hY4108uzG+L7SG/1kUwIVnzE+7fmgPhmE41ut1Ntb7vC5haOZh55SlNLrJpAO0hC64VAdQKpYk+w/PMg6BK2A4P27cwBNTAZimGYwoEVnFGLMM8jgFfrATgI2i6WFJkRW9SONPldvhdYu5TNhME8uK1heIJp2x1u9uqvO5nYI98I+TOE1oaa6urXI31Xlt94DTjlCy4jgT29fkzWB9Eo5latx8vd9FSlZZPXG0cD95RANaNODgczqFXRyCQqHE4blC7D9WnxKqAZJh87BvyFogLOm6ASF0OXi3U0gseWaa2DBJI0dQBCyDBJ5rqPWOWCsU2xMOSekPRAMp3Rzr/W7iI8p6DJ5j66vJZvapFs5qfGfLPjv+p7D75J//XAUKJZM3OLE4BI0IolCsriQkRC+vj+JDt92Ztl4Zha31JVe77AsQoXPS/GkAgB1798TXV3tBtZfUTz7eLQ0EAgLniykA64NN9XbmC4LQ5RTqqz12i+CMYHIuZajiv5Z63kzSP2kdySWxVu070tkyte6dLWSMgKxHASrge1/9sK0I7asZCEkbW49/sLs9FFHSfi6sCPRg94g6wC4TbS9THUCpcEgjeI50Ks9rLytQkXSrLdt1TVQKSQSzm7VY+kCY0ZimWGZ7T/hoW++yhVOzHGT/ke5Z02rTvWICEBvRHzzWO33K1DQD/6jSH4yGoqTSUPqjZ/iik8OKEp7YRd+I9C+0wtTefUetAzHAiF3VFWfNveT02Uc7A2s3H953rC9xwmTDeusSdUBaFClaVVs/0NNV2FVRKJMJiEjURr4/UZi+rg0lzyig0Ydn5MSHwMpOQa33w6n1vKrpM5v9d994ziesx4evPDUcji6YWb9gZn2moyXdN7U+11AMU6xFAelfX9RIYVD62yrh4MHjUjA4zc9N83MeznQiPemRS0TQxQlVMag3mFLJEA+wmLcCsCodUxWQBrRyw+Hn3z3+wsYRSiPkTZknW/FCcv/c0FrkKVtquZZazoj2xVPAkjZo6yG1KwqGZZDf42iodrsc3IiaJa0OUKTonNlDhZiyVwqiUCYv0CFyeaWADUHlfzrYn//ETgzDdkQlscCYhtbRhzHmOXbuCbOBYW7efWzd5oOhqOxzO5afMuf6uc2qrL789q73dx0LRWQAgK4lN1hnudq1O2UIwTnzRVAQfYFsbXgZhGY2D42dcxT+2TPLIASkxmypDYUQApFn66zqQIqqK5qRdC96q/zBgf7sB1kyp2Hf/kPZt6G5wZTJDYTAIfAFBGk89vAD8XLQlLQ+AGj7V8giApxbiklL02zr6N+ypy0cVQSemzejftaUGlJLQUM1Pqff45CDJKmVG25JIpm0yDAM0zDMd7ZnHD737OgZipisctX4XAJv9SewuOaycwwTb997LBCWjnT0B8OyohmKqjEMd/5CK0g0Ls5zvh+yb4ggKTCS/Qjvvb/tvffTDMC/9plbsh0ZQY+Tb6h2h6JqXzCa2jU+UQek9QYrUvTCs056c/1W+yn1BlMqEAYh3vZAFsSjD/390Yf+nrjm2txaQqYtrVNkTbdxAvv4X/5hL93ysX8Z/pLVkTGibNvX3jNAEjEcPLtl+84jRw6LPBuR1PaeYDiarZ1LYiXkES6CGd7dzZLsb61/X0/w95LwUwDIbMKQ128bplT+9GSsBJvdEhIAcMpCEsI04gxgwYJ5kqLZpeIgAOzbh7IM/9PK/Tj//bvHNSXblCUv0uoAFzfs86QlIiiVBoNiP9XxwIrSVeW84brrSK8Ub67txz2eoUr1l5x7UmmigOKagCiDmy4FADzy2Cs9A5HO3pAdbCMFuSovqbJtmDgiq6mNIWNHJEG6SNOMHKNCSeRl4qTOivLUdVPVjGBY6gtG1ZwPNfK5hj9ds+H9xKeZMgCaaz3Pvfo2GAfccNnpT7/6bnYdQA1BlEnGs08+RgTuTbcKPGvPALLbcpOaAVxw3d1gfPP0c89lefWG666zdYPbkvtJb/z1tVuLUQbpw0Aff/I1O/NrICTZ0p8MjQfPrGp6JKoaRpqcL1JHgWWspLBcM8IwxoZVNQhCsqxZhqOoog0EpbBEBv6gfAxGBkAIDAOT6J8MM4DrLjsbAJBdDcycN99uVA0hCISVqKxaQaWkcymEMCwpwYiSpT4S6wB6oDP7JCCxOEQWqA6gTFY1kMRVV1yeafv/+GxO5p3xz4jqAQAwdUpDYcqAjY+trfpuQ4V9dMMIhCVJHu7dxSTRKSKpkkIKQqQejmNJFGVqwHsWSPItib0hO+qGKStaVNYikhIIy6Ua+MdJO3CwijUTM/28WY1T6v1rNg3lvqVVAy1Tq4q6BkxyxLbs7dhxoCscJR9j/E2yvoYRdUBiXhg1BFEomfjJb9IojMnH0xnUwy9/G1u44brr7voQseikhRjUYmIRksr+1gJZE5HUUESxB/IIDqVRyKoWjJB6cKnHQghxLKsb+RWEgHZ7BxZpOpH+EUmNSGqmRgJZVAjpPp/DuZKep/YZPu+UE3hoD7STQ5tsOjtHSN3KhROaXQ3epo6e0LHuIOkcGZ8ZVJPqhlm6hilS1OGrkQJDG1BDEIWSlhdffoV+Mk8/91yWOQSpUxYLpYlZQ4hC0HRS1dlu+UJ6Y0FoG3x0w1QietSqB5d6LJKiDYGeoRN6JiAixT1MEwfDpNC0pGiabuReSpplGZfI64ahSyO0+c0FVTewifk02c0lhkHQ4xIgBCpRe3o0aaaVdRJw8ZLm59+yFEDWeDhqCKJMeqiIL5KE2Bs7ENSK3glFlVCEDEsZUvSftU1DGGNZ1cJRJa31n2UQzzLEgp9nPTirrgcMRZWBkBSWVFXLVfpDCJ0iX+d31/icORaHwiO91heIHO8eAKMCZ1VUbaxx+70OO24qjteaB2RJDfvwNecmSv/n12wv88VSKJRJCEpX9F8fsGJvICR5dwIf66Vl1V5OX+rZjqQkRpN8rP+D+5I2A6GILCkqaS6Wm92fZZkqj6Ox1lvnd3PDq1JnO9dIr3X2ho60j5CTVUIYq29BY43b7eCTRvOJOiCVcDCQy/Hvu+czxV4ihUKpFAWAgWGagbAcloiRh+cYt1Owiy5gUuTG1BMK3STCsohlGVKbLc/hP4TQNLGkaPkO/Ov97qZar88t5tg3JscZwEBIipTClJQjkDS3Y2qrXHVVLiGzGks7CbjmvMWJTzNNAqgOoFAomRgmPTEAshIr+o8gdDsEp8jHIyMzJXYhCAWOtSP3Qf5YeoUUk85lY25w4F/rd4tCQj+yEs0AVE0veehRdqzPmW+ocVd5RIaBeRmCctQBFAqFkpZh+beGQYb/kkIckoLA+jyOXOoukfY8ECoFiU6McY4Jw3GLf3zgX47i3sX0MS4YlkV+r6Pe6l2clyGIpOxdSSJTs0MnARQKZQQFQEI/ZTUYlg3DtGzTDpeDHxphZxCMCEGBY3QrewuUAXuMbw/8myyLf2zgX5D0H1G6j/Lw34aUuODZ+mpXrc+ZReOmnQRI4WDiU2oIolAo+SsATKwfAyFJVkltHKfI+9yOeGU0q5Z2eskocCwkmV+lt5zYzdkdAlfnd9sDf69bZNmiBv4j7joWEwACg6DXKTTUuH1uq49xPpOAJEMQhUKhgNz4/1MBtmMA42yBAAAAAElFTkSuQmCC"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "With its hook released, the axe has clear space toward the FRONT camera; the opposite depth direction is blocked by the horizontal sword.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - WrongSelection\n\n\r\n\r\nReset context:\nSeed 7. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The selected forward vector leads out through the opened depth corridor.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-70a50da27c70", "created_at_unix_s": 1788845984.740539, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 8, "requested_group_seed": 8, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [8], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], 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"}], 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"}], 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P0O4wZveZErAvORJ6c8fxAd9QL9YTViw6YcWixOdn6YE9B47uOXA06aVlRW3r9vR5AhyHRYFXVO2vf/jZP5765T+e+hU7YeOFN37zp3+akrcNAEAOc+kkVvF5kQmYyRBQUvynUh8ZH6e1rYeJgdsbfnPboXBUYR4Ag/kBWLfr7Pb5tSUnr6qLhmNpgKWLYjOdd+9vGukCVjcsJAS193he3nZEUbSGhVWL55YX28yiwGGMB0NDhoee3m8vtldVlT9y3+em4GMAACDP2JKZcc/9KFCueACpxF2BkmJzZanNFxkW+meQhDRNc4fng/0dvgAbLEMLQ9lXqjcQZ+e+pg92Hz7c0uvxhW+97NTT1iz42yt7/vX2gaPt/ZKsCDynOwTqbZsX33TunF279k/NGwUAIM/4/XNDMxDz2gmYMQ8gafnPDpKcAEaF0/LzJ98dCMphvZ+Ew8wlRoHixwihUpuwdmm13WaiuQE9DbBm5SJaNKpDBr0BVSPd/T7XQFDVNH2/N7np8k1JL/q3V/bU15Ysqiu3mETmZ7Bg0WXXfibLHwQAAHnIlgyMe+57ADmUBB6FSzatentX8/6jPYk5ALOB1636EJpG/CHJqLeLYKngXfuaYjKgewNrdFdA0TR51xF/KBocud3FR89ZTRM+/965sK6svqbEaadlpqLAs8nRAs9fdPnNU/++AQDIUX4/6qaw3Df9ORQCYvU/o8DzeNXiqmJ9ac8wG3hVURKX/7R/kRxbrCd5Erv2Ne3c20TFQI8KCTy3dPG8qEYj/Qih1OV/nCvOX3vi8rpt+1oUddgLKar69yd+/tc//GzcbxUAgFnBk3o5UCqXXvv5fLH+MyYAqfEfpgHxWFCcCqflN397n1UElTutiXdJ2rAhbE4LH5WVlt5waZmztY36CokwYYjJAEJOu3nDirm+KHaHUrYjp3DBaQ1Bf5rNycwhYD4BAACFxqXDDX1+mf4cCgElln6mctFZK/SoC7d8QUV7jzcyqrvQ6/K3dXudFmHZ4mWYpnkb08oAxqhh6fxjHa45JbTSdDJ0dCaLDQAABcWl+Wb3c0gA0iZ+00KD+6IQkRQW/0kdw+y08J6Q2t7jFStLdu5txBitWblYDwGlkYEDh48Xm9D7R/rYhJmRKoVo1sFDR8/vOXA0dR8ZWH8AKGQuzVvTP2MCMFL5f+pWABb/YR4AIaTXHQhGpJHiP/HjAY93tzeMMTYZxK5+n9NuWbV8AUKo8Whr0pVYTIaKolhPaVYgNIoM0HMOHMX67oGJv3kAAIBCLgMdff9XIhVOS68ntrFLktVX3mtq7vQQPQMcltS0AuAJqSUWXhSH0sUMUeBVzsgyDSctr4yrhzcQae3yrFo81OSPjRaIKwFb/jNaOl2JzYUSl/+fufu/JvBRAAAAFHQVUGriNy3hiOwPRpn11+M/wyA67Pi5Z55OfbhMe0mHRCT5Q9GXtre+uL21rWcgGJbMRtFqMSSeybRh577GkbYQs+ZCiS0lwPoDAJCPcLmw/yt2TJAk0SExSfU/jO5+vzcYTYz/FFmMqfEfdvzMX/74zF/+mPYCSiy4xEIffqjd986B7r1NnR5fePvBtqTTOIzjG8fizKvJNF0BAACQ+8y8B8A2cNmL7UfbXa99cOy9Pa2tXQOhsKSqGov+s/hPe69XUYaK8W+/4uQl9WV4MA88ENYGwlpis6C4DKRVAiYDJRZaBuoOpWkyEXcFRsE1QAcYzKmt+v3PfzDetwwAAFDQVUAsIq9q5IP97f0Dwf6BUDAsYYQPHu8rtplqK4pqKortVqNB5L2BaJ87SBCJ1/9gjGrK7fvEnqg8rEnc8+niP0wDbrv9Nl8gEpUUkrB/mHkD7hB5cXvrh9bNTXog0eP+iQv/eTWlWDSFI3JPTy+7paXL/f7uYVVGAAAAeYEwg/EfQojHFz7a7lFU1RNUmVUmiERlxR8M72/0HzjWZzbSvg6qqnkHu7zF63+qSovm1Tj7+mJzApJiQak89uhjCKFv3/dVRVG9gYjbGwpGJE0fL8NkIJXUWzWNNDW17j7mUhSNZR3MAp1T9rFPXPfMX34/8Y8GAACgoDyAeXOrLO7g0a6A2xcr7kyEGnpNiepDgJP4zCdOppcucCcsqXqpz83K/zN80f/65vcRQg/c99UyB91XrGokFJHc3tCAn2YCzEaD0252FFlMhlh5KEpwAnrc/l2H2kMR2RcckpzUycYAAAB5wbSWgSZ6AHH6PcG3djZ39fsTb0wt9Iz3/5E0zASA1vbI6paXdilqLPr/zgtbxntJD9z3VXYQjsrNne7Q4D4DhDCHsdkklhRbim3mORWOnYfbe92BwaIj6jd4QiTJ5wAnAACAPEKYwfoftgNAiUbqKh29btqcmd070kbf1P1f/S4Pa9M2evBnFO4ddAiMBrHYZg5H5cFyUqIREgzTjqFtyLO/qSu2gVgHY44QzTk8cMSx+lAAAIA8YUargHRLay2y9bjo8h+PbOgTOf+UxU2trqZWV2NL/2vvH2P2OvMQUFru/eb3v/Gt7xfbTGwkfborHSnQQ+UHY1RZavvsNedN5hoAAABmbQhoxAkwBLl9oa4+/9E2V1e/XyPEwJFUAYjHf0SBHxIPA/0fxtgTUicQ/0nLtZ/8VIIfMAyMY3rptJvXNcw9eKzL4w0LAr9mWW2501bscLB7L77i1qxcCQAAwGwQgCTrH5WU+Fo71gqCoGBY2tvY3drl8QdCaQUgtf1DYv1P2gLQifHAfV/VCIlIiscXGvCF45WjGHM8h5fNr1w8rwIPdoZACC2YU253FCc+A2jAzHLjHf+ZdEtxcdEkn/Oh73xtks8AALnGTFYBDesChJHVYtiwcs6KheXPvXFYGiz6HIW49Z+K0DuHscUoWsqLa8uLCUKKovmCEVnRTlhaaxqUrnk1pc4SJxswkEhvb/+vf/rdmz739Sm4LiAjcz8V/MfXH4wf/89374GfBDALEGbK7qfObNEnf3Fvbj92YkPt2ztbEgtA06aFnRY+JGO7je4Ue/Lxx7N4hfd+8/vx6iAmMKLAlRbTqZBx68/Yta+JzZsEZpm5Hx0Qgxnhhltuqyor/v73/ntmXn42IsxU+8+5dZVp+z8jhBbWlSqq9t6etkQNSNv/2WQUTl41l6jRJ9GMsXNfE502PCgDvb39M3cts5bzP35zbU1Vhifbhnf3i0HGGDs6JhgRgtIUm4EYTDUP/fCB3YfbEULV5TTQes89dHH24IO0fg/IgxzABPo/qxrZcaBj1+FOVuWZmhaO130W2Ysa5jm+8a3/yfplJzoBo7SEi2cC2PT5RAGAKNDkOf/jN7ODkQQgvbkfzsL+RpbVJ6xxLELtNQ3ju47hRQFplSARiBFli49cfu05Jy9ft2Ke/rOOtXk3WukPvWH9pVl7mUJlBkJA/mC0yJp+EGN8/AvP4Xk1joPHe5WwNFL/ZzYAvrV74M+//S3KDXbta6qpiNUCAdkw/dRiJ4b+MjH3C/qPxLsExmG/Lfp/9J/67kPjuhhV09qqlw89GxqqRxjdLQAlmDzM+iOEAqGIzWJi1p8O9ftgC2hArgtAavVnKBAoshpHiv8wCCE9roA0WPaTuvyPl2lOeAvYeDMBcVLbwyU6AXFg+T/pVf+Q6a+nU6OJ0Zhm3bCw/0hKHUD2ywJ4jqvvOcyONY20Vi/LUAyu+/QXf//zH2X9egoKfyA2FQohVFpuT7zrwAe0+BtkIP+qgNIGghL7P3f1+RWV/mmlpn89IdVh5gghA2Ft6gQgcxI1oLN3oKbCAdZ/0qY/Zsd10x8jGo36vL6TuYHhD5ruDdgch4fEgJDWqjHE4LpPfxEhBDIwMW6/8syR7urvzmiWFJArO4FZ/+eRSBz/MuCP9LgCbP5X2pPZAICR+j9nC9YoIm2P6NQz054MZMj5H7/5/I/flGD6cX1dZaL19/l8Df7WFOs/w3CYikF9z2FT0y5D067EuzBSFXmompnJADB5+rtd7As+zFxPAqfu/6IJHL2ScpT575pGdh7q3HGgQ9E0m1l0FFl8dBYY0f+dwv1fI5E2EJQYBbr+tq9M9TXMYjZs+pjTWZIS8BnC5/OdjKfV7nf7w+xgVXXJxJ7hHcPQlGk6etoXZAfgB4yXfzxJu7gzAoFQlV4IlMqZF9807qcGpjkENHf4H/ZI8Z9IVOnu97PecIosL55Xtmx+eVOr6729bb7g2BvEsk68YVzaTABY/yyBRzT9OAumfFzWvNxmTr2xLzD0VGOyGB17NWSeMycmA8V2K9MACAeNl0BCAgDIYwEYpfozsf6H5nxC0X5PUI//CBhpTjv9a1xYRx/+zq4WhNS4EzANy/848QjPSOWhwGTIxPRPwJqnNeXjteYTY5Ml/Gp7V6oGQGZ4MnT3edM6AW8892twAnJLAMZV/h8/JoT0e4KyPv6X41BlqYMJAMZowZyScDDY1j3gD8ViQWgmSHQIYPk/eZYvWWAyDZX3nEX66P9o5x7zVFhzdcPZZospXkwqvf4vlFW2yrFadbpRUSRt7V11oAFArjJ9HsDoGjBs/nuPV9brf2RZcRabDWLM0HMcLrYZrfMrWjo93rAPzSiQ8s0KGzZ9jAb9IlTRRfaD5rK5PFc3nD36CYazLmAHRN9yyJDf+neqNZ8YZpH0d3dGFMxcgWI7nUMXdwWAMfnEjXf/5f9+Ah/UbKgCGml6YmL9j8cX7vPQPw9a/0NIc4cn6WSBx4vmljYsrJzO+A8wbWTL+vcuXNO7cI3LTbPH0eEMeLyhYCj5KxRmX1SKTj+ffWXlSlYR/wbe197eFb+FyQAwMbr7vGlvf+O5X8NHmiseQOr+L0lW9xzpEgXeaTc77WZR4DmO7s9MjP9oGunq84cjMvtW0rDLO5QC6ulxza2rbG3rMYj8vPKhRwGzhlWhzkxOG31hvnBZvDCfrjhcbo/NOsZvC9GG1iZMA6j3oCpr16+l7Z4+2DnsIrVh40vj1OpTptNBb69F6osDAafDxgTJZJzJRrwAwJim38Kjba5uV6DfJ2OMRZEv0TXAZjFWlxdZjMKHT19OCI3yRyWls8+n6PU/qh4Fio//jaeRWSlRfKQMkNcsX7Ig8dvJh1zspeV9fa7y8mEp4kAwNKYGJBKJDBWbiaKwdv3ayiPbE+4fe/3uDceHSw9BsOr2eK16Q5tfPgy7RiYeBRopFQzkhAAkDX9/ZdtR1oxFJQQRokS1jj65s8+Psd5dmajz68rtVmN1uT0ckftj8R9a/2MdoWUQQuhL9/wQTT033/aZ6y855Z2dtJVYv9v3k4f+dxpetNASAIxYAiBL+P0BmkguosvtMTUgcfmfZP1ZIsrn85vSGfRx8R6iyxdq/QntLDTJZwPSArVAuegBXH3JRoTQn597P2nKri4HmqqoB472UjEwihaTyDaLcRjVVJbU1zjj5/f0uCoqSlhLr+lZ/t946+3s4NS1ixFCW17+YBpeFEhipHjLSLGXdxAyDdb4JMlAogZIkmwwiEnPk2T6A4ExUrXxhlQqIZpGZE2TFE0lJKqoYVlVNC0iaxFF0TSiaKSNC4miIPBcOBJNkhwgO6lg+FDHz/QFIq+6eEP8+Gd/eTd+rDd6o39KoYgUisTWWYqqVJRYF80dVjXU2+sefTNBFvniF+9SVO22a86N33Lr1edqGvmf/3l4ei6gAOM/8z0t6Wy6dcxIS2K8ZUW47e0Be1GR1Tpo6xNlIBAMxWZK0ylvemMS3YibzeZE659o+hVVtRzZ06WqIUlRCJEULSQpKiGqbu41/VdXG6m8IQFFUBVV5fQVDDB5WBRI0zRJVnvd/s4+r8cX9gcj//3gN75yz7fgE54xAUiK/6QlMbKfKAYM1v85Eh1qHJS05J+e+E8qt117Hsb4obwKBC3ccPHR959DOcnvHvvRlTfeQZs8Wy0VFeX9Nlt975FRYuijE5KVsG6aF2ghyat5qhdytMyA3mW1Wvz+ADP9RG8pGlv+64ZbVhTZT/0MKRp7UVXTfL5gd0+f1JI873MyxK3/9tf/lsWnLRAONfdIsirwnCQrGOODzfp+kcEGIkyCMUZdrsAXvnDXj3/80Exfb6EKAJv1OHrXhzHFQCEci/YkMT3Bny9+8a6RmkxijO6++/M/+Ul+aMDSUy6tX7g4GPB0H3wb5SRP/N8jV954RyAYChxvqa6saK5YUt97hNAAYewEfYRLLNyiaERWaYxFowtwwpbh9EAj3f6wd9B9jNFD07ZBRxXPcUajochmMYiCaBAtZhNHbxGLimyRcCQ+JSbgD/a7Blwer6pvCDB7hqo2J0+PABnLMfjwR69mGmk0CBzHjHrMvOu/C2TnYVohxqpFEjwuQrShv1Ue00fKivYfd/0Hx+GfgAzMVDO479xPbSgjczEYCSYqRoNQWVna0+Oaag+ACYCmkVuvPif13p//8WWMUV5owDnXfbV+wcJ/Pf37zn1vTPNL37lptCW8aFv642ePx7/dtPkaURREUeB4wecPRiLR8qibZYliod14uD0dVoMQktVRT6FESuhWLIywaBA1jYZtCCGqqsbMDUJGV0ZFqAyBH8paS8pQF+hBYgsYHuNuwakRDSON5zDHcQZRfOuFJ1AB8PGrb2ALKfaDGZzJFgv5RiVlKHQ2eDPVAJ4zCDz9OSHMc3jwgxzG/Z+9eJTX5Xj6iF8++aaqkf95KPuzAmcTU54DMBqExPnvkxeDaYv/pBoUd58bIXT5h9a+/n4sUpHj9BzZLoq82TxiMdV0Gv1RePX5P9LBT2fRCX+E0L/eTs5eoWTUAZTDmOc4ksHIX5O7a6QNkDFrzvPpTHki1BphfT4MM+4mkecwFswGu8lADzjsMNMVrKKpIkeV5bB9XgVCmqpFI2FRFDBCj//iobw26JR40+6YszYYhRkejdForjt2b3yEH09tO/30OJMYicqqGlMHno/l5DHGSxdUX3zGipLiSe31ueWKM2jY9sozG065bDLPM7uZwnbQzAmIN3/WNNLe0Zt4QoZikNhDYhqW/1/98t2KXqShauTTgx4AM/1xXnv/MEb4P+8fO9sx4yzc+OFQwK8EvX0t+3NHAETbUoRQohPASNQA2iQqMw2glmUwbDTWwjzNo5m14jHFSMMPmMfYZhREnh7bTQZFVXkOm0V2CxI4jq5ME2L6aTlor2cHoWAglh7LMdN/2dU3JE06TrTpsUN2T3ylTu04/cA0/Yekf2YxORzXIp1xqLn3T//cHk6K3ek/j9VLai48bXlp8US2TDMPgPHujsM3fe6eCTxJgTDlHkBUUpih5zhcWuZ09XumyDPIFt//wU+++uW79fVdst2PkzpyNmeJ+L3YaCLhmZ+blgnbX9+y7qxLMWZRH9wrODBG1Xol6AjWnP4gDDxXW2zp9Iaiijqm0WdBhUqbqcxmsoiCgacLdYF+UaOeNvM0XmbQ9Ke36YPubHxtzg5UfaETt+n0HIz0Hfpxg47HZdDHS0WJzWo2pBEAQvYc6ejodt9y2emlk/ADwPrPpACk2nSr2eBKOSEqKT09rtHFQNMIC9ROZ/3P7KgqNglIMhiJMPYs9ennC5fMT3UCtr++ZeM5H6d19fRHgA0GwW+aYzIaVqr9JpGnYXR9hc4OgpLMAi8Wg0A00joQM7gYYYNAF+jsZE7/CslKVJcHQkhvIKJoZGFpkc2YvBsgp0z/x6++IR5DjxlrFlFJ+PVMzIvGbTqLoRNCOAp9+6lPPhU2fVyYjYZim7nfEygtHurzqmoarQDBeFl9+VvbD+861H7v7Rczlytz3t1BZ3bC2n+GJ4I9cN9/sCVVkk1vbethGV32bTgi9+lrbRYvSpwcyW5hQaRpiP8wmAfAFk+XnL067TmvbjvM4fyIAlWtOkPRNNXb72mPTbLNkRBQ2ihQnMuv/wxbty5cOF83CDETUNWyO/XkZrf/cK+PmcUKm/mEGmdqXMIXkZr6/f3BSDz3aOC5OQ7rXIfVJArTY/qHhdHxGDY9KfbC4jMYYxqZGn9qNAdRNfLXl3Z9sL+1zG4gOOakEkJkRW2YXyEM7tjYdaj99ivPrigtir/lf78Zm77JMvmpH4fDblk0rwomBMxwCOjeb/5PWjFI0gOzKbYKi0qK0SCwe5lPwG6Z5iU5iwLFihVGII929AiKRIwWJX+cAMZTj/8sfnz59Z9hB4sWLeied0L8diYGgah83B1gBtQiCg2VjrRRabvJsLa2xBOWjvR5B9gOMsccL0IdLR1ziq1znVbjoMXJkLYa2sP8+NFD9Jtw2/B4C/741TfEf3/iv0eqpsXMfSwcE1up6xeM892gjxeOw2UOG+aop4KRSjCvEVJb4fjUVWc/8+w78b/9NcvmPPrEawShk1fMiZdsIYTmVMVWkNAXKA92Ag8Xg7uY/YwrQfwgXunPlvyJzzADFjd/rPwoGHmsaKrBZkd5yyhioGna9t0H3cheiTwCx62sdppS2gr9qU02Gpn+YYJMyGxiw2YwK02Zt7SdoP72pgWlReVW01vRhD8K3aYP2vHYkmBobd54kNa6JC7hNZYXpeXoesFQwdl0lLJI1/RFuu60JJ9DCGrr9hQZUDgimU1GOvFJUj91FR3h8LFLTkUI/eWZN9n2vTXL5uw81P7uvnaE0VN//C30hsuPENC4dgykhv7jiQEWL7r+5q9P21VlEgVixaB5EQWqWXWGyVka6mnrPpzY1XLmQ0CjR4HG5OPX3e7zBTy+8PxaOkIsVa4lWe320KsSBzv/6C2o9CP9bEexTf8/FkkoKUcaO1339nSbnoth9GkjMerCcnKZrI6+8PUHMnz+az95I89z127eWFdXEb+x6Wg7O9jb1M1iQexHs/mMBqt5mEcLw+Inxsw3Jf/P+x8aSQxY9392yzT3f45HgWYBhpIFJg6HfUFJJkJxPcdzvCCEB5suzCwEoSuu/dQow4Jip410txoutvHFVhvbvhur6YzfixGLI59z1oZ5c2vTPoGguwvP/uM1XuUKyqAn2nRV1VhqIYsGfbzEX72tjRaL19VVxK0/7Qm4qAohVGY3vbStiRD0/Jv7z9m4pGzEAQxA/ghAhmIwUwMABi1LPmGuaOB4XtNUORrCGBNVbW1uEUSDokg07KGqikaVgGga5riiIqsoGmUNeVp2pH023UDjkQyxqmnhiBy73fe78V5qi+1MGiPQpSCem2XVO8NN0mi26f7PXjLKvY+/cmzMy7jkorNPrpvW6XhTwZip0Wm26eMCY7xmSc3+xpjR39/YvohuoUNdvcP2gpy3cRFC6KVtTa9sO3Ly6vo5lQ7mmcGEgHwNAWXCd+6/K1EbpjMKxBglCuTxhX7441zZbn7FtTeOuGIetAX0rsEMZFrDGguSjGA79PrMxEdjVVWjsooIWZKxAMRDQLd8/UdoimECMJIHEHcCclAA4gadfez5ZdDHyydvvHnNkhq6c0WKDQRkjLIX7KVtTWevX1TujLX7hihQ3nsAIzH91p9Fgb78pbtGr/YhiLR0JU8tzpArrr1xyE6nvAjrjDKyGadmOvVevciEMlKDYhbLHsxRjgTOcHHNkGTl6Zf3fLB/qJkzMHZqVDfomdQZ5KlBHy+/+79fLfvm11Jvd3mDI8kA8wbiTSbACZi1AjBTNLb0WszGkmKLqtGdSbT3pKr94pmtiX+zhOMuu4ZGsdP+IbNOk4m3xE+L9SMbwciz/9gvN203k8ZOpFaYjG2spwKDKJy2dv6x9n6Uft/0bCYx6sKYxYv0qeae+76HEHpwBBkYyRUY8FGFcBYPTX9LAsaEjULhCsCHL72K/k+3rDzP0R3w6Uo9orLW2R946A+vDd6QEjfBdKYga55Cd5zSp0l6qfQmYabs9VRQW+FYv2Juf1vGSblIe8g0B+UeiVGXeFHQmIBBnwYZGMkVGMX0A7MkB5B1Nl92jaIkZHd1y08X2hwWBV7guVlsrKcIty/0+LPbyo8MFeyPTlwAJp8G+Odrw9LXRB8YQINcuhYTQlzEOUoO4PCh2AbpuqLYFnRcMh8hdPdtN0/ywoBJktYbSJSBVOufNhMAW4JHohAFYPNl12gawlzKtk99sVdRYrviQ2vra2Lzh4HM+eBA65Envpz5+UwDqk+6hnXXm+QH/sV70sRVLrnmNvpLvmRuc3O7wzHiVjirxWTUJ4XRHYhVQ3XooAE5rgEZCgBowEgUlgBsvuyacmdRj4t2l0wjAIOUlxR94nzQgOSVdbyTATPUqfY6Isnht3+c+Y8jNQo0mU1ho1j/+C3/ff9onYG/9s2hPX0gA7kvA0Qji+vp/oAkwAnInAISgJ8/TGOLvmBk9+GOfm8QDzaf0lsCqFVl9njOtscdKi8puuL8NfNrSvPID0iOhCCauB7FXk9ycT0S935oHP01g6Y5eAoF4PbMrX8ckIF8kQFV1Zra+jefuSrpNBCAzCkgAWD8/OHvBcPRPY1dvQMhohFMaH/gmgoHnQ6o96GNV8CfceKCshLHgjll07O4VuhuTDTYQmaqjPWCNZvCQa/eGsFkshYf2fZPlG3GJQBJTkAWBeCSa25HCMUFIEPrHwdkIPdlICopzZ3uDAUAokBpKTgBYDz0g2+9uJU2cVwyP+ZCJv4atXf1sYO2LlrYaDEm10qxCk7WlXcylzGulXVWWLz+/KDXxUTOYi9p2v7iVLzKhJ2AbAlAkvX3eX2P/oT6f1nRgD0HjhFCwuFov2vA6wvKihIv9Nr92pPZuHwgAw0gqM8T6OzzEkIuP39d0gngBGRIgZaBuj2+dcvotsOPnr+B3WKy0v6QnZ1pGk6EogrTgF5PcKQtwa9tO4zzYTyAwWCOCgaEOVWRVXmCI3yzC86sO3TmXExD/6hh0Pp7B63/jZ//z/SvOgI9btrZv7KEphl7unvjMrC6YQHtt2GxyrLS2+/ZtedwT58nGqUf5glnXwEyMA3cc9/3vv2Nr4QiUnxfeibAhoBUCtQDQAh9454vscDL5ReexKw/Y/v2g+qgeWAeAMMfihCCLt001Iw+TzuDLll3fjjowxzPcbzZVnzw3Wen4lUmHAiapACwxG/DknnxW75//9dvvvu/2PGGE2kH/wnQfOx4Wm/AbLG63ANtHb2HjrT4AyGFegMxwBuYaj732dv7PAFC0BUfTvYAwAnIkMIVgLgGEISWzC1PbX7jD0ZcvnDSLRjjfO8OXbN0IyFIkaIcLxQVlzTteHF2CcDtida/zxOkfS8QOvWkNewWWR7WaiYtne0daW+XJCmtDERlLRSKHG/pbG7tCoYiqhqbS4wR3vXaXybxboAxuOKq62IHKRoAUaBMKNAQEONbD/4wLgOJ1t8fjLCDUrs5UQOKrKZAKDrSs2maxqUbRJVrdB7etmT9h6VwiCDEC1P1C/DAi3LmGmDJ0sZgFvpnoxP14I8Xc2Lc9COE+nt7g8FQ6gNJZpEhg8EQ14Ce7t64BhhFrrjSaTYb6+ZUHjzc3NHZq6iaqqoEkRPOvoLnuB2vPDH5dwek8uSffx/XgEyAKFASeWCwpppvPfjDxtZY1tcfjMStP6PUbi61DwWI0rXuiZEX1p9hMFl0xVKlSHjJhgvQrIBZ/4al+vIfU+svo2TrT/d8Wel7T4JuQ8ts5JxBhx33dPey3IDJRDeROeyWupqydWuWnbxhZXmZw2AQeZ6WGquadsLZV6w7V289AmSbJ//8+7S3d/fRardUfvG/0IhpiIIOASXy/370zdFPYK7A7IgCrT3ro74Bb8A/YDLbbI7S/W//bYpeKHMnIO4BTCwENMz6I+Qd8G44aUOS6Y+T1glI5KrNrZm86O+eGdyFhNG8eXVJ9+7ad7TpWPuAN0ArjAeDQgZReP/FP2Xy5MC4uOKq6zKMAh0+1okQuvXz98InDAIwEQ0IhKL5XguEEDrtousjoUA4EsWcsP+dv0+pAHgV2sYrwyEBExCAm++6p7fXHbf+NNG9dEla08+45Gx9jHuW+N3fhm1GTVKCt97dd6y5Q5JkQjcuURngMLZYzW8/99ssXgNABwj/309SPwfQgNHJm6jFNPDZL943+glJ4aBU8mjbcDSqBGhdk2a2mM76yE1T9CpeJZih9Z8wt9z1nxjhVOvf39ubav0vOftQdq0/nWTy0W72xb5taWlraWmL33v6ySs/edWHb/nkxQZR4HU0QgKB0InnXnX2R6fqYy9MPnHj0ASnTPgFxIIgBDQBV6Clm86oGyUKlBceAN37ds3tra1tiqJabY4PXvrzzI6JZx4AQegnGXsAt9wVq+svHZwJlWj9B2+I5WwuOetwRjH+KXYIfvn482w+BPMGBIGvKHP+88+ZtlAFxusEjLQr+PCxTogCgQcwbleAWf9R0DTynfu/nhd/qNXVVfX1tbW1FVJ0WLXrDJK5iU5r/RcvWdTT3dXa0urzB9o7e482t+87eOyDnYdKDG/t2eMe6AmjqDpyFn86HIJbrt986ycvvvWTFzNvQFHU7l7PGVPmgQEjpYLB+jMKugx0FD77xftG8QNGrQXKmyhQKBgaGPA1Hz1uNFtXnHrJ/nemZEdY1klr+l9/94CzyLTvYHM4HJFkBSEiCILJaJhb3WkW0Y5DOBJVTUa+zGFYMs+6cI7F4TQgkZ9Sn4BpAHMI4hoQdwhu/eTFCKFHf/OsRojPP7VRssLhEzfenTYTAIwEVAGNxm2333bCYtoxghGR5B43/VtlkxpnQRTo4ms/d+jgflE0G0yW3a//dcZDQGMmgdNa//2Hmge8fo0Qs9EoCHxVZWlNVdn1H6IzikMRfcmPUTCshiPq8c7w8c4QRqjYJsyrtiyotRRTJeCmSglUsmef5729A0YDhwynxG+eNy9508NnbhhHMTuQlVTwmReD4wUewKg89uhjt99+2+pBDZAVNZNkr0ZoFCgvNMDuKD5h7dpQKByJDvUwYNz7q7+3HNjz+I9ypVourekXjFajQE5at8LrD/T1D9RUlRXbbR/ZdBihDva7bbHEfFxW/D93vu2siBqOqh6v3NQeeuHdPoOI59dYVi+2Y4uQdRno7Qq9scPd2Rehe+7wS6pGli/9kO4QtCcpwc9+S4vZQQaAaQZCQGOz+0jn+hXUcy+ymPY2ddVWOEc/P3UocM7yx58++PGbv3jkwN459UtO23z9288/zm6vXXl6S+OB4mLHHd96pL+vS+FEm935m2//R05Z/xJncTxrUF7qLC91IITHKPLhMLIIZotgdhpr5lpJWO3sixxrD72xw1VfY5k3z4qEbNbFBcKqohJR4GjvDZWOID14ONZ4I1EJkmQAlGAyQBRoXIAAjMGjjz5266c//cH+tvUr6ghCJkNsZ5M+Bz49Z21YwnaE5QVHjrQoyNDR0WU0D9nW8y6/vK3psFxeLWDkC4Z4o4n4B26+/xE1Gh1wu/722IMzbPrLytvb2kucw4T2krNjo30zhcPYKtRabbW6EjS2Bnfs9iyqs9rLjRlNgs+ABQuKbi0xdvRG/CHlaHuopSvs8UnsF8dspQOIwkHfSA4BeANZpLvPmxoFeuO5X0MUCHIAGXHF1dc7i8yrl9Rs3d08p2rIA5gFO8IQQivOvEKKRowmy77XacuauWvOPem8Te3HjlTUzO3taEUEL1iyXCVkoLcXEU0QRNFoFDjMc9yfH/7WlOYAbvr81/XB7jhu/aNRqchRYrWYOvR+bXNq6D727JT2E+Lriza2Be1WYfECGzKMODF0/M+s/6toN9/Tx4KH5aW9J554WfxOV7/Lah7aMj1v3py8s/71y06PH2uYvpfWg6/mfj3omQWfBgAPICOe/NPjV1x9/evbj2qa1tTau2juUCfIfN8RRtEUDiN+KPhBouGw0WgO+X1KVC5xlpYW2Tq6OhQpEgoFMeYQbzAa6NdFN91lstqefjj7zVVO/vDVPM8da+ksKbaqqtbeQStquvv9pSXFGHesXL6A5eHbO3s+c40nOy+Jsb3CtK7E4O6LHmsJ1pabjHaRhoyy8Mz0n5vv6bvlU5cNDR3qjLWfYi8Qf5kBXzD3rf/chk2DdXCkVuwkCCtqr6bpwS5EOA5LCqpctTSsezuEEB+upm/58BszeM3gBKQFBGB8GsBxnKbRYC6rBZodUaD9bz0TP65fd3406A96XKLBKHD8okVLVEXVNM1R7JQlSRCFiKRYzJZwOKJqiqpEMc9f+5XvRSOhp/53DG8gcz5x812RqBQMhm0WY1SSFdpaUzv99I3s3sYjjdt3HZ5b4yxx0ihKlhG4kmpzSdjQ0hGKdoWX1FuROTt/I6edtPLgQRqkWr6cOjqJsOX/gC9WDPqZL/3Xz374bZRjzFl6JvVd+B4aHyOdvIBFHmmqinlOlTSBw7JGWx7xPEdogwEFIyzymM1rcWpdAs8VL1sc5OmUHozQ8f0vz/QbAigQAhofV1x9vaZpzANgIwRGigK9+t5hjsubKFCcqqUnbbr0I+1HDiFCaubO9/t80Uh07vx6juOONx+NRCVeNHE8b7NaI0E/QSpdumLeVmS3O5yPf/erkw8B+RZegxAqG769K75eRggdbWo63NRRUVZcP49WZ2XNA0hCI97+qGtAKncaimwiMg6WiqqDqk8QkjVk5seXMNDIS6/3vPJ+f8Oy8+O3WYocHW2xNECc3NGA+hXnqqpawXfyHCcrKschjkOEIFHAmqJg2gSXwwTLKvWXMEbU6GONTtjmOEIwz+OIrhCKRgQDL8uIaDhspNsjeEE8suPfU3TZEAXKBPAAxg3HcU2tvQvqykev9smjHWFxqpefZjCZrfZiW7HDWVzK83xfb5/DWaKqpKOzJRwOm6x2k9WiyIq7r586AIpksVoMJj7g9/n93gtuuruiqvZ3D9L5CpMhyfon3VtfP7e+ni4kd+468P2vm0fd2avv2Iub7OH3xI+UCB3xHHfnCH0Q0TQUDCsaIe/vH+h2S5UlhiKLQAga8MthiZ6O9KHQF55WbiiOdYfOCA7XVZnF4bVGcZ8y15i/8hxCcCnXaTSSSIRgQkQeqxoxirwsE5VWDnOaingeqRoNIdJPhQZ9WAyUo58jwYqCBDo+m2oAUjWB5xREjNEuQcBRflpbEUMUKBUQgAkGgpo7XOUlRTaLYXZEgaj1bzgdEe3kc87e+9ZrGuK9bp/JZKqorCxylIZCUaPB5pxfZTSZ+t39ne3tIqHyZrHZBQ5zBEuRMOHpejDg8171hW/5err+8YcJ9rf55UPfQQjddU9sG/bevQdWrWpIPOH4sZb5C+Z5PZ4F86of+yM684RmRWFGe8iq69/HrLzHL7NoXVLEjhDEc9hq4QMhRW/WGcMbkHvdUUJQKEob9hBCopK2/2gsHaBRoxY7meNwTblxw4aycb1Bk5i8NIjotUC5Q8PGzdFIyKH1ELUT6Z6PRtf7HNKIwGMOE7aKp6diDiGsqghjIicILU+wwPOSov8QCH04R6sGiKoRTp+bofdGJQa5e9XGc/dug3DQjAECMEENkBQ1FJasJsOzr+0ZKQqkEXL3XXf+5KGHUc5TvuhEORqmS/6udk40ElUhmhIJR4KhgMfjFgTRZDLZrCYBYZvZZuRFKRwSRIMmR5BgCvl9CCNFVjVFMRjNRp5zlJbd+JXvDPi8brtDU9WyHcmhoTF56MGhdkyf+tyX5i+gc9gZhA51sblc7n6X9xPnDUgyrcilpUJ0zUlXnswxwxRUZBFqK0z0RjahcTBBP3gOslkETj+Tfatv+/AdPB6Q5ORVeeoqXVXJGzvci+qszqrResQmPw+9XDTgp6Pl5tTGOsd5BugA+hzhwLbnTzqxQZWJTDP+hKcRHcRjLCNVI/RDpL8dCPOYLvB5DqsaKjIL/qCiEY3m5vWd8mYjL6sqzyFFUQTq8dBYEMIkKqs8xzN3gdNwVBlxxN5UbAhI6wQUMiAAE9eAQChqsxit5hGdAEJIjzvw7fu+9l/f/B7KVRYvXm4xiR19Lt5eWVdfF/L56IUrsspriEii0WQwmjkeD3i6ggGXRjRHae3ChpWyJPV1tFmMRsQh30A//ePmeaJpAb9vQFG9Pp/ZZNQ4HAgFOI73L7qHFwSzxVa+4ysTuMLf/PSH9zzw/fi3c+uqunpdnV39FotpeUP8j3n4qnoS4TdHkZh5VL9/QHp5W//lF9QiQ6Y7yBR1lOqBXGFAK3OKLlXVsKbrIkGShlQN84gUmcUw1pgnRFQVY+rRyCriRYxUjt6hN5mMKprA089RzwTQn4iqaiajoCiy7i7QbRgaQmK4b/UpF+3Z+o8ZfLNvFPCGAJgHMEGe/NPjsqJ29A509nm7Xf7mTvfB4z27j3QePN6z/WDbmzuPvfju4WBUnVdNNw18+76voVzFIPClRWZsLV2waJ4iR6mHTzROFBHCotFoMpvcro6Q320wmux2p9VWEvAHO442drUcNRh5JBCCSXFZhabREhBN03heNBrNiBND4Sgh1E4okhT2+w0G08SsP+PBe7864A24PX6X29vXP9DvGihx2mkVEFvwsxV+4tck0Bfo43iKfU3+A0cyi+EopK89tP+oP/cF4PCuN5BKDDw2CNStCivU8dLX7TSlIvKYaBp1nDhOVWlaOBxVzSaBZoc5rBJMEKYJcjNPYz48z1RBI1iSFX3tT3geaUSTaeSNk6LDhrDO4ISAAgQ8gElBCPIFI4///V3E6asdjM1GodhmLnPa2EEoHMYIj7JteMbpV22ufm35qoaw32c0W+hfp6YRTRNEg8lo5jmhuKhEEEXRaA4Fg6GoJEXpGBmMsNEgREIhQRBKnc4ii9Xn94WjktFoNhrNBGuY5wJulyxJtEbIXlS1555JXqfJZKQhnWJn0OedX1rW1UkH+2UfgvxBWnea+SOisvbK+/3za8zmEjoZeCQiHmnXEd8HBwa6+qOaRkKhiMViQjmMG5dViv2BKM32UMOPaMpE0VA0KhuNPMbMj6GFnjRGhLGs0J0ZmOO5WCAIxzIrhGoAQgRjTlE1nqMVQgQLiMoEoUehjhNPu3jH289Nz/uCVHAiIAATRxB4m9ngsJsVRVu1ZI7NYjQZRafdLApCLOg8OCWYOQE5GAiae8K5kaD/xFM39He08QYTpj3qBYw5jDiTxXLwg/c2bjrfZCkKeD0D/d1hSSVIjwFjJIgC1uucZCno9SGrxVZWXhXw+yVFaW9rLLLZRYMx5BvQkFBcVra07YeTv1ROoObVbDKaTWPswpsUGBkNNCmQWCc0Jp19kde2uy48pxrpy4BkImpTS2DrnoFj7cGIpMnqYl7gXR7vhnUr46cMTQzIGY7seqNsTQNd8msIUbcQEQ5jupxXdcuOaRCfFn7SrAZGJBKSzVaTrBBFo1kBRJCmIYOAo1GFupWDHwz7WLGmcZjTNYD+bMOh9F37pwdtPHo/y4AQ0MT5658fd9otRoNoNRtWLqqurympKi0yirTaLW0EIQcDQa27Xz79Q+d6XS5OEDVMJCkaDgdEg9Fksdq9hzc21HYc3rNn6ysIkaKSSqPJzGFsMBp5gyCaDJqmlpSWms1WRZbcrr6e9lZCNJvNVlMzl2iqt78nEonyBsOKzh+jvEJWxm0OCEHbD3pbmlMSuSrp7ww9/0bP0y93Hzjuj0iahpfxAn/BeaddcN5p7JTmY8f27TuIchI3KqfJHQFhzNFKHvpWMSFYVQnN6CI9m6E7jNRvJEiRFKKoWCMiJrymqrKqKhpLCrNTkH42T+uANFpHS/0IJgnZa7yRQRQoPiVGU7VCtv4gAJPFaBCYrX/2tT1pTzhrwxJC6MopZzXg6Z99t7SqtrSmzmorlsIRo9liK3K+/Oef+SWei/qWOkKnLSuV+xubdr1jtxeZzVbE0cVcJBKORIKIw5FwKBIJK5Iky1EpEvF6+gTB4CytNJhNZpv1pMAvs3KRX/rmD9LfwVKOWYXj8CgDf0YiGFZffLdfDcjxW6ID0vs7XH/5d9fWPR6PX9bIUg0vSzL9zceOlZWVoFzl0K7XY/VRtIiTJxrS9LH2bBe8fo++gUIPBnEclhVaH6Rhuj9QRljT/0U8psaeOgqcSjiN6FuFMSdgQmuDqPeAuEjP6o0fns63BqafASGgLDB655/c7wv07K9+hBC67mvfm7dshWA0/vI/P4sQeunll2i3rE0fthF3vcNSa7F19zf2e0KBQGj9qWcTjityOII+r6oqUkTCCDtLnbIihcJ0MAsviI7ymn/9+scnbcrmdZaVOgghh47EusUF/f6t21wbVzh4q5Cdpj06HA2BjS8ExDjeGXpnt+eMk8qQQo61Brfu9RxtC4WjqqIt5ni66o+f2XzsGH07g6a/fm51c2sXykmCfLmR9NKPQ48FxTJdmKPLGg4rKk356gW0BOtl/kaRU1Uc31SnB414SVZFESOF8LG/Bfpwhe4eoE+IaSYAyfJUpYJFEazciMBHMym+/4OffPXL1Mcc01pUldq6XTlU653K77+Xxjt549UX2MGm0zY6bcRaYgw7zC07t3kDkZVnnG61O2z2Eg77NU0TzaZgXx/W8IC712Kx2xzj2x41Oj+878vfe+QX7HjZkvl0cXrkuLWo6M19RbuP7Fm5qOiklU7Rlh0Z0DsPTcSx0DTy5k73gF9WVNLUGvT4ZUVbgjmO41GS9U9d9eesBhzY+fr6E1aoqsYCmyzHoZt4vSsgT7cIxCI8GhJFPhJVDQae5nYH1z2qSrNlCCOO1zWE7gpGnEDTAFRI6P8R5jgx2jPDb7UggRxA1hgtCoRIrztYVTrU4SDvePXtbc+88GZQ5q1qaJ4TL6qxug7s3P3i3xv3HjCYTEUOZzgQUGRaI4RkYjBYnn2UzgwYryGN6HtH0/K1O27d/sHO+LfLlsxnSuCTVr9zYP4vn2l9450+ySulb/wwHowiR9M4E8IfVN7dM/D+fq/bJ6toGea4xJhP05HGUWI+9XNpy8wcRJFVI0fnZrLmbjKNAumFbURDPMI8wZjQhC7CeqQH8YjwmLYHorUEVCaQySAoskZD/5y+q4xqBhZEPRMw2D5oz4Fs9PROx8euuzOT017bkp1wZX4BApAdRjcY8axwXmsA/SN57eVnX3vXrVhENVxt06qKzKtOPikcigT9Pp4XzNYik9nqKK164+lfsfMb+zRvmDr4GRIYdVvok7/5fyPJgF9e/e6h+b98pu3fr3e7OkNJMvCJm3alfcJPfq4x9caIpCmTSC1ohHT3VSja4tRwf1VV+egR/9zUgF0HDtEuDoiL6JuCaYUbj/SmnxotCqM2RO8Hp5cEGQyComqcINClPcEqQSrCCuEEkQoFT7dd6406NGIQef1R9MZ9Bw9M85vy+MPT/Iq5CYSAshcFyuFi/+zyzps0PVC5ZP1Jm87tam2lWz01he4FI5qmaG/87dfxM9sGtN4AKrfhqiLObqLlpaNjN4299O7pG2g+3uxyedatX5sUFAooq3cdQ3sad65aWHTisuLyajPi8Y1f6gtGkiceM3rd/hu+0BsZ6IyGA3/7U2ykidlEzZQ6IU+ipYPu++N4UlNTzSYWsHB/YsR/dHIzFvT2rtjy/MRVDTwtCcUa5rDGEQ3Rnm4KVQL9h0vX/JIkiXoWgJX+sGohg0EMhKJmoyjq+4MJIe/tnG6jD6QCApA1MMYj9QVijeFu/XyuDFifPPNO/JAa8Xn6+2kcQM/6BoNerGnlc+oXLj8xEpWwaGk/vG1RGdfu1ToGSK9frbDhyiKu2IwdojWtmXfJxCxmGnspLXU2H292uwZOXL8mUQZ+/6vfIkTef8f/Y99hfyBwzafvrqnhi22Wi69487knz0h8hkuv2TZ/4bK6+gVKZO47L/7zkiveUVQFY14PY2BJkctK7ETTFjYYMjf9esGMQDPVjS21NeVBrztz05/jGsDYsTe91T5lzXKOruY5WSNGg0GSVIMo0I2+dMsw4mnyl+N5XtHIzr05WvNamIAAZIFgWLKYxNRqn89/9xclJnTf3beyxsTfuf/reTceYCRadrz4qW/99OC2dyVZFgQaEcYIlVbVhRq3nt5Q5ouingBvtZ27Nxq9ZtF7IYn4omQgTLwBFAihoEmzm7DVSNeM8Y8sqqAIQQmDETOipNTxh18/7g+GJUkuttlK7E6rsUhRaX9nAyecdeElA70d1TXVjf1di0+v/eKjLb6+QNAbrqwvFQR+3oklwa5gZ0vT0uXL5y1aEPV6pWiU7nETOINo1FObiCBj11FDJBpWNRKOhG024/xl/Cimf+hWJXzowMG1Jyyf2Mebm7GgUdi6K71ZX7e6gRO493fvm/YrAjICBCALPPLITz/2iWttFmOxzRwIS7KsRmUlGJZOXOY8crDn5fcOn7x6/rjay+QFv/nG5y667eshvy/k9xuNhpLy0gUHHkQC2um+oNIcaSgVpRKuO2h5zXuCxx8KhqXrF7REFOKLIG9Ec4WIwNGAj1nEQYlEFVJt52r4Do9Sm8lLv/P6O8GAZDEZjPq2NIsTE02VFdXn71YURZE0UTS6A16jQezubFGIdGDfXueAifYlVjUicc0DrRhjVdHCA6iqbt6+3TtLK6sOt7bTahbap0BRaY8zvbEB5hFtbCwYjabSYhvGfOtBuc/jKS42G510HA1r8pFo+m2DUaxwVJVlZWI1iP391HWYBWzfA3GenAYEIDtoGvEFIr5A5NEn3qB9MfX9McVmRZHkF15+c+t770VlVdXIpvPOJoS89vLrSQ/ftHa9X1Ft5Y72zh6HlZZMaCqRZPnQkdydKHDr1RfYDtFpwLvr7nI6i4pLS5H+x75W+xcKIhRE+/lNNXZOMdiUcrs/Ir3gKfEGIqFI9FOLjoUV0uEl3jBxh2iKmMM0W2DkMbGPEXkvn7My3Oe2iCar0xAI+vw+r6qpRJX1zmK0HR3NRXJiRFJq585rPd7U39l1/oc3WUy0jQQhxOvZFn8qNjAgLFU27t2JuQXIYuzv7N646TyWs1cUdWDAryi05IXRemSXKPB2m7Wm3I6QIezu9QYDpXMXp5p+lj5we3x9roGaqrLCNP25j8cfdhaNo4/3rARGQmaHSy+ngwzDfCnHC0jTeKJ6vb4Tl0fff+uwRP0BEo2oRlETOM5sNvjdQjhE9wQYORXrjTQ53iQLpKbKgXlD/bwySVLlqNzS1rV1136Uq9x91pBxHIX30YU2MjCvokjjDP4o6fWEej0D0ah8+dwmDqGgRHoDxB8dqhR6/kCarfkr15wejSgOu00UtAGvJxiMXnjZx0Z/3ZdeeOmW229JvOWNt7cnfnvh+cNSAqk0Hm3bum1vogYwOg9tMxkER1GxyWyXieAPBIxm3llFfZekzHF5qWP92uWGzJyARNP/g2/NnnRRjvDM79OM5XCmCMDZlw77nZn1gAcwxEev/zKbzafo+x5FoyEalhDiFUVSZDkaDcpyVFHkaDiEEZblqKaq+l5HnhBt7+FuzGkItbNeaRziVVWuPrWGN9s3zhfLS21uT0RTiSjwkajkerfJbEYhjdSY+JCKAwq2iUqr2VxXb9dozbWf9kzXtCLzVDVImTA9972g7tlJpMhn+36KUHcmD9mA/kmjX33oA3R+qRieW2qpd5bLCO/0lbh9QX8wevDQaFnBirmra8srzaJGDMTj7fYGQpdedXUmr3veh89rPt7c3+9Zv2FthhY/iXl1Va3tPa6e5HzskhNi4+kRQn1H9ttsdpvJ1n28pbx+XtKZbo+/r3+gtnpsJwCsPzAjzHIBuOSTXzaYrHSAVF9vKBBSFFmKSrIiK7KsKAptBKXq0RZawKjt2LlTr1xT6dZ0/QtjGhLWdzrS3S7UkOmdT1jBmx4roA3Qae8Ajtp/kdZH02N936Ph3d29y5aa2o65wqGIeyAqa7LVYuB57qmjLekvd7DYLjfpfeAVJElIU+/0/FKO+NE4vef16N9IRj95hj930zmLnGhBqXX5nGJPUJpTUdTvj/hC0aOHDyeeXzt/7cK6BQZBdrn7ut3uY8eH3ZshZWW0WIiNPUh7gj6qZGgjd1KixmogvfSnPWJgqmrxCuor7HqvqqISef1tfZ11i+hoe4aqqS1tXeVljlGcADD9wAwyCwXguru+Iyt8f3e3b2CgufGYFAmpqkx7j9BmhCwUrNE9i/E6ZdquROA43XRzPMYCpsaeToOl2qDqJ9NiR41D2pED7470uh/Ro0AMfe4pJSgFA9EBAc+vWGDbfMaK/Y0d9+ZeU+hMrT/9yLT7LG92Bj1mPMFJfneeoT786itsCOy5J5+wor587rLavpDc4fIHI0pEM5VUVtqwbMJmo0iaW/a1tiTb/Ud/9J0rb7rD6RzfYD9Jj+TEZcDhyOjhvCj29A2EwmO0qVm85iSaJNi/q6qyxt3SWjKPzqxn0ExAv6e2ujz1UWD6gRln9uQAbvji9zyecE9nm9/rVaJhTZViLWjpv6ydoUBnkfKCpiqqFJGi4fbGoZTg5EkrAFjr8g1oRSW1GONTT5gflSSE0L3355kGnPK1x7bYaLaTBPy3/PsGUfWZeMVqJLaMSuSHkFXk1+tBt+wbqvfceOo5JWIwKJMer7T05LO3/uuFE5ev6OhqJg4nFsW+5ube5jSVJFfedAeN4Y5TBhgVGRfmE0Kajne0tHVn2hsO456jjSWO0l5PX+2gK5A2EwDWf5qBHMCs9QCu/4//bjq4/73X35LlKF3R661rY41qEWnel1xyM0X8/ak/JmoAg3DVRbq1oZuDmnvmlNtFugM+z3j3aBc6QS93URVOCYi8ahKJzUL7O2b4DDJ1qEhYRhGZmAR000bl19tiv3vb3tF9C4Q2XHBpz96DDfPn7j24e/U558hE9rk9RmN6kXni149MUgYyAWNcVVnS1+8Z0wmIU7mQflAlnOTtbi6uqk91AsD05w6egi8Eym8P4Jo7v+vq6/G4XKGgPxoONe7898xeT1onIA7G+OTV9bIs55cHYLj62x6ZBNfQTgk3/+UiMxcxi6rdgkrsnFFEfa6M5mmEJEL7BRBkNtDMiT5oFv3k9SEtLKldfuLyFY3HG6tPWLV4ZUPTrn2KqnpdroHujkqHae+uof4/qYxXBjL3ACbmBLD/97btLy13trX21y9ZXVbi2HDicp/XV2h1Pnduoi4v4+FXx+kwZhtwAmabB/DHh7+O8gdCyOHm3nlVDpQ/2K77Tn9QilpjttWIowZBMxuR3YoNot4KLDNMIp39pM+VTXPvxtPPMoiGl156im6sPbobBT+uGE3WklJFDnNhi8NmOPWMs955c0RPLtEbyLpDMAEngFFRt2Jd5Lf+4lMc1qAUUR555FEDLy5bsjjok9eJT965KSdsYhbtO5CP5LcHkGuM7gEwazK/xllSbMkXJ0C57L6AwaqYi7QFsYj2fz5/lrMI2y2Yo5PCaZjN5Z54F7zfHl1+2knLGw83H9q3OysXnIkMZOgBPPLT39ICL47nBcFoMMiyFImGFVlGiKxcEystTWVd9Hcj3bWlZeWl84Y1RRBtS3/8bGy+zawXgBl/s+ABzDYPIO8ghPR5Ak67BeUJId6oGi2a0RS/xWbGVhPmeFYcSzWg1Ilcnoye7fmB00scznDIF5XVZSvn2W3CfKXp2b9uGfOBptKFGNNuYoIoWswWWtOlyHQfBtFc7ftSvYHR40K9/e5MNOCOz90QP/YHQ3v3Hx2vE5BEkvUHgBkHBGDK88BJ+MNyIDTBGsrpRzZZNaOJGC0kFOEsVAbsViwKbDsEtf50BghGFaWo1zX2s212vPWXY0usRRaVkPltf6AjJwW0PYOB4BHX0fjxQGZXnt0ssc1iLi9zjiMTAAD5AAyEmSo0tqs4FUJ63X6UJ2gGk2a0EJOFGGJOwOdOeoXQmR56SF+3/nSyK4frqjJKCHxiwZHN5bs+UpGdgM+YPPHrR5749SMej5d9TTITYDHTnkKJ8Dj5a7x84RLaxbpAyME36ynsyTAgADNAHnkAdO1v1K2/aFCNdv02fMuKf+n3EdrlnRk+DgkcWlKXo79OTAboX3sGMjDg9ab9UiSpyGqapLkHcp/XCmk2JCSBpzsPzCphyhy2xx59FOUDXff+CxktyGBCBiMfjAV6Htt+lj4XnJl+LPJ0YKxBQEYB72jKqElcnMRi0OmBxYUqK8Y3p4UQ1N7Z298/LAT15j//PMm8KDvI01Rw3r1ZyAMnATmAGUgD0GWmP4TyBX35jwQR8YJaWse72hAht617/Rfbz6LLf44W9fM8FpgG8OjMFYKBRy/tST+FMReIpwdG0oD//d63pv2iAGAGAAGYQjRNS+sEqDQ7kNH+qVyg+htnd/1wOxUA+iWotUv5Dtqf59Z1r/9m19k8F4v/iDwy8EjgqTcgcOiC1fy/9qg5uPxPrRfKBb5wyfw8dQImQEG92RwnR4O2QG4hGJAg0C9eQDyvLWxgN39q9SsCFzP6Bvov9QDoLl9CJIWcPB+vqSErykeUuqKKZdP4HgAASAYEYMa46rqhMvM4d26ScnB3ZfVdq3TTT78wjxCPtBW6BnDcVSteG7T+1Amg4SCEFBVFokSSNEUhiooWOtT6IrXOptVYSKUZlZoFh8VaVFL/rScbZ/qd5RA5WCFTIG/WU8CFQBACmj7q+n6ddAvrB5AXPnL17Yu6ftOJeUx/ZXiEeUTWNeDtBxAhH1n8qubq3xq9klp/jBSVRKKaJBFZpUqgakTTkEawRui/KuI1JKii/Y51v6145pyZflsAUNCAB5B9/v7UH8feDZCHVH+qJmb94/+e1sB6n3GlZadWvCBwiGhEkkhUoiEgVSGqhqj11+LWn9MQrwq2OzYmF88AubYunmoK6s3mLCAAOUpu/nlUXVXFrD9TAioD58TyAZjn15a/vMb+XEQikkytv0JNPx32G7P+RLf+nJmYxzcnHcgXigXrKF8oB/jYdXeOec7ZhTQWGEJAwPio/GhVz7+6Y06ArgHcBQ00K/A8HduCzeZNi15Dmvr0rrNVTfcAqOnXvxCvYoNmdN5xwmOz+EMfl6XLn1rg9EzSrOdaqPPsQjL9DNgINn3bwVJzAKNvmcmpv40kel/TNSAuAzg2WpfJAN00NdgWf3DsbjIV987aBMC9HxqadzYmIdOcpFty+ede4G929gEhoClPA8yyKBCj4uwqGgLiEOYGrb8Od2EDd34sMRADmqcBQK4CAjDlzKY8cCLlp1TRtT8TAGboNYRURFSEz2rApzXgk/T0QKIY6Cwt42bx8h8h9MCLcuYnWyLteST8WX+zwMwCOYCcJteCpEmUrauiQ253dVPrrwsA0TUg/oXXNCAFcXKUk6O8HDXIkRIl7Ln9zJm+cAAAKOABAJOlbE1V2dqqVOsf0wBFworMKbKgykVKNFQY1r+g1sUF9WZnGSAAuU6+BATK1leVn1SVYv0VZv15VbbIEfl2OlkeAIAcAQRgqpit28FGp/zMqvJzqsrPryq/qAqrGlbk0qsWOq5fbrtxDbrtVFRIjGtdXLBvNl/WN7MVEIA8IE//SMo+VlN6ZV5e+fRTUIGRgnqzOQ4IAABMFeAEADkOCMBMkvmm+Tx1AoDMKah1cUG92VwGBGD6toNl3h0F/jyAfKSgPJ7ZAQjANFE4eWAgESiRjONVgolfcoDOlQNmFtgINn3sc1650vNEhidbIu2JjVNIzm8KA0bHqwQz+YjG0Vgnn98mkCOAAADA1AI2EchZIAQ0rQRWfjbzkxMzAWnaaQIAAEwOEIDpzAOTlQurJ/Y8BHpqAgCQbUAApgkOY6vJUFlatOlz/28CTkBKS00AAIDJAgIwHWCMzCaxosRmMRkm9gzgAQAAkHVAAKYcjJHJIFY4bUVWE4vlj8sJiD8JbAcDACC7QBXQlLPlyVga4L57v/LmB41nrF88roezetD0YxUBAAAmAQjA9PHNB/57wsNUAQAAsg6EgPIA6AwBAMBUAB5Afmwggp8TAABZBwzLjPHAi/Kdm6QMT4YEAAAAWQdCQAAAAAUKCEB+ABuBAQDIOiAAeQIoAAAA2QYEID+AVhAAAGQdEAAAAIACBQQAAACgQAEBAAAAKFBAAPIDyAEDAJB1QADyA9gIBgBA1gEByA/AAwAAIOuAAAAAABQoIAAAAAAFCghAfgA5AAAAsg4IAAAAQIECAgAAwAxQLFihtGHGgXkAADADFAvWtLeH0Ox8X2mBwOaMAx5AfkBgsZS3PPyqoViwFgtWu/4v+5qt4z8feFHO/OR8f7OzABCA/AC6gc4CYMEL5BogAPkBOACFsy7OdwrqzeY7IAD5ASweC4eCCowU1JvNQSAJnB8Q0IA854EX5Xs/JI5yL5pFzLK3M4sBAQCAaQLMIpBrQAgoP4AQEAAAWQcEAAAAoEABAQAAAChQQAAAAAAKFEgC58m++Uh7yDRnaq8GAIACAzwAAACAAgUEYCaBxikAAMwgIAAAAAAFCgjADAObgwAAmCkgCZxfMnB8qq8EAIDCAS+fXznT1wAAAADMABACAgAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAAoUEAAAAIACBQQAAACgQAEBAAAAKFBAAAAAAFBh8v8B8r4a1enZNxQAAAAASUVORK5CYII="}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "The axe’s immediate lateral and vertical clearances are crowded; its clear extraction is toward the FRONT camera, with no visible need to disturb another weapon or release a retaining hook.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The SIDE view shows the axe’s retaining hook directly in its forward path, so release the selected axe’s hook before extraction.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - ExtraHookRelease\n\n\r\n\r\nReset context:\nSeed 8. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The hook is clear and the configured forward path remains open.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-8af6b336c020", "created_at_unix_s": 1788845866.969342, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 9, "requested_group_seed": 9, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [9], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 5, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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p0wC673zlNuMOeiJau7xDnTl72dq74gVlBQEACvb6n37vq6pjQsl6TJZe6wuPMiLyRDOWVhUwtKpb3sCengbQW4Le3Nn65s5Wwyv06PG2I8dO+wNVd3/ygmxHi8u2738F5QMBAAq2eLpXdHvcvlL1mZkcett/ctwvTcytrw0GFkueDNBzAM++upeMhoHDx04fP3kmucOB4x3ZMixdfUOKOjZWGTNAQDGQBAYzZCooZbkc+VL/2D210eHh1VnekUgBJKa38zcuGeo6WLpk8HWXrnzhjQM00zLATI4ILl/y6bsftDXWVc2bUZd+Mr2Dwx631DStGpPBQfEQAMCMeDSiKJx4bVD0p7yaEgkoFSiVKBVEQYwNhRTvHEIGSnuiaRNC//alN1N26Q9G3mo9WVft+9ETfxr3VlFSGevsC3ncrtoqL/qDQpHQBARmcFUlVk+aVrrSP2XPpf7WBedfS7WWf73ZRyGUS5IwNHg61N8zGB27E7dK2toAGTK5TI4QQvyBqsTL/HRH/wfHOzMeTZaVjt5gTFbQARSKhAAAZjBFLp8ZoZ/6Sb5DqEYsuTseDCVaYpKDc7kix0LRmBwLC6zUCyuONes8++re9Nt/naqy1qOZZ/jhhIQj8a7eEDqAQpHQBASmO9FPRg7g5uZ9E+7zjS//LSHEveK78Q/u1rewpfcMhULDw2FVlmNKhBM1FovJw7I2jb4gyu+8FVeUumn1iX21rkCcc7fXGw7FAlWu4ZjiI2JJ0wD64/QrmKgEVMnqSPUgGMq6LAHnvC8Yzv9ze3qsH+QMFQABAMziZVH6a536ZyYK0oF77jswu7qmmjNCT/9htGu9yjmjRCCUilQknIiiJIiCNgGc9lhkoiRJksvtFgRx9syqcERW5RItB5Y5CyB4ql2q1vhTKGNfIABzEADADEoom6xKQP4+uvJsxu2HpeuUeFyVZa5qBb6iyIkYoHWoT/TIoZxxzlS3JDCPJJcmsP3HQ0/c8Xe3p0SA9Csoh/qJr05/HAkP5Tig11XiMAUOgAAAplBKtGWBJzUA8HEDtkZ6Uya35JiaaKnygpbtGh24Nn/tLY+/erC+0auVwFSrHGi1BK07EAl4RJmKpERBIEtPIFn05VMJuOLaK//yyuvJp0gAQPGQBAYzRJdbECf77iFljK42gYNhS/4jeOMqm16t3T5zxjjX1oTRfwRKamrdpS790+eEIITMW7W2pB8KkBECAJjuBmrLSWmWnH97dzAS0iYy4owxLQYk/i5MVRa69vf2TlbXpmTFZXTDmf7Imf5Isv0nN7T/gCUQAMAMveeM5dfu5uZ9xp8J96+5/qFCP+LsQKR9IKyonHMxkcsQEj+i9mch4wksGQ2QNG3v1/UHLldeHZDQ/gOWQAAAU8Y60ZdWSpQpPugcHRAPnokojHIi8kRbfGIqUP1nMo37uN3uLZtrXthc88LK5pn6rETVgQzDrCUtd12wKz7y6SLOEyoZAgCYoTKFqfEpafcvxrSbHqiaObduxqwZ85aNHCu5IMxobClpJcCYBkgyBrULVi9YNLuBCrSvr8+4z/RurfZQXxPA9A9gIQQAMJsEdpXTTEB5qLv5gbZjh0+fOHG2rb2rt09vxkoMaeOJx5PdrT5jMKsNeC9Ys2DtirkZ3zK9virgdSMBAFZBAABTxq9qW/4GVt+7c+trb762/fSJU0o8yrQhwZRrPVn1lcG0McKTcBo50gAjKJnVWLPx/MUZX/S4pZmNNUgAgFUQAMAMrdN8ImtqFwPdZ9uOHImHh+ThIOUKU2VRELUBAIlXEwsDjAWAyWoFGvtEnkgDXHOnFh4EgWZr66eU1mTKDQCYY6ffYSgfWuK0nGYD1c0Qsq6WtbDzcTkWoVzVRv0mFrUffWUq6zEmKh37j3aU4kzAmRAAwAymypyV3dqECuM1ataO/D5R9giKSBSBMJckJkaBJUv/kvRqnZhxYHPOHU0vU/aFe79p8p3gAAgAYDYJLJXdXDQqI6rK3RE546tcVQXCPRKRBF5dXaOoht20NHA+y4dZngbIXK7/y4PPG5+uk5+s8ozsWWgCQI5PRmctsCkEADCDaROrlct6AOMCgPbD2VCGUu/yucei8Thn/Mq5x2YM/D4xsZCQvPOf/ApASmfQ3ALu8pp3DyoDJoMDMwRRSswGWl4xQFG5woii1QNIrC8mq6SmaXQGuITNi7WF1zknkj7eNlGoJgaCjawOZrz9v6ixVWUjcYES4pVorY/mN1C3mAUiM9NzFoUuAfaFe7+JGgDkgBoAmKE1mWh9KMvLyO0/JyrXIgEnZLArNtgVS9mNUlLlpqoS19YI0KaUy3rAsRQBISrXQov1FYXRT/+Hv9s84b5o/wFrIQCAGUxVy3AcgMq4ykcagtjoCF+tD2hXbKBzXBgQBSKwWGJVgJFeoNoMa7Wrcrf+J8aNWaDzla8ZnpW2bWd4eLikxwdbQwAAM5iqzaZWtjkA42JZySFrA52x0R8tQxCTVVHUGnQo4ZRq3YD2tuf6dRgZNVz6qaFTrJOfNH18lP6QGwIAmDFzznx/oKbcrt3qpR699E8W+pkKbK3NZ6BLXhnfRrWxYONnBDJIKe5VPi6uWInm6gJk2qe+NLJCMkA2CABQsHUfvTsyHJZjZlayLbUr73lMW1PdWHSnFOwjGV9tNRm/1yOKLqYNaMjQuqM1IhkPw4mspm60qBUIYGogAEDBujvPxKMRRU5NrpaJrsC6rqp1WeYrGrvZplTgnEku92gFIK1oT9ugpZdLUgmg+WSACxXHCACYCAIAFCwyHFETq2j5F19Rtpevq2pddyIMGCQ7fGoTGVFBYGpMFEfG2KbXABLT8qdi44eMTfJogILEorGhhEg0WqKPALvDOAAoWCjYL0iS2+cbDgXL/PJ1V4/EgKah3cbGHyoI2lCGeEwQ/PpEEOnletnluItw9OjRqT4FKEcIAFCw4eBAfDjs8flJ5267XL5kJDCaLsU741WJwJAhAFjV3J9N5ytfm3HVtyecBML4dGHL+hP7duV5/FA44vMaputIDmkDGIUmICgY7d6ztFGYH6iEhgVtaWDRbV0X/zJqBXr1mceYMYiNNnUBJKEGAI6mxMOS5NY6Ak3UtM9LXydIJ5z/v/UZIGKyTLa+Uejbz5ztmjtnRvKptupNkYtqQmVBDQAcTY5HRa0jUF5Fu8K4UoIgcM6KBbl30Ep/rfwuuAdSV0//cKQSKmpQIggA4GiLGgRBEBKrm01Qsut3zsNxbTSAVUyMBljYsj7/nWOx+ImTZ1UlMWef9vfD7T+MgwAATjfQ0y5KUj4zW0iCtobkYFSbaW7SFDoDqJEkisGhcG//oFb667luAAMEAHA8ylxuTz6Fo0CJ361lAoJRbc5Ry+XuAmRCz+GdnPPu3gGmT92KBACMhyQwOJ0kSqLkSe8FtKtvzSR8+h1XTZABHkkA6DgjWmtVASRRjEQiqqK6EwMgijlVqDyoAYDTNTeKmQYCT5IJM8BFpgF0jDFFmbinEzgNAgAA6T57gmac+WGqFZMA0PUcTqwhzImiqqo6+R1ZoawhAAAQQlVRsHqxx6Lt3HvSysMlYkAZruMGUwgBAICIoiSI5ZUPY0QLSJetW+ySxp9Y4aMBxt7KudYQBDAKAQCAUG2Ve8O0OVMh4zow2RqmTKQBNNp01qgBwBgEAADS3CiUZ4L0hW37p/oUKsrGmz678abPTvVZlJHyqvYCTJW24/sS3YEm1QPfnfR1wcoxzE2ebc/9TA8DyccOhwAAoNEXEi5DLpco63M5mB0NEGvfI0xbYv2Z2RbCQBICAEBCmZb/ZPmC6a1H2lNjAFgUBlZefD0h5MCOPzjziiIAQFFubt5XGVdwSZNUbl2AdG/vO+XzpCaoKaWLVm843vr2pJ8aVBQkgcGkm5v3VUzpP+UydgGCSXAgce+v1wMcCAEAwAHKcZhzeVnpyBiAAADWNJ4ne1FmfKpv4Ymn+kvpT50m/y5APq/Lo81XOoYzjOeyzAGnJgAQAMCym8vkRJMZn+pbaOKp/lL6U8imb3C4yIsjSSIqATkccGpDUBklvqD8odF/Erhcrphs8XjdeCRk7QGhMqAJCMCuGeBFqzdYfS7OdcCRlQAEADAvpeE+/3b8lHb/3E8LPXgF8LjwiwmTAd8zMC+l4T7/dvyUdv/cTws9uC0UOglEOBIt2bmAcysByAEA2IPH7YnFY8mnnKm0/NYwsLvTbWeIk6AGAAAVQmxcPtWnYDMIAFA5njnW8uj93yd21jM88iuJNIAJas9BS/4VquadR5wBAQDAHl2AMqYB0BHIWqHT7xEnQQ4AbO+ZYy3EVh6+/754HEldmHqoAQBMtjxLf7QCQakhAACUl47+rOEBMwKBtRAAwN5s1/5TDIwGAGshAICN2bHPz8P335ftpRtvuWVyzwWcDklgsJlKuuWfsAuQxyVMODFcPkuDrT1//bvv7Cr8BCucb+65kbb3M75UNe88J/QIQg0AoOwywEdbd0/KuUAGTij3kxAAAMqi/SdPiuqkWfGgxBAAAGwMS4MVI5Kl/cc5kAMAh7a/l5tsGeB80gATUlQsIVmA8FCQOANqAADlkgH+6WPPlC4NIBCm/1h7WFsLzHH6DQ0CAIBtEgDZ0gCYEQjMQQAAKKMuQC5h4rYaSpAHtkb4zL4Ml7euWUuuDBwjDoAAAFBGaKaVz4yTApkr/Tu6eos7L6hMCAAAk+Guu+7geaxrTAnNkQbIWPrn0xGo+9CO/E4TnAUBAGAyvH/wTMa7e92pg3v0B6KY9VfSWPqrKnK5YAEEAIDJ0Nk39OJr70w4CURKkOCjP8VjQ+36jxUHgwqBAAAwGXK3/9z5tW/pD9yuzENz3IY0QEboCAQmYCAYQMktX7/pb//rRdU+ccI9RWncPsdadzevXlfKU4MMXYCcAzUAgMnw1nt5rVeeI08AExIbl1tylbgz+oAiAABMvWQGmBBy8803MDJxRSElD+ydqIHIIdSevKIsJOF7A1AuBK786ukXs73qcuUVGKAYXI1xNeaca4gAAFBay9dv+uxNFxi3pHcBeuThRx95+FFtMvpQuHnh7NWXbk6+dGyiSYHQamSZ6jnEYRAAAEqMs7ffP3jVxauyvb7y4muTjz0e17ETZ+Oykv/hJVEQBO0XGR2BrDF0hjgGAgDAFDuw4+WU3qIDA0P5pwEESn0eVylP0Em3/0MOKv0RAADKSywmu0QSHo7mnwbghLgk3MmBGfjeAJTQ8nUbc+/gnzbD+FRlLHhqDy9k8K/KGOdk5cIms+cIxJm3/wgAACU3b+a0HK/mU9LnyANTSlWVRWIyZogGE1ADACgtz/gp/lO7ABlK7lBbq/4g/8FgiqK6JDEUiYUj8WJP1LGqHXr7j6kgACy24cOfTNnSJZPPXZ5rgoHbvvT3x0+2v7f30KxzmqPhgWVX39p28mRstjYDhCAKzQtmr1m1ePebr+k7+72u4ahsfPuNH7tVf7CeEPLxW7/3j/da/XeqaNWO6/pphLmAAKxRvWAtIWTvgZOCQD93+w3jXwxlfx/nhC5unrukeR7n/Gxnz959RwcjnCZ6dnJOjp44e/TE2Xl1I3sraRNB/+jhp41TzfnntPgD1T2H/op/1wIMOfH2HwEAwGIuEiWMHPrggHFjy8Xzsu0fCoW3vbZt3O8kY35JlscPBegaFKbXan0909v6RTVHdIGsxJrZWgo96NCiX4caAID1mMoEUdD76eeY3i1OfURl2oz/TN+TMsYopS5JYoyr6ljygCV2AEuL/rO4nkgCA1hP65iZWN5LHB2mmzEDrL0mUFGgor6fQAVR6+kviILbLRnfaHTBmgXGpw1VqaPA0P6TjVgzW6yZrQbPovTXoQYAYD2m3daPuDl7+0+icpD4T1vvUftPWxFSoFx7IBBOYvG48VC6rt68xglDOpT7KRAAAKwxdPLdqjkjE/7s3bvf63FPGAB6u7sK+QSP/r++4HDqK+q4fkEAeUITEID14nHrS+SOnpEb/2gs18FnNNZa/tFQqRAAACwTOrM/+Xg4knk+n6Su/rC5T0nvCNRQ6zd3KHA4NAEBWIm6AiO/Wh7XlVddlGPPplnzojFt+K4kiUxlnHNBFBjjAqVc60ek8sR8n5RSRaNyNtYpaOP5zdveGbdsIRe9+IeEQiEAAJREIOBL2ZLSBWjWzIahIa01XxRFTkbKfZpY3kUr+hVVEKiSCAyMsb7+4HB4rOm/qw99/8ECCAAAJTGttpoQ8qHmrDfmkiTV1VYzrt3j0+T0P3rxn/iTE04J1XYg1O/zHjp8or1fnjVN6/TZM2Cy+QjACDkAAMtUL9yQfFxfXzPh/nR0BAAV6MgPHftT367tIAop9YnhaK6p37A0GOQJAQCgJGqqR5IBpcAYv+GyFuOWxir8LkPB8KUBKAmfd6TbfkZzl6ws5uCM88QUEgBFQQAAKAlRG9RbQtkWjj9v2aySfi5UEgQAgBL8XiVKf2MGOHUdGLPa+0dGgfUHIxl3eGnnCUs+CJwAAQDAej7vyDwQpdPZN3TjptXGLUgDQKEQAACsV11lfQZ47Xnj0gaDQ5lrADp0BIJ8IAAAWK9+Wk3pMsC5cwAA+UMAALDetDptFJiqMn1NGGvpaQCuxYBxy80DFAoBAMD6UWBVAd+FC0bSACUKA4SQYDiSMQ2AjkCQJwQAAOt5PONW6frXh58n3PoY0NWLGYGgKAgAAFaj2pxuGbZbHQP6gpgRCIqCAABgMZdU8jkW9TRALJ45D/x6aw86AkE+EAAALOb3Z5+an7PLrr7aqg9KzBWdYUKI4TCahiAvCAAAFqurqUpmgEcSAAbHTpyx6oMY54xzDAcD0xAAACzWUD95q/JGopnXB0ZHIMgHFoQBsFhtTQkngk7ROxiu8ueadhQqwz3/8/MpW3710tspW0598H6hh0UAALB4EIDPlzUHcNk111rVBKSvDtbTH14wqz7bPotWbzjemlpMOEc+hSYxVW5WDDQBAVjs0iW+bAkAy4UiMUII0gBgDgIAgPUTQU+aeFzRFg8GMAVNQABW4CNd8r2u7H1ACens7D5x/KTpD6EkZfIfl8p5LK543eMGHoNRXbU/+fgLt24ihPzn028Yd1i06vzj+99x5kVDAACw9DdKEifch3OScaRwoTr6ozOneUPDsYwB4Lxls9471G7Bx1QsTuzgn79xV8oWPYCJwrhvmsrMzAyIAABgpa9++tJsLy0/Z63lMYAQMjAUaayrunHT6t+/1mrNER0gMYKO69N2EELmrzjX3HHWntOS557rVy+ccJ+HfvGHYgJYtr9Fjiw3AgBAqWTLAFOqBQCrhCPx9I279p1a3zIfHYFK7d09+4xPz129SmWME+LOoyJYeIlvlHEMeMEQAAAsMHTy3eoFYzf4k0lRSjLXNJjwfut+49M1LSMr//DEvB0CpQ8/abrELwkEAICpYFH7T44p4SB/nbHaGZ5By6/Y3n0Hko9bDY/LB7qBAlhg+bqNE+xgSABoLGoC6uiPRuKyfrCU0QBQkEEyvQKuWB9rdE8vYMFRBAAAy3z7zv8yYQKgZdVSa2sAiqIqSoYeILv2ncKMQPljnM1edcmHrvsEsa3OWC1nTJaVJevznXEWAQDA3jghkVjmKeEgf16vNxAYGzFgU5xzVdFccO3H8tkfOQAAazRV57qdenP7zuRjVdbmbzBBEDMO+PIGw9GaQNYBaA6fESgfYrw/3NO/Y+sp6vIT4m3wRvN/rz64zBx/TcP3//O3xAqdsVpBENxut8frEUXxxeee1/8iuWc6QgAAsEaO4bi9Qxl6alqlo3twzvTaudPrSvcRFSdrBobLw488+MPcbz7y16etOo/hYG8x8SPp8x8bSUE9+Ku/yF0dR7sONHjySjUhAABYkwH+b5+8PM/9RZfHRCUgy+2/Zig8cjQMB8sPNRaN11zSYpxR1cLyvfwhBwBgxS+SMO5XqdSTgKaIROWM44J27Ts1madhH3n1wWIWjtYrV6gBAFjgMzdeUND+5ioB2SiqqqjMlWn0aXnOCLRly03Gp6/89VC2Pa+6aFmpawDZCFZN1lHGEAAALLDzvYNXXrRqCi9lLK5kDAC2kFcpz/npzsGjp3vzvDGnhNx2XSV07S8pBAAAC+Qo/U8dyjpHm7dxQfJxtMf8NNFHO8mFaxbkSAOUZUcgPjoaQmu+Stxt57zjptoEO4JAmZpXAOCJ41IH3MUXAwEAoCjL122URNFcAqCYQj9FXM48G/CufackV9mtFvD4489t2XJjsh1m687DVBA4Y5IkqYxdsWFxMqXx6ltH2o98kHzjpz51w2u7jumvccaML225/catbx8lhpeCG9fWVo+tzgbpkAQGKNbnP3rxlF/EYLiAruvlgY7e8htv0hNTNFMiCFT/SX9bxo36DNspDp3skrPERdAhAAAUy+2a+sb3gaEIsR+9FWis5BZHOlNljA0aQaDZUh00rTA72x083Tlg7RlXGAQAgGK98uYeEwkAa731xuvJxymzwpXxjEBjpbz+qL7WbyjyM97pZw0A6fsrqnrkVDdxsFMHs34zdQgAAEWprw1cceGqqRoBYJRYiYTYdDSA36slKmZPr5mwOlVQfSs0bFlf24qEAABg3vJ1G2+/bn2ZXMGYnGthgEWrN5AyNqOhmhBSG/DMaqrJsRtjLOBz59+zR1/3EbJBAAAoSvn0M7T13e6MhqpEO74wO2cAiMYVt0tE506rIAAAlMqkJQB0g4Y8sO0Wh/F6Rvqqej25+qYPDkW9HpcTxuhaYsLF7hEAAMxbOLuBqeWyIuPLL/2R2Jbhpj5X4d4fHPZ5pHLodlUZMBAMwLybLl9tDABTmAHOTZHlMhwOZkIwHJNEQZJEUtQaOFOcGPjpUy8nH3/uk9dO4ZkgAACUah1gsFwsrrpcYtE1gLwmgzNXoBNCZFGfjD87ww4P/vq13PtasmBANggAAKaVXUs0J5xmP6uynBGoMIwxURCq/O7egeEiinBeugJ9Sjz8m22MM+1RWnZk/opzcywKhgAAYNKmdYtzJAAmOQOs+/GDT3zpC1sqeHEYbegwJQGvW5sVLlHiFV8D+NP2fSOlZ9kU6KZ1x2u11Undg3nujyQwgEnnLp9jfFq2CQCdIlfMwvHU73O5JJRdE4SBfKAGAABTvCCMvh5AxlUBtI3jt3PGOnqGYnFFGzSwbGwM9ta3jiQfu1zSh6+/8mhbSFHNVxNsrTteK7DYhKvbI4oCgJ1QgVb5PbmHAjDGQ5H4zIaaHBkRc7fM5czE3wIBAMCMr9xzZ45XpyQBoHvgwcftMxyMj18Sko//k2fZSD1uUcrZBMQ5l2V12cLpAb87n/OoDxDVZf8YEOke95MHNAEBmDF/1rTyGQKWvzLpCPT448+lbjEs7WKJM4fIpeevWjKvacI9r/lQy4LZ9cmn/uqxx72D4Re37+/paCN20FTr7h6MJ5+yWJB4J4h/CAAAFijzDHCFDQfLE6V0/sxphb5reKgv+dgnkI9uXEzIYmNU0Lvb/vsjz5Iy1lSbV9UHAQAAKpYrnyFjtLCoYByfZQwM33/kGWKTcj8JAQDAYlOYAEhXkaMBysewITAkh+yWW1TIAQEAoGA/+u7XyzkB8MCDjyeHg0GZRAVvYFyS+Qc/S82CTAkEAABHJAAqbjgYsddkcNHwoDES3PXZm8ohGCAAADhLmXQEmlIWTwZnLhKUQzBAAACo2ASADmmANOWyTmTU0mCQY9K3bBAAAApWzgkAHdIAZVsDKDQYGLdbDgEAoKIywFABoqUs9I3+P1hq1VJw1EVzAAAAAElFTkSuQmCC"}], 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"}], 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OAxi+//UbzDsYiWj98g60Z+xlW9oVLygqCACQszf/8idvZS0TCtZjsvCaX3qMEZFHm7H0qoCpVd32BvbkNIDREvT29ua3tzebXqGHj7YcOnKywld552fPSne0iFLy/a+geCAAQM7mT/aILrfLW6g+M+PDaPuPj/ul0bn19cHAYsGTAUYO4LnXd5PhMHDwyMmjx0/Fd9h3tC1dhqWjZ0DVRsYqYwYIyAeSwGCFQgW1KJcjX1gxck9tdnBoRZp3RFMA0entKhoWDHTsL1wy+Irzl7701j6aahlgpgQF2Rt/+v5HLQ21lbOm1CafTHf/kNslNU6qwmRwkD8EALAiEgqqKieeEij6E15NiASUCpRKlAqiIIYHBlXPDEL6CnuiSRNC/+GVtxN26fUH32k+Xlvl/dmTfxn1VlHSGGvvGXS75JpKD/qDQp7QBARWcE0jdk+aVrjSP2HPhRXNc9ZcTvWWf6PZRyWUS5Iw0H9ysLerPzRyJ26XpLUBUmRymRIkhFT4KqMv85NtvR8dbU95NEVR27r9YUVFB1DIEwIAWMFUpXhmhH76F9kOoYpZcGfEPxhtiYkPzuWqEh4MhZVwQGCFXlhxpFnnudd3J9/+GzSNNR9OPcMPJyQQjHR0D6IDKOQJTUBguRP9eOQArm3ak+HVW1e9SQj59lc3EEJcS+6NfHSnsZ0tvGtgcHBoKKApSlgNcqKFw2FlSNGn0RdE5b13IqpaO6nO+KsYXB5PYDDsq5SHwqqXiAVNAxiPk69gtBJQqWix6oF/MO2yBJzzHn8g+8/t6rJ/kDOUAQQAsIpPcOlPCHnggwv0/02NFqR9d92zb3pVdRVnhJ58cbhrvcY5o0QglIpUJJyIoiSIgj4BnP5YZKIkSZLscgmCOH1qZSCoaEqBlgNLnQUQ3FWypjf+5MrcFwjAGgQAsIISysarEpC9Ty49nXL7QekKNRLRFIVreoGvqko0Bugd6qM9cihnnDPNJQnMLSmFCWz/8eCTt/3jTQkRIPkKKoO9xFtrPA4GBjIc0CMXOEyBAyAAgCWUEn1Z4HENAHzUgK1Yb8r4lgxTEy1UX9KzXcMD12avvu6J1/fXNXj0EpjqlQO9lqB3ByI+t6hQkRQoCKTpCaSI3mwqARddfvHfXtObvAxIAED+kAQGK0TZJYjjffeQMEZXn8DBtCX7EbwRjU2u0m+fOWOc62vCGD8CJdU1rkKX/slzQhBCZi1bXdAPBUgJAQAsdwMtyUlpFqy5qdMfHNQnMuKMMT0GRP8uTFPnynu7u8era1O84jK84VRv8FRvMN7+kxnaf8AWCABghdFzxvZrd23THvPPmPtXX/lgrh9xui/Y2hdQNc65GM1lCNEfUf8zl/EEtowGiJu0+1vGA1nOqgMS2n/AFggAYMlIJ/rCSogy+Qedw33i/lNBlVFORB5ti49OBWr8jKdRH7fTdfOm6pc2Vb+0tGmqMStRlS/FMGtJz13n7KJPfD6P84RyhgAAVmhMZVpkQtr98zHpmvsrp86snTJtyqxFsWPFF4QZji0FrQSY0wBx5qB21oo586bXU4H29PSY95ncqdce6qp9mP4BbIQAAFaTwHIxzQSUhdpr7285cvDksWOnW1o7unuMZqzokDYefTze3epTBrMan+eslXNWL5mZ8i2T6yp9HhcSAGAXBACwZPSqtsWvb8Xd2ze/8fYbW08eO6FGQkwfEky53pPVWBlMHyM8DqeRIQ0QQ8m0huoNa+anfNHtkqY2VCMBAHZBAAAr9E7z0axpqejrPN1y6FAkMKAM+SlXmaaIgqgPAIi+Gl0YYCQAjFcr0Mgn8mga4LLb9fAgCDRdWz+ltDpVbgDAmlL6HYbioSdOi2k2UMMUIe1qWXPbn1DCQco1fdRvdFH74Vcmsh5jodKx93BbIc4EnAkBAKxgmsJZ0a1NqDJeraXtyO8VFbegikQVCJMlMToKLF76F6RX69jMA5sz7mh5mbJb7/4Xi+8EB0AAAKtJYKno5qLRGNE07goqKV/lmiYQ7paIJPCqqmpVM+2mp4GzWT7M9jRA6nL93x54wfx0rfJUpTu2Z64JACUyHp21oEQhAIAVTJ9YrVjWAxgVAPQfzgZSlHoXzjwSikQ44xfPPDKl70/RiYWE+J3/+FcAEjqDZuZzFde8e1AeMBkcWCGIUnQ20OKKAarGVUZUvR5Awj1hRSPVjcMzwEVtmq8vvM45kYzxttFCNToQLLY6mPn2/5yGZo3F4gIlxCPRGi/NbqBuPgtEpmbkLHJdAuzWu/8FNQDIADUAsEJvMtH7UBaX2O0/JxrXIwEnpL8j3N8RTtiNUlLpopoa0dcI0KeUS3vAkRQBIRrXQ4v9FYXhT/+nf9w05r5o/wF7IQCAFUzTinAcgMa4xmMNQWx4hK/eB7Qj3Nc+KgyIAhFYOLoqQKwXqD7DWs2ye6u+m+H40XFjNmh/7ZumZ4Vt2xkaGiro8aGkIQCAFUzTZ1Mr2hyAebGs+JC1vvbw8I+eIQgrmijqDTqUcEr1bkD/h92Y4eCxUcOFnxo6wVrlKcvHR+kPmSEAgBVTZ8yu8FUX27X78bVvGaV/vNBPVWDrbT59HcrSyBaqjwUbPSOQSUJxr/FRccVONFMXIMtu/EpshWSAdBAAIGdrP3lncCighK2sZFtQVJJ/8elt+prq5qI7oWCPZXz11WQqPG5RlJk+oCFF647eiGQ+DCeKlrjRplYggImBAAA562w/FQkFVSUxuVoMqCg+9rntj1zwVJr5ikZutikVOGeS7BquACQV7Ukb9PRyQSoBNJsMcK4iGAEAY0EAgJwFh4JadBWtivkXFeXlo+LcpkevfPHhJT9K2B6//ddXABYEpoVFMTbG1qgBXNP+dHzv6LT8idjoIWPjPBogJ+FQeCAqGAoV6COg1GEcAORs0N8rSJLL6x0a9BfV5VO2PEk4ky/4nD5bXX0jra17tPEp1nqKcP5fDn3N3PhDBUEfyhAJC0KFMRFEcrledDnuPBw+fHiiTwGKEQIA5GzI3xcZCri9FaR9Z3FdPn1+Oq688ZgeBi68hYqiOG++MHmKtn/vwwt+kPIdZ598NRoY9ABgvv1PzgHYrv21b0655HtjTgJhfjp3+bpje3ZkefzBQNDrMU3XER/SBjAMTUCQM9q5a2GDMNtXhA0Lxo28/qO8/rD6+kP62foqpVXrhNlziJDi264vDSy67OviX0StQK8/+zgzB7Hhpi6AONQAoIwYpX+sPUf/UTc/GB/nJZ33Re3gR7y/L757lXhSjQQkyaV3BBqraZ8Xvk6QTFjzv4wZIMKKQja/levbT53umDljSvypvupNnotqQnlBAIDyMtyj32jXiZf+lBC29ZfCcApY1MmS5FIiIW9VJQtnNVxWZfoME7Y3o5yxZE7mHfTS32jgynERno6u3rq6mrxODsoamoCgfDxa8/AzN30surhXvBIwgsY7AOlESRJl2UWv/gdBEKKrm41xe2+U+kMRfTSAXSyMBpi7fF32O4fDkWPHT2tqdM6+4bAIEIcaAJSba3r1NOmfateQhGIvOu9brAeQKEmSfM7hV8lhcqBLqaqtz2ZmC0nQ15DsD/FJFVQar3unXGcANZNE0T8QEAVRvwz6vKPG/wBiEACgPF3d95756Yu1q/QaAKEf707quUSZ7HJnkx4VKHHLJKgQf4jXemnKgQL5SNUFSM8BWNZ1cHtt07rO7j7GmD7zHRIAMBoCADjClX0fpHtJEiVRcif3AtrRs7Lw50Vuu2SMDHAsAWDIPQ0giWIwGNRUzRUdAJHPqUL5QQ4AnK6pQUya+W38jJkBzjMNYGCMqerYPZ3AaRAAAEjn6WP2N+hMdALA0HUwuoYwJ6qmadr4d2SFooYAAEAI1fRMaZHZvvu4nYeLxoAiXMcNJhACAAARRUkQiysfxogekC5YO1+WpKTpLizinOsNQQDDEAAACNVXuTdNmzMRUq4Dk65hykIaQKdPZ40aAIxAAAAgTQ1CcSZIX9qyd6JPoaxsuOaLG6754kSfRREprmovwERpObon2h1oXN1/77ivC1aMYW78bHn+l0YYiD92OAQAAJ2xkHARkmVRMeZysDoaINy6S5i0wP4zK1kIA3EIAABRRVr+k8VzJjcfak2MAWBTGFh67pWEkH3bXnTmFUUAgLxc27SnPK7ggkap2LoAGd7dc8LrTkxQU0rnrVh/tPndcT81KCtIAoNF1zbtKZvSf8Kl7AIE42Bf9N7fqAc4EAIAgAMU4zDn4rLUkTEAAQDsaTyP96JM+dTYElumi6d+6jTZdwHyemS3Pl/pCM4wnss2+5yaAEAAANtuLuMTTaZ8Ojwbv7EqS+qnkE5Pf1YLlmUgSSIqARnsc2pDUBElvqD4odF/HMiyHFZsHq8bCQ7ae0AoD2gCAijVDPC8FevtPhfn2ufISgACAFiX0HCffTt+Qrt/5qe5HrwMuGX8YsJ4wPcMrEtouM++HT+h3T/z01wPXhJynQQiEAwV7FzAuZUA5AAASoPb5Q5HwvGnnGm0+NYwKHUnW04RJ0ENAADKhNiweKJPocQgAED5ePbI8sfu+xEpZV1DsV9JpAEs0Lr22/KvUDlrFXEGBACA0ugClDINgI5A9ho8+QFxEuQAoOQ9e2Q5KSkP3XdPJIKkLkw81AAAxluWpT9agaDQEAAAiktbb9rwgBmBwF4IAFDaSq79Jx8YDQD2QgCAElaKfX4euu+edC9dfd1143su4HRIAkOJKadb/jG7ALllYcyJ4bJZGmz1mnXvv7cj9xMsc96ZZwZbPkz5UuWsVU7oEYQaAEDRZYAPN+8cl3OBFJxQ7schAAAURftPllTNSbPiQYEhAACUMCwNlo9gmvYf50AOABza/l5s0mWAs0kDjEnVsIRkDgIDfuIMqAEAFEsG+NHHny1cGkAgzPix97AlzTfD6Tc0CAAAJZMASJcGwIxAYA0CAEARdQGShbHbaihBHtgegVN7Ulze2iY9udJ3hDgAAgBAEaGpVj4zTwpkrfRv6+jO77ygPCEAAIyHO+64jWexrjElNEMaIGXpn01HoM4D27I7TXAWBACA8fDh/lMp7+4NJ/bvMh6IYtpfSXPpr2nI5YINEAAAxkN7z8DLb7w35iQQCUGCD//kjw20Gj92HAzKBAIAwHjI3P5z+ze/YzxwyamH5rhMaYCU0BEILMBAMICCW7xu4z/853OqvOKYe4rSqH2ONO9sWrG2kKcGKboAOQdqAADj4Z0PslqvPEOeAMYkNiy25SpxZ/QBRQAAmHjxDDAh5Nprr2Jk7IpCQh7YM1YDkUNoXVlFWYjD9wagWAhc/e0zL6d7VZazCgyQD66FuRZ2zjVEAAAorMXrNn7xmrPMW5K7AD3y0GOPPPSYPhn9YKBp7vQV52+Kv3RkrEmB0Gpkm6oZxGEQAAAKjLN3P9x/ybnL0r2+9NzL44/dbvnIsdMRRc3+8JIoCIL+i4yOQPYYOEUcAwEAYILt2/ZqQm/Rvr6B7NMAAqVet1zIE3TS7f+Ag0p/BACA4hIOK7JIAkOh7NMAnBBZwp0cWIHvDUABLV67IfMOFZOmmJ9qjPlP7OK5DP7VGOOcLJ3baPUcgTjz9h8BAKDgZk2dlOHVbEr6DHlgSqmmsWBYwQzRYAFqAACF5R49xX9iFyBTyT3Y0mw8yH4wmKpqsiQOBsOBYCTfE3WsKofe/mMqCACbrf/4ZxO2dCjklgszTTBww1e+dvR46we7D0w7oykU6Ft06fUtx4+Hp+szQAii0DRn+spl83e+/Yaxc4VHHgop5rdf/anrjQfrCCGfvv6H/3y33X+nslbluK6fZpgLCMAeVXNWE0J27zsuCPSWm64a/eJg+vdxTuj8ppkLmmZxzk+3d+3ec7g/yGm0Zyfn5PCx04ePnZ5VG9tbTZoI+mcPPWOeaq5ixvIKX1XXgb/j3zUHA068/UcAALCZTEKEkQMf7TNvXH7urHT7Dw4GtryxZdTvJGMVkqKMHgrQ0S9MrtH7eia39YtahugCaYnV0/UUut+hRb8BNQAA+zGNCaJg9NPPML1bhHqJxvQZ/5mxJ2WMUUplSWKMa9pI8oBFdwBbi/7TuJ5IAgPYT++YGV3eSxweppsyA6y/JlBRoKKxn0AFUe/pL4iCyyWZ32h21so55qf1lYmjwND+k45YPV2snq75T6P0N6AGAGA/pt/Wx1ybvv0nWjmI/qev96j/p68IKVCuPxAIJ+FIxHwoQ0d3VuOEIRnK/QQIAAD2GDj+fuWM2IQ/u3fv9bhdYwaA7s6OXD7Bbfyvxz+U+Io2ql8QQJbQBARgv0jE/hK5rSt24x8KZzr4lIYa2z8ayhUCAIBtBk/tjT8eCqaezyeuozdg7VOSOwLV11RYOxQ4HJqAAOxEZV/sV8stX3zJORn2bJw2KxTWh+9Kksg0xjkXRIExLlDK9X5EGo/O90kpVXUaZyOdgjasadry3qhlC7nowT8k5AoBAKAgfD5vwpaELkDTptYPDOit+aIochIr92l0eRe96Fc1QaBqNDAwxnp6/UOBkab/jh70/QcbIAAAFMSkmipCyMea0t6YS5JUW1PFuH6PT+PT/xjFf/RPTjglVN+B0Aqv58DBY629yrRJeqfPrj6LzUcAZsgBANimau76+OO6uuox96fDIwCoQGM/dORPY7u+gygk1CeGQpmmfsPSYJAlBACAgqiuiiUDCoExftUFy81bGirxuww5w5cGoCC8nli3/ZRmLliaz8EZ59EpJADyggAAUBCiPqi3gNItHL9q0bSCfi6UEwQAgAL8XkVLf3MGOHEdGKtae2OjwHr9wZQ7vLL9mC0fBE6AAABgP68nNg9E4bT3DFy9cYV5C9IAkCsEAAD7VVXanwFevWpU2qB/IHUNwICOQJANBAAA+9VNqi5cBjhzDgAgewgAAPabVKuPAtM0ZqwJYy8jDcD1GDBquXmAXCEAANg/CqzS5z17TiwNUKAwQAjxB4Ip0wDoCARZQgAAsJ/bPWqVrh889ALh9seAjm7MCAR5QQAAsBvV53RLsd3uGNDjx4xAkBcEAACbyVLB51g00gDhSOo88JvNXegIBNlAAACwWUVF+qn5Obvg0kvt+qDoXNEpJoQYCqBpCLKCAABgs9rqyngGOJYAMDly7JRdH8Q4Z5xjOBhYhgAAYLP6uvFblTcYSr0+MDoCQTawIAyAzWqqCzgRdILu/kBlRaZpR6E83PU/vpSw5bevvJuw5cRHH+Z6WAQAAJsHAXi9aXMAF1x2uV1NQMbqYF29gTnT6tLtM2/F+qPNicWEc2RTaBJL5WbZQBMQgM3OX+BNlwCw3WAwTAhBGgCsQQAAsH8i6HETiaj64sEAlqAJCMAOPNYl3yOn7wNKSHt757Gjxy1/CCUJk//IGufhiOpxjRp4DGa1VRXxx7dev5EQ8vAzb5l3mLdszdG97znzoiEAANj6GyWJY+7DOUk5UjhXbb2hqZM8g0PhlAFg1aJpHxxoteFjyhYnpeC7374jYYsRwERh1DdNY1ZmBkQAALDTNz5/frqXFp+x2vYYQAjpGwg21FZevXHFn95otueIDhAdQceNaTsIIbOXnGntOKvPWJ7lnutWzB1znwd//WI+ASzd3yJDlhsBAKBQ0mWAKdUDgF0CwUjyxh17TqxbPhsdgQrt/V17zE/PXLFMY4wT4sqiIph7iW+Wcgx4zhAAAGwwcPz9qjkjN/jjSVULMtc0WPBh817z05XLYyv/8Oi8HQKlDz1lucQvCAQAgIlgU/tPhinhIHvt4Zop7n7br9juPfvij5tNj4sHuoEC2GDx2g1j7GBKAOhsagJq6w0FI4pxsITRAJCTfjK5DK5YD2twTc5hwVEEAADbfO/2/zRmAmD5soX21gBUVVPVFD1Aduw5gRmBssc4m77svI9d8RlSstrDNZwxRVEXrMt2xlkEAIDSxgkJhlNPCQfZ83g8Pt/IiIESxTnXVN1Zl38qm/2RAwCwR2NVptupt7dujz/WFH3+BgsEMeWAL48/EKr2pR2A5vAZgbIhRnoDXb3bNp+gcgUhnnpPKPv3GoPLrKmorv/Rw38gdmgP1wiC4HK53B63KIovP/+C8RfJPNMRAgCAPTIMx+0eSNFT0y5tnf0zJtfMnFxbuI8oO2kzMFwZeuSBn2Z+86G/P2PXeQz5u/OJH3Ff+lQsBfXAb/+mdLQd7thX784q1YQAAGBPBvi/fvbCLPcXZbeFSkCa23/dQCB2NAwHyw41F42XnbfcPKOqjeV78UMOAMCOXyRh1K9SoScBTRAMKSnHBe3Yc2I8T6N0ZNUHi9k4Wq9YoQYAYIMvXH1WTvtbqwSko2qaqjE51ejT4pwR6OabrzE/fe3vB9Lteck5iwpdA0hHsGuyjiKGAABgg+0f7L/4nGUTeCnDETVlACgJWZXynJ9s7z98sjvLG3NKyA1XlEPX/oJCAACwQYbS/8SBtHO0eRrmxB+HuqxPE324nZy9ck6GNEBRdgTiw6Mh9Oar6N12xjtuqk+wIwiUaVkFAB49LnXAXXw+EAAA8rJ47QZJFK0lAPIp9BNElNSzAe/Yc0KSi261gCeeeP7mm6+Ot8Ns3n6QCgJnTJIkjbGL1s+PpzRef+dQ66GP4m+88car3thxxHiNM2Z+6eabrt787mFiesm/YXVN1cjqbJAMSWCAfH3pk+dO+EX0B3Loul4c6PAtv/kmPTpFMyWCQI2f5Lel3GjMsJ3gwPEOJU1cBAMCAEC+XPLEN773DQRJ6TFagUZKbjHWmSplbNAJAk2X6qBJhdnpTv/J9j57z7jMIAAA5Ou1t3dZSADY65233ow/TpgVrohnBBop5Y1HdTUVpiI/5Z1+2gCQvL+qaYdOdBIHO7E/7TfTgAAAkJe6Gt9FZy+bqBEAZtGVSEiJjgao8OiJiumTq8esTuVU3xocsq2vbVlCAACwbvHaDTddsa5IrmBYybQwwLwV60kRm1JfRQip8bmnNVZn2I0x5vO6su/ZY6z7COkgAADkpXj6GZb03e6U+spoO74wPWMACEVUlyyic6ddEAAACmXcEgCGflMeuOQWh/G4Y31VPe5MfdP7B0Iet+yEMbq2GHOxewQAAOvmTq9nWrGsyPjqK38mJct0U5+pcO/1D3ndUjF0uyoPGAgGYN01F64wB4AJzABnpipKEQ4Hs8AfCEuiIEkiyWsNnAlODDz69Kvxx7d89vIJPBMEAIBCrQMMtgtHNFkW864BZDUZnLUCnRCiiMZk/OmZdnjgd29k3teWBQPSQQAAsKzoWqI54TT9WRXljEC5YYyJglBZ4eruG8qjCOeFK9AnxEO/38I40x8lZUdmLzkzw6JgCAAAFm1cOz9DAmCcM8CGnz/w5FduvbmMF4fRhw5T4vO49FnhoiVe/jWAv2zdEys9i6ZAt6wzUqOvTurqz3J/JIEBLDpz8Qzz06JNABhUpWwWjqcVXlmWUHaNEQaygRoAAEzwgjDGegApVwXQN47ezhlr6xoIR1R90MCikTHYm985FH8sy9LHr7z4cMugqlmvJpS0zkiNwMJjrm6PKAoApYQKtLLCnXkoAGN8MBiZWl+dISNi7Za5mFn4WyAAAFjx9btuz/DqhCQADPc/8ETpDAfjo5eE5KP/5Gk2UrdLlDI2AXHOFUVbNHeyr8KVzXnU+Ygml34MCHaO+skCmoAArJg9bVLxDAHLXpF0BHriiecTt5iWdrHFqQPk/DXLFsxqHHPPyz62fM70uvjTiqqRx939gZe37u1qayGloLHG1dkfiT9lYT/xjBH/EAAAbFDkGeAyGw6WJUrp7KmTcn3X0EBP/LFXIJ/cMJ+Q+eaoYHS3/fEjz5Ei1liTVdUHAQAAypaczZAxmltUMI/PMgeGHz3yLCmRcj8OAQDAZhOYAEhWlqMBiseQKTDEh+wWW1TIAAEAIGc/u/dbxZwAuP+BJ+LDwaBIooLHNyrJ/JNfJmZBJgQCAIAjEgBlNxyMlNZkcKFAvzkS3PHFa4ohGCAAADhLkXQEmlA2TwZnLRIUQzBAAAAo2wSAAWmAJMWyTmTI1mCQYdK3dBAAAHJWzAkAA9IARVsDyDUYmLfbDgEAoKwywFAGQoUs9M3+PwlqKDTKL/2iAAAAAElFTkSuQmCC"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass nomination band is on the axe, item 1.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The axe’s handle is retained by its own visible hook, so release hook 1 only.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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iVoP0BwC8vPG9uqPPiPeHIYSCyw0tQiyTWBaxgCBJvoDPHwh5fN54tOudt17s6WiFJon19WmyIsdj8aisqbosy92d7V/4wa/BhARznNMJ2CK0EJA9IWBMUKiuzlAAyG7iloJJ/0kzA/jspz6a+efv/nQvmBK8/tRdJ59/HZhCfOD6L+7fuolKf7cvGSxJx8wQcxzPi5qi9PYc8Hi9EMJgqL6tdX/TzMWz5y86uGc3Qtjj8UgCBxFSFbn98MHPfvcXv7v9y+P9hbLR1AQh1INtyxIrkaA+bcbEhAy2vrKg3ZECjaP0n93gA5NqEtAfiYPqYPqSE+RoXHJ7OZdb1WTMcYZp8Jzo8QX9wVp/ICgIUkPD9LqGGbX1LRhzoivU3dGOEJ61cJEgcDwihqbJ0aipqKosR/p7wQTjizffYVmmaer20NIixFQVebxPilEQAsiqBen4ziwkgZmyK4QbX+l/xy2f+9YPfwsmEjv2ti2e11LN0p8C4UtPPn7u5VdE+/s6jxxS1YTPX+f2+F979klF1RBCl338U32dRxLRiGkYqmEaNDiKxKNh0SWF6kJut//IgT00vdaC0ECxaPgz3/l/v/+vfxvvbwX+7Zb/Hlg0IIS6rhJCTNMwTdMyza/dRqNXf/q9/xjfk2TkknfIf+hI94zmOna5hsP4a84JqAOqWvTbtG57s2XR8c/886Erb/g0Qkgzdb+vdu+rj86s82D/gm1bNz3459+ef+U1kd4eWVF1OzrUgeexaRp9vZ2Busa+7nZiAmLpKoSGpnz8ptvj/ZF//vn/xk/oDwIjbBk09IdYwDLT/QAcNeDAlMEEAQ42+sdlBQDgdgmHjnQDAGY010kCp2ismlPZwCVzkvmu42v8mQg64LIrr0ndZYvntSya3Zgr+g91Ri4/59i8b59iPgAAwLQlJwJifeIbNyOM/+9rX2io8R2/ZBoWPYdl39atmwxDP++KD0fC/XIiDiAyLQMj7PF5LJMaV0KhYKQ/nEjEaOtdt9ciJOT3TZs2Q9O0X337a+Ml9DPZte9QV+dhQoBlGaFQ7dErlhXZWXL7f/AfXxrRc2SUwUc/cX3KBORIfwBAfY0/tcOM5jpHAbTUB3PfzsJAx3MGMPFN/w6ZY4y80r/aOLz9zeZFx/31p/9DDD3Wd8QjcTvfa182t3FhELhPOH7ta68/89B9H7j0SkKIqik8z4uiJEiSoaqqnlDkeFPLtP7+vnAkjDAHTLMvEjOM9wiGH/nGt32+0O//6ysje7ZfvPkODIceA27ftt1Z4Nx+QmjSAvUDQ7T+7XcBAMced3TedymJSGpmwKYFY0+u9M/i0JHu+to8op8x0U1AE8oQlAo0Y9Lf4cjOt1MXp+PIIYvMCMeVFQtbFrRg8azTXn9j/XOPPHTy+88WREHT9UQijjlcW1sboRWjQSIRCwTrOMGlG6YgiLIcl5W44HLJ8Zgiy1d++T8e/MUPRtasP6TQT/7KhFxQh55XkCxrofqGcF8PIXZWMACOGnAopAyYjWhcyBX9Xb2RzEkAYyIqgNKH/xNEBzCK0NV+iG+ZsX7rIVXT507Tly2etWHTrrUvvQAh/MBlV8iKbOmqpqmJRIwQKx6JSK64y+NF0OpoP9Q0fabb74+H+2xjkQU58LF//57k8vzxO+n6ekV4/qm7D9z70RvuGRS39pmv3laq0Afgglq76sOAsZ8QwgsCgtjj9ZuWdVmzZBLKY+2avVtJyuCmW7//s+/fyu6Z0abQwJ8xoRXAZDH+pHByQ1mUcRHa2g5Nnz5r064jhm7Nn96QmD9t7/4O09Ce/eeDV974GY83sHvLO9QJoJsYYtPQEcdJmAsGAv2d7ZZl2T1YMC+KLsTd8+OC4jtX9B+4l95Ld37MSukAR/oriipJYl6hnxrs0yIVOei6xnG8aZqCIBFiQog5uhu8bJpkKwKgmdZTnbrz1rzKwElNqkId8MErPkcI6Nr5MkbAMAyB5wghhkV7KhBC3AJUdCBxBEFgWkDRTEKAKNDkLd0kigF4RGSdzrcIANhuxGCYJoCYEDBn1bkQgmceYgPBKjMBTZBJACsIWQqtrQfs/0JNM4+a1cCBujUvvVQzY/HDf/nTJdd9ghdEOZ7QNV3wugTJFe7r5kTR56tR5IQcjWiG5Q2GvMHgA//3nbJEfxaZY/9cC895tbZhv+jBqe+aADkel1xuDlFZ5KynSoBmvAEXwpdPwzQDmgDDsp7s0AFdnVYGxxy7ylmoHh1QM2PFglqzc9vL9mWya2nbgpwWSYQ4I8KKtlo2TILRQKsFW6ECMLjzpp2CQedk9nEIAfveeRYQsHjhfMMzc8+7L47DN6wy0EQb/t9xy+dG7YwYI0nb4YP72np37T+yvI4798wzjlq6wjTMf/3lL9PmLAiG6rz+oMvnwwKXkKOGrgMIXV4f5rmmWbN3bdw4pPR//qm77/yYlVf03/kxK1P6d3d1+nxeAMB5NfCCWnRBHbqoHnNDSX9HTxCaD6wSy4Kw4IOAIOQQlDh8WYt0cZN4UaNwQQNHAEEIbXh3U2q3m279PqgCCKAFQTLX2GY1p7DS4JV2+m7Wm7NCLegKW4tk72jR0j/lnltXT3+5b2GMlgKYdMYfRgW0th442BFes/ngnCBuRL0rl6+CtrnFMHVPIIAQ7mlv5yAfDfd0th8AAM5fdeI7r7xycMtrxQ9bSPSnuPHiBc5Cd1cnAOA0MXFhnT3kt0foJWKZpqFrhFjOv0PuT4tdI8hjJNjKwJFP1aYDIDEIhjoAdDAPQbJ+Eg38tSDRMTAwNAEiwFQBoDkgVIybFrBonqCtPgxgaRARDpoImBCYdKZgWpCW96FHshxXPoLQop4YRnWZgMbFEHT+ZdcCRGevBAx9z33qStoNpr+fhrczAAAHDh2YOX3Wq1sPnbCw2SX0SiuOOnLwoJxQTMN0mqupCi3TiAj21dT/4bYvFL9od36spKL8m//0cVB3uyP9TxcrLAOpKbKhaQQCVU4ItAUEX+IbHR1zSRP/SDutJLHh3U2rjl5RLbagnHE51buE/mvR5gp2eb18PTMGHcMe3VNtTZwJRFaaV3qfclm6YEbe9Vtee8gpIbf0pMsrOOwUBpoqHcJQDZ3BiqUfGPKdP/31j5L3g/1Lnfv+Y5Yv+cAIDv/HTAekFIAEkwoAAnDG8Qvz7vyB0/NHg4wSk+V+nT59Fo9RfU1g1ikXhHt7EYKWpXu9IVWRVTmuy9rMpcvu/vG3hi/6Uyz/1N0/vPO1iqU/AOCvO3tVnSLwvN/nvWaWp9wjEAIe6aA6gAaqD+gAAMAU1gE105bMrUeKamIIBI4O2y3DQrSiKpENM+DlFM3iMTR1A3HYMgFGSNVMO0UcGZYFiGVZFsDYMCyeQ9GEIWCqey1Ic31NC+gGnRKIIrKkxvkL5qY+98TF9XnPJysM9Iz3LS9y8k4N0cnyTI0NnKzSn2cQBKzb8LSzePyq85yF//vtT1I62Rb6A5J/QE8/++I7//urH537/mN++ZO/TCLjz/mXXwsmMNvW/nNsPmiYT0Vr64H6xhlnXPTRw3v20scZYZT0qRKek2YevfxP//214p7ecj9x858+fro4rNTrhJIwTdN2ZSuqxgNQtgKA0J4HHKGRQlNgHuAMk4tz+jW3UXONMw0igEPARHQBEWoc4xCyfbx06mdP/+iOA3W2IYKIBtoCZ8ZATXbOv4QQTBUAoKFAA0ce8B0zRhf47LN351s/IOvT87Ck/E9J/swuDanQapphv37zSCmA0Z4EfP5LX6ZVzAjp7E9oujkBZwCTiLu3o0O7drTu3imIiBdchFhyIiogoWHW/G9dtiivpqlM9KdY/qm7X7/nucree8+BRFfbIdMweFEyNMXt89301c87m3iOiz73yMScB5Qio0eV06+5bV4IGgadriEIRB4Yljng8sXIFuIIWZphYNo+jtbaNiwqJKiqoDE/hmkRWoXJri1uUfc77TFBezNDpBlJUwTmIPG1zJs3ezRmAGwSMKQCyJT6A/8lIOB3hSPyoKH/wI6WRSCicnMEpf9o64Bf//wHjo9q/aY9As919EZTwQhMAVTAB2/40VlXXNV1+HAi0i/wyDB0AfFNcxfcfHF+Vbr2V5eCYXPSF/9VaNOzf0hOnu5r1U1DJ4RaH+wBKL2dZVlOxMOWaemaKoiS5HJhQfzKl25wFAAAwNEB9x5UPjpTGvI0CACOPwAA8ItbrwZTl9OvuW1ugDbkwrYHmMO0nQ71AlDhTkf6FgEI0nJQdoNQlDQX2A8WNQBRmU9jrqyBpAE6ARgYRhq2S9iybEkdaJ43b87wFcATL6ZT2WsCnlOOX+osM0NQIQWQK/1BrvEnT4wXICMu/UdPAfz+Fz9ct2k3XaIxCBbH4Y6eaMoTVUQByKo+4idz8QdOAJOfMz/2XY7nz7j4ili4z9J1t1f0eIO7XnlUDEz/1X9eM+KivxQdkOLj37pLkWXLMhDCkiQJohiLRkI1NZjjsJ3+ZeiaIitury8SDuuaRu3UALjcnju/85ERPNUpwOnX3LawDuu6SaW7HcFpX1U7m4tgezoNEKJRQXZ0pxOZReUHIfYMABg0xJMqAJp5YUt/027KRu1F9A964WlKnuluWjJ/Wupzj16cXi6iAGIJp1FwfmoCaSvfKccvZTqgaBRQhiIAIDn8T62syEU/gSKCktKfQr8JLWhcGva97FyGtDa0b2WYqS3pI2Fvt3Nc6Jizs3dnQ+3ivMd89Lm3wJgwqprm5XtuP/Pj33/p4ftPPu9CX41/+pw5v//2t45fMmNmSPnm/z76k69fPOKiv3TuviPtLbjsiz9HCezzB3SNNq0UBNHQNVESOZ7v7mwXBBFjbOi65HIz6Z87iCYASRzkIbInz5YjuJMjJwKoP5cAhJET3WOZzlOQjPenEwSI7YE/sOwYMboPnT3QPZznBGP6X4RgPCOtrHQG7NTp3IKUbdp5QInT1w6A19Ztq/h2msIKYLDUdxbyGn/AWAz/R08H+OedoSiK3RPKxJapHH4nc2sR7ebIc/teKuSkSq63bzP7NrQXGmqS5QoGtEfK3JTcP7UmrWDsBfp4pbbZN/iAvqGHtjcmj+PslZWSM5aa5mvXfeB//vL0m88/ff3Xvnb/7+60TGvHe+1ekVsxHX/jxw9d4cnrbRoua391aSmTgBSXX0FV0f33PeQPhGpq6hDGpin29nR97rNJJeGYgCavjB5N6C2X0EzFsIdNNMiHBn1iSA0+FoGaCXlq9UnKfZoKQLfTqg/2RAA6ZiL7SEQzAYcIQpZhEpNAiLBJAGdbhngMiVSJE9i++W3xP/j5TC0nnw4CPv2VksqQVJUJqKDxZ0ABjKnxJ4vh6ICf3v1kd19CUXWE0LYN6xNR1Q48hk6+IbUDcFjgMMdzFIwtXU/EZTtMgUY2BOtC8Ujc7a1BGCGECSC6RQ+lxOOAEJeXpqECYi5xHypyDq0H3p4+6zgw2mT8RKl7PbUmtY+tQgZp+6SDf+A/dnZ+shG3HeftRHbAzDlNoTiNX/zjJZfkufFSOtv47Z8esCzzmIXNS+Y0bWyVr6h9cmS/buiC3y0us6HF3S/udxZmzWz57R/+XlNXf2j/3q9/7bOpHVIK4MSZaBLK6NzhhP2vM3xL/prEGS44P7ezJikc7VFH6s5J3UGpcfSPfv/4wlorrtHULTszDmi67hYQj6CqJy+XcxOJPK0PYZfoTw4dBQ5YAGq6nRJm52ETQASOqLqpGoDnsG7SRDBAgIuHmmfGsvmNo2QC+vS/MemfJHuwI/CcpqfK6mZK/4xVk4dv/OyBeDS67u1trftbI72dup2UpBmabXC07PIj1DXoOAbt2Sy2FcNAeRK6hhaLpwqDOq/oCgAte+ZKtyBOTCRiECKOw7t8PkdDII6+6HaEsK4gTti3b9+pC0feeTDE9KPwGmc5Z02uQB94s/OPM5NPpf9n3A1peQPAl685y5E4Dbvv+M9T6ZrvvEr6Y8pxi6a/Ej2vQ3Ov+dZvIxYgAnB5wG9/2FDxd31U/kjjy+t37q4tMunJZev27oHq1kcOHekwBLduwbfeXJ/aweNxOwvde8bgJ0uZSDL+68zzBoS1LYiTC/YvBYvI6GzsvZMdtZydBmawmXdB+tdPzW8zbof0NNY+Qc0gCECRA5pJXcEihwQOYQg1I2kfcEYHA4fI+ILUY5CaqQ7YfBDkaSUm+g4IgYigahC9vJyQ8mDSP68CSN57mm5kDP+p9C9wk47p8L8CQ9BNP/yzLKvbN246cvCgkpCJqRtqwrn1DCtdON7JXaRuQIu6AZyJqnP70hAF55a2LEwNBXQHJ4zErh5P38YRZ3qLNAgT4V7nMcGYOFoDACgJkmXqkb7eRAK9snp1TW2wu1cReDrh5TjuhKOLRa1NOAoqCPrwKq0v9h3eaapxSAwMTA6ThobkkPC/Tt9/+8tWJK68e+tL3RYwewD0AySACACXf6PTi8BdPy5PDTwqU98shNDrEm0XIhWYadmVYzFL1nejfkvnlGxxSggWJcEfFCIRZ0CaMVx2JGza22PHqyQDVzKGz/SAznJ6HJ0MZE/L6OR9lXnBBgRjhm0ic2taoaXEcWaRC8ecPvj6F2bwRwy101CHsGi8pmFaJrQgMQnBhKZ3YTukn/bXtAwzYxRFZ8ZOrJ09eMJ0V2RHAg3oLrqzYdFyTPQKWqaF6OAKQ+orHgWY9M+rAJLXWuA5Nd1XM7myqO93jKR/uTrgs9/+07bNuzsPH1biMWIZpq6agJZytKW/SezMFHrP2ZKcflMafUYj2Wg6CrVg0omBHdlGrUQ0X5rWNUkenI5cnBkDoIEN9siFyntC4xoGLCe2dwxCGNMsCE3TNBQFBlzSzDpc55ZUXaM6AFrP3Pk3iZCoSdwcNADUCICc2KHDlhlBYFqzZoVkxUIAtnfHzzuPlqCYsKhtr/Qd3mVqiaT0R4RH4GC/5eZhnYde9++eeeCjJ+wM90ZJwIdEgKcDl50qpgIQVcGHb+r8x88aShf9Di6RD/pc9NpnibDcQazNAXNa56HWlEi2m8Jr8XjMDkx0hC7sjlhxTW6qddtJLumjpHw/yZlhcovt18z92OxTGOQOynNqkyfniRZ6oDc9oZU+qdiHg6oo5a3hltJ2mTtaBFKXcWof25VsAfvgkIaVV3RRXtt00FYq2ba7Gr/nT39gJaaLmoCyjD/p4X9h32+W9J8I/Pv//PVga3j/nt2H9++zDN3QEjQizQ5AJoSORuiyOZC6YhnOc09HMclhm/3Y27MAuyah6cygBw3RMgaLtPpJcsprF7qyH3Mn2XHgLjRNk454VI0c6ejSjJis0qmE20U1gNf+sEaemlARhLJhikjD0Jw5Y5okYJdAOGCoquF3T+hu13rHa32tO00tDi0dAYtKfwwETC25QRdsCeBZ8/jjyWvhaL/pD9DplQhEBOYD+noLgHYRKAlw3Zc67/plMR3wqPyRw32cz+0KuiXD0AxdSyT0lzsO6vTXxAZtRCObpg5pMTHTifjXDd00LGBBzHHQ521pmebyev21zQd3bA/Wh+jdL0q6ovCY/lJvvbmxceZsOREJ+L1bOg71Hmg1dB1iYpv0aPnPZJwLFDiOFyU3x4kQEp7nOJ7neYHnBcwJxywSCn+DySPmi4IQQNQFQK03mevtxyJbbjtuo9xyoeknKcNfiyA0be+UM0975KH7Ursffeuw+kj3RysvGTK14RxJ5kj/XOMPHf4XNv5k/T0GVR+KTwJuuuPP+/ft27phm+ZYeyzTouZE27Fk17BFgE4tLV3jeYHjBU50O1N4ty/g9vp1TaFTVyo76K1tmaZFVFrnkPYxMXVdt6cEdn14Z2JrzxCS2IrSUQ0WsLDjY3C8CJBONXTD2t/W1xtVZNXkML3LLUICwZmaLBu6SnTdMnTdIIalm4DsX7vPtCyBh4Y9FNJU89Hnfzja1/aH372l9J1vnf3VXl03IDQR/sG6PxhqzJH+PLI4DHgMXAIMuOC0IJ41mwOXelt/0266vUB30yLxOo0F1OzSj7yjPwmIJ8A93efaMy5oGlBTsaIkbE1s6YbeF4n0htcAAAUe1wW8kFoakr8E1d0Q0d/FkTR2uZnkFI6eKSQEGoDM8vv37dotSiLR9ZjVh7vsrCWrVo1HBI5/ac0zlkXCG3qwwLdDWNNQd7ink0ojmvOUDGJxxqnUykFSdnkLDQg3mgUL0epXID0vi9CAAsTTkAEIMOYFCWPORBgSYgVr/JiTdFVXEwlMcG8k9umPfBCMNzf/5x2l7BZsWUKvAkfnxE7otD22sm062YN6JxTBniYlJz9piZEKyrQsO0GYpgPYDyrNL3N2LTUyuxSS9wKjmBM4TwhoRjhQzjvH0vhTig74t+/9dduGje/t2UWTUez4TlqnyqRjZ16SeJGzLIsX3G5vgKPZ/+q65/6cdYTLrrxmyESwF9/e21DjPXHF/J5wvC+SiMaVuKwpmrF5V49FY9qo39fHGxg6+gEkTJeuaQD1ENDf0X6koz2r6tl7xb/sDfegY085b/1rydJMo8ott5eqY7i72/u0pPQnHPzWx7dSQS6CH/+sjkPJsb9fgtMCaO5MDnzI+63X12qJ9YTDQFOgkiARn9oHDoRAu60GTAKIAgwFvHHTPb+8OzkJ+OXeCwkn2uYGy8LI6w95/TV0DmfolmmYpmHa+t1WznaNevsXp0rXiS6xsTW0vQThgf37MKL/c7nchgpOPOV9dbXBh198wzTNk9+3AoAlTz/xLORIPNIPIIzEwvFo1Db3Oxa/TNe37d/B1N1PRwnIcfYjGv1Oj88JdgoIohFkHITolmNeH/qCDjRe7l506/Bl9OhC2wFQrUd/Cysp3J2h0MAOKd80rQ7gLND1yH45ZaHTMWVOOJA9WrPTCpxfzla6I6kAGMUUQHL4D7KH//2Fh/+71qfLoE8EPnf7HzavW3/wvT12VA+VEYCOvLDHG3T7ApZpvPvKP0o/WpHRQmONz++VlsxtpJML3VQ1PS5r0YT6/hOsz3zp5iLHnH1SMge9XMZG+peOcE97j6rrVPrTsu5U9PMACNSwc8t/d+Mg+O0t9X4JtgTQ/Ok8OM8DhDnrtm+3Yv00LZTnCE/9A+YBTyIGZK89XYoDEgMgARIZzvmjj1m+Qm4b5qkqs6enlv/+zLZI3xHLtOoamqZNb3ZWQlqvJim5zrvwXADAC8+9iDjOH2w6/SOrpnvLiwIS9+eLA9ZKUAATSsSXgN0JADpuMW5Qi68BUskpTp5XMrwo7Q1IFn0bOB7CdsqAbQWisUK2SbWSE3POzi5NUcn7qw9O4LmswH9H+tt/pFaOv/GnyCTga3fc89ba12N9vYahEGJhHgdrG1wu75tPZ4/xh4/PQ/O5IO1sBzkRSSIf8NkOw6EMvN5g2cUmJyCiLf01AC0q/ZEj+qn0lwB0A+QHWABf/WmXCMDOe6eDc9zAPwuAmyUeknA3MVVgykCPW0qExLxWjw+IIg2ZolWANaQqdnwteNnzTZ6rJAs0F+m91tTy9Yv8ANCY8fvakkPLV17fqCXiwL7/U5z9gfc7YaDrN++b1zQiZzGlSIZJ2S/OvnROqYdkWE9avGe8JUtaDG4BZlLbbNLlizEyjUF1Z8ol5He7JCHgde051E37DA8gTMLkvrGBXpeswH+H/nBy+D9xjD+FdMC+PTv7ujsIsWoamhGH1j833IzTIsI8b+s7x6L5+lN3nXx+wQLFRzomfQ+ZzrNvBH9OZ9AkHek3fQ+KALqo9Ockej+JdmHlcz7a+jw4G4AvA3DG49eBmvVvGJ39RInARD/0eIHLDSU3FATaiQEiWjRA170ez0vkE6PtKj3p+KM6Ontjcdk0DYQQJ7pOOPEY5aUnN7mT8j4ha3W1wZXLFoDuVL0QRgpMzfQYGjqiDXRs35edUEN9LQSALxy7s8SL9X/rFtIy0nQeQd9o+9mhQWgMKK0bV3J/nkxOWDbLJfEtDaFQwL3vUHdfVNZ0QxL4ZQua72e/YT6yhv9J44/zV9Z/R9z488TqLReetmyYB7nkxv+IhftaZk5/7h8/B6NPqpZIubz7zqDG5ZNS+ucD/oyqBPTz72FXUvq7AQgB0AjAN8ELPxnYTYCWbqpWTztJhKHbA11uIkqQFwDmqFK1LGRZZ227U6irVYkCQukykCPLU1FpJcYulwghNA0naove39JZF5wAwF/+ct+8eXODQTeEYOH8GUwB5AUhaJqEw/Rf21xjcQMdlStW3nZveCppMKZRGHZEaCUHqwvRtHyPJJywbNaCGfUH2/v2tnbPbAqtXJQ/kZgxKPA/Jf3t4b+9aqgrNMzh/3B0wB23fG6bNu/un34TjCUFcy6nMo88ctdJ/+/VIjuYX7nNBMD7h+95bOnfAEATAAEAvgNADwD/D/xpxYL5b214m5g6iSlWrB+KIkgqALsXCCFBQdy4v6e2V+FQR0zf/rn3rRrZr9A1/+ja2tDKIx3UkCXwosCblok53jLMRFxOB4yH/C4Xrf/c0d7ZeOL74ZsvjuxpTHboeN9O0RoI47HDn1KU82hkjqNoqFxGxlyyhfAw4DGur/HVBD1zptV6XKIkMBNQflI/XpbXpKTh/3Ck/xOrt2QtVMC3rp8PxpbKhv+Tne4HPlbKbr2fvi1gS/9mAIJ2xEcfAB0AnAU+9exHv9lcE+CgCS0NmipIREBfJ+lqAx2tsKvNq0Q/7upddMwZ3f0x3dBmHX3clnkrnyY1j4WLhNWXQdd82sbnpVfXOX+6XVIsGjVNA9hhRandrr7q4nVvb0i1DanKn3pInKoNTuokfaVFRPmXa1A9Ksea5FQGdT5oeEAAOIRqAx4m/YuAco0//eFEXu/NCEr/1dva/bV1YNg8cu8zYHR4Zd2u/BuqUijccE+pZdG2fPq25z99G02ysqV/OwBdAIQBWBTc8db8/10+b66bgzwwMM0F0DmiS8iaWR/yP/GLjs6evr7IjMUntsWsfe917t31HnZxLYvnPhx33bU/PUKvWPo7ZeycNU6pu2SGnh0onJf29k7wvvcP56OnHk6eu1MJJWdT+SJ78FucenVOGRbaVJQxZqUgMkz/ScYyimo4hqBtmx9euvwyMFZU7AOoKn7+6ds+8YfvddjSvx+AuB3vv+TTh1/832aylNoQro983TTM951w6unPXvxfD9Jhfn84SrPDNO2EE05dv3Xzlje6BEkMNTbOXrigs6v73iNwbt3Qnblymb/E6cFANm7Zu3TRTOe27ursshPGCO92GeqgO/2qKy964MHHzzzjpNSatXr2o1EWNLZ0ijI4EZhSVgGfLJuDExhqpeysLI5zTEi2cE6RGv6PnvFn9bZ2ZyFzElCxIWj0JgEFYNK/JP766ds6bemfsKW/bteGtxv+Ed0gP4U//q76k9OfvVjRyNdOUVaddkkqAygWT5yyapllaGoiARHNC164bP6OjUnrTWXSP90PNiP+3KLDTFy8t1F7e+dJp6WVAYNW0LO7fDmjoKxRelnR95k70/y6gRj+ZIXqYSiAtq5+9kuVCBoU+D9A1rUfDenvMCKGoG2bHwYjSpFbr2pH/w0v/LHct2z49G1xu9wbrc4DgBUDp3/8iG6SuGxF4lZcJqpONIPomSmfhKiqrqo6z/MQWuHOzvaDre2Hj2BEXn8rXbG5FHw1tc4RAQCbt+1bvGCGs76rowsAsHDxAssybVGW+eF0AHrVlRe9/MrazEMdc/wx5X73KQ2109MUYIt2cKTtHy3LME2nSkoZR7Fx7HC0fIbdTtJpD2YX8hjNktCMAVCm9HeG//mD/0ef4UwCRlwHMEaE7k/flpT+cUAStOTD+y/viCSsmEwU3VEAQDXgqtgDqbcYphlPyKces0KLxTRFbtu7b9e6t5EuKxEquMsiHI4U2Upr1wxUdU1J/1za2ztFaaCbW9VDAPAIhMdEpC8gccAt8pLAizyHMS5rfAQh5DDmOSwKvCRgAQMe0SpSPCIup0QUY/QpnMadO/wnQw//9aJGwNXb2mkhlcE/7YgYgkaWYvdeFWeYVzAJAADIn77NSiSlP1Hp69prulSNaDod+6sG7SOoW+nUX0WOJWRF1fTzzj7zjGOP1qIRLRrBxBAwevWVF8oc/oNwOBoOR1PDf4eW6S12JTiT1rSx+0AwSgQhZNgh+hYBBgG6BVTd1HSD1uajZd3KMgHRkb9hWjot7moZdjkg5wAm7a/AnMBjAcoz/C9k/IFDS38+1zE0wOpt7QKPvW4hN9F/+Dpg7CYBbGBSPsZXbiOyrQAUAFRC1AQd+9vSX7eo9NdoQYmBoEBCFFWPJxTDoKL5nNNPveiiC9//wQtPPevc0884u4JPP3g4PXVw7D+0gYGi2lXBaRmzRCIRj8cz33LRhedkWYGYJ8DBaZ2AMeIxbf/rFPfBSbcArZJX+u9iN9ez30PzQOyoTQhooVy7pKiZMSZgjB5opEa4xaW/XXIO1wbc0+r9Aa+UVUl8pJwBjIk5CaC31X/fRr0BKiFKAiiJr1+1wRn7axbSAA/dyQG7g2lZsbgcTyjD8QRKA3ab3OG/3Z5Bo8mnGFM/sE0iQYNNZVmRZSVPPCjDJtq+NazCuMlFNRjToGIAVceKjhQdqQZSzTIUgGYizaRvsRewaoCEDhI6kk1OQ94DW19il3wM4AYN/4sYf4oO/59eu+PsExcV+ZjXdnTUBd0zGgN+r4gxklVaRLPQ011ZVKgzCRiRkNCqzPYtlYYX/lioLERxbPuPDJQE/VeVv33xC9986Fydtgau2cUNRNoQqwVptOKMrtUl9Om8KvGVjAQPgaRGOXi4K8v9m0jIjm4glkmDjAa6QTo+A0FIpp5ddOE5jz/xfGY86EmnnRR+6amyT2XKDWQP9oJ5jbQdvFMr26DJwABjQLvmlaOveVtZIAR0gx6N1oom1LlMnUack0nCGHXSGrti448j/Ysbf1wi11zra6zxBr1SY8hTG3Bzo2AIGqmIICb9RwP4u9tS0t95aYS3pNBu/pTkHsSqN5JuW9MiUVWPaXoFc4BDDfNSy1nDf4+P3saKoiqKSvv+IGyZNBNYtylyTDYJSBFt32bXbaYme+ehp40BKnpqnLcMFIGwi8HaMUAHthWrO8IY4RlAMva/otn2kGP/l9+m1QEjCfBOT/c7G0fXm/rIvc9c8tHxb6405RnGJCAB6EsGmgKUhCkE9wqnOpsgyXbGqoYZUXS/KAh5K84XxRnj5w7/k+HnhNqrqRSj/QBor7e8B8mdBDAc9rfLCxv4gW6Ppj0XoDWBnL57JWL31nGOYBeAgI42IToKsus8ZnBD+36LRv6cfQKV/sWt/w6qVIKVXzky/PTg4RuCmAloSP5T//V/818o98Lif/3ePOtKqgDkBFQSv1n1X+duTwb20GryiAcwPS80CZBN4nNJzTl5KsV5p6uHNi8M+pYMHv6bhBp5EEI8Tzs2+oL1c+YtMRVZUdTUPpnLWbS3dzaddX5o0+vlOcCnYk4SRBxVnzgpvqkH2On7mMzkLe0g0KKVwGlvGdpRkupkZL8fTTmr2QRmYGyVoQF2vV2q9Fd0GhFWRPo/s6VHc9WhYLO/ZfbM5uDCWXUzmoOSyBWaLnKhZKumCRUVysjld68KlTmE8UsPAiUB1QRUZKQqG9Y8lrExe/wo01K1ZU8VL6+hb+mLpH25nfbwX9P0SDTe3ROWZccEZClywtD1WCyRSMhJW9Ngk9Ppp52YFQ7EoM6SI9uT8T72XIoqgErc9XQSZvfvdHp4Od03weFdb7KLPD79AEbW+LMrIiyYM72l3n/eKYsCcxYBQMdxbz/3gGGYnb3xPQe7O3vjBjUfDv74+vpIV9kpP1lWoFEqEHSoM1LlqQAjYguCigxVGeuKS8uOt+nig/V6X+pPiRp/SG8ie7cScGUN/7v7oqZpxuKyXQgOeD0uQIimyMSyurr73G5JUanz2e/z0g6/VBQV8ASsOJm8UUaNaNqEbCqyu11f1MyZWjI5PtkTr7JEeac5sAUwD/rU/I3zvv39n3771q8N96QZOdCpXCZ0+A+HHv4/vXbH+4cy/nS0tXrdQo2kvr1B8+/elUoAwxgF/a7GWl9c1iJx1S7/N+gs/Bk6YFwMQUPcxcxHPDwdQKW/qkiKHJIErehlbvC6xIEwzbIgdruqzOF/JBo3Dd00LYSgLKPtuw44o0/TNPr6I/GETItPABKLxQMB2leQ4+xw9Kot/TEkEHOYk4HOWdjO6XUquZXlrbEvMMDEok1gDAgExHUemFjNxqc8qALjz9Nrd5x2zILij8bqHZ08h2qD7qY6n9clZKb/Qgglgauv8dSFPG6Jt6t05aQF1NdXbAga8/Jw1U65tiC4d5OoySGR1/56a+Z6ktEU3u7pgeq8Ulnq1qQlZegra/i/c2+rrmmmHa1iWURVVdOyEIRyPMbxgqyo4XCkvz8cicb7+2NtbR2H2zpicdpNkBBy+qknZFqB2ts7IasRDUC4fcfWNsMlUhMCx0OEaTJXWbYEe+QHEYZ2TyDgEvCRxLAKrzIqIEdjD/W8Pb12x/HL5gh2EG+h4f/qHZ0cRkGfq6nO5wT+Z38qgj632Fzvb6jxukR+lIZZI5sbnLT/2Cagfz5fXmGyKU/DC38sSw0EOazflWwvPNgNALr4ZAx4rUf0CpwTtJMepjjLOYKGENIbV4+EE+1R+RUjkDX8J4OrFJim1VwfWDhvpojMmc31Fi1kRnTdVGkGshyNJcKR2KHWI4dajzhqoMyLkXGqU5rwke3bD6u0Q6RBv2pGD/ZSccpwWCbBCLWGcc/BzDDBsqmvZeFDZYPyDP8L1/x5eu2OZQume11CceMPtbG6hWmN/vqQhyvQ2IHjUG3APb0xUBN02TkBozIJ2LbvOTDiTy6zChSgRB3Q1DzTvPs/i++DIfQKfL+s9claWNGiqh5X9UhqWdNlzVB0UzNM3aT1ZLpiys7O8L7e6L6eSNbwf8fuA4V+YWHAAmqXoLSc4pS6biQSSjSaiETira3tu3fvP/aYFVmuYPi+9+umpRmmcw6aYTrnE1P1hGbImhFVdPs8K1Uek4Ro526aUA1pQ99yy3c6ZT9tMxA52G+GjwyraTaT/sPKBE5Kf1gs7evptTvouMzvymwCmsvqHZ0Cj+trvM11PrfEF2qfAiEURa426FY0I57Q+qOK02t0hJ0B8RgYcViU6FA6oLhXwLrn9iGvMYSgMyb3xBWEaJgJRhBDqNk2HOz8aa/BCDllRY5EEmGFhvNL85ZmDf/z/X5Ff17qGKAljk3TVFWV5zlh9+bseNCmhkP9cd2kJYwxQnRnu4iublrYtmlqpoUgrWvfPNVLnLy7bcfxyxfaUaAEWGUMjohF38IBS4VWvHt/ZZ/e1Rupr5mqjvaxoFSnjSP9Tz16PscNYfxBCPq9YlOt1+sWUVFdgSAUBK4u5Gmo8dp9O4e4e8qaBwxnEsCcwKNnEcq7PmUFSrkBTItEFL1P1nrjandc6YzKRyJyd0yhyzG6fDicOBROHOyLvddLXxFb+gMAsob/23fuK/20Bw1ibf+waZqevo79vdFGPZY1CZh34SWH+mKt9jkc7I+3heOt4URHVDkSSRyOJDpjsr08rGaWk4V1m2n/VExru5XxLojoW+hvtLtC6Z+CDf+HNQMYZPwpXPXhuKNmiwIe0vjjcfGNtb6Q30VTA4cCQeiRhMZabySuaoZpGNlRoZmTgIrZtu+5pXM/AEYI1hWyRDJlfefZN5buJOjiQ6lg0IzuFE6t0IHlgQ3YHmg7q3OH//5Ef8QdhOncoiGH/5ZT0pLYmcmeaE86BJXPngTYxc9z3RF5lqY8r72z4+RVi8tyA/CYZmK/vWU3GBPWPPGXUy/8JJjMfOXWH9F+q9+/eQSPiQZF/hQ2/ixbMN3nTtbJKsTqHZ2SwDXW+prrfJLIl2grxxhSd3Gt10ODhfLsULEzYNTCgZgTYOzKiA4Jn9GHZMmCmZmbDr/7jqMDnKL/pTdzdqS/L9abtZ4lhRXh9Q07aI5nyYgifmfbGEn/Sc1Xbv1RSvSPrPQfbAIq8HQ8vXbHwjnNdQG301W1sPGnAyEY9LumNQQCXinZgrUEqDNA4OpDnvqQR+BH2BCUojJvcF6Y+J84QJoshh3v0cDwnwYXZln/HR1gGUWyDtJYNLspj/T3J8KZf7a3d15w/XXD/wpTiVfeLuPZfHMjtSozxkXu5ygAW6rlDv+fXrtj1rSGphovjdUtfiCEfG6xqdZXE3DltnwZ6r3Q6xEba71Bn2R/ULGIoLKgk4ABP/BwdICaGc7BNMAokOsGKAVkFw+w7BDPrOF/JhCAWoWKb2IaqVfmDsQyUq+80p8xYSFFuxBORq77wi2jLffz1ALKK/1p0F6tNxXKWcT365L4pnpfQ61X4HEFIpLDqMbvbqj1uiVBj/Xqsb6sV+bOlReLrlQHJJT04DE3Wokx4qSyAYqDaYdCKv4HW/87U/YfBwQBh5BfjjgvZ2VSEwwIfX8inHoV+jh/IpyVFMYmAeNOPOPZnAJc94Vb7vr1D8fs44aw2a1aPNNDQzmLSf8X11E90R8F/R3tW/Imco/EkNnl4mQ5PWorPSqUVgf6zBXDifO0CDnU3h8Kep0/aaGYArz+1F0nn8/MAmMHh6BBK0kWG/47P6ia4aBM6QDGZIcQcKCtd86Mxqz1bV39LfWTLy/surGV/ukZwOzG7OH/8+v2HjV/esgnDTdNt+S360JNnheffnH+BjBsKpgE0OIBmemgzAQ0ypRuBdJNS9XN4sN/p+XISE3b2CRgQmFZVl/EbmcyVSw/YwxXWPq3hLxiypdbaPj/zKZOJNXWBdzNtOYP75Z4SeAkkRd5LPKY5zDPIZoaY9eLJYQ4aQR2J2iHjDGaZrR1Rw92RI50R9t7Y6pmJ5g7Oww8vZy/wYh0lj0J+P1DmZOAcqNCO3ujsUS6TLxTt5Yx4mxY89iqUz+UNxi0EIZFEITFh/+jjSRJ4/jpVY5pErui8BSR/neN7fC/mAloer1P0Yo1yQMAPLup0+8Wj17YdNTcBp9byLSNdHR2Ex1oOijLPufhwJJpniXTaElYTTfbuqN7Wnt7I/Igs3uoJtLXO6o6ABbxAFNYKvAoYt9EToV5K1kspsjOCPJzl2UP/9evG80TBP5Y78svv3bmmclOlu+9d/Dyz3/mn7/6zah+KCMvhp2zPTUuzl1jLv3pWDzv8P/9JyzIjOQpNPz3i6QlyIVcQI5Furp6Ojq7U6/hnxktJhpwN9R4aMXBwZ/vD9VUcMBHfv9QZWeiDFYApYeTM8oFIYQxFgTe5/XkL8k/GDzew/8Ul3/x8+N9CtUILfA9+YMyrhtz03+K/Mm6LlqYAfjcUhHpv3ZHe41Paq7z+dyDqj2PFBBCt8g1hjw1fhefU1EupQPKSw3L0AG5zgC/JzmXz7yhLIso6qCZUGZdScbI4nG7/D5P0E/riMybNycWmok5QRBEnhc4XsAcjzgeYvoCmAOYaz7xtKwjtL77LkHcMF8gYyH5wskFaH+uX469/PJrqQ99772Djg5wzi11nhw/RO4kY5joevk1SCcY142f9M9jAnp+3d4PvG/hkAJ9zdY2v0eaVu+vD7oL1fscPhijgFdqrPHGZD0cy1MtroI6cZm2oGxDEIR+jxSJK5lfXtWMrBhQQsiQWRGMylj99D8uvfazpmlACDU5dtLxy1VtSePhraZFDMsyLGrwNSxi2sv6/OW1A6FZ9NfctkM5mFv4cxAQALfAQUgLySFE+5DYPU2RRYjIYYsQ0yIiR0vbm4TQVhWQLiAIeRpvakEIBIxp1wFAXBzOMgTNnj1zaVOQ7ozpzrREXWg2kEcsA5GRBSGg8mLdVez4LaYATl45RxjK+LN6a5vI4/qgu7HG66bV/EdLGt79rzUl7jlyOgD4PFIsni4AE02oumE6fgHTtBTNcEsZRWEYIw0hFq0haPcY3LNn71FLFycWHVe/7x1CsFNA2Pl3d+28LOlv/3pDTM54jJp9klfkebuIqEkI1QR2A0hHDViE8HZvE4sQjhazomlGTsFRy9YECEJaAJmmIMAWP3gkQwcAABZdemX0hcedkIfdobleCEFVlIMbHwghWebZSUdb25Hn//XXiaIAnl+397yTF6cEuqKkQ18y4Tlc43fb1Xucfl6VMGt6duhuLv/+2Ut3Hux5bdPBjt64k+056NQlYEQqrBOX5RPOwud1RWPJB5cGI9lFywkh3f1xn1t0ooB003Sim0oxVTPK+Gn+/odLPvLpVKDV1m07jlq6uHvuMQ3737VX0PVbArNzpb968D1IS/zTO9IiKVVht5sduHegnXJY5xEFjJ16csmfLzPYLLWctZBnxSCcSYDv7IuUl5/cHpgDAYhGCuaUMYaPrpvyYPMso3IF8Py6vWccO8/phFqENduO1PpdMxr8NQFXbquv0jnQ2lHKbn4RnHfcDFnTD3dGDndFe6OyppuAgIRJpymcvz6lA8ptGJDSAYMnAckGxX6vSxToSF8dMDJ298V1g7qcaFqAZry6fk9twBPyu0MBt0cSnMaWTBWMpA4YELSODuicc/SADsiDI/1rPWKdRyJ2NWlao98iJiExVY8ouiPbDcvqS6j1HkmghsucnwvmWy6614dnB/6RYwjaHpgDqlX6H/vZv5W66z0fG+ZnJRQtNTufjJxz6ScmkAJYuWiahxo3ig3/V29pk0SuocbTUEOfobG58BACiecaQl4AoGlZPRHZMokbm8OJCs0iUwdkfilCiH2HUfqjsiBg3bAI0Lr7Ym09cQ4jUeCaav2Ndb7agMfnkTwSc/qNqA4YIFMH5A7/1YPvUQeyyC2sD3jsLl+EGnPovyYhbeFEXDPo72bPDKK0zYDqoRpgZO7fXB1w9NFLXn3lDVCVrP/dtSXvOyzfISEgoeiTvTb78+Nq/0n/Bs+v21sf9KT6txQy/tDSzV6pMURN/0UqIoz8WSLodfGNNZ6GkNclZPcQriwqtGBQUMbRCQEpBUA9hCbRdKOrN6bpptPOPiFre1u739p84Pk3dj65eusTa7ZWdiaMPL/O3/+Q+edW286TK/2doCwEwIyAJyAJPEYCRiKHXTx2C5xH4AMuujL1FtU0w7LmOGlHj9PPeB/7TUcVy7Jofj6LyBseyQfjlFVzRB4Xl/6vbTti1/v0BjIyhMcMjJFHojqgNuD0EM4CVtg9OEMH5H4lwzRT2XB0UGlZjk/YLQkXnb7snBMXnbhi9onLZx2/bNbKhdMaa31UYzBGTQd09vblSn/tEI388Yhck9+dOyZBELh5zkfHK8k1FgFxzYhR186IneeHZwdyo0IBAH+78/+N2GcwcnOABwZnjGEpgOfX7Q16JWcmVUj6r97a5hL5ljpfU41d73M8wBj5PWJjjYdmHQ/WQP5QaDR0QFzWFDUdZkBtyqYlClxznX9mc82SuU0nrZhz6tHzTlox+6RVc849aXFlBihGKTqgvqkx2/FLgCP9aatqjyQVKELu4nGtRxIyJgFxzeiNq/qITgJWrlyVpQPYJGBUMWjHzkmcA3zOBHAAUAXw/Lq9Hzx5UXHp75Rrrg24Wup83sElH8YYgcd1AU990CMKuenBaR1QLo4OyO3vF5O1zERzQghCsKnWz/PUV44g5OxIIIwQh5Ak8EGfq7ITYJQ+D0hhHU5Kfx7BRq+r0F3JIRRyCT5RSG3XTas3ocbVEZsEOF7fXH57V8keUUaZ6MbUKQIxjnBnHjefNtUryuvbjwS9UlMNNf7c98Toe7cqeizjqpkK46nAG/zI7x8aXHeOomp61skEvC7a7ZIxtuQO/2UV9nCheo36/+u8UtAlFOtTLXABl9CbUE1b5BMAYpreK6tekctNMi+XVMzP3DlzclPDhnlwRiE0Y9IXgXh+vD3AVAF4B56cQsP/l+1y/52xcGd7R7oXho3qKqdRFwQeSfC4BZpmaVqyqit0CDZiPyHnBkZf+3B0QC405DQDjGnXs8kcdDApufGm22ppDFiSbVvpDdnT000g38nXTLf6Zwa9xUN6OIS8IifxOD6QN6QZVm9crXWLRTRHKWRFfObqgAXDOTqjSA6w5rQCYoxEGGgR408eKQ9pJyaew0GBc0m81y34PaJhWn0RmRDA81jgkMBzooAFntN0ozcsAwgEDgs8FgVO5DlBwLKi94ZlhGBypYBFnuN5HIur/VGFw0gQkjuLAuYwCscUXbcM02rviR7ujNAU8Jxfnws1ZeqA4V0Z+v5UCJCDU+l6mIdlDF/6d3VH6NCBaBALYPrCoBQtfhAIgV/kAxIv22G8zo8bVfWoqvulyuPZcuP9Ha9vZm4wYzSwLEszWAjQCCmAItL/ma09fq+4eE7DyoXNdSFPx+Be2yWTtzWPBGZkFyKlBDgAaDnoLELupHl90XRvXzS491DP4Y6wqpnZgr6+LtLl1CKFT6zeeuFpR5V1ooPLwFmZhaAhhD7PQLN7NvIYD+nvoCimNhCahYE5a0Zzj39BkRwxBxfP1bilfllLDMzqNNOMqrpuWmKZLayLS386aR5cH4Ix4pgW0Y1J7ABYefL5XZ3JvibjCyoi/Vfv6PSLZHqtOK1G0JRYpdJ/hEEIOt3nA17JTufM7SBfl1p+YnXlgflZZeB4DntS5gJmBRoPtm3d8dh9f3nukbuduT+t7OaWWg+1QoS65h2beuV9L0Iw5BZ8Ip/66QgBYVmLqnrphoTW6ctTr0LSHwDwxouPZUYEMUYcWhNwMseAbnz9KTAx4IZIv3ILTbW04DMeyBGbCPAcqgm4mup8pkUiMcUeCxR8iCuYBzhEE2qmD8DnFjN7JDDG3vjz2H1/cZY3rHl81akXYYw9bheAYPNmquaXL0/+ypk6oH7vemcB2pMAr8j3JFTHDGS7go3ehOYTBdFuVJcXR9ZnsW/vPrsXmFgo3v+NFx9jtqDRQ9eNSR0DOnEoqADW7Oz0uKj0rw26h1PzZzSAELpEvrHW5yT990ezK0X704agMo9M5QKtQWMR0tMfzzxsKOBO70fAP59ff/k5+Qebxbnhnol1MSeN6X/wQH3DmsdPOfcqnucsk1ZpBhA6asAhVxnU712PIQxIgovHMdXuNmoPJHsTai2tDTfIt19E6GeiKKokicWzvXYv/LGz4EQEfe660islMApAQFzRzYxn88HnN115zgp2vUZMAaze0SkKuLHW29Lgd0ujWPC5YjBCfi99aDXNlFVDVvSsSUCmDihzEkC/rKabnb2xzLXvtXbPnzXQlX7CXY+pLP23btkOAXzs/uyYudeefcCuG+p4dal+cLpJApCcE2QqA0cTWLph7T2I9m5Jx4Oqel9C9Ym8gFGu3M8V+lkUkf5ZkwAWFTpSGKZl9+hOP++aMin7wq88+fxxtwVxBaMmPFJzvT/oc41UzawRBgIeY79HaqzzRhOKrjtpgcPVASRdZ1wHsPBQnXUFHjVuvOm2zOsci8vEIo8/+Nchc8Qu/vCN9lsc0U5rdsPBymD58qN4nps7e/pOXW8/0pUKQe7TXf2uaY11NbBkuf/Pe39XynfJ1QG/vetvbBIwTFTN0LShQ4Dauvpb6vOGnzCKKoDVOzpdItdQ660NuGlzmAkp/ymQFqcL+VyNtb64rEdiykiFBdMisyYpkh432WsQTmgI8HlFWaaxCaZp7tq5+7lH/l7K+x79xx/TmsD5dZLKwG7jPOAqAASYuh4IePv7afAoISQWTxxqbe/t6kzZ9Icp94sztXXAGFg4FU2fAjnAG19/auXJ5084BbB6R6fA49qgp7HWm1tuYaIBIRQFrj7kicRURTXsqM1hTwKSbYaKKpOJfVkmL5/40i3RaLSnt9c0LQ7jcF9PBamCKU2QnhbYct/+n625IVQTUUg0qhvsTXI8IvH+vHp9mEI/ryFoauuAUYUQ2qKDZYCNigJYvaMDIxTyu6Y1+gPegZj3iU0yKrTOG0uo3f1xk4btDEsH0NG/OYTQYV3ARoOrb7xp//5DxM7AoG17iW5Z1upn0qX6KiBHGSQzBDG1bEKnKZgdV26Z9q0zgiP9FMwZMIJYFi3JPoIVBKqcwTMACF0S31zvbxi/kp8VwHGoxu9uqFXjihZPaMO6Nwjt9jV0otck0IyTjPOv+WxHRxeEkHZcIwARDUA4TOlfSBlceNX1dhE/mJIjhJDnH70XjBpvvPhYVmoYmwRUhmVZWfn5pbPmib+ceuEnwURi5Xj7gdMKYPXOTkng6kLU+DMxI38KASGUJK6hxhuJKZpm2sH7FU4CaIJJCfHFjq2ANgfPuUyvP3XXyedfV/F3qU5O/9B1FiEzpzdpuq7rhq4bsXDfS0/cP0of98QDfwZjTm6dOKYDKk0BG3hCJ42ImrhugKTHZvWODrvgs3tag9/rFiaR9HfAkLYKaKrz+b0iT5N6KkkPJrQDcGmzS0JbBWza3XbwSG9fNBGJK7RVMc0aYDPTSmhurFu2aGZTQ8205voZLQ2JaHj0pP94kZUenGoawygLw7RM06LP9yQTUROU5AwAQpr0O63RXxf00PoKk4q7739pRI5jmjRMvJQ9ZVU/0hXuDu+SBM7jFjmMvG6xLuhZOKuxLpinkBGjCJ/88rea6gPOMsZoy87dqdakUwxmCBoW9vDKDtCbQsMsU1t54tkb33xhPBXAi3bB564I6DrcBqYwGRVC8xqCSqwwLiv6ke6IYdej0nQjElccDRryuxfNbhyVM5+63PDV2+prMnK+7GKfzz0yZfuoMEPQMNENk0zyNgCZbHzzhZUnnj2OOoBzpP/ExHBPdxY4jFySEPS5Gmt80xuDWY23ZtQlB90E0JaN/TF58+62zbvb+qNypj0H24WczWhHIR2g5zgPckkoWnt3JNcNRQgReOySWLHoMrjxptvqcop9Pp6T8TuVYBFBw4G2gNaHCtGbnDoAjBOcKTVMbwiuXDRt/ox6r1uc1AGOEEAO47qA9/Rj5y+d2/TujtZt+9qjCSVTqmNfYyEdMGR2SVzOL/0dFFVXNcMtDqvBSDWzNaPcWya3/+jn3735K2CqwAxBw4sBHXqUNkl1wMbxmASgoM+1bEHLkjlNvkku/TPBEDXW+M9536Krzz161aLp7sFdn7Avv6FmSPdvEelvJygaCTldPpoxJH/82fdkWUm9Lrvswj/944GsfW7/0c8BAF+++fbJdT2/cfv3vnH79+7M+ToAgC/e/N2EYtiFKujjxrzBpWOYFu3SOhXZOE7zAO6EZbPnz6inSb+jJv137D0ExgkCyPRat6UHDrX3JRSaIuAE6vCBOiXcXW6duOIByLph2jWqGGXw8zu+/ZVvfdtZ3rhx68qVR/3H93+S2spxVHP39fZMumtq1yIC27ftvPMfD+zd+17mplhc9biSpkII4csvvvLDl54cp9OcTBDbAFtxEsDEZ+N4zAM4DhhHOrraO6bI2D8LCKAk8vUhb1zWLKfGw0AjUakiHVAE0yK9kbiqT80RymjrACe14vW16xsaalObMkX/l2++/Rc/+i6YDJx05nkQJnXAtq07xcElhlLS/+UXX1nLRH/JEEIVwBRyAOdnjHUAkmiPpPGX/vbAPPXbDvqRiQO9AQavd7YMemPGpoGVGCGfR2qq89UFaY7AoA5iI/u9CYnLtLwMo1x+fse333n7Xcuy+uwCbbn84kffnSzSHwCw9uWn/+e7t72+eg0tXKPmtwoy6V8uJm3ROgUdAJk4on8sbUFcxR2xR5bBSmjQKRXKSqNvKXDuWRsEDtcGPBBC3TC6+kyT9pKgt5Hkr1Mi3YBUMAnI/8Gyog9MMBjlseb5R2m+Xm2DZf86tGCbPYj+5Y8njdzPYu3LT699+enPfyOjurXNyy++QreysX+ZmDRLf8raf7J0wLEnnb1+7VjMA6Zmxk2eWhECX+P31Id8bpeQrVDsP7cc6iv9eIU2yKpeSiYBIy9rnn800tPZ0dXb0dnb2d3X1dM/eaV/it/8z/ey1qx96Ukm/SvALthXLQ/X+gHpf+xJozsbqAoFkCocXRf01AbcPM+lpgiuQL29GRx4r8QkuGITJtpEmLUqHQZrnn9UllVZVmOxRDgSH86hGFMMw7CmQBuA4WiC0VAGxZrCTzEQhB6X2BDyxRJarxnXE7Tjo+SSZGdzSZawIXZSNVOvglnqqNLSXGc7fCjXf/X2eFxubKiZNaNJFIS16zYvXzqvoT5UynF03RjBs7Isq72jp6OrLxyJqaquqpphmglZMWlpGlqbIOmpqpYR6jhAx1ZVfH3Xj45FqIoUgFM4Ouh31yfUjvZ2R/pTu3MwFOkv3f5TDM0w7HRiRoVc+tHP7NuXXSXt0HvhQ+/td5bffKP35FPeV+QIbtcQXb1ysQhZv2Hnlm17h5Qued1RTY21559zUsqXtmf3rqwdFDk5xmAMB12fUkUgJgjVpQAggE+9tI7eTHxAwEC2CARAQlr+gkHlYxiWOqIDz2rjX/f+/tKPfqb4Pq+/9kaRrdNaGir4XCUWljizsgE80eL79uyu4I2MsqsAsUs20lSRAnjwKVqM9+RVc+bPn9/eHWnt6O+Pyp29EUCSkwC7Q8iwPsIwre37OkfsjKuSDClMAKA9WwbG3ek/Mxq50LBeyyLJfxE83NYxraXsknzDsd6kapcmY5LhECFqjMqwK/UyFTAZFMCr6/IX3Hfo6Oq96oLTwHhI/2985nLnzxq/Z860upisbtrVuttO1PQHQ/bgf1h3mGVZHDdpOqlNTDLMLHTB/gvmbE0GiTp/YUxVAsZOcYVK5K5ulNAGrgCpzqlO63nGKDGFc4DHEe6BJ9dcdcGpI3KsA4eTg99ZLTS05kBbV+bWjq60nf2BJ1dnvXf0VIIj+i8+74yFM9MpphyHfJzo84ibd5ojZP6hWITQXrOMYRDu60/IiSF3a25pgdCeE9CkgUxtAQ63dZZlCCKEGMMQLimNtXXL9iF3fuZff6/4g6qcChRAW1d/S31wdE5nCs0AHnhyjS2CTx2+6C8k8R2+9IkLnRpY/+/PNOsnk1yVMHytkFf0Z7L/ULsno07c8EMM7DEpUwDD4qUn7z/rgquH1AFH2oqF7aolqJAUFiHxuExKC9/KnSNGw31bt0RK/zhGZbAA69EAHlz/CADgh79+oAId8MCTrxUaM3/5kxc55Q5p6BYhlkXsLtz2LNm24RJCjbYEEAQhKqAVnDPM/KP0M3zwqdeKi35nobsv/ubGnYZKKxBIvvpB/QNwtn0sq10Y5vIY0E5aOZdEO771w9+WeJ6MvJSiA4pQEypj3KcbZiIxqHVE6QqAxha7XSVpfQieZcP/YXDJFVfnv66Q+mCuvSh/bFjWDGCiNYUfd5Ii7JYvXPXDXz9QylTAFvqZULvJv33qQ3lLShDayyV7PfXj2XZTCxLTslI23n/71MVOKLVpJrXFL//yeM6n0zPMIuuEs8z9hUR/8uwhcKT/iMwAUtxxy+eYDhgOFudasXJecrww4BmGEFrEMfgkfyy6nrp/LUQ3AV3XCSGiKHAYNzbWZlr1MheyOHS4c+fug6UogGQF5wyCAd/pJ69KuQGK8O9f+lyJ353BGDPSY9hbvnCVMxXIUgM5Ej/JF667wBbTeZ6KFHnXp3ZHCCKUdJk6jx9CyDAt27ZL3/tv13+I+vpoeCWNAIMA/vquJ3IPmKkVIITTZ0z/8PknDCn6U/urYnKWILIYg4lEZmfg9D0zsJD6r1Pvj/5rd45L7TnIlTx4IQvOdh9Xpv/pnUnfyOx+jEkJt+e9tvmzW1J/O1MBAMD9j78CUbaJ4yufuoj2wk0Ox+hd71hvhn8eqYPQlvR2V/rUiCwZ9mF/4pc+eRGyVY5lEdu2RJ+9pFYg5Cs3XGxa5JEXN27eeXD5opnFRf8wyWv/yYRNAobD4vnJbqCV3UVN9vC/RLCjAEogIyY1iUmxsH3HFufHv/wtmwQwJhpUiu0ZKIPjaIKrP3RWc733//74L2flFz9xAUya6emf/FiFOWY+aQLPZcdrQxr/YVGnK/nSJ5O+ZWeHo+Y2lSX6fW4hntBGo8gI0wGTAowRteFUFAdky/+qK1AzAXnw+U1XnrNivM9i8jFoGOtoAoQFQsCXP3WJSa0xgyfR4x3ikppvYAgBSp69XYAzaRp+9KVNjgJ45a1tM5trSjnmgkb3wXZdoa3mWEv3CcEZF39irBVApTe2aRGTVX+aAGhK5SED1UyeqWt3bz9CkMNIFDiB53gOO0OkcZf+hbB9CckYo+Zab2842humhd5KpLHWWxtwSbQp5ghABjrOO9xxC3P9TXSc+7uy9xLLGk4OAWPkmKDSaYKT/77ftGPcuviOBI5Dr1SfXtAntdT7a4OeUTobpgMmOM4AorL3WoSUXnb0x79kwcGjRaU/YLUzRrWA/vfP2QGdNS7h+g+fO1qfV85kRRK4hhoPhOBIN5tFTmIPcMUghFAJXtyxqTvNqAxWgmUCKYCf3fWUrmkYF3MX98paplaosTNyR1ElFAZB6Jb4+tBozQCYN3giOwAcBeAEklaGpusjejqMSvCWXwacMWIKIGuAj4qK/rz0ylrmcYJi8sRuuPa8MfidEIQucQgPcFYacLmwiKAJC4KwlDjOQqg0fIAxPjhpwACAgJf29sgKGrQL9KY9NFz5cmnKw42sYWcQydqIA8li9p+mHb+fmcyZl37VsOzoujv/9vTYqAFWxK1qgQgWn60WR1OZAhh/vG4xU+7npRp6yo+RAvjJnY+kkngL4RJ5WueHetggpi+qqxXaSs+CCJqmpahDG0/77X0cNTBmE4JRgk0CJqADwJkBcNwYzQBYOtjoQDt+j9KhpzbcSI36B4b5dKSPILIQrA+5MUIcRhgn/yWExBKaaVkIQUU1jnRHkzVaSElqYCwnBKME0wETEQhpNYhKa4HrupHqTMAYFxAcZPZhlA43AgYfKvGpBhZ5Gk+NMaSVe4jRXOd3xv50EmAnExMCPC7ByadXVCMSU02LpMp80+Kgdm0vChl6QjB51QBj4niAnQBybqjCHkWqQdhRQKwc0HiCEFo4J13PhlE6XLmiH0JarkfVTJ6jct0p5sxxKOCVvG4BYxpQEUvopmH4vaLzqKT/Twg/MNeWRK6pzqcZZsJ2/xIAFNvuDyE0TMswLeA4XfMNzBw18MeH1kA1Nnw1cM/DyVYEop/2scnPyI3v2CRgtCmrEBAF0iDCzE6TZaEbhmXRfmQVvJdRIld/7FOqXDC7kxYQY4ysAkgoGoLwV397JrXGseZzGHndQm+/FfK7HHGPMRR4HPBKbklwBvsAyP0RGl2dfVBbW9hLREC4pcGv6YasGAhR53A0rtK2ARDKqp5Q9XBExph6Dmi5Rafbag5E9Gq6oemmZRHNMO999PXjl80GowC2y5SO1NGYDphQ2DMAXHE/OLsaUEn14ByYG2DEGY4Lp8opqACc6pvJZUgnWS6Rk0Re5HEo4FJUvaXB70h/hBCHqAmI42ilTicpY6jiKnSrzy2YFu9104pDlkX8HtEZgcmqHpf1aExxibwkCqpmaDotQmrX/CnQJ8guBFfZJTBdtaZ95CKBxDyHVZ3V/JqCHuDMckBWRbeQaVqmZbEyUqNKkeF/VtxnJkGfa9TOaMqbgJz+2rZ9XxI5SeACPpffI/Ic9rqFnr54TcDtVGZ2enZUUCkIUucbRA3H1AbdAIDWQ63OegGAAAAt8+my001M1w1V02VZ3bvjXcOw6HQg9ajSMyCWSTIbcrvdVJh3h+N5P3fOtKSpJxqjqb+3fPL0mKwpqvG3J94pdKopy9VIwSYBE8QBMKglQEVQBTBys0NGBVRcyOtDlyW7jN14ZbHus5d89MvVOANQNN0t8TyHQz6Xxy343KJb4qlfF0NJ4ISidaGPO+sSOdqbu37GjGmZfzo+35Toz3MaNEkHIiQIPOeShOCJp21553VNN1MGGZ62HMMuEWqZGfmOTy7333wcae8GQzEaRkamAyZcReiKsIbXU54xfLwelgY80gpAFDhCSEPIKwpc0C+5RFoWVPfMtQAwAJi1dI5S+KA+F8gr/Wn7vUOHU8szZkwrIvozoTMMjAQaSAoaarxxWZMHsm/yJnDR6YE9RYDOVMGZLjhrKnrMWQ7h1C8IOpBTWlFBUFYOaNyAEIZ81ITAqIBiN72saM31voZaj98jrt2p6J65ufvEEjSGpzIylUEmglTw54QQNNX5mut9dUWLd9omqQEjlvOqVPQ7SAOlKUYWVih0gkAjFiouBwRpVmNZ72BlQUcQjGgtr5E8YjXBFTKUb9xJK0K73UE6yQXg1KPcIJ6nu1YAAJD/AJVjxml/10K4XAGXC9SGwJyM83Q444RFBXXS4M42Ds53399RZDJjvwuCgNcFQD8YBZghKAUiZmfn0Oa4IanAYaOommXqkJRmyRnkKoYulygnEh3trCDE+IAxZhP0ihmn8KkMm0yeyIuMNU5cEBlyzcChTJMc6Q5XchqF4Tjs86RLTTGmHsQuUVXJOyGoqfELQnkj0P/93u2VfBYjHzxXuf+GMUgBZD0CNPg+tcYRspk7kHyvAmSm2Dgx/cnI/oFluuB8XGrrwKen/k0J+jxrBg7VE45H42rB08j5M30yhU+ex0jgSyoWJom0qHW5MEPQuGPZaegVvJHDuCboZ/JnHCnx2QQAnHnxDaN8LpMPzgmuT8XAZf3p5GCl+q1nmlKgHf+QuWe6AGjqz7TdHVpWcucsWzwsvJCXgQ9I75faX1WNjp5okSc568gDSWn5841T2PluozvEYIYgAMBLj91z2cc+C8YDuwpJ+QoAQr/PI0mVaH3GSDEZqwA999i9BIDpR526e+2j43smXGarXyoJB4dDD/orJ1IaFdqaJeftFo2pnUemclbOESyLtPdEBgWD5pA/FtRZVfiUaF5byR7CLYf6ls0IlbgzY4JgJ/OWrQAQhA11wcq9x4yRQBqqk8co8dxj96aWpx11Rs72bH9SpqxPRGiQ5LhLf0cBpP+AReTlUFI7/+A635+jNJbuj8p9kVHp6ShQBVDSWTtKsDIdwCYB9EcMx4OBUWzNVswJbPefKAuOw16vm5mfxxeXyDeXW/2pTB659xe5Kx0h7rB77cNgclJ09jRoLgAmMnZx6ciQg7hKvgSkvSaGqmyRhOqJYaQEMR0wLpiWFbNzwsuFwzR/oNx3MQ/weKUBf+jSK2+8Kj1U7+qLPfLCuyN1GrllYicFE2D2mgrFqbCWDzX+dPRElBIaM1XwCRjB3IoiuED1YNdANMiWQ33lfxSDugHG/iooipaQhwgFzovtGpp8z/xUAkIolRmCNUrASSj9R6spfOkkHW80CIculTjQziKhaAmlpCjsCo7OYVx6msn0puALL26eNavy0uRsEvDwPb878YxhlfhuO3QADNWurnIyfMVhNfbaa51lvfvNl58chXOqXgQej5cPYGownjMAQoii6qpm0PrPilaBF845iG5Yoxel4/QyK3HnF15MthYYziSARYW++crTkjS8Oo7WRCzOw6T/iOMSeb7kMFDGqM8AECypMoRFyP7Dve3dsSyrjMn5gj7XyatmzmwOJsXo1oPFD0WIXamttKlDBX2beA6XWAkudQoHDrQNZxLAAAC88gz1qp3xwcsURa7wgljmKM4DyoSJ/lFCEviynug/PvBKphuAMYQCWHbUzCGv0f59mTI6HRMtKwWVwZqttgOd89I3EPqEuyR+6byG9VsPJyLy86/SrS0N/kWz6ob89GQb4tKoYJrgcQtOO/shydzN0QEVh4QyQ5DDK888PLF0QEXJYkz6jx5i4SJdrBnAcBUAgtq2bXvAyOEaSJn54HHNNPFn3b7UJlnRVW3QtH00zPrJhOGS/fUQgpDPXeLO9TVeOV4b6e0pfR7w+KubLzp9ed5NTAdk6gDbVStPunkAE/2jjYs5AIYHV5cTdt0bpiFx05tCrR15qr+NBpZlNtR4t+/rNE2axoUxF/AK86YHCQACHhSeoZnDqMlDaMZ/XNZ6I4narG9dwDaEEHLbSuvx1duLHxshuGRO497tW7LWF58EFJL+jFxzEACgQufwOOkAJv1Hn4kSAjQ1ZwAYWapux7YDsOdQn2kaC2fVuSRxy+62gFeivhcCWhqD67YcXDav0eUSD7T10nYxCNb4C9Zz7uyN1oV8q9fv9bioYDVNA2N6DjV+V084LesxRiZt8pVNSh/0RZTt70VuvPyU5AYI47L2xwdeKdQTmBCg6kYkpvRFEnHFOHmlU0t0gAJDfIxgiS4mjyRMa6B+C5eAZC2ZUsQmASPLm688PcJHZExmIACCMFHcPJOUIazbdg4sxSVytJyD3RfeLQkBn8sl8h43FeJ+j+jUAPJ7RKdrmKzq1HMKLedFAEhVaJAEmnvMc1jMSN+g8ziY2ebRSc0vITPTrvg/hBnILvQWS6gd3dGOnkgsoXb2Rk374HUBDwJwX1tfW0+0N5rHwkB7IJeQZgIhmD+zPuR366563VXPBRpTmw4caCseDnTR6csff3Vzoa0sIojBKPzcQaFARg5jZBSAI5Kh7Qud3hhwmqzU1yTtJ7S+G6AdWpx0GFGgpeUgpEoiM3IG2rYUZ5l2XgKgud4X8KWNOW6J74sMkr+maZVmeR9KAxBgWFZ/JNHWFe6NxDWduhksQto60yWjrz1vxeVnLb3glIW57/Z7JL5o50sHnsNzp9dS1YgN5+WvSeemHzjQVsIXYTAY5UHlTMm9H1KlQP/4wCvsQqcoqD+b6oNHutpSkrUu6HEM9FTQ85zTlt1RD+JANT4EoUuiEwVot5LPPFpKHzjr64KeuJyOEYormk5Fc/q35DAqRfIWwz49VTPCMbm7j35C5sadBzpnNCVN8zOOHbAj/S0dxZ8qAjGkFoIAtNQHGmp8zh2Zymbw16Qdwk+s2XrhqUcVnwQwbzCDURYYodFo1j1JqaxzfakTKIHHcV1PSXU65M+XBU+nCCUM3LnBFfZlxQCAZOZbeVwCxuUUn8s38I/LWl8kEYkpek7P7mhcyVIJeT+hlFLjCKHFcxodLSjwWFHT5UgzdUBxrvjgiQ898ybTAQxGyUBJpL1gRrsS3KSQ4xVTqgKAAETiKkbI4xJH5IMHO1ezw6sryu5LH0SxB/59EVmmVR7zxG4TAPa19sxo8AMA3n7yXwCA4y64NGsfhGBqclMEv1dqrvM7CinglXQjbpp5PrH4JECVR6WOKYMxVaF2adfQE/RMHvvXgx+69EowFeX4yPcEfmvzPgCkzMze9u4ogrCxlto6hg8ezdpJ7T2RSFyxzUoF2X84qQAcHDWQicBxrqGqACEIF89u8A80jGys8SYUPZZQUwlDpRuC2CSAwSgdjJGrcBZYdUrzCijjCoZjSlmJkLs7OafR6oJGMswM3nLpiySGrCykFm0d4+QADxllLAjczOYaOFCJqCHkicQVVTMcb3NZhiBnElDEGcBgMFJgjCroBf/Yvx5M/QsA+NBlV1f5JaVm98yOuFnLmRQJzcyM4ExhmXaf1SINGos04RootKuZUt6Xxx087qiZG3Yc3LD94Jp39tz/zPp/PPW21+ve/l7n9vc6K6srl4VbEor3gYEQzGoO1YXSOWV+j9gQ8hbJTnxizdYiB7zigyc6OiDvVhYSymCk4FAZVRoZhUADDdbTspoQUqRFdl7RGo3nKfvjkgRqyi8q5QttSsnQrL7wI8sza3es23bwwZd2//PVfQ+v3j9449AOAIzQwlkNmdFKPIdDfpfPk605MqNCi+iAIT0BTAcwGA4cN+qduqsBuyXkQNt3xzuabt6b7w1xWcsKvdVNs7Uj3JDjG4j2dSqqbpgWaEwnRmUiCpysqPlPa+Az0r/wgA4o2r+9crI0HoRAGsrCWBv0NA3+1hACt8jX+N19ESWuaJknWqIhyAkJZYYgBqMokMM4ZXotkWs+eh3N+7ng+Clv2S8dW8YVaNgL8wnaI92RTN8pVQkJLWobvpcunZ9ZPG7JnGRJ50LwhZM4dN1MDsDtE3JUUurcBv3so6EN7E/0uovFO3EYLV/Q4s6JieI4VBd090VlTTd0I7/FrLg32KGQDmBF4hgMaAcKVtY/KsUff3lHiS0hc3sCTxnlUbQaKEobdmRFO2VVnjI7sqIFfa4Tl88klpUp/XNrQS9dOn//voMuSZgzd6ZTQXrlwuaX1tEEYIy55QuaNu9ud+oC0Rpqe7tOP3ZwuZ6RAEIoChzPcXR4XhQ8UAauEH6vNLulJvcOxAj6PWJTjTee0Pqjiu0FLy8iqHhxCKYDGAyIoFA0UTS3FvSVH/6YaVoj5TZ4JF+b+BKZUMpj7CppbNu2xykHPbh/QBItJ1drxJutYoy8LjHkd9UEvRt2Hi6yJ4QQF3UxQQhmt9QU0hAiz9UFPZG4Kqu02VnmphINQaefuPzVN5khiMHID4K0CAScnD17HxmG8hhx/TGEAiCApBJ+Cy1n7JxeNeTOKTCi/t49B/qdhRTb9/bOaakdETXgDPwDXinkc7skfkZTaOOutoJubnt/pwZeoR3ckrBkTlNujNC9j7xW/EziclofFJkE+AcMS8wQxGAUiAFFjXWB0i8OhLR22ciPKyek/ihdSRRTABahI9xMMUnsAnB9Ubm9K+KUUkAI2uFDhOew3yPVBKiE1XSzP0oLL6vaELH2WUI/k5GK+UkN/H12ZTdaixTjGr+7p0AGnHOvUAVQ+JiNNb6gP3uO+ebmgyqX5450FImjbzgfMKLdpeiAC8869omX1hf5XswZwKgSrv7Yp1Q5lrmm3FphZ158w9XXfnzyC/8RUBJZuqE8ExAhQNPNcFR2sqgwRhghw7BLH9gXlxCiakZCpk3ecyvwlIVbEoavryWRD3pdQT8tXp0p0hfPaXxtQ7ofGYLIIimHLaQTzML2H4zg0nnNfL4klC9deYJpkWhCPdQR2bKvsy8i03jawaqM89Vl6oBCQDPppWARQQxGFqLAVVAGrmrkfxm6obyLaFkkGlfiskoIQQhJAm83ZE9updU3dTMcUyJxRVb04WRjuSWhNugp3WqXa8+BEHrdYlOtr77Gm0zpoi0HktSHvJJd6B9CKIl8S0N65A6hXbO68GeLAjetIZB7NxECErGomogJQJ9dL52ypH7xNG9QAm7OdOOBF0/cPPEHQ6WkBVx27gnOAksNYzCyHm3nCS2zEhxMxYAyylYAtKmWpodpcU1arF8SObfEp+3ghKYKxxJqJK4Mc/iPEGyq8/OlaXgCgGGaWbYmDqOAV2qpD4T8bsE2+zikhDaCcN6MeoyR3yM11fqXzWtKvRfaw/+CCgCCY4+a7SiP7C2pd2CMec7vdzc1BPweMT3zcJoi5GRTF9IBekYXXKYDGFVLlv1H4LBL5CsYzk8YN/DkVACWZUXialzRCCEcRj63KAzOlVV1IyarimoM0/gT9Lr8XqmUCZtlEUXVe8OJrE+sC3mb6wNet+gM/FNkThPmtNTUBb3N9X7HPZAK6cG061nByyLyeOGshmIZKAOmIZ7DtV6hIeShJavsnjh6rI++EmE9EXYJJV15pzgEg8FIIfCV2H/sPiVMA2RTqg+A2jcUPRyTDcOEEHpcgtctZpY8syxiWrRaEBgGHEaiwDfW+YesFUqcCYes9oUT4Zxujg0hL/URFT2GwHMNNXQ356OWzm16c+MeGgBaJL8E0h5hXk/+BLEXXngZVEohb3BmcQgWEcRgAACp3bnwEC0v11x7Xd56ZQyuLpCuZebQG6ZCZ8XiGQCArbt3pdbX+EGNn+YAH+6S+8NhkQ8kFYB9YXO9neWCIPS4xYYan9MiuCCEfpaarviv535uIemftY7mktir9hzsmDO9/s2NdgCo/cr7+TTJyysVGn189+sfdhShczb9UXndtsPv7jwSjat5rwsnASPSNaQOcMpEO8tMBzCqHKdNU/EqjbnYgYq0W+2onddkpZJEMKdZi60PxFlNeYbDR7pjh9p6jl46vchB9h7smjujLt8WC4DkiH5/a8/MadPzDPwTal8kEU3QSkP5j17gh84OK8r4A9tjCir9C99bbonOewpt3b3nkH0gDMzkWZ130oJzTph3qCO8ZsOBPa29mRMmB85fn6kD8qLKiWBdQ393Z/HdGIxqACIatVGuIIf569owyowCGnsErGS+RE5xi1pDCE5vEDTdmN0SuuHyU260Xx8+/7hYLLFkdsOS2Q2FjpZ139QFPOkYpmSLAtriMe9IAUJQG/A4eqIYA9LfUQn79x+WI5EZIX5GiPfxlhsZWa9SIoLOzqiKwbzBjGqGeoClshWAXemYqYA8oMfeOPD4W4efWFesNEIljPJkK1VI7oU3tg3zI+fU8XPqeDPR6wQg5daYfXz1dielwOMuVh2oEBxGIZ+rscbrcfFDzkHz6gBVTsyfly7EVLxSEIMxdYEuiS8rBSwNk//54H76YycxjDgRldQCY5l6ey8hROC5BQvnAdPasLP1tQ37owkl4HWdcez8Sxe0aIr29Os73tnRGo0rAABDz26wzvF1a7YrEIJTFifbJZZLbzjbtZsJRmh2S3rsXKLwL55ZBiGgNWYLDP9p1FNFdx6tLC1w9XZ1IFUzVN3Muhf9wVCkv6/4QVbOb9yz973i+7DcYMbUBkLgEoUKioDe/7e7U+WgGXl9ANDxr9BFBHivnJSWltXW3rdxV1ssoYoCv2hWw9xptbSWgo5qA+6Qz6VEaFIrP9guQjNpkWmalmlab26JggJ0b+1OR0wGPbUBjyjY/QlsLjr3FNMiW3a3hmPywfa+SExRdVPVdIz505faQaIpcV7y/VB8RwRpgZG8mzBCNQE3hPDtdza//U6eAfg3P39VsSMj6HMLjTXeaELrjSRyu8Zn6oC83mBVTpx10oqX1m5y/mTeYEYVghESHA9kRdx371333XtX5poPldYSMm9pnWHWdJsgcA/8+e/O0lXXf2TwJrsjY1zdvOdIdz9NxHAJ3MYt2w8ePCAJXFzWjnRHYoli7VwyKyEPcRJ4cOlNW7K/uvYdI8PfS8NPAaCzCVNZu3mQUvnjQ8kSbE5LSADAsUtpCNOQM4AlSxbJqu6UioMAcK+/VyhOIOCVtm3bWeQr/OQ3D+hqsSlLWeTVAR5+0PVkJSIY1QZGyUd1InDJyFXlvOzii6mQ8WcHZBbC5/Omls85dcXIRAGlNAFVBld8AADwj/uf6e6Pd/REnWAbOcI7FdBMi8QVTSlQ6M0O0kW6bpYYFZosvTk4ytMwLE03IzG5N5LQSj7U0J81+M/Vb7yT+WehDICLTlvy6LOvgwnAZeee8PCzbxXXAcwQxJhiPPLQ/VTgXnG1KHDODKC4LTerGcCZF98AJjYPP/poka2XXXyxoxu8ttzP+uLPr9k0HGWQPwz0gYeeczK/+qOyI/3p0HjgkzXdiCc008yT80XrKHDYTgorNSOMEGLaVYMgpMu6bThKqHp/RI7JdOAPRo+ByAAIgWkSuwJ0/vvq4nNPBgAUVwOzFy12GlVDCMIxNaFodlAp7VwKIYzJaiSuFqmPxLmAEe4oPgnILA5RBKYDGFNVDWRxwXkfLLT/v3+hJPPOxGdI9QAAmD6tsTJlwKXG1nZ9t3RhH8M0wzFZVgZ7dwlNdIrLmqzSghC5h+M5GkWZG/A+VPcV2oAFIWiYlqLqCUWPy2o4pozUwD9F3oGDXayZmukXzW2a1hBavT6d+5ZXDcyZHhzWORCaI7Zxd/vWfZ2xBL2MqS/JBRqH1AGZeWHMEMRgFOLHv86jMKYeDxdQD7/4TXLhsosv/uQ11KKTF2pQS4pFSCv72wt0TVzWonHVGcgjmE6jUDQ9Eqf14HKPhRDiOc4wyysIAZ32DhzSDSr947IWl7UhGwnkqhDafb6Ez8r6O7fP8GnHLhSgM9DODm1y6OgYInWrFBa2eBr9ze3d0dauCO0cmZoZ1NDqhkW6hqlywhWolcPpHZghiMHIy5NPP8OuzMOPPlpkDkHrlCVDaZLWEKoQdINWdXZavmCadw0dg49hWmrcSNj14HKPRVO0ITAKdEIvBES0uIdlkUiMFpqWVV03zNJLSXMc9kiCYZqGPESb3wkFRtDnESEEGlV7RiJrplV0EnD2ypbHX7UVQNF4OGYIYkx5mIgfJhmxN04gqB29E02o0TgdlmJa9J9zTEOEEEXTYwk1r/Wfw0jgMLXgl1kPzq7rAaMJtT8qx2RN00uV/hBCtyTUh7y1AXeJxaHIRMoT4e2Kqk213pDf5cRNpfDb84AiqWEfvujUTOn/+Ooto3yyDAZjCoLyFf03+u3YGwhp3p0oJHtp2bWX85d6diIpqdGkHOv/wHtpm4FoXJFVjTYXK83uz3E46HM11fnrQ15+cFXqYp9V4bbRAtt9C5pqvV6XkDWaz9QBucQi4VKOf8ctnxvuKTIYjGpRAASYlhWOKTGZGnkEHnvdolN0gdAiN5aRUegmE45DHIdpbbYyh/8QQssisqqXO/BvCHmb6/wBr1Ri35gJOANwlI4o4Lqgpz7oEQursbyTgItOW5b5Z6FJANMBDAajEIOkJwFAUZNF/xGEXpfoloRUZGShxC4EochzTuQ+KB9br9Bi0qXszA8M/OtCXknM6Ec2OWcAztXzuoTGWm/QJ2EMyzIElagDGAwGIy+D8m9Nkw7/ZZU6JEWRC/hcpdRdou15IFR1o4KoTUJIiQnDKYt/auA/ZYp7cxwK+V0Ndu/isgxBNGXvfBqZWhw2CWAwGEMoABr6qWiRmGKalm2bdnlcQnqEXUBKIwRFHht29hYYBZwxvjPwb7Yt/smBP5w6BQFpiQuBa6jx1AXcRTRu3kmAHItk/skMQQwGo3wFQGh+b39UVjRaG8ctCQGvK1UZza6lnV94ijwHaeZXJcP/4jjN2V0iXx/yOgN/2o2LG9bAf8LOGWivMbfYWOsNeO0+xuVMArIMQQwGgwFK4/8DBbHJzhwYn5YAAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "assistant": "With the retaining hook released, the axe’s clear corridor is toward the FRONT camera; no other rack item needs rotation.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 1 Forward", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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iVoP0BwC8vPG9uqPPiPeHIYSCyw0tQiyTWBaxgCBJvoDPHwh5fN54tOudt17s6WiFJon19WmyIsdj8aisqbosy92d7V/4wa/BhARznNMJ2CK0EJA9IWBMUKiuzlAAyG7iloJJ/0kzA/jspz6a+efv/nQvmBK8/tRdJ59/HZhCfOD6L+7fuolKf7cvGSxJx8wQcxzPi5qi9PYc8Hi9EMJgqL6tdX/TzMWz5y86uGc3Qtjj8UgCBxFSFbn98MHPfvcXv7v9y+P9hbLR1AQh1INtyxIrkaA+bcbEhAy2vrKg3ZECjaP0n93gA5NqEtAfiYPqYPqSE+RoXHJ7OZdb1WTMcYZp8Jzo8QX9wVp/ICgIUkPD9LqGGbX1LRhzoivU3dGOEJ61cJEgcDwihqbJ0aipqKosR/p7wQTjizffYVmmaer20NIixFQVebxPilEQAsiqBen4ziwkgZmyK4QbX+l/xy2f+9YPfwsmEjv2ti2e11LN0p8C4UtPPn7u5VdE+/s6jxxS1YTPX+f2+F979klF1RBCl338U32dRxLRiGkYqmEaNDiKxKNh0SWF6kJut//IgT00vdaC0ECxaPgz3/l/v/+vfxvvbwX+7Zb/Hlg0IIS6rhJCTNMwTdMyza/dRqNXf/q9/xjfk2TkknfIf+hI94zmOna5hsP4a84JqAOqWvTbtG57s2XR8c/886Erb/g0Qkgzdb+vdu+rj86s82D/gm1bNz3459+ef+U1kd4eWVF1OzrUgeexaRp9vZ2Busa+7nZiAmLpKoSGpnz8ptvj/ZF//vn/xk/oDwIjbBk09IdYwDLT/QAcNeDAlMEEAQ42+sdlBQDgdgmHjnQDAGY010kCp2ismlPZwCVzkvmu42v8mQg64LIrr0ndZYvntSya3Zgr+g91Ri4/59i8b59iPgAAwLQlJwJifeIbNyOM/+9rX2io8R2/ZBoWPYdl39atmwxDP++KD0fC/XIiDiAyLQMj7PF5LJMaV0KhYKQ/nEjEaOtdt9ciJOT3TZs2Q9O0X337a+Ml9DPZte9QV+dhQoBlGaFQ7dErlhXZWXL7f/AfXxrRc2SUwUc/cX3KBORIfwBAfY0/tcOM5jpHAbTUB3PfzsJAx3MGMPFN/w6ZY4y80r/aOLz9zeZFx/31p/9DDD3Wd8QjcTvfa182t3FhELhPOH7ta68/89B9H7j0SkKIqik8z4uiJEiSoaqqnlDkeFPLtP7+vnAkjDAHTLMvEjOM9wiGH/nGt32+0O//6ysje7ZfvPkODIceA27ftt1Z4Nx+QmjSAvUDQ7T+7XcBAMced3TedymJSGpmwKYFY0+u9M/i0JHu+to8op8x0U1AE8oQlAo0Y9Lf4cjOt1MXp+PIIYvMCMeVFQtbFrRg8azTXn9j/XOPPHTy+88WREHT9UQijjlcW1sboRWjQSIRCwTrOMGlG6YgiLIcl5W44HLJ8Zgiy1d++T8e/MUPRtasP6TQT/7KhFxQh55XkCxrofqGcF8PIXZWMACOGnAopAyYjWhcyBX9Xb2RzEkAYyIqgNKH/xNEBzCK0NV+iG+ZsX7rIVXT507Tly2etWHTrrUvvQAh/MBlV8iKbOmqpqmJRIwQKx6JSK64y+NF0OpoP9Q0fabb74+H+2xjkQU58LF//57k8vzxO+n6ekV4/qm7D9z70RvuGRS39pmv3laq0Afgglq76sOAsZ8QwgsCgtjj9ZuWdVmzZBLKY+2avVtJyuCmW7//s+/fyu6Z0abQwJ8xoRXAZDH+pHByQ1mUcRHa2g5Nnz5r064jhm7Nn96QmD9t7/4O09Ce/eeDV974GY83sHvLO9QJoJsYYtPQEcdJmAsGAv2d7ZZl2T1YMC+KLsTd8+OC4jtX9B+4l95Ld37MSukAR/oriipJYl6hnxrs0yIVOei6xnG8aZqCIBFiQog5uhu8bJpkKwKgmdZTnbrz1rzKwElNqkId8MErPkcI6Nr5MkbAMAyB5wghhkV7KhBC3AJUdCBxBEFgWkDRTEKAKNDkLd0kigF4RGSdzrcIANhuxGCYJoCYEDBn1bkQgmceYgPBKjMBTZBJACsIWQqtrQfs/0JNM4+a1cCBujUvvVQzY/HDf/nTJdd9ghdEOZ7QNV3wugTJFe7r5kTR56tR5IQcjWiG5Q2GvMHgA//3nbJEfxaZY/9cC895tbZhv+jBqe+aADkel1xuDlFZ5KynSoBmvAEXwpdPwzQDmgDDsp7s0AFdnVYGxxy7ylmoHh1QM2PFglqzc9vL9mWya2nbgpwWSYQ4I8KKtlo2TILRQKsFW6ECMLjzpp2CQedk9nEIAfveeRYQsHjhfMMzc8+7L47DN6wy0EQb/t9xy+dG7YwYI0nb4YP72np37T+yvI4798wzjlq6wjTMf/3lL9PmLAiG6rz+oMvnwwKXkKOGrgMIXV4f5rmmWbN3bdw4pPR//qm77/yYlVf03/kxK1P6d3d1+nxeAMB5NfCCWnRBHbqoHnNDSX9HTxCaD6wSy4Kw4IOAIOQQlDh8WYt0cZN4UaNwQQNHAEEIbXh3U2q3m279PqgCCKAFQTLX2GY1p7DS4JV2+m7Wm7NCLegKW4tk72jR0j/lnltXT3+5b2GMlgKYdMYfRgW0th442BFes/ngnCBuRL0rl6+CtrnFMHVPIIAQ7mlv5yAfDfd0th8AAM5fdeI7r7xycMtrxQ9bSPSnuPHiBc5Cd1cnAOA0MXFhnT3kt0foJWKZpqFrhFjOv0PuT4tdI8hjJNjKwJFP1aYDIDEIhjoAdDAPQbJ+Eg38tSDRMTAwNAEiwFQBoDkgVIybFrBonqCtPgxgaRARDpoImBCYdKZgWpCW96FHshxXPoLQop4YRnWZgMbFEHT+ZdcCRGevBAx9z33qStoNpr+fhrczAAAHDh2YOX3Wq1sPnbCw2SX0SiuOOnLwoJxQTMN0mqupCi3TiAj21dT/4bYvFL9od36spKL8m//0cVB3uyP9TxcrLAOpKbKhaQQCVU4ItAUEX+IbHR1zSRP/SDutJLHh3U2rjl5RLbagnHE51buE/mvR5gp2eb18PTMGHcMe3VNtTZwJRFaaV3qfclm6YEbe9Vtee8gpIbf0pMsrOOwUBpoqHcJQDZ3BiqUfGPKdP/31j5L3g/1Lnfv+Y5Yv+cAIDv/HTAekFIAEkwoAAnDG8Qvz7vyB0/NHg4wSk+V+nT59Fo9RfU1g1ikXhHt7EYKWpXu9IVWRVTmuy9rMpcvu/vG3hi/6Uyz/1N0/vPO1iqU/AOCvO3tVnSLwvN/nvWaWp9wjEAIe6aA6gAaqD+gAAMAU1gE105bMrUeKamIIBI4O2y3DQrSiKpENM+DlFM3iMTR1A3HYMgFGSNVMO0UcGZYFiGVZFsDYMCyeQ9GEIWCqey1Ic31NC+gGnRKIIrKkxvkL5qY+98TF9XnPJysM9Iz3LS9y8k4N0cnyTI0NnKzSn2cQBKzb8LSzePyq85yF//vtT1I62Rb6A5J/QE8/++I7//urH537/mN++ZO/TCLjz/mXXwsmMNvW/nNsPmiYT0Vr64H6xhlnXPTRw3v20scZYZT0qRKek2YevfxP//214p7ecj9x858+fro4rNTrhJIwTdN2ZSuqxgNQtgKA0J4HHKGRQlNgHuAMk4tz+jW3UXONMw0igEPARHQBEWoc4xCyfbx06mdP/+iOA3W2IYKIBtoCZ8ZATXbOv4QQTBUAoKFAA0ce8B0zRhf47LN351s/IOvT87Ck/E9J/swuDanQapphv37zSCmA0Z4EfP5LX6ZVzAjp7E9oujkBZwCTiLu3o0O7drTu3imIiBdchFhyIiogoWHW/G9dtiivpqlM9KdY/qm7X7/nucree8+BRFfbIdMweFEyNMXt89301c87m3iOiz73yMScB5Qio0eV06+5bV4IGgadriEIRB4Yljng8sXIFuIIWZphYNo+jtbaNiwqJKiqoDE/hmkRWoXJri1uUfc77TFBezNDpBlJUwTmIPG1zJs3ezRmAGwSMKQCyJT6A/8lIOB3hSPyoKH/wI6WRSCicnMEpf9o64Bf//wHjo9q/aY9As919EZTwQhMAVTAB2/40VlXXNV1+HAi0i/wyDB0AfFNcxfcfHF+Vbr2V5eCYXPSF/9VaNOzf0hOnu5r1U1DJ4RaH+wBKL2dZVlOxMOWaemaKoiS5HJhQfzKl25wFAAAwNEB9x5UPjpTGvI0CACOPwAA8ItbrwZTl9OvuW1ugDbkwrYHmMO0nQ71AlDhTkf6FgEI0nJQdoNQlDQX2A8WNQBRmU9jrqyBpAE6ARgYRhq2S9iybEkdaJ43b87wFcATL6ZT2WsCnlOOX+osM0NQIQWQK/1BrvEnT4wXICMu/UdPAfz+Fz9ct2k3XaIxCBbH4Y6eaMoTVUQByKo+4idz8QdOAJOfMz/2XY7nz7j4ili4z9J1t1f0eIO7XnlUDEz/1X9eM+KivxQdkOLj37pLkWXLMhDCkiQJohiLRkI1NZjjsJ3+ZeiaIitury8SDuuaRu3UALjcnju/85ERPNUpwOnX3LawDuu6SaW7HcFpX1U7m4tgezoNEKJRQXZ0pxOZReUHIfYMABg0xJMqAJp5YUt/027KRu1F9A964WlKnuluWjJ/Wupzj16cXi6iAGIJp1FwfmoCaSvfKccvZTqgaBRQhiIAIDn8T62syEU/gSKCktKfQr8JLWhcGva97FyGtDa0b2WYqS3pI2Fvt3Nc6Jizs3dnQ+3ivMd89Lm3wJgwqprm5XtuP/Pj33/p4ftPPu9CX41/+pw5v//2t45fMmNmSPnm/z76k69fPOKiv3TuviPtLbjsiz9HCezzB3SNNq0UBNHQNVESOZ7v7mwXBBFjbOi65HIz6Z87iCYASRzkIbInz5YjuJMjJwKoP5cAhJET3WOZzlOQjPenEwSI7YE/sOwYMboPnT3QPZznBGP6X4RgPCOtrHQG7NTp3IKUbdp5QInT1w6A19Ztq/h2msIKYLDUdxbyGn/AWAz/R08H+OedoSiK3RPKxJapHH4nc2sR7ebIc/teKuSkSq63bzP7NrQXGmqS5QoGtEfK3JTcP7UmrWDsBfp4pbbZN/iAvqGHtjcmj+PslZWSM5aa5mvXfeB//vL0m88/ff3Xvnb/7+60TGvHe+1ekVsxHX/jxw9d4cnrbRoua391aSmTgBSXX0FV0f33PeQPhGpq6hDGpin29nR97rNJJeGYgCavjB5N6C2X0EzFsIdNNMiHBn1iSA0+FoGaCXlq9UnKfZoKQLfTqg/2RAA6ZiL7SEQzAYcIQpZhEpNAiLBJAGdbhngMiVSJE9i++W3xP/j5TC0nnw4CPv2VksqQVJUJqKDxZ0ABjKnxJ4vh6ICf3v1kd19CUXWE0LYN6xNR1Q48hk6+IbUDcFjgMMdzFIwtXU/EZTtMgUY2BOtC8Ujc7a1BGCGECSC6RQ+lxOOAEJeXpqECYi5xHypyDq0H3p4+6zgw2mT8RKl7PbUmtY+tQgZp+6SDf+A/dnZ+shG3HeftRHbAzDlNoTiNX/zjJZfkufFSOtv47Z8esCzzmIXNS+Y0bWyVr6h9cmS/buiC3y0us6HF3S/udxZmzWz57R/+XlNXf2j/3q9/7bOpHVIK4MSZaBLK6NzhhP2vM3xL/prEGS44P7ezJikc7VFH6s5J3UGpcfSPfv/4wlorrtHULTszDmi67hYQj6CqJy+XcxOJPK0PYZfoTw4dBQ5YAGq6nRJm52ETQASOqLqpGoDnsG7SRDBAgIuHmmfGsvmNo2QC+vS/MemfJHuwI/CcpqfK6mZK/4xVk4dv/OyBeDS67u1trftbI72dup2UpBmabXC07PIj1DXoOAbt2Sy2FcNAeRK6hhaLpwqDOq/oCgAte+ZKtyBOTCRiECKOw7t8PkdDII6+6HaEsK4gTti3b9+pC0feeTDE9KPwGmc5Z02uQB94s/OPM5NPpf9n3A1peQPAl685y5E4Dbvv+M9T6ZrvvEr6Y8pxi6a/Ej2vQ3Ov+dZvIxYgAnB5wG9/2FDxd31U/kjjy+t37q4tMunJZev27oHq1kcOHekwBLduwbfeXJ/aweNxOwvde8bgJ0uZSDL+68zzBoS1LYiTC/YvBYvI6GzsvZMdtZydBmawmXdB+tdPzW8zbof0NNY+Qc0gCECRA5pJXcEihwQOYQg1I2kfcEYHA4fI+ILUY5CaqQ7YfBDkaSUm+g4IgYigahC9vJyQ8mDSP68CSN57mm5kDP+p9C9wk47p8L8CQ9BNP/yzLKvbN246cvCgkpCJqRtqwrn1DCtdON7JXaRuQIu6AZyJqnP70hAF55a2LEwNBXQHJ4zErh5P38YRZ3qLNAgT4V7nMcGYOFoDACgJkmXqkb7eRAK9snp1TW2wu1cReDrh5TjuhKOLRa1NOAoqCPrwKq0v9h3eaapxSAwMTA6ThobkkPC/Tt9/+8tWJK68e+tL3RYwewD0AySACACXf6PTi8BdPy5PDTwqU98shNDrEm0XIhWYadmVYzFL1nejfkvnlGxxSggWJcEfFCIRZ0CaMVx2JGza22PHqyQDVzKGz/SAznJ6HJ0MZE/L6OR9lXnBBgRjhm0ic2taoaXEcWaRC8ecPvj6F2bwRwy101CHsGi8pmFaJrQgMQnBhKZ3YTukn/bXtAwzYxRFZ8ZOrJ09eMJ0V2RHAg3oLrqzYdFyTPQKWqaF6OAKQ+orHgWY9M+rAJLXWuA5Nd1XM7myqO93jKR/uTrgs9/+07bNuzsPH1biMWIZpq6agJZytKW/SezMFHrP2ZKcflMafUYj2Wg6CrVg0omBHdlGrUQ0X5rWNUkenI5cnBkDoIEN9siFyntC4xoGLCe2dwxCGNMsCE3TNBQFBlzSzDpc55ZUXaM6AFrP3Pk3iZCoSdwcNADUCICc2KHDlhlBYFqzZoVkxUIAtnfHzzuPlqCYsKhtr/Qd3mVqiaT0R4RH4GC/5eZhnYde9++eeeCjJ+wM90ZJwIdEgKcDl50qpgIQVcGHb+r8x88aShf9Di6RD/pc9NpnibDcQazNAXNa56HWlEi2m8Jr8XjMDkx0hC7sjlhxTW6qddtJLumjpHw/yZlhcovt18z92OxTGOQOynNqkyfniRZ6oDc9oZU+qdiHg6oo5a3hltJ2mTtaBFKXcWof25VsAfvgkIaVV3RRXtt00FYq2ba7Gr/nT39gJaaLmoCyjD/p4X9h32+W9J8I/Pv//PVga3j/nt2H9++zDN3QEjQizQ5AJoSORuiyOZC6YhnOc09HMclhm/3Y27MAuyah6cygBw3RMgaLtPpJcsprF7qyH3Mn2XHgLjRNk454VI0c6ejSjJis0qmE20U1gNf+sEaemlARhLJhikjD0Jw5Y5okYJdAOGCoquF3T+hu13rHa32tO00tDi0dAYtKfwwETC25QRdsCeBZ8/jjyWvhaL/pD9DplQhEBOYD+noLgHYRKAlw3Zc67/plMR3wqPyRw32cz+0KuiXD0AxdSyT0lzsO6vTXxAZtRCObpg5pMTHTifjXDd00LGBBzHHQ521pmebyev21zQd3bA/Wh+jdL0q6ovCY/lJvvbmxceZsOREJ+L1bOg71Hmg1dB1iYpv0aPnPZJwLFDiOFyU3x4kQEp7nOJ7neYHnBcwJxywSCn+DySPmi4IQQNQFQK03mevtxyJbbjtuo9xyoeknKcNfiyA0be+UM0975KH7Ursffeuw+kj3RysvGTK14RxJ5kj/XOMPHf4XNv5k/T0GVR+KTwJuuuPP+/ft27phm+ZYeyzTouZE27Fk17BFgE4tLV3jeYHjBU50O1N4ty/g9vp1TaFTVyo76K1tmaZFVFrnkPYxMXVdt6cEdn14Z2JrzxCS2IrSUQ0WsLDjY3C8CJBONXTD2t/W1xtVZNXkML3LLUICwZmaLBu6SnTdMnTdIIalm4DsX7vPtCyBh4Y9FNJU89Hnfzja1/aH372l9J1vnf3VXl03IDQR/sG6PxhqzJH+PLI4DHgMXAIMuOC0IJ41mwOXelt/0266vUB30yLxOo0F1OzSj7yjPwmIJ8A93efaMy5oGlBTsaIkbE1s6YbeF4n0htcAAAUe1wW8kFoakr8E1d0Q0d/FkTR2uZnkFI6eKSQEGoDM8vv37dotSiLR9ZjVh7vsrCWrVo1HBI5/ac0zlkXCG3qwwLdDWNNQd7ink0ojmvOUDGJxxqnUykFSdnkLDQg3mgUL0epXID0vi9CAAsTTkAEIMOYFCWPORBgSYgVr/JiTdFVXEwlMcG8k9umPfBCMNzf/5x2l7BZsWUKvAkfnxE7otD22sm062YN6JxTBniYlJz9piZEKyrQsO0GYpgPYDyrNL3N2LTUyuxSS9wKjmBM4TwhoRjhQzjvH0vhTig74t+/9dduGje/t2UWTUez4TlqnyqRjZ16SeJGzLIsX3G5vgKPZ/+q65/6cdYTLrrxmyESwF9/e21DjPXHF/J5wvC+SiMaVuKwpmrF5V49FY9qo39fHGxg6+gEkTJeuaQD1ENDf0X6koz2r6tl7xb/sDfegY085b/1rydJMo8ott5eqY7i72/u0pPQnHPzWx7dSQS6CH/+sjkPJsb9fgtMCaO5MDnzI+63X12qJ9YTDQFOgkiARn9oHDoRAu60GTAKIAgwFvHHTPb+8OzkJ+OXeCwkn2uYGy8LI6w95/TV0DmfolmmYpmHa+t1WznaNevsXp0rXiS6xsTW0vQThgf37MKL/c7nchgpOPOV9dbXBh198wzTNk9+3AoAlTz/xLORIPNIPIIzEwvFo1Db3Oxa/TNe37d/B1N1PRwnIcfYjGv1Oj88JdgoIohFkHITolmNeH/qCDjRe7l506/Bl9OhC2wFQrUd/Cysp3J2h0MAOKd80rQ7gLND1yH45ZaHTMWVOOJA9WrPTCpxfzla6I6kAGMUUQHL4D7KH//2Fh/+71qfLoE8EPnf7HzavW3/wvT12VA+VEYCOvLDHG3T7ApZpvPvKP0o/WpHRQmONz++VlsxtpJML3VQ1PS5r0YT6/hOsz3zp5iLHnH1SMge9XMZG+peOcE97j6rrVPrTsu5U9PMACNSwc8t/d+Mg+O0t9X4JtgTQ/Ok8OM8DhDnrtm+3Yv00LZTnCE/9A+YBTyIGZK89XYoDEgMgARIZzvmjj1m+Qm4b5qkqs6enlv/+zLZI3xHLtOoamqZNb3ZWQlqvJim5zrvwXADAC8+9iDjOH2w6/SOrpnvLiwIS9+eLA9ZKUAATSsSXgN0JADpuMW5Qi68BUskpTp5XMrwo7Q1IFn0bOB7CdsqAbQWisUK2SbWSE3POzi5NUcn7qw9O4LmswH9H+tt/pFaOv/GnyCTga3fc89ba12N9vYahEGJhHgdrG1wu75tPZ4/xh4/PQ/O5IO1sBzkRSSIf8NkOw6EMvN5g2cUmJyCiLf01AC0q/ZEj+qn0lwB0A+QHWABf/WmXCMDOe6eDc9zAPwuAmyUeknA3MVVgykCPW0qExLxWjw+IIg2ZolWANaQqdnwteNnzTZ6rJAs0F+m91tTy9Yv8ANCY8fvakkPLV17fqCXiwL7/U5z9gfc7YaDrN++b1zQiZzGlSIZJ2S/OvnROqYdkWE9avGe8JUtaDG4BZlLbbNLlizEyjUF1Z8ol5He7JCHgde051E37DA8gTMLkvrGBXpeswH+H/nBy+D9xjD+FdMC+PTv7ujsIsWoamhGH1j833IzTIsI8b+s7x6L5+lN3nXx+wQLFRzomfQ+ZzrNvBH9OZ9AkHek3fQ+KALqo9Ockej+JdmHlcz7a+jw4G4AvA3DG49eBmvVvGJ39RInARD/0eIHLDSU3FATaiQEiWjRA170ez0vkE6PtKj3p+KM6Ontjcdk0DYQQJ7pOOPEY5aUnN7mT8j4ha3W1wZXLFoDuVL0QRgpMzfQYGjqiDXRs35edUEN9LQSALxy7s8SL9X/rFtIy0nQeQd9o+9mhQWgMKK0bV3J/nkxOWDbLJfEtDaFQwL3vUHdfVNZ0QxL4ZQua72e/YT6yhv9J44/zV9Z/R9z488TqLReetmyYB7nkxv+IhftaZk5/7h8/B6NPqpZIubz7zqDG5ZNS+ucD/oyqBPTz72FXUvq7AQgB0AjAN8ELPxnYTYCWbqpWTztJhKHbA11uIkqQFwDmqFK1LGRZZ227U6irVYkCQukykCPLU1FpJcYulwghNA0naove39JZF5wAwF/+ct+8eXODQTeEYOH8GUwB5AUhaJqEw/Rf21xjcQMdlStW3nZveCppMKZRGHZEaCUHqwvRtHyPJJywbNaCGfUH2/v2tnbPbAqtXJQ/kZgxKPA/Jf3t4b+9aqgrNMzh/3B0wB23fG6bNu/un34TjCUFcy6nMo88ctdJ/+/VIjuYX7nNBMD7h+95bOnfAEATAAEAvgNADwD/D/xpxYL5b214m5g6iSlWrB+KIkgqALsXCCFBQdy4v6e2V+FQR0zf/rn3rRrZr9A1/+ja2tDKIx3UkCXwosCblok53jLMRFxOB4yH/C4Xrf/c0d7ZeOL74ZsvjuxpTHboeN9O0RoI47HDn1KU82hkjqNoqFxGxlyyhfAw4DGur/HVBD1zptV6XKIkMBNQflI/XpbXpKTh/3Ck/xOrt2QtVMC3rp8PxpbKhv+Tne4HPlbKbr2fvi1gS/9mAIJ2xEcfAB0AnAU+9exHv9lcE+CgCS0NmipIREBfJ+lqAx2tsKvNq0Q/7upddMwZ3f0x3dBmHX3clnkrnyY1j4WLhNWXQdd82sbnpVfXOX+6XVIsGjVNA9hhRandrr7q4nVvb0i1DanKn3pInKoNTuokfaVFRPmXa1A9Ksea5FQGdT5oeEAAOIRqAx4m/YuAco0//eFEXu/NCEr/1dva/bV1YNg8cu8zYHR4Zd2u/BuqUijccE+pZdG2fPq25z99G02ysqV/OwBdAIQBWBTc8db8/10+b66bgzwwMM0F0DmiS8iaWR/yP/GLjs6evr7IjMUntsWsfe917t31HnZxLYvnPhx33bU/PUKvWPo7ZeycNU6pu2SGnh0onJf29k7wvvcP56OnHk6eu1MJJWdT+SJ78FucenVOGRbaVJQxZqUgMkz/ScYyimo4hqBtmx9euvwyMFZU7AOoKn7+6ds+8YfvddjSvx+AuB3vv+TTh1/832aylNoQro983TTM951w6unPXvxfD9Jhfn84SrPDNO2EE05dv3Xzlje6BEkMNTbOXrigs6v73iNwbt3Qnblymb/E6cFANm7Zu3TRTOe27ursshPGCO92GeqgO/2qKy964MHHzzzjpNSatXr2o1EWNLZ0ijI4EZhSVgGfLJuDExhqpeysLI5zTEi2cE6RGv6PnvFn9bZ2ZyFzElCxIWj0JgEFYNK/JP766ds6bemfsKW/bteGtxv+Ed0gP4U//q76k9OfvVjRyNdOUVaddkkqAygWT5yyapllaGoiARHNC164bP6OjUnrTWXSP90PNiP+3KLDTFy8t1F7e+dJp6WVAYNW0LO7fDmjoKxRelnR95k70/y6gRj+ZIXqYSiAtq5+9kuVCBoU+D9A1rUfDenvMCKGoG2bHwYjSpFbr2pH/w0v/LHct2z49G1xu9wbrc4DgBUDp3/8iG6SuGxF4lZcJqpONIPomSmfhKiqrqo6z/MQWuHOzvaDre2Hj2BEXn8rXbG5FHw1tc4RAQCbt+1bvGCGs76rowsAsHDxAssybVGW+eF0AHrVlRe9/MrazEMdc/wx5X73KQ2109MUYIt2cKTtHy3LME2nSkoZR7Fx7HC0fIbdTtJpD2YX8hjNktCMAVCm9HeG//mD/0ef4UwCRlwHMEaE7k/flpT+cUAStOTD+y/viCSsmEwU3VEAQDXgqtgDqbcYphlPyKces0KLxTRFbtu7b9e6t5EuKxEquMsiHI4U2Upr1wxUdU1J/1za2ztFaaCbW9VDAPAIhMdEpC8gccAt8pLAizyHMS5rfAQh5DDmOSwKvCRgAQMe0SpSPCIup0QUY/QpnMadO/wnQw//9aJGwNXb2mkhlcE/7YgYgkaWYvdeFWeYVzAJAADIn77NSiSlP1Hp69prulSNaDod+6sG7SOoW+nUX0WOJWRF1fTzzj7zjGOP1qIRLRrBxBAwevWVF8oc/oNwOBoOR1PDf4eW6S12JTiT1rSx+0AwSgQhZNgh+hYBBgG6BVTd1HSD1uajZd3KMgHRkb9hWjot7moZdjkg5wAm7a/AnMBjAcoz/C9k/IFDS38+1zE0wOpt7QKPvW4hN9F/+Dpg7CYBbGBSPsZXbiOyrQAUAFRC1AQd+9vSX7eo9NdoQYmBoEBCFFWPJxTDoKL5nNNPveiiC9//wQtPPevc0884u4JPP3g4PXVw7D+0gYGi2lXBaRmzRCIRj8cz33LRhedkWYGYJ8DBaZ2AMeIxbf/rFPfBSbcArZJX+u9iN9ez30PzQOyoTQhooVy7pKiZMSZgjB5opEa4xaW/XXIO1wbc0+r9Aa+UVUl8pJwBjIk5CaC31X/fRr0BKiFKAiiJr1+1wRn7axbSAA/dyQG7g2lZsbgcTyjD8QRKA3ab3OG/3Z5Bo8mnGFM/sE0iQYNNZVmRZSVPPCjDJtq+NazCuMlFNRjToGIAVceKjhQdqQZSzTIUgGYizaRvsRewaoCEDhI6kk1OQ94DW19il3wM4AYN/4sYf4oO/59eu+PsExcV+ZjXdnTUBd0zGgN+r4gxklVaRLPQ011ZVKgzCRiRkNCqzPYtlYYX/lioLERxbPuPDJQE/VeVv33xC9986Fydtgau2cUNRNoQqwVptOKMrtUl9Om8KvGVjAQPgaRGOXi4K8v9m0jIjm4glkmDjAa6QTo+A0FIpp5ddOE5jz/xfGY86EmnnRR+6amyT2XKDWQP9oJ5jbQdvFMr26DJwABjQLvmlaOveVtZIAR0gx6N1oom1LlMnUack0nCGHXSGrti448j/Ysbf1wi11zra6zxBr1SY8hTG3Bzo2AIGqmIICb9RwP4u9tS0t95aYS3pNBu/pTkHsSqN5JuW9MiUVWPaXoFc4BDDfNSy1nDf4+P3saKoiqKSvv+IGyZNBNYtylyTDYJSBFt32bXbaYme+ehp40BKnpqnLcMFIGwi8HaMUAHthWrO8IY4RlAMva/otn2kGP/l9+m1QEjCfBOT/c7G0fXm/rIvc9c8tHxb6405RnGJCAB6EsGmgKUhCkE9wqnOpsgyXbGqoYZUXS/KAh5K84XxRnj5w7/k+HnhNqrqRSj/QBor7e8B8mdBDAc9rfLCxv4gW6Ppj0XoDWBnL57JWL31nGOYBeAgI42IToKsus8ZnBD+36LRv6cfQKV/sWt/w6qVIKVXzky/PTg4RuCmAloSP5T//V/818o98Lif/3ePOtKqgDkBFQSv1n1X+duTwb20GryiAcwPS80CZBN4nNJzTl5KsV5p6uHNi8M+pYMHv6bhBp5EEI8Tzs2+oL1c+YtMRVZUdTUPpnLWbS3dzaddX5o0+vlOcCnYk4SRBxVnzgpvqkH2On7mMzkLe0g0KKVwGlvGdpRkupkZL8fTTmr2QRmYGyVoQF2vV2q9Fd0GhFWRPo/s6VHc9WhYLO/ZfbM5uDCWXUzmoOSyBWaLnKhZKumCRUVysjld68KlTmE8UsPAiUB1QRUZKQqG9Y8lrExe/wo01K1ZU8VL6+hb+mLpH25nfbwX9P0SDTe3ROWZccEZClywtD1WCyRSMhJW9Ngk9Ppp52YFQ7EoM6SI9uT8T72XIoqgErc9XQSZvfvdHp4Od03weFdb7KLPD79AEbW+LMrIiyYM72l3n/eKYsCcxYBQMdxbz/3gGGYnb3xPQe7O3vjBjUfDv74+vpIV9kpP1lWoFEqEHSoM1LlqQAjYguCigxVGeuKS8uOt+nig/V6X+pPiRp/SG8ie7cScGUN/7v7oqZpxuKyXQgOeD0uQIimyMSyurr73G5JUanz2e/z0g6/VBQV8ASsOJm8UUaNaNqEbCqyu11f1MyZWjI5PtkTr7JEeac5sAUwD/rU/I3zvv39n3771q8N96QZOdCpXCZ0+A+HHv4/vXbH+4cy/nS0tXrdQo2kvr1B8+/elUoAwxgF/a7GWl9c1iJx1S7/N+gs/Bk6YFwMQUPcxcxHPDwdQKW/qkiKHJIErehlbvC6xIEwzbIgdruqzOF/JBo3Dd00LYSgLKPtuw44o0/TNPr6I/GETItPABKLxQMB2leQ4+xw9Kot/TEkEHOYk4HOWdjO6XUquZXlrbEvMMDEok1gDAgExHUemFjNxqc8qALjz9Nrd5x2zILij8bqHZ08h2qD7qY6n9clZKb/Qgglgauv8dSFPG6Jt6t05aQF1NdXbAga8/Jw1U65tiC4d5OoySGR1/56a+Z6ktEU3u7pgeq8Ulnq1qQlZegra/i/c2+rrmmmHa1iWURVVdOyEIRyPMbxgqyo4XCkvz8cicb7+2NtbR2H2zpicdpNkBBy+qknZFqB2ts7IasRDUC4fcfWNsMlUhMCx0OEaTJXWbYEe+QHEYZ2TyDgEvCRxLAKrzIqIEdjD/W8Pb12x/HL5gh2EG+h4f/qHZ0cRkGfq6nO5wT+Z38qgj632Fzvb6jxukR+lIZZI5sbnLT/2Cagfz5fXmGyKU/DC38sSw0EOazflWwvPNgNALr4ZAx4rUf0CpwTtJMepjjLOYKGENIbV4+EE+1R+RUjkDX8J4OrFJim1VwfWDhvpojMmc31Fi1kRnTdVGkGshyNJcKR2KHWI4dajzhqoMyLkXGqU5rwke3bD6u0Q6RBv2pGD/ZSccpwWCbBCLWGcc/BzDDBsqmvZeFDZYPyDP8L1/x5eu2OZQume11CceMPtbG6hWmN/vqQhyvQ2IHjUG3APb0xUBN02TkBozIJ2LbvOTDiTy6zChSgRB3Q1DzTvPs/i++DIfQKfL+s9claWNGiqh5X9UhqWdNlzVB0UzNM3aT1ZLpiys7O8L7e6L6eSNbwf8fuA4V+YWHAAmqXoLSc4pS6biQSSjSaiETira3tu3fvP/aYFVmuYPi+9+umpRmmcw6aYTrnE1P1hGbImhFVdPs8K1Uek4Ro526aUA1pQ99yy3c6ZT9tMxA52G+GjwyraTaT/sPKBE5Kf1gs7evptTvouMzvymwCmsvqHZ0Cj+trvM11PrfEF2qfAiEURa426FY0I57Q+qOK02t0hJ0B8RgYcViU6FA6oLhXwLrn9iGvMYSgMyb3xBWEaJgJRhBDqNk2HOz8aa/BCDllRY5EEmGFhvNL85ZmDf/z/X5Ff17qGKAljk3TVFWV5zlh9+bseNCmhkP9cd2kJYwxQnRnu4iublrYtmlqpoUgrWvfPNVLnLy7bcfxyxfaUaAEWGUMjohF38IBS4VWvHt/ZZ/e1Rupr5mqjvaxoFSnjSP9Tz16PscNYfxBCPq9YlOt1+sWUVFdgSAUBK4u5Gmo8dp9O4e4e8qaBwxnEsCcwKNnEcq7PmUFSrkBTItEFL1P1nrjandc6YzKRyJyd0yhyzG6fDicOBROHOyLvddLXxFb+gMAsob/23fuK/20Bw1ibf+waZqevo79vdFGPZY1CZh34SWH+mKt9jkc7I+3heOt4URHVDkSSRyOJDpjsr08rGaWk4V1m2n/VExru5XxLojoW+hvtLtC6Z+CDf+HNQMYZPwpXPXhuKNmiwIe0vjjcfGNtb6Q30VTA4cCQeiRhMZabySuaoZpGNlRoZmTgIrZtu+5pXM/AEYI1hWyRDJlfefZN5buJOjiQ6lg0IzuFE6t0IHlgQ3YHmg7q3OH//5Ef8QdhOncoiGH/5ZT0pLYmcmeaE86BJXPngTYxc9z3RF5lqY8r72z4+RVi8tyA/CYZmK/vWU3GBPWPPGXUy/8JJjMfOXWH9F+q9+/eQSPiQZF/hQ2/ixbMN3nTtbJKsTqHZ2SwDXW+prrfJLIl2grxxhSd3Gt10ODhfLsULEzYNTCgZgTYOzKiA4Jn9GHZMmCmZmbDr/7jqMDnKL/pTdzdqS/L9abtZ4lhRXh9Q07aI5nyYgifmfbGEn/Sc1Xbv1RSvSPrPQfbAIq8HQ8vXbHwjnNdQG301W1sPGnAyEY9LumNQQCXinZgrUEqDNA4OpDnvqQR+BH2BCUojJvcF6Y+J84QJoshh3v0cDwnwYXZln/HR1gGUWyDtJYNLspj/T3J8KZf7a3d15w/XXD/wpTiVfeLuPZfHMjtSozxkXu5ygAW6rlDv+fXrtj1rSGphovjdUtfiCEfG6xqdZXE3DltnwZ6r3Q6xEba71Bn2R/ULGIoLKgk4ABP/BwdICaGc7BNMAokOsGKAVkFw+w7BDPrOF/JhCAWoWKb2IaqVfmDsQyUq+80p8xYSFFuxBORq77wi2jLffz1ALKK/1p0F6tNxXKWcT365L4pnpfQ61X4HEFIpLDqMbvbqj1uiVBj/Xqsb6sV+bOlReLrlQHJJT04DE3Wokx4qSyAYqDaYdCKv4HW/87U/YfBwQBh5BfjjgvZ2VSEwwIfX8inHoV+jh/IpyVFMYmAeNOPOPZnAJc94Vb7vr1D8fs44aw2a1aPNNDQzmLSf8X11E90R8F/R3tW/Imco/EkNnl4mQ5PWorPSqUVgf6zBXDifO0CDnU3h8Kep0/aaGYArz+1F0nn8/MAmMHh6BBK0kWG/47P6ia4aBM6QDGZIcQcKCtd86Mxqz1bV39LfWTLy/surGV/ukZwOzG7OH/8+v2HjV/esgnDTdNt+S360JNnheffnH+BjBsKpgE0OIBmemgzAQ0ypRuBdJNS9XN4sN/p+XISE3b2CRgQmFZVl/EbmcyVSw/YwxXWPq3hLxiypdbaPj/zKZOJNXWBdzNtOYP75Z4SeAkkRd5LPKY5zDPIZoaY9eLJYQ4aQR2J2iHjDGaZrR1Rw92RI50R9t7Y6pmJ5g7Oww8vZy/wYh0lj0J+P1DmZOAcqNCO3ujsUS6TLxTt5Yx4mxY89iqUz+UNxi0EIZFEITFh/+jjSRJ4/jpVY5pErui8BSR/neN7fC/mAloer1P0Yo1yQMAPLup0+8Wj17YdNTcBp9byLSNdHR2Ex1oOijLPufhwJJpniXTaElYTTfbuqN7Wnt7I/Igs3uoJtLXO6o6ABbxAFNYKvAoYt9EToV5K1kspsjOCPJzl2UP/9evG80TBP5Y78svv3bmmclOlu+9d/Dyz3/mn7/6zah+KCMvhp2zPTUuzl1jLv3pWDzv8P/9JyzIjOQpNPz3i6QlyIVcQI5Furp6Ojq7U6/hnxktJhpwN9R4aMXBwZ/vD9VUcMBHfv9QZWeiDFYApYeTM8oFIYQxFgTe5/XkL8k/GDzew/8Ul3/x8+N9CtUILfA9+YMyrhtz03+K/Mm6LlqYAfjcUhHpv3ZHe41Paq7z+dyDqj2PFBBCt8g1hjw1fhefU1EupQPKSw3L0AG5zgC/JzmXz7yhLIso6qCZUGZdScbI4nG7/D5P0E/riMybNycWmok5QRBEnhc4XsAcjzgeYvoCmAOYaz7xtKwjtL77LkHcMF8gYyH5wskFaH+uX469/PJrqQ99772Djg5wzi11nhw/RO4kY5joevk1SCcY142f9M9jAnp+3d4PvG/hkAJ9zdY2v0eaVu+vD7oL1fscPhijgFdqrPHGZD0cy1MtroI6cZm2oGxDEIR+jxSJK5lfXtWMrBhQQsiQWRGMylj99D8uvfazpmlACDU5dtLxy1VtSePhraZFDMsyLGrwNSxi2sv6/OW1A6FZ9NfctkM5mFv4cxAQALfAQUgLySFE+5DYPU2RRYjIYYsQ0yIiR0vbm4TQVhWQLiAIeRpvakEIBIxp1wFAXBzOMgTNnj1zaVOQ7ozpzrREXWg2kEcsA5GRBSGg8mLdVez4LaYATl45RxjK+LN6a5vI4/qgu7HG66bV/EdLGt79rzUl7jlyOgD4PFIsni4AE02oumE6fgHTtBTNcEsZRWEYIw0hFq0haPcY3LNn71FLFycWHVe/7x1CsFNA2Pl3d+28LOlv/3pDTM54jJp9klfkebuIqEkI1QR2A0hHDViE8HZvE4sQjhazomlGTsFRy9YECEJaAJmmIMAWP3gkQwcAABZdemX0hcedkIfdobleCEFVlIMbHwghWebZSUdb25Hn//XXiaIAnl+397yTF6cEuqKkQ18y4Tlc43fb1Xucfl6VMGt6duhuLv/+2Ut3Hux5bdPBjt64k+056NQlYEQqrBOX5RPOwud1RWPJB5cGI9lFywkh3f1xn1t0ooB003Sim0oxVTPK+Gn+/odLPvLpVKDV1m07jlq6uHvuMQ3737VX0PVbArNzpb968D1IS/zTO9IiKVVht5sduHegnXJY5xEFjJ16csmfLzPYLLWctZBnxSCcSYDv7IuUl5/cHpgDAYhGCuaUMYaPrpvyYPMso3IF8Py6vWccO8/phFqENduO1PpdMxr8NQFXbquv0jnQ2lHKbn4RnHfcDFnTD3dGDndFe6OyppuAgIRJpymcvz6lA8ptGJDSAYMnAckGxX6vSxToSF8dMDJ298V1g7qcaFqAZry6fk9twBPyu0MBt0cSnMaWTBWMpA4YELSODuicc/SADsiDI/1rPWKdRyJ2NWlao98iJiExVY8ouiPbDcvqS6j1HkmghsucnwvmWy6614dnB/6RYwjaHpgDqlX6H/vZv5W66z0fG+ZnJRQtNTufjJxz6ScmkAJYuWiahxo3ig3/V29pk0SuocbTUEOfobG58BACiecaQl4AoGlZPRHZMokbm8OJCs0iUwdkfilCiH2HUfqjsiBg3bAI0Lr7Ym09cQ4jUeCaav2Ndb7agMfnkTwSc/qNqA4YIFMH5A7/1YPvUQeyyC2sD3jsLl+EGnPovyYhbeFEXDPo72bPDKK0zYDqoRpgZO7fXB1w9NFLXn3lDVCVrP/dtSXvOyzfISEgoeiTvTb78+Nq/0n/Bs+v21sf9KT6txQy/tDSzV6pMURN/0UqIoz8WSLodfGNNZ6GkNclZPcQriwqtGBQUMbRCQEpBUA9hCbRdKOrN6bpptPOPiFre1u739p84Pk3dj65eusTa7ZWdiaMPL/O3/+Q+edW286TK/2doCwEwIyAJyAJPEYCRiKHXTx2C5xH4AMuujL1FtU0w7LmOGlHj9PPeB/7TUcVy7Jofj6LyBseyQfjlFVzRB4Xl/6vbTti1/v0BjIyhMcMjJFHojqgNuD0EM4CVtg9OEMH5H4lwzRT2XB0UGlZjk/YLQkXnb7snBMXnbhi9onLZx2/bNbKhdMaa31UYzBGTQd09vblSn/tEI388Yhck9+dOyZBELh5zkfHK8k1FgFxzYhR186IneeHZwdyo0IBAH+78/+N2GcwcnOABwZnjGEpgOfX7Q16JWcmVUj6r97a5hL5ljpfU41d73M8wBj5PWJjjYdmHQ/WQP5QaDR0QFzWFDUdZkBtyqYlClxznX9mc82SuU0nrZhz6tHzTlox+6RVc849aXFlBihGKTqgvqkx2/FLgCP9aatqjyQVKELu4nGtRxIyJgFxzeiNq/qITgJWrlyVpQPYJGBUMWjHzkmcA3zOBHAAUAXw/Lq9Hzx5UXHp75Rrrg24Wup83sElH8YYgcd1AU990CMKuenBaR1QLo4OyO3vF5O1zERzQghCsKnWz/PUV44g5OxIIIwQh5Ak8EGfq7ITYJQ+D0hhHU5Kfx7BRq+r0F3JIRRyCT5RSG3XTas3ocbVEZsEOF7fXH57V8keUUaZ6MbUKQIxjnBnHjefNtUryuvbjwS9UlMNNf7c98Toe7cqeizjqpkK46nAG/zI7x8aXHeOomp61skEvC7a7ZIxtuQO/2UV9nCheo36/+u8UtAlFOtTLXABl9CbUE1b5BMAYpreK6tekctNMi+XVMzP3DlzclPDhnlwRiE0Y9IXgXh+vD3AVAF4B56cQsP/l+1y/52xcGd7R7oXho3qKqdRFwQeSfC4BZpmaVqyqit0CDZiPyHnBkZf+3B0QC405DQDjGnXs8kcdDApufGm22ppDFiSbVvpDdnT000g38nXTLf6Zwa9xUN6OIS8IifxOD6QN6QZVm9crXWLRTRHKWRFfObqgAXDOTqjSA6w5rQCYoxEGGgR408eKQ9pJyaew0GBc0m81y34PaJhWn0RmRDA81jgkMBzooAFntN0ozcsAwgEDgs8FgVO5DlBwLKi94ZlhGBypYBFnuN5HIur/VGFw0gQkjuLAuYwCscUXbcM02rviR7ujNAU8Jxfnws1ZeqA4V0Z+v5UCJCDU+l6mIdlDF/6d3VH6NCBaBALYPrCoBQtfhAIgV/kAxIv22G8zo8bVfWoqvulyuPZcuP9Ha9vZm4wYzSwLEszWAjQCCmAItL/ma09fq+4eE7DyoXNdSFPx+Be2yWTtzWPBGZkFyKlBDgAaDnoLELupHl90XRvXzS491DP4Y6wqpnZgr6+LtLl1CKFT6zeeuFpR5V1ooPLwFmZhaAhhD7PQLN7NvIYD+nvoCimNhCahYE5a0Zzj39BkRwxBxfP1bilfllLDMzqNNOMqrpuWmKZLayLS386aR5cH4Ix4pgW0Y1J7ABYefL5XZ3JvibjCyoi/Vfv6PSLZHqtOK1G0JRYpdJ/hEEIOt3nA17JTufM7SBfl1p+YnXlgflZZeB4DntS5gJmBRoPtm3d8dh9f3nukbuduT+t7OaWWg+1QoS65h2beuV9L0Iw5BZ8Ip/66QgBYVmLqnrphoTW6ctTr0LSHwDwxouPZUYEMUYcWhNwMseAbnz9KTAx4IZIv3ILTbW04DMeyBGbCPAcqgm4mup8pkUiMcUeCxR8iCuYBzhEE2qmD8DnFjN7JDDG3vjz2H1/cZY3rHl81akXYYw9bheAYPNmquaXL0/+ypk6oH7vemcB2pMAr8j3JFTHDGS7go3ehOYTBdFuVJcXR9ZnsW/vPrsXmFgo3v+NFx9jtqDRQ9eNSR0DOnEoqADW7Oz0uKj0rw26h1PzZzSAELpEvrHW5yT990ezK0X704agMo9M5QKtQWMR0tMfzzxsKOBO70fAP59ff/k5+Qebxbnhnol1MSeN6X/wQH3DmsdPOfcqnucsk1ZpBhA6asAhVxnU712PIQxIgovHMdXuNmoPJHsTai2tDTfIt19E6GeiKKokicWzvXYv/LGz4EQEfe660islMApAQFzRzYxn88HnN115zgp2vUZMAaze0SkKuLHW29Lgd0ujWPC5YjBCfi99aDXNlFVDVvSsSUCmDihzEkC/rKabnb2xzLXvtXbPnzXQlX7CXY+pLP23btkOAXzs/uyYudeefcCuG+p4dal+cLpJApCcE2QqA0cTWLph7T2I9m5Jx4Oqel9C9Ym8gFGu3M8V+lkUkf5ZkwAWFTpSGKZl9+hOP++aMin7wq88+fxxtwVxBaMmPFJzvT/oc41UzawRBgIeY79HaqzzRhOKrjtpgcPVASRdZ1wHsPBQnXUFHjVuvOm2zOsci8vEIo8/+Nchc8Qu/vCN9lsc0U5rdsPBymD58qN4nps7e/pOXW8/0pUKQe7TXf2uaY11NbBkuf/Pe39XynfJ1QG/vetvbBIwTFTN0LShQ4Dauvpb6vOGnzCKKoDVOzpdItdQ660NuGlzmAkp/ymQFqcL+VyNtb64rEdiykiFBdMisyYpkh432WsQTmgI8HlFWaaxCaZp7tq5+7lH/l7K+x79xx/TmsD5dZLKwG7jPOAqAASYuh4IePv7afAoISQWTxxqbe/t6kzZ9Icp94sztXXAGFg4FU2fAjnAG19/auXJ5084BbB6R6fA49qgp7HWm1tuYaIBIRQFrj7kicRURTXsqM1hTwKSbYaKKpOJfVkmL5/40i3RaLSnt9c0LQ7jcF9PBamCKU2QnhbYct/+n625IVQTUUg0qhvsTXI8IvH+vHp9mEI/ryFoauuAUYUQ2qKDZYCNigJYvaMDIxTyu6Y1+gPegZj3iU0yKrTOG0uo3f1xk4btDEsH0NG/OYTQYV3ARoOrb7xp//5DxM7AoG17iW5Z1upn0qX6KiBHGSQzBDG1bEKnKZgdV26Z9q0zgiP9FMwZMIJYFi3JPoIVBKqcwTMACF0S31zvbxi/kp8VwHGoxu9uqFXjihZPaMO6Nwjt9jV0otck0IyTjPOv+WxHRxeEkHZcIwARDUA4TOlfSBlceNX1dhE/mJIjhJDnH70XjBpvvPhYVmoYmwRUhmVZWfn5pbPmib+ceuEnwURi5Xj7gdMKYPXOTkng6kLU+DMxI38KASGUJK6hxhuJKZpm2sH7FU4CaIJJCfHFjq2ANgfPuUyvP3XXyedfV/F3qU5O/9B1FiEzpzdpuq7rhq4bsXDfS0/cP0of98QDfwZjTm6dOKYDKk0BG3hCJ42ImrhugKTHZvWODrvgs3tag9/rFiaR9HfAkLYKaKrz+b0iT5N6KkkPJrQDcGmzS0JbBWza3XbwSG9fNBGJK7RVMc0aYDPTSmhurFu2aGZTQ8205voZLQ2JaHj0pP94kZUenGoawygLw7RM06LP9yQTUROU5AwAQpr0O63RXxf00PoKk4q7739pRI5jmjRMvJQ9ZVU/0hXuDu+SBM7jFjmMvG6xLuhZOKuxLpinkBGjCJ/88rea6gPOMsZoy87dqdakUwxmCBoW9vDKDtCbQsMsU1t54tkb33xhPBXAi3bB564I6DrcBqYwGRVC8xqCSqwwLiv6ke6IYdej0nQjElccDRryuxfNbhyVM5+63PDV2+prMnK+7GKfzz0yZfuoMEPQMNENk0zyNgCZbHzzhZUnnj2OOoBzpP/ExHBPdxY4jFySEPS5Gmt80xuDWY23ZtQlB90E0JaN/TF58+62zbvb+qNypj0H24WczWhHIR2g5zgPckkoWnt3JNcNRQgReOySWLHoMrjxptvqcop9Pp6T8TuVYBFBw4G2gNaHCtGbnDoAjBOcKTVMbwiuXDRt/ox6r1uc1AGOEEAO47qA9/Rj5y+d2/TujtZt+9qjCSVTqmNfYyEdMGR2SVzOL/0dFFVXNcMtDqvBSDWzNaPcWya3/+jn3735K2CqwAxBw4sBHXqUNkl1wMbxmASgoM+1bEHLkjlNvkku/TPBEDXW+M9536Krzz161aLp7sFdn7Avv6FmSPdvEelvJygaCTldPpoxJH/82fdkWUm9Lrvswj/944GsfW7/0c8BAF+++fbJdT2/cfv3vnH79+7M+ToAgC/e/N2EYtiFKujjxrzBpWOYFu3SOhXZOE7zAO6EZbPnz6inSb+jJv137D0ExgkCyPRat6UHDrX3JRSaIuAE6vCBOiXcXW6duOIByLph2jWqGGXw8zu+/ZVvfdtZ3rhx68qVR/3H93+S2spxVHP39fZMumtq1yIC27ftvPMfD+zd+17mplhc9biSpkII4csvvvLDl54cp9OcTBDbAFtxEsDEZ+N4zAM4DhhHOrraO6bI2D8LCKAk8vUhb1zWLKfGw0AjUakiHVAE0yK9kbiqT80RymjrACe14vW16xsaalObMkX/l2++/Rc/+i6YDJx05nkQJnXAtq07xcElhlLS/+UXX1nLRH/JEEIVwBRyAOdnjHUAkmiPpPGX/vbAPPXbDvqRiQO9AQavd7YMemPGpoGVGCGfR2qq89UFaY7AoA5iI/u9CYnLtLwMo1x+fse333n7Xcuy+uwCbbn84kffnSzSHwCw9uWn/+e7t72+eg0tXKPmtwoy6V8uJm3ROgUdAJk4on8sbUFcxR2xR5bBSmjQKRXKSqNvKXDuWRsEDtcGPBBC3TC6+kyT9pKgt5Hkr1Mi3YBUMAnI/8Gyog9MMBjlseb5R2m+Xm2DZf86tGCbPYj+5Y8njdzPYu3LT699+enPfyOjurXNyy++QreysX+ZmDRLf8raf7J0wLEnnb1+7VjMA6Zmxk2eWhECX+P31Id8bpeQrVDsP7cc6iv9eIU2yKpeSiYBIy9rnn800tPZ0dXb0dnb2d3X1dM/eaV/it/8z/ey1qx96Ukm/SvALthXLQ/X+gHpf+xJozsbqAoFkCocXRf01AbcPM+lpgiuQL29GRx4r8QkuGITJtpEmLUqHQZrnn9UllVZVmOxRDgSH86hGFMMw7CmQBuA4WiC0VAGxZrCTzEQhB6X2BDyxRJarxnXE7Tjo+SSZGdzSZawIXZSNVOvglnqqNLSXGc7fCjXf/X2eFxubKiZNaNJFIS16zYvXzqvoT5UynF03RjBs7Isq72jp6OrLxyJqaquqpphmglZMWlpGlqbIOmpqpYR6jhAx1ZVfH3Xj45FqIoUgFM4Ouh31yfUjvZ2R/pTu3MwFOkv3f5TDM0w7HRiRoVc+tHP7NuXXSXt0HvhQ+/td5bffKP35FPeV+QIbtcQXb1ysQhZv2Hnlm17h5Qued1RTY21559zUsqXtmf3rqwdFDk5xmAMB12fUkUgJgjVpQAggE+9tI7eTHxAwEC2CARAQlr+gkHlYxiWOqIDz2rjX/f+/tKPfqb4Pq+/9kaRrdNaGir4XCUWljizsgE80eL79uyu4I2MsqsAsUs20lSRAnjwKVqM9+RVc+bPn9/eHWnt6O+Pyp29EUCSkwC7Q8iwPsIwre37OkfsjKuSDClMAKA9WwbG3ek/Mxq50LBeyyLJfxE83NYxraXsknzDsd6kapcmY5LhECFqjMqwK/UyFTAZFMCr6/IX3Hfo6Oq96oLTwHhI/2985nLnzxq/Z860upisbtrVuttO1PQHQ/bgf1h3mGVZHDdpOqlNTDLMLHTB/gvmbE0GiTp/YUxVAsZOcYVK5K5ulNAGrgCpzqlO63nGKDGFc4DHEe6BJ9dcdcGpI3KsA4eTg99ZLTS05kBbV+bWjq60nf2BJ1dnvXf0VIIj+i8+74yFM9MpphyHfJzo84ibd5ojZP6hWITQXrOMYRDu60/IiSF3a25pgdCeE9CkgUxtAQ63dZZlCCKEGMMQLimNtXXL9iF3fuZff6/4g6qcChRAW1d/S31wdE5nCs0AHnhyjS2CTx2+6C8k8R2+9IkLnRpY/+/PNOsnk1yVMHytkFf0Z7L/ULsno07c8EMM7DEpUwDD4qUn7z/rgquH1AFH2oqF7aolqJAUFiHxuExKC9/KnSNGw31bt0RK/zhGZbAA69EAHlz/CADgh79+oAId8MCTrxUaM3/5kxc55Q5p6BYhlkXsLtz2LNm24RJCjbYEEAQhKqAVnDPM/KP0M3zwqdeKi35nobsv/ubGnYZKKxBIvvpB/QNwtn0sq10Y5vIY0E5aOZdEO771w9+WeJ6MvJSiA4pQEypj3KcbZiIxqHVE6QqAxha7XSVpfQieZcP/YXDJFVfnv66Q+mCuvSh/bFjWDGCiNYUfd5Ii7JYvXPXDXz9QylTAFvqZULvJv33qQ3lLShDayyV7PfXj2XZTCxLTslI23n/71MVOKLVpJrXFL//yeM6n0zPMIuuEs8z9hUR/8uwhcKT/iMwAUtxxy+eYDhgOFudasXJecrww4BmGEFrEMfgkfyy6nrp/LUQ3AV3XCSGiKHAYNzbWZlr1MheyOHS4c+fug6UogGQF5wyCAd/pJ69KuQGK8O9f+lyJ353BGDPSY9hbvnCVMxXIUgM5Ej/JF667wBbTeZ6KFHnXp3ZHCCKUdJk6jx9CyDAt27ZL3/tv13+I+vpoeCWNAIMA/vquJ3IPmKkVIITTZ0z/8PknDCn6U/urYnKWILIYg4lEZmfg9D0zsJD6r1Pvj/5rd45L7TnIlTx4IQvOdh9Xpv/pnUnfyOx+jEkJt+e9tvmzW1J/O1MBAMD9j78CUbaJ4yufuoj2wk0Ox+hd71hvhn8eqYPQlvR2V/rUiCwZ9mF/4pc+eRGyVY5lEdu2RJ+9pFYg5Cs3XGxa5JEXN27eeXD5opnFRf8wyWv/yYRNAobD4vnJbqCV3UVN9vC/RLCjAEogIyY1iUmxsH3HFufHv/wtmwQwJhpUiu0ZKIPjaIKrP3RWc733//74L2flFz9xAUya6emf/FiFOWY+aQLPZcdrQxr/YVGnK/nSJ5O+ZWeHo+Y2lSX6fW4hntBGo8gI0wGTAowRteFUFAdky/+qK1AzAXnw+U1XnrNivM9i8jFoGOtoAoQFQsCXP3WJSa0xgyfR4x3ikppvYAgBSp69XYAzaRp+9KVNjgJ45a1tM5trSjnmgkb3wXZdoa3mWEv3CcEZF39irBVApTe2aRGTVX+aAGhK5SED1UyeqWt3bz9CkMNIFDiB53gOO0OkcZf+hbB9CckYo+Zab2842humhd5KpLHWWxtwSbQp5ghABjrOO9xxC3P9TXSc+7uy9xLLGk4OAWPkmKDSaYKT/77ftGPcuviOBI5Dr1SfXtAntdT7a4OeUTobpgMmOM4AorL3WoSUXnb0x79kwcGjRaU/YLUzRrWA/vfP2QGdNS7h+g+fO1qfV85kRRK4hhoPhOBIN5tFTmIPcMUghFAJXtyxqTvNqAxWgmUCKYCf3fWUrmkYF3MX98paplaosTNyR1ElFAZB6Jb4+tBozQCYN3giOwAcBeAEklaGpusjejqMSvCWXwacMWIKIGuAj4qK/rz0ylrmcYJi8sRuuPa8MfidEIQucQgPcFYacLmwiKAJC4KwlDjOQqg0fIAxPjhpwACAgJf29sgKGrQL9KY9NFz5cmnKw42sYWcQydqIA8li9p+mHb+fmcyZl37VsOzoujv/9vTYqAFWxK1qgQgWn60WR1OZAhh/vG4xU+7npRp6yo+RAvjJnY+kkngL4RJ5WueHetggpi+qqxXaSs+CCJqmpahDG0/77X0cNTBmE4JRgk0CJqADwJkBcNwYzQBYOtjoQDt+j9KhpzbcSI36B4b5dKSPILIQrA+5MUIcRhgn/yWExBKaaVkIQUU1jnRHkzVaSElqYCwnBKME0wETEQhpNYhKa4HrupHqTMAYFxAcZPZhlA43AgYfKvGpBhZ5Gk+NMaSVe4jRXOd3xv50EmAnExMCPC7ByadXVCMSU02LpMp80+Kgdm0vChl6QjB51QBj4niAnQBybqjCHkWqQdhRQKwc0HiCEFo4J13PhlE6XLmiH0JarkfVTJ6jct0p5sxxKOCVvG4BYxpQEUvopmH4vaLzqKT/Twg/MNeWRK6pzqcZZsJ2/xIAFNvuDyE0TMswLeA4XfMNzBw18MeH1kA1Nnw1cM/DyVYEop/2scnPyI3v2CRgtCmrEBAF0iDCzE6TZaEbhmXRfmQVvJdRIld/7FOqXDC7kxYQY4ysAkgoGoLwV397JrXGseZzGHndQm+/FfK7HHGPMRR4HPBKbklwBvsAyP0RGl2dfVBbW9hLREC4pcGv6YasGAhR53A0rtK2ARDKqp5Q9XBExph6Dmi5Rafbag5E9Gq6oemmZRHNMO999PXjl80GowC2y5SO1NGYDphQ2DMAXHE/OLsaUEn14ByYG2DEGY4Lp8opqACc6pvJZUgnWS6Rk0Re5HEo4FJUvaXB70h/hBCHqAmI42ilTicpY6jiKnSrzy2YFu9104pDlkX8HtEZgcmqHpf1aExxibwkCqpmaDotQmrX/CnQJ8guBFfZJTBdtaZ95CKBxDyHVZ3V/JqCHuDMckBWRbeQaVqmZbEyUqNKkeF/VtxnJkGfa9TOaMqbgJz+2rZ9XxI5SeACPpffI/Ic9rqFnr54TcDtVGZ2enZUUCkIUucbRA3H1AbdAIDWQ63OegGAAAAt8+my001M1w1V02VZ3bvjXcOw6HQg9ajSMyCWSTIbcrvdVJh3h+N5P3fOtKSpJxqjqb+3fPL0mKwpqvG3J94pdKopy9VIwSYBE8QBMKglQEVQBTBys0NGBVRcyOtDlyW7jN14ZbHus5d89MvVOANQNN0t8TyHQz6Xxy343KJb4qlfF0NJ4ISidaGPO+sSOdqbu37GjGmZfzo+35Toz3MaNEkHIiQIPOeShOCJp21553VNN1MGGZ62HMMuEWqZGfmOTy7333wcae8GQzEaRkamAyZcReiKsIbXU54xfLwelgY80gpAFDhCSEPIKwpc0C+5RFoWVPfMtQAwAJi1dI5S+KA+F8gr/Wn7vUOHU8szZkwrIvozoTMMjAQaSAoaarxxWZMHsm/yJnDR6YE9RYDOVMGZLjhrKnrMWQ7h1C8IOpBTWlFBUFYOaNyAEIZ81ITAqIBiN72saM31voZaj98jrt2p6J65ufvEEjSGpzIylUEmglTw54QQNNX5mut9dUWLd9omqQEjlvOqVPQ7SAOlKUYWVih0gkAjFiouBwRpVmNZ72BlQUcQjGgtr5E8YjXBFTKUb9xJK0K73UE6yQXg1KPcIJ6nu1YAAJD/AJVjxml/10K4XAGXC9SGwJyM83Q444RFBXXS4M42Ds53399RZDJjvwuCgNcFQD8YBZghKAUiZmfn0Oa4IanAYaOommXqkJRmyRnkKoYulygnEh3trCDE+IAxZhP0ihmn8KkMm0yeyIuMNU5cEBlyzcChTJMc6Q5XchqF4Tjs86RLTTGmHsQuUVXJOyGoqfELQnkj0P/93u2VfBYjHzxXuf+GMUgBZD0CNPg+tcYRspk7kHyvAmSm2Dgx/cnI/oFluuB8XGrrwKen/k0J+jxrBg7VE45H42rB08j5M30yhU+ex0jgSyoWJom0qHW5MEPQuGPZaegVvJHDuCboZ/JnHCnx2QQAnHnxDaN8LpMPzgmuT8XAZf3p5GCl+q1nmlKgHf+QuWe6AGjqz7TdHVpWcucsWzwsvJCXgQ9I75faX1WNjp5okSc568gDSWn5841T2PluozvEYIYgAMBLj91z2cc+C8YDuwpJ+QoAQr/PI0mVaH3GSDEZqwA999i9BIDpR526e+2j43smXGarXyoJB4dDD/orJ1IaFdqaJeftFo2pnUemclbOESyLtPdEBgWD5pA/FtRZVfiUaF5byR7CLYf6ls0IlbgzY4JgJ/OWrQAQhA11wcq9x4yRQBqqk8co8dxj96aWpx11Rs72bH9SpqxPRGiQ5LhLf0cBpP+AReTlUFI7/+A635+jNJbuj8p9kVHp6ShQBVDSWTtKsDIdwCYB9EcMx4OBUWzNVswJbPefKAuOw16vm5mfxxeXyDeXW/2pTB659xe5Kx0h7rB77cNgclJ09jRoLgAmMnZx6ciQg7hKvgSkvSaGqmyRhOqJYaQEMR0wLpiWFbNzwsuFwzR/oNx3MQ/weKUBf+jSK2+8Kj1U7+qLPfLCuyN1GrllYicFE2D2mgrFqbCWDzX+dPRElBIaM1XwCRjB3IoiuED1YNdANMiWQ33lfxSDugHG/iooipaQhwgFzovtGpp8z/xUAkIolRmCNUrASSj9R6spfOkkHW80CIculTjQziKhaAmlpCjsCo7OYVx6msn0puALL26eNavy0uRsEvDwPb878YxhlfhuO3QADNWurnIyfMVhNfbaa51lvfvNl58chXOqXgQej5cPYGownjMAQoii6qpm0PrPilaBF845iG5Yoxel4/QyK3HnF15MthYYziSARYW++crTkjS8Oo7WRCzOw6T/iOMSeb7kMFDGqM8AECypMoRFyP7Dve3dsSyrjMn5gj7XyatmzmwOJsXo1oPFD0WIXamttKlDBX2beA6XWAkudQoHDrQNZxLAAAC88gz1qp3xwcsURa7wgljmKM4DyoSJ/lFCEviynug/PvBKphuAMYQCWHbUzCGv0f59mTI6HRMtKwWVwZqttgOd89I3EPqEuyR+6byG9VsPJyLy86/SrS0N/kWz6ob89GQb4tKoYJrgcQtOO/shydzN0QEVh4QyQ5DDK888PLF0QEXJYkz6jx5i4SJdrBnAcBUAgtq2bXvAyOEaSJn54HHNNPFn3b7UJlnRVW3QtH00zPrJhOGS/fUQgpDPXeLO9TVeOV4b6e0pfR7w+KubLzp9ed5NTAdk6gDbVStPunkAE/2jjYs5AIYHV5cTdt0bpiFx05tCrR15qr+NBpZlNtR4t+/rNE2axoUxF/AK86YHCQACHhSeoZnDqMlDaMZ/XNZ6I4narG9dwDaEEHLbSuvx1duLHxshuGRO497tW7LWF58EFJL+jFxzEACgQufwOOkAJv1Hn4kSAjQ1ZwAYWapux7YDsOdQn2kaC2fVuSRxy+62gFeivhcCWhqD67YcXDav0eUSD7T10nYxCNb4C9Zz7uyN1oV8q9fv9bioYDVNA2N6DjV+V084LesxRiZt8pVNSh/0RZTt70VuvPyU5AYI47L2xwdeKdQTmBCg6kYkpvRFEnHFOHmlU0t0gAJDfIxgiS4mjyRMa6B+C5eAZC2ZUsQmASPLm688PcJHZExmIACCMFHcPJOUIazbdg4sxSVytJyD3RfeLQkBn8sl8h43FeJ+j+jUAPJ7RKdrmKzq1HMKLedFAEhVaJAEmnvMc1jMSN+g8ziY2ebRSc0vITPTrvg/hBnILvQWS6gd3dGOnkgsoXb2Rk374HUBDwJwX1tfW0+0N5rHwkB7IJeQZgIhmD+zPuR366563VXPBRpTmw4caCseDnTR6csff3Vzoa0sIojBKPzcQaFARg5jZBSAI5Kh7Qud3hhwmqzU1yTtJ7S+G6AdWpx0GFGgpeUgpEoiM3IG2rYUZ5l2XgKgud4X8KWNOW6J74sMkr+maZVmeR9KAxBgWFZ/JNHWFe6NxDWduhksQto60yWjrz1vxeVnLb3glIW57/Z7JL5o50sHnsNzp9dS1YgN5+WvSeemHzjQVsIXYTAY5UHlTMm9H1KlQP/4wCvsQqcoqD+b6oNHutpSkrUu6HEM9FTQ85zTlt1RD+JANT4EoUuiEwVot5LPPFpKHzjr64KeuJyOEYormk5Fc/q35DAqRfIWwz49VTPCMbm7j35C5sadBzpnNCVN8zOOHbAj/S0dxZ8qAjGkFoIAtNQHGmp8zh2Zymbw16Qdwk+s2XrhqUcVnwQwbzCDURYYodFo1j1JqaxzfakTKIHHcV1PSXU65M+XBU+nCCUM3LnBFfZlxQCAZOZbeVwCxuUUn8s38I/LWl8kEYkpek7P7mhcyVIJeT+hlFLjCKHFcxodLSjwWFHT5UgzdUBxrvjgiQ898ybTAQxGyUBJpL1gRrsS3KSQ4xVTqgKAAETiKkbI4xJH5IMHO1ezw6sryu5LH0SxB/59EVmmVR7zxG4TAPa19sxo8AMA3n7yXwCA4y64NGsfhGBqclMEv1dqrvM7CinglXQjbpp5PrH4JECVR6WOKYMxVaF2adfQE/RMHvvXgx+69EowFeX4yPcEfmvzPgCkzMze9u4ogrCxlto6hg8ezdpJ7T2RSFyxzUoF2X84qQAcHDWQicBxrqGqACEIF89u8A80jGys8SYUPZZQUwlDpRuC2CSAwSgdjJGrcBZYdUrzCijjCoZjSlmJkLs7OafR6oJGMswM3nLpiySGrCykFm0d4+QADxllLAjczOYaOFCJqCHkicQVVTMcb3NZhiBnElDEGcBgMFJgjCroBf/Yvx5M/QsA+NBlV1f5JaVm98yOuFnLmRQJzcyM4ExhmXaf1SINGos04RootKuZUt6Xxx087qiZG3Yc3LD94Jp39tz/zPp/PPW21+ve/l7n9vc6K6srl4VbEor3gYEQzGoO1YXSOWV+j9gQ8hbJTnxizdYiB7zigyc6OiDvVhYSymCk4FAZVRoZhUADDdbTspoQUqRFdl7RGo3nKfvjkgRqyi8q5QttSsnQrL7wI8sza3es23bwwZd2//PVfQ+v3j9449AOAIzQwlkNmdFKPIdDfpfPk605MqNCi+iAIT0BTAcwGA4cN+qduqsBuyXkQNt3xzuabt6b7w1xWcsKvdVNs7Uj3JDjG4j2dSqqbpgWaEwnRmUiCpysqPlPa+Az0r/wgA4o2r+9crI0HoRAGsrCWBv0NA3+1hACt8jX+N19ESWuaJknWqIhyAkJZYYgBqMokMM4ZXotkWs+eh3N+7ng+Clv2S8dW8YVaNgL8wnaI92RTN8pVQkJLWobvpcunZ9ZPG7JnGRJ50LwhZM4dN1MDsDtE3JUUurcBv3so6EN7E/0uovFO3EYLV/Q4s6JieI4VBd090VlTTd0I7/FrLg32KGQDmBF4hgMaAcKVtY/KsUff3lHiS0hc3sCTxnlUbQaKEobdmRFO2VVnjI7sqIFfa4Tl88klpUp/XNrQS9dOn//voMuSZgzd6ZTQXrlwuaX1tEEYIy55QuaNu9ud+oC0Rpqe7tOP3ZwuZ6RAEIoChzPcXR4XhQ8UAauEH6vNLulJvcOxAj6PWJTjTee0Pqjiu0FLy8iqHhxCKYDGAyIoFA0UTS3FvSVH/6YaVoj5TZ4JF+b+BKZUMpj7CppbNu2xykHPbh/QBItJ1drxJutYoy8LjHkd9UEvRt2Hi6yJ4QQF3UxQQhmt9QU0hAiz9UFPZG4Kqu02VnmphINQaefuPzVN5khiMHID4K0CAScnD17HxmG8hhx/TGEAiCApBJ+Cy1n7JxeNeTOKTCi/t49B/qdhRTb9/bOaakdETXgDPwDXinkc7skfkZTaOOutoJubnt/pwZeoR3ckrBkTlNujNC9j7xW/EziclofFJkE+AcMS8wQxGAUiAFFjXWB0i8OhLR22ciPKyek/ihdSRRTABahI9xMMUnsAnB9Ubm9K+KUUkAI2uFDhOew3yPVBKiE1XSzP0oLL6vaELH2WUI/k5GK+UkN/H12ZTdaixTjGr+7p0AGnHOvUAVQ+JiNNb6gP3uO+ebmgyqX5450FImjbzgfMKLdpeiAC8869omX1hf5XswZwKgSrv7Yp1Q5lrmm3FphZ158w9XXfnzyC/8RUBJZuqE8ExAhQNPNcFR2sqgwRhghw7BLH9gXlxCiakZCpk3ecyvwlIVbEoavryWRD3pdQT8tXp0p0hfPaXxtQ7ofGYLIIimHLaQTzML2H4zg0nnNfL4klC9deYJpkWhCPdQR2bKvsy8i03jawaqM89Vl6oBCQDPppWARQQxGFqLAVVAGrmrkfxm6obyLaFkkGlfiskoIQQhJAm83ZE9updU3dTMcUyJxRVb04WRjuSWhNugp3WqXa8+BEHrdYlOtr77Gm0zpoi0HktSHvJJd6B9CKIl8S0N65A6hXbO68GeLAjetIZB7NxECErGomogJQJ9dL52ypH7xNG9QAm7OdOOBF0/cPPEHQ6WkBVx27gnOAksNYzCyHm3nCS2zEhxMxYAyylYAtKmWpodpcU1arF8SObfEp+3ghKYKxxJqJK4Mc/iPEGyq8/OlaXgCgGGaWbYmDqOAV2qpD4T8bsE2+zikhDaCcN6MeoyR3yM11fqXzWtKvRfaw/+CCgCCY4+a7SiP7C2pd2CMec7vdzc1BPweMT3zcJoi5GRTF9IBekYXXKYDGFVLlv1H4LBL5CsYzk8YN/DkVACWZUXialzRCCEcRj63KAzOlVV1IyarimoM0/gT9Lr8XqmUCZtlEUXVe8OJrE+sC3mb6wNet+gM/FNkThPmtNTUBb3N9X7HPZAK6cG061nByyLyeOGshmIZKAOmIZ7DtV6hIeShJavsnjh6rI++EmE9EXYJJV15pzgEg8FIIfCV2H/sPiVMA2RTqg+A2jcUPRyTDcOEEHpcgtctZpY8syxiWrRaEBgGHEaiwDfW+YesFUqcCYes9oUT4Zxujg0hL/URFT2GwHMNNXQ356OWzm16c+MeGgBaJL8E0h5hXk/+BLEXXngZVEohb3BmcQgWEcRgAACp3bnwEC0v11x7Xd56ZQyuLpCuZebQG6ZCZ8XiGQCArbt3pdbX+EGNn+YAH+6S+8NhkQ8kFYB9YXO9neWCIPS4xYYan9MiuCCEfpaarviv535uIemftY7mktir9hzsmDO9/s2NdgCo/cr7+TTJyysVGn189+sfdhShczb9UXndtsPv7jwSjat5rwsnASPSNaQOcMpEO8tMBzCqHKdNU/EqjbnYgYq0W+2onddkpZJEMKdZi60PxFlNeYbDR7pjh9p6jl46vchB9h7smjujLt8WC4DkiH5/a8/MadPzDPwTal8kEU3QSkP5j17gh84OK8r4A9tjCir9C99bbonOewpt3b3nkH0gDMzkWZ130oJzTph3qCO8ZsOBPa29mRMmB85fn6kD8qLKiWBdQ393Z/HdGIxqACIatVGuIIf569owyowCGnsErGS+RE5xi1pDCE5vEDTdmN0SuuHyU260Xx8+/7hYLLFkdsOS2Q2FjpZ139QFPOkYpmSLAtriMe9IAUJQG/A4eqIYA9LfUQn79x+WI5EZIX5GiPfxlhsZWa9SIoLOzqiKwbzBjGqGeoClshWAXemYqYA8oMfeOPD4W4efWFesNEIljPJkK1VI7oU3tg3zI+fU8XPqeDPR6wQg5daYfXz1dielwOMuVh2oEBxGIZ+rscbrcfFDzkHz6gBVTsyfly7EVLxSEIMxdYEuiS8rBSwNk//54H76YycxjDgRldQCY5l6ey8hROC5BQvnAdPasLP1tQ37owkl4HWdcez8Sxe0aIr29Os73tnRGo0rAABDz26wzvF1a7YrEIJTFifbJZZLbzjbtZsJRmh2S3rsXKLwL55ZBiGgNWYLDP9p1FNFdx6tLC1w9XZ1IFUzVN3Muhf9wVCkv6/4QVbOb9yz973i+7DcYMbUBkLgEoUKioDe/7e7U+WgGXl9ANDxr9BFBHivnJSWltXW3rdxV1ssoYoCv2hWw9xptbSWgo5qA+6Qz6VEaFIrP9guQjNpkWmalmlab26JggJ0b+1OR0wGPbUBjyjY/QlsLjr3FNMiW3a3hmPywfa+SExRdVPVdIz505faQaIpcV7y/VB8RwRpgZG8mzBCNQE3hPDtdza//U6eAfg3P39VsSMj6HMLjTXeaELrjSRyu8Zn6oC83mBVTpx10oqX1m5y/mTeYEYVghESHA9kRdx371333XtX5poPldYSMm9pnWHWdJsgcA/8+e/O0lXXf2TwJrsjY1zdvOdIdz9NxHAJ3MYt2w8ePCAJXFzWjnRHYoli7VwyKyEPcRJ4cOlNW7K/uvYdI8PfS8NPAaCzCVNZu3mQUvnjQ8kSbE5LSADAsUtpCNOQM4AlSxbJqu6UioMAcK+/VyhOIOCVtm3bWeQr/OQ3D+hqsSlLWeTVAR5+0PVkJSIY1QZGyUd1InDJyFXlvOzii6mQ8WcHZBbC5/Omls85dcXIRAGlNAFVBld8AADwj/uf6e6Pd/REnWAbOcI7FdBMi8QVTSlQ6M0O0kW6bpYYFZosvTk4ytMwLE03IzG5N5LQSj7U0J81+M/Vb7yT+WehDICLTlvy6LOvgwnAZeee8PCzbxXXAcwQxJhiPPLQ/VTgXnG1KHDODKC4LTerGcCZF98AJjYPP/poka2XXXyxoxu8ttzP+uLPr9k0HGWQPwz0gYeeczK/+qOyI/3p0HjgkzXdiCc008yT80XrKHDYTgorNSOMEGLaVYMgpMu6bThKqHp/RI7JdOAPRo+ByAAIgWkSuwJ0/vvq4nNPBgAUVwOzFy12GlVDCMIxNaFodlAp7VwKIYzJaiSuFqmPxLmAEe4oPgnILA5RBKYDGFNVDWRxwXkfLLT/v3+hJPPOxGdI9QAAmD6tsTJlwKXG1nZ9t3RhH8M0wzFZVgZ7dwlNdIrLmqzSghC5h+M5GkWZG/A+VPcV2oAFIWiYlqLqCUWPy2o4pozUwD9F3oGDXayZmukXzW2a1hBavT6d+5ZXDcyZHhzWORCaI7Zxd/vWfZ2xBL2MqS/JBRqH1AGZeWHMEMRgFOLHv86jMKYeDxdQD7/4TXLhsosv/uQ11KKTF2pQS4pFSCv72wt0TVzWonHVGcgjmE6jUDQ9Eqf14HKPhRDiOc4wyysIAZ32DhzSDSr947IWl7UhGwnkqhDafb6Ez8r6O7fP8GnHLhSgM9DODm1y6OgYInWrFBa2eBr9ze3d0dauCO0cmZoZ1NDqhkW6hqlywhWolcPpHZghiMHIy5NPP8OuzMOPPlpkDkHrlCVDaZLWEKoQdINWdXZavmCadw0dg49hWmrcSNj14HKPRVO0ITAKdEIvBES0uIdlkUiMFpqWVV03zNJLSXMc9kiCYZqGPESb3wkFRtDnESEEGlV7RiJrplV0EnD2ypbHX7UVQNF4OGYIYkx5mIgfJhmxN04gqB29E02o0TgdlmJa9J9zTEOEEEXTYwk1r/Wfw0jgMLXgl1kPzq7rAaMJtT8qx2RN00uV/hBCtyTUh7y1AXeJxaHIRMoT4e2Kqk213pDf5cRNpfDb84AiqWEfvujUTOn/+Ooto3yyDAZjCoLyFf03+u3YGwhp3p0oJHtp2bWX85d6diIpqdGkHOv/wHtpm4FoXJFVjTYXK83uz3E46HM11fnrQ15+cFXqYp9V4bbRAtt9C5pqvV6XkDWaz9QBucQi4VKOf8ctnxvuKTIYjGpRAASYlhWOKTGZGnkEHnvdolN0gdAiN5aRUegmE45DHIdpbbYyh/8QQssisqqXO/BvCHmb6/wBr1Ri35gJOANwlI4o4Lqgpz7oEQursbyTgItOW5b5Z6FJANMBDAajEIOkJwFAUZNF/xGEXpfoloRUZGShxC4EochzTuQ+KB9br9Bi0qXszA8M/OtCXknM6Ec2OWcAztXzuoTGWm/QJ2EMyzIElagDGAwGIy+D8m9Nkw7/ZZU6JEWRC/hcpdRdou15IFR1o4KoTUJIiQnDKYt/auA/ZYp7cxwK+V0Ndu/isgxBNGXvfBqZWhw2CWAwGEMoABr6qWiRmGKalm2bdnlcQnqEXUBKIwRFHht29hYYBZwxvjPwb7Yt/smBP5w6BQFpiQuBa6jx1AXcRTRu3kmAHItk/skMQQwGo3wFQGh+b39UVjRaG8ctCQGvK1UZza6lnV94ijwHaeZXJcP/4jjN2V0iXx/yOgN/2o2LG9bAf8LOGWivMbfYWOsNeO0+xuVMArIMQQwGgwFK4/8DBbHJzhwYn5YAAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "assistant": "The SIDE view shows the sword’s vertical blade in the forward corridor, so rotate only item 0 to its safe quarter-turn.\n{\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":0,\"rotationSlot\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nROTATED ITEM 0 TO SLOT 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - ItemInterlock\n\n\r\n\r\nReset context:\nSeed 9. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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vkGFQPNK3b89bLACxSLTnaGd4cDAaCoUGI4ZhhIcHM4fnUw2GZe1OwCYmWs0aEFCmKERXZygAZDVxS0Gl/7QZAXz6Ex/KfPuH22dIVZlXnrjj9As/CmYQZ37sS3veeN0wdN7hhixrGgbxklt+csHhhAD09R52uVy6rgIIdu/cvGTVyfFwdKi/j2U5ty/AMQhDPDw0cO03fvCP/5mKjiBViWNsIsaWJWY8Hp7sM6IUBI/0vtKg3WqBJlH6z2/0gGk1CBgOx0Bt8LWf3959uN0wTcSLmqFb9jFiOUF0eb2BukCg3jRMr9vvDzQFgk0ul1cQvQfe3tM8d37znLnEtaJJSjymy4qp6eHhwfd/Zcp5gT5/4y2maRiGZpmWJsaGIkuTfVKUgmCAj12Sju/MQuSpK3vKK4C80v+Wmz4DpokOqB3pDwB44C+3cbzQNG+B0+XRdTUaGdJ0TXR4GJZ97tFHNEP3B5t4waHIkmngWEyWFU1X1aP79xqmHqzz1zW1aJqsq4qqyJoqm6bxqe/9EkwBvnTTD+wXA3UIoaYpGGPD0A3DMA3jazf/99du/u/JPkdKHvKa/Ee6+unFGiOTrzlvuekz37z192AKU1Oi30ZV5GcffdjUtSs+cYOyWzKw6Xb5X33u6YDXy7Lc4/fdDSG88Kr3D/f3KqqqakbqATUMxRvwRyJDXn/dYF8XZFmg4cjwYLC+6erP/Md9v//pxP8tX7rpB3nXM4gxdRL6QzxbRrofQKYO+PkP/2uiTpNSDDjS6R+TZACA08HbOmBOS73Is7JKqzlNVQVQ3PkzRXRAIsTHGgQcs6i1NkW/Tdfbm2evOBUA8ODtf/rYN76pG/rdv/xfB1RPmO/oinm27+9SpNijd//9wquvlXu6GAStksokpl4QBSkaIwtuF2JZbGEYRsfBfXUB/7d/eYemabd+/frJEvqZkNBP4vwhZw4APn7tqtxtUspAdHr/+7++MA5nSikbW/pncqSrf05LPb2UU1QBTH3Xv02mjbFsflPNSn+bjrZXAQAty078+69+wUJ09O03m1vnHuroXrmwWVja0CfPbtu544n7/nnhNR8a6O0GEKim6hSdgihKsbimxAyBW7T4mMOH9muayvEORVP6Bgc1VVFN8xPf/untP/iPqp/w52+8hYGj24C72nbZC6zTizFWFYXMA0O0+Y23AAAnnHhc3m/J8XBKGdBhwcSTmgDIlf42R7r6G+r8E3tSM4HJdwFNtUGATY1L/xRde95YeOw5B7a8CADo7mw3TRyOq2uOmeVxqOyxx769Z+/T/7rnlHPPUw1V4ARN1xRFqasLRsPINI1oNDR/4bKe3h6G5ZzQK8uxgeGQw+0a7O/54Ne/Gx4afOz2X1bV2NdHFfo2GOOL6tE6GUmSGmhoDA0NYGxlBQNgqwGbQsqA+ogmhVzR3zcYbgh6J+dsZgpo6pj/U3BCmAIAsKW/TW/3ka7+0KZth0xNXuyNrl6zCmP82gvr/D6fKDoYxFreIKBqiiTHwsODfd2dHIKaKsViYafb46tvJFV3MJDiMbfX/4GvlhEdtO6JO2/7cHa1hk995eZC2+9q25V62ar9wjp4YR28qB5d0kBsfowxKWYHGZfba5jmFS3ie5uFS5o4220FAN78xlupV6GjfPVbP6I3yQRQyPCnTOkRwHRx/qSwc0NplHERuruOIjh788721YtbFwZ48ZyzX9uw4flHHnnPlVc2trZwnNDdcUBTFUwK6unYwHXNsx0ADPT3xCPDCDFKPGaSmG7WVLXWBYtL+VHWPXHn4bs+dPguci/d9mHz+r+jTOkvy4ooCnkt/ZSxb2exZaFpKstyhmHwvIixAe0MNwCvmCXaKkA1zCd6NfureUcGdmrSV7/1o//90bdALfGe930GY9C35wUGAV3XeY5M9ugmKQSCMXbyUNaAyGIEgWECWTUwBgJPkrc0A8s64BCWNDLewgAwViMG3TAAJJbBgmPfDSF46oHJdwbUCFPFBTRFHEG0IGQpdHZ2tM6a+9buoysX6i0N5uq1q3Zs3/XUgw8yLHP5x64zdFPXdF3VRdEpOFyR4UFG4J1ub3ioX4pGdRM7PX6Hz/vgr/+7LNFfxPbP9fBcUGc59ovuXIrHAAZSLCY6nCwissheT5QAaRMGHIi5chZjkjQBoJvm4z0aIKvTyuD4E461F2pHBwTnrFlSZ/S2vWBdJquWtiXISZFEyGREWJFWy7qBGZRstWApVABGdt60UjBIQzZrPxiDA28+DTA4Zuli3TV331vPTcJfWGOgqWb+U0fQtKDzaLuim1ve7jza3bvMEV3U0sBwxJr+99/+OmveYp53spzgcLs5URgK9cYiIZfLK7pckEFOb4AXxVGlv+3wySv6b/uwmSn9+/t6PR43AOCCILyoDtkeHnY06W/rCeKtUhSrwFHBBwFByCIosswVreKlxEfEX9TIYoARQlve2lZrviBSCdAYYSZZFULswkojVyZba2R+OSvUgqywtEj2hiSqrGxjrG9guNyvUMZLAUw75w+lXDo7DnV0HN55oHfPwc6T5/CLG72D7buwiR+88w5vsK5l3gKXzz/Y2yNwTlWV+vuO8oJz1uLlb2/d8vjtPy++50KiP8UNly6xF/r7egEAZwnxi+stk9+y0EvENAxdUzE27X9H3R5CwCLIMYi3lIEtn2pNB0CsYwZqgNQEgRAk6icZJkmkwBoDdAYaAGFgKAAYRJBj8ikwTUvaY4B1YKoQYRYaCBgQGGSkYJiQROWSPZn2VD6C0FQn+2+tCaaWC2hSHEEXXnEtQGT0isHo99wnriLdYIaHoxNyatOA9iOHdcNUNOPEJS1u8ew48i488dTu9nan02EYpmliVYlhYHKsytc5NzzxWPuOl4vsLXeaNy/bb/8IqP+OLf3PFiosA6nKkq6qGAJFivOkBQRX4hdtHXNZM/dQN6kkseWtbccet6ZWfEE5drk1n07+NUkiiFVeL1/PjBH7sKx7oq2xPYDISvNKb1MuK5bMybt+x8sP2CXkVpx2ZQW7ncGwO3Y/QxTvyJ9szYp3jfrNn//2x4n7wfql3v3O41cvf1e1zP9Jnwwojt9PfA5ZtG38V9UPNC3u187OIxBCSdVmNQabTzy1p6MDIqSossvlJ8Y1wLokO92+155dd3j7y2MU/SluuHTJrbf1Viz9bT8DREjXNIarZBwMAbisiXuop/Z0QBK7OJs96IIAqDr2uRlZTf+OiQFZyj2ULr9NPuJYFInrvFWMz27KjBAZLSQ3gBe+7yNVP+e2jf+aFs/UhMFKipG9DoNNW560F0869gJ74f9+/9OUTraEflLyJ3/Rp59782e/+fG733n8r3/612nk/LnwymvBFGY8lEpexvhUHD3arpuzT3z/Zw/t2gUhZjnW0Ino5wWRgax/7qJXn1u3/60Xisz0lnvE7bd/5GxhTOVX43LcMAwAoKrKisoB4Cp3DxBa44AuEik0A3SAbSYX5+z330wktT0MwoBFwEBkAWHiHGMRsuZ4MQaQ/J/MrKTqbEMEkUGEiD1iIC47+1+MMQNJAwkSCpTcc3LumDK+wKefvjPf+qSsT4/DEvI/JfkzuzQkdTxZfnvz9mopgPEeBHz2C1/UDRNg3DscVzVDhAkXEATgnJOW5v3Ku87OnxxE+cPrkeG+viN7drEc5HkRAKAoEtBNb13T1tdee/i3X8zVNJWJ/hSrP3HnK38n49cK+PvheF/nEUPXOUHUVdnp8Xz1K5+1P+JYNvJMGX2eMQb2OIAkrCZ1AABgPHRAKTJ6XDn7/TcvCkBdJ4Y6gkDggG4aySlfhpjwBkDIVHWdQQibxLzXTSIkiKogMT+6YWJShcnqxWmS6XdsWjMFCCJVT7giGBZiT+uiRfNTxz3lmIa855OVCHbOqauLnHyqiwAdBBRXAJlSP/lfDHxeRygsjTD9kxuaZDRN5GYVpf9464Df/uK/7Tmqzdv28RzbMxhJBSNQBVAB53/ke++69iMde/eZhspzjKrKCMOGWfM2vbj+kd99KXf7jb+5fKw/IQCnff7fhT56+k+JwdM9HZqhaxhjMiNBDFByO0uSFI+FTMPUVIUXRNHhYHjhy18gRYo4lkyM2Trgrnb5Q3OJMisOBsCeDwAA/Opb14CZy9nvv3mhjzTkYqwZYJYh7XTsrDoTE0vfxABBkxQSJxMCKOEusB4s0yq7Zwl9ZCaTBsgAIGlG6taUsGlaktrXsmjRgrErgMeeeyO1HPS5zjhphb1MdUAhBZAr/UGu8ydPjBfAVZf+46cA/virWzdt20uWSAyCybJMz0AkNRNVRAFIilb1k7n0XSeDac4Fn/k/JRZBCJ196ZVDPd0QG4KDd7l8O156+p7f/Md4iP5SdECKj3zzDlmSTFNHiBFFkReEaCQcCAYZlmWs9C9dU2UyUeEJh0KaqpqWIepwum773gereKozgLPff/PSekbTDCLdrQhO66pa2VyYsYbTACESFWRFd9qRWUR+YGyNAIBOQjyJAiCZF5b0N6ymbMRfRN6QC09S8gxn8/LFs1LHPe6Y9HIRBRCN242C8xP0pb18Z5y0guqAolFAGYoAgIT5n1pZ0RR9JYzTbHBC+hPIX6LpORMhBbDuZfsypLWhdSvDTG1JHgnrcyvHhdicvYN7GuuOybvPh595HUwI46dpnvz9V4j9de331j/y4OkXvpdncH1z0/2/+dkJy+Z87du/UsSm31h2sS36T9t5HujqALoOGps3Hl8sKKha3HlLerbgis//AsUZj9enqZokSTwv6JoqiALLcf293TwvMAyja5rocFLpn2tEY4BEFnIQWYNn0xbcCcsJAxJLhQFikB3dYxr2U5CI9ycDBMhYhj8wjcSzQ3xAVoCo/ZwwDPkvQjCWkVZWOkk/dTq3IOWbth9QbOWdAQBe3tRW8e00g0cA+c3/Is6fcTX/U1RdB3z91jtkmTQqMQwDm4Z89M1wTM6MRTu3WiOAVGUJfBBAa0ibuEVT7qbEUVNr0grGWiCPV+ozu0hNQt+QXVsfJvZjb5WVkjPB/M9fHhdE/trPfu5ft98xeHTvgtbg6avmSQboRfMvR79c+WCzMzTA6rr1jFphHwwLHK6NVw+N9yAgxZ3PHQQA3HvPA15fIBisRwxjGPrgQN9nPp1QErYLiLgd5qLpKKPHE3zrHx5dEjRl3TKbrERfbOgMJA4fE7OqATnL65P0GRiABIWSqg/WQADabiJrT1g1AItMhAzdwAaGEDEGBqx183IM1D1zVi1qqmgEULCYS3oEgMEnv1ywilTNKoCCzp+kAphQ508VdcDP73y8fyguKxpCqG3L5nhEsQKPrSr2mDQHZ1iGZxmWYwkMY2paPCZZYQokssFfH4iFY053EDEIIQYDrJlkV3IsBjB2uK2QUGwsdx4pcg4dh9+YPe9EMN5k/EQJTZBnOt9WISPGeIkJ/uR/rOz8RCNuK87bjuyAmWOaQnEav/zHs6JD/NQVZwAAfv2nu31u8bQVs9836DOfedinSEFsElMQWS5khOwzBQwHZi3ceN7ucv/cwEV/OGZBWkyUrgAAAPPmtv7+T/8M1jccObj/61/7dGqDcVIAEyWjc80J61/bfEv8mtg2F+yf216TMB2SfR3sb6buoJQd/eM/Prq0zoypJHXLyowDqqY5ecQhqGiJy2XfRAJH6kNYPVoSpiPPAhNAVUtoCHsszbNY0QxFBxzLaIbVcxoDBwdV15xViytTAAVJKYBPfolK/wIuIJ5jVS1VVjdT+mesmj5843/vi0Uim95o6zjYER7s1WQSNq7qquVwNK3yI2Rq0J4YtEazjKUYkuVJrGa4JE8FEWentRYBaFojV/IJYoV4PAohYlnmbY/H1hCIJS+rjS5iNBmx/IEDB85cWv3JgzwkI65h0TX2cs6aXIGeDuC2arWkFxMLWYMWiy998Dxb4jTuveV754D/fnnx828dOGv34YAiQSIB4ElvDYVNgHngcIGtx88Fsgp0FXTsP+3FlRvP2Vn63/qw9MGmFzbv2VtX1qBn565EH8Gerq4jXT0679RM+Pprm1MbuFxOe6F/3wT8ZCkXScZ/7XFeUlhbgjixYP1SsIiMzsbaOtFRy97I2kPWXZD+9ZOfplYkf3h7M7IvVccIQIEFqkGmggUW8SxiIFT1hG/Atg6Su8j4A8mMQWqkmvT5IMiRSkzkGxACAUFFx1p5OSHlQaV/XgWQuPdUTc8w/4n0L3CTTqj5X8FkwFdv/YskKbu2butqb5fjEjY0XYnbt55upgvH27mLZBrQJNMA9kDVvn1JiIJ9S5smQxwFZAM7jMSqHk++xmJ7eItUCOOhQfsxYRhsaw0AoMiLpqGFhwbjcfTi+vXBOn//oMxzZMDLsuzJxxWLWptyFFQQ1sMb2tK9700tPgxMjQEGi8zGJmIS/tcZ+wbvdGuKZJjGmU+E+k1gDADoBYgHYQBaXm93I7B3eSPRAe37SzyRhyUyNwshdDsEawqRCMy07MrxmCXqu5F5S9tKtcQpxowg8l4/Hw4nKkCnzWVbwqZne6x4lUTgSob5THZoL6ft6EQge1pGJ+6rzAuWSolKXdJMDZyh0FLiOLPIhe1OH3n9S/jVStpotF2YJF5TN0wDmhAbGDPYNAFmrJB+Ugnc1I0MK4qMjO1YO8t4YsimyIoESuousrFuknJM5AqahomIccVAMlc8DlDpn1cBJK41z7FKuq9mYmXRud8Jkv7l6oBPf/f2tu17e48elWNRbOqGphiAlHK0pL+BrcwUcs9Zkpz8pST6jESykXQUksNCBgZWZJvV6xBAg9Q1SeycWC72iAGQwAZL+BF5j0lcQ9JzYs2OQQijqgmhYRi6LEOfQ5xbz9Q7RUVTiQ6A5lO3/UPEOGJgJwt1AFUMICv0aLB1jh8Y5rx5AUk2EYDd/bELLiAlKKYsTHRn1/4tmhRKSn/MInBoyHRwMLTrdHd8o2YYF95xKDQYwT4PEgAzGzgQKb+pABBRwLxtvYdX1gNVOe3h1o2Xdo4q+m0cAuf3OMi1zxJhuUasxWFjVu+RjpRItprCq7FY1ApMtIUu7A+bMVVqrnNaSS7pvdh+r6QiSQloa14z97DZp5Ayn0ecZP7lqQ0p9EBuekwqfRKxD0dUUcpbwy0jATi9oYkhmTJObWNNJZvA2jkkYeUVXZSXt7VbSiXbdxf0um7/09StLDAlXEBZzp+0+V9o3jcn6n8q8J//87f2jtDBfXuPHjxAqhKrcRKRZgUgk+a05G7FppFMXTF1+7knVkzCbLMbxFqKgUhxwx5BjzDRMoxFUv0kMeS1Cl1Zj7md7Ji8Cw2rJI6pqLirp0/Vo5JChhJOB9EAbutgTRxxoSIIJd0QkMpAY+6cWSLPOHjMAl1RdK9zSne75uU9R/duVuNDwFBRQvpjngEiCz0CPHRwL6Nr37htRygybHh9ZHglAAGBxYC8XgegWwByHCzZ0r93bT2IFCzo+LD0waNDrMfp8DtFUmxaU+Nx7YWedo38moyuKaoqGYYGSTExw47413TN0E1gQoZlocfd2jrL4XZ761rad+/yNwTI3S+ImixzVk2D11/b2jR3vhQP+7zuHT1HBg936JoGGWy59Ej5z0ScC+RZlhNEJ8sKEGKOY1mO4zie43iG5Y9fxhe+TtNHzBeFzN2QKQDivclcbz0W2XLbnjbKLReafpJSLihrNsKwpofscdpDD9yT2vy4b31tLOc8HKm8ZMjMhrUlmS39c50/xPwv7PzJej8BVR+KDwK+estfDh44sHNLm2p7e0zDJO5Ea2LJqmGLABlamprKcTzL8azgtIfwTo/P6fZqqkyGrkR2kFvbNAwTK6TOoUnChTRNs4YEVn14e2BrjRASWIrSVg0mMBl7jsGeRYBkqKHp5sHOocGILCkGy5C73MTY55+rSpKuKVjTTF3TdKybmgHwwY0HDNPkOahbppCqGA+vu3W8r+2t37+prO2/OOsLCgB3HPpax55NSmwQGFpK+nMMEDki/Zu96JV41IFxR3e34XQDzUmKxGskFlC1Sj9ytv7EIBYnGlfStDt73m3oUFUYWY5bmtjUdG0oHB4MbQAA8hxT73ND4mlI/BJEd0NEfhdb0hBtbv02lufBGs9BHeB5Xu+Bt/cKooA1LWoOMX1W1pJZp8TCPMs9v+Ep08ShLQMMz3VDGGysPzrQS6QRyXlKBLHYdirxcuCUX95ESeFGsmAhWv8iJOdlkmIYLOJIyAAEDMPxIsOwBmIgxqY/6GVYUVM0JR5nMDMYjn7yg+8Bk82N376llM38rcvJVWDJmNgOnbZsK8unk23UJyb4yTApMfhJS4xUUKZpWgnCJB3AelBJcIC9aamR2aWQuBcoxSaBcb6lwnO/E+n8KUUHfOmHf2vbsvXQvrdJMooV3wlM3TSI7cyJIiewpmlyvNPp9rEk+1/Z9MxfsvZwxVXvHzUR7Lk39jcG3aesWTwQig2F45GYHJNUWdW3vz1gImL1I8R4OBIYZ+kHEDccmqoCNIDBcE93V093VtWzQ8X/2Ov/jk4444LNLydKM40rN32nVB3j/md3b1wnBX8R+uDSX4KVAPDgR79pZGFS+rPQLcAmL1rRzNyr64cePaxu3IxZBqgylOM47FGGwOEA6LbUgIEBloEugxPWDz56bvNHmp4m4UP7L8asYLkbTJNBbm/A7Q2SMZyumYZukNBBot8t5WzVqLcjejH58TOmAMyEgwLCwwcPMIj8z+Fw6go45YxT6+v8Dz73qmEYp5+6BoDlTz72NGRxLDwMIAxHQ7FIxHL32x6/zKlva36HIdP9xEpA9mQ/ItHvZP8sb6WAIBJBxkKIbjr+ldEv6J5EgFD/sm+NXUaPL6QdANF65LcwE8LdNoWSG6Tmpkl1AHuBrCehX5aMT0SaJT5Jmk/kk6TOJvKaVIqmTIwCSJj/INv8Hy5s/r+9OV0GfSrwme/8afumze2H9llRPURGkNhChnG5/U6PzzT0t168u/S9FbEWmoIer1tcvrCJDC40Q1G1mKRG4so7TzY/9YUbi+xz/mmJHPRymRjpXzqeu7t7Y7oMsIGQySJiw/PEkr/5pl7oBL/4Ub0t/Rvd8JhGBp4sIqRs2rXLjA6TtFCOxRzCHDIOu+JRILmt4VIM4CgAcRA39YHkUY47fvUaqdhkQCnI82enlv/5VFt4qMs0zPrG5lmzW+yVkNSrSUiuCy5+NwDg2WeeQyzr9Tef/cFjZ7vLiwISDuaLA1ZLUABTSsSXgNUJANrTYuyIFl9JUskpdp5XIrwoPRuQKPqW3B9irJQBywtEYoUsl2olJ2afnVWaopLv1x4sz7G5aV/JN6mVk+/8KTII+Notf3994yvRoUFdlzE2GY7x1zU6HO7Xnsy28ceOx0Xaz0LS2Q6yAhIFzuexJgxHc/C6/WUXm5yC+O7p7onqki39GYbYDxx5QQFAB0Bu8I1f97MAPPSDpqWNLHeiCFY2skKvyEEc6seGAgwJaDFTDuOo2xzwAEEgIVOGCTQVKTKAZh+Er7r+g2MryQLNRTzUkVr++DIvACRm/J7OhGn54itb1XgMWPd/ivPe9U47DHTz9gOLmqtyFjOKRJiU9WKtS2eXekiE9aTFe8ZXsqTFyBZgBvHNJqZ8GQYZ+oi6M+US8DodIu9zO/Yd6Sd9hpPwydwOShbkumQF/tsMhxLm/9Rx/hTSAQf27Rnq78HYDDa2IBZtfiZvfdMyKCLM87a+sz2arzxxx+kXFixQ3NUz7XvI9J53Q+8f0xk0xJz73A8zpT/jIfcTD8B13+7xAfASOAWAi3wt61Z/5T2vfOR7eu8wlsMwPgxdbuBwQtEJeZ604oGIFA3QNLfL1efM02ihupx20sqe3sFoTDIMHSHECo6TTzlefv7xbc6EvI9Lan2df+2qJaA/VS+EkoIhbnoG6hoiDXSsuS8roYbMtWAAPnfCnhIv1v9tWkrKSJNxBPmiNc8OdUxiQEnduJL782Ry8qp5DpFrbQwEfM4DR/qHIpKq6SLPrVrSci/9DfORZf4nnD/2u6z/Vt3589j6HReftWqMO7nshv+KhoZa585+5u5fgPEnVUukXN56c0Tj8mlH73k35K6EvyX6AP6/H0IPQN6E9HdalnYDADeA1/4MwBc2fPs7rc08NDVDMQe6cTwEnS7ocGJBhBxPSkGQgFgTmeZ1P/3k4GeviPS3B5vTZSCryxMRcS3DOBwChNAgFSnsmUogvuOikwH461/vWbRood/vhBAsXTyHKoC8IAQNA7MM+ddy15hssqNyxXFOVm94ImkYhkRhWBGhleysPkAMCJfIn7xq3pI5De3dQ/s7+uc2B9Yuy59ITBkR+J+S/pb5b60a7QqN0fwfiw645abPtKmL7vx5drHJ8aVgzuVM5ue3//XDd20o9Cn+3s0YAOFPP+QAcCSlfyMA2wBwf/PR6C2XrHjXpWuWLH59yxvY0HBUNqPDUBBAQgFYvUAw9vNCrKGpt71v8dzGiDywFhfL6a+AvsXH1dUF1nb1EGc9zwk8Z5gGw3KmbsRjUjpgPOB1OEj9557u3qZT3glfe666pzHdIfa+laKVDOOxwp9SlPNoZNpRJFQuI2Mu0UJ4DHAM0xD0BP2uBbPqXA5B5KkLKD+pHy9r1qQk838s0v+x9TuyFirgmx9fDCaWyS21NlmE/jV62xbpkzeHPnmzF4B6AJoBOAzAth8+b8qS6+sPvB65/4K//qAl6GOhAU0VGgqIh8FQL+7rBD0dsK/TLUeuu/cnTwfcbe19O/Z1doajL7laNwjzqnX+fYtJG5/nX9pkv3U6xGgkYhg6sMKKUptdc/Wlm97YkmobUpM/9ajYVRvs1EnySouI8i/XiHpUtjfJrgxqH2hsQABYhOp8Lir9i4BynT/DoXje2ZsqSv/1bd3eunowZh666ykwPry46e38H9SkUPjeo6U6ZPd88uYWaxBwGABjYACS6Hj4h0W39S3Yd3X7j1YvWuhkIQd0huQCaCzWRGTObQh87bWHOi+8omXJCdgE244MDSocZjnfLN86pn5Ly7FVkf52GTt7jV3qLpGhZwUK56W7uxec+s4xHn2GYee525VQcj4qX2SP/Ipdr84uw4LsjsOUiSkFkeH6TzCRUVRjcQS1bX9wxeorwERR8RxA7XDfJ8nEAH7Pl0g1JLtuBgS/XHj7jR3XX7zvW1cyIPpG/WPvu97QjVNPPvPcF5/49v0//zMAx4UiGID6+Wuko4eOHjzQfaTdHfCtPul4A8eeAF4n7+VjqRjRMli83O7BgLfu2L9i2Vz7tu7r7bMSxjDndOjKiDv96qsuue/+R88957TUmo1a9qNRFiS2dIYyMhGYUFYBnyyfgx0Yaqb8rDSOc0JItHBOkTL/x8/5s76t217IHARU7Agav0FAAaj0L+0yPfXLlPS3o7t/Mvd2ngEcAnVn9n2q79Yf6f/zjnWXyrHffuMs+dizLktlAC1fulyNx6LDQ4PdPYf3HYjrRrCpcf/ON8ci/dP9YDPiz01iZjLFext1d/eedlZaGVBIBT2ry5dtBWVZ6WVF32dubN0j1oJdIW5sgfydfQULilCyQCMC/5NkXfvxkP42VXEEtW1/EFSVIrcetf5LxxF6MyX9bcn7nebbEMCqhgejZiRuyipZTgchW1JBUbRTj10jCCQNeLCnt6u9o6+3z9DVroFwWT+iJ1iX+jG3tx04Zskce31fTx8AYOkxS0zTsERZ5sGJAXr1VZe88OLGzF0df9LxZR16pkP89CQF2CQdHEn7R9PUSWMlUpijjL1Y2H44Uj7DaidptwezCnmMZ0loShKUKf1t8z9/8P/4M5ZBQNV1ACWLxmf/XME1cQxtTkl/C/yfDX+UZDMWNyUFKxpWdaDo4PjYfamv6IYRi0tnHr9GiYYjQ0Mdb+99+43Nsb6e4fat5R49FCqmM0jtmmRV15T0z6W7u1cQSfYfxRYMLh5zDBbIC4gscAqcyHMCxzIMU5Z9BCFkGYZjGYHnRJ6xR4cc+Rc77BJRlPGncBp3rvmPRzf/taJOwPVt3aSQysiftiqOoOpS7N6jGeZlIvZvyuh6aQCs/2jebzUdyxppCqjoQDWQaqbvQ1mKxiVZUbUL3nmOLsWk8HD/kSOxSDgUVd7Y9GqZ5j8IhSKhUCRl/tu0zm61KsEZpKaN1QeCUiIIId0K0Tcx0DHQTKBohqrppDYfKetWlguIWP66YWqkuKupW+WA7B0YpL8CnQSeCFAe87+Q8weOLv253ImhJOvbunmOcTv53ET/seuAiRsE1LBhUtkgIIkt/Q3SDpzU6QaKBlQi/aFqIg1bRQWSdcJkRYvFZV03zjvj9Heedup7L77one++6NSzzlfVSlp0tR8lPp9M/w8AQJEVqyo4KWMWj8djsVjmVy65+PwsLxCdCbCxWycwDOIY0v7XLu7DJKYFSJW80n8Xq7me9R2SB2JFbUJACuVaJUUNszrlQCjFQdWycItLf6vkHFPnc85q8PrcYlYl8WpNBlCmpg4QezclpD/WycvUf3PM/xgmUHWoEenPaKwvc3vDNKMxKRaXsyzK0884q4yDJv02uea/1Z5BJcmnDEPmgS3icZIOJkmyJMl54kEpFpHunSEFxgw2osKoCmWiyBlZQ7KGFB0pRhkKgIz8DPIVa4FRdBDXQFxDksGqyH145/P0kk8A7Ajzv4jzp6j5/+TG3eedsqzIYV7e3VPvd85p8nndAsMgSSFFNAuNFyuLCrUHAVUJCa3JbN/xRex7S65bScx/gygAMggwE7a/xnoPOM9NbIfNVqSSijOaWh/XZnOKyFViCR4BCRdQ+9G+rOnfeFyydQM2DWSVsEmpmVAozPOJji6XXHz+o4+ty4wHPe2s00LPP1H2qcw4Q7Z9ECxqIu3g7VrZOkkGBgwDSNe8csxHzlIWCAFNJ3sjtaIxmVwmDX1Y0q6HMgGkNXbFzh9b+hd3/jgEtqXO0xR0+91iU8BV53Oy4+AIqlZEEJX+4+EIEgd2JqU/GQ18d8FPNMyqjMfXvDCxBTYb9MS0rWHiiKJFVa2CaMAjjYtSy1nmv8tDbmNZVmRZIX1/EGMaxK2kWRTZJx0EpIh0t1l1m4nL3n7oSWOAip4a+yvJIhAwkQSA8eG2l8rfGaXSEUAi9r8i58+otv8Lb5DqgOE4eHOg/82t4zub+tBdT132oclvrjTjaXz2z3lrw41OQvqTf5HLqTJub9P8t8LzrZzQ7MlYRTfCsuYVeD5vxfmi2DZ+rvmfCD/HxF9NpBjpB0B6veXdSe4ggGJzsFta2sgluz0a1liA1ASy++6ViNVbx96DVQCC5A2S30dDfnqdJwx29LnfopE/551MpH9x77+NIpbg5Ze7xp4ePHZHEHUBjcq3td/+gPtcuRdWjB6WHU3WTCJGHOtumLslssBO/yTV5BEHYHpcaGAgGdjjEFty8lSK82YfyRn2+z3LR5r/BiZOHoQQx5GOjR5/w4JFyw1ZkuV04bnM5Sy6u3ub33FhYFsZDV5IVOtMzEmCiCXqk0mIbzIDbPd9TGTylrYTaJJK4KS3DOkoSXQysr6PZpzXbAqTtK0yNMDbb5Qq/WWNRIQVkf5P7RhQHfXI3+JtnT+3xb90Xv2cFr8osIWGi2wg0appSkWFUnL5w0t8hb4g04TYQCyLIPhZyye2bHgk47Ns+1EipWrLHipeGSRfGQqn53J7LfNfVbVwJNY/EJIk2wVkylJc17RoNB6PSwlf00iX09lnnZIVDkQhkyVduxLxPtZYiiiAShJ3ySDM6t9p9/BKZI4fffs1epEnpx9AdZ0/b4f5JQtmtzZ4LzhjmW/BMqtUMHjjmft03egdjO1r7+8djOnEfTjy8A0N4b503F5lXqBxKhB0pNfyUNNUgDH4gkSlT+X9jENEmsor6TrMNn2cv0EbSm9MnD94MJ4dllMCjizzv38oYhhGNCZZheCA2+UAGKuyhE2zr3/I6RRlhUw+ez1u0uHXTl7LOxOw5nT8ahk1okkTspnI3m5tWQtrqInk+ERPvMoS5e3mwCZgODCk5G+c990f/fy73/raWE+akgMZymVCzH84uvn/5Mbd7xzN+dPT2eF28kFReWOL6t37dioBjGGQ3+toqvPEJDUcU6zyfyPOwpuhAybFETTKXUzniMemA0jDdI7llbivaP1ICECj2yEkwzTLAlvtqjLN/3AkZuiaYZgIQUlCu94+bFufhqEPDYdjcYnjOAhwNBrz+UhfQZa1wtFp6Y+CPw/DMqwENNZkrJxeu5JbWbM11gUGDDZJExgdAh6xvYenVrPxGQ+qwPnz5MbdZx2/pPijsX53L8eiOr+zud7jdvCZ6b8QQpFnG4Ku+oDLKXJWla6ctICGhoodQRNeHq7WKdcXxPI8Z2geYIJ/fDdzPTZHVGbmGFTvFstStwYpKUNeWeb/nv0dmqoaVrSKaWJFUQzTRBBKsSjL8ZKshELh4eFQOBIbHo52dvYc7eyJxkg3QYzx2WeenOkF6u7uhbRGNACh7t07O3WHQFwILAcRQ5K5yvIlWJYfRAy0egIBB890xcdUeJVSATkae7Tn7cmNu09atYC3gngLmf/rd/eyDPJ7HM31HjvwP/uoCHqcQkuDtzHodgjcOJlZ1c0NTvh/LBfQv9ZtruKea04HhHs8pr5kXqLv48hpANDHJWLA61yCm2ftoJ20mWIv5wgajPFgTOkKxbsj0ou6L8v8xyOrFBiG2dLgW7poroCMuS0NJilkhjXNUEgGshSJxkPh6JGOriMdXbYaKO9apM8JzGxCXbt2HVVIh0id/KkZPdhLxS7DYRqYQagjxAyUX+4pk4Y6Gj5UNiiP+V+45s+TG3evWjLb7eCLO3+Ij9XJz2ryNgRcbIHGDiyL6nzO2U2+oN9h5QSMyyCg7cAzoOpPLnUB5aPx2T+XqAaC9a0t9Q37b72uyDYMhG6eG5bUIUkNyWpE0WKKFk4tq5qk6rJmqLqhGaSeTF9U3tMbOjAYOTAQzjL/d+89XOgX5pMeUKsEpWkXp9Q0PR6XI5F4OBzr6Ojeu/fgCcevyZoKhqe+UzNMVTfsc1B1wz6fqKLFVV1S9YisWedZqfKYJkR695KEakga+pZbvtMu+2m5gXD7sBHqGlPTbCr9x5QJnJD+sFja15MbdxO7zOvIbAKay/rdvTzHNATdLfUep8gVap8CIRQEts7vlFU9FleHI7Lda7TKkwGxKKg6NEq0MLYOKD4r0Fxf1/vrLxa/xhCC3qg0EJMRImEmDIIMhKrlw2Hst9YaBiG7rEhXOB6SSTi/uGhFlvmf7/cr+vOSiQFS4tgwDEVROI7l927PjgdtbjwyHNMMUsKYQYhsbBXR1QyTsXyaqmEiSCpht8z0Eidvte0+afVSKwoUA7MM4wib5CssMBVoxvoPVnb0vsFwQ3CmTrRPBKVO2tjS/8zjFrPsKM4fhKDXLTTXud1OARXVFQhCnmfrA67GoNvq2znK3VPWOGAsgwA6CTyuo4H+33w5a03KC5SaBjBMHJa1IUkdjCn9Mbk3InWFpf6oTJajZPloKH4kFG8fih4aJK+wJf0BAFnm/649B0o/7RFGrDU/bBiGa6jn4GCkSYtmDQIWXXzZkaFoh3UO7cOxzlCsIxTvichd4fjRcLw3KlnL2WFOM5JN20n/VIbUdivjWxCRr5DfaG+F0j8FNf8rBpXo/AEAnLhyvsAzozp/XA6uqc4T8DpIauCoh4fQJfJNdW6/12GplmKOoIqpzBFUiLH0KqpBNZCpCUp0E/VxgZSr37RKBRjkhTOXdZOY24phalZvX/snyTX/vfHhkQWfcYk6AFuZya7IgKwbcc2QNCO3MoRmYtU6B3IyJjZMbJ8YmYu2TtKomVvl5Td3CzzkEg6FkuAYwHPojR17wYSw4bG/gmnOl7/14y9/68fV3ScaEflT2PmzaslsjzNRJ6sQ63f3ijzbVOdpqfeIAleir5xhIJkurnO7SLBQng0qngwYt3AgOgkwVk1QRbiMPiTLl8zN/OjoW29m6oDSmznb0t8THcxaT5PCivDKlt0kx7NkBIF5s22CpP/MkPu/+NGNv/jRjdXdeYaRXuDpeHLj7qULWup9TruramHnTw9C0O91zGr0+dxiogVrCZDJAJ5tCLgaAi6eq7IjaDwGAVT8Tx0gSRZj7AFA0vwnwYVZ3n9bB5g6SfUaFZNkN+WR/t54KPNtd3fvRR//6Nj/hJnEi2+U8Wy+tpV4lSmTIvdzFIAl1XLN/yc37p43q7E56CaxusV3hJDHKTTXeYI+R27Ll9G+C90ugTiCPKJ1oKo5gsggIDkPPBYdoGSGc1ANMA7kTgOUArKKB5iWpyXL/M8EAlAnE/GNDT31ytwAm3rqlVf6U6YsxN02s/jo524ab7mfpxZQXulPYjbq3KlQziJzvw6Ra27wNNa5eY6pQESyDAp6nY11bqfIa9FBLTqU9crcuPJi0ZXqgLicNh5zo5UoVSeVDVAchnQoJOJ/pPe/N+X/sUEQsAh5pbD9slcmNEFS6HvjodSr0OG88VBWUhgdBEw6sYxncwbw0c/ddMdvb52ww43iszv2mLkuEspZTPo/t4noieEIGO7p3pE3kbsaJrPDwUpS2morPSqUVAf61PvGEudpYnykezjgd9tvSaGYArzyxB2nX0jdAhMHi6BOKkkWM//tH1TJyFNK6QDKdAdjcLhzcMGcpqz1nX3DrQ3TLy/soxMr/dMjgPlN2eb/uk37Vy6eHfCIY03TLfnrGh/M8+LSL9bbCMZMBYMAUjwgMx2UuoDGmdK9QCQQSDOKm/92y5FqDdvoIGBKYZrmUNhqZzJTPD8TDFtY+rcG3EJqLreQ+f/Utl4k1tX7nC2k5g/nFDmRZ0WBEzhG4BiOZTgWkdQYq14sxthOI7A6Qdtk2Giq3tkfae8Jd/VHugejimolmNsbJJ9e1tuoh3vLHgT88YHMQUDbgWdWLHxX6deodzASjafLxNt1aylVZ8uGR449872pt31cILMyaF50EyMIi5v/440oipN49BrHMLBVUXiGSP87Jtb8L+YCmt3gkdViTfIAAE9v6/U6heOWNq9c2Ohx8pm+kZ7efqwBVQNl+edcLFg+y7V8FikJq2pGZ39kX8fgYFga4XYPBMNDg+OqA2CRGWACTQUeR6ybyK4wbyaKxRTZGEFu4aps83/zpvE8QeCNDr7wwsvnnnuG/fbQofYrP/upf/3md+N6UEpedCtne2ZcnDsmXPoTWzyv+f/Ok5dkRvIUMv+9Am71swEHkKLhvr6Bnt7+1GvsZ0aKifqcjUEXqTg48vjeQLCCHT70xwcqOxN5pAIoPZycUi4IIYZheJ7zuF35S/KPhJls8z/FlZ//7GSfQi1CCnxP/6CMj0646z9F/mRdBynMADxOsYj037i7O+gRW+o9HueIas/VAkLoFNimgCvodXA5FeVSOqC81LAMHZA7GeB1JcbymTeUaWJZGTESopnA44fL6fB6XH4vqSOyaNGCaGAuw/I8L3Acz3I8w3KI5SBDXoBhAcO2nHJW1h463noLI3aML5CxkHgxiQVoHdcrRV944eXUQQ8dard1gH1uqfNkuVFyJyljRBuZoT0d+ejkSf88LqB1m/a/69Slowr0DTs7vS5xVoO3we8sVO9z7DAM8rnFpqA7KmmhaJ5qcRXUicv0BWU7giD0usRwTM784xVVz4oBxRiPmhVBqYz1T959+bWfNgwdQqhK0dNOWq2oy5uO7jRMrJuk4oJu1V0wrGVt8eq6ZGgW+TXbdsvtuYU/RwABcPIshKSQHEKkD4nV0xSZGAssY2JSxUFgSWl7A2PSqgKSBQQhR+JNTQgBzzCk0gPADpbJcgTNnz93RbOfbMyQjUmJusB8IFWzDAklE4xB5cW6a3jit5gCOH3tAn4058/6nZ0CxzT4nU1Bt5NU8x8vaXjnvzeUuGX1dADwuMRoLN2DMBJXNN2w5wUMw5RV3Smmu01Rqg7GJqkhaPUY3Ldv/8oVx8SXndhw4E2MGbuAsP3v3rpFWdLf+vVG8QZwDGrxiG6B46wiogbGRBNYDSBtNWBizFm9TUyMWVLMiqQZ2QVHTUsTIAhJAWSSggBbveChDB0AAFh2+VWRZx+1Qx72Bha6IQQ1UQ5ucsAYZ7lnpx2dnV3r/v23qaIA1m3af8Hpx6QEuiynQ18y4Vgm6HVa1Xvsfl6VMG92duhuLv/56cv3tA+8vK29ZzBmZ3uOOHUR6OEKuwdnzQln4XE7ItHEg0uCkayi5Rjj/uGYxynYUUCaYdjRTaW4qill/DT//NNlH/xkKtBqZ9vulSuO6V94fOPBt6wVZP0O3/xc6a+0H4KkxD+5I02cUhVWu9nkvQOtlMN6l8AzjNVoJvnzZQabpZazFvKsGIE9CPCcd4n8wuO7fAsgAJFwwZwyytjRNEMa6Z6lVK4A1m3af84Ji+xOqEXY0NZV53XMafQGfY7cVl+lc7ijp5TNvAK44MQ5kqod7Q0f7YsMRiRVMwAGcYMMU1hvQ0oHlNswIKUDRg4CEg2KvW6HwBNLX0k6GfuHYppOppxIWoCqv7R5X53PFfA6Az6nS+TtxpZUFVRTByQFra0Dehccl9QBebClf51LqHeJ2KomTWr0W+U5o4oWljVbtuumORRXGlwiTxyXOT8XzLdcdKsPzPfdneMI2uUjzc5qU/qf8Ol/lLrp3z88xmPFZTU1Op+OnH/5x6aQAli7bJaLODeKmf/rd3SKAtsYdDUGyTM0MRceQiBybGPADQA0THMgLJkGdjLGWKJCs8jUAZl/FMbYusMIwxGJ5xlNNzFQ+4einQMxlkECzzbXeZvqPXU+l8clukQ66VdVHZAkUwfkmv9K+yEygSywSxt8LqvLFybOHPKvgXFnKB5TdfK7WSODCGkzoLiIBqjO/ZurA447bvlLL74KapLNf7i25G3HNHeIMYjLGukpNp3trnWT6v9J/wbrNu1v8LtS/VsKOX9I6Wa32BQgrv8iFRGqf5YIuh1cU9DVGHA7+OwewpVFhRYMCsrYO8YgpQDIDKGBVU3vG4yqmmG3s49L6v6O/te3H1736p7H1+98bMPOys6EkufX+eefMt/utPw8udLfDspCAMzxuXwizzGIZ5DAMg6OcfKsi+d8DrIy9RXFMEKSak/Sjh9nn3Mq/U3HFdM0SX7+tA8BnWQSD8YZxy4QOKa49H+5rcuq9+n2ZWQITxgMg1wi0QF1PruHcBawwu7BGTog90/SDSOVDUeMStO054SdIn/J2avOP2XZKWvmn7J63kmr5q1dOqupzkM0BmXcdEDv4FCu9FePkMgfl8A2e525NgmCwMmxHmKvJNaYGMRUPUqmdqp2nh+Y78uNCgUA/OO2X1btGJTcHOCkcUYZkwJYt2m/3y3aI6lC0n/9zk6HwLXWe5qDVr3PyYBhkNclNAVdJOt4pAbyBgLjoQNikior6TAD4lM2TIFnW+q9c1uCyxc2n7ZmwZnHLTptzfzTjl3w7tOOqcwBRSlFBzQ0N2VP/GJgS3/SqtoligWKkDs4ps4l8hmDgJiqD8YUraqDgLVrj83SAXQQMK7opGPnNM4BPn8KTAAQBbBu0/73nL6suPS3yzXX+Ryt9R73yJIPEwzPMfU+V4PfJfC56cFpHVAutg6wexBmEpXUzERzjDFCsLnOy3FkrhxByFqRQAxCLEIiz/k9jspOgFL6OCCFeTQh/TkEm9yOQncli1DAwXsEPvW5ZpiDcSWmVG0QYM/65vL7O0qeEaWUiabPnCIQkwh77omLSVO9oryyq8vvFpuDxPlzz2PjP7tV0WMZU4xUGE8Fs8EP/fGBkXXnCIqqZZ2Mz+0g3S4pE0uu+S8pcIANNKhk/r/eLfodfLE+1Tzrc/CDccVu0osBiKraoKS4BTY3ybxcUjE/CxcsyE0NG+POKYVQ9WlfBGLdZM8AEwXgTj45hcz/F6xy/73RUG93T7oXhoXiKKdRFwQukXc5eZJmaZiSosnEBKvaT8g6gT7UPRYdkAsJOc2AYUjXs+kcdDAtueGrN9eRGLAEbTvJDTkw0I8h18sFZ5vDc/3u4iE9LEJugRU5JpbMG1J1czCm1DmFIpqjFLIiPnN1wJKx7J1SJAdYtVsBUaoRBlrE+ZNHykPSiYljGT/POkTO7eS9LkE3zKGwhDHgOIZnEc+xAs/wHKtq+mBIAhDwLMNzjMCzAsfyPCPJ2mBIQggmVvKMwLEcx0RjynBEZhnE84mNBZ5hGRSKyppm6obZPRA52hsmKeA5vz4baM7UAWO7MuT7qRAgG7vS9Rh3Sxm79O/rDxPTAauQ4cHspX4xUnwnEAKvwPlETrLCeO0fN6JoEUXzipXHs+XG+9uzvpm5wZTxwDRNVachQFVSAEWk/1M7B7xu4ZgFjWuXttQHXD0je22XTN7WPCKYk12IlOBjASDloLMIOBPu9WWz3UMR//4jA0d7QopqZAv6hvpwn12LFD62fufFZ60s60RHloEzMwtBQwg9rmSze2p5TIb0t5FlQ02GZjHAmDenZcC7pEiOmI2DY4NOcVhS48lRnWoYEUXTDFMos4V1celPBs0j60NQqo5hYk2fxhMAa0+/sK830ddkckFFpP/63b1eAc+uE2YFeVWOVir9qwxC0O4+73OLVjpnbgf5+tTyY+srD8zPKgPHsYwr5S6gXqDJoG3n7kfu+eszD91pj/1JZTen2HGkAyLUt+iE1CvvdxGCASfvEbjUT4cxCElqRNFKdyR0zF6dehWS/gCAV597JDMiiFJ1SE3A6RwDuvWVJ8DUgB0l/crJN9eRgs9MMkdsKsCxKOhzNNd7DBOHo7JlCxR8iCsYB9hE4krmHIDHKWT2SKBMvPPnkXv+ai9v2fDosWdewjCMy+kAEGzfTtT86tWJXzlTBzTs32wvQGsQ4Ba4gbhiu4GsqWB9MK56BF6wGtXlxZb1WRzYf8DqBSYUivd/9blHqC9o/NA0fVrHgE4dCiqADXt6XQ4i/ev8zrHU/BkPIIQOgWuq89hJ/8OR7ErR3rQjqMw9E7lAatCYGA8MxzJ3G/A509th8K91m688P7+xWZzr/z61Lua0cf2PNNS3bHj0jHdfzXGsaZAqzQBCWw3Y5CqDhv2bGQh9Iu/gmKhidRu1DMnBuFJHasONmNsvIvQzkWVFFIXi2V57l/7EXrAjgj7z0dIrJVAKgEFM1oyMZ/P+dduuOn8NvV5VUwDrd/cKPNNU525t9DrFcSz4XDEMQl43eWhV1ZAUXZK1rEFApg4ocxBA/lhVM3oHo5lrD3X0L56X7Eo/5a7HTJb+O3fsggA+cm92zNzLT99n1Q21Z3WJfrC7SQKQGBNkKgNbE5iabu5vR/t3pONBFW0orngEjmdQrtzPFfpZFJH+WYMAGhVaLXTDtHp0p593VZ6WfeHXnn7hpPuC2IJREy6xpcHr9ziqVTOrykDAMYzXJTbVuyNxWdPstMCx6gCcrjOuAVjYVKddgceNG756c+Z1jsYkbOJH7//bqDlil37gBusrtmgnNbvhSGWwevVKjmMXzp+9R9O6u/pSIchDmmPYMaupPghLlvv/uusPpfwtuTrg93f8gw4Cxoii6qo6eghQZ99wa0Pe8BNKUQWwfnevQ2Ab69x1PidpDjMl5T8BkuJ0AY+jqc4Tk7RwVK5WWDApMmvgIulx070G4ZQGA49bkCQSm2AYxtt79j7z0D9L+d7Dd/85rQnsXyehDKw2zsmpAoCBoWk+n3t4mASPYoyjsfiRju7Bvt6UT3+Mcr84M1sHTICHU1a1GZADvPWVJ9aefuGUUwDrd/fyHFPndzXVuXPLLUw1IIQCzzYEXOGoIiu6FbU55kFAos1QUWUytS/L9OVjX7gpEokMDA4ahskyTGhooIJUwZQmSA8LLLlv/c/S3BAq8QjEKtEN1kdSLCxy3rx6fYxCP68jaGbrgHEFY9Kig2aAjYsCWL+7h0Eo4HXMavL63MmY96lNIiq03h2NK/3DMYOE7YxJBxDr3xhF6NAuYOPBNTd89eDBI9jKwCBte7Fmmub6p9Kl+iogRxkkMgQZ4tmEdlMwK67cNKxbp4qWfgo6GVBFTJOUZK9iBYEaZ+QIAEKHyLU0eBsnr+RnBbAsCnqdjXVKTFZjcXVM9wYm3b5GT/SaBppxmnHh+z/d09MHISQd1zBAWAUQjlH6F1IGF1/9cauIH0zJEYzxuofvAuPGq889kpUaRgcBlWGaZlZ+fulseOyvZ158HZhKrJ3seeC0Ali/p1fk2foAcf5MzcifQkAIRZFtDLrDUVlVDSt4v8JBAEkwKSG+2PYVkObgOZfplSfuOP3Cj1b8t9QmZ7/3oybGc2c3q5qmabqm6dHQ0POP3TtOh3vsvr+ACSe3ThzVAZWmgCWf0GkjoqbuNEBixmb97h6r4LNzVqPX7eSnkfS3YSBpFdBc7/G6BY4k9VSSHoxJB+DSRpeYtArYtrezvWtwKBIPx2TSqphkDdCRaSW0NNWvWja3uTE4q6VhTmtjPBIaP+k/WWSlB6eaxlDKQjdMwzDJ8z3NRNQUJTECgJAk/c5q8tb7XaS+wrTiznufr8p+DIOEiZeypaRoXX2h/tDbIs+6nALLILdTqPe7ls5rqvfnKWREKcJ1X/xmc4PPXmYYtGPP3lRr0hkGdQSNCcu8sgL0ZpCZZahrTzlv62vPTqYCeM4q+NwXBn1HO8EMJqNCaF5HUIkVxiVZ6+oP61Y9KlXTwzHZ1qABr3PZ/KZxOfOZy/VfubkhmJHzZRX7fOahGdtHhTqCxoimG3iatwHIZOtrz6495bxJ1AGsLf2nJrpztr3AMsgh8n6Poynomd3kz2q8Nac+YXRjQFo2Dkel7Xs7t+/tHI5Imf4cxirkbER6CukALWfyIJe4rHb3h3OnoTDGPMc4RFosugxu+OrN9TnFPh/NyfidSdCIoLFAWkBro4XoTU8dACYJ1hAbZzf61y6btXhOg9spTOsARwggyzD1PvfZJyxesbD5rd0dbQe6I3E5U6oznqZCOmDU7JKYlF/628iKpqi6UxhTg5FaZmdGubdMvvPjX3z/xi+DmQJ1BI0tBnR0K22a6oCtkzEIQH6PY9WS1uULmj3TXPpnwkDUFPSef+qya9593LHLZjtHdn1iPPkdNaNO/xaR/laCoh6X0uWjKaPy5//9oSTJqdcVV1x8+933ZW3znR//AgDwxRu/M72u5ze+88NvfOeHt+X8OQCAz9/4/bisW4UqyONGZ4NLRzdM0qV1JrJ1ksYB7Mmr5i+e00CSfsdN+u/efwRMEhjg2XVOU/Md6R6KyyRFwA7U4Xz1cqi/3DpxxQOQNd2walRRyuAXt3z3y9/8rr28devOtWtX/tePfpr6lGWJ5h4aHJh219SqRQR2te257e779u8/lPlRNKa4HAlXIYTwhedevPX5xyfpNKcT2HLAVpwEMPXZOhnjAJYFeldPX3fPDLH9s4AAigLXEHDHJNW0azwkG4mKFemAIhgmHgzHFG1mWijjrQPs1IpXNm5ubKxLfZQp+r9443d+9ePvg+nAaedeAGFCB7Tt3COMLDGUkv4vPPfiRir6SwZjogBm0ARwfiZYByCR9EiafOlvGeap33bEj4xtyA0wcr39yYgvZnyUXMkg5HGJzfWeej/JERjRQay6fzfGMYmUl6GUyy9u+e6bb7xlmuaQVaAtl1/9+PvTRfoDADa+8OT/fP/mV9ZvIIVrlPxeQSr9y8UgLVpn4ARAJrbon0hfEFtxR+zqMlIJjTilQllp5CsFzj3rA55l6nwuCKGm631DhkF6SZDbSPTWy+F+gCsYBOQ/sCRryQEGpTw2rHuY5OvVNZrWr0MKtllG9K9/Mm3kfhYbX3hy4wtPfvYbGdWtLV547kXyKbX9y8QgWfoz1v+TpQNOOO28zRsnYhwwMzNu8tSK4Lmg19UQ8DgdfLZCsd7uODJU+v4KfSApWimZBJS8bFj3cHigt6dvsKd3sLd/qG9gePpK/xS/+58fZq3Z+PzjVPpXgFWwr1Yers1J6X/CaeM7GqgJBZAqHF3vd9X5nBzHpoYIDl+D9TE4fKjEJLhiAybSRJi2Kh0DG9Y9LEmKJCnRaDwUjo1lV5QZhq6bM6ANwFg0wXgog2JN4WcYCEKXQ2gMeKJxddCIaXHS8VF0iJL9cUmesFE2UlRDq4FR6rjS2lJvTfgQPv6V78RiUlNjcN6cZoHnN27avnrFosaGQCn70TS9imdlmmZ3z0BP31AoHFUUTVFU3TDikmyQ0jSkNkFipqpWLNRJgNhWNXx9N4+PR6iGFIBdONrvdTbElZ7ublv6E7+zPxAeLt3/UwxV1610YkqFXP6hTx04kF0l7cih0JFDB+3l114dPP2MU4vswekYpatXLibGm7fs2dG2f1Tpknc6qrmp7sLzT0vNpe3b+3bWBrKUsDEoY0HTZlQRiClCbSkACOATz28iNxPn4xkgmRgCICI1f8Gg8tF1U6mq4Vlr/PuuP17+oU8V3+aVl18t8ums1sYKjitHQyJrVGbAYzV2YN/eCr5IKbsKEL1k1aaGFMD9T5BivKcfu2Dx4sXd/eGOnuHhiNQ7GAY4MQiwOoSM6RC6Ye460Fu1M65JMqQwBoD0bEna3em3GY1cSFivaeLEvwge7eyZ1Vp2Sb6xeG9StUsTMclwlBA1SmVYlXqpCpgOCuClTfkL7tv09A1efdFZYDKk/zc+daX9Nuh1LZhVH5WUbW937LUSNb3+gGX8j+kOM02TZadNJ7WpSYabhSxY72DOp4kgUfsdwxCVwDB2cYVK5K6ml9AGrgCpzql263nKODGDc4AnEfa+xzdcfdGZVdnX4aMJ43deKwmtOdzZl/lpT1/az37f4+uzvjt+KsEW/ZdecM7SuekUU5ZFHlbwuITte4wquX8IJsak1yxlDISGhuNSfNTNWlpbIbTGBCRpIFNbgKOdvWU5gjDG+hiES0pj7dyxa9SNn/r3Pys+UI1TgQLo7BtubfCPz+nMoBHAfY9vsETwmWMX/YUkvs0XPnaxXQPrl38hWT+Z5KqEsWuFvKI/k4NHul0ZdeLGHmJg2aRUAYyJ5x+/9x0XXTOqDujqLBa2q5SgQlKYGMdiEi4tfCt3jBgJDe3cES79cJTKoAHW4wFs3/wQAODW395XgQ647/GXC9nMX7zuErvcIQndwtg0sdWF2xolWz5cjInTFgOMIEQFtIJ9hplvSj/D+594ubjotxf6h2Kvbd2jK6QCgehpGNE/gMn2j2W1C2PYPA6009YuxJGeb976+xLPk5KXUnRAEYKBMuw+TTfi8RGtI0pXACS22OkoSetD8DQ1/8fAZe+7Jv91hWQO5tpL8seGZY0AplpT+EknIcJu+tzVt/72vlKGApbQz4T4Tb70iffmLSmBSS+X7PVkHs/ym5oQG6aZ8vF+6ROX2qHUhpHQFr/+66M5RydnmEXWCWe5+wuJ/sTZQ2BL/6qMAFLcctNnqA4YCybrWLN2UcJeSM4MQwhNbDt8Ej8WWU+mf01EPgKapmGMBYFnGaapqS7Tq5e5kMWRo7179raXogASFZwz8Ps8Z59+bGoaoAj/+YXPlPi3UygTRtqGvelzV9tDgSw1kCPxE3zuoxdZYjrPU5Ei7/rU5ghBhBJTpvbjhxDSDdPy7ZLvfunj7yVzfSS8kkSAQQB/e8djuTvM1AoQwtlzZn/gwpNHFf2p7RUhMUoQaIzBVCKzM3D6nkkupP5r1/sj/1qd41JbjphKHrmQBWtNH1em/8mdSb5I/X6UaQm771Dn4vmtqff2UAAAcO+jL0KU7eL48icuIb1wE+YYuett783YzyO1E9KS3upKn7LIEmEf1hG/cN0lyFI5pokt3xJ59hJaAeMvX3+pYeKHntu6fU/76mVzi4v+MZLX/5MJHQSMhWMWJ7qBVnYXNVvmf4kwtgIogYyY1AQGwWSsO7Y4P/n17+kggDLVIFJsX7IMjq0JrnnvO1oa3P/353/bKz//sYtgwk1P3nITFeaY+aTxHJsdrw1J/IdJJl3xF65LzC3bG6xc2FyW6Pc4+VhcHY8iI1QHTAsYBhEfTkVxQJb8r7kCNVOQ+9dtu+r8NZN9FtOPEWasrQkQw2MMvviJywzijRk5iJ7sEJfUeIOBEKDE2VsFOBOu4Yef32YrgBdfb5vbEixln0uanO3dmkxazdGW7lOCcy792EQrgEpvbMPEBq3+NAVQ5cpDBmqZPEPX/sFhhCDLIIFneY7lWMY2kSZd+hfCmktIxBi11LkHQ5HBECn0ViJNde46n0MkTTGrAE52nLe55SY69TfVse/vyr6LTXMsOQSU6jFFpdMUJ/99v233pHXxrQb2hF6pc3p+j9ja4K3zu8bpbKgOmOLYBkRl3zUxLr3s6E9+TYODx4tKf8BaZ4JqAf3sL9kBnUEH//EPvHu8jlfOYEXk2cagC0LQ1U9HkdN4BrhiEEKohFnciak7TakMWoJlCimA/73jCU1VGabYdPGgpGZqhaCVkTuOKqEwCEKnyDUExmsEQGeDp/IEgK0A7EDSylA1raqnQ6kEd/llwClVUwBZBj4qKvrzMiipmfvxC4kTu/7aCybgd0IQOoRRZoCz0oDLhUYETVkQhKXEcRZCIeEDlMnBTgMGAPjcpLdHVtCgVaA3PUPDli+XZjxsdR07I0jURkwmi1lvDSt+PzOZMy/Dim5a0XW3/ePJiVEDtIhbzQIRLD5aLY6qUAUw+bidQqbcz0st9JSfIAXw09seSiXxFsIhcKTOD5lhgwx5EV0tk1Z6JkTQMExZGd15OmxtY6uBCRsQjBN0EDAFJwDsEQDLTtAIgKaDjQ+k4/c47Xpmw1bL6k+a+cTSRxCZCDYEnAxCLIMYJvEvxjgaVw3TRAjKit7VH0nUaMElqYGJHBCME1QHTEUgJNUgKq0Frml6qjMBZVJAcITbh1I6bBUcPkTiEw0scCSemmEgqdyD9ZZ6r237k0GAlUyMMXA5eDufXlb0cFQxTJwq802Kg1q1vQh49AHB9FUDlKkzA2wHkLOjFfYoUg3CigKi5YAmE4TQ0gXpejaU0mHLFf0QknI9impwLJHrdjFnlkU+t+h28gxDAiqicc3Qda9bsB+V9P8x5pJjbVFgm+s9qm7ErelfDIBs+f0hhLph6oYJ7EnXfIaZrQb+/MAGqETHrgb+/mCiFYHgJX1s8lM9+44OAsabsgoBESAJIszsNFkWmq6bJulHVsF3KSVyzYc/oUgFsztJATFKdRVAXFYRhL/5x1OpNbY3n2WQ28kPDpsBr8MW9wwDeY7xuUWnyNvGPgDScJhEV2fv1NIW1hLmEdPa6FU1XZJ1hMjkcCSmkLYBEEqKFle0UFhiGDJzQMot2t1Wc8CCW9V0VTNME6u6cdfDr5y0aj4YBxirTGm19kZ1wJTCGgEwFfeDs6oBlVQPzoZOA1SdsUzh1DgFFYBdfTOxDMkgyyGwosAJHBPwOWRFa2302tIfIcQi4gJiWVKp007KGK24CvnU4+QNk3M7ScUh08Rel2BbYJKixSQtEpUdAicKvKLqqkaKkFo1fwr0CbIKwVV2CQxHnWHtuUggMccyikZrfs3AGeDMckBmRbeQYZiGadIyUuNKEfM/K+4zE7/HMW5nNONdQHZ/bcu/LwqsyLM+j8PrEjiWcTv5gaFY0Oe0KzPbPTsqqBQEyeQbRI3H1/mdAICOIx32eh4AHwCti8my3U1M03RF1SRJ2b/7LV03yXAg9aiSM8CmgTMbcjudRJj3h2J5j7tgVsLVE4mS1N+brjs7Kqmyov/jsTcLnWrKc1Ut6CBgikwAjGgJUBFEAVRvdEipgIoLeb33ikSXsRuuKtZ99rIPfbEWRwCyqjlFjmOZgMfhcvIep+AUOTKvy0CRZ/midaFPfMdlUmQwd/2cObMy39pzvinRn+c0SJIORIjnOdYh8v5Tztrx5iuqZqQcMhxpOcY4BKhmZuTbc3K5/+ajq7sfjMZ4OBmpDphyFaErwhxbT3nK2HG7aBpwtRWAwLMY48aAW+BZv1d0CKQsqOZaaAKgAzBvxQK58E49DpBX+pP2e0eOppbnzJlVRPRnQkYYDOJJICloDLpjkiols2/yJnCR4YE1RID2UMEeLthrKnrMaQ7hzC8ImswpraggKC0HNGlACAMe4kKgVECxm16S1ZYGT2Ody+sSNu6RNdfC3G2icRLDUxmZyiATXiz4c0IImus9LQ2e+qLFOy2XVNKJZb8qFf02YrI0RXWhhUKnCCRioeJyQJBkNZb1DVoWtIowiNTyquYeawm2kKN86x5SEdrp9JNBLgBnrnSCWJ7uWj4AQP4dVI4RI/1dC+Fw+BwOUBcACzLO0+ack5cV1EkjO9vY2H/7wZ4igxnrWxD43A4AhsE4QB1BKRA2entHd8eNSgUTNrKimoYGcWmenBFTxdDhEKR4vKebFoSYHBiGoQP0ipmk8KkMn0yeyIuMNXZcEB51TXJXhoG7+kOVnEZhWJbxuNKlpigzD2yVqKrkmxAEg16eL88C/dkPv1PJsSj54NjK528oIxRA1iNAgu9Ta2whm7kBzvcqQGaKjR3Tn4jsTy6TBftwqU+TR0/9mxL0edYkdzUQikViSsHTyHmbPpnCJ88xiOdKKhYmCqSodblQR9CkY1pp6BV8kWWYoN9L5c8kUuKzCQA499Lrx/lcph+sHVyfioHLemvnYKX6rWe6UqAV/5C5ZboAaOpt2u8OTTOxcZYvHhZeyEvyAOntUtsrit4zECnyJGftOZmUlj/fOIWV7za+JgZ1BAEAnn/k71d8+NNgMrCqkJSvACD0elyiWInWp1SL6VgF6JlH7sIAzF555t6ND0/umbCZrX6JJBwZDj3iXU6kNCr0aZact1o0pjauTuWsnD2YJu4eCI8IBs0hfyyovarwKZG8tpJnCHccGVo1J1DixpQpgpXMW7YCQBA21vsrnz2mVANxtE4e48Qzj9yVWp618pycz7PnkzJlfTxMgiQnXfrbCiD9BhaRl6NJ7fzGdb6342RLD0ekofC49HTkiQIo6axtJViZDqCDAPIjhmJ+3zi2Zis2CWz1nygLlmXcbid1P08uDoFrKbf6U5k8dNevclfaQtxm78YHwfSk6OhpxFgATGWs4tLhUY24Sv4ISHpNjFbZIgHRE2NICaI6YFIwTDNq5YSXC8uQ/IFyv0VngCcrDfi9l191w9VpU71vKPrQs29V6zRyy8ROC6bA6DUVilNhLR/i/OkZCMslNGaq4AgMgrkVRZgC1YMdyWiQHUeGyj8UhUwDTPxVkGU1Lo0SCpwXa2po+j3zMwkIoVhmCNY4Aaeh9B+vpvClk5h4I0E4ZKlEQzuLuKzG5ZKisCvYO8swpaeZzG72P/vc9nnzKi9NTgcBD/79D6ecM6YS351HDoPR2tVVTsZccUiJvvxyb1nffu2Fx8fhnGoXnmMmaw5gZjCZIwCMsaxoiqqT+s+yWsEsnL0TTTfHL0rH7mVW4sbPPpdoLTCWQQCNCn3txSdFcWx1HM2pWJyHSv+q4xA4ruQwUMq4jwAQLKkyhInxwaOD3f3RLK+MwXr8Hsfpx86d2+JPiNGd7cV3hbFVqa20oUMFfZs4limxElzqFA4f7hzLIIACAHjxKTKrds57rpBlqcILYhrjOA4oEyr6xwmR58p6ov9834uZ0wCUURTAqpVzR71GBw9kyuh0TLQkF1QGG3ZaE+ism3wBkyfcIXIrFjVu3nk0HpbWvUQ+bW30LptXP+rRE22IS6OCYYLLydvt7EclczNbB1QcEkodQTYvPvXg1NIBFSWLUek/fgiFi3TRZgBjVQAIqm1t+0D1cCRTZt5zYgtJ/Nl0IPWRJGuKOmLYPh5u/UTCcMnz9RCCgMdZ4sYNQbcUqwsPDpQ+Dnj0pe2XnL0670dUB2TqAGuqVpp24wAq+scbB50AGBtsfU7Y9WCIhMTNbg509OSp/jYemKbRGHTvOtBrGCSNi2FYn5tfNNuPAeCZEeEZqjGGmjyYZPzHJHUwHK/L+qsL+IYQQk5LaT26flfxfSMEly9o2r9rR9b64oOAQtKfkusOAgBUODk8STqASv/xZ6qEAM3MEQCDTEWzYtsB2HdkyDD0pfPqHaKwY2+nzy2SuRcMWpv8m3a0r1rU5HAIhzsHSbsYBIPegvWcewcj9QHP+s37XQ4iWA1DZxhyDkGvYyCUlvUMgwzS5CublD4YCsu7DoVvuPKMxAcQxiT1z/e9WKgnMMZA0fRwVB4Kx2Oyfvpau5ZokgImPoNgiVNMLpGf1UjmLRw8ktREShEdBFSX1158ssp7pExnIAA8P1WmeaYpo3i3rRxYgkNgSTkHqy+8U+R9HodD4FxOIsS9LsGuAeR1CXbXMEnRyMwpNO0XBiBVoUHkSe4xxzJCRvoGGcfBzDaPdmp+CZmZVsX/UdxAVqG3aFzp6Y/0DISjcaV3MGJYO6/3uRCABzqHOgcig5E8HgbSA7mENBMIweK5DQGvU3M0aI4G1teU+ujw4c7i4UCXnL360Ze2F/qURgRRKIWfO8gXyMihVEcB2CIZWnOhs5t8dpOVhmDCf0LquwHSocVOhxF4UloOQqIkMiNnoOVLsZdJ5yUAWho8Pk/ameMUuaHwCPlrGGZpnvfRNAAGumkOh+OdfaHBcEzVyDSDiXFnb7pk9LUXrLnyHSsuOmNp7re9LpEr2vnShmOZhbPriGpkdPvlDaZz0w8f7izhD6FQKOVB5EzJvR9SpUD/fN+L9EKnKKg/mxv8XX2dKcla73fZDnoi6DnWbstuqwchWY0PQegQyUABWq3kM/eW0gf2+nq/KyalY4RisqoR0Zz+LVkGlSJ5i2GdnqLqoajUP0SOkPnhnsO9c5oTrvk5JyT9SP9IR/GnikCMqoUgAK0Nvsagx74jU9kM3mB6QvixDTsvPnNl8UEAnQ2mUMqCQWg8mnVPUyrrXF/qAIrnmJimpaQ6MfnzZcGTIUIJhjs7ssK+JOsA4Mx8K5eDZ5hyis/lM/xjkjoUjoejspbTszsSk7NUQt4jlFJqHCF0zIImWwvyHCMr6XKkmTqgOO97zykPPPUa1QEUSslAUSC9YMa7Ety0kOMVU6oCgACEYwqDkMshVOXAIydXs8OrK8ruS+9Etgz/obAkkSqPeWK3MQAHOgbmNHoBAG88/m8AwIkXXZ61DUIwNbgpgtctttR7bYXkc4uaHjOMPEcsPghQpHGpY0qhzFSIX9ox+gA9k0f+ff97L78KzEQ5Xv2ewK9vPwCAmJnZ290fQRA21RFfx9hhxrN2UvdAOByTLbdSQQ4eTSgAG1sNZMKzrGO0KkAIwmPmN3qTDSObgu64rEXjSiphqHRHEB0EUCilwzDIUTgLrDaleQWUcQVDUbmsRMi9vazdaHVJEx5jBm+5DIXjo1YWUoq2jrFzgEeNMuZ5dm5LECYrETUGXOGYrKi6PdtcliPIHgQUmQygUCgpGAZV0Av+kX/fn/oXAPDeK66p8UtK3O6ZHXGzljMpEpqZGcGZwjSsPqtFGjQWacKVLLSrGmLel8vpP3Hl3C2727fsat/w5r57n9p89xNvuN3OXYd6dx3qrayuXBZOkS/eBwZCMK8lUB9I55R5XUJjwF0kO/GxDTuL7PB97znF1gF5P6UhoRRKChaVUaWRUgiUbLCeltUY4yItsvOK1kgsT9kfh8gTV35RKV/oo5QMzeoLX12e2rh7U1v7/c/v/ddLBx5cf3Dkh6NPADAILZ3XmBmtxLFMwOvwuLI1R2ZUaBEdMOpMANUBFIoNy457p+5awGoJmWz7bs+Oppv35vtCTFKzQm81w+joCTXmzA1EhnplRdMNEzSlE6MyEXhWkpX8p5U8RvoXTuqAov3bKydL40EIxNE8jHV+V/PIvxpC4BS4oNc5FJZjspp5oiU6guyQUOoIolCKAlmGSbleS+T9H/ooyfu56KQZ79kvHUvGFWjYC/MJ2q7+cObcKVEJcTViOb5XrFicWTxu+YJESedCcIWTODTNSBjg1gnZKil1biN+9vHQBtYR3c5i8U4sg1YvaXXmxESxLKr3O4cikqrpmp7fY1Z8NtimkA6gReIoFGgFClbWPyrFn399S4ktIXN7As8Y5VG0GihKO3YkWT3j2DxldiRZ9Xscp6yei00zU/rn1oJesWLxwQPtDpFfsHCuXUF67dKW5zeRBGCGYVcvad6+t9uuC0RqqO3vO/uEkeV6qgGEUOBZjmWJeV4UJlkGrhBetzi/NZh7BzIIel1Cc9Adi6vDEdmaBS8vIqh4cQiqAygUiCBfNFE0txb0VR/4sGGY1Zo2eChfm/gSmVLKY+IqabS17bPLQY/sH5BAzcnVqnqzVYZBbocQ8DqCfveWPUeLbAkhZIpOMUEI5rcGC2kIgWPr/a5wTJEU0uws86MSHUFnn7L6pdeoI4hCyQ+CpAgEnJ49ex8ag/Kouv4YRQFggFMJv4WWMzZOrxp14xQMIvO9+w4P2wspdu0fXNBaVxU1YBv+PrcY8DgdIjenObD17c6C09zW9nYNvEIbOEV++YLm3Bihux56ufiZxKS0PigyCPAmHUvUEUShFIgBRU31vtIvDoSkdln17copqT9KVxLFFICJiYWbKSaxVQBuKCJ194XtUgoIQSt8CHMs43WJQR+RsKpmDEdI4WVFHSXWPkvoZ1KtmJ+U4e+xKruRWqQME/Q6BwpkwNn3ClEAhffZFPT4vdljzNe2tytsnjvSViS2vmE9QI/0l6IDLn7HCY89v7nI30UnAyg1wjUf/oQiRTPXlFsr7NxLr7/m2o9Mf+FfBSWRpRvKcwFhDFTNCEUkO4uKYRCDkK5bpQ+si4sxVlQ9LpEm77kVeMrCKfJj19eiwPndDr+XFK/OFOnHLGh6eUu6HxmCyMSpCVtIBpiF/T8MgisWtXD5klC+cNXJhokjceVIT3jHgd6hsETiaUeqMtZTn6kDCgGNxCwFjQiiULIQeLaCMnA1I//L0A3lXUTTxJGYHJMUjDFCSOQ5qyF74lNSfVMzQlE5HJMlWRtLNpZT5Ov8rtK9drn+HAih2yk013kagu5EShdpOZCgIeAWrUL/EEJR4Fob05Y7hFbN6sLHFnh2VqMv927CGMSjESUe5YE2v0E8Y3nDMbPcfhE4WcPJJF8cdnLY6w+UkhZwxbtPthdoahiFkvVo209omZXgYCoGlFK2AiBNtVQtRIprkmL9osA6RS7tB8ckVTgaV8IxeYzmP0Kwud7LlabhMQC6YWT5mlgG+dxia4Mv4HXyltvHJiW0EYSL5jQwDPK6xOY676pFzanvQsv8L6gAIDhh5XxbeWR/kvoGwzAc6/U6mxt9XpeQHnnYTRFysqkL6QAtowsu1QGUmiXL/8OzjEPgKjDnp8w08PRUAKZphmNKTFYxxiyDPE6BH5krq2h6VFJkRR+j88fvdnjdYikDNtPEsqINhuJZR6wPuFsafG6nYBv+KTKHCQtag/V+d0uD154eSIX0MKTrWcHLInDM0nmNxTJQkq4hjmXq3HxjwEVKVlk9cbToEHnFQ1o85OBLuvJ2cQgKhZKC5yrx/1h9SqgGyKbUOQDi35C1UFTSdQNC6HLwbqeQWfLMNLFhkmpBYAywDBJ4rqneO2qtUGwPOCRlKBQP5XRzbAy4yRxR0X3wHNsYJJvZh1qxsPm1rftIAGiR/BJIeoS5XfkTxJ599gVQKYVmgzOLQ9CIIAoFAEj8zoVNtLy8/9qP5q1XRmHrfelaZjaDISJ01hwzBwCwc+/bqfVBLwh6SQ7w0T5pOBQSOF9CAVgXNne2s1wQhC6n0Bj02C2CC4LJsZR0xX8t97iFpH/WOpJLYq3a196zYHbDa1utAFDrlff4JMnLLRayPr7/9Q/YitA+m+GItKnt6Ft7uiIxJe91YUWgh/tG1QF2mWh7meoASo1jt2kqXqUxFytQkXSrHbfzmq5UkghmN2ux9IEwrzmPOdzVHz3SOXDcitlFdrK/vW/hnPp8n5gAJCz6gx0Dc2fNzmP4x5WhcDwSJ5WG8u+9wA+dHVaU8YaxbAoi/QvfW06RjHsKfbp33xFrRwwwEmd1wWlLzj950ZGe0IYth/d1DGYOmGxYb0OmDsiLIsX99Y3D/b3FN6NQagGISNRGuYIc5q9rQykzCmji4Rk58yWwslNQGwNwdiOvavr81sD1V55xg/X6wIUnRqPx5fMbl89vLLS3rPum3udKxzAlWhSQFo95LQUIQZ3PZeuJYiSlv60SDh48KoXDcwLcnADn4Uwn0rNepUQEnZdRFYPOBlNqGTIDLJatAKxKx1QF5AE98urhR18/+timYqURKmGcB1upQnLPvto2xkMuqOcW1HNGfNAOQMqtMfvo+l12SoHLWaw6UCFYBgU8jqag2+XgRh2D5tUBihRfvChdiKl4pSAKZeYCHSJXVgpYGir/88H+/Cd2Yhi2IyqJB8Y0tO5BjDHPsUuWLgKGuWVPx8tbDkbiss/tOOeExZcvaVVl9clXdr+5uyMSkwEAupbdYJ3l6jfskiEEZxyTaJdYLoOh7KndTBiE5rembecShX/xzDIIAakxW8D8J1FPFd15pLI0zzZY1YEUVVc0I+te9PoD4eGh4jtZu7hp3/5DxbehucGUmQ2EwCHwFRQBvfcfd6bKQVPyzgFAe36FLCLAuaWEtDTNzu6hrW93RuOKwHPL5jUunFVHailoqM7nDHgccpgktXIj/SIkkxYZhmEahvnajggoQP/O/nTEpN9V53MJvNWfwOKSd59hmHjH3o5QVGrvHgpHZUUzFFVjGO7sFVaQaEqcl3w/FN8QQVJgJO9HDEJBnxNC+Mab2994M48B/h+fvbrYnhH0OPmmoDsSVwfD8dyu8Zk6IO9ssCLF33Hamuc3brPf0tlgSg3CIMTbM5AVcc9dd9xz1x2Za95bWkvIvKV1xljTbYrA3veXf9pLV3/8gyM/sjoyxpTt+7r6h0kihoNnt+7Y1d5+WOTZmKR29Yej8WLtXDIrIY9yEszI0puWZH9p45t6xnwvCT8FgIwmDHnj9hFK5c8PJEqw2S0hAQAnrCAhTKOOAJYvXyYpml0qDgLAvnKoUJyAzy22te0p8if89Hf3aUqxIUtZ5NUBLm7E9aQlIii1BoMSj+pU4LLqVeW84tJLiZDxZgdkFsLjcaeWzz9zTXWigFKagCiD970LAHD3vU/1D8d6BiJ2sI0U5uwKaIaJY7IqFyj0ZgXpIk0zSowKTZTeHBnlqeumqhnhqDQYjqsl72r0Y418u/7VNzPfFsoAuOSs5Q8//QqYAlzx7pMffPr14jqAOoIoM4yHHriXCNz3XSPwrD0CKO7LzWoGcO6l14OpzYMPP1zk0ysuvdTWDW5L7mf94es2bBuLMsgfBnrfA8/YmV/DEcmW/sQ0Th5Z1fRYXDWMPDlfpI4Cy1hJYaVmhGGMDatqEIRkWbMcR3FFGw5LUYkY/mD8SEYGQAgMA1sVoPPfV5e++3QAQHE1MH/ZMXajaghBKKrEZdUKKiWdSyGEUUkJx5Qi9ZFYB9BDPcUHAZnFIYpAdQBlpqqBLC664D2Ftv/Pz5Xk3pn6jKoeAACzZzVVpgzYlG1t1XdLF/bRDSMUlSR55OwuJolOMUmVFFIQInd3HEuiKHMD3kfrvkIasCAEdcOUFS0uazFJCUXlahn+KfIaDlaxZuKmX7aweVZjYP3mdO5bXjWwYLZ/TOeASY7Y1r3dOw/0RuPkMqb+SNbXNKoOyMwLo44gCqUQP/ltHoUx83iwgHr41e8SC1dceul17ycenbwQh1pCLEJS2d9aIGtikhqJKbYhj2A6jUJWtXCM1IPL3RdCiGNZ3SivIAS02zuwSNOJ9I9JakxSR20kkKtCSPf5Eo6V9T63z/BZJyzloW1oZ4c22fT0jJK6VQpLW11N3pbu/khHX5h0jkyNDIKkumGRrmGKFHf46qRQegPqCKJQ8vL4k0/RK/Pgww8XGUOQOmWJUJqEN4QoBE0nVZ3tli8MybuGtsNHN0wlpsetenC5+yIp2hDoBTqhFwIiUtzDNHE4SgpNS4qm6UbppaRZlnGJvG4YujRKm98pBYOgxyVACFSi9vR41kir6CDgvLWtj75kKYCi8XDUEUSZ8VARP0YyYm/sQFAreicSVyIxYpYypOg/a7uGMMayqkXjSl7vP8sgnmWIB7/MenBWXQ8YiSvDESkqqapWqvSHEDpFviHgrvM5SywOhadSnghnVVRtrnMHvA47biqF1xoHFEkN+8AlZ2ZK/0fX7xjnk6VQKDMQlK/ovz5sxd5ASPLuBD7RS8uqvZy/1LMdSUmcJuV4/5PfJW0GIjFZUlTSXKw0vz/LMn6Po7ne2xBwcyOrUhc7VoWfjReM1beguc7tdvBZ1nymDsglGg6Vsv9bbvrMWE+RQqHUigLAwDDNUFSOSsTJw3OM2ynYRRcwKXJj6hmFbjJhWcSyDKnNVqb5DyE0TSwpWrmGf2PA3VLv9bnFEvvGTMERgK10BJ6p97sa/C6hsBrLOwi45KxVmW8LDQKoDqBQKIUYIT0xALKSKPqPIHQ7BKfIpyIjCyV2IQgFjrUj90H5WHqFFJMuZWMuafjXB9yikNGPbHqOAOyr53bwTXVuv0dkGFiWI6hEHUChUCh5GZF/axjE/JcUMiEpCKzP4yil7hJpzwOhoukVRG1ijEtMGE55/FOG/4wp7s2yKOB1NFq9i8tyBJGUvQtJZGpx6CCAQqGMogBI6KeshqOyYZiWb9rhcvBpC7uAlEYIChyjW9lbYBywbXzb8G+xPP4Jwx/OnIKApMQFzzYGXfU+ZxGNm3cQIEXDmW+pI4hCoZSvADDJ7x2OSLJKauM4Rd7ndqQqo1m1tPMLT4FjIcn8qsT8L47dnN0hcA0Bt234k25c7JgM/yk7ZiC9xpxCU53b57b6GJczCMhyBFEoFAoojf8PqMRMBRnSmtUAAAAASUVORK5CYII="}], 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"}], 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x9u6B2WG63deVQH/I8eibW474gwO9WFccs2DFMQvSz6+nB3bsObRjz6GMj5YVtbXL1+sLcxwWBV5RyTO//9k/nv7FP57+pX7A2otuvO+nfyzIbQMAUMRcOoFRfElkAqYyBJQR/6nVloxP0dLarYuBNxB7c9O+WELRPQAd3Q/Aml3X989tdJ903MxELJkGWLwguabz9t1NQ13A8mXzKUVt3b5XNh1QFLJsft3CWdUVNrMocBjj/tCQ4aFndzsqHHV11Y/c8+kCfA0AAJQY60dn3Is/ClQsHkA2KVfAXWGurbQF44NC/zo0LU1ztN333u72YFhfWIYVhuqPbG8gxdZdTe9t37+/uccXjN1y+SnvWznvr6/u+Od/9xxq65NkReA5zSFQb1238KZzZmzbtrswNwoAQInxuxcG1kAsaSdgyjyAjOG/vpHhBOjUuCyPP/WOPyLHtH4STjOXHgVKbSOEKm3CqsX1DpuJ5Qa0NMDKYxewolEN2u8NqIR29QU9/ohKiDbfm9704bMyPvSvr+6Y0+heMLPaYhJ1P0MPFl1+7Sfz/EUAAFCCrB+FcS9+D6CIksDDcMlZx/1329Hdh7rTcwBmA69Z9QEIoaGoZNTaReip4G27mpIyoHkDKzVXQCFE3nYgFE1Ehm538cGzl7OEz7+3zp9ZNafB7XKwMlNR4PWVowWef/+HP174+wYAoEj53bCTworf9BdRCEiv/xkGnsfHLayr0Ib2OmYDrypK+vCf9S+Sk4P1DE9i266mrTubmBhoUSGB5xYvnJ0gLNKPEMoe/qe44vxVxy+duWlXs6IO+iBFVf/258ef+f3PxnyrAABMC57SyoGyufTaz5WK9Z8yAciO/+gakIoFpahxWX7z13f1iqBqlzX9JYkMWoTNZeETstLcE6uscrW0Ml8hHV0YkjKAkMthXnPMrGACe6NZ05GzuPB9yyKhHJOTdYdA9wkAACg3Lh1s6EvL9BdRCCi99DOb959xjBZ14ZbOq2nrDsSHdRd6PKHWroDLIixZuASzNO/BnDKAMVq2eO7hds8MN6s0nQjtHZliAwBAWXFpqdn9IhKAnInfnLDgvijEJUWP/2Qvw+yy8L6o2tYdEGvdW3cexBitPHahFgLKIQN79h+pMKF3D/TqK8wMVSnEsg4+tvT8jj2HsueRgfUHgHLm0pI1/VMmAEOV/2dPBdDjP7oHQCnt8YYjcWmo+E9q2+8LbA/EMMYmg9jZF3Q5LMctnYcQOnioJeNKLCZDjT3ZU1ovEBpGBtgxew5hbfbA+G8eAACgnMtAh5//lU6Ny9LjS07skmT11Y1NRzt8VMsAxyQ1pwD4oqrbwoviQLpYRxR4lTPqmYYTl9am1CMQjrd0+o5bONDkT19aIKUE+vBfp7nDk95cKH34/8k7vjaOrwIAAKCsq4CyE785icXlUCShW38t/jMIqqFvv/Dcs9lvl1kv6aiIpFA08fLmlpc2t7R2+yMxyWwUrRZD+pG6NmzddXCoKcR6c6H0lhJg/QEAKEW4Ypj/ldymSJLYIjEZ9T86XX2hQCSRHv+xW4zZ8R99+7m//OG5v/wh5wW4LdhtYW/f1xZ8e0/XzqYOXzC2eW9rxmEcxqmJYylmN4w2XQEAAFD8TL0HoE/gclQ4DrV5Xnvv8MYdLS2d/mhMUlWiR//1+E9bT0BRBorxb7vipEVzqnB/HtgfI/4YSW8WlJKBnEqgy4DbwspAvdEcTSZSrsAwePxsAYMZjXW/e/x7Y71lAACAsq4C0iPyKqHv7W7r80f6/NFITMII7z3SW2EzNdbYG2oqHFajQeQD4USvN0IRTdX/YIwaqh27xO6EPKhJ3N9zxX90Dbj1tluD4XhCUmja/GHdG/BG6UubW85bPSvjjVSL+6cP/Gc3VGLRFIvL3d09+p7mTu+72wdVGQEAAJQEwhTGfyilvmDsUJtPUVVfRNWtMkU0ISuhSGz3wdCew71mI+vroKok0N/lLVX/U1dpn93g6u1NrhOQEQvK5rFHH0MIfeOeLyuKGgjHvYFoJC4RbXkZXQayyd5LCG1qatl+2KMoRM86mAW2TtllV1733F9+N/6vBgAAoKw8gNmz6izeyKHOsDeYLO5Mhxl6oiS0RYAz+OSVJ7FLF7gVi+pe7vXq5f+j/NCv3fcdhNAD93y5ysnmFauERuOSNxD1h1gmwGw0uBxmp91iMiTLQ1GaE9DtDW3b1xaNy8HIgORkr2wMAABQEkxqGWi6B5Cizxd5a+vRzr5Q+s7sQs9U/x+JYF0AWG2PrK5/eZuiJqP/b/9r/Vgv6YF7vqxvxBLy0Q5vtH+eAUKYw9hsEt0VlgqbeUaNc+v+th5vuL/oiPkNvijN8DnACQAAoIQQprD+R58BoCTiM2udPV7WnFl/daiJvtnzv/o8Pr1N2/DBn2G4u98hMBrECps5lpD7y0kpoTQSYx1DW5Fvd1NncgKxBsYcpcQ1OHDE6fWhAAAAJcKUVgFpltZqt3V72PAfD23o0zn/5IVNLZ6mFs/B5r7X3j2s2+vRh4Bycvd93/n6/d+psJn0JelzXelQgR4mPxij2krbp645dyLXAAAAMG1DQEOuAEORNxjt7A0davV09oUIpQaOZgtAKv4jCvyAeBjYfzDGvqg6jvhPTq796MfS/IBBYJzUS5fDvHrZrL2HO32BmCDwK5c0VrtsFU6n/urFV9ySlysBAACYDgKQYf0TkpIaaydbQVAUiUk7D3a1dPpC4WhOAchu/5Be/5OzAHR8PHDPlwmlcUnxBaP+YCxVOYoxx3N4ydzahbNrcH9nCITQvBnVDmdF+hlAA6aWGz/zvxl7KirsEzznQ9/8ygTPAADFxlRWAQ3qAoSR1WJYc+yMY+ZXv/DGfqm/6HMYUta/EKF3DmOLUbRUVzRWV1CEFIUEI3FZISsWN5r6pWt2Q6XL7dIXGEinp6fvVz/51k2f/moBrgsYlbkvBJ//6oOp7R9/6y74SQDTAGGq7H72mi3ayl/cm5sPH7+s8b9bm9MLQHOmhV0WPipjh43NFHvqySfzeIV33/edVHWQLjCiwFVWsFUhU9ZfZ9uuJn29SWCamfvhATGYEm64+da6qorvfPu7U/Px0xFhqtp/zppZm7P/M0Jo/sxKRSUbd7Sma0DO/s8mo3DScbOokngKTRlbdzWx1Yb7ZaCnp2/qrmXacv6HPt7YUDfKg22Du/sloSMsOzoiGFGKchSbgRgUmoe+/8D2/W0IofpqFmi96y42OHvwQVa/B5RADmAc/Z9VQrfsad+2v0Ov8sxOC6fqPu0O+7LZzq/f/+O8X3a6EzBMS7hUJkBffT5dACAKNHHO/9DH9Y2hBCC3uR/M/L6Delaf6o1jEWprWDa26xhcFJBTCdKBGFG++MCHrz37pKWrj5mt/ayTbd6NVvZDX3bCpXn7mHJlCkJAoUjCbs29EGNq+Reew7MbnHuP9Cgxaaj+z/oC8C1d/j898QQqDrbtamqoSdYCAfkw/cxip4f+RmPu5/UdSHUJTKH/tmj/Z//M6do3potRCWmtXzpwNjRQjzC8WwBKMHF0648QCkfjNotJt/5sUb/31oMGFLsAZFd/RsNhu9U4VPxHh1La7QlL/WU/2cP/VJnmuKeAjTUTkCK7PVy6E5AChv8THvUPmP45bNVoajTmGDfM7zuQVQeQ/7IAnuPmdO/XtwmhLfVLRikG133izt89/oO8X09ZEQonV4VCCFVWO9Jf2vMeK/4GGSi9KqCcgaD0/s+dvSFFZX9a2elfX1R1mjlKqT9GCicAoyddAzp6/A01TrD+Ezb9STuumf4kiUQiGAiexPkHv2myJ2BzHB4QA0pb6kYQg+s+cSdCCGRgfNx21elDvdTXNaq1pIBimQms938eivTlX/yheLcnrK//lfNgfQGAofo/5wu9UUTOHtHZR+Y8GBgl53/o4+d/6KY004/nzKxNt/7BYHBZqCXL+k8xHGZiMKd7v6lpm6FpW/pLGKmKPFDNrMsAMHH6ujz6A77MYk8CZ8//YgkcrZJymPXfCaFb93Vs2dOuEGIzi067JcjWAqPavwWc/zUUOQNB6VGg62/9n0JfwzRmzVmXuVzurIDPAMFg8CQ8qXa/KxTTN46rd4/vDG8bBlaZZktPByP6BvgBY+UfT7Eu7jrhcLROKwTK5vSLbxrzqYFJDgHNGvyHPVT8J55QuvpCem84RZYXzq5aMre6qcWzcWdrMDLyBLG8k2oYlzMTANY/T+AhTT/OgykfkzWvtpmzd/aGB041IgvR4f9EzTNmJGWgwmHVNQDCQWMlnJYAAEpYAIap/kyv/2E5n2iizxfR4j8CRsTlYH+N82eyt7+9rRkhNeUETMLwP0UqwjNUeSgwEUZj+sdhzXOa8rFa8/FxliX2n7bObA2AzPBE6OoN5HQC3njhV+AEFJcAjKn8P7VNKe3zRWRt+V+OQ7WVTl0AMEbzZrhjkUhrlz8UTcaC0FSQ7hDA8H/iLF00z2QaKO85g/ay/7DOPeZCWHN1zZlmiylVTCq9/k+UVzbIyVp1NlFRpK1tnTNBA4BiZfI8gOE1YND6790BWav/kWXFVWE2iElDz3G4wma0zq1p7vAFYkE0pUDKNy+sOesyFvSLM0UX9R80l8/hubrmzOEPMJxxob5BtSmHOvJb/8625uPDLNK+ro64gnVXoMLB1qFLuQLAiFx54x1/+fWP4IuaDlVAQ62emF7/4wvGen3sz4PV/1B6tN2XcbDA4wWzKpfNr53M+A8waeTL+vfMX9kzf6XHy7LHicH4fYFoJJr5iMb0B5OiU8/XH3m5kuNoaA0fbGvrTO3RZQAYH129gZz733jhV/CVFosHkD3/S5LVHQc6RYF3Ocwuh1kUeI5j8zPT4z+E0M7eUCwu608lgj2BgRRQd7dn1szaltZug8jPrh54F1DqpOZ1HxftGM3xww/M5y9JFeazEYfH67NZR/htoWRgbKJrAPMeVGXVCatYu6f3tqYffBwZtHxpikZtlelcsP2NSH3JH3Y5bbogmYxT2YgXAHQm6bfwUKunyxPuC8oYY1Hk3ZoG2CzG+mq7xShccOpSSlmUPyEpHb1BRav/UbUoUGr531QaWS8lSi0pA5Q0FQ77wcMtbBxQ7a6wWycecnFUVvf2eqqrB6WIw5HoiBqQTjw+UGwmisKqE1bVHtic9vrI4/dALLW49AAUq15fwKo1tPnFwzBrZPxRoKFSwUBRCEDG4u+vbjqkN2NRKUWUKgnS3it39IYw1rorU3XuzGqH1dhQ7YjG5b5k/IfV/1iHaBmEEPriXd9Hhefjt37y+ktOfnsrayXW5w3+6KH/m4QPLR/WnHWZ15d059vauzp53siTSpfDYBAneOZQKMwSyXY23B5RA9KH/xnWX09EBYMhUy6DPiY2IjZ8Ydafss5CEzwbkBOoBSpGD+DqS9YihP70wrsZq+xqckBURd1zqIeJgVG0mER9shiHUUOte06DK3V8d7enpsatBwomZ/h/4y236RunrFqIEFr/ynuT8KHljKqqvnAkGI7aLGZXhc1iNiE8ZLxlqNjL2wiZ+mt8MmQgXQMkSc6WmQzTHw6PkKpNNaRSKSWEyoRIClEpTShqTFYVQuIyiSsKIVQhtJWLiqIg8FwsnsiQHCA/qWD4UsfO5AUiP3LxmtT2z/7yTmpba/TG/pSicSkaT46zFFWpcVsXzBpUNdTT4x1+MkEeufPO2xWV3HrNOak9t1x9DiH0xz9+eHIuoBxQ0wpvWDWwHEj+PsYkJRaw2M3Lal1Vw+ZL9UhLerzlmFjrf/0Ou91q7bf16TIQjkSTa0qzVd60xiSaETebzenWP930K6pqObCjU1WjkqJQKikkKikqpapm7on2q0uGKm9IQxFURVU5bQQDTBw9CkQIkWS1xxvq6A34grFQJP7dB7/+P3fdD9/wlAlARvwnJ+mR/XQx0NH7P8cTA42DMob8kxP/yebWa8/FGD8EgaA8seWN9avPuDQZHiS4i6+oUQZaPnSFYs5kDfCojGZUVmKaaZ5HolKA+Ornc6zMgL1ktVpCobBu+qnWUjQ5/NcMt6wocoj5GVIiKSQqIcFgpKu7V2rOXO9zIqSs/+bX/5rH05YJ+452S5IiCLwkKxjjvUe1+SL9DUR0CcYYdXrCX/jC7T/84UNTfb3lKgD6Wo/Dd30YUQwUyunRngwmJ/hz5523D2V1MEZ33PG5H/0IkgH5YfPrTAMopRxmpUA9gjOlAW6zocFhIXqsUPtHIVRWWYyFsAE41YfhbIPQrlAs0O8+JulmaduIs47nOKPRYLdZDKIgGkSL2cSxPaLdbovH2KhfP3s4FOnz+D2+gO6XmH0DVZsTp1uAjOUIXPDBq3WNNBoEjtONetK8a7adbt3PKsT0apE0j4tSMvC3ymP2Tlkhn7/98xyHfwQyMFXN4L55L7OhOqMXg6HQRcVoEGprK7u7PYX2AHQBIITecvXZ2a8+/odXMEagAXlE9wO0v+bkn3KN4ueZJmg708PtubAahKisDnsII+5mU7EwwqJBJISFbSilqqomzQ1CRs+oilB1BH5gIrqkDHSB7ic5gOEx7hJchBKMCM9hjuMMovjWv/6MyoAPXX2DPpDSfzD9a7IlQ74JSRkInfXvZhrAcwaBZz8nhHkO93+Rg7j3UxcP87kcz97xi6feVAn98UP5XytwOlHwHIDRIKSv/z5xMZi0+E+2QfH2ehFCHz5v1evvHpicaygTNr/OlvVYfcalWPMDWL4nzRUYHg5jnuPoKJb8NXk7h5oAmbTmPJ/LlKfDrBHW1ofRjbtJ5DmMBbPBYTKwDQ47zWwEqxBV5Jiy7HfMrkGIqCQRj4migBF68ucPlbRBZ6SadrNBeloUZnA0hrBcd/LV1FQPntl29u1xJjGekFU1qQ48n8zJY4wXz6u/+LRj3BUTmutz8xWnsbDtVacvO/nyiZxnelMoAfjfex9KOQH6yJ0Q2tbeMw4x0Od/pbZRgfnyl+5Q9CKNtJGHbvpTUES/ee9X//fekbMdwOjZ/Pr6T589YMerRPZT2Bg7b/h3EUpDCRkjrFmiEQfm2bCfsqwSHjNsRpEpCtsQRJ7jMHaYDIqq8hw2i/oeJHAcG5mmxfRzwe91zGHJBoSiEZaFNohCsZn+y6++IWOl43SbntzUX9H+y7wmZsfZl0Y0oda+M7aAq3YUHv0gXWff0Z4/vrg5Njh2Rynd1dTJcfii9y2trJjolOl3tiSX7gGmxgNISIpuvjkOV1a5PH2+AnkG+eI73/vRl790hza+y7T7KbKXnAXGTbrRz+ZE80tMG6QLh7Dm7Adh4LnGCktHIJpQ1BGNvh5UqLWZqmwmiygYeDZQF9iDmbGcmaexopt+1G/6WWXEZJn+3Da9351Njc31DVUb6KRsOjsGI22GfspBwmMy6GOlxm2zmg0ZAqBf7o4D7e1d3psvP7VyAn7AO1v23/TpuyZ6ldOaAgpAtk23mg2erAMSktLd7RleDAiheqB2Mut/oKp4Cu1+BqsN/0QGJFdezcLo2ghd34hIsh54sRgESmiLP2lwMcIGgQ3Q9YM57RGVlYQmD5TSnnBcIXR+pd1mnOiks4Ka/g9dfUMqhp401npEJe3XMz0vmrLpegydZdcZ7PazT14Imz4mzEZDhc3c5wtXVgz0eVUJYRUgGC+ZU/3W5v3b9rXdfdvF/U7G2Ab+YP2neEWwB+75vD6kyrDpLa3delxIfxqLy73aWFtfLCx95Uh9j94HYhLSvzq6B6APni45c3nOY/6zaT+HMUSBCmf0q3BuCXbNvjF751FvaH9PUDeLNTbzigaXHqZPJxiXmvpCfZF4Kvdo4LkZTussp9UkCpNj+geF0fEINj29f6Ju0xHbj1lkauyp0SJEJfSZl7e9t7ulymGgOBm7o5TKirpsbo3QP2Nj27622646s6bSnrrlf7+ZXH1Tz+Rnfx1Oh2XB7DpYIWCKQ0B33/fjnGKQoQdmU3IUlpAUo0HQX9V9An3PJA/J9ShQslhhCGBGTyGM/mjwNf86cXrSttY1b2fzthLyEW9YN6AWUVhW68y2/qxNkMmwqtHti0kHegN+fQaZc0YAofbm9hkV1lkuq7Hf4oyS1gbWw/zIoX3sSax1cLwFf+jqG1K/P6nfI5WQpLlPhmOSI3XtgnGpG/SxwnG4ymnDegkwUinmCaWNNc6PfeTM555/O/W3v3LJjEf//BpF6KRjZqRKthBCM+qSI0joC1QCM4EHi8HtumKnlCA7zasP+dPPMAVxd5i3WTR2P52mpsMIoQUL5nXNXkEI2bx9rxc5apFP4Lhj612m/gUkUvyxVTYa9f4QmCITMpv0xWawXpoye3EbRX1tTfMq7dVW01uJtD8Kzab32/HkkGBgbH5wL6t1SR/CEz0vysrRtYKhsrPpKGuQrs/k0JyWzGMoRa1dPrsBxeKS2WRkKz5J6sc+wpZwuOySUxBCf3nuTX363solM7bua3tnVxvC6Ok/PAG94UojBDQaUsVC2aH/VGJAjxdd//GvTtpVjSYKpBeDQhSoQEZ/qBAQRej+V5Iu44euuy0YDPuCsbmN9pxDBElWu3xssC/2d/7RWlBpW9rRzgqb9l8s0mhGjjR5uObtaTa9GMPok0Z61EXPyY1mdPSFrz4wyvNf+9EbeZ67dt3amTNrUjubDrXpGzubuvRYkP6jWXfaMqs5taobAxaLHx9T35T8f+99aCgx0Lv/63smuf9zKgoEDM9lJ0gGHhlYESWKykjk2EKebPxbGIdNJmh/3c2Xs0mjGmqswsZXWG39bYW0cpbU0RjpceSzz1gze1ZjzhMKmrvw/D9e41WurAx6uk1XVaKnFvJo0MdK6tNbW3sQQjNn1qSsP1uDYUEdGxM4TC9vaqIU/f3N3WevXVQ15AIMQOkIwCjFYKoWAMhoWFaGXHHtx1JGNWdSREUolvNVzRpXtv+c11b+yYsgNDV+IjtOf++nLhnmLU++yuJFw3PJ+888aeakro5XCEZMjU6yTR8TGOOVixp2H0wa/d0H2xawKXSos2fQfMBz1y5ACL28qenVTQdOWj5nRq1T98xghYBSDQGNhm/ee3u6NkxmFEhnmCiQLxj9/g+LZbr5FdfeOKSh7rcFemed/j05LEQySDKE7dCmd6a/m1kZ9keo/W8Yi1Pd8fOJh4Cu+sbzYzpJSgCG8gBSTkARCkDKoOtfe2kZ9LHy0Rs/vnJRA+vYISUXBNQZZi7Yy5uazjxhQbUr2e4bokAl7wEMxeRbfz0K9KUv3j58tQ9FtLkzc9XiUXLFtTcO2OmsD9E7owxtxpmZzn5VKzJhDNWgWI9l9+cohwKPcnA9Jn5y99gEYJqnRjWDPpo6gxI16GPlt7/+5ZL7vpK93xOIDCUDujeQajIBTsC09QCmig9ecY3FbHRXWK44fzVhXSeJqpKfP7ch/W920Hg6i2QfyzQGvTc57T6D1Ng6+cvN2s2MYuyXR2OdX35y9wfymAQeqxNQUA8gPeqiM40H6ZPDg7lkYHhXgM0OqWB+ADgBY6V8BeCCSz/C/qNZVp7n2Az4IUo9BhvxrLgJZmsK6s1T2IxTdppRXUDR2utisP7DCMBYNWBMApAedUkVBY0IGPQplAHd9OuAAEzPEFDeWXf5NYqiZXc1066orD0wG2hzWBR4gefK01gXifV38axKX0WIJ9HRHP/ia1vSn1JtwQAW5NK0uBJTDx1YWzSb3Tv3aP+imfZkGSt2z2Vm/daPj+Pigbxw1z3fzikDGRGhdOsPUaBxUI4ewLrLryEEYS6rnEQb7NW4bVect2pOQ3L9YWDSTL9u97PJlgHHOfdlH3bnXTniKpdccyv7JV806+jRNqfTMdRHWy0mo7ZSGJuBWDdQh34HaEBxuwIZAgBOwFgpLwFYd/k11S57t4et/5dDAPqpdtuvPB80IHNknepkoCtjhkAefvnecfxEhjL6w8vAff0TwYYnZf1Te75773C9Ib9y30B/b5CB4pcBSujCOWx+QAY5A0HQF6jcBeDxh5lTGYzEt+9v7wtEcH/zKa0lgFpX5UiF+7u90Wq3/YrzV85tqCwhPyAzEoIoKzUZwl6PifEZ94kb/aFkYNQCcNvorX8KkIFSkQFVJU2tfetOPy7jMBCA0VNGAqDz+MPfjsQSOw529vijlFBMWX/ghhonWx1Q60ObqoA/7fh5VW7nvBlVkzO4VthszGQp/USMdc5ISDG0dpiI3U/ngZcG1YkPxSXX3IYQSgnAKK1/CpCB4peBhKQc7fCOUgDACchJ2QmAzkPfu/+lDayJ46K5SRcy/deorbNX32jtZE2qLcbMVLm+oqzelXcil5FHY10IJi4Ak2z0h7L+wUDw0R8x/y8vGrBjz2FKaSyW6PP4A8GIrCipurDtrz011k8BxqkBFPX6wh29AUrph89fnXEAOAGjpEwF4Ot3fVHf+OD5a/QNk5WZqo4OT7YApDSgxxcZakrwa5v242m3PMD4BCBfRn8cdl/n4mtuxQgv67f+gX7rf+Pn/nfgoFELd63bllMGzBarLCs9fb5tO/Z39/oSiYFlrUAGJoFvfP1/2rr93gALCV5xwagEAJyAbMq0DPT+B7+va8DTL2788EUn6tafxYIaKjdv3pttHqIJJRSND706QH6WEixppmqwnzPxu2zR7NSeR3/07Y/f8TV9e83xrIP/ODh6+AhrSNXVk5KBWJTVIzbWV5uMYmt7z74DzaFwVGHeAFpx5hUgA4Xma/d/99Ofum1w69YBYFbwKClTD0BH1wCK0KJZ1dnNb0KRuCcYy9iDMS6f7tCj8QDyYvcnaPTTueSa29Ktf68vwvpeIHTKiSv1PbI88md1tLXn3C9JUk5vICGTaDR+pLnjaEtnJBpX1eS6xBjhba/9ZQJ3A4zAFR+5LrkxOicAaoEyKFMPQOf+B7+fkoF06x+KxPWNSoc5XQPsVlM4mhjqbIQQLtdCVNObVHWmylmm0O6nh/71pRO14E8Ac2LK9COE+np6IpEck8sGzfMeGoPBkNKA7q6elAYYRa6i1mU2G2fOqN27/2h7R4+iElVVKaIrzryC57gtr/45T7cIDOKpP/0upQGj4Y0XfgUakE7ZGaxs7n/w+wdbkkH/UCSesv46lQ5zpWNgkJurdU+SMrT+6fAkOpqJuw+8JKcehbD+yxZrw3/MrL+MMq0/m/NlzSFUuP8xIgYNfbu7q0cPCplMbBKZ02GZ2VC1euWSk9YcW13lNBhEnmelxiohK868YvU5WusRIN889aff5dzf1RvIuf/n/1fUlReTTFmHgNL56Q9yTC5NR3cFyioKNJoQUM6OPRneQN5t/QjWH6GAP7DmxDUZpj9FTicgnY+saxnNh/72uf5ZSBjNnj0z49Vtuw41HW7zB8Kswrg/KGQQhXdf+uNoTg6MiSs+ct0oo0D7D3cghG753N3wDYMAjEcDwtFEmdQCjVsAdEY5XWvifPz2u3p6vCnrz6p7Fy/Kafp1LjlTW8Y9T/z2r4Mmo2YowVvv7Dp8tF2SZMomLjEZ4DC2WM3/feGJPF4DwBYQ/vWPsr8H0IDhAQ9gbBqAEGru8oMHUDwCcPPtrLizsn9VkJT1nwTTP4wSZDsET/zxn5LMBFWXAZ7nHXbLa3/9VeGupwzJ1oBhBACBHwACMA4ZaO5ia9QNowHgAUyOAOimfyTrn9SnS87YX5hVisfmEPziyb/rrcV1GRAEvqbK9eKffjYZV1YGjFIAdA24BaJAIABj1QDd+g8jAP/ZuJ/jpkkUqJhDQDmt/8JFC3q6uxMJWVGUYCiakKRYLBGPS+ef2Ge38rPqzE6nARn4yVeCbIfg5799QZcBjuNtNsubfwNXYFKjQFALpFPWZaDD8Kk77xnGDxi2FqjcZ4QVmpym//V39rjspl17j8ZicS3SQgVBMBkNs+o7zCLasg/HE6rJyFc5DYtmW+fPsDhdBiQWVgk++sGulAw0N7dmKMEtH72YTVL7zfOE0mCIzSkDJs6VN96RUwOAoYAcwHDcetutKxayhap14pLc7WV/q/pKjdM+ClSEHkBO679731F/IEQoNRuNgsDX1VY21FVdf14zm8IdV/UF7iMxNRZXj3TEjnREMUIVNmF2vWVeo6WCKQFXKCVQ6Y5dvo07/UYDhwwnp3bPnj0j48BP3jCGYnZgGMAJGBMgACNw2223Lu/XgFA07g0OzBIYMgq0aT83LWqBikoAcpp+wWg1CqzldSAU7u3zN9RVVThsHzhr/5BnIRTF1VhC9QXkprZolydhEPHcBsvyhQ5sEfIuAz1tkb+81NnRG6cI8RirhC5dfF76ARlKADIwcUAAxgSEgEZm+4GOE45hnrvdYtrZ1NlYM9z6gnqd39h+CMC4rL/bVZGavFVd6aqudCKER6jz4TCyCGaLYHYZG2ZZaUzt6I0fbou+scUzp8Eye7YVCfmczReOqYpKRYGjlLX7Rgjt3f+S/pKuBM3Nbeky8LMnknOaQAnGDUSBxkRZT14dDY8++hih9L3dLIZLETIZkqNabR343JyxJlmEDkycm2//31ShZ8r6u6uqo7FExtTdS87cP7YqTw5jq9A4x3baKdWnH1+ZkMiW7b5gTzx3d7FxMW+e/ZbLZl17UeOlZ9auXFzhrjCkxgZmq8NsTS5R2dzcpj9Sb0wpAZAXcs4KfuMFSLyDAIyCnz/+uDcYfXdXC+sCH09OauUwfv61HUO9hVL6zXu/ikocf4yoJG/WcBzc9Lmv6vn2lOlPJCSD2aZXUrZ1dOs7Lzlz34QK/DUlWLTEsWCm9WBr5OD+IJKSE3cnCo+tlcZFSypWH1955fsb9x2q6Omr6fWk9ZS2OkxWRySW/KVKyUDJeQBzlpyaesxaetaspWdN4cVceeMdU/jppQWEgEbFU3988oqrr3998yFCSFNLz4JZA3/D07g7dHuAWgy0xsZZDKyx5WR+9EkXXM3z3OHmDneFVVVJWzurqOnqC1W6KzBuP3bpPD0P39bR/clrfPn5SIwdNabVboO3N3G4OdJYbTI6RBYyysOZ2T8fv6v35o9dntrX1pFsP6V/QOpj/MFI8Vv/WcvO6q+Do41iB0VYUXsI0YJdiHIclhRUe9zimMSEmlIaxPXslve/MYXXnLNB9Btl3xsOBGBsGsBxHCEkNcwf6uAz1izS+wKVNISicAIlFOIy40oWHp8kDbjy47fHE1IkErNZjAlJVlhrTXLqqWv1Vw8eOLh52/5ZDS63KxlCyScC5643u2OG5vZoojO2aI4VmfPzN/K+E4/du5dlp5cuXZzxktXM4or+YLIY9JNf/NrPvv8NVGTMWHw6y7Xw3Yj14O/gBSzyiKgq5jlVIgKHZcJaHvE8R1lgQcEIizzWHUgX6RR4rmLJwgjPVunBCB3Z/cpU3xDAgCqgsXHF1dcTQnQPQF9CYBrPCDt9abL3NUbIbkJ1ds4o4EJXAekR/6rB07tS42WE0KGmpv1N7TVVFXNms+qsvHkAGRAa6Et4/FK1y2C3icjYXyqq9t8vRUgmyMwzgziW0778ever7/YtW3J+ap/F7mxvHUgA6BSPBsw55hxVVWv4Dp7jZEXlOMRxLFEiCpgoCmZNcDlMsawyfwljNm5AmLAVtjmOUszzOK4phEKoYOBlGVGCY0Y2PYIXxANb/j21s4JPv/gmVMaABzBmOI5raumZN7N6+Gqf6TQjjCIUjCNJJXV2zm4s+H1lWP+MV+fMmTVnDhtIbt225ztfNQ89J69/xl7KZA9+JbWlxNkSzyl3jrI3UUJQJKYQSt/d7e/ySrVug90iUIr8ITkmscORtij0Re+rNlQku0OPCg7PrDOLg2uNUj5lsTH32LMpxZVch9FI43GKKRV5VsxqFHlZpiorEuaIingeqQTxmipoQR89Bsqx75FiRUECWz6baQBSicBzCqLGRKcg4AQ/qa2IIQqUDQjAOANBR9s91W67zWKY3lGgdOIyavOTaht2W3DhSl1/8dA3EUK335Wchr1z557jjluWfsCRw81z580O+HzzZtc/9gd0+oqjiqIb7QGrrj1PWnlfSNajdRkRO0oRz2GrhQ9HFa1ZZ5JAWO7xJihF0QRr2EMpTUhk96FkOoAwo5Y8mONwQ7VxzZqqMd2gScwcGsQjQVRMLFu7LhGPOkk3VTuQ5vkQNt7nEKECjzlM9VE8OxRzCGFVRRhTOU1oeYoFnpcU7YdA2ds5DvMcVQnltHUztN6o1CB3Hbf2nJ2bIBw0ZYAAjFMDJEWNxiSryfD8azuGigIRSu+4/bM/euhhNF1QCOoK0YSCamxI5AvoCjz04D2p7Y99+otz581LPaVsURebx+Pt8wSuPNcvyawiF2vBB04beerahBnIbhEaa0xsp75CY3+Cvv8YZLMInHak/lSb9hHceyQsyZmj8uxRuqrSN7Z4F8y0uurGsC6mdrnIH2LhtRmNyX5BPn8YFQ17Nv39xOOXqTKVMeYQ5VlEh01kk5FK2Kw7pCqUIMyz9Xgxz2GVILtZCEUUQgnLzWsz5c1GXlZVnkOKogjM42GxIIRpQlZ5jtfdBY7ghDLkEnuFmBAAawVnAAIwfg0IRxM2i9FqHtIJoJR2e8PfuOcrX7vv22i6QCnyRmlCofUOzixORpjrNz/5/l0PfCf1dNbMus4eT0dnn8ViWrosFdUdfCUTuC6nXRy9e9Pnl17Z1PfhCxuRYbRTahR1mOqBYsFPqlyiR1UJJpouUiQRpBLMI2o3izFMdE+IqirGzKORVcSLGKkce0GbX5RQiKANEbRMAPuJqCoxGQVFkTV3gRXfEoTEWO/yk9+/Y8M/pvBm3yjjWiCYCDZOnvrjk7Kitvf4O3oDXZ7Q0Q7v3iPd2w907D3SvXlv65tbD7/0zv5IQp1dz6YNf+Oer6DpRURCLX7CJgoM0xgvfzx495f9gbDXF/J4A719/j6P3+1ysCogfcCvj/DTHxNAG6CP4RS7mkJ7DowuhqPQ3rbo7kOh4heA/dveQCo18NggMLcqprCfsjZuZykVkceUEOY4cZzW0BTFEqrZJLDsMIdViinCLEFu5lnMh+d1VSAUS7Kijf0pzyNCicwib5yUGLQIax6BCQEjAh7AhKAUBSPxJ//2DuK00Q7GZqNQYTNXuWxmo1hhM0djMYzwMNOGi5YvPvKnmK+PququP2srrWchKagjSHGMOkzYJCaNJs8l2y3nPQVuMhlZSKfCFQkG5lZWdXYk1/TIMxSFIqzudPTvSMjk1Xf75jaYzW62MvBQxH3StgPB9/b4O/sShNBoNG6xmFAR48VVtWJfOEHZT1NTeZ5V8qBEQjYaeYx1P4YVerIYEcaywmZmYI7nkoEgnMysUKYBCFGMOUUlPMcqhCgWEJMJyrai7ce/7+It/2X9sScBSAWnAwIwfgSBt5kNTodZUchxi2bYLEaTUXQ5zKIgJIPO/asE605ACQWC6hav7Ww55LI5KELO932FN9tmzVscCvib/nxL+mEqQb0R6o1SZu61WxY4ZBRwrR07TXlWAE5g5tVsMppNI8zCmxAYGQ0sKZBeJzQiHb3x1zZ7Ljq7HuXMi8TVpubwhh3+w22RuERkdSEv8B5fYM3qY1OHJLtFFxMHtr1RtXIZG/IThDClBFEOYzacVzXLjlkQnxV+sqwGRjQelc1Wk6xQhbCsAKKIEGQQcCKhsMRx/xejf62YEA5zmgawn20smnsB98mBjEXvpxkQAho/z/zpSZfDYjSIVrPh2AX1cxrcdZV2o8iq3XIavxIKBJ131UeaDze19/a0d7QqlHkwLYcPhIP+hgu+V3vug7XnPDhwqGYgZBXJCvMJohIKxqkpa7pACSErYzYHlKLNewPNR7MSuSrt64j+/Y3uZ1/p2nMkFJcIwUt4gb/w3PddeO779EOOHj68a9deVJR4UTXPY15AGHOskofdKqYUqyplGV2kZTMo0XK/lFCkSApVVEyoiClPVFVWVYXoSWH9EKQdzbM6IMLqaJkfoUsCP5lRoFRrIKKScrb+IAATxWhImrqh+gKdsWYRpWzkVEIaMPv482LhCGLti9XOzo7u9o4qp1sURX9Pd29Xh6+vNxQKuk+/p/L0e3O+3WnGxnw7ll+873u5XyhAqyKOw+PIa0Ri6kvv9KlheSA05Jfe3eL5y787N+zw+UIyoYsJXpJh+o8ePlxV5UbFyr5tryfro1gRJ08JItqy9voseO0VbQKFFgziOCwrrD6IYKRiJCNMtH8Rj5mxZ44Cp1KOUG2qMOYETFltEPMeEBfvXr72gsm8NTD9OhACygPDd/4pub5AqhQPB3xGozkSDAm8OKO+3mY2hfwehIgsx4lKBYpj8YTZaDSecIfRYku8cv/AmzGqshbqhqsqnZTSfQeO6E8j4dCGTZ61xzh5q5DHnAOH9VkOY9aAIx3Rt7f7TjuxCin0cEtkw07fodZoLKEqZCHHs1F/6sijhw+z2+k3/XNm1R9t6URFSYSvNtIe9nVorl4y04U5NqzhsKKylK9WQEuxVuZvFDlVTc0O14NGvCSrooiRQvnkrwZ7u8JmD7ATYpYJQLJcqFSwKIKVGxIIAU2I73wvWWg8orWoqxyY3VrMzFpx1gVXXJEIBYkiOx32efPmK6oSj8ecTpeJBeCNvNksGtjUTooUQtREIua4+NuN1ySXNRc4ZCvAVOHv3/OlKtbun7Fk0dwli+ayFjo2+5u75vz8uZa33umVg3K+vAGt89B4TkUIfXOr9/mXO597pfOZVzp3HwqF4/MJXpJt/auq3BkD/zmzWLu0ImTP1td5rY6T04w1j9mGqmJZ4RTC8TwWOMxjFhiiBIkiH0+oWBNjbR4G21BVli1jUSSeIqoiTDGmHAsrES0CpOcYODGR7O0KTCYgAHljuCgQoj3eSElogBSPxaNhJREXEDLwYktzczgUiscTiUTC4+1jSUxt7Ge22+Js7XVVliVvX3dr82HrRQ/UXPHjChPO65IqA3zlM7dsfm9r6mlKBoLS8rf3zP3Fcy1vvN0rBaTcjR/GglHkxt32LhRR3tnhf3d3wBuUVbQEc1x6zKfpwMFhYj5FqwGKrBo5tm6m3txNZlEgrbCNEsQjzDODzhK6CGuRHsQjqukEKw1g7hRbRUNQZMJC/5w2q4ywGWSCqGUC+tsH7dgzgYbew3LZdZ8dzWGvrf8FKj9AAPLD8AYjlRUucg2oX/Y+o9WmKIrdVWl3uAVBQAgbTBZCcVt7K+b4Clelq8odj8e6W9vCwVDIH1UShMO8qih+b19Xe6uw7uHCBbye+s1Ph5KBkLz8nX1zf/Fc679f7/J0RDNk4MqbtuU84Uc/fTB7Z1wiygScCUJpV2+NQhZmh/vr6qqHj/gXpwZs27OPdXFAXFybFMwq3HikNf0kSK8R1R5YKwkyGARFJZwgUCYSWKVIRVihnCAyoeDZtGutUQehBpHX3sV27tq7Z5JvyheKTfInFicQHZso3/nej778JVZpUALzO4eldvFJiJDla45v2bcnHInbbA6O40wWs8Pp9vv8dnvlvLpaQklLW7O3t1fEPMacxWrFRFXiCUWWiVb+7/P0xJbd7zCbDUYjeuXOQlxnd6//6JGjHo9v9Qmr9D26Buw7cCSsLN92GO04uPW4+fbjl1RU15sRj2/8Ym8knntx4x5v6IYv9MT9HYlY+K9/PFXfaTYxM6WOy5Nobmfz/jieNjTU6ysW6OH+9Ij/8BRnPuC/25LD8+OPW8azklBMMIcJRwliPd0UpgR64AdxWJIkUcsC6KU/erWQwSCGowmzURS1+cGU0o1bJ9voA9mAAOQNrK0RlrMvkN4Y7pbP3Y2Klco5yylRjzlxbaCvD/G8xWokagIhQygYCIeCHMebzYZwsLvCWTV31nxVIt7uLlYSSGTeYIqEgxzPqyohshzghbjAS2azwWC84IZPm20Oi9XW2Xz0P3/+eZ4vuNJ19MhRr8d//Akr02Xgd798AiH67tuhHwb3h8Lhaz5xR0MDX2GzXHzFmy88dVr6GS69ZtPc+UtmzpmnxGe9/dKLl1zxtqIqGPNaGANLilzldlBC5i8zjN70awUzAstUH2xubKiOBLyjN/1FrgE6W3bmttonr1zKsdE8JxNqNBgkSTWIApvoy6YMI54lfzme5xVCt+4s0prX8gQEIA9EYpLFJGZX+3zuWz93m9A9d7DJUxSxRSKLcHmAJYuXVVVYDrb3rTj9ku6WI2abgxKFqConCIQkzBYbzxuMJqOnt9Vmc3p6O3v7vHZX5cIVKz2dbUZBpAjFYhFJinMsXqRPjY6FojGzwcCbDJFQIBIKWuyOS2/7yvpH8z8Pzl3p/P2vngxFYpIkV9hsbofLarQrKuvvbOCEMy66xN/TXt9Qf7Cvc+GpjXc+2hzsDUcCsdo5lYLAzz7eHemMdDQ3LV66dPaCeYlAQEokEEaCwBlEIwtTs2pQY+chQzwRUwmNxWM2m3HuEn4Y0z+wV4nt27N31Yql47uv4owFDcOGbbnN+urlyziBe3f7rkm/ImBUgADkgUce+cllV15rsxgrbOZwTJJlNSErkZh0/BLXgb3dr2zcf9LyuZO8pOLoYTldhIjRHehqE9i0H+bXY4yJoopGI8dhn6fTYrY6nNUCzyckospKT1uLosh2hw0RGXO81WGXe+NEUfRGPDZbhTZtVEWUU2SJqKoal07ct3nZ8uP1T/zWji0TvOa3X387EpYsJoPRYDTyBosLU3ZdajDUpSiKIhFRNHrDAaNB7OpoVqi0Z9dOl9/E+hKrhErcUX8LxlhVSMyP6mbO3rV9a2Vt3f6WNlbTzvoUKCrrcaY1NsA8Yo2NBaPRVFlhw5hv2Sv3+nwVFWajiy1Hozf5SDf9tv4p0LGEKsvK+GoQ+/qY6zAN2LwD4jxFDQhAfiCEBsPxYDj+6J/fwDzPTAlFFWZFkeR/vfLmho0bE7KqEnrWuWdSSl975fWMt5+16oSQotqqnW0d3U4rK5kgKpVked+Bwq4osODEdZ6eroCfX7R0vpRIGC1WbREPijBntlhsNgehpKKikumBrPoD4bjEDDqL9HJs9ddoJIqoWumuXLB4WSQSCYYjosFICLHY7YSQQG8XIYQXTRbXoJWYvrr8+HFrQPWMY2O9XotosroM4UgwFAyoRKWqrHUWY1EolovkxLikNM6a3XKkqa+j8/wLzrKYWBsJSmnAtyl1Kn3BgJhUe3DnVszNQxZjX0fX2rPO1XP2iqL6/SFFGVgdvuXANlHgHTZrQ7UDIUPM2xOIhCtnLcw2/Xr6wOsL9nr8DXVV5Wn6ix9fKOayj6GP97QEloTMD5d++Bo26OMrOV5AhPBUDQSCxy9NvPvWfon5AzQRV40iETjObDaEvEIsytoGGDkVE0KoyvEmWaANdU7MG+bMrpIkVU7Iza2dG7btRgWmfskpx6xcFujt4YwmVqstitpaV4K/eX+CGhavXCWarCG/J+DrS8gqYSWgPEXEZDJZ7TZVVhQpajJZRIPRYLDIkoQF3uvtRaxDJB+PRSgSK+vr1m7/b8aHjlUAjl15aiKuOB02USD+gC8SSVx0+WXDv+Xlf7188203p+9547+b059edP6glEA2Bw+1bti0M10DdDr2bTIZBKe9wmR2yFQIhcNGM++qa0xfL1KnutJ5wqqlhtE5Aemm/3v3F2+6qER57nc5luVwZQnAmZcO+p2Z9oAHMMAHr/+SvjafosWyRaMhEZMQ4hVFUmQ5kYjIckJR5EQsihGW5QQbC7O5jjylZOf+LswRhNrY6JGtTMKrqlx/SgNvdqydK1ZX2rw+NodWFPh4QvK802Q2oyihDSY+quKwgm2i0mI2z5zjIKzmOsR6phNiNxeqQUo6J5x6ctDbJ1psbEQfDXGS4HTXBPZtWlDHann83Qea/dG+3r4Vp5wXi8WCfi/FhBcMFFNZktxud8CnxmLhcCggcKLDVS2KBre7Ohz0RcNBWVYr6+tfefLhtf3Bn3FQM2t5tdPtMPHUQH2BrkA4eulHrh7NG8+94NyjR4729flOWLNqlBY/g9kz61rauj3dmfnYRSuSy9MjhHoP7LbZHDaTretIc/Wc2RlHen2h3j5/Y/3ITgBYf2BKmOYCcMlHv2QwWdkCUr090XBUUWQpIcmKrMiyorCJrETVoi2UEkK2bN2qVa6pbGq69mChD1VLB7KSPq08ROt8ohe8abEC1gCd9Q7gmP0XWX0022YawBve2d6zZLGp9bAnFo17/QmZyFaLgee5pw81577c/mK7yeT5X/zgg7d+GXFYliSlXXHW1r70xCMsoFFzGe5unVFlqWsQgtV1TXs39EWkxtmLBJORxchVQgmbMxuPR1RZQSrizCYpHvN6u1xV9ZU1Mzihy4bw639KzhAeB9aqhRxvjEZjAa43GlKOHNk/jpNUVbFiIYSQQcitptpSJQMTuTMSNVYD7WE/7SFLQusWHsN8hW0b62pqUSDU2tsxc8Hi1KsqUZtbO6urnMM4AWD6gSlkGgrAdbd/U1b4vq6uoN9/9OBhKR5VVZn1HmHNCPVQMGFzFlN1yqxdicBxmunmeIwFzIw9Ww2WaYOqHcyymoRD5MCed4b63A9oUSAdbd1TRkSKhBN+Ac+tmWdbd9oxuw+23118TaH/+lhyva1bv/XoY19Ndv9/46Xn9I0zzjzbbYwsrRZDldbucGd7ezTuj6694AJbhTPg8VBCpbjMsbnBDhbXwjjo74uEAjZn5T9//cPxXc/yNadxitjW3RGJeGOBjnDfoFcf/cE3r7rpM67BeYURkbRITkoGnM5RvZ0Xxe5efzQ2QpuahStPZEmC3dvqahu8zS3u2WzNeh2WCejzNdZXZ78LTD8w5UyfHMANd37b54t1d7SGAgElESOqlGxBy/7V2xkKbC1SXiCqokpxKRFrOziQEpw4OQUAk86gn9jdjRjjU1bMTUgSQujue4tOA0bk9HPWmaVek9kUV1AkTvuCUigaO+XiDxCCPF0dvCDwoujp7CDMq1Istop3XvhD6r1fzQoBDZMDqGpcevzSY9o7j+zePShkn8FVN32GxXDHKAM6NaMuzKeUNh1pb25lkx5G9QaMuw8ddDsre3y9jf2uQM5MAFj/SQZyANPWA7j+899t2rt74+tvyXKCjei11rXJRrWIHt2VWXJTIP729B/SNUCHcvV2zdqwyUFHu2dUO0Q2A770eOOVv+sb55x6SqMN20ROrnQ2vfni0nM/lEgkBEUhsTjTW83VclWPs4x97doLTAa0c+/2zvYcTRrS+fOvHpmgDIwGjHFdrbu3zzeiE5Cidj6rC3JzUqDraEXdnGwnAEx/8eAr+0Kg0haAaz77LU9v9/49O6ORkKpKh7a/hIoDQkjKCUjhDUQXza6R5YGW8aXIK2+9jRA69dQzUTzQEzc4W1oURVUV2cTmClTGI2FHQ82Lv/7BWE/r1gb+B48cbD60ffTvmgQZsFnM1VWu0ToBzNlkeQSzqy7UuluNd7S29M1ZtPxoS1d1lSsYCJZbnc9nz2Iur87D/xnVnOoCcdl1n83pBGTw2vpflFUhUGkLwB8e/ioqHSil+4/2zK5LtjUuabpIRTQUOf6007rb21gTMLb+H5ETEY7jd7718ljPtvbUMwyi4eWXnx7fxaTLQN6VYBxOgE7NzGNWx58IVZzstEakuPLII48aeHHJooWRoLxafOqzZ029TcyvfQdKkdIWgJLDF4w6bcYH7v1KKaYB0pHjkRPOO9/T3sHS4wgLojES8RlFMx9smV3vNplWYdF6ZNdbI57HPWPR+05cenD/0X27xjDwH14GfL5AXmTgkZ88wQq8OJ4XBKPBIMtSPBFTmANHj12ZLC3NZnX8ifSn51RsQB62sXAOWt987JLYH5A48OoXLpn7w+eT69tMe8rqZksFEIBJhVLa6wu7HBZU4jRvefnCq6/1dfckZJVjywASi8Vhd1ZGvE1LG92LGy0hSaxyXRgIS4lQR+pdz2Cj2+mKRYNLVp685NjZDpuwe2fT88+sH/HjsK1R77XKWjJUVlKKFEVm8zAo8bTtypaB4eNCPX3e0eSBP/PpG1LboUh05+5DY3UCMrh0NrTEAYoLEICC54EzCMXkcDSBSp/HvnTjxZ/6mslekYhGDAJXP3NG1dv/g4wI9aF9pousfOy4auRzUEmqeiUQDYRikbh6vR399vB+q92iUvrX3/1p9J9Fw+36RiKCOryHJj89MLZMAACUCCAAk5oHZlDa4w2h0ueOM1S0O7kufPeJDzLr38+S+IsIoXfC73fzwSqH0WmqEGZUdvqlf/qiJrM5FIx2to3KiE+cfKUHhsoEaGvkToiyCowU4c36yrsQCARgCih1D6B93b33Ot5BHX8fqH3ceFf2YSfhfyCCkB9tohfYqW9WjbPRXRWKq119vsYahy8YO9Q0STOfx5Qe8AfYMdlQQu1WUyJeqLXLgSLhtXIqBJo+E8GKhJzTwTLgOVTltD326KOo1Oj++j/VHVuoLN3W+dACu5bcHAUm35rUdsfcioYjAf+SmkBM9gTDnmA8FEnsPzCpPTB0h6C2ZmzrtFCK2jp6+vr86TvffPFPEymMEW3JyWLFNi4eJSV3szAdLAPwAKYgDcCGmaEoKjV6HngVJRKIkE/1/hQpkfGdpOEIG1/vbfUuqLHOmFtJsdDhCdXVOnyhWCAUbz40wvyv/DoEQ2nA/337/km4DACYckAApiANoLKmRHpjopKy/uyq1c/0PSrHg2ZuQtPZXtuw6TWETj713FqxZ15DZU2lXaJ4f3OPvX6+wWIP+ryVjQ09zUcVhbTuzGwlnS9S9ULFQBEGxwtHWd1skQMCAIyM/fpv9uhbkpSI+M04YeBJTEXj7ld9xxnqj17nN/RPGTvplNMXVfNRaly0YjkiJBxwEYzkmkqVUiV6bOchqJ4EgIKQO0gNTAIfuW6gzDzFZ8+SinB2pT+avKSbX7nJiKIGXjUbqN2St0Uu33n7jd+u/49qqDS67Ngo2lxOqiiuKrfJyFaaWXpcctn36c0XLmGL2pcJRXWzvlAMlSvgAUweM3t/lbFH7wdQ5D4yvvK+aP/sVU6JiLxqFKnNjG0mLNpwr4dMxAlI37Px389s/Pczp33g6mWnnaQQBWMcC4WMAue0CmtPOW3T22/m424AABgAPID887enBzohE32NsZLFePU3wiqSTNbkU042CcRqQg4LFoWJzoliMwmyePNvf9zx6jt+n8/sdCGOM/B0w4ZNZWL9i2pcXGjK6maLFvAAipQicQKCcTlhdRKDSX/6yIdf/fo/z6iwYIOIKGal8W439nrzPDl2w4t/HPd7r/zkV//ys2/l9XKA0VIhJAcKOSmGurfR9AQ9s2wmAYAHAIxAzGQjBhM1DkyVtFuwyYhZz2O9Ow+llWOrpx/ZCZgIlKBb7hpzM+o8UiFYR/9AJc4Eb6fYnIAzL725rKw/eADACKhGCzGaicmC4hJnYr2LbSbMc2yRTLY6MnMD2Dq6tVWoe/DCjVPCuo/daamokgm+/La7n3n0gSm5hgdeku8+L63h57BY4m1R04wi9Pwm52anhMuu++xUX0IRATmAgqcBxk0xjI+04b8FGc1UTEaBPrn2P2yhNc36I235Ew4jnsOz6vJWFDRujEZTJBTYv2uzvcI11dcCACUACEDBKek8cPXjN1OThRrMSDSopmQLnVtWvcKiP5r1x5r15zkkcGjxrCn+dXr2sW+KWJEigWBf20dvv2eqLuOBl8YwS84SbytC4Z+0mwWmFhCAoqYYbAE1mJBg0B6i6mpguzCHKE2O/ZkAMOsv8Fjk0QkL+VOWssoCPUEwJTTOnINZkmLqPRIAKHJAAICREI1IZNYf8SLik2Vjt57wOkbJ4A+z/hyz/gZNAww8On+FoK2MOwXwmNpt1o7Ww0aRv/rWL03NRZTZuLisbnaaAQJQ7Ey5E1D/tdOZ9RcE7SGqM5bo+29Z/boe/GEawCORxwbtX4FjbsEFx/GnLRxZBDLmgk2c3/3kW7IsGwzG7u4Oq63ky2wAoKCAABSK6TQdrP7OVUkN4HnMc2TBMn3/DSte04I/SB/4syiQFg6ihCYk9lheT5dVkUmz/joVFRUVTidCVJLipTIuLnXGfbNTPr4pc0AASoCi+CNh1l9/IMQjeqymAZRedcxrl9U9Z+CxngMQeIQpkhUal6gkU1WhSi77X2F12GuSnkT+oZitvdzTEwwFP3f391EpUFaBkbK62SIHBAAYFfWfmId4AfPa5HFdA05YxmqAEMIVznNn/Jt5ABx7RVFpLEElmSgqs/4qyZEOJkb3Z1Y8VqCv/uc/+LrR5HBX1WFsCEWmss8XOAFAkQMCMJWMfoJoMTgB9TfU69YfC0wG2L+nLsNnMlcA8/zJ9a+sNj2rqjSeIJJEZQUpCook6KHeHC7AZ1Y/UdBLNZusPC9QSiOR0CXXfRqVAmU1Li6rmy1mQAAmbzrY6DsBFO2fR91VdTilATxiDgGP8Pn9roCj4vi6V9/n+quk0M4A2XBUeb1J2dM9BfmPSCQQ8nqa9uzydXe5q2sm/wLKk7LyeKYHIACTRKnngVPUXlqXigLpD8wjbl0yLcxkwO64UHz0Y/yDXUGSUJI7D+w5cGjrzpatO1t37Olq70QF/jae/fVDVntFbW09RTQeGefqlXkBSiRTBJRI+kMO75/CnwugA91AJ49drquO9f15fI1TaDF1iam9oK7n9a7k8J/TmgFhxF28DFFE/r6HUkJjrPPjLxd/DyH0+Wfv88qb5hFiRtSAEMZYDYd+9KWzA5XunxbyIm1OVzQUVBQl5PddftPtz/zqITSlBEa3ivJoG+sUK6O8TaBIAAEAxkPNGXUIod53u5gPmSr3p4i7cJl6CKGt7yGE7nzlIbPHU60qTkItiJoQMrCJWkwEZKJGenrvtloewIUKGnh7ujCH4+GoIIi8kbWxmyrAJgJFC4SAJpXwsZ8a/cHpmYDibGtQvaZOH/4zWIs4RGNI3vi2QtDX3/2Fpa9vhiLXElKJiBtRN6KVmFYhVE1pPUIziDo3Evkml+wxl3f+9fQvDWaLyWpViYpVcuPt3yjQBwFA6QICMJl5YHrs/PrxnWcKW+sMT9WquqqVdcz6E4RUpB4Ik8MHEVVjza31qlpJqRPRCoRcGLkxrmQPVIVxFUa1GNVT0hgOF+7aBF6MRUKIUG9fTzQcLNwHAUCJAgIwWV80xlaTobbSftanfzoOJ2CqWuuMQQZW11EVkY5WpMp3v/5/tYrsRsSBqINZf2b6azns5jgHz9k4bMfYjVEVQjVEfZwUZD4wg8ecwMuqrKhSJB4q1KcAQMkCAjAZYIzMJrHGbbNoa6pMJw8gneqT6xq+dhaiquz1OSm1U2RDyIGxCyMnx/ECx1pGCNqEMY41krZh7EbUJUkFup6//fZhi92hKHGEaCwSvvLW/ynQBwFAiQICUHAwRiaDWOOy2a0mPVw+JicgdZIimQ42IrNeuMchy1ZKrRjZMLZjZONYxzhkEJHVhKxmZBSZDDANYAphpwUsCTWabCqhkqxGozE5AVXqADAIEICCs/6pP/z590/8+P8ewRi/+d7Bsb5djwKVggMwgFGr+TEhZNY0gNl6gWfWv8aFapzIYmJ6oHXs5xAyFfLeRMFgNNk4XlBVVZLiH/zo7QX8MAAoNaAMdPK474Hvpj8d/WKqJQeHMY9Z0acRYUGr+0x6AE4bezkSR1wi6dRgxA4oGOt/+9B5l9+gKgnM4Vg0gMzQIBoABgABKAEs8bZIEaymPXqoKFIpgZFu3DUDTyiSZOTRSnESEmJhn+TIn/B8YS+GkFg8bBCM/niE5wr7WQBQWkAIqHhJnzePSopEfZ3EcWrKxlOKiIpiCeQJsEcsgVTCJIGyZYVjpkJNBdBxumsxFkWTieON4Ujsus99vaAfBwAlBHgAU8YDL8mfPWu0BTDFXQWayaM9bQ8YDNFEPIGoXc9gqBRRBSlqvx4QvbApygsXkEJVAem0thw2GiwcFq1WkyxJvd2dBf04ACghwAMACsLdBuQThCDCEaqN9AlbGYAJgP5QmQAQju+rqiz0D2DjS8+KAh8NBUN+nxSNBnp7Cv2JAFAqgAdQGtBScwIQQp8yC7+JY0GREUVW1iaCpt0Dlnm+2+26MOQv6DWceN6ViCJVlYM+r8liURUlEY+ddM6loXAiFo1qiWjM85gQoshK857/FvRiAKDYAAEoEUpRARD6mIn/JTades0DgRXn4eo6vppDHOKaO/fduvo0jqBo/mfnHn/aukQiLiUSXV1dRqO5+dBBo8nMsdwvjkbjhIkQ39LcPnNWo81qVFUiJRLRSPjQzrfzfiUAUPyAAJQGRd4KYhg+TnNMv2LWvzBsefPvqe2UvCw/+Ty7w2E0mjmOUxUlEglvfHV9gS4AAEoIEABg+rNjw0v6xtqzPxjEYqi9Y6qvCACKAkgCA2VEl9ePMC/nWqceAMoQEACgjBDkmKLIlCvZgBoA5BUQgNIAhqx54fDujYHudtHmqJ5zXH7OCAClDOQASgMYsuYLA1WxwUB5+M0HAPAASgTwAPKFkeeIJAsW6AoHACAAQJlxePdGNRoRLdba+Sum+loAYIqBHABQdthEzsgLCDqDAmUPCEBpADmAPCIaDGazddai45affEE+zwsApQakwoDyA/MWg8FitnBW1qsUAMoW8ACAsmPfe69sfPF3Not548tPT/W1lC8VghVKG6Yc8ACAMuX53/5wCj+9QshdhhRFpc1Q95UTCGxOOeABlAba6ilASfLwfwwVgrVCsDq0f/XHUAdb4m2olHngpRy9/6brzU4DQABKg9LtBgqkgJ8hUGyAAJQG4ACUz7i41Cmrmy11QABKAxg8lg9lFRgpq5stQiAJXBqU5oJgwAAPvCTffZ5YJqPmaXY70xgQAACYJMAsAsUGhIBKAxj+AwCQd0AAAAAAyhQQAAAAgDIFBAAAAKBMgSRwicybj7dFTTMKezUAAJQZ4AEAAACUKSAAUwk0TgEAYAoBAQAAAChTQACmGJgcBADAVAFJ4NKSgSOFvhIAAMoH8ACmHnACAACYEkAAAAAAyhQQAAAAgDIFBAAAAKBMAQEAAAAoU0AAAAAAyhQQAAAAgDIFBAAAAKBMgYlgQOEh9MPe9za+14UQanRZt6w8G750ACgG8NK5tVN9DcB0xnH9g0dVk1xZTy0OJAiYKHw84Ix4hcc/M9WXBgDlDggAUEDkD93jq5mvVDZQgxkZjFgUkSBwiOCIx+Jrt3//Wvj2AWAKgRwAUCgab/m+r3quUj2Tmu3IakM2O7JXYHsFraik9XNjdXPiX/kjfPsAMIVADgAoFLvDRJ0/g5ptyGzBFiu2mrAZIV57TTVSYWaEyCb4+gFg6gAPACgUkquGWmzM9NsdXKVJmInEJUhchPh6xLkQchlUZyV//7PwAwCAqQI8AKAg1N783W4LG/sjq41ziHwdMjUgB0IqQmETksyIcAgFbWGz3Qw/AQCYIsADAAoCpQTxPDYYsMmArYivQxUInYLQMoRMCHE2hI2I5YRFEX4AADBVgAAABaHnl1/BSMUcxQaEDewXjSDkRyiEEEUIs8kBCFFixgR+AAAwVYAAAIVClKIoEcaY/ZbRKDP92xA6jFAcIZJAKIp4OYY+D5PCAGDKAAEACkW9FODCHhQOoQQiYSQpKMzMPlLiiPQiHKbmWAC+fQCYQmAiGFBAEv/zZLB2Nqmqxy47tiFk0CI/CYTDxOjvtd+4Ar59AJhCQACAAv+G3f1Hj6tOdbiRxYpEkQWEpJgtGjB84kT46gFgagEBACaDym8/6xEtqmgwYyp99hz40gGgGAABAAAAKFMgCQwAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAQJkCAgAAAFCmgAAAAACUKSAAAAAAZQoIAAAAACpP/h8AWnMdpoe8JQAAAABJRU5ErkJggg=="}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The hook and corridor blocker are cleared and the forward direction is set; extract the axe.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} -{"format": "agentark_trajectory", "version": 1, "trajectory_id": "traj-0ea2e7c02ebb", "created_at_unix_s": 1788845986.204682, "source": {"kind": "ark_eval", "case_id": "ThreeViewWeaponRackExtraction3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex"}, "task": {"task_name": "ThreeViewWeaponRackExtraction3D", "requested_task_name": "ThreeViewWeaponRackExtraction3D", "group_seed": 10, "requested_group_seed": 10, "env_id": 0, "requested_env_id": 0}, "rollout": {"max_attempts": 1, "max_steps_per_attempt": 9, "rollout_step_budget": 9, "attempt_group_seed_history": [10], "score_reward": -1.0, "last_attempt_reward": -1.0, "best_attempt_reward": -1.0, "rollout_success": false, "ever_attempt_success": false, "rollout_truncated": true, "attempt_rewards": [{"index": 1, "reward_total": -1.0, "turns": 4, "done": true, "success": false, "auto_reset": false}]}, "prefix": {"requested_attempts": null, "attempt_count": 1, "target_attempt_index": 2, "include_images": true}, "history_snapshot": {"0": [[{"obs": {"task_prompt": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}\n\n\n[task prompt]\nYou are inspecting a crowded medieval weapon rack through three synchronized fixed cameras labeled FRONT, SIDE, and TOP. A brass nomination band marks the target weapon: item 0 Sword, item 1 Axe, item 2 Bow, or item 3 Shield. The safe extraction path is defined by real weapon silhouettes, handles, rack hooks, and interlocking clearances. Each view by itself remains ambiguous, so compare the same geometry jointly across all three views.\n\nFirst select the nominated target. If the views show an obstructing rack item that must move, rotate only that item from rotationSlot 0 (home) to rotationSlot 1 (safe quarter-turn). If the target's retaining hook blocks the path, release only that hook. Set one extraction direction: 0 Up, 1 Forward (toward the FRONT camera), 2 Right, 3 Left, 4 Down, or 5 Back. Then extract and submit. Use SelectTarget(itemId), RotateRackItem(itemId,rotationSlot), ReleaseHook(itemId), SetExtractionDirection(directionSlot), ExtractTarget(), and SubmitExtraction().\n\nSelection and arrangement edits are nonterminal and give reward 0. A dangerous extraction through a blocker ends the attempt with -1. A safe extraction remains visibly displaced and must be submitted; a correct final submission gives +1, while an invalid final submission gives -1 and ends the attempt. Repeated or malformed edits are safe and deterministic. The attempt keeps the same seed and has no cross-attempt history.\r\n\r\n\r\nAvailable tools:\r\n- ExtractTarget\n kind: method\n docstring: Attempt the physical extraction. A safe path remains nonterminal and visible; collision with the first blocker gives -1 and ends the attempt.\n signature: ExtractTarget() : bool\n return_type: bool\n access: call\n example: {\"name\":\"ExtractTarget\",\"arguments\":{}}\n- ReleaseHook\n kind: method\n docstring: Release one rack hook by item ID 0-3. Releasing a non-required hook disturbs the rack. Nonterminal, reward 0.\n signature: ReleaseHook(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Hook/item ID 0-3.\n example: 1\n example: {\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}\n- RotateRackItem\n kind: method\n docstring: Set rack item 0-3 to rotationSlot 0 home or 1 safe quarter-turn. Extra disturbance blocks a safe extraction. Nonterminal, reward 0.\n signature: RotateRackItem(int itemId, int rotationSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Rack item ID 0-3.\n example: 3\n - rotationSlot (int) [required]: Rotation slot 0 home or 1 safe quarter-turn.\n example: 1\n example: {\"name\":\"RotateRackItem\",\"arguments\":{\"itemId\":3,\"rotationSlot\":1}}\n- SelectTarget\n kind: method\n docstring: Select the brass-banded target item 0 Sword, 1 Axe, 2 Bow, or 3 Shield. Nonterminal, reward 0.\n signature: SelectTarget(int itemId) : bool\n return_type: bool\n access: call\n arguments:\r\n - itemId (int) [required]: Visible item ID 0-3.\n example: 0\n example: {\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":0}}\n- SetExtractionDirection\n kind: method\n docstring: Set extraction direction 0 Up, 1 Forward, 2 Right, 3 Left, 4 Down, or 5 Back. Nonterminal, reward 0.\n signature: SetExtractionDirection(int directionSlot) : bool\n return_type: bool\n access: call\n arguments:\r\n - directionSlot (int) [required]: Direction slot 0-5.\n example: 0\n example: {\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":0}}\n- SubmitExtraction\n kind: method\n docstring: Submit the visibly extracted target. A valid safe extraction gives +1; every other submission gives -1. Submission ends the attempt.\n signature: SubmitExtraction() : bool\n return_type: bool\n access: call\n example: {\"name\":\"SubmitExtraction\",\"arguments\":{}}\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 9 operation step(s).\n- Across the whole task, you can take at most 9 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.", "reset_context": "Seed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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miX6KZeqxaEhTI8A08+/ZtXvDrOWnP3T3bz75jW8hjvuvr3+ppcFz6vJZnBTjT1izffuWpx6875LrP2wFR6xYFEBkWgaHOEEUVCVumabb6ZAlube7V9M0UJVCf+eOnQAATdMBACefsia/MmA+ouqBSv9UmClQ1Qqg+l3/lNQxxrL5rTNP+gMARFFQ44oKsNvjnXDnYzvfal92yp9++u/Y0CPD3S6Z332oZ+XC1qV+4Dzt1PWvv/Hsw/dfdM0NGGNViwuCIEmyKMuGqqp6LK5Em1ra9+w+iKdqxi7p1i9E7ifBAEMANr7zHn2ZSxMwH9H0cuKS9lzSn3K0e6C5cSwFjFEzLqBqMwIoM1L6E6vZ6YrHIha2dq57pJD9u3e/k1zu7T5q4TnBaPyEpR1LOjjp/HPeeHPj848+fOYHLhAlUdP1WCzK8VxjY2OIVIwG3V3HojFNlFzT4tbPJ/QxvqwJQVszYSCZFjYs/HS/nqoJCjELmI9oKskU/f1DoeaGiccxjOlUAIUP/6tEB8xsLMsYHuo3zRJDJvp7jgodczZuP6pq+sJZ+srj5m3asmf9yy9CCC+69nolrli6qmlqLBaxLPPw/s5YOIh4AVSMz/3T7QWP9MFljUTiQxLMOgYEEHGQ58C17ZyJCY/3EJ8VhAUpg6995/v/+f3vlOW7MPKQa+DPqGoFUCvOn3H5JjM6ytg0SbyOaZQeM9fVdXT27Hlb9nQburV4dkts8az9B3tNQ3vubw/d8NnPudy+vdvetSwzOBTq7xvEFoZQApWU/vG4KstSVqGfHOyTIhV5sTUD4Mlu8NpZsq0IgGZaT/fp9K1ZlQFNTapDHfDB67+AMejf/QqHgGEYosBjjA0LkkIgGDtFGNeBzGMEgWmBuGZiDCSRJG/pJo4bQEBY0YnnDQPAkTcBwzQB5DAGC068GELw7MNsIFhnLqAqMQLGcg1nKJaFAanlQGY+S6az87D9X6hp5vHzWnjQtO7llxvmHPfIH39/9c2fFEQpFokOD42YpoU4wdBilR77Z3p4LmlE3IRSPwdECZCMN+BA3HWzOJIBjYFhWU/16sReSFEGJ518Il2oHx3QMOeEJY1m345X7J+J2El00ETGFZBkVFA4BMiPZmIO2fvYj5ZGxh6QTw30Jb8tyS+kx8EYHHj3OYDBcUsXG665+957aRq+YZ2Bqm34f9dtX6jYGTHIeA0JIhFpk6br2JEDXUN7DnavauIvfv95x684wTTMv//xj7MWLHG4vKFwHPEiRMgwJ6VsJpT+A/19Ho8bAHBJA7ysEV3WhK5o5vhSpX8mCEIeQZnnru2Qr2qTrmgVL2vhMcAIoU3vbUnu9rXvfB/UARiQgiCZxtN4v1rChk6p3ZB4c1qoBVlha5H0HS1S+qfYc+sfHCn2LYxKKYCac/7UCaauW6ZBCvqUg87Ow0d6g+u2Hlng51rR0OpVJ0JE7qiR4WEyIoRIi0dRue+xNOkPADhHil3eZA/56cRuZYAQ8AgKHBJtZUDlU73pAIgNzEEdADKYh8C+2nZTCNOCWOeAwUETIAxMFQCTCHJMtgKL5Ana6sMAlgYR5qGJgAmBSSwF04KkvA85kkWn8hGE1jRED9ch1VgMboqNgEuv/eil13/i0us/UciQ49M3nOf3k/FmjWL3b7F93WXi8NHDwUj8te1HXUibLQ2dcsLxPUePjAwHNTVuGhoAsOQJ5wKl/7lSbIrnbKjb4+o2oR51QMZDQl1tnJ2oRe8rTFw9eY9hj+6ptqZvyzTYUku/Fc6KJXP8fk/m37bXH972+sM71v+thGPObPhtu563XcPj1p6w4qIJ3/nT//lR4n6wr9TFHzhp1fKLyjX8n/bJgPxk1QGVuL1WnHFdeQ9Ixv4QlfZ05eJI5+HZs+e9tu1oc4Nv3lknB4eGbJ8MmQAgm43KSn8wTRAd0Co82kuCRze9t+XENSfU23wALc5GjS4IgGZgn5uLa2OiJGGQJd1DdNp3dJPAo3DMEG3VgW3dgRCxFkZ3gJde/4myn/OO9X8r+zNV0/CKmpERisGGTc/QxVNPvIQu/NevfpKUGrbQH5X8o1f0uZfe/Y///tHFHzjpFz/5Yw05fy69rvAyNdPAlI1ZJvlUdHYebm6dc94VHzu2bz8AltMlS5Kk6+RpRoifedKfAiGxAx7tJpFCM0AHbHv94Qn3OfdDtxNJTUfuGPAImIgsIEycYzxC9hwvxgCS/5MGcaSNkP1WiCAigbYAQgw4RFx29F+MMUeicUlyesImIKF4MzgWr4rg33hzW7b1Cbn+X7/6SdqapOSn60atucSsz3MvbSzXmU2BEfDFL3+FVDHDuG8kpukTlEaY2Uxe0/x5Jzq6Z5euqaKEJFl2uVx9vT0IcrLbX4XSv3fRmnlzs1eXFHg+/HyhfZ6pL2jK7IBCZHTFMTEJ98HAMgEm0T4koBNAIHC8qgHLRAjZ88QWwBbEEFjYVgbkfWRn0wIcgBaGmg4EEiRkx9xigExIrFMSpEYM1bRZ5TLCjIBUso7OMv0D5Ar5vI5gSEnbiV4ny8IQkWu8Z+PWMg7/K6oD/udnP6BzVBu37GvzCr1DLNNkUtz773edf/2NDp8/FhoRsSrJHAc52RN45fdfXf/fr0y9xfPcb3MeqndR9qyuJJ6Lri5CB1A7oCehA37+nZvo+s9cVgXCugLQEDJudAYY2R58jElcAXmcbLnNITv3wjYOBJSI6CG+HmITWMQ1lAjzSXh+xnZAxB1txyiUx0v55EtjqewNPtdZp65gOiC/Ahj73ZOOYroQDCm0MUjGjrRBDy6v9K8ov/n5Dzds2UuWbO+jbcngQsYcjz3/dtlP5qqLTgO1j6qqLzx4/3lXXc8LoqXrsxeIwYgZ6tr7kX+++x8XTsP5XHxrdqfWn19KNJXNj+eiq0+fW0SIxMSTZjMFXuAwMZfJ828H+FgY2dlcJJzH7t2IsGVZRMxjohuo5x9j2wIgk1DEP2TZWWMkUwxbxJggj2FiV4Qgx0MTAxFokxH3WXl9ww66wOwASm7/bIoiAGB0+D9eJUwBFTICEtKfQL4JKWhcGMQUIv9LnQohS2NhDLZTDBHPJnlh39Xkvu4b2t3SeNyUKZWp1zSv3HvH+z/x/ZcfeeDMSy73NHgbW9veeuYxt0Pu7dwLpkMBMMpFqlTFAMk8FCCyjWeSVgjhaH9GTMwCsgeHaAVAy6RPQSLen6hTyFFlQNxH9iaLWA+JcRiJJuLIfxGC0ZS0ssJJzFMmRnWE0VMbjVCife1SNEGdw+eT+nRh1PmTMe877o2VG/5XQgd4F50Xj8ctyzRNk7PM+LF3U7fm0W5Untv3Ui57YTTkgXpGE0YxbGkYrYiQuEUTH5KMXE+uGVMw9gJ5vJLb7Bt8VN+QQ9sbE8ehe+VxnlZa03z95ov+/Y/PvPXCM7d8/esP/Pp3Ik/COiShgrWAGAWOfMsEueVimhk37GGTSQKBsGlx0CKB+xhqJiQOH3sG2IYYCqZFJgmIRMbQwqNGFcaaCXiEEbIME5sYQsSZGPB2OQmBg1guZQ4gYUQknr2U9eN2ILvc+tWcVaTqVgFkdf6kCsMszh/6utLOn0nqgJ/++amB4Vhc1RFCOzZtjIXVd99+F5CZKLvXKM9zfLvo4HiBJ3CcpevPvqfYYQokssHfFIiGok53AyKuTQ4DrFvkUPFoFGDscNshodhc7jya5bNHb73OI4Oz5y1IrsnMWBpbk6lgUrdlHHrs5qb6ZtQySd7rNikXkuqV1PTLUZWTGD8lsvMTjbiJhzcR2QFTbZqscRrf+NQlP7/v5ft++dvPXnMaAKf86vcPWnoRkyvFqqga8p5NlYzOHE7Y/9LhW+Jq4oQTPuF5Txk62KOO5J2TvIOS42g6sqETvwJHooA0C8siJyCo6lDkxnaTBGCaUDMgIDV+yFqRJ3lemp7wCPEc+QSR51TdVA1yNN0kOxAbIiVgtHgm1hxM+ud0AYkCr+lGpvNnTCWAWuIb//lgNBze8M6OzoOdoaE+PU5CRzRDgyQW3rLLjxAfpi3QqDXL2YphtDwJWWPPciEIiFeTrADQsi1XsgXxUiwWgRDxPLfH46EaAvHkj2xHiNNJRYQDBw6cvbT8FREmMD9yr6HLGWsyn5xUHUM1RnLHVO2SIm8A+MqHzk9KnM/fcgNZtfeuojxsVL8krJ9UCVQOm2b7zoHR6tbdWXdwuZx0YWDfFFyybNNq1M4bFda2IE4s2FcK5pHR6dh7Jzpq0Z1GLdjUu2Ds6ift25TbYcyMtU9QMzACUOKBZpLwTYlHIo84CDUj4R+go4PRQ+DxqiNpqY76fBAUeGR/SbJGQlA1sB0/XClu/Uc29s+iABL3nqYbqXO/Pq8jx02aZIrmfos1Ar72wz8oirpz85buI0fiMQWbuqHG6K1nWGO5SbZlSv41LDINQA1VevtijIk5S8IeLI7jTLuFlh3HDMgu9tt4eyhEah5AGAsO0ceE4zDVGgBAWZQtUw8ND8Vi6NW1axsa/QNDcVEgBi/P86etWQVqiJwKgjy88c6Xho/tNtUoxAYHTJ7DTWcX/aSNFXMYrdYPUmQiHaKOrUr8N3F/jsmuDI9Zor4bwIhEoYy618ZPZKXEN9OIgFEtlJjDxNQSIpvGhs/kgHR5bBydOPExGZ24r1J/sOQIN8V8S1XW46y68QtJd/r43z/PbwqK2WmiQ1gAQWSYlgktiE2MOUyidjg7pJ+EB1mGmTKKIpYxjbWzB08c2ZXE+ZD3UN1FdialSSw7lsgyLUQGVxwsS7mqTJj0z6oAEr+1KPCqNm74T4N/cs/9TmnkT+E64PPf/f2OrXv7jh2LRyPYMkxdNQEp5WhLfxPb8WvknrMluR3SbIco21mypN4JWbRf21Wp7HoGNDSNQGQUtRhIaDMduRB5j1HiPqdFDuktH9EsCE3TNOJx6HPIc5u4Jqes6hrRAdB69nd/lTEOm9jJQwNADQPIS7067JjjB6Y1b15AiVsIwJ6B6CWXnAeqGLXr1eFje0wtlpD+CAsIKG9/zylAvGYS4fApQg/mXlOYxwwcNmf1He1MiuT0+mW28h4IWVFNaWt02uEsY0dJzv0kLMPEFnteM/Nj009h3HRQllOrnZwnDLBJbnpMKn0SsU+C98dtzpTb2fw5pE44mTJO7mNPJVvAPjgkYeUl/SivbzliK5X08K0Gr+v3v63eygJV4QJKc/6MDf9zz/2mSf9q4Jv//qcjncGD+/YeO3jAMnRDi1lkaI9J/BkmoxGybJqJkaJlUClARjGJYZv92NtWgB3wbFILetwQLTH4sxeT9XDJGw36mNNkx9G7kFTgtyxL1XB3b79mRBSVmBJOB9EAbvvDWgUkECcTVAxTQhoHzblzZski5xAxDwxVNbzOcpbTKTt67+vDnbtNLQotHQGLSH8OiBxwCNDvgHN7fzTvJHn9u2VIs3hvHzYM3TI0w9AMXTMM3dR1nVxNziCNaBTT1CEpPWHa6hvohm4aFrAgx/PQ4+7omOVwu72N7Ud27fQ3B9IO/vZbm1vnzldiIZ/Xva336NDhTkPXIYdtlx4p/5mIc4EizwuS7OR5CUIsCDwvCIIgCoLI8eJJy8Tcp187Yj4vCAFEqrcl6v+A8UZa2rek00aZ5ULHnqSU+VoEoWnPTlE77dGH70/uvuY7X5/MOY+EpzlpvGrhqSSj0j/T+WNnfuV0/qS9noLA//xGwNfu+sPBAwe2b9qhUW+PZVrEnWhPLNk1bEneoYUtXRMEkRdEXnJSE97p8TndXl2LE9OVyA5ya1umaWGV1Dm0SLiQruu2SWDXh6eGrW0hJLAVJVUNFrA4OsdAZxEgMTV0wzrYNTwUjiuqyXPkLrcw9vnnaopi6CrWdcvQdQMblm4CfHD9AdOyRAEa9lBIU83HXvhhWX5AuX357JVrol1Hu7evTdv0wztvK/w435n/T0O6bkBoIu4HG35rqBEq/QVk8RyZ0HOI0OeAs/zcvPk8uMZ9xjWnrL9jXeoRfrr7IjU0EGhwNzQ3uLxugNXhwRDu2wAwNA2oqVw8HrM1saUbuqZrpmGQuRiEyGW1TMMW8uSP1KUkked0btPW5va1sT0Ptj0HDYDneb0H9uyVZAnresQa5vqJjEH2/Y/JpA4JaQxuGuREoQfChpamY4N9RBoBk054UvFF7DySppr0y1toVLhxCAGI1r4KyXlZmAQUIIGEDEDAcYIocxxvIg5ibPkbvBwv66quxmIc5oZCkVs/8kEw3Xzr/xQ0T+PvWE5+BZ7YxDR02h5b2T6d9EH9aF6YnQ423qM3FpRpWaZ9GcnR7AcmMfFjrygbiXuBkW8SGOcNB8p453SlfeXSAf/4vT/t2LT50L49JBnFju8ElmHZpSgFWRYk3rIsQXQ63T5ekg1N3fD8H9KOcO0NH6ILEIDzTl2a9dNfemd/S4P79BMWDwajw6FYOBqPKlpcM7buGbRITBuZ9/UIBgepfgAx06FrGkCDGIz09nT39oDffTzVZj6U/8t+5l508lmXbHw9UZpp8nC8aOoGl/qgjnLbHYXqGP7PPcNaQvpjHn77E9uBAIAEfvyfTTxKjP29MpzlQwvn8uBKNwCLAfjpGXfOBwCsv6OFHuTry57/4aYzv7BoVA9BAJpA/9J/pa/uf3oz5iXb3WBZZKjJ8QJxzZmGbmFoWnZWkUmVs12j3r7iROkm5pEJtoamB4eHDx7gEPmfw+E0VHD6We9rGt9D/Jknn4M8joZGAIShSDAaDtvufurxS536tud3ODvXlVS8o5P9iES/k+Pzop0CQrKZSNVVdNtJb0z8g442Xh5Y9p3Jy+jKQtoBEK1HroWVEO50KDS6Q3JumlQHoAtkPbL/aFnosZgye77FvmIksmJUTttKt65Ls0ypAkgM/0H68H8k9/B/z8axErjVwBfu+O3WDRuPHNpnR/WQkveAjLw4l9vv9Pgs03jv1fsKP1qe0UJrg8frlpcvbCXGhW6qmh5VtHBM/cBp1ue+/K08x5x/BslBL4EySn8CRMG+Xq9Uemy+eG/PoKrrRPqTsu5E9AsAiABK4LZ/G+D84Fe3NXtl2OFDi2cL4BIXEBcAcCcARPoDAM6489b1d/yWLt92Yrpw3H4sEXiz5qRVJyhdYHLE589OLv/vsztCw92WaTW1tM2a3Z658yWXXwwAePH5lxDPe/1t537kxNnu4qKApIPZ4oC1AhRAVYn4ArA7AUA6LcaPa/E1SjI5heZ5JcKLxmYDEkXfRo+HODtlwPYC2WGmieClok+Mnp1dmqKU99cfvCjwaYH/VPrbL5Irp9/5k8cI+Ppd9769/o3I8JBhxDG2OIHzN7Y4HO63nkkf408ej4vkc0HS2Q7yEpIlweexJwwncvC6/S5QBXCcYBkW5yyxQqdkS38NQItIf0RFP5H+MoBOgLyAE8E//bRfAmD3X2aDC53AOw+AbwGQaJ1oa4KXz/i4b/29wTx2usCXkgWaiXyoM7l8yzIvAF5iW3TlG1pecNEHaBjoxq0HFrWV5SxmFIkwKfuPt0WHHThnhwLZZT5t0uYGxh9ifAswk/hmE1O+HIdMY1zR4WIJeJ0OWfS5HfuODpA+w6OIfHX1vq0eyO+SFvhPGQkmhv/V4/zJpQMO7Ns9PNCLsdXQ0o54tPH5P0/y4HmEedbWd9Sj+cbT95x56c253tjdGwFVgGWoZPgGS9FGfRd8FvxhLLIzMZH+te9BCUAHkf68TO4nCQAXABd+rPMFcAEAXwEgGb/0EwCeAwf7wRZ1aYewp2tsfI0BVg0QJNkmfdCd8BFViDNOPb63bygSVQZHIv19Azd/gpQMir/81BZnQt7HFK2p0b965RIwkKwXwkjCETc9Bw0dESvSnvuyE2rIXAsG4Esn7y7wx/qvDUtJGWliR5A32vPs0MAkBpRUEiWDi6I5beU8hyx0tAQCPueBowPDYUXTDVkUVi5pf4Bdw2ykDf8Tzh/6Ku2/ZXf+PLl22+XnrJzkQa7+7L9GgsMdc2c/f9/PQOVJ1hIplvfeHde4fLpwiCIktstoXYqipH824H8SlYB+9j3OkZD+TgACALQC8C/gxWQxcQB+AcDjoLMPvKcODZihOG7wckMh6uMHYRWH4nYwQrxXxXEQsFOmK8DTYXk1xzkcEoRwZHiYiPuo4nQ55PMvOw2AP/7x/kWLFvr9TgjB0sVzmALICkLQNDHPkX9td43FJ7IrSo9zsnvDE0nDcSQKw44ILeVgTQGSlu+SxdNWzlsyp/lIz/D+zoG5bYHVy2aVemoznHGB/0npbw//7VUTvX+Sw//J6IC7bvvCDm3Rn3/6L2AqyZ2VWuX4Zq2QZJckE+k20NXp7Vge6iqbTjK/ersJgPu333PZ0r8FgDYAfAD8XwAGAfh/4PcAPAz6esE78ZF+MxjHGgn3BhEVKzqO6TiuA5cE/TJ8fNcxHvVG9J1feF/Sa1Qe+hevaWwMrO7uJY4sUZBEoae758orLqA6ILlbQ8DrcMgkT7inr/X0D8C3XirvadQ6ZLxvp2iNhvHY4U9Jink0UsdRJFQuJWMu0UJ4Eggc19zgafC7FsxqdDkkWWQuoOwkL156XmTaf7MO/ycj/Z9cuy1toQS+fctiMLVUrk9FeWleeKp/9qqGeSc2LzjZP3tl04KTHE5ysTQ1FldiiOMcLm9gzipP27IyfujQrbf7bOnfDoDfjvgYBqAXgPPBp8FIN3gzHuwzRxRb+ttypNmLhmJ4RCEDSQRBTMcDIxHd0OatOWXbotXP4IYnw0QWT57+xaQBwMuvbaAvnQ45Eg5LUnrM/k03XrXhnU2jxS1ZT6qs0KoNNHWS/I2JiOKfjLTEbnJX0Mqg9IMmBwSAR6jR52LSf2IFkOr8GQnGss7elFH6r93R421sApPm0b88CyrDqxv2ZN9QC/K/dcn7DFLviJyraZkOlwchzjA0EpwuSoIoIoRMQxdlEhTbMPfEwNzVgTknNC04ZfIfve3W21+49XaaZDUMQA8A/QAEAVjm3xXuNYZt6U9kiIU1HUdiVrMMGpzIKZLIzriBz7/shq6IdeBQ3/49hzgH37Z0/rOw8WnFNXnpT8vY0TW01N3ZZ09QSK6npw+87wOT+eiZB81zH23nkrapeJE9/i20Xh0tw4Jox2HG1JSCSHH9J5jKKKrJOIJ2bH1kxaprwVRR8hzAlILx8JHNaet8HSui4SANwIYQ8oLk4AWMMS+IyeI4bUvP6NmzfvKf/7Nbb//kb7/Xa0v/EQCigLT2WH7rsZf+o52O/TUDRxUrFLNicdzaiOjQD2N4AIArr7j6qZdf2fZmvyhLgdbW+UuXdHf3veafJ0QGSziTxctpDwa8edv+Fcvm0tu6v68/1yW88YYrHnzoifefd0ZyzXo9/dEoChJbOkMZnwhMKKqAT5rPgQaGWkk/K4vjnBISLZyTJIf/lXP+rN3RQxdSjYCSHUGVMwJyUPXSH4DefW9lruRIXX6MOB4hkjcrOZy9e9/q27+hb//bffvf7t+/of/AO2WR/pQ/3Xp7ny39Y7b01+3a8MRpYGGdSv+oFVVwXMN7j+gSDx0CdIiQBoCed/Jqy9DUWAwikhe8YOncN19+YTLSf7RpXXr8+YT09PSdcc6YMmCQBu52ly86CkobpRcVfZ+6M8mvG43hT1SonoQC6OofYVeqQNC4wP9R0n77Skh/SlkcQTu2PgLKSp5br/pH/7mwq1/IHC9wnCBIRfvWW168u9i3bLr19igAKrCr8wBgRcC5n+jWzTHpr+rEFCDtBW1Sf1pBECC0gn19PUc6+3v6XDK/cVO6TZMfT0Nj8mJu3XHguCVz6Pr+3n4AwNLjlmS+hVbvuPGGK155dZwiPOnUk4r97jMa4qcnKcAW6QFJ6nJYlmGatEpKEUexoZWySPkMu50kbQ9mF/KoZEloxigoVfrT4X/24P/KMxkjoOw6YOZhi367XIE9FTDWw7KSDNx6e0L6RwGOARwHH7iuNxSzIgqO61QBANWAa7enV7s779Q1WiSixZWu/Qd2rn9LCw6KRtHDumAwVPjOuYRXT0+fJBcdNTtTwQC4RCxwWCJ/QOaBUxJkUZAEnuO4osZHxA/JcQLPSaIgi5zIkfbxAvkXO4SasLRnArnTuDOH/3ji4b+e1wm4dkcPKaQy/tKWxRFUXvLde7WbYW7b1ChRBsgu31V5IwAAoNx6uxVLSH+skr+Pfqhf1cgksG4S6a+ZULfGUn+joeFEbYYLzj/v5DVaOBQPjcSi0WBYffP1V4oc/oNgMBwMhpPDf0rH7A66oMbVWIzViSwChJBhh+hbGBgY6BZQdVPTDVKbj5R1K8oFREb+hmnppLirZdjlgOgBTNJfgU0CTwV8luF/LucPnFj6C5kTQ6Os3dEjCpws8XHV0JJmv423sSk0ODCZCWFqBEzFbHDNDkxIATPbCBgtXTp1GF+9Hf6f7xEFEAdAxViN3Xjng1n3fP2+9DTDC889e5KffuRYf5r/h4r+5A6xWAxj7HSOmcJXXH7hE0++kDoVfMY5Z6xfW7YJktqFtsUjpe7sUnuk2DYAPI3YKTI6gjTXS4xAkV311u4pb/eDsSzSabhS34GRQkZ+RKmSIb/0t0vOcY0+p98jD4zEhoIxc7ytkKoDGJVAkiWep5UsedPQD219rYSDtLx4d66U4Dw83v4bcPdvUtdszLWrr4iS1PmRR/02mcN/uz2DJonj8gBiMcXpdChKlr4FPT19bW2VLVBRK4R7tgcbVggCb2kGrbOOMWfYxqQd31sEmplop4MxNkgpOFo0FJFZZk46vP3lSn0HRqYCSAz/8zh/8g7/n1m/64LT86UUvb6rt8nvnNPq87oljkOKSopo5hqGTrsRULPZvvnw+zw6KeKJBEG0TG2COtTl1gEFEg5HVhbv68/kKGjMNfyPxRSqG5I6IOm4CAZD4qhWyGoEBF9+uuhTmXED2SNDYFEraQdPa2UbJBkYcBwgXfOK0QACHfwjoBvkaKRWNCaTy2TSiE9v18OoEGOOtpKdP1T653f+OCS+vdHT2uD2u+XWgKvR5+QzKj6WZTKgLLPBM0/6EwUQCHA8kp0OySE5R/ueVxsr9YSLZjIcbVmUXE4b/rs85DaOx9V4iguI2K82EySFMWzCPTvsus3EAKAPPWkMUNJTQ98yWgTC7stj6+PDO0oxTxklWgCJ2P+SnD8Tjv1feYdUBwzFwLuDA++mBvJVwAv96F+evfpj099cqTqBEBq6LskOXddcdrkbUJVGQFmgY/ys3n/afgQiSHWAldvXnGkEMCgHe5SlLcJot0fTtgVITSDad69A7N469Ah2AQhItQnW0bhGPYyKwk8895s38ueC04j0z+/9p6hyASH/8e7JpwdP3hE0I11AAGMlFhFlORIO+r0LJ3mwCumA+Y2+yR/k3X6SM+z3e5aPH/6bmDh5EEKCQCJhIYThUNjpdCRnC6hxkOuwZCbg/EsDW94obgJ8JuYkQcQTI4BLiG8y+0v7PiYyeQs7CLRI81/SW8YOSSMNP+33oxnnNasBF1CKBtjzTqHSP66TiLA80v/ZbYOaown5270d8+e2+5fOa5rT7pclPpd85QPjWjVVSVTozMCyTARR77Ejsihw5Si0UlpU6BRwXQO5m4dDY9O5ffbwX9P0UDg6MBhUFOICisdVwzBjMSUSicVipBcBYbwb+9xzTk9LCmOQyZLunYkoTduWIgqglLgyUgnInjmmPbxo901wbE+WPHbGVPQDKK/zZ09IXLJgdkez95KzlvkWLAOAhNm98/yDhmH2DUX3HRnoG4oaxH04/uObm0P9/ZP0AlUoJPRoX6h2UwGi0Vg4EjUNUyf9f8rzHcpuB2zyzp/bs6scR3KkDf8HhsOmaUaiil0JCbhdDsMg2Wc8z5uhqNMpx1WNTER53KTDLxFFOWYCTjgTv1lEjWjShGwmsrdHX9bOm1oiOT7RE6+07ELaHNgCnACG1eyF/777/Z9+9ztfn+xJMzIgplwqZPgPJx7+P7N+1wcmcv70dnW6nWKDrL6zSfPu3ZOMEuY45Pc6Whs9UUULRVW7/N/4kNAUHTAtjqAJ7uLadA8ND4/omm5ZVjQay+PomBnzAdhuV5U6/A+Fo6ahm6aFEFQUpOuGabcMpP1jozGFFJ8AOBKJ+nykryDP2xlztVv6o9JAjud4Bei8xdk5vbSSW1GWpf0DAw5bJPDfgEBEfN/h6mo2PuNBJTh/nlm/65yTluR/NNbu6hN41Oh3tjV53A4xNUcEQiiLfHODqyngcsqCXaUr/Vje5uaSHUFTXh6uNlBVjQZqx+NqKBwt45HL6AuaZIKaSUrKkL+04f/u/Z26ppl2tIplYVVVw+GwEleVuBpT4kpcDQZDIyPBUDg6MhLp6uo91tUbiZJughjjc88+LdUL1NPTB1mNaACCPbu2dxkOibgQeAEijpR0Lery2SM/iDhIktMhcIhcd2xShVcZJZChsSca8TyzftepKxeIdhBvruH/2l19PIf8Hkdbk4cG/qd/KoIep9Te7G1pcDskoULDrPIWCEr4f2wX0N9eyJnJVLVgjBEigzUAUTRa5voHLS/eXRY1MDwS2t+0JCnHKYke5BS6nCFoMMZDUbU7GOsJK68avrThPx5fpcA0yaEt07D/TIsUMsO6bqqqHo0p4UgsGIoc7ew+2tlN1UCJX6Y2XYWFE+zeufOYSjpEGuSrpvRgLxTLfotlYg6hziA3mFHDvCiaG1n4UNGgLMP/3DV/nlm/a+WS2W6HmN/5Q3ysTnFWq7c54EqkiWfA86jR55zd6mvwO+ycgIoYATsOPA/K/uTWqFeARFmQcl0I8ZqWL+a9ZCavA/YdPHq0s6c7FOsNKwPR+GBUHVa0YFwLq3pU1UPJZU1XNCOum5ph6iapJ9Mfie/uCx4YCh8YDKUN/3ftPTzhFbZLUBKtYJqWrhuxWDwcjoVC0c7Onr17D5580glpU8HwfR/QTUszTHoOmmHS84moekwzFM0Ix3X7PEtVHjVCuG8v6eYLSUPfYst30rKfthsIHxkxg92TalDKpP+kMoET0h/mS/t6Zj2ZnWv0OlKbgGaydlefKHDNDe72Jo9TFnIVCIEQShLf6HfGNSMa00bCcdprtMyTAdEIKDu1GSUKIUdK7tLlFBd5JXRAybMCsVj8mNrHWWFScAYCzi47gyAk1QEg1GwfDkdf2mvs6qbkYnSHYsE40WryohVpw/9i9TvGxEQgVY5NU1VVQeDFvVszK0McHYnqJilhzCFEdraL6Oqmxdk+Tc20EOlzCdvLUO+8qnlvx65TVy21o0AxsIp4MLBF3sIDS4VWdOBgaZ/ePxRqbpipE+1TQaG9kqn0P3vNYp6fwPmDEPS6pbZGt9spoby6AkEoinxTwBWOqnHViJEHON/DWZQOSCaF7Tjw/IqFFwFQ75PAtNeqfUVKC9qbCjWALcMEfDTF60InCmmzEOLGGV22QwZJLCIdYaikmgwhbfi/c/cBYvcU+OnYohXK7BfAtAwMoT8yeBCCVgBeeXV9alLYosuvfv5Pf7ZDioBpj2Tt0jiJtub22NY+05muAAAAG7buOeuk4zhU3G0F7fIP5BrtLVH6J2HD/5JBBTp/AACnHD9fErkJnT8uh9Da6Al4HSQ1cMKPh9Ali62Nbr/XYauWfI6gkinNEZSLyfQqmkYSzvRRCToF0ImBEvxCfUIg6eq37FIBJvnDqcuGRYbbqmnpJukpQi9J5vDfGxvB1NNc6PA/YSRhTN7lCg/GDTOmm8r4+rW0MoRuYc0+B3IypIAlpidG5jDskzRr81Ypgdff3SWRhm5FvEXggCigd7btBVPCuif/CGqcr37nR1/9zo/Ke0w0LvInt/Nn5ZLZHue46omZrN3VJ4t8a6OnvckjS0KBgobjIJkubnS7SLBQlh1KngyoWDhQTZoAo6JtpgU2Cil9SJYvmZu66dh776bqgMLLFVPp74kMpa1nSWF5eGPTLpLjWTCSxL27Y4qk/8yQ+z/7/rd+9v1vlffgKRcsx9PxzPpdSxe0N/mctKtqbudPL0IkwH9Wi8/nlhMtWAuATAaIfHPAFbIdQSqZNyubIyhJCY6gXNSqALX7vo82xp1SPnPvxLbgiWdfWcKRIQAyz4XVVO8/+XZ9veNqt3ljIyGnn3h4uIkllGVqEHGZ0t8bC4acY/FFPT19l91y81N/uKeE056pvPrOtptpG+YCeGtzWTL+ZixfrZjQT2X0ybRlQubw/5n1u+bNamlrcCcaSeU5EEIep9TW6GnwOWhr7yJOAkG3SyKOII9sf1DZHEHECBidB56MI8hWS7WtAUjDJduJUp3xiZvWPU4XsFVE5AyyiwdYtqclbfifCgSgMR4kBzeN5F/qDtgykn9Zx/6MqoW422YWN3/ptikQ/ZSxKa+s0h8A0NboToZy5pn7dchCW7OnpdEtClwJIpLnUIPX2dLodsqiHhnSI8Npf6k7l14sulQdEIuTOgGUzGilmoC4QexcHTtipdq/Qr9QUEV4jnQoJOJ/vPe/L+n/oSAIeIS8Soj+0ZUJTTAq9L2xYPIv18d5Y8G0pLDLbrm51K/IKA/RlGdzBnDzl267539+OGUfN4FFfOJxc10klDOf9H9pA9ETI2Ew0tuzLWsidzmGzA4HryhGCY4gEg70uesnE+dpYXy0ZyTgd9OXedqpv/H0PWdeWqUSwS62ZYfOkPOvdgVQIDyChh3bmmf4Ty+ompKnlNQBjFoHY3C4a2jBnNa09V39Ix3NtZcXdvPUSv8xC2B+a/rw/4UN+49fPDvgkSc7aVjw23WxIcufMPbHe8vQlq8EI4AUD9Br2wU0f/UFpEAO4kkyMAkDBdVM4V4gEgikm/mH/7TlSLnMNmYEVBWWZQ2HypzWPr2enymGzy39OwJuKTmXm2v4/+yWPiQ3Nvmc7aTmj+CUBVnkZUmQBE4SOIHnBB6R1Bg79BxjTNMISGdaeyia1C5kjKYZXQPhI72h7oFwz1BE1ewEc7rD6NPLe1uMUF/RRsBvHk41AoqdEO4bCkdiY9XTaN3a2iKtFlN16rBN6x5PnQruFwLN+jjXXyaGhRGE+Yf/lUaWJ9VdhzEZTBPbFYVniPS/Z2qH//lcQLObPfGJCgY8t6XP65TWLG07fmGLxymm+kZ6+wawDjQdFOWfc/Fg+SzX8lmkJKymm10D4X2dQ0MhZZzbPdAQGh6qqA6AeWaAazUVGNppwIlY0IPvlTMxorzYNxGtMG8lisXk2RlBYeHK9OH/xg0VPUNvZOiVV15///vPoi8PHTpy3Rc/97f//mVFP5SRFcPO2Z4ZP849Uy79yVg86/D/A6ctSY3kyTX890q4w88HHECJhPr7B3v7BpJ/kz8zUkzU52xpcJGKg+M/3xtoKOGAj/7m4dLOJD5eARQeTl498LxAqi/aXffGkl2rD4QQx3GiKHjcrkLsFG66h/9JrvuHL073KdQjpMB3bQZlTK/rP0l2WeAQiWXgccp5pP/6XT0NHrm9yeNxjqv2XC4ghE6Jbw24GrwOIaOiXFIHFJcalqIDMicDvK6ELZ96Q1kWjtth5jWdCUwiZUZdP6iKW+65nA6vx+X3kjoiixYtiATmcrwoipIgiLwgcryAeAFy5A9wPOD49tPPSTtC53vvYcRP8g+kLCT+uMQCySTgeK8SeeWV15MfeujQEaoD6Lklz5MXJsidZEwSfXyGdi1y8/RJ/ywuoBc27L/ofUsnFOjrtnd5XfKsZm+z35mr3ufk4Tjkc8utDe6IogcjWarFlVIjKMUXlO4IgtDrkkPReOqXVzUjLQYUYzxhVkQVAgHENAqIq14FsPaZ+6756OdN04AQakrkjFNXqdry1mPbTQsbFqm4YNh1F0x7WV+8qnE0NItczR274kcyC3+OAwLgFEk3YFpmjtxRpKcpsjCWeM7CpIqDxJPS9ibGpFUFJAsIQoHEm1oQApHjSKUHgB08l+YImj9/7oo2P9mZIzuTEnWB+UCpXm9brYMxKL1Ydx1P/OZTAGeuXiBO5PxZu71LErhmv7O1we0k1fwrJQ3//Pd1Be5ZPh0APC45Eh1rJxuOqTopNEa+o2lacc1wypUqpVk5UKJegt2CAxVTsWXKwdgiFevsum/79u0/fsVxsWWnNB94F2OOFlmj/+5tXJQm/e2rN4FxJnCo3SO7JUGwi4iaGBNNYJtGVA1YGAt2bxMLY54UsyJpRrTgqGVrAgQhKYBMUhBghxc8mqIDAADLrrkh/OITNORhb2ChG0KgVPLHqm8wxmnu2Zqjq6v7hb//aRpPYJwseGHD/kvOPC4p0HM1DhR4rsHrtKv30H5epTBvdnrobibf/Pw1u48Mvr7lSO9QlGZ7jjt1GRihErsHp80Jp+FxO8KRxINLgpHsAgoY44GRqMcp0Sgg3TRpdFOVhtSMxxZciPSE4Th7IqB6efR/f3v1R25NBlpt37Hr+BXHDSw8qeXge/YKsn6bb36m9FePHIIAiDz5ehZOqgq73ezovQPtlMMmlyRy3LjaeKnBZsnltIUsK8ZBjQDPBVfEX3lqp28BBCAcyplTxpg8um4q492zjNIVwAsb9p938iLaCTUP63Z0N3odc1q8DT5HZquvwjnc2VvIbl4JXHLKHEXTj/WFjvWHh8KKppsAg5hJzBTe25zUAcXWCErqgPFGQCJJyut2SCIZ6aujTsaB4SjpI2tZJC1AM17buK/R5wp4nQGf0yWLtLFl1UpWXVchGVZbo0WUQQ3ogFFBS3VA34I1ozogC1T6N7qkJpeM7d6QpEa/XZ4zouqhuE5lu2FZwzG12SWLxHGZcblgtuW8e314vu++DEfQTt8CUK/S/+TP/7XQXe/9+CQ/KxbXktZ5LXLhNZ+sIgWwetksF3Fu5Bv+r93WJUt8S4OrpYE8Q1Pzw0MIZIFvCbgBgKZlDYYUy8ROzpxMVGgaqTog9UthjO07jDASVkSR0w0LA21gONI1GOU5JIl8W6O3tcnT6HN5XLJLrtJJP1IymTRBNBHHkZmAqiehA0ZJ1QGZw3/1yCEygSzxS5t9LpGnPiJqBJgYdwVjUc0g1822DMJxfVhRXUQDlOd3yNQBa9Ysf+3VN0FdsvHXHy1430kNRDAGsbhO+i7Uwv2ci+n1/4xdgxc27G/2u5L9W3I5f0jpZrfcGiCu/zwVEcp/lgi6HUJrg6sl4HaI6T2ES4sKzRkUlHJ0jEFSAZAZQhNrutE/FNF0k7azjyna/s6Bt7cefuHN3U+t3f7kuu2gKuGou8quBsrzVT0HkOTR//1t6svttp8nU/rToCwEwByfyyeLAodEDkk85xA4p8i7RMHnICuTb1FNM6hodJK2cpx73vsqenyGZVkkP7/2IvKqi8SDcdaJCySByy/9X9/Rbdf7dPtSMoSnDI5DLpnogEYf7SGcBiyxe3CKDsj8SoZpJrPhyKDSsuicsFMWrzh35YWnLzv9hPmnr5p36sp5q5fOam30VLrTVunY0p+2hKzac5xQB/QNDWdKf+0oifxxSXyb15k5JkEQOAXeQ8YriTUWBlHNiJCpnbKd54fn+zKjQgEAf/3d/yvbZzAyc4BHB2eMSSmAFzbs97tlaknlkv5rt3c5JKGjydPWYNf7nA44DnldUmuDi2Qdj9dA3kCgEjogqmhxdSzMgPiUTUsS+fYm79z2huUL2844YcHZaxadccL8M05ccPEZx5XmgJoCoC3saHilXRS69nRAc1tr+sQvBlT6k1bVLlnOUYTcIXCNLllMMQKimjEUVfWyGgGrV5+YpgOYEVBRDNOs6SIQF1bBBABRAC9s2P/BM5fll/60XHOjz9HR5HGPL/kwxYgC1+RzNftdkpiZHjymA4qF6gDagzCViKKlJpqTKBoE2xq9gkDmyhGEvO1a4ewqa7Io+D0OUJ3YP4xFKyvUkAmQzQ5IYh1LSH8BwVa3I9ddySMUcIgeSUxu101rKKZG1bIZAXTWN5Nf3VPwjCijSHRj5hSBmEb495+ymDTVy8sbO7v9brmtgTh/7n+y8rNbJT2WUZXag8TXUcJs8KO/eXh83TmCqulpJ+NzO0i3y1qDVgAlE6OQGgM1RubwX1HhIB9o1sj8f5Nb9jvEfH2qRd7nEIdiKm3SiwGIaPqQorolPjPJvFiSMT8LFyzITA2b5MEZudCMmi8C8cJ0zwATBeAefXJyDf9fscv990WCfT29Y70wbFRHMY26IHDJosspkjRL01JUPU6GYGW7hLwTGMM9k9EBmZCQ0xQ4jnQ9q8mgA5KwQGYx7GywGvsCn/3a7Y0kBizBju3khhwcHMBQ6BMaZlsjc/3u/CE9PEJuiZcFLjqaN6QZ1lBUbXRKeTRHIaRFfGbqgCWTOTojTw6wRlsBMSZFIiAkj/Mni5SHpBOTwHN+kXfIgtspel2SYVrDIQVjIAicyCNR4CWREwVe042hoAIgEHlOFDhJ5CWBF0VOietDQQUhmFgpcpLACwIXiaoj4TjPIVFM7CyJHM+hYCSu65ZhWj2D4WN9IZICnnH1+UBbqg6Y3C9D3p8MAaLQStegBiGx/5gkAddE2tqE0r9/IESGDliDnAhmL/XL4fwHgRB4JcEnC4odxksvbljVw6rulUuPZ8uM96ezvqm5wYxKYFmWZrAQoDIpgDzS/9ntg163dNyCltVL25sCrt7e0jJvs7bmkcGc9EKkBB8PACkHnUbAmXCvL5vtHg779x8dPNYbVDUzXdA3N4X6aS1S+OTa7Zefc3xRJzq+DJyVWggaQuhxjTa7r7WRB8eTPp2kDByZBAY1Kv0p8bipjYZmccCcN6d90LskT44YxSHwDU55RNFio1adZpphVddNSyqyhXV+6U+M5vH1IRhlx7SwbtTwBMDqMy/t70v0NZleUB7pv3ZXn1fCsxulWQ2iFo+UKv3LDEKQdp/3uWU7nTOzg3xTcvnJtaUH5qeVgRN4zpV0F9TYMBo4ZQeHSK1l8qJmDecd23c9fv8fn3/0z9T2J5XdnHLn0U6IUP+ik5N/Wd+LEAw4RY8kJC8dxiCoaGFVL/z36Jy9KvmXS/oDAN586fHUiCBG2SE1AWs5BnTzG0+D6oCfIP3KKbY1koLP3GiOWDUg8KjB52hr8pgWDkXi9lgg50Ncgh1ACcfU1DkAj1NK7ZFQW8gOWZLEuEIuYvV3hM/l/Hn8/j/S5U3rnjjx7Cs4jnM5HQCCrVuJml+1KnGVU3VA8/6NdAHaRoBbEgZjKnUD2VPBxlBM80iiZDeqywqV9Wkc2H/A7gUm5Yr3f/Olx5kvqHLoulHTMaDVQ04FsG53n8tBpH+j3zmZmj+VAELokITWRg9N+h8Jp1eK9o45goo8ckI+koqPgyPR1MMGfM6x/TD42wsbr7sw+2AzP5+5dxp+TIyxw+HQNA0DribkfxbX//iB+qZ1T5x18Y2CwFtmYmabqgFKpjJo3r+Rg9Aniw6Bi6h2t1F7IDkUUxtJbbhxc/t5hH4q8bgqy1L+bK+9S39MF2hE0BduLrxSAiMHGETjupnybD70wpYbLjyB/V5lUwBrd/VJItfa6O5o8TrlChZ8LhkOIa+bPLSaZiqqocT1NCMgVQcUaQSQL6vpZt9QJHXtoc6BxfNGu9JX3e8xAUo8jgTB5fFoumWauLak//ZtOyGAjz+QHjP3+nMP2nVD6awu0Q+0myQACZsgVRlQTWDphrX/CNq/bSweVNWHY6pHEkQOZcr9TKGfRh7pn2YEsKjQcmGYlt2je+w21uI12Rd+9ZmXTrsviM8ZNeGS25u9fo+jXDWzygwEAsd5XXJrkzsci+s6TQucrA7AY3XGdZCnamatdQVW4yqwNEHgnIIUClf10/LZr90+9gKDSFTBFn7ioT9NmCN21Yc/a7+Fina7A9p4ZbBq1fGCwC+cP3u3rvd09ydDkId1x4hjVmtTAyxY7v/tL78u5Ltk6oBf3fNXZgRMElUzNG3iEKCu/pGO5qzhJ4y8CmDtrj6HxLc0uht9TtIcpmolHSTF6QIeR2ujJ6rooUi8XLObpMisifOkx9VcDUKEOCWuYcx7vK5D7/0dVDMYeNySopDYBNM09+ze+/yj/1vI+x677+4xTUCvTkIZJLphJjQBBqau+3zukRESPIoxjkRjRzt7hvr7kj79Scr9/MxsHTAFHs64ps+AHODNbzy9+sxLq04BrN3VJwpco9/V2ujOLLdQbUAIJZFvDrhCETWuGnbU5qSNgESbobzKpLp/lkxkh0OJBlVVS4+prDI++eXbwuHw4NCQaVo8xwWHB0tIFUxqgjGzIDH3TSpE28oAqrEwxBrRDfYmJRqSBW9WvT5JoZ/VETSzdUBFwZi06KjZQLaqY5wCWLurl0Mo4HXMavX63KMx79VNIiq0yR2JqQMjUZOE7UxKB5DR/0Re8ppLp/J4XPGorChx4guqVm767NcOHjyK7QwM0rYX65ZlrX12rFRfCWQog0SGIEc8mzAZEEW6x9i3ThlH+knYZEAZsSxSkr2MFQTqnPEWAIQOWWhv9rZMX8nPEuB51OB1tjSq0bgWjWmTujcw6fY1caJXjcl/0NjgM7Xo4OCIpo1lNlQVl37o8729/RBC0nENA4Q1AOEkpX8uZXD5jbfYRfxIcQy6BmP8wmN/ARXjzZceT0sNY0ZAaViWlZafXzjrnvzj2Zd/ClQTq6d7HnhMAazd3SeLfFOAOH+qM/InFxBCWeZbGtyhSFzTTDt4v0QjgCSYFBBfTH0FpDl4xs/0xtP3nHnpzaDKkGXR6ZQM3TUSioLq49wrb7Ywnju7TdN1XTd03YgEh19+8oEKfdyTD/4BTDmZdeKYDig1BWz0Ca0ZEVW90wCJGZu1u3rtgs/OWS1et1OsIelP4SBpFdDW5PG6JYEk9ZSSHoxJB+DCrEtMWgVs2dt1pHtoOBwLReOkVTHJGqhSyxRjSxJFt8dVne3A2lubVi6b29bSMKu9eU5HSywcrJz0ny7S0oOTTWMYRWGYlmla5PmuMRFVpSTEAYQk6XdWq7fJ7yL1FWqKPz/wclmOY5okTLyQPRVV7+4PDgT3yCLvcko8h9xOqcnvWjqvtcmfpZDRtEMc3hw0LbMKO8J/6ivfbmv20WWOQ9t27022Jp1hMEfQpLCHV3aAXpUOs0rB1FaffsHmt14E06gAXrILPveHQP+xLjCDSakQmtURVGCFcSWudw+EDLselaYboWicatCA17lsfiuoUqBhmAMDw6ZZXbL1M/90e3NDSs6XXezz+UdnbB8V5giaJLphkrYWM4XNb724+vQLplEH8FT6VyeGczZd4DnkkEW/x9Ha4Jnd6k9rvDWnKTHoxoC0bByJKFv3dm3d2zUSVlL9OZxdyNkM9+bSAXrG5EEmsbjWMxDKnIbCGIsC55CrtFi0ZZkjI6FoNCbJVWSgfPZrtzdlFPt8IiPjdybBIoImA2kBrVd9IntJOgBME7wpt8xu8a9eNmvxnGa3U6q5AMdUIIA8xzX53OeevHjFwrb3dnXuONATjsVTpTrnac2lAybMLokq2aU/Ja7qqmY4pUk1GKkQwZFQf9+gaZA2eqBa2Z5S7i2VO370szu/9VUwU2COoMnFgE48SqtRHbB5OowA5Pc4Vi7pWL6gzVPj0j8VDqLWBu+F71t208VrTlw22zm+6xPnye6omXD6N4/0txMUjZhSpUGWwVBU13UIEZqo/edUcvd/fk9R4sm/a6+9/Pf3PZi2zx0/+hkA4CvfugPUFN+443vfuON7v8v4OgCAf/jWnbG4YReqII8bmw0uHMO0SJfWmcjmabID+NNWzl88p5kk/VZM+u/afxRMExjg2Y1OS/cd7RmOkVoIiWLIgq8pHhwotk5c/gBk3TDtGlVVCZH9yA5bra45gJ/d9d2vfvu7dHnz5u2rVx//r9//SXIrzxPNPTw0CGoNuxYR2Llj9+/ue3D//kOpmyJR1eVIuAohhK+89OoPX35qmk6zlsC2A7bkJIDqZ/N02AE8D4zu3v6e3hky9k8DAihLQnPAHVU0i9Z4GG0kKpekA/JgWngoFFX1ahyh+Lzu4SFZJWWQq858pjqApla8sX5jS0tjclOq6P/Kt+74+Y/uBLXAGe+/hDRes3XAju27pfElhpLS/5WXXl3PRH/BYEwUwAyaAM7OFOsAJJMeSdMv/W3BlLy24y4yppAbYPx6umXcG1M2ja7kEPK45LYmT5Of5AiM6yBW3u+NcVQh5WWqEJfb5XLKsixN/5XOxs/u+u6777xnWdawXaAtk5//6M5akf4AgPWvPPPvd97+xtp1pHCNmt0ryKR/sZikResMnABIhYr+qfQF8SV3xC4v45XQuFPKlZVG3pLj3NM2iDzX6HNBCHXD6B82TdJLgtxGsrcpHhoAuAQjIPsHK3F91MCoMiB0OByC6BwJVmMmMEnTf+Exkq/X2GLZV4cUbLMH0b/4cc3I/TTWv/LM+lee+eI3Uqpb27zy0qtkKxv7F4lJsvRnrP8nTQecfMYFG9dPhR1QXR7hCtaKEIUGr6s54HE6xHSFYr/cdnS48OPl2qCoeiGZBFOPruscx7s9LkmWQbWy7oXHQoN9vf1DvX1DfQPD/YMjtSv9k/zy37+Xtmb9y08x6V8CdsG+any4KkFS+p98RmWtgbpQAMnC0U1+V6PPKQh80kRw+JrtzeDwoQKT4PIZTKSJcFXGWUIAo5GgwHOBhqpukbHuhccURVUUNRKJBauybBFjurBjmKvx4ZoyTVAJZVCNlWEqBILQ5ZBaAp5ITBsyo3qMdHyUHbJCNxfkCZtgJ1Uz9aq0Uk0MwuGoy6fKjirvCAA62pvsCR/CLf90RzSqtLY0zJvTJoni+g1bV61Y1NIcKOQ4um6U8awsy+rpHeztHw6GIqqqq6pmmGZMiZukNA2pTZCYqaqXEeo0QMZWdfz7bqyMR6iOFAAtHO33Optjam9PD5X+xO/sD4RGCvf/5EMzDDuduOoYHhoeGhrmRJfDVaWZCpRrPva5AwfSq6QdPRQ8euggXX7rzaEzz3pfniM4HRN09crEwnjjpt3bduyfULpknY5qa2289MIzknNp+/buSdshriTGGIzJoOszqghElVBfCgAC+PTLG8jNJPhEDigWhgDISMteMKh4DMNSyzrwLBe9PX1xRYlGY/F4NUapJvn7X35zzcc+l3+fN15/M8/WWR0tJXxuPBKUebO0ATzWogf27S3hjYyiqwCxn6zc1JECeOhpUoz3zBMXLF68uGcg1Nk7MhJW+oZCACeMALtDyKQ+wjCtnQf6QPVhWnZnXCRUfxxFihTGAJCeLaPj7rGXKY1cSFivZeHEvwge6+qd1VF0Sb7JeG+StUsTMclwghA1RmnYlXqZCqgFBfDahuwF9ym9/UM3XnYOmA7p/43PXUdfNnhdC2Y1RRR1y57OvXaiptcfsAf/k7rDLMvi+SqqtZAk0RsdoUPvPAmqmxQ3C1mwX8GMrYkgUfqK44hK4DhaXKEUuasbBbSBy0GycyptPc+oEDM4B3ga4R98at2Nl51dlmMdPpYY/M7rIKE1h7v6U7f29o/52R98am3aeyunEqjov+qS85bOHUsx5Xnk4SWPS9q62yyT+4dgYUx6zVYfCCKOFxCqRuWURnB4JKbEJtytvaMDQtsmIEkDqdoCHOvqK8oRhDE2JiFckhpr+7adE+787N//t+QPqnNKUABd/SMdzVUd9lYVFsCDT62zRfDZkxf9uSQ+5cufvJzWwPp/fyBZP6lkqoTJa4Wsoj+Vg0d7XCl14iYfYmCPSatRAXC8IDlcHFcDHr+Xn3rg/MtumlAHdHflC9tVC1AhSSyMo1EFF+Ycy7QRw8Hh7dtChX8cozSqM8C61uFv+9KNAIAf/s+DJZgCDz71eq4x81c+dcX7TloGIamQgzG2LEwsZWojQ/CVW67EmDhtMcAIQpRPK4yTp4Wf4UNPv55f9NMF0W6RaKikAgEvliFJCiF4121f+PYPfwWqCUQMALEmLIDCdUC5ME3LKjXAHEHIkSp7BewKwXNs+D8JqrCM1QwgMSS87Us3Uh0woZC1hX4qxG/yj5++MmtJCUx6uaSvJ/N4tt/Ugti0rKSP9x8/fRUNpTZNoi0ghL/44xMZn07OMI20E05z9+cS/YmzhwnpXxYLIEm16QAIAccJllmNEUpZsXjHCasX2V51cmPRKVoIoYWpwydxsch6Mv1rIbKJJDxjjCVJ5DmutbUx1auXupDG0WN9u/ceKWQWOFHBOQW/z3PumScmpwHy8M0vf6HA785gTBljPoFUUyBVqmZI/ARfuvkyW0xneSqSZF2f3B0hUp+eLtPHDyFkmJbt2yXv/cdbriRzfSS8kkSAQQD/554sc5ipWgFCOHvO7A9fetqEoj+5vyolrARp5g4xOMRJkoytWppGS+0MPHbPjC4k/0vr/ZF/7c5xyT3HTSWPX0iDt6ePS9P/5M4kb6xGvx+DMSH8vkNdi+d3JF9TUwAA8MATr0KU7jL+6qevIL1wE8MxctdT7w2YNMmDkJb0dlf65IgsEfZhf+KXP3WF7UmCloVt3xJ59hJaAeOvfuYq08KPvrR56+4jq5bNzS/6Jwln+45qxQjQdRUitHv9I6BGOG5xohtoaXdRmz38LxCOKoACSIlJTWASLM6+Y/Pz41/8ihkBjGqDSLF9o2VwqCa46crz25vd/3X33+nKf/jkZaSNyKicF6YqzDH1SRMFPj1eG5L4D4tMuuIvfyoxt0x3OH5hW1Gi3+MUozGtEhNM1aMDdr3xt+POzO4TY3AcIj6ckqwjW/6zycnp56EXttxw4QnTfRa1x7hhLNUEiBMxBl/59NUm8caMN6KnO8QlaW9wEAKUOHu7AGfCNfzYy1uoAnj17R1z2xsKOeaSVueRHj1OWs1VaUv3soBxzcip86765FQrgFJvbNPCZtXn1tUDWnyKQgZmGFlM14GhEYQgzyFJ5EWBF3iODpGmXfrnwp5LoL4o2N7oHgqGh4Kk0FuBtDa6G30OmTTFLAN4tOM85a7bqmXqb/f6hEnHSIPe36X9LNiyJpNDwCgfVSqdqpzs9/2WXdPWxbcc0Am9Quf0/B65o9nb6HdV6GyqRwcw8gwgSvtxLIwLLzv6419UhT9wRlLqBax3pigz6D/+kB7Q2eAQb/nwxZX6vGKMFVnkWxpcEILuAWZF1vAMcMkghFABs7hTU3eaURrVWYKlThXAf97ztK5pHJfvkgwpWqpWaLAzciuoEnKDIHTKQnOgUhZAVc0GVz9TPAFAFQANJC0NTa/qAqt1grv4MuCMsimAtAE+yiv6szKkaKnH8UuJE/vMRy+ZguuEIHRIE8wAk1qEk4DpgKqFZPNOwgJQSfgAY3qAMHHhfG45M2jQLtA7NkPDFy+XZjx8eR0740jURhxNFrNfmnb8fmoyZ1ZGVINm5//ur89MjRqoziJujCkAIpjfWs2PpjIFMP24nVKq3M9K9ddCrxkF8JPfPTphYRmHJJA6P2SGDXLkj+jqOGmlZ0EETdOKqxM7T0fsfagamDKDoEIwI6AKJwCoBcDzU2QBsHSwykA6flfo0DMbvlyj/tFhPhnpI4gsBJsDTg4hnkMcl/gXYxyJaaZlIQTjqtE9EE7UaMEFqYGpNAgqBNMB1QiEpBpEqbXAdd1IdiZgTAsIjnP7MAqHL4PDh0h8ooElgcRTcxwklXuw0d7kpWN/YgTYycQYA5dDpPn0cdUIRVTTwsky36Q4qF3bi4AnNghqVw0wqmcGmAaQ8xMV9shTDcKOAmLlgKYThNDSBWP1bBiFwxcr+iEk5XpUzRR4ItdpMWeeRz637HaKHEcCKiIx3TQMr1uij8rY/zEWRm1tWeLbmjyaYcbs6V8MQNz2+0MIDdMyTAvQSddsAzOqBu5+eB1UI5NXA/c+kmhFIHlJH5vslG98x4yASlNUISACJEGEqZ0mi0I3DMsi/chKeC+jQG76+KdVJWd2JykgxiivAojFNQThf//12eQa6s3nOeR2ikMjVsDroOKe46AocD637JRFOtgHQBkJkejq9IPa2sJewiLiOlq8mm4ocQMhMjkcjqqkbQCEiqrHVD0YUjiOzByQcou022oGWHJruqHppmVhzTD/8tgbp66cDyoAZ5cpLdfRmA6oKmwLgCu5H5xdDaigenAUNg1QdiYzhVPn5FQAtPpmYhkSI8sh8bIkSAIX8Dniqt7R4qXSHyHEI+IC4nlSqZMmZUxUXIVs9ThF0xLcTlJxyLKw1yXREZii6lFFD0fiDkmQJVHVDE0nRUjtmj85+gTZheBK+wlMR6NpHzlPILHAc6peM7V0apepnwFOLQdklXQLmaZlWtZMLiNVBeQZ/qfFfabi9zgqdkYz3gVE+2vb/n1Z4mWR93kcXpck8JzbKQ4ORxt8TlqZmfbsKKFSECSTbxC1nNTodwIAOo920vUiAD4AOhaTZdpNTNcNVdMVRd2/6z3DsIg5kHxUyRlgy8SpDbmdTiLMB4LRrJ+7YFbC1ROOkNTf2z51bkTR4qrx1yffzXWqSc9VuWBGQJVMAIxrCVASRAGwboXTSsmFvK689ia68Nkb8nWfvfpjXwF1aAHENd0pCwLPBTwOl1P0OCWnLJB5XQ7KIi/mzb0+5fyrlfBQ5vo5c2alvqRzvknRn+U0SJIOREgUBd4hi/7Tz9n27huabiYdMgJpOcY5JKilZuTTObnMf7PR3TMAJqISTkamA6quInRJWJPrKc+YPG4XSwMutwKQRB5j3BJwSyLv98oOiZQF1V0LLQAMAOatWBDPfVCPA2SV/qT93tFjyeU5c2blEf2pEAuDQyIJJAUtDe6ooimj2TdZE7iIeWCbCJCaCtRcoGtKesxZDuHMLwg6mlNaUkFQVg5o2oAQBjzEhcAogXw3vRLX2ps9LY0ur0tavzuuuxZm7hOJkRie0khVBqmIcs7LCSFoa/K0N3ua8hbvtF1So06sZC/6SYRpyKOlKcoLKxRaJZCIhZLLAUGS1VjUO1hZ0DLCIVLLq5xHrCf4XI7yzbtJRWin00+MXADOPt4Jolm6a/kAANkPUDpmlPR3zYXD4XM4QGMALEg5T8p5py3LqZPGd7ah0O9+sDePMWO/CwKf2wHACKgAzBGUBGGzr29id9yElDBhE1c1y9QhLsyTM26qGDockhKL9fawghDTA8dxzEAvmWkKn0rxyWSJvEhZQ+OC8IRrRg9lmrh7IFjKaeSG5zmPa6zUFGPmge0SVaW8E4KGBq8oFjcC/Y/v3VHKZzGyIfClz98wximAtEeABN8n11Ahm7oDzvaXg9QUGxrTn4jsH10mC/TjkltHPz35b1LQZ1kzeqjBYDQcVXOeRsbLsZPJffICh0ShoGJhskSKWhcLcwRNO5adhl7CG3mOa/B7mfyZRgp8NgEA77/qMxU+l9qDp8H1yRi4tJc0ByvZbz3VlQLt+IfUPccKgCZfjvndoWUldk7zxcPcC1kZ/YCx/ZL7q6rROxjO8ySnHXk0KS17vnESO9+tskMM5ggCALz8+L3XfvzzYDqwq5AUrwAg9HpcslyK1meUi1qsAvT843/BAMw+/uy96x+b3jPhU1v9Ekk4Phx63KuMSGmUa2uanLdbNCZ3Lk/lrIwjWBbuGQyNCwbNIHssKF2V+5RIXlvBM4Tbjg6vnBMocGdGlWAn8xatABCELU3+0mePGeVAnqiTR4V4/vG/JJdnHX9exvb0+aRUWR8LkSDJaZf+VAGMvYB55OVEUjv74DrbywqNpUfCynCoIj0dRaIACjprqgRL0wHMCCAXMRj1+yrYmi3fJLDdf6IoeJ5zu53M/Ty9OCShvdjqT0Xy6F9+nrmSCnHK3vWPgNokr/U0zhYA1YxdXDo04SCulC8BSa+JiSpbJCB6YhIpQUwHTAumZUXsnPBi4TmSP1Dsu9gM8HSlAV95zQ2fvXFsqN4/HHn0xffKdRqZZWJrgiqwXpOhOCXW8iHOn97BULyAxkwlfAKHYGZFES5H9WDHaDTItqPDxX8Ug0wDTP2vEI9rMWWCUOCs2FNDtffMzyQghHKRIVgVAtag9K9UU/jCSUy8kSAcslTgQDuNWFyLxQuKwi7h6DzHFZ5mMrvN/+JLW+fNK700OTMCHrn316efN6kS311HD4OJ2tWVTspccVCNvP56X1HvfuuVpypwTvWLKHDTNQcwM5hOCwBjHFd1VTNI/ee4VsIsHD2IbliVi9KhvcwK3PnFlxKtBSZjBLCo0LdefUaWJ1fH0arG4jxM+pcdhyQIBYeBMipuASBYUGUIC+ODx4Z6BiJpXhmT9/g9jjNPnDu33Z8Qo9uP5D8UxnaltsJMhxL6Ngk8V2AluOQpHD7cNRkjgAEAePVZMqt23gevjceVEn8Qy6ygHVAkTPRXCFkUinqi737w1dRpAMYECmDl8XMn/I0OHkiV0WMx0Uo8pzJYt92eQOfd5A2YPOEOWVixqGXj9mOxkPLCa2RrR4t32bymCT890Ya4MEowE1xOkbazn5DU3agOKDkklDmCKK8++0h16YCSksWY9K8cUu4iXawZwGQVAILajh37QPlwjKbMfPCUdpL4s+FAcpMS11VtnNleCbd+ImG44Pl6CEHA4yxw5+YGtxJtDA0NFm4HPPHa1ivOXZV1E9MBqTrAnqpVas4OYKK/0jjYBMDk4Jsywq6HgiQkbnZboLM3S/W3SmBZZkuDe+eBPtMkaVwcx/vc4qLZfgyAyI0Lz9DMSdTkwSTjP6poQ6FYY9q3zuEbQgg5baX1xNqd+Y+NEFy+oHX/zm1p6/MbAbmkPyPTHQQAKHFyeJp0AJP+ladaQoBmpgXAIUvV7dh2APYdHTZNY+m8Jocsbdvb5XPLZO4Fg45W/4ZtR1YuanU4pMNdQ6RdDIIN3pz1nPuGwk0Bz9qN+10OIlhN0+A4cg4NXsdgcEzWcxwySZOvdJL6YDgU33ko9NnrzkpsgDCqaHc/+GqunsAYA1U3QpH4cCgWjRtnrqa1REfJMcTnECxwiskli7NayLyFQ0SKlkgpYkZAeXnr1WfKfERGLQMBEMVqmeapUSbwbts5sASHxJNyDnZfeKcs+jwOhyS4nESIe10SrQHkdUm0a5ii6mTmFFr0DwOQrNAgiyT3WOA5KSV9g9hxMLXNI03NLyAz0674P4EbyC70FompvQPh3sFQJKb2DYVN++BNPhcC8EDXcNdgeCicxcNAeiAXkGYCIVg8tzngdeqOZt3RzPtak5sOH+7KHw50xbmrnnhta66tLCKIwcj93EExR0YOozwKgIpkaM+Fzm710SYrzQ0J/wmp7wZIhxaaDiOJpLQchERJpEbOQNuXQpdJ5yUA2ps9Ps+YM8cpC8OhcfLXNK3CPO8TaQAMDMsaCcW6+oNDoaimk2kGC+OuvrGS0R+95ITrzl9x2VlLM9/tdclC3s6XFIHnFs5uJKqRM+ift2EsN/3w4a4CvgiDwSgOImcK7v2QLAV694Ovsh86SU792dbs7+7vSkrWJr+LOuiJoBd42padqgdptBofgtAhE0MB2q3kU4+W1Ad0fZPfFVXGYoSicU0nonnsWvIcKkTy5sM+PVUzghFlYJh8QurG3Yf75rQlXPNzTh71I/11LIo/WQRiQi0EAeho9rU0eOgdmcxm8DaMTQg/uW775Wcfn98IYLPBDEZRcAhVoll3jVJa5/pCDShR4KK6npTqZMifLQuemAgFDNz58RX2lbgBAE7Nt3I5RI4rpvhctoF/VNGGQ7FQJK5n9OwOR+NpKiHrJxRSahwhdNyCVqoFRYGLq2PlSFN1QH6u/+DpDz/7FtMBDEbBQFkivWAqXQmuJuR4yRSqACAAoajKIeRySGX54PGTq+nh1SVl940dJG4P/IdDikKqPGaJ3cYAHOgcnNPiBQC889TfAQCnXHZN2j4IwaRxkwevW25v8lKF5HPLuhE1zSyfmN8IUJWK1DFlMGYqxC/tmNhAT+Xxvz905TU3gJkox8vfE/jtrQcAkFMze3sGwgjC1kbi65g8XCVrJ/UMhkLRuO1WysnBYwkFQKFqIBWR5x0TVQFCEB43v8U72jCytcEdi+uRmJpMGCrcEcSMAAajcDgOOXJngdWnNC+BIn7BYCReVCLk3j6eNlpd0oonmcFbLMOh2ISVhdS8rWNoDvCEUcaiyM9tb4CjlYhaAq5QNK5qBp1tLsoRRI2APJMBDAYjCcehEnrBP/73h5L/AgCuvPamOv9Jids9tSNu2nIqeUIzUyM4k1im3Wc1T4PGPE24Rgvtaqac9c/l9J9y/NxNu45s2nlk3bv7Hnh2431Pv+N2O3ce6tt5qK+0unJpOGUxfx8YCMG89kBTYCynzOuSWgLuPNmJT67bnueA13/wdKoDsm5lIaEMRhIeFVGlkZELNNpgfUxWY4zztMjOKlrD0SxlfxyySFz5eaV8rk1JGZrWF768PLt+14YdRx56ee/fXjvwyNqD4zdOPAHAIbR0XktqtJLAcwGvw+NK1xypUaF5dMCEMwFMBzAYFJ6veKfuesBuCTna9p3Ojo417832hqiipYXe6qbZ2RtsyZgbCA/3xVXdMC3QOpYYlYok8kpczX5ao58xdoVHdUDe/u2lk6bxIATyRB7GRr+rbfy3hhA4JaHB6xwOxaNxLfVEC3QE0ZBQ5ghiMPICeY5Lul4L5EMfu5nk/Vx26oz37BeOLeNyNOyF2QRt90Aode6UqISYFrYd3ytWLE4tHrd8QaKkcy6E3Ekcum4mBuD2CVGVlDy3cZe9EtrA/kS3M1+8E8+hVUs6nBkxUTyPmvzO4bCi6YZuZPeY5Z8NpuTSAaxIHIMB7UDB0vpHJbn7F3cV2BIysyfwjFEeeauBojHHjhLXzjoxS5kdJa75PY7TV83FlpUq/TNrQa9YsfjggSMOWVywcC6tIL16afvLG0gCMMfxq5a0bd3bQ+sCkRpq+/vPPXl8uZ5yACGURF7geTI8zws3WgYuF163PL+jIfMO5BD0uqS2Bnc0po2E4/YseHERQfmLQzAdwGBABMW8iaKZtaBv+PDHTdMq17TBo9naxBdIVSmPqauksWPHPloOenz/gARaRq5W2ZutchxyO6SA19Hgd2/afSzPnhBCLu8UE4RgfkdDLg0hCXyT3xWKqopKmp2lbirQEXTu6atee4s5ghiM7CBIikDA2uzZ++gklEfZ9ccECgADnEz4zbWcsvPYqgl3TsIhMt+77/AIXUiyc//Qgo7GsqgBOvD3ueWAx+mQhTltgc17unJOc9v70xp4uXZwyuLyBW2ZMUJ/efT1/GcSVcb0QR4jwDvqWGKOIAYjRwwoam3yFf7jQEhql5V/XFmV+qNwJZFPAViYjHBTxSS2C8ANh5We/hAtpYAQtMOHsMBzXpfc4CMSVtPNkTApvKxqE8Tapwn9VMoV85Mc+Hvsym6kFinHNXidgzky4Oi9QhRA7mO2Nnj83nQb862tR1Q+yx1JFQnVN7wHGOGBQnTA5eef/OTLG/N8LzYZwKgTbvr4p1Ulkrqm2Fph77/qMzd99BO1L/zLoCTSdENxLiCMgaabwbBCs6g4DnEIGYZd+sD+cTHGqmbEFNLkPbMCT1E4ZXHy+lqWBL/b4feS4tWpIv24Ba2vbxrrR4YgsnBywhYSAzO3/4dDcMWidiFbEsqXbzjNtHA4ph7tDW070DccUkg87XhVxnuaUnVALqCZmKVgEUEMRhqSyJdQBq5u5H8RuqG4H9GycDgajyoqxhghJIuC3ZA9sZVU39TNYCQeisaVuD6ZbCynLDb6XYV77TL9ORBCt1Nqa/Q0N7gTKV2k5UCC5oBbtgv9QwhlSehoGRu5Q2jXrM792ZLIz2rxZd5NGINYJKzGIiLQ5zfLZy1vPm6W2y8DJ286udE/ATsF7PUHCkkLuPbi0+gCSw1jMNIebfqEFlkJDiZjQBlFKwDSVEvTg6S4JinWL0u8UxbG/OCYpApHYmooGp/k8B8h2NbkFQrT8BgAwzTTfE08h3xuuaPZF/A6RdvtQ0kKbQThojnNHIe8Lrmt0btyUVvyvdAe/udUABCcfPx8qjzStyTfwXGcwHu9zrYWn9cljVketClCRjZ1Lh2gp3TBZTqAUbek+X9EnnNIQgnD+aqZBq5NBWBZViiqRuMaxpjnkMcpieNzZVXdiChqXDUm6fzxux1et1yIwWZZOK7qQ8FY2ic2BdztzT63U6ID/ySpZsKCjoYmv7u92UunB5IhPRzpepbzZ5EEbum8lnwZKKOuIYHnGt1iS8BFSlbZPXH0yDD5iwX1WNAhFvTL0+IQDAYjiSiU4v+x+5QwDZBOoXMAxL8R14MRxTBMCKHLIbqdUmrJM8vCpkWqBYFJwHNIEoXWJu+EtUIxNTgUdTgYC2Z0c2wJuMkcUd5jiALf0kB2ox+1YmHbW5v3kQDQPPklkPQIc7uyJ4i9+OIroFRyzQanFodgEUEMBgCQ+J1zD9Gy8qGP3py1XhmDb/KN1TKjDAWJ0DnhuDkAgO179yTXN3hBg5fkAB/rV0aCQUnwJRSA/cNmznYWC4LQ5ZRaGjy0RXBOMPksdaziv575ubmkf9o6kktir9p3pHfB7Oa3NtsBoPZf1s8nSV5uOdfo485//jBVhPRsRsLKhh3H3tvdHY6qWX8XXgZGqH9CHUDLRNNlpgMYdQ5t05S/SmMmdqAi6VZbsfOqVUpJBKPNWmx9IM1ryzIc7h6IHO0aXLNidp6D7D/Sv3BOU7YtFgCJEf3BzsG5s2ZnGfjH1OFQLBwjlYayHz3HhU4PK0p5wdljCiL9c99bTpnYPbm27t131D4QB8zEWV1yxpILT1t0tDe4btPhfZ1DqQYThfc2p+qArKhKzN/UMjLQl383BqMegIhEbRQryGH2ujaMIqOAph6Ri6f+SXzcKWktATi7RdR0Y35H4DPXnfVZ++/Dl54SicSWz29ZPr8l19HS7psmn2sshinRooC0eMw6UoAQNPpcVE/kY1T6U5Vw8OAxJRSaExDmBASPYDmRkfZXSETQBSlVMdhsMKOeITPActEKwK50zFRAFtDjbx5+4u1jT27IVxqhFCpsbCULyb345o5JfuSCJmFBk2DGhmgAUmaN2SfW7qQpBS5nvupAueA5FPA4WhvcLocwoQ2aVQeoSmzxorFCTPkrBTEYMxfokIWiUsDGYPI/G/xPf0wTwzCNqCQeGMvUe4YwxqLAL1m6CJjWpt2dr286GI7FfW7HeScvvmZJhxbXnnlj17u7OsPROADA0NMbrPNC07qdcQjBWccl2iUWy1AwfWo3FQ6h+R1jY+cChX/+zDIIAakxm2P4T6KeSrrzSGVpkW+2qwOpmqHqZtq96PUHQiPD+Q+yenHrvv2H8u/DcoMZMxsIgUMSSygC+sBf/5wsB83IOgcA6fwKWURAcCsJaWlZXT3Dm/d0RWKqJArL5rUsnNVIainoqNHnDHgc8RBJahXG+0VIJi0yTdMyTeutbWGQg4HtA2MRk35Xo88liXZ/ApsrLj7LtPC2vZ3BiHKkZzgUiau6qWo6xwnnrrCDRJPivOD7If+OCJICI1k3cQg1+JwQwnfe3frOu1kG4P/yxRvzHRlBj1NsbXCHY9pQKJbZNT5VB2SdDVaV2PlnnPDy+i30JZsNZtQhHEIinYEsifv/cs/9f7kndc2VhbWEzFpaZ5I13aoE/sE//C9duvGWj4zfZHdkjKpb93UPjJBEDIfIb96288iRw7LIRxWteyAUieVr55JaCXmCk+DGl960Jftr6981UuZ7SfgpAMSaMOPrt45TKnc/nCjBRltCAgBOXkFCmCa0AJYvX6aoOi0VBwHg3ziUK07A55Z37Nid5yv85JcP6mo+k6UosuoAlzDu92QlIhj1BocSj2o1cHX5qnJee9VVRMh40wMyc+HxuJPLF559QnmigJKagCiD6y8CANz3wLMDI9HewTANtlFCAq2AZlo4GtfiOQq92UG6SNfNAqNCE6U3x0d5Goal6WYoogyFYlrBh5r4s8a/XPvmu6kvc2UAXHHO8seeewNUAddefNojz72dXwcwRxBjhvHoww8QgXv9TZLIUwsgvy83rRnA+6/6DKhuHnnssTxbr73qKqob3LbcT/viL6zbMhllkD0M9MGHn6eZXyNhhUp/MjQe/WRNN6IxzTSz5HyROgo8ZyeFFZoRhjE27apBEJJl3XYcxVR9JKREFDLwB5VjNDIAQmCa2K4Anf2+uuriMwEA+dXA/GXH0UbVEIJgRI3FNTuolHQuhRBGFDUUVfPUR+IdwAj25jcCUotD5IHpAMZMVQNpXHbJB3Pt/80vFeTeqX4mVA8AgNmzWktTBnxybG3Xdxsr7GOYZjCiKPHxs7uYJDpFFU1RSUGIzMMJPImizAx4n6j7CmnAghA0TCuu6rG4HlXUYCReroF/kqwDB7tYM3HTL1vYNqslsHbjWO5bVjWwYLZ/UueASY7Y5r092w/0RWLkZ0x+Sd7XOqEOSM0LY44gBiMXP/6fLApj5vFIDvXw818mFq696qpPfYh4dLJCHGoJsQhJZX97gayJKlo4qtKBPIJjaRRxTQ9FST24zGMhhASeN8ziCkJA2t6BR7pBpH9U0aKKNmEjgUwVQrrPF/BZaa8z+wyfc/JSEdKBdnpoE6W3d4LUrUJY2uFq9bb3DIQ7+0Okc2TSMmgg1Q3zdA1TlZjD16gEx3ZgjiAGIytPPfMs+2UeeeyxPDYEqVOWCKVJeEOIQtANUtWZtnzhSN41pA4fw7TUqBGz68FlHoukaENg5OiEnguISHEPy8KhCCk0rai6bpiFl5Lmec4li4ZpGsoEbX6rCg5Bj0uCEGhE7RmxNEsrrxFwweqOJ16zFUDeeDjmCGLMeJiInyQpsTc0ENSO3gnH1HCUDEs5UvSfp64hjHFc0yMxNav3n+eQyHPEg19kPTi7rgcMx9SRsBJRNE0vVPpDCJ2y2BxwN/qcBRaHwtWUJyLYFVXbGt0Br4PGTSXx2nZAntSwD19xdqr0f2LttgqfLIPBmIGgbEX/jRE79gZCkncniYleWnbt5eylnmkkJXGaFOP9H30vaTMQjsYVVSPNxQrz+/M85/c42pq8zQG3ML4qdb7PKnFbpeDsvgVtjW63Q0wbzafqgEwioWAhx7/rti9M9hQZDEa9KAAMTMsKRuIRhTh5RIFzOyVadAGTIjeWkVLoJhWeRzzPkdpsRQ7/IYSWhRVVL3bg3xJwtzd5fW65wL4xVWgBUKUjiVyT39Xsd0m51VhWI+CKc1amvsxlBDAdwGAwcjFOemIA4mqi6D+C0O2QnLKYjIzMldiFIJQEnkbug+Kx9QopJl3IzsLowL8p4JallH5ktWkB0F/P7RBbG91+j8xxsChHUIE6gMFgMLIyLv/WNMnwX1HJhKQk8T6Po5C6S6Q9D4SqbpQQtYkxLjBhOOnxTw78Z0xxb55HAa+jxe5dXJQjiKTsXUoiU/PDjAAGgzGBAiChn3EtFImbpmX7ph0uhzg2ws4hpRGCksAZdvYWqAB0jE8H/u22xz8x8IczpyAgKXEh8i0NriafM4/GzWoEKJFQ6kvmCGIwGMUrAEzye0fCSlwjtXGcsuhzO5KV0exa2tmFpyTwkGR+lTL8zw9tzu6QhOaAmw78STcuflID/6q1GUivMafU2uj2ue0+xsUYAWmOIAaDwQCF8f8BeE1YxN3q1FQAAAAASUVORK5CYII="}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "assistant": "The brass nomination band is on the axe (item 1). I will select it first.\n{\"name\":\"SelectTarget\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nSELECTED 1 Axe", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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/O4zZfaYE7EMOB9/ZdqzX29+LdfniucsXz41/fZYe2LXvyK59RxK+tayozR3ubref47Ao8Iqq/ePPD/3r2cf+9ezv2QnHn3ftvb/964TcNgAAGcxFY1jFZ0UmYCpDQAnxnzJ9ZHyMpuZOJgYuT+idLQdCEYV5AAzmB2DdrrPHZ1UVnri0OhKKpgEWzI3OdN65t36wC1hWN4cQ1NLpfn3LIUXR6uaUz5tZkm8ziwKHMe4LDRnuf26vPd9eXl7y4N23TMDbAABAlrExPeOe+VGgTPEAkom5AoX55rIimzc8IPTPIHFpmoZW98d7W71+NliGFoayj2RvIMb2PfUf7zx4sLHL7Q3dePHJp6yY/c83dv3nvX1HWnokWRF4TncI1JvWz7vu7Bk7duydmBsFACDLeOql/hmIWe0ETJkHkLD8ZwcJTgCj1GF59JkPegNySO8nUWDm4qNAsWOEUJFNWLmgwm4z0dyAngZYsWQuLRrVIX3egKqRjh6vszegapq+35tcd8nahG/6zzd21VYVzq0usZhE5mewYNHFV35pnN8IAACykI1pGPfM9wAyKAk8BBeuXfrejoa9RzrjcwBmA69b9X40jfiCklFvF8FSwTv21EdlQPcGVuiugKJp8o5DvmAkMHi7i0+ftYwmfP67fU51cW1locNOy0xFgWeTowWeP/+S6yf+vgEAyFCeGnJTWOab/gwKAbH6nyHgebx0Xnm+vrRnmA28qijxy3/av0iOLtYTPIkde+q3766nYqBHhQSeWzCvJqLRSD9CKHn5H2PDupXHLaresqdRUQd8I0VVX3j60X/8+aER3yoAANOCZ/RyoGQuuvKr2WL9p0wAkuM/TANisaAYpQ7LE//8iFUElTis8U9J2oAhbA4LH5GVxq5QUbGjqZn6CvEwYYjKAEIOu3nN4pneCHYFk7YjJ3HuKXUBX4rNycwhYD4BAAC5xkUDDX12mf4MCgHFl34mc/4Zi/WoC7dodmlLpyc8pLvQ5fQ1d3gcFmHhvIWYpnkPp5QBjFHdgllHW50zCmml6VhobUsUGwAAcoqLss3uZ5AApEz8poQG90UhLCks/pM8htlh4d1BtaXTI5YVbt99GGO0Ysk8PQSUQgb2HTyWb0IfHepmE2YGqxSiWQc3HT2/a9+R5H1kYP0BIJe5KGtN/5QJwGDl/8lbAVj8h3kAhJAulz8QlgaL/8SOe92enZ4QxthkENt7vA67Zemi2Qihw0eaEq7EYjKU5kV7SrMCoSFkgJ6z7wjWdw+M/uYBAAByuQx06P1f8ZQ6LF3u6MYuSVbf+LC+oc1N9AxwSFJTCoA7qBZaeFHsTxczRIFXOSPLNJywqCymHh5/uKndvXRef5M/NlogpgRs+c9obHPGNxeKX/5/6fbvjuKtAAAAyOkqoOTEb0pCYdkXiDDrr8d/BkB02PFLzz+X/OUy7SUdFJHkC0Ze29r06tam5s7eQEgyG0WrxRB/JtOG7XsOD7aFmDUXim8pAdYfAIBshMuE/V/RY4IkiQ6JSaj/YXT0+DyBSHz8J89iTI7/sOPn//6X5//+l5QXUGjBhRb65QdavO/v69hd3+b2hrbub044jcM4tnEsRk1luukKAACAzGfqPQC2gcuebz/S4tz08dEPdzU1tfcGQ5Kqaiz6z+I/LV0eRekvxr95w4nza4txXx64N6T1hrT4ZkExGUipBEwGCi20DNQVTNFkIuYKDIGzlw4wmFFV/tSjPxvpLQMAAOR0FRCLyKsa+XhvS09voKc3GAhJGOH9x7rzbaaq0rzK0ny71WgQeY8/0u0KEERi9T8Yo8oS+x6xMyIPaBL3cqr4D9OAm26+yesPRySFxO0fZt6AK0he3dr0iVUzE76Q6HH/+IV/TWURFk2hsNzZ2cUeaWx3fbRzQJURAABAViBMYfyHEOL2ho60uBVVdQdUZpUJIhFZ8QVCew/79h3tNhtpXwdV1Tx9Xd5i9T/lRXk1lY7u7uicgIRYUDKPPPwIQugHd39TUVSPP+zyBANhSdPHyzAZSCb5UU0j9fVNO486FUVjWQezQOeUfebSq57/+1Ojf2sAAAByygOomVlucQWOtPtd3mhxZzzU0GtKRB8CnMCXLj2RXrrALZ9f/lq3i5X/p/lNv3vvTxFC9939zeICuq9Y1UgwLLk8wV4fzQSYjQaH3VyQZzEZouWhKM4J6HT5dhxoCYZlb6BfcpInGwMAAGQFk1oGGu8BxOhxB97d3tDe44t/MLnQM9b/R9IwEwBa2yOrG1/boajR6P/7r2wc6SXdd/c32UEoIje0uYJ9+wwQwhzGZpNYmG/Jt5lnlBZsP9jS5fL3FR1Rv8EdJAk+BzgBAABkEcIU1v+wHQBKJFxdVtDlos2Z2bODbfRN3v/V43SzNm1DB3+G4K4+h8BoEPNt5lBE7isnJRohgRDtGNqM3Hvr26MbiHUw5gjRHAMDRxyrDwUAAMgSprQKSLe01jxbp5Mu//Hghj6edSfNq29y1jc5Dzf2bProKLPX6YeAUnLXvT/93vd/mm8zsZH0qa50sEAPlR+MUVmR7ctXnDOWawAAAJi2IaBBJ8AQ5PIG27t9R5qd7T0+jRADR5IFIBb/EQW+XzwM9H8YY3dQHUX8JyVXfv4LcX7AADCO6qXDbl5VN3P/0Xa3JyQI/IqFVSUOW35BAXv2gg03jsuVAAAATAcBSLD+EUmJrbWjrSAICoSk3Yc7mtrdPn8wpQAkt3+Ir/9JWQA6Ou67+5saIWFJcXuDvd5QrHIUY47n8MJZZfNqSnFfZwiE0OwZJfaC/PhXAA2YWq79yncSHsnPzxvja97/w2+N8RUAINOYyiqgAV2AMLJaDGuWzFg8p+Sltw9KfUWfQxCz/hMReucwthhFS0l+VUk+QUhRNG8gLCva8gVVpj7pqqkschQ62ICBeLq6eh7/7Y+vu+XbE3BdQFrmfiL4n2//KHb86x/fCT8JYBogTJXdT57Zok/+4t7ZevS4uqr3tjfGF4CmTAs7LHxQxnYb3Sn2zJNPjuMV3nXvT2PVQUxgRIEryqdTIWPWn7FjTz2bNwlMM3M/NCAGU8I1N9xUXpz/05/879R8++mIMFXtP2dWl6Xs/4wQmlNdpKjah7ua4zUgZf9nk1E4celMokaeQVPG9j31dNpwnwx0dfVM3bVMW9Z99vqqyvI0T7YN7O4XhQwzdnRYMCIEpSg2AzGYaO7/+X07D7YghCpKaKD1zjvp4uxHP6L1e0AW5ABG0f9Z1ci2fa07DraxKs/ktHCs7jPPnldXU/C97/963C873gkYoiVcLBPAps/HCwBEgcbOus9ezw4GE4DU5n4gc3oOs6w+YY1jEWqprBvZdQwsCkipBPFAjGi8+NQlV5514qJVi2v0n3W0zbvRSn/odasvGrdvk6tMQQjIF4jkWVMPYoyNf+E5XFNZsP9YlxKSBuv/zAbAN3X0/u2Pf0SZwY499ZWl0VogYDxMP7XY8aG/dMz97J5DsS6BMdhvi/4f/ae248CILkbVtOaKRf2vhvrrEYZ2C0AJxg6z/gghfzBss5iY9adD/T7eCBqQ6QKQXP0Z9PvzrMbB4j8MQkin0y/1lf0kL/9jZZqj3gI20kxAjOT2cPFOQAxY/o951d9v+mvp1GhiNKZYN8zpOZRUBzD+ZQE8x9V2HmTHmkaaKhamKQZXffGOpx79xbhfT07h80enQiGEikrs8U/t+5gWf4MMZF8VUMpAUHz/5/Zun6LSP63k9K87qBaYOUJIb0ibOAFIn3gNaOvqrSwtAOs/ZtMfteO66Y8SiUS8Hu+JXO/AL5rsDdgch/vFgJCm8mHE4Kov3oEQAhkYHTdfdvpgT/V0pDVLCsiUncCs//NgxI9/6fWFO51+Nv8r5clsAMBg/Z/HC9YoImWP6OQzU54MpMm6z16/7rPXxZl+XFtdFm/9vV5vna8pyfpPMRymYlDbedBUv8NQvyP+KYxURe6vZmYyAIydng4n+4A3M9OTwMn7v2gCR6+kHGL+u6aR7Qfatu1rVTTNZhYL8ixeOguM6P9O4P6vwUgZCIqPAl190/+b6GuYxqxZ+xmHozAp4NOP1+s9EU+q3e/whdjB0orC0b3C+4b+KdN09LQ3wA7ADxgp/3qGdnFn+P3Bcr0QKJnTL7huxC8NTHIIaObAP+zB4j/hiNLR42O94RRZnldTvHBWSX2T88Pdzd7A8BvExp1Yw7iUmQCw/uMEHtT043Ew5SOy5iU2c/KD3f7+lxqWeejom0HzjBlRGci3W5kGQDhopPjjEgBAFgvAENWf8fU/NOcTjPS4A3r8R8BIc9jpX+Ocavrl7+9oREiNOQGTsPyPEYvwDFYeCoyFdEz/KKx5SlM+Ums+OtZaQm+2tCdrAGSGx0JHtyelE/D2S4+DE5BZAjCi8v/YMSGkxx2Q9fG/HIfKigqYAGCMZs8oDAUCzR29vmA0FoSmgniHAJb/Y2fR/NkmU395zxmkm/6Pdu4xT4Q1V9ecabaYYsWk0lv/QePKZjlaq043KoqkuaW9GjQAyFQmzwMYWgMGzH/v9Mh6/Y8sK458s0GMGnqOw/k2o3VWaWOb2xPyoikFUr7jwpq1n6FBvzBVdJH9oLnxXJ6ra84c+gTDGeeyA6JvOWTI7/432ZqPDrNIejrawgpmrkC+nc6hi7kCwLBceu3tf//Dr+CNmg5VQINNT4yv/3F7Q91u+udB638IaWh1J5ws8HjuzKK6OWWTGf8BJo3xsv5dc1Z0zVnhdNHscWQgvW5PMBBM/AiG2AeVolPXsY9xuZKlxLeG97a0tMceYTIAjI6Obk/Kx99+6XF4SzPFA0je/yXJ6q5D7aLAO+xmh90sCjzH0f2Z8fEfTSPt3b5QWGafShp2evpTQJ2dzpnVZU3NnQaRrynp/ypg2rA02JbOaUMvzOcsjBXm0xWH0+W2WYf5bSFa/9qEaQD1HlRl5eqVtN3Tx9sHXKQ2YHxpjCp9ynQq6ONVSH211+8osDFBMhmnshEvADAm6bfwSLOzw+nv8coYY1HkC3UNsFmMFSV5FqPwyVMXEUKj/BFJaev2Knr9j6pHgWLjf2NpZFZKFBspA2Q1i+bPjv907CEXe1FJd7ezpGRAitgfCA6rAfGEw/3FZqIorFy9suzQ1rjnh1+/e0Kx4dL9EKy63B6r3tDmsQdg18joo0CDpYKBjBCAhOHvb2w5wpqxqIQgQpSI1tott3X7MNa7KxN1VnWJ3WqsKLGHwnJPNP5D63+sg7QMQgh9/c6fo4nn+pu+dPWFJ72/nbYS63F5f3X/bybhm+ZaAoARTQCMEz6fnyaS8+hye1gNiF/+J1h/lojyen2mVAZ9RHyI6PKFWn9COwuN8dWAlEAtUCZ6AJdfeDxC6G8vfZQwZVeXA01V1H1HuqgYGEWLSWSbxTiMKssKaysdsfM7O52lpYWspdfkLP+vvfFmdnDyynkIoY2vfzwJ3xRIYLB4y2Cxl/cRMvXV+CTIQLwGSJJsMIgJr5Ng+v3+YVK1sYZUKiGaRmRNkxRNJSSiqCFZVTQtLGthRdE0omikmQuKoiDwXCgcSZAcYHxSwfCmjpzJC0R+7oI1seOH/v5B7Fhv9Eb/lIJhKRiOrrMUVSkttM6dOaBqqKvLNfRmgnHkjjtuU1TtpivOjj1y4+Vnaxr59a8fmJwLyMH4zyx3Yyqbbh020hIfb1kcan6v156XZ7X22fp4GfAHgtGZ0nTKm96YRDfiZrM53vrHm35FVS2HdrWralBSFEIkRQtKikqIqpt7Tf/V1QYrb4hDEVRFVTl9BQOMHRYF0jRNktUul6+t2+P2hnyB8P/+6Hv/787vwzs8ZQKQEP9JSXxkP14MGKz/czjS3zgoYck/OfGfZG668hyM8f1ZFQias+aCIx+9hDKSPz3yi8uu/Qpt8my1lJaW9NhstV2HhoihD01QVkK6aZ6tBSWP5q6Yw9EyA/qU1Wrx+fzM9BO9pWh0+a8bbllRZB/1M6RI9Juqmub1Bjo6u6XGxHmfYyFm/be+9c9xfNkc4UBDpySrAs9JsoIx3t+g7xfpayDCJBhj1O70f+1rt/3yl/dP9fXmqgCwWY9Dd30YVgwUwrFoTwKTE/y5447bBmsyiTG6/fav/upX2aEBC066qHbOvIDf3bH/PZSRPP2HBy+79iv+QNB/rLGirLShdH5t1yFCA4TRE/QRLtFwi6IRWaUxFo0uwAlbhtMDjXT4Qp4+9zFKJ03bBgrKeY4zGg15NotBFESDaDGbOPqImJdnC4fCsSkxfl+gx9nrdHtUfUOA2d1ftTl2OgXIWA7DJz99OdNIo0HgOGbUo+Zd/10g2w/SCjFWLRLncRGi9f+t8ph+paxo/3Pb/3Ac/hXIwFQ1g/vhPdSGMtIXg8FgomI0CGVlRZ2dzon2AJgAaBq58fKzkp999C+vY4yyQgPOuuqbtbPn/Oe5p9r2vI0ymLXrrxBFQRQFjhe8vkA4HCmJuFiWKBrajYXbU2E1CEFZHfIUSriQbsXCCIsGUdNo2IYQoqpq1NwgZHSmVYTKEPj+rLWk9HeB7iO6gOEx7hAcGtEw0ngOcxxnEMV3X3ka5QCfvfwatpBiP5i+mWzRkG9EUvpDZ30PUw3gOYPA058TwjyH+97IAdzz5QuG+L4cT7/isWfeUTXy6/vHf1bgdGLCcwBGgxA//33sYjBp8Z9kg+LqdiGELvnEyrc+ikYqMpzOQ1tFkTebBy2myhDefPkvdPDTGXTCHyH0r7eNs5cqaXUA5TDmOY6kMfLX5GofbANk1JrzfCpTHg+1RlifD8OMu0nkOYwFs8FuMtADDheY6QpW0VSRo8py0F5TipCmapFwSBQFjNCTv7s/qw06Jda0O+qs9UVhBkZjNJrrjj4bG+HHU9tO3z3OJIYjsqpG1YHnozl5jPGC2RUXnLa4MH9Me31u2HAaDdtednrdSReP5XWmNxPYDpo5AbHmz5pGWlq74k9IUwzie0hMwvL/m9+4XdGLNFSNfLHPA2CmP8amjw5ihL9zz/DZjilnzvGfDPp9SsDT3bgXZTzxGkCbRKWnAdSy9IWNhluYp/hqZq14TDHS8APmMbYZBZGnx3aTQVFVnsNmkT2CBI6jK9O4mH5K9ttr2UEw4I+mxzLM9F98+TUJk47jbXr0kD0TW6lTO07fME3/IenvWVQOR7RIZxxo6Prrv7eGEmJ3+s9j2fzK805ZVJQ/mi3TzANgfLDt4HW33DmKF8kRJtwDiEgKM/Qch4uKHc4e9wR5BuPFT3/2q29+43Z9fZdo92Mkj5zNWMI+DzaaSGjq56alw9a3Nq464yKMWdQHdwkFGKMKvRJ0EGtOfxAGnqvKt7R5ghFFHdbos6BCmc1UbDNZRMHA04W6QD+oUU+ZeRopU2j6U9v0Pnc2tjZnB6q+0InZdHoORvoO/ZhBxyMy6COltNBmNRtSCAAhuw61tna4brj41KIx+AFg/adSAJJtutVscCadEJGUzk7n0GKgaYQFaiez/md6VBWbBCQZjEQYfpZ6hrD1rY3Hn/VZWldPfwTYYBB8phkmo2GJ2mMSeRpG11fo7CAgySzwYjEIRCNNvVGDixE2CHSBzk7m9I+grER0eSCEdPnDikbmFOXZjIm7ATLK9H/28mtiMfSosWYRlbhfz/i8aMymsxg6IYSj0NtPfvGJsOkjwmw05NvMPW5/UX5/n1dV02gFCMYLa0ve3Xpwx4GWu26+gLlc6fPBNjqzE9b+UzwR7L67/4ctqRJselNzJ8vosk9DYblbX2uzeFH85Ej2CAsiTUL8h8E8ALZ4uvDMZSnPeXPLQQ5nRxSofOlpiqapnh53S3SSbVZwydVfYuvWOXNm6QYhagLKG3cmn9zg8h3s8jKzWGozL690JMclvGGpvsfXEwjHco8GnptRYJ1ZYDWJwuSY/gFhdDyMTU+IvbD4DMaYRqZGnhrNQFSN/OO1HR/vbSq2GwiOOqmEEFlR62aVCn07NnYcaLn5sjNLi/Jit/zfd6LTN1kmP/ntKLBb5taUw4SAKQ4B3XXvr1OKQYIemE3RVVhEUowGgT3LfAL2yCQvyVkUKFqsMAhZtKNHUCRitCjZ4wQwnn3yodjxJVd/iR3MnTu7o2Z57HEmBv6IfMzlZwbUIgp1ZQUpo9J2k2FlVaE7JB3q9vSyHWQFMzwItTa2zsi3znRYjX0WJ02aK2kP82NHDtBPQs0D4y34s5dfE/v9if0eqZoWNffRcEx0pa5fMM52gz5SOA4XF9gwRz0VjFSCeY2QqtKCL3zuzOdffD/2t79i4YyHn95EEDpx8YxYyRZCaEZ5dAUJfYGyYCfwQDG4jdnPmBLEDmKV/mzJH/8KU2Bxs8fKD4GRx4qmGmx2lLUMIQaapm3dud+F7GXILXDckgqHKamt0F+bZaOR6R8myITMJjZsBrPSlJoFLQT1tNTPLsorsZrejcT9Ueg2vc+OR5cE/Wvzw/tprUv8El5jeVFajq4XDOWcTUdJi3RNX6TrTkviOYSg5g53ngGFwpLZZKQTnyT1C5+jIxw+c+HJCKG/P/8O2763YuGM7QdaPtjTgjB69i9/hN5w2RECGtGOgeTQfywxwOJFV1//7Um7qnSiQKwYNCuiQJVLTzM5ioKdzR0H47taZj2fvepmr9fv9oZmVdERYslyLclqh5su9sW+zj96Cyr9SD+7IN+m/x+LJJiQI42ernt7uk3PxDD6pBEfdWE5uXRWR1/79n1pvv6Vn7+W57kr1x9fXV0ae7D+SAs72F3fwWJB7Eez/rQ6q3mARwvD4kfH1Dcl/8499w8mBqz7P3tkkvs/x6JA0wBD4WwTh0PegCQTIb+W4zleEEJ9TRcmnw1XfiH5waF3cQ0ai1ND+TY+32pj23ejNZ2xZzFiceSzzlhTM7Mq5QsIurvw4r828SqXUwY93qarqsZSC+No0EdK7Ls3N9Ni8erq0pj1pz0B55YjhIrtpte21BOCXn5n71nHzy8edAADkD0CkKYYTNUAgD7Lkk2YS+s4ntc0VY4EMcZEVZsaGgXRoCgSDXuoqqJRJSCahjkuL88qikZZQ+7GbYPb6+gf59C7bROfHWiNo7v26Vvad9qAJ/tbqrHqnYEmaSjbdM+XLxzi2SffOIqG48LzzzyxelKn400Ew6ZGJ9mmjwiM8Yr5lXsPR43+3sMtc+kWOtTeNWAvyDnHz0UIvbal/o0th05cVjujrIB5ZjAhIFtDQOnww3tui9eGyYwCMYaIArm9wZ//MlO2m2+48tpBDXWfLaBP9WUgUxrWaJBkENuh12fGfzW1Mn0R3rQsztD2etxhAjCYBxBzAjJQAGIGnb3t2WXQR8rnr71+xfxKunNFig4EZAyxF+y1LfVnrp5b4oi2+4YoUNZ7AIMx+dafRYG+8fXbhq72IYg0tidOLU6TDVde22+nk74J64wyuBmnZjr5Wb3IhDJYg2IWy+7LUQ4GnipjnUOpUd2gp1NnkKUGfaT86Q+/X3jvt5Ifd3oCg8kA8wZiTSbACZi2HsBU8ekNV1jMxsJ8y4Z1qzTadVJTVe13z28eGLqIW08nwTpNxj8yMCYyoPo7/hT2H/vlpu1m0lj7gbGefA8gPuoS/eFN30X65PCjVDIwtCuAEHLkUz8AnIDp6QFMBJ+86HP0f7pl5XmO7oBPVeoRkbW2Hv/9f97U90BS3ATTmYKseQrdcUpfJuFbpTYJYK8zkPioS6woaFjAoI8jd979k5QyMJgrwEw/MDpy1ANYf/EVihKX3dUtP11oc1gUeIFPXAyCsc5k/r1pQPqa6AMDaJBL12JCiJM4hvAADh6IbpCuzotuQceFsxBCt990/QRfODBWbyDZ+qd0AmBL8GDkogCsv/gKTUOYS9r2qS/2SgttGz6xsrYyOn8YmFBjHc0q6zupxviG33FnirjKhVfcRH/J589saGgpKBh0K5zVYjLqk8LoDsTy/jp00IAM14A0BQA0YDBySwDWX3xFiSOv00m7S6YQgD5KCvMuXQcakMJYs04GzFCPxV6nNNbjTsz6xx7533uG6gz8rXv79/SBDGS+DBCNzKul+wMSACcgfXJIAB59gMYWvYHwzoOtPZ4A7ms+pbcEUMuL7bGcbacrWFKYt2HdilmVRVnkByRGQhBtqZlF9nrcufCKm9O3/jFABrJFBlRVq2/uWX/60oTTQADSJ4cEgPHoAz8JhCK7Drd39QaJRjCh/YErSwvodEC9D22sAv6042YXFxbMnlE8OYtrhe7GRH0tZCbKWM9esTYU8OitEUwma/6hLf9G05QLr7gZIRQTgDStfwyQgcyXgYikNLS50hQAiAKlJOcEgHH/z77/6mbaxHH+rKgLGf9r1NLezQ6a22mTaosxsVaKTZRlXXnHchmTv7Ket3pdwONkImexF9ZvfRXlgPX3erwP/4r6f+OiAbv2HSWEhEKRHmevxxuQFSVW6LVz0zPjcflAGhpAULfb39btIYRcsm5VwgngBKRJjpaButzeVQvptsNPr1vDHjFZaX/ItrYUDSeCEYVpQJc7MNiW4E1bDuJsGA9gMJgjggFhTlVkVU6axDQtuICG/lFdn/X39Fn/a7/6nf6T0hDuThft7F9WSNOMnR1dMRlYVjeb9tuwWGVZ6epx79h1sLPbHYnQN3P5mRtABiaBO+/+yQ++9/+CYSm2Lz0d3n7pcSgHSiBHPQCE0Pfu/DoLvFxy3gnM+jO2bt2v9pkH5gEwfMEwIeiitf3N6LO0M+j8VetCAS/meI7jzbb8/R+8iKYRLPFbN78m9shP7/n29bd/lx2vOY528B8FDUePpfQGzBar09Xb3Np14FCjzx9UqDcQBbyBieaWL9/c7fYTgjZ8MtEDACcgTXJXAGIaQBCaP7MkufmNLxB2ekMJj2CMs707dOWC4wlBihTheCEvv7B+27SKAl14xc3x1r/bHaB9LxA6+YQV7BFZHtBqJiVtLa0pH5ckKaUMRGQtGAwfa2xraGoPBMOqGp1LjBHesenvY7gbYBg2fO6q6EGSBkAUKB1yNATE+P6Pfh6TgXjr7wuE2UGR3RyvAXlWkz8YGezVNE3jUg2iyjTaDm6Zv/qTUihIEOIFYfqF/tnoRD3448GcGDP9CKGerq5AIJj8hSS9yJDBYIhpQGdHV0wDjCKXX+Ywm43VM8r2H2xobetSVE2lXU/J8jM38By37Y2nx+kWgQE887enYhqQDhAFSiALDNZE8/0f/fxwUzTr6wuEY9afUWQ3F9n7A0SpWvdEyQrrzzCYLLpiqVI4NH/NuWgaWf+6BfryH1PrL6NE60/3fFnpvSdAt6GlN3LOoMOOOzu6WG7AZKKbyArslurK4lUrFp64ZklJcYHBIPI8LTVWNW35mRtWna23HgHGm2f+9lTKxzu6abVbMr/7TVbWNE8QOR0Ciuf/fnHv0CcwV2B6RIFWnvFpb6/H7+s1mW22gqK97/0TTSfrj5Cn17PmhDUJpj9GSicgns+tb0rnm/7p+b5dSBjV1FQnPLtjz5H6oy29Hj+tMO4LChlE4aNX/5rOiwMjYsPnrkozCnTwaBtC6Mav3gXvMAjAaDTAH4xkey0QQuiU868OB/2hcARzwt73X0DZzPW33dnV5YpZf5roXjA/pelnXHimPsZ9nPjTPwdsRk1Qgnc/2HO0oVWSZEI3LlEZ4DC2WM3vvfTHcbwGgA4Q/sOvkt8H0IChAQ9gZBqAEGrs6M12DwAhtPrsy32+Xg4Ta57darG99cLjKDu54TZa3FnUNxUkZv0nwfQPoQTJDsEf//ofSaY1QkwGeJ6351k2/TNb3/Zs0YAhBACBHwACMAoZaOygM+qG0ICsEAC69+2Km5uamhVFtdoKPn7tbyg7Tf9w1j+as7nwjINpxfgn2CF47MmX2XwIJgOCwJcWO/79t4cm48pygDQFgGnAjRAFAgEYqQYw6z+EALz54UGOy44o0A133NPZ2RYKhbu6PLve2Yiy3/rPmz+3q7MzEpEVRfH6ghFJCoUi4bC07oSePCs/s9xcUGBABn7ylSDZIfjdn15iMsBxvM1meSdrPbAsjQLBjjDGtKoCHEe+fMfdQ/gBQ9YCZU3zuGAg2NvrbThyzGi2Lj75wr3vv5i9pv+tD/Y58kx79jeEQmE90kIEQTAZDTMr2swi2nYAhyOqycgXFxjm11jnzLAUOAxInFgl+PynO2Iy0NjYnKAEN37+AoTQw0+8qBHi9dFRJ8DYufTa21NqADAYkAMYiptuvmn5PNoxghGW5E4X/VtlkxqnQRTogitvObB/ryiaDSbLzrf+gbLT+u890NDr8WmEmI1GQeDLy4oqy4uv/kQjFbmwygbcB0JqKKweawsdawtihPJtQk2FZXaVJZ8qATdRSqCSXXvcH+7uNRo4ZDgp9nBNzYyEE790zQiK2YEhACdgRIAADMPNN9+0rE8DfMGwy9u/S2DQKNCWg1yW1AJdccudkXAoGAyFI8qbzz0W/9Rdv3+hcd+uJ39xVyabfsFoNQq05bXH5+/u6a0sL8632z61NjrhKwUaQWE1FFHdHrm+JdjhjBhEPKvSsmyeHVuEcZeBrpbA319tb+sO0z13GKsaWbTgE/EnJCgByMDYAQEYESAAw3DzzTcRglYvpp67LKvv7zpWVepgT02DWiCE0Gevv2Pnxx/MqJ2vKMp7Lz/JHqxacupZF1yQb6Gmtqe7XeFEm93xxA/+J6Osf6Ejf+DmLbrUH0Gdj0ZISG3rDh9tCWqE1FZaamqsSBjP3XxHD3tfeKvT7ZVp7w1VnxzdR7wSgEMwJang0y+4bpy/cRYCAjA8N37xixzGqxdXS7K6/UBLUQG1QRohgzWGy64o0JLTLvF5naJoNJpte995lj14zT33N9cfLC2pEDBy9bp5o8lgNBUUFKmRSK/L+c9HfjTFpr+4pKW5ZUZl2fiUeOpKcLgp4A8qc6ut9hJjWpPg00ElgV6ptSvsCypHWoKN7SG3V2IycNxxFyOEQgFv/OnxSgDewKgBJyB9QADSYsPlVzvyzMvmV27e2TCjPOoBDOEEZNGOMITQ4tM3SJGw0WTZ8xZtWTNzxdknnLO25eih0sqZXa1NiODZ8xephPR2dSGiCQIVC4HDPMf97YHvT+iFXffVb+uD3XHM+kciUl5BodViatX7tTENGJ/qfkK83ZHDzQG7VZg320aLhcYLtvBXtOvv7GYDJEqKupgAsCedPU6rWYydXlMzI+usf+3CU2PHGqb30rT/zSm8HnAC0gSqgNLimb8+ueHyq9/aekTTtPqmrrkz+ztBpiSLBklSNIXDiO8PfpBIKGQ0moM+rxKRCx1FRXm21vZWRQoHgwGMOcQbjAb6cf51t5mstuceGP/mKid+8nKe5442thXmW1VVa2mlFTUdPb6iwnyMW5csms3y8C1tnV+6wj0+3xJje6lpVaHB1R052hioKjEZ7SIal5ou/TWuv7P7hi9c3D90qC3afop9g9i36fUGMt/6z6xb21cHR6rENoKwonZpGhEFjiDCcVhSUNnSBSHd2yGEeHEFveWDb0/hNXd0e5IDQW/n/IQAEICRaQDHcZqmsUfYjp6UnLFmPssEZAV7330+dly7al0k4Au4naLBKHD83LnzVUXVNK0g3yFLkiAKYUmxmC2hUFjVFFWJYJ6/8v/9JBIOPvubcfMGLr3+tnBECgRCNosxIskKba2pnXrq8ezZw4cOb91xcGalo9BBxziPMwJXWGEuDBkaW4OR9tD8Wisyj8/fyCknLNm/n2anFy1akPAUW/73eqPFoF/6+ncf+vkPUIYxY8Hp1HfhO2l8jLTxAhZ5pKkq5jlV0gQOyxptecTzHKE9JhWMsMhjNq/FobULPJe/cF6Ap1N6MELH9r4+1TcEUCAENDI2XH61pmnMA2AjBKbBjrB4yhecsPaiT7UcOoAIqZw5y+f1RsKRmbNqOY471nAkHJF40cTxvM1qDQd8BKl06Yp5W57dXuB48sffHK+gf/HA7V2x9TJC6Eh9/cH61tLi/NoaWp01bh5AAhrx9EScvVKJw5BnE5Gxr1RU7VN9gpCsITM/soSBRl57q/ONj3rqFq6LPWbJK2htbkk4MXM0oHbx2aqqlvJtPMfJispxiOMQIUgUsKYomDbB5TDBskr9JYxppRXCGp2wzXGEYJ7HYV0hFI0IBl6WEdFwyEi3R/CCeGjbfyfosiEKlA7gAYwYjuPqm7pmV5fQ6PRQp2VVFEinYtEpBpPZas+35Rc48ot4nu/u6i5wFKoqaW1rDIVCJqvdZLUosuLq7qEOgCJZrBaDiff7vD6f59zrbi8tr/rTj+h8hbGQYP0Tnq2tnVlbSxeS23fs++m3zYPvyevbsRcz2QOfiR0pYTriOebOEfpFRNNQIKRohHy0t7fDJZUVGvIsAiGo1yeHJHo60odCn3dKiSE/2h06LThcXW4WB9YaxXzKTGPWkrMIwUVcm9FIwmGCCRF5WsxqFHlZJirtbMRpKuJ5pGo0hEjfFRr0YTFQjr6PBCsKEuj4bKoBSNUEnlMQMUbaBQFH+EltRQxRoGRAAEYZCGpodZYU5tkshukRBaLWv+5URLQTzzpz97ubNMR7XF6TyVRaVpZXUBQMRowGm2NWudFk6nH1tLW0iITKm8VmFzjMESyFQ4Sn68Fel/O0S25a4Xxg6O8l2hb88sX+IYvxPHb/DxFCt90Z3Ya9e/e+pUvr4k84drRx1uwaj9s9u6bikb+g05c3KAoz2v1WXf88auXdPplF6xIidoQgnsNWC+8PKnqzzigev9zlihCCghHasIcQEpG0vUei6QCNGrXoyRyHK0uMa9YUj+h9NomJS4PwwFqgKafu+PWRcLBA6yRqG9I9H42u9zmkEYHHHCZsFU9PxRxCWFURxkSOE1qeYIHnJUX/IRD65RytGiCqRjh9bobeG5UY5I6lx5+9ewuEg6YMEIBRaoCkqMGQZDUZXty0a7AokEbI7bfd+qv7h7GGmUDJ3OPkSIgu+dtbONFIVIVoSjgUDgT9brdLEESTyWSzmgSEbWabkRelUFAQDZocRoIp6PMijEKB8CmRP6NO1N+UeQzc/6O7Y8dfuOXrs2bTOewMQoe62JxOV4/Tc+k5vZJM6+tpqRBdc9KVJ3PMMAXlWYSqUhN9kE1o7EvQ952DbBaB089kn9JZvoe8+4/5JTlxVZ68SldV8vY219xqq6O8f2TQsOiXi3p9dLTcjKpovyB3Lx1AnyHs2/LyCcfVqTKRacaf8DSiQzeyyUjV6K47RH87EOYxXeDzHFY1lGcWfAFFIxrNzes75c1GXlZVnkOKogjU46GxIIRJRFZ5jmfuAqfhiDLoiL2JaAuR0gnIZUAARq8B/mDEZjFazYM6AYSQTpf/B3d/67v3/gRlKvPmLbKYxNZuJ28vq66tDnq99MIVWeU1RCSR7gAwczzudbcH/E6NaAVFVXPqlsiS1N3abDEaEYe8vT2Y5329XjQCM4i+duGswZyABJ747c/vvO+nsU9nVpe3dznb2nssFtOiutgf88BV9RjCbwV5YvpR/Z5e6fUtPZecW4UM6e4gU9QhqgcyhV6t2CE6VVXDmq6LBEkaUjXMI5JnFkNYY54QUVWMqUcjq4gXMVI5+oQ+aTCiaAJP30c9E0B/IqqqmYyCosi6u0DnImgIiaHuZSedv2vzv6bwZt/O4VqgrJlimGk889cnZUVt7ept6/Z0OH0Nba79xzp3Hmrbf6xz6/7md7YfffWDg4GIWlNBNw384O5voUzFIPBFeWZsLZo9t0aRI9TDJxonighh0Wg0mU0uZ2vQ5zIYTXa7w2or9PsCrUcOtzceMRh5JBCCSX5xqaaqoWD/8ORx50d3fbPX43e5fU6Xp7unt8fZW+iw0yogtuBnK/z4jzGgL9BH8BJ76n37DqUXw1FId0tw7xFf5gvAwR1vI5UYeGwQqFsVUqjjpa/baUpF5DHRNOo4cZze0BSFIqrZJNDsMIdVggnCNEFu5mnMh+eZKmgES7Kir/0JzyONaDKNvHFSZMAQ1nHk0mtvn6BXnjaABzAmCEHeQPjJFz5AnL7awdhsFPJt5mKHjR0EQyGM8IAmABlGj2pz9miLltaFfF6j2UL/OjWNaJogGkxGM88J+XmFgiiKRnMwEKAdliNhQgPA2GgQwsGgIAhFDofVWNPdNeJqnPSdADZ3l4Z08h0Br2dWUXF7W3SmxzhDkC9A607T/4qIrL3xUc+sSrO5kE4GHoywW9pxyPvxvt72noimkWAwbLGYUAbjwsVlYo8/QrM91PAjmjJRNBSJyEYjjzHzY2ihJ40RYSwrdGcG5nguGgjC0cwKoRqAEMGYU1SN52iFEMECojJB6FGw9bhTLtj2Hu2PPQlAKjgeEIDRIwi8zWwosJsVRVs6f4bNYjQZRYfdLApCNOjc1xeIOQEZGAiaufzscMB33MlrelqbeYMJ8zzPCxhzGHEmi2X/xx8ev3adyZLn97h7ezpCkkqQHgPGSBAFrNc5yVLA40U8J2I8kmKYkcMJ1LyaTUazaZhdeGMCI6OBJgXi64SGpa07vGmr87yzKpC+DEgkrNY3+jfv6j3aEghLmqzO4wXe6fasWbUkdkq0W3QmcWjH28Ur6uiSX0OIuoWIcBjT5byqW3ZMg/i08JNmNTAi4aBstppkhSgazQoggjQNGQQciSjUrex7Y9jbijWNw5yuAfRnGwqmHuA+OWgj0ftpBoSARs8//vakw24xGkSr2bBkbkVtZWF5UZ5RpNVuKSMIGRgIatr5+qmfONvjdHKCqGEiSZFQyC8ajCaL1e45eHxdVevBXbs2v4EQySssM5rMHMYGo5E3CKLJoGlqYVGR2WxVZKmrs93vcY3iAr524SyUYcjKiM0BIWjrfk9jQ1IiVyU9bcGX3+587vWOfcd8YUnT8EJe4M8955RzzzmFndJw9OiePftRRuJCJTyPeQFhzNFKHnqrmBCsqoRmdJGezdAdRuo3EqRIClFUrBERE15TVVlVFY0lhdkpSD+bp3VAGq2jpX4Ek4Txa7yRRhSoozuqN5qq5bL1BwEYK0aDwGz9i5t2pTzhjDXzCaErp4zVgOce+nFReVVRZbXVli+FwkazxZbneP1vD/kknot4FxQET1lYJPccrt/xvt2eZzZbEUcXc+FwKBwOIA6HQ8FwKNjb41ZVeeIu8uv3/iz1EyzlOK5wHB5i4M9gBELqqx/0qP7+NyHSK320zfn3/7Zv3uV2+2SNLNDwwgTT33D0aHFxIcpUDux4K1ofRYs4eaIhTR9rz3bB68/oGyj0YBDHYVmh9UEapvsDZYQ1/V/EY2rsqaPAqYTTiL5VGHMCJrQ2iHoPiAt3Ljv+k5N5a2D6GRACGgeG7vyT+X2BXvz9LxBCV33rJzULFwtG42Pf+TJC6LXXX6Mtc9d+0kZctQWWKouto+dwjzvo9wdXn3wm4bi8goKA16OqSjgUCQSCvDCxISC6QayogBBy4FA0bRDw+TZvcR6/uIC3CuPTtEeHoyGwkYWAGMfagu/vdJ92QjFSyNGmwObd7iPNwVBEVbR5HE9X/bEzG44epbfTZ/prZ1Y0NLWjjCTAlxhJF3079FhQNNOFObqs4bCi0pSvXkBLsF7mbxQ5VcWxTXV60IiXZFUUMVIIH/1boF+u0N0D9AUxzQQgWZ6oVLAogpUbFHhrxsRPf/arb36D+pjDWovyIluHM4NqvZN56icpvJO333yFHaw95XiHjVgLjaECc+P2LR5/eMlpp1rtBTZ7YSTUpdJd/qP8XUonFfzzu7/xkwd/x44XzqdRowOHjlnz8t7Zk7fz0K4lc/NOWOIQbeMjA3rnodE4FppG3tnu6vXJikrqmwJun6xo8zHHcTxKsP7Jq/6M1YB9299avXyxqmossMlyHLqJ17sC8nSLQDTCoyFR5MMR1WDgaW63b92jqjRbhjDieF1D6K5gxAk0DUCFhP4fYY4TI51TfKs5CeQAxo2hokCIdLkC5UX9HQ6yjjff2/L8K+8EZN6qBmsceG6l1blv+85XXzi8e5+GsCAaVVma0Av41ldu3Prx9tinC+fPYkrglZa9v2/WY883vf1+t+SRUjd+GAlGkaNpnFHhCygf7Or9aK/H5ZVVtBBzXHzMp/7Q4SFiPrUzacvMDESRVSNH52ay5m4yjQLphW1EQzzCPMGY0IQuwnqkB/GI8Ji2B6K1BFQmkMkgKLJGQ/+cvquMagYWRD0T0Nc+aNe+8ejpnYrPXHVrOqdt2jhgIl6OAAIwPgxtMGJZ4azWAPpHsun1Fzd94FIsohqqsGnleealJ54ghSVFlvS/9FGSZir4mSf+bzAZ8MnLPjgw67Hnm//7VoezLZggA5detyPlC37+lsPJD4YlTRlDakEjpKO7VNHmJYf7y8tLho74Z6YG7Nh3gHZxQFxY3xRMK9x4pDf91GhRGLUhej84vSTIYBAUVeMEgS7tCVYJUhFWCCeIVCh4uu1ab9ShEYPI619FH9yzf98k35TbN4HbVrIICAGNXxQog4v9x5f336HpgbL5q09Ye3Z7U1Nefp7X0xsOTawHEKOzu7fhWIPT6V61emVCUMivLNtxFO06vH3pnLzjFuaXVJgRj6/9encgTPuWJdPl8l3zta5wb1sk5P/nX6MjTcwmaqbUUXkSja103x/Hk8rKCjaxgIX74yP+Q5OZsaD3dkSX58ctreNpSSjWMIc1jmiI9nRTqBLoXhNd80uSJOpZAFb6w6qFDAbRH4yYjaKo7w8mhHy4fbKNPpAMCMC4gTEerC8Qawx341czZcD62Kk57hNq2Ovu6UFE4zlSXFrc3NA4lhcc0aYwOiGyyNFwrMHl7D1u9Yp4GXjq939EiHz0vu+X3oM+v/+KL95eWcnn2ywXbHjnpWdOi3+Fi67YMmvOwura2Up45vuv/vvCDe8rqoIxr4cxsKTIxYV2omlz6gzpm369YEagmerDjVWVJQG9NHakdT6ZqQGMbbtTW+2TVizi6GqekzViNBgkSTWIAt3oS7cMI54mfzme5xWNbN+doTWvuQkIwDgQCEkWk5hc7fPVH/+u0ITuvv1G1pj4h/d8O+vGAwxG47ZXv/D93+7f8oEky4KAjUbRarPmC9ahv4pNUKCEW4Km/vm3o6awqODPjz/pC4QkSc632QrtDqsxT1Fpf2cDJ5xx3oW9Xa0VlRWHe9rnnVp1x8ON3m5/wBMqqy0SBL7muMJAe6CtsX7BokU1c2dHPB4pEqF73ATOIBr11CYiyNh+xBCOhFSNhMIhm804ayE/hOnvf1QJHdi3f+XyRaO7r8yMBQ3B5h2pzfqqZXWcwH20c8+kXxGQFiAA48CDD/72M5deabMY821mf0iSZTUiK4GQdNxCx6H9na9/ePDEZbNG1F4mK3jie7ecf9O3gz5v0OczGg0LlpVt277puBlDacDQb8FX18/6zctpOQHvv/V+wC9ZTAajvi3N4sBEU2VF9fo6FEVRJE0UjS6/x2gQO9oaFSLt27Pb0WuifYlVjUhcQ28TxlhVtFAvKq+u2bNze1FZ+cGmFlrNQvsUKCrtcaY3NsA8oo2NBaPRVJRvw5hv2i93u935+Wajg46jYU0+4k2/zRS9y1BElWVldDWIPT2j2VWXgWzdBXGejAYEYHzQNOL1h73+8MNPv415npoSgvLNiiLJr7z+zuYPP4zIqqqRteecSQjZ9PpbCV++duVqn6LaSgpa2joLrLRkQlOJJMsHDmXuRIEbLz/XdoBOA95ZfZvDkWd3FHalTrWmxjLQCVBY18nhKJmxJNTtsogmq8PgD3h9Xo+qqUSV9c5iKp1CResuxbCkVM2saTpW39PWvu6Tay0m2kaCEOJxb4m9FBsYEJLKDu/ejrnZyGLsaes4fu05LGevKGpvr09RaMkLo+nQDlHg7TZrZYkdIUPI1eUJ+Itmzks2/Sx94HJ7u529leXFuWn6Mx+3L+TIG0kD2+kIjIQcHy665Aq66OOLOF5AmsYT1ePxHrco8tG7ByXqD5BIWDWKmsBxZrPB5xJCQbonwMipWNM0onK8SRZIZXkB5g21NcWSpMoRubG5ffOOvShTuf0MNaEdQrtX80XI0E5APPEC4AwQlaCn3mxIeeaSFadGwkqB3SYKWq/HHQhEzrv4M0O/+GuvvHbDzTfEP/L2e1vjPz1v3YCUQDKHjzRv3rI7XgMYbQe2mAxCQV6+yWyXieDz+41m3lFeFT8vklFSVLB65SJDek5AvOn/2fenT7ooQ3j+qRRjORxJAnDmRQN+Z6Y94AH08+mrv8Fm8ym6ORONhgitbOEVRVJkORIJyHJEUeRIKIgRluWIpqr6XkeeEG33wQ7MaQi1sF5pHOJVVa44uZI324+fJZYU2VzusKYSUeDDEcn5Qb3ZjIIaqTTxQRX7FWwTlSazubrWrtGaax/tma5peeaJapAyajrvfkXdtZ1I4S93/xahjvinMEZFVjxIuc0wToBKUFifHpVM6cxlVSVlZlEjBuL2dHj8wYs+d3k6L37OJ89pONbQ0+NevWZlmhY/gZrq8qaWTmdnYj52/vLoeHqEUPehvTab3WaydRxrLKlNnIXjcvu6e3qrKoZ3AsD6A1PCNBeACz//DYPJSgdIdXcF/UFFkaWIJCuyIsuKotBGUKoebaEtkLVt27frlWsq3Zquf2BMQ8L6Tke624XGsPXOJ6zgTY8V0AbotHcAR+2/SOuj6bG+79Hwwc6uhQtMzUedoWDY1RuRNdlqMfA89+yRQQpm+ortMpOu+95AkoQ09Vb3Y3LYlzz+xShgh2U0r/x2a543GFldNqAZQNWslXOqZxsE2enq7nC5jh47OIpXLi6mxUJs7MEQeemY+CREoawG0kV/2oOWhJbPW0x9hR0flpeWIY+vubuteu6C2LOqpjY2t5cUFwzhBIDpB6aQaSgAV932Q1nhezo6vL29DYePSuGgqsq09whtRshCwRrdsxirU6btSgSO0003x2MsYGrs6TRYqg2qfjJthqtxSDu074PBvu+n9CgQQ597SglIAX+kV8CzSmfb1p+2eO/h1rsyryl0utafvmXa3ZZ32gJuM049yS/fhA92BReUWkbkBJy4eGar0/fWvpaC6sWFZWU2LJuw2SiShsY9TY2Jdv/hX/zwsuu+4nCMbLCfpEdyYjJQUJDWl/Oi2NndGwwN06Zm3ooTaJJg747yskpXY1NhDZ1Zz6CZgB53VUVJ8leB6QemnOkjANfc8RO3O9TZ1rztw21KJKSpUrQFLf2XtjMUeIHOIuUFTVVUKSxFQi2H+1OCE0Q44OVNNCbu7A3UN3UjjO6751t33ZNlGnDStx7ZyI5kuePgB0YUMvCqX0K2pBJ5Tg8EjfT1jzV3B2Rit+WtOfHMzf95Ze6ixa3tDf4CR96sOaWY72pIrCR5+vEHL7vuKzSGOyoZKE27MN9mMZcUOxqbO9LpDTdz8QqEsXzksOx0dbm7q3RXQNW0xuaOkmJHghMA1h/IBKaDAFz9P/9bv3/vh2+9K8sRuqLXW9dGG9Ui0rAnseRmgnjh2b/EOwEMwlXk6daGbg5q6JxRYhfpDvgs44Mj7Wi5Xu6iKpziF3nVJBKbhfZ3TD7ZLOImd2imI936Cku4ZZaZqCYUti/v3L2/btbM3ft3LjvrLJnIXpfbaEy9D+vpxx9ECI1OBtIHY1xeVtjd4x7WCYhRNoe+UYWc5OloyC+vTXYCwPRnDu6cLwTKbgG44tYfO7s7D+7bHQz4VFU6svNVlBlomhaLAsVweYLza0pleQL75k8Ehst/4Gaz/RC64ZUrjZxiEjSrCdkt2CjibmeKeRppOgGyinrDJCiR4gr8x22m4xaJh50NFcuXnnPWGfU79iiq6nX5IpHI0hUrd+/o7/8zyTIwIidAHyhM793sKPc171XDbc1NPbXzlzU0USfA6/HmWp3PrWv7G4Q88OaENwwfgs9cdWvKQqAENm18LKcKgbJbAP7ywLdR9kAIOdjQVVNegLIH21U/7AlIEWvUthpxxCBoZiOyW7FB1FuBDUJAClsN/TNvCUJBibaHlFRq9/0REpTpzHGrARl49J57Zt18wzLz1mV16JfPPYUCn1WMJmthkSKHuJClwGY4+bQz3n9nUE8uXgbGXQlG4QQwSqsXrwr/0Zd/UoE1IIWVBx982MCLC+fPC3jlVeIzt66deps4vvYdyEZgH8B4kjIPPODtxnhWpaMw35ItaQDl4rv9BqtiztNmR4tbvvPyGY48bLdgjk4Kp2E2p2v4pbHVYKLxOP1E1g+SacdDB2adcsKiwwcbzp8VXR2PqCNQMunIQJo5gAd/+0da4MXxvCAYDQZZlsKRkEIdOLJkRbS0NJlVkT8N9tTGxiUX1QxoiiDaFozxfrNIAKb8ZmErwHTzALIOQki32++wj6pYcioI8kbVaNGM/Wt5mxlbTZjjWXEs1YAiB3K6h3mdgESXzy/3nlpY4AgFvRFZXbikxm4TZoXql3v/tbwCBdFQrYFMRXMwpt3EBFG0mC20pkuR6T4Mojlb9iR7A0PHhbp6XOlowFduuSZ27AsEd+89MlInIIEE6w8AUw4IwITngRPwhWR/MHUNZQYim6ya0USMFhIMcxYqA3YrFgW2HYJafzoDBKPSItTlHP7V1he8+/ej8615FpWQWc1/Vjj1ZAPysL1j/oOirb+CPoGw80jsuDe9Kx/f9MDIMgEAkCXAQJiJQmO7ipMhpMvlQ1mCZjBpRgsxWUhfQP+WE94gNIajh3N0608nu3K4ujytxO+lsw+tL9nxqdKdaFJ4+vEHn378Qbfbwz7GmAmwmGlPoXh4nPgxQcNwpgcZeLPu3J4MAwIwBWSRB0DX/kbd+osG1WjXH8M3LP6P/hyhXd6Z4eOQwKH51SP7dRI0fjJlgP61pyEDvR5Pyg9FkvKspjGaeyDz2ZRLsyEhBDQFaITcdPPNjzz8MMp4Sn97dftd/0GiEQkGJIhIVy5sMBK9AojOA9SX/wIVACzy6Li5/Lb6xO5pQyBovMKN4PyxEJ8ecLs9ZaUjm9OiOzuJj73z778lPLJCL++Z0GE4Wc0U3myalaA5BQjAFKQB6DLTF0TZgr78p9afF9Siat7ZjAi5adVbv9t6Rsz68zwWeDo03MCj0xcLBh69tmskbeEml1h6YDAN+M1Pvj/pFwUAUwAIwGRvB9PbA9AnUZZQ8b0z23++lQoA/RDUqgV8K+3Pc+Oqt57YcSbPReM/Ik8r+gWe+gECh85dxv9n1/BL+0lb/g/hEGQC4AQAUwLkAIA0oPEfgX7wAuJ5bU4de/gLy96gwR/d6Bvov9QDEDiECZEUcuIsvKKSLC4ZVOryShfCuw8AUwgIwJTxuav6y8xj3LpWysDdlRW3LdVNP/3APEI80hbrGsBxn1u8qc/6UyeAhoMQUlQUjhBJ0hSFKCqaU6DW5qnVNq3SQsrMqMgsFFiseYW17p7MHXcz+WRghUyO3Kw7hwuBIAQ0eVR3P57wCOsHkBUBgYqb57Y/0YZ5TH9leIR5RFbV4a37ECGfmvem5uzZHLmMWn+MFJWEI5okEVmlSqBqRNOQRrBG6L8q4jUkqKLd3dtf2g8AwJQAHsD488Kzfxl+N0AWUvGFyqj1j/17Sh3rfcYVFZ9c+orAIaIRSSIRiYaAVIWoGqLWX4tZf05DvCrY3L6Wqb6bTCSj1sUTTU7dbMYCApChZOafR/nnypn1Z0pAZeCsaD4A8/zKktdX2F8KS0SSqfVXqOknuunXrT/RrT9ndgU6p/o+gAkhX7AO8ZEJb/pnrrp12HPOhG6gADAYZZ8u7/xPR9QJ0DWAO7eOZgVepmNbsNm8du4mpKnP7ThT1XQPgJp+/QPxKjY4Q/2jz6clI7J02VMLnJoxmvVMC3WemUumnwHdQCevLWhyDmAwWFecjPrbSKBrk64BMRlgu8JIVAZibfHjxu5SfvD6qVl912ly1yfE9E8OmhK74GXXO5BTNzv9gBDQhKcBplkUiFF6ZjkNAXEIc33WX4c7r45bF00MRIlrnvbds9+d9CsFAGBQQAAmnOmUB46n5KRyuvZnAsAMvYaQioiK8Bl1+JQ6fIKeHogXg9zQgPteHcHQN0u4JYuEf9xvFphaoAw0o8m0IGkCxavK6ZDbHR3U+usCQHQNiH3gFXVIQZwc4eQIL0cMcvgbf14+1VcNAEAU8ACAsVK8orx4ZXmy9Y9qgCJhReYU+TtPLMgd659T6+KcutlpBghAppMtAYHi1eUlJ5QnWX+FWf+7/jBvqi8QAIBEQAAmium6HWxoSk4vLzmrvGRdecn55VjVsCLf80j1dx+fg3KPEa2Lc/Zms2V9M10BAcgCsvSPpPgzlfc8XDXVV5Ed5FRgJKduNsMBAQCAiQKcACDDAQGYStLfNJ+lTgCQPjm1Ls6pm81kQAAmbztY+t1R4M8DyEZyyuOZHoAATBK5kwcG4oESyRgeJRD/IfvpXDlgaoGNYJPHHsdlS9xPp3myJdwS3ziFZPymMGBoPEognbdoBI11svk2gQwBBAAAJhawiUDGAiGgScW/5MvpnxyfCRjQTwcAAGA8AAGYzDwwWTKnYnSvQ+J6amY1QwwMmS63CABZAwjAZL3RGFtNhrKivLW3/N8onICBLTWzhhGNhcrOWwSALAYEYDLAGJlNYmmhzWIy5JQHMNIamOy8SwDIVkAAJhyMkckgljpseVYTW+WOyAmIvUgubAcDJwAAJhMQgAln4zN/efrPf/z1bx7EGL/z8eGRfjmLAmXv0hgK4QEgY4Ey0Mnj3vv+d9TDVKcrsHcUAKYQEIAswBJuCSRN084iwMoDQGYCApAdG4jg5wQAwLgDhmXKuO9V+da1UponQ3YUAIBxB5LAAAAAOQoIQHaQvVVAAABkLCAAWQIoAAAA4w0IQHaQpa0gAADIZEAAAAAAchQQAAAAgBwFBAAAACBHAQHIDiAHDADAuAMCkB1ADhgAgHEHBCA7AA8AAIBxBwQAAAAgRwEBAAAAyFFAALIDyAEAADDugAAAAADkKCAAAABMAfmCFUobphyYBwAAU0C+YE35eBBNz/tKCQQ2pxzwALIDAoulrOWBNw3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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "assistant": "Across the top view, the axe has an unobstructed clearance below its handle; the sword crosses above and the shield blocks to the right. I will set the extraction downward.\n{\"name\":\"SetExtractionDirection\",\"arguments\":{\"directionSlot\":4}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nDIRECTION 4 Down", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "assistant": "The axe is retained at its rack contact while its downward route is otherwise clear, so I will release only its own hook before extracting.\n{\"name\":\"ReleaseHook\",\"arguments\":{\"itemId\":1}}", "reward": 0.0, "done": false}, {"obs": {"step_msg": "Step context:\nRELEASED HOOK 1", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"}]], "skip_infer": false, "video_raw": null, "video_pil": null}, "next_obs": {"step_msg": "Step context:\nDANGEROUS EXTRACTION - RackGuard\n\n\r\n\r\nReset context:\nSeed 10. Identify the brass-banded weapon in the rendered rack, then fuse FRONT, SIDE, and TOP geometry.", "task_prompt": null, "reset_context": null, "step_context": null, "observation_context": null, "vis": [[{"__agentark_type__": "agentark.pil_image_png_base64.v1", "mime_type": "image/png", "source_type": "pil", "mode": "RGB", "size": [512, 288], "data": 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"}], 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"}], 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"skip_infer": false, "video_raw": null, "video_pil": null}, "action": "{\"name\":\"ExtractTarget\",\"arguments\":{}}", "assistant": "The released axe has settled free of its hook, and the selected downward path clears the surrounding silhouettes. I will extract it.\n{\"name\":\"ExtractTarget\",\"arguments\":{}}", "reward": -1.0, "done": true}]]}} diff --git a/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_results.jsonl b/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_results.jsonl deleted file mode 100644 index 589075557547eaed3f6cf28088e0b2d853987f34..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_results.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0001", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 1, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 1, "actual_rollout_group_seed": 1, "initial_attempt_group_seed": 1, "final_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.20492364466190338, "last_attempt_index": 1, "last_attempt_reward": 0.20492364466190338, "best_attempt_reward": 0.20492364466190338, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.20492364466190338, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.20492364466190338, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 123.7017, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "assistant_preview": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "action_extracted": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 284, "reasoning_output_tokens": 232, "total_tokens": 7048}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 284, "reasoning_output_tokens": 232, "total_tokens": 7048}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}", "assistant_preview": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}, {"role": "assistant", "content": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49166 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10636, "output_tokens": 580, "reasoning_output_tokens": 516, "total_tokens": 11216}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 17400, "output_tokens": 864, "reasoning_output_tokens": 748, "total_tokens": 18264}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}", "assistant_preview": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}, {"role": "assistant", "content": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49166 chars]"}}]}, {"role": "assistant", "content": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 15696, "output_tokens": 975, "reasoning_output_tokens": 893, "total_tokens": 16671}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 33096, "output_tokens": 1839, "reasoning_output_tokens": 1641, "total_tokens": 34935}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}", "assistant_preview": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}, {"role": "assistant", "content": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49166 chars]"}}]}, {"role": "assistant", "content": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}]}, {"role": "assistant", "content": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53906 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14592, "input_tokens": 22071, "output_tokens": 790, "reasoning_output_tokens": 715, "total_tokens": 22861}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 32512, "input_tokens": 55167, "output_tokens": 2629, "reasoning_output_tokens": 2356, "total_tokens": 57796}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}", "assistant_preview": "The cyan perimeter selector has propagated to R3_EG62. Both examples also soften this complete marked perimeter; use a moderate bevel matching the visible facet width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}, {"role": "assistant", "content": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49166 chars]"}}]}, {"role": "assistant", "content": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}]}, {"role": "assistant", "content": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53906 chars]"}}]}, {"role": "assistant", "content": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52554 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan perimeter selector has propagated to R3_EG62. Both examples also soften this complete marked perimeter; use a moderate bevel matching the visible facet width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 29168, "output_tokens": 189, "reasoning_output_tokens": 116, "total_tokens": 29357}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 34304, "input_tokens": 84335, "output_tokens": 2818, "reasoning_output_tokens": 2472, "total_tokens": 87153}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R3_EG62 by 0.12 | rev R4 | mesh 986890EE | handles R4_F10,R4_F10A,R4_F23,R4_F36,R4_F88,R4_EG18\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan perimeter selector has propagated to R3_EG62. Both examples also soften this complete marked perimeter; use a moderate bevel matching the visible facet width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG62 by 0.12 | rev R4 | mesh 986890EE | handles R4_F10,R4_F10A,R4_F23,R4_F36,R4_F88,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The connected, recessed, contrasting center and marked-perimeter bevel now match the composed example pattern. Commit the transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.20492364466190338, "reward_by_agent": {"0": 0.20492364466190338}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 1, "attempt_group_seed": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 1, "rollout_group_seed": 1, "current_attempt_group_seed": 1, "attempt_group_seed_history": [1], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R3_EG62 by 0.12 | rev R4 | mesh 986890EE | handles R4_F10,R4_F10A,R4_F23,R4_F36,R4_F88,R4_EG18\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan perimeter selector has propagated to R3_EG62. Both examples also soften this complete marked perimeter; use a moderate bevel matching the visible facet width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG62 by 0.12 | rev R4 | mesh 986890EE | handles R4_F10,R4_F10A,R4_F23,R4_F36,R4_F88,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49286 chars]"}}]}, {"role": "assistant", "content": "Inspect an alternate query angle to identify the marked face and perimeter before applying the inferred composed edit.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49166 chars]"}}]}, {"role": "assistant", "content": "The magenta selector is F44. Both examples show its face being connected to form a center region before the inward recessed treatment; begin that topology operation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F44\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F44 | rev R1 | mesh 046BA7A3 | handles R1_F23,R1_F26,R1_F43,R1_F50,R1_F76,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50254 chars]"}}]}, {"role": "assistant", "content": "The newly highlighted center-face handle is R1_F26. The examples’ central inset is a negative-normal tapered extrusion, so recess it with the apparent three-quarter cap scale.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F26\",\"distance\":-0.3,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F26 by -0.30 scale 0.75 | rev R2 | mesh 0975898D | handles R2_F59,R2_F62,R2_F67,R2_F75,R2_F77,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53906 chars]"}}]}, {"role": "assistant", "content": "The extruded cap remains the highlighted face R2_F62. The examples assign the contrasting recessed-center material, so set that cap to the alternate material slot before finishing the marked perimeter treatment.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R2_F62\",\"slot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R2_F62 material slot 1 | rev R3 | mesh 17E3D316 | handles R3_F22,R3_F34,R3_F77,R3_F79,R3_F86,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52554 chars]"}}]}, {"role": "assistant", "content": "The cyan perimeter selector has propagated to R3_EG62. Both examples also soften this complete marked perimeter; use a moderate bevel matching the visible facet width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG62\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG62 by 0.12 | rev R4 | mesh 986890EE | handles R4_F10,R4_F10A,R4_F23,R4_F36,R4_F88,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33586 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31610 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53354 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected, recessed, contrasting center and marked-perimeter bevel now match the composed example pattern. Commit the transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 29056, "input_tokens": 36571, "output_tokens": 104, "reasoning_output_tokens": 55, "total_tokens": 36675}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 63360, "input_tokens": 120906, "output_tokens": 2922, "reasoning_output_tokens": 2527, "total_tokens": 123828}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 986890EE | Q 0.205 | geometry=0.000 ruleTopology=0.000 material=0.433 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.204924 caps=0.40 final=0.204924\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected, recessed, contrasting center and marked-perimeter bevel now match the composed example pattern. Commit the transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.20492364466190338, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 986890EE | Q 0.205 | geometry=0.000 ruleTopology=0.000 material=0.433 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.204924 caps=0.40 final=0.204924\n\n\r\n\r\nReset context:\nSeed 1. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-acd989b62491", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0001::codex-gpt-5.6-terra-medium", "job_index": 0, "case_index": 0, "model_index": 0, "worker_index": 0, "parallel_slot_index": 0, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0002", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 2, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 2, "actual_rollout_group_seed": 2, "initial_attempt_group_seed": 2, "final_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.14310725033283234, "last_attempt_index": 1, "last_attempt_reward": 0.14310725033283234, "best_attempt_reward": 0.14310725033283234, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.14310725033283234, "turns": 3, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.14310725033283234, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 113.5968, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}", "assistant_preview": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48366 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 851, "reasoning_output_tokens": 795, "total_tokens": 7615}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 851, "reasoning_output_tokens": 795, "total_tokens": 7615}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}", "assistant_preview": "The new center face is now marked by the magenta glyph, while the transferred cyan perimeter group is R1_EG87. The examples indicate beveling that marked perimeter after connecting.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48366 chars]"}}]}, {"role": "assistant", "content": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49906 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The new center face is now marked by the magenta glyph, while the transferred cyan perimeter group is R1_EG87. The examples indicate beveling that marked perimeter after connecting.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 11240, "output_tokens": 742, "reasoning_output_tokens": 664, "total_tokens": 11982}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 18004, "output_tokens": 1593, "reasoning_output_tokens": 1459, "total_tokens": 19597}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R1_EG87 by 0.12 | rev R2 | mesh C0BF87A1 | handles R2_F12,R2_F31,R2_F67,R2_F71,R2_F79,R2_EG61\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new center face is now marked by the magenta glyph, while the transferred cyan perimeter group is R1_EG87. The examples indicate beveling that marked perimeter after connecting.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R1_EG87 by 0.12 | rev R2 | mesh C0BF87A1 | handles R2_F12,R2_F31,R2_F67,R2_F71,R2_F79,R2_EG61\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The connected-and-beveled inset now matches the common structural change in both demonstrations; no additional visible face recoloring is indicated.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.14310725033283234, "reward_by_agent": {"0": 0.14310725033283234}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 2, "attempt_group_seed": 2, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 2, "rollout_group_seed": 2, "current_attempt_group_seed": 2, "attempt_group_seed_history": [2], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R1_EG87 by 0.12 | rev R2 | mesh C0BF87A1 | handles R2_F12,R2_F31,R2_F67,R2_F71,R2_F79,R2_EG61\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new center face is now marked by the magenta glyph, while the transferred cyan perimeter group is R1_EG87. The examples indicate beveling that marked perimeter after connecting.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R1_EG87 by 0.12 | rev R2 | mesh C0BF87A1 | handles R2_F12,R2_F31,R2_F67,R2_F71,R2_F79,R2_EG61\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 2. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48366 chars]"}}]}, {"role": "assistant", "content": "The magenta selector identifies the face to be connected first; F24 is the cyan-highlighted marked query face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F24\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F24 | rev R1 | mesh 6099EE8C | handles R1_F35,R1_F48,R1_F54,R1_F60,R1_F75,R1_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49906 chars]"}}]}, {"role": "assistant", "content": "The new center face is now marked by the magenta glyph, while the transferred cyan perimeter group is R1_EG87. The examples indicate beveling that marked perimeter after connecting.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R1_EG87\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R1_EG87 by 0.12 | rev R2 | mesh C0BF87A1 | handles R2_F12,R2_F31,R2_F67,R2_F71,R2_F79,R2_EG61\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31190 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31350 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50694 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected-and-beveled inset now matches the common structural change in both demonstrations; no additional visible face recoloring is indicated.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16520, "output_tokens": 1234, "reasoning_output_tokens": 1182, "total_tokens": 17754}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 34524, "output_tokens": 2827, "reasoning_output_tokens": 2641, "total_tokens": 37351}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh C0BF87A1 | Q 0.143 | geometry=0.000 ruleTopology=0.000 material=0.021 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.143107 caps=0.40 final=0.143107\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected-and-beveled inset now matches the common structural change in both demonstrations; no additional visible face recoloring is indicated.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.14310725033283234, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh C0BF87A1 | Q 0.143 | geometry=0.000 ruleTopology=0.000 material=0.021 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.143107 caps=0.40 final=0.143107\n\n\r\n\r\nReset context:\nSeed 2. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-e4d056746438", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0002::codex-gpt-5.6-terra-medium", "job_index": 1, "case_index": 1, "model_index": 0, "worker_index": 1, "parallel_slot_index": 1, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0003", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 3, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 3, "actual_rollout_group_seed": 3, "initial_attempt_group_seed": 3, "final_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.14000000059604645, "last_attempt_index": 1, "last_attempt_reward": 0.14000000059604645, "best_attempt_reward": 0.14000000059604645, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.14000000059604645, "turns": 7, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.14000000059604645, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 145.5527, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "assistant_preview": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "action_extracted": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6768, "output_tokens": 172, "reasoning_output_tokens": 123, "total_tokens": 6940}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6768, "output_tokens": 172, "reasoning_output_tokens": 123, "total_tokens": 6940}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "assistant_preview": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "action_extracted": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10525, "output_tokens": 691, "reasoning_output_tokens": 630, "total_tokens": 11216}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 17293, "output_tokens": 863, "reasoning_output_tokens": 753, "total_tokens": 18156}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}", "assistant_preview": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}, {"role": "assistant", "content": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49406 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10368, "input_tokens": 15657, "output_tokens": 738, "reasoning_output_tokens": 673, "total_tokens": 16395}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13952, "input_tokens": 32950, "output_tokens": 1601, "reasoning_output_tokens": 1426, "total_tokens": 34551}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}", "assistant_preview": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}, {"role": "assistant", "content": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49406 chars]"}}]}, {"role": "assistant", "content": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50658 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 21727, "output_tokens": 997, "reasoning_output_tokens": 916, "total_tokens": 22724}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 15744, "input_tokens": 54677, "output_tokens": 2598, "reasoning_output_tokens": 2342, "total_tokens": 57275}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}", "assistant_preview": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}, {"role": "assistant", "content": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49406 chars]"}}]}, {"role": "assistant", "content": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50658 chars]"}}]}, {"role": "assistant", "content": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54562 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 14592, "input_tokens": 28978, "output_tokens": 137, "reasoning_output_tokens": 69, "total_tokens": 29115}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 30336, "input_tokens": 83655, "output_tokens": 2735, "reasoning_output_tokens": 2411, "total_tokens": 86390}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}", "assistant_preview": "The inset cap remains the material-associated central handle R3_F37. Both examples recolor that new cap to the third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}, {"role": "assistant", "content": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49406 chars]"}}]}, {"role": "assistant", "content": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50658 chars]"}}]}, {"role": "assistant", "content": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54562 chars]"}}]}, {"role": "assistant", "content": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54686 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The inset cap remains the material-associated central handle R3_F37. Both examples recolor that new cap to the third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 27904, "input_tokens": 36273, "output_tokens": 474, "reasoning_output_tokens": 409, "total_tokens": 36747}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 58240, "input_tokens": 119928, "output_tokens": 3209, "reasoning_output_tokens": 2820, "total_tokens": 123137}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F37 material slot 2 | rev R4 | mesh C8518FBC | handles R4_F18,R4_F31,R4_F36,R4_F70,R4_F73,R4_EG49\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The inset cap remains the material-associated central handle R3_F37. Both examples recolor that new cap to the third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F37 material slot 2 | rev R4 | mesh C8518FBC | handles R4_F18,R4_F31,R4_F36,R4_F70,R4_F73,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 6, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The full inferred composition is committed: connect and recess the selected center, bevel the marked perimeter, and assign the cap material. Submit this snapshot.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.14000000059604645, "reward_by_agent": {"0": 0.14000000059604645}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 3, "attempt_group_seed": 3, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 3, "rollout_group_seed": 3, "current_attempt_group_seed": 3, "attempt_group_seed_history": [3], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 6, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F37 material slot 2 | rev R4 | mesh C8518FBC | handles R4_F18,R4_F31,R4_F36,R4_F70,R4_F73,R4_EG49\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The inset cap remains the material-associated central handle R3_F37. Both examples recolor that new cap to the third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F37 material slot 2 | rev R4 | mesh C8518FBC | handles R4_F18,R4_F31,R4_F36,R4_F70,R4_F73,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48738 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the marked face and perimeter edge group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48578 chars]"}}]}, {"role": "assistant", "content": "The selected magenta face is the central back wall; inspect top view to resolve which opaque face handle corresponds to it and the cyan perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49406 chars]"}}]}, {"role": "assistant", "content": "The magenta-selected broad face corresponds to the central listed face handle R0_F25. The examples’ inset/rim topology indicates first creating its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F25\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F25 | rev R1 | mesh 290BCFAA | handles R1_F11,R1_F29,R1_F39,R1_F46,R1_F70,R1_EG70\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50658 chars]"}}]}, {"role": "assistant", "content": "The connected center face retains the selected face’s material-associated handle position, now R1_F29. Extrude it inward with the examples’ moderate recessed, reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F29\",\"distance\":-0.32,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F29 by -0.32 scale 0.72 | rev R2 | mesh 87D5FF5D | handles R2_F15,R2_F26,R2_F26A,R2_F46,R2_F53,R2_EG39\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54562 chars]"}}]}, {"role": "assistant", "content": "The cyan-marked perimeter group persists as the sole current group R2_EG39; apply the medium bevel evident in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG39\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG39 by 0.12 | rev R3 | mesh 08D4AEEA | handles R3_F21,R3_F37,R3_F39,R3_F59,R3_F91,R3_EG64\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54686 chars]"}}]}, {"role": "assistant", "content": "The inset cap remains the material-associated central handle R3_F37. Both examples recolor that new cap to the third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F37\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F37 material slot 2 | rev R4 | mesh C8518FBC | handles R4_F18,R4_F31,R4_F36,R4_F70,R4_F73,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=34450 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32238 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53102 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The full inferred composition is committed: connect and recess the selected center, bevel the marked perimeter, and assign the cap material. Submit this snapshot.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 35840, "input_tokens": 44801, "output_tokens": 176, "reasoning_output_tokens": 121, "total_tokens": 44977}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 94080, "input_tokens": 164729, "output_tokens": 3385, "reasoning_output_tokens": 2941, "total_tokens": 168114}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh C8518FBC | Q 0.140 | geometry=0.000 ruleTopology=0.000 material=0.000 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.140000 caps=0.40 final=0.140000\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 16, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The full inferred composition is committed: connect and recess the selected center, bevel the marked perimeter, and assign the cap material. Submit this snapshot.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.14000000059604645, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh C8518FBC | Q 0.140 | geometry=0.000 ruleTopology=0.000 material=0.000 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.140000 caps=0.40 final=0.140000\n\n\r\n\r\nReset context:\nSeed 3. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-11e3ab70ad50", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0003::codex-gpt-5.6-terra-medium", "job_index": 2, "case_index": 2, "model_index": 0, "worker_index": 2, "parallel_slot_index": 2, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0004", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 4, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 4, "actual_rollout_group_seed": 4, "initial_attempt_group_seed": 4, "final_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.2579776346683502, "last_attempt_index": 1, "last_attempt_reward": 0.2579776346683502, "best_attempt_reward": 0.2579776346683502, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.2579776346683502, "turns": 9, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.2579776346683502, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 243.0172, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "assistant_preview": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "action_extracted": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 564, "reasoning_output_tokens": 511, "total_tokens": 7330}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 564, "reasoning_output_tokens": 511, "total_tokens": 7330}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}", "assistant_preview": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10919, "output_tokens": 1142, "reasoning_output_tokens": 1072, "total_tokens": 12061}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 17685, "output_tokens": 1706, "reasoning_output_tokens": 1583, "total_tokens": 19391}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}", "assistant_preview": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16548, "output_tokens": 2409, "reasoning_output_tokens": 2331, "total_tokens": 18957}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16128, "input_tokens": 34233, "output_tokens": 4115, "reasoning_output_tokens": 3914, "total_tokens": 38348}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "assistant_preview": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "action_extracted": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15616, "input_tokens": 24360, "output_tokens": 717, "reasoning_output_tokens": 665, "total_tokens": 25077}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 31744, "input_tokens": 58593, "output_tokens": 4832, "reasoning_output_tokens": 4579, "total_tokens": 63425}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}", "assistant_preview": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 23168, "input_tokens": 31342, "output_tokens": 931, "reasoning_output_tokens": 850, "total_tokens": 32273}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 54912, "input_tokens": 89935, "output_tokens": 5763, "reasoning_output_tokens": 5429, "total_tokens": 95698}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "assistant_preview": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "action_extracted": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 31104, "input_tokens": 39454, "output_tokens": 534, "reasoning_output_tokens": 471, "total_tokens": 39988}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 86016, "input_tokens": 129389, "output_tokens": 6297, "reasoning_output_tokens": 5900, "total_tokens": 135686}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 6, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}", "assistant_preview": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 6, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53390 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 38784, "input_tokens": 48042, "output_tokens": 127, "reasoning_output_tokens": 51, "total_tokens": 48169}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 124800, "input_tokens": 177431, "output_tokens": 6424, "reasoning_output_tokens": 5951, "total_tokens": 183855}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 16, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 7, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}", "assistant_preview": "The correct recessed center panel is now visible. Finish the composite rule by beveling the current cyan-marked perimeter group with the same moderate width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 7, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 16, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53390 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56594 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The correct recessed center panel is now visible. Finish the composite rule by beveling the current cyan-marked perimeter group with the same moderate width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 47872, "input_tokens": 57137, "output_tokens": 274, "reasoning_output_tokens": 204, "total_tokens": 57411}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 172672, "input_tokens": 234568, "output_tokens": 6698, "reasoning_output_tokens": 6155, "total_tokens": 241266}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nREJECTED [MUTATION_QUOTA] - six committed mutations have been used | unchanged mesh 8449DA33 | rev R6 | snapshots 2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 18, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The correct recessed center panel is now visible. Finish the composite rule by beveling the current cyan-marked perimeter group with the same moderate width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [MUTATION_QUOTA] - six committed mutations have been used | unchanged mesh 8449DA33 | rev R6 | snapshots 2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 8, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The recessed connected panel matches the examples’ primary local topology and silhouette; submit the committed result.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.2579776346683502, "reward_by_agent": {"0": 0.2579776346683502}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 4, "attempt_group_seed": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 4, "rollout_group_seed": 4, "current_attempt_group_seed": 4, "attempt_group_seed_history": [4], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 8, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nREJECTED [MUTATION_QUOTA] - six committed mutations have been used | unchanged mesh 8449DA33 | rev R6 | snapshots 2\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 18, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The correct recessed center panel is now visible. Finish the composite rule by beveling the current cyan-marked perimeter group with the same moderate width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [MUTATION_QUOTA] - six committed mutations have been used | unchanged mesh 8449DA33 | rev R6 | snapshots 2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48058 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from above to identify the magenta-marked face and cyan-marked perimeter group.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"TOP_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to TOP_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50062 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The cyan-highlighted face handle is R0_F48, corresponding to the magenta center-marked top face. Start the inferred inset construction by connecting it to create the center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F48\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F48 | rev R1 | mesh 6099EE8C | handles R1_F10,R1_F58,R1_F85,R1_F88,R1_F90,R1_EG63\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51842 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published final face handle. Extrude it inward with a reduced cap to form the recessed central panel seen in both examples.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F90\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F90 by -0.35 scale 0.70 | rev R2 | mesh BD8DAEAA | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F81,R2_EG29\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53950 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The last extrusion affected a non-center face, so restore the connected-face snapshot before applying the recess to the correctly marked center.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R3 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54358 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The examples’ visible change is a connected center panel framed by a uniform bevel of the cyan-marked perimeter, rather than a deep cap extrusion. Apply the perimeter bevel to the current marked edge group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R3_EG86\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R3_EG86 by 0.12 | rev R4 | mesh C0BF87A1 | handles R4_F11,R4_F21,R4_F28,R4_F63,R4_F91,R4_EG49\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52514 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The marker sits on the leftmost connected center region, matching the first displayed handle rather than the prior final handle. Undo the perimeter bevel so the center region can be recessed first.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 6099EE8C\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53390 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The leftmost current handle R5_F28 corresponds to the magenta-marked connected center region. Recess it inward with the scaled cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R5_F28\",\"distance\":-0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R5_F28 by -0.35 scale 0.70 | rev R6 | mesh 8449DA33 | handles R6_F19,R6_F48,R6_F67,R6_F71,R6_F83,R6_EG50\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56594 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}, {"role": "assistant", "content": "The correct recessed center panel is now visible. Finish the composite rule by beveling the current cyan-marked perimeter group with the same moderate width.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R6_EG50\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [MUTATION_QUOTA] - six committed mutations have been used | unchanged mesh 8449DA33 | rev R6 | snapshots 2\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32014 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54690 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32678 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The recessed connected panel matches the examples’ primary local topology and silhouette; submit the committed result.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 56832, "input_tokens": 67262, "output_tokens": 52, "reasoning_output_tokens": 7, "total_tokens": 67314}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 229504, "input_tokens": 301830, "output_tokens": 6750, "reasoning_output_tokens": 6162, "total_tokens": 308580}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 8449DA33 | Q 0.258 | geometry=0.000 ruleTopology=0.000 material=0.787 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.257978 caps=0.40 final=0.257978\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 20, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The recessed connected panel matches the examples’ primary local topology and silhouette; submit the committed result.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.2579776346683502, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 8449DA33 | Q 0.258 | geometry=0.000 ruleTopology=0.000 material=0.787 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.257978 caps=0.40 final=0.257978\n\n\r\n\r\nReset context:\nSeed 4. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-50965d59118f", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0004::codex-gpt-5.6-terra-medium", "job_index": 3, "case_index": 3, "model_index": 0, "worker_index": 3, "parallel_slot_index": 3, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0005", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 5, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 5, "actual_rollout_group_seed": 5, "initial_attempt_group_seed": 5, "final_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.29977667331695557, "last_attempt_index": 1, "last_attempt_reward": 0.29977667331695557, "best_attempt_reward": 0.29977667331695557, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.29977667331695557, "turns": 8, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.29977667331695557, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 149.9952, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}", "assistant_preview": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6766, "output_tokens": 415, "reasoning_output_tokens": 358, "total_tokens": 7181}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6766, "output_tokens": 415, "reasoning_output_tokens": 358, "total_tokens": 7181}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}", "assistant_preview": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10808, "output_tokens": 961, "reasoning_output_tokens": 887, "total_tokens": 11769}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 17574, "output_tokens": 1376, "reasoning_output_tokens": 1245, "total_tokens": 18950}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}", "assistant_preview": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 16309, "output_tokens": 544, "reasoning_output_tokens": 475, "total_tokens": 16853}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 9472, "input_tokens": 33883, "output_tokens": 1920, "reasoning_output_tokens": 1720, "total_tokens": 35803}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}", "assistant_preview": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}, {"role": "assistant", "content": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57746 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15744, "input_tokens": 22297, "output_tokens": 910, "reasoning_output_tokens": 840, "total_tokens": 23207}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 25216, "input_tokens": 56180, "output_tokens": 2830, "reasoning_output_tokens": 2560, "total_tokens": 59010}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "assistant_preview": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}, {"role": "assistant", "content": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57746 chars]"}}]}, {"role": "assistant", "content": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55226 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "action_extracted": "{\"name\":\"UndoLastEdit\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21120, "input_tokens": 29552, "output_tokens": 275, "reasoning_output_tokens": 218, "total_tokens": 29827}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 46336, "input_tokens": 85732, "output_tokens": 3105, "reasoning_output_tokens": 2778, "total_tokens": 88837}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}", "assistant_preview": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}, {"role": "assistant", "content": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57746 chars]"}}]}, {"role": "assistant", "content": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55226 chars]"}}]}, {"role": "assistant", "content": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56206 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 28928, "input_tokens": 37039, "output_tokens": 428, "reasoning_output_tokens": 362, "total_tokens": 37467}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 75264, "input_tokens": 122771, "output_tokens": 3533, "reasoning_output_tokens": 3140, "total_tokens": 126304}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 6, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}", "assistant_preview": "The cap is already in base slot 0, so the examples’ material change must be the remaining non-orange visible material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 6, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}, {"role": "assistant", "content": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57746 chars]"}}]}, {"role": "assistant", "content": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55226 chars]"}}]}, {"role": "assistant", "content": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56206 chars]"}}]}, {"role": "assistant", "content": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56862 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cap is already in base slot 0, so the examples’ material change must be the remaining non-orange visible material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 36864, "input_tokens": 45556, "output_tokens": 111, "reasoning_output_tokens": 48, "total_tokens": 45667}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 112128, "input_tokens": 168327, "output_tokens": 3644, "reasoning_output_tokens": 3188, "total_tokens": 171971}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R5_F11 material slot 1 | rev R6 | mesh 288D9677 | handles R6_F14,R6_F23,R6_F69,R6_F80,R6_F88,R6_EG87\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 16, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cap is already in base slot 0, so the examples’ material change must be the remaining non-orange visible material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R5_F11 material slot 1 | rev R6 | mesh 288D9677 | handles R6_F14,R6_F23,R6_F69,R6_F80,R6_F88,R6_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 7, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The completed local transformation preserves the demonstrated violet appearance while including the connected, extruded, beveled, and material-assigned feature. Submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.29977667331695557, "reward_by_agent": {"0": 0.29977667331695557}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 5, "attempt_group_seed": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 5, "rollout_group_seed": 5, "current_attempt_group_seed": 5, "attempt_group_seed_history": [5], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 7, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R5_F11 material slot 1 | rev R6 | mesh 288D9677 | handles R6_F14,R6_F23,R6_F69,R6_F80,R6_F88,R6_EG87\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 16, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cap is already in base slot 0, so the examples’ material change must be the remaining non-orange visible material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R5_F11 material slot 1 | rev R6 | mesh 288D9677 | handles R6_F14,R6_F23,R6_F69,R6_F80,R6_F88,R6_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49030 chars]"}}]}, {"role": "assistant", "content": "The magenta glyph selects the face, and the demonstrated transformation begins by connecting that selected face to create its center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F56\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F56 | rev R1 | mesh 96EBC2C5 | handles R1_F34,R1_F35,R1_F40,R1_F43,R1_F80,R1_EG90\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50346 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the newly published first face handle. The examples show that center pushed outward into the prominent band with a modest enlargement.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F34\",\"distance\":0.32,\"capScale\":1.2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F34 by 0.32 scale 1.20 | rev R2 | mesh 32DA50D9 | handles R2_F13,R2_F25,R2_F39,R2_F57,R2_F76,R2_EG65\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57302 chars]"}}]}, {"role": "assistant", "content": "The cyan selector persists as the current marked perimeter group. The remaining demonstrated local operation is its moderate bevel, producing the surrounding diagonal transition facets.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG65\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG65 by 0.12 | rev R3 | mesh 298C3F92 | handles R3_F24,R3_F34,R3_F47,R3_F66,R3_F89,R3_EG84\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57746 chars]"}}]}, {"role": "assistant", "content": "The transformation also changes the newly created cap’s material region; the first carried-forward face handle is that cap, and the bright demonstrated band uses material slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F24\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F24 material slot 2 | rev R4 | mesh 9338EE38 | handles R4_F10,R4_F40,R4_F43,R4_F77,R4_F91,R4_EG26\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55226 chars]"}}]}, {"role": "assistant", "content": "Slot 2 is visibly the orange palette region, unlike the uniformly violet demonstrated target. Revert only that mistaken material assignment while preserving the geometry.\n{\"name\":\"UndoLastEdit\",\"arguments\":{}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - Undo restored the complete prior committed mesh/audit snapshot; fresh handles published | rev R5 | mesh 298C3F92\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56206 chars]"}}]}, {"role": "assistant", "content": "The violet target palette indicates the intended material is the base visible slot rather than the orange slot; assign slot 0 to the carried-forward cap.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":0}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nREJECTED [INAPPLICABLE] - requested material already assigned | unchanged mesh 298C3F92 | rev R5 | snapshots 3\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=56862 chars]"}}]}, {"role": "assistant", "content": "The cap is already in base slot 0, so the examples’ material change must be the remaining non-orange visible material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R5_F11\",\"slot\":1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R5_F11 material slot 1 | rev R6 | mesh 288D9677 | handles R6_F14,R6_F23,R6_F69,R6_F80,R6_F88,R6_EG87\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33590 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=57334 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The completed local transformation preserves the demonstrated violet appearance while including the connected, extruded, beveled, and material-assigned feature. Submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 44672, "input_tokens": 54650, "output_tokens": 88, "reasoning_output_tokens": 31, "total_tokens": 54738}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 156800, "input_tokens": 222977, "output_tokens": 3732, "reasoning_output_tokens": 3219, "total_tokens": 226709}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 288D9677 | Q 0.300 | geometry=0.141 ruleTopology=0.124 material=0.482 protected=0.700 scoreBudget=1.000 effect=0.310 raw=0.299777 caps=0.40 final=0.299777\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 18, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The completed local transformation preserves the demonstrated violet appearance while including the connected, extruded, beveled, and material-assigned feature. Submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.29977667331695557, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 288D9677 | Q 0.300 | geometry=0.141 ruleTopology=0.124 material=0.482 protected=0.700 scoreBudget=1.000 effect=0.310 raw=0.299777 caps=0.40 final=0.299777\n\n\r\n\r\nReset context:\nSeed 5. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-6db584da506b", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0005::codex-gpt-5.6-terra-medium", "job_index": 4, "case_index": 4, "model_index": 0, "worker_index": 4, "parallel_slot_index": 4, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0006", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 6, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 6, "actual_rollout_group_seed": 6, "initial_attempt_group_seed": 6, "final_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.15775568783283234, "last_attempt_index": 1, "last_attempt_reward": 0.15775568783283234, "best_attempt_reward": 0.15775568783283234, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.15775568783283234, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.15775568783283234, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 103.511, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}", "assistant_preview": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49958 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 403, "reasoning_output_tokens": 330, "total_tokens": 7169}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 403, "reasoning_output_tokens": 330, "total_tokens": 7169}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}", "assistant_preview": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49958 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 10809, "output_tokens": 591, "reasoning_output_tokens": 506, "total_tokens": 11400}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 17575, "output_tokens": 994, "reasoning_output_tokens": 836, "total_tokens": 18569}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "assistant_preview": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49958 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53710 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 15963, "output_tokens": 165, "reasoning_output_tokens": 85, "total_tokens": 16128}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16128, "input_tokens": 33538, "output_tokens": 1159, "reasoning_output_tokens": 921, "total_tokens": 34697}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}", "assistant_preview": "The recessed cap is the face retaining the magenta selector, R3_F19. In both completed examples this center feature has the contrasting dark material, so assign the contrasting material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49958 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53710 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53998 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The recessed cap is the face retaining the magenta selector, R3_F19. In both completed examples this center feature has the contrasting dark material, so assign the contrasting material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15744, "input_tokens": 21605, "output_tokens": 250, "reasoning_output_tokens": 176, "total_tokens": 21855}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 31872, "input_tokens": 55143, "output_tokens": 1409, "reasoning_output_tokens": 1097, "total_tokens": 56552}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F19 material slot 2 | rev R4 | mesh 314C1315 | handles R4_F19,R4_F28,R4_F34,R4_F87,R4_F91,R4_EG27\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The recessed cap is the face retaining the magenta selector, R3_F19. In both completed examples this center feature has the contrasting dark material, so assign the contrasting material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F19 material slot 2 | rev R4 | mesh 314C1315 | handles R4_F19,R4_F28,R4_F34,R4_F87,R4_F91,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The intended local topology and contrasting cap material are in place. Preserve the remaining original surfaces rather than making speculative changes and submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.15775568783283234, "reward_by_agent": {"0": 0.15775568783283234}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 6, "attempt_group_seed": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 6, "rollout_group_seed": 6, "current_attempt_group_seed": 6, "attempt_group_seed_history": [6], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F19 material slot 2 | rev R4 | mesh 314C1315 | handles R4_F19,R4_F28,R4_F34,R4_F87,R4_F91,R4_EG27\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The recessed cap is the face retaining the magenta selector, R3_F19. In both completed examples this center feature has the contrasting dark material, so assign the contrasting material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F19 material slot 2 | rev R4 | mesh 314C1315 | handles R4_F19,R4_F28,R4_F34,R4_F87,R4_F91,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49958 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The examples both turn the magenta-selected face into a smaller center region, then form a recessed, beveled feature while retaining the surrounding original surface. Start by connecting the query’s magenta face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F23 | rev R1 | mesh 073404CE | handles R1_F10,R1_F28,R1_F62,R1_F85,R1_F88,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The new center face is the face carrying the persistent magenta selector (R1_F10). The example feature is recessed and tapered, so extrude this center inward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F10\",\"distance\":-0.3,\"capScale\":0.72}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F10 by -0.30 scale 0.72 | rev R2 | mesh B403DA6C | handles R2_F46,R2_F48,R2_F76,R2_F79,R2_F89,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53710 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The cyan-marked perimeter has been republished as R2_EG74. Both examples show a clear chamfer band around that selected perimeter, so bevel the full group at a moderate amount.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 2D500067 | handles R3_F19,R3_F23,R3_F47,R3_F55,R3_F80,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53998 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}, {"role": "assistant", "content": "The recessed cap is the face retaining the magenta selector, R3_F19. In both completed examples this center feature has the contrasting dark material, so assign the contrasting material slot.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F19\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F19 material slot 2 | rev R4 | mesh 314C1315 | handles R4_F19,R4_F28,R4_F34,R4_F87,R4_F91,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52218 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33434 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The intended local topology and contrasting cap material are in place. Preserve the remaining original surfaces rather than making speculative changes and submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21248, "input_tokens": 28237, "output_tokens": 757, "reasoning_output_tokens": 703, "total_tokens": 28994}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 53120, "input_tokens": 83380, "output_tokens": 2166, "reasoning_output_tokens": 1800, "total_tokens": 85546}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 314C1315 | Q 0.158 | geometry=0.000 ruleTopology=0.000 material=0.118 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.157756 caps=0.40 final=0.157756\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The intended local topology and contrasting cap material are in place. Preserve the remaining original surfaces rather than making speculative changes and submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.15775568783283234, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 314C1315 | Q 0.158 | geometry=0.000 ruleTopology=0.000 material=0.118 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.157756 caps=0.40 final=0.157756\n\n\r\n\r\nReset context:\nSeed 6. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-db058a68aece", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0006::codex-gpt-5.6-terra-medium", "job_index": 5, "case_index": 5, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0007", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 7, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 7, "actual_rollout_group_seed": 7, "initial_attempt_group_seed": 7, "final_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 4, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.28999999165534973, "last_attempt_index": 1, "last_attempt_reward": 0.28999999165534973, "best_attempt_reward": 0.28999999165534973, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.28999999165534973, "turns": 4, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.28999999165534973, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 89.478, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}", "assistant_preview": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47690 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6766, "output_tokens": 394, "reasoning_output_tokens": 335, "total_tokens": 7160}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6766, "output_tokens": 394, "reasoning_output_tokens": 335, "total_tokens": 7160}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}", "assistant_preview": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47690 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48878 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10789, "output_tokens": 591, "reasoning_output_tokens": 516, "total_tokens": 11380}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 3584, "input_tokens": 17555, "output_tokens": 985, "reasoning_output_tokens": 851, "total_tokens": 18540}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}", "assistant_preview": "The cyan bar marks the current perimeter group R2_EG81; the examples add a moderate bevel around that group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47690 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48878 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51874 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan bar marks the current perimeter group R2_EG81; the examples add a moderate bevel around that group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 15921, "output_tokens": 215, "reasoning_output_tokens": 150, "total_tokens": 16136}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 13824, "input_tokens": 33476, "output_tokens": 1200, "reasoning_output_tokens": 1001, "total_tokens": 34676}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG81 by 0.12 | rev R3 | mesh AF5CF106 | handles R3_F15,R3_F30,R3_F33,R3_F42,R3_F62,R3_EG62\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan bar marks the current perimeter group R2_EG81; the examples add a moderate bevel around that group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG81 by 0.12 | rev R3 | mesh AF5CF106 | handles R3_F15,R3_F30,R3_F33,R3_F42,R3_F62,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The connected inset extrusion and marked-perimeter bevel complete the inferred local geometry rule; no separate material reassignment is visible in both examples.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.28999999165534973, "reward_by_agent": {"0": 0.28999999165534973}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 7, "attempt_group_seed": 7, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 7, "rollout_group_seed": 7, "current_attempt_group_seed": 7, "attempt_group_seed_history": [7], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG81 by 0.12 | rev R3 | mesh AF5CF106 | handles R3_F15,R3_F30,R3_F33,R3_F42,R3_F62,R3_EG62\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan bar marks the current perimeter group R2_EG81; the examples add a moderate bevel around that group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG81 by 0.12 | rev R3 | mesh AF5CF106 | handles R3_F15,R3_F30,R3_F33,R3_F42,R3_F62,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 7. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=47690 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The magenta disc selects query face R0_F52. The examples indicate first connecting the marked face to create a center face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F52\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F52 | rev R1 | mesh 07B6DBF8 | handles R1_F48,R1_F54,R1_F59,R1_F60,R1_F75,R1_EG73\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48878 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The newly marked center face is R1_F59. The paired examples show it being extruded as an inset, with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F59\",\"distance\":-0.3,\"capScale\":0.65}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F59 by -0.30 scale 0.65 | rev R2 | mesh 86FEB995 | handles R2_F12,R2_F21,R2_F31,R2_F67,R2_F71,R2_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51874 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}, {"role": "assistant", "content": "The cyan bar marks the current perimeter group R2_EG81; the examples add a moderate bevel around that group.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG81\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG81 by 0.12 | rev R3 | mesh AF5CF106 | handles R3_F15,R3_F30,R3_F33,R3_F42,R3_F62,R3_EG62\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53178 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31734 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31838 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected inset extrusion and marked-perimeter bevel complete the inferred local geometry rule; no separate material reassignment is visible in both examples.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 21575, "output_tokens": 546, "reasoning_output_tokens": 493, "total_tokens": 22121}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 19712, "input_tokens": 55051, "output_tokens": 1746, "reasoning_output_tokens": 1494, "total_tokens": 56797}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh AF5CF106 | Q 0.290 | geometry=0.000 ruleTopology=0.000 material=1.000 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.290000 caps=0.40 final=0.290000\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected inset extrusion and marked-perimeter bevel complete the inferred local geometry rule; no separate material reassignment is visible in both examples.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.28999999165534973, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh AF5CF106 | Q 0.290 | geometry=0.000 ruleTopology=0.000 material=1.000 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.290000 caps=0.40 final=0.290000\n\n\r\n\r\nReset context:\nSeed 7. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-2b362532a571", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0007::codex-gpt-5.6-terra-medium", "job_index": 6, "case_index": 6, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0008", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 8, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 8, "actual_rollout_group_seed": 8, "initial_attempt_group_seed": 8, "final_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.1208629310131073, "last_attempt_index": 1, "last_attempt_reward": 0.1208629310131073, "best_attempt_reward": 0.1208629310131073, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.1208629310131073, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.1208629310131073, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 115.4998, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}", "assistant_preview": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48382 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6764, "output_tokens": 647, "reasoning_output_tokens": 592, "total_tokens": 7411}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6764, "output_tokens": 647, "reasoning_output_tokens": 592, "total_tokens": 7411}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}", "assistant_preview": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48382 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49146 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 11033, "output_tokens": 1305, "reasoning_output_tokens": 1226, "total_tokens": 12338}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 17797, "output_tokens": 1952, "reasoning_output_tokens": 1818, "total_tokens": 19749}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}", "assistant_preview": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48382 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49146 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53598 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16877, "output_tokens": 473, "reasoning_output_tokens": 406, "total_tokens": 17350}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 16128, "input_tokens": 34674, "output_tokens": 2425, "reasoning_output_tokens": 2224, "total_tokens": 37099}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}", "assistant_preview": "The glyph-tracked extruded cap is now R3_F21. In both examples this new center cap carries the designated third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48382 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49146 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53598 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The glyph-tracked extruded cap is now R3_F21. In both examples this new center cap carries the designated third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15616, "input_tokens": 22790, "output_tokens": 251, "reasoning_output_tokens": 185, "total_tokens": 23041}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 31744, "input_tokens": 57464, "output_tokens": 2676, "reasoning_output_tokens": 2409, "total_tokens": 60140}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F21 material slot 2 | rev R4 | mesh F8A5025A | handles R4_F20,R4_F60,R4_F60A,R4_F65,R4_F91,R4_EG77\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The glyph-tracked extruded cap is now R3_F21. In both examples this new center cap carries the designated third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F21 material slot 2 | rev R4 | mesh F8A5025A | handles R4_F20,R4_F60,R4_F60A,R4_F65,R4_F91,R4_EG77\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The face-centered extrusion, its perimeter bevel, and the cap material assignment complete the inferred local rule; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.1208629310131073, "reward_by_agent": {"0": 0.1208629310131073}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 8, "attempt_group_seed": 8, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 8, "rollout_group_seed": 8, "current_attempt_group_seed": 8, "attempt_group_seed_history": [8], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F21 material slot 2 | rev R4 | mesh F8A5025A | handles R4_F20,R4_F60,R4_F60A,R4_F65,R4_F91,R4_EG77\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The glyph-tracked extruded cap is now R3_F21. In both examples this new center cap carries the designated third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F21 material slot 2 | rev R4 | mesh F8A5025A | handles R4_F20,R4_F60,R4_F60A,R4_F65,R4_F91,R4_EG77\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48382 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The magenta face selector identifies the face to connect first, creating the central face for the subsequent local transformation.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F47\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F47 | rev R1 | mesh 073404CE | handles R1_F35,R1_F49,R1_F64,R1_F77,R1_F82,R1_EG19\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49146 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The connected center face is the glyph-tracked R1_F49. The examples show it being extruded outward with a reduced cap, producing the raised central feature.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F49\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F49 by 0.32 scale 0.75 | rev R2 | mesh 8852F555 | handles R2_F14,R2_F19,R2_F30,R2_F52,R2_F75,R2_EG57\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53598 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The cyan selector now tracks the corresponding current perimeter group R2_EG57; the visible example rim width is a moderate bevel.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG57\",\"amount\":0.1}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG57 by 0.10 | rev R3 | mesh E725FCB0 | handles R3_F18,R3_F21,R3_F39,R3_F46,R3_F57,R3_EG12\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53546 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}, {"role": "assistant", "content": "The glyph-tracked extruded cap is now R3_F21. In both examples this new center cap carries the designated third visible material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F21\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F21 material slot 2 | rev R4 | mesh F8A5025A | handles R4_F20,R4_F60,R4_F60A,R4_F65,R4_F91,R4_EG77\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53826 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32542 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32842 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The face-centered extrusion, its perimeter bevel, and the cap material assignment complete the inferred local rule; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 22272, "input_tokens": 29380, "output_tokens": 121, "reasoning_output_tokens": 70, "total_tokens": 29501}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 54016, "input_tokens": 86844, "output_tokens": 2797, "reasoning_output_tokens": 2479, "total_tokens": 89641}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh F8A5025A | Q 0.121 | geometry=0.000 ruleTopology=0.000 material=0.139 protected=0.500 scoreBudget=1.000 effect=0.000 raw=0.120863 caps=0.40 final=0.120863\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The face-centered extrusion, its perimeter bevel, and the cap material assignment complete the inferred local rule; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.1208629310131073, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh F8A5025A | Q 0.121 | geometry=0.000 ruleTopology=0.000 material=0.139 protected=0.500 scoreBudget=1.000 effect=0.000 raw=0.120863 caps=0.40 final=0.120863\n\n\r\n\r\nReset context:\nSeed 8. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-a1665cfbeb54", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0008::codex-gpt-5.6-terra-medium", "job_index": 7, "case_index": 7, "model_index": 0, "worker_index": 7, "parallel_slot_index": 7, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0009", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 9, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 9, "actual_rollout_group_seed": 9, "initial_attempt_group_seed": 9, "final_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 5, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.18527698516845703, "last_attempt_index": 1, "last_attempt_reward": 0.18527698516845703, "best_attempt_reward": 0.18527698516845703, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.18527698516845703, "turns": 5, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.18527698516845703, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 76.3815, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}", "assistant_preview": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49230 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 487, "reasoning_output_tokens": 417, "total_tokens": 7253}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 0, "input_tokens": 6766, "output_tokens": 487, "reasoning_output_tokens": 417, "total_tokens": 7253}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}", "assistant_preview": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49230 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49702 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 10893, "output_tokens": 726, "reasoning_output_tokens": 635, "total_tokens": 11619}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 17659, "output_tokens": 1213, "reasoning_output_tokens": 1052, "total_tokens": 18872}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "assistant_preview": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49230 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49702 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53082 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16188, "output_tokens": 206, "reasoning_output_tokens": 132, "total_tokens": 16394}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 12032, "input_tokens": 33847, "output_tokens": 1419, "reasoning_output_tokens": 1184, "total_tokens": 35266}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}", "assistant_preview": "The remaining magenta marker is on the new cap face R3_F58. Both examples recolor that derived face to the third visible material region, so assign slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49230 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49702 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53082 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The remaining magenta marker is on the new cap face R3_F58. Both examples recolor that derived face to the third visible material region, so assign slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15744, "input_tokens": 21873, "output_tokens": 371, "reasoning_output_tokens": 298, "total_tokens": 22244}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 27776, "input_tokens": 55720, "output_tokens": 1790, "reasoning_output_tokens": 1482, "total_tokens": 57510}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F58 material slot 2 | rev R4 | mesh 8FD28DF2 | handles R4_F13,R4_F37,R4_F57,R4_F69,R4_F78,R4_EG27\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The remaining magenta marker is on the new cap face R3_F58. Both examples recolor that derived face to the third visible material region, so assign slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F58 material slot 2 | rev R4 | mesh 8FD28DF2 | handles R4_F13,R4_F37,R4_F57,R4_F69,R4_F78,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The inferred face-connect, tapered extrusion, perimeter bevel, and cap material assignment are complete; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.18527698516845703, "reward_by_agent": {"0": 0.18527698516845703}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 9, "attempt_group_seed": 9, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 9, "rollout_group_seed": 9, "current_attempt_group_seed": 9, "attempt_group_seed_history": [9], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F58 material slot 2 | rev R4 | mesh 8FD28DF2 | handles R4_F13,R4_F37,R4_F57,R4_F69,R4_F78,R4_EG27\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The remaining magenta marker is on the new cap face R3_F58. Both examples recolor that derived face to the third visible material region, so assign slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F58 material slot 2 | rev R4 | mesh 8FD28DF2 | handles R4_F13,R4_F37,R4_F57,R4_F69,R4_F78,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49230 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The examples indicate a face-centered connect operation on the magenta-selected face, followed by using the cyan-selected perimeter edge group. Start by connecting the query’s magenta-marked face.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R0_F40\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R0_F40 | rev R1 | mesh 9DBF4CCB | handles R1_F14,R1_F25,R1_F30,R1_F62,R1_F79,R1_EG81\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=49702 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "Connecting exposed the new magenta-marked center face R1_F25. The example results show that center face being made into a tapered local extrusion before the perimeter treatment, so extrude it outward with a reduced cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R1_F25\",\"distance\":0.32,\"capScale\":0.75}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R1_F25 by 0.32 scale 0.75 | rev R2 | mesh 033FEB1F | handles R2_F22,R2_F28,R2_F79,R2_F83,R2_F91,R2_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=53082 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The cyan marker now identifies the current extrusion perimeter group R2_EG74. Apply the matching moderate perimeter bevel to create the faceted band seen in both examples.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R2_EG74\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R2_EG74 by 0.12 | rev R3 | mesh 0BCAE86C | handles R3_F53,R3_F58,R3_F72,R3_F86,R3_F86A,R3_EG14\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=54642 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}, {"role": "assistant", "content": "The remaining magenta marker is on the new cap face R3_F58. Both examples recolor that derived face to the third visible material region, so assign slot 2.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F58\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F58 material slot 2 | rev R4 | mesh 8FD28DF2 | handles R4_F13,R4_F37,R4_F57,R4_F69,R4_F78,R4_EG27\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=51554 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31402 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=33790 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The inferred face-connect, tapered extrusion, perimeter bevel, and cap material assignment are complete; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21248, "input_tokens": 28628, "output_tokens": 72, "reasoning_output_tokens": 23, "total_tokens": 28700}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 49024, "input_tokens": 84348, "output_tokens": 1862, "reasoning_output_tokens": 1505, "total_tokens": 86210}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 8FD28DF2 | Q 0.185 | geometry=0.000 ruleTopology=0.000 material=0.302 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.185277 caps=0.40 final=0.185277\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The inferred face-connect, tapered extrusion, perimeter bevel, and cap material assignment are complete; submit the committed mesh.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.18527698516845703, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 8FD28DF2 | Q 0.185 | geometry=0.000 ruleTopology=0.000 material=0.302 protected=0.700 scoreBudget=1.000 effect=0.000 raw=0.185277 caps=0.40 final=0.185277\n\n\r\n\r\nReset context:\nSeed 9. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-7b93bf2398e0", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0009::codex-gpt-5.6-terra-medium", "job_index": 8, "case_index": 8, "model_index": 0, "worker_index": 6, "parallel_slot_index": 6, "parallel_eval": true} -{"status": "ok", "case_id": "Task168_MeshEditRuleInduction3D-seed-0010", "model_name": "codex-gpt-5.6-terra-medium", "model": "gpt-5.6-terra", "provider": "codex", "base_url": null, "api_key_env": null, "thinking": null, "reasoning_effort": "medium", "codex_context_mode": "lean", "codex_disabled_mcp_servers": ["node_repl", "unityMCP"], "max_completion_tokens": null, "requested_task_name": "Task168_MeshEditRuleInduction3D", "requested_group_seed": 10, "requested_env_id": 0, "actual_task_name": "Task168_MeshEditRuleInduction3D", "actual_group_seed": 10, "actual_rollout_group_seed": 10, "initial_attempt_group_seed": 10, "final_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "actual_env_id": 0, "unity_id": 0, "start_attempt_index": 1, "loaded_history_attempt_count": 0, "trajectory_ref_resolved": null, "turns": 6, "max_attempts": 1, "max_steps_per_attempt": 12, "rollout_step_budget": 12, "rollout_budget_exhausted": false, "attempt_count": 1, "score_reward": 0.17450284957885742, "last_attempt_index": 1, "last_attempt_reward": 0.17450284957885742, "best_attempt_reward": 0.17450284957885742, "final_attempt_success": false, "ever_attempt_success": false, "first_success_attempt_index": null, "success_attempt_count": 0, "attempt_rewards": [{"index": 1, "reward_total": 0.17450284957885742, "turns": 6, "done": true, "success": false, "auto_reset": false}], "done_all": true, "truncated_all": true, "rollout_success": false, "rollout_truncated": true, "rollout_terminal": true, "total_reward": 0.17450284957885742, "reset_info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "auto_reset": false, "done": false, "success": false}, "rollout": {"started": true, "success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12, "history_snapshot_seeded": true}, "sub_env": {"reset": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "initial_step_msg_preview": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from...[truncated]", "initial_obs_summary": {"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}, "duration_s": 105.7038, "steps": [{"turn_index": 0, "action_preview": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "assistant_preview": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 0, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "\nYou are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Not...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 2, "message_roles": ["system", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "action_extracted": "{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 678, "reasoning_output_tokens": 625, "total_tokens": 7442}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 1792, "input_tokens": 6764, "output_tokens": 678, "reasoning_output_tokens": 625, "total_tokens": 7442}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 1, "action_preview": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}", "assistant_preview": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 1, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 4, "message_roles": ["system", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}", "action_extracted": "{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 5888, "input_tokens": 11031, "output_tokens": 890, "reasoning_output_tokens": 827, "total_tokens": 11921}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 7680, "input_tokens": 17795, "output_tokens": 1568, "reasoning_output_tokens": 1452, "total_tokens": 19363}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 2, "action_preview": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}", "assistant_preview": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 2, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 6, "message_roles": ["system", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}", "action_extracted": "{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 10240, "input_tokens": 16407, "output_tokens": 95, "reasoning_output_tokens": 34, "total_tokens": 16502}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 17920, "input_tokens": 34202, "output_tokens": 1663, "reasoning_output_tokens": 1486, "total_tokens": 35865}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 3, "action_preview": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}", "assistant_preview": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 3, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 8, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}", "action_extracted": "{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 15744, "input_tokens": 21877, "output_tokens": 398, "reasoning_output_tokens": 316, "total_tokens": 22275}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 33664, "input_tokens": 56079, "output_tokens": 2061, "reasoning_output_tokens": 1802, "total_tokens": 58140}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 4, "action_preview": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}", "assistant_preview": "The extrusion cap is the magenta-following face R3_F63. The examples give this newly created feature its contrasting third material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}", "reward_total": 0.0, "reward_by_agent": {"0": 0.0}, "done": {"0": false, "__all__": false}, "info": {"truncated": {"0": false, "__all__": false}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": false, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": false, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 4, "truncated": {}}, "attempt_done": {"__all__": false, "0": false}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 10, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55466 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The extrusion cap is the magenta-following face R3_F63. The examples give this newly created feature its contrasting third material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}", "action_extracted": "{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 21248, "input_tokens": 28571, "output_tokens": 650, "reasoning_output_tokens": 585, "total_tokens": 29221}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 54912, "input_tokens": 84650, "output_tokens": 2711, "reasoning_output_tokens": 2387, "total_tokens": 87361}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F63 material slot 2 | rev R4 | mesh 9B3E05A0 | handles R4_F11,R4_F30,R4_F45,R4_F64,R4_F81,R4_EG18\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The extrusion cap is the magenta-following face R3_F63. The examples give this newly created feature its contrasting third material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F63 material slot 2 | rev R4 | mesh 9B3E05A0 | handles R4_F11,R4_F30,R4_F45,R4_F64,R4_F81,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}, {"turn_index": 5, "action_preview": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "assistant_preview": "The selected perimeter bevel, connected-and-reduced outward extrusion, and contrasting cap material have been applied; submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "reward_total": 0.17450284957885742, "reward_by_agent": {"0": 0.17450284957885742}, "done": {"0": true, "__all__": true}, "info": {"truncated": {"0": true, "__all__": true}, "attempt": {"index": 1, "group_seed": 10, "attempt_group_seed": 10, "max_attempts": 1, "max_steps_per_attempt": 12, "done": true, "success": false, "auto_reset": false}, "rollout": {"success": false, "truncated": true, "task_name": "Task168_MeshEditRuleInduction3D", "group_seed": 10, "rollout_group_seed": 10, "current_attempt_group_seed": 10, "attempt_group_seed_history": [10], "reroll_group_seed_on_same_task": false, "env_id": 0, "current_attempt_index": 1, "max_attempts": 1, "max_steps_per_attempt": 12, "max_rollout_steps": 12}, "sub_env": {"step": {"turn_index": 5, "truncated": {}}, "attempt_done": {"__all__": true, "0": true}, "attempt_truncated": {}}, "history": {"bucket_key": null, "history_bucket_key": null}}, "request_messages": [{"unity_id": 0, "step_msg": "['\\nACCEPTED - set R3_F63 material slot 2 | rev R4 | mesh 9B3E05A0 | handles R4_F11,R4_F30,R4_F45,R4_F64,R4_F81,R4_EG18\\n']", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 12, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The extrusion cap is the magenta-following face R3_F63. The examples give this newly created feature its contrasting third material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}"}, {"role": "user", "text_preview": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F63 material slot 2 | rev R4 | mesh 9B3E05A0 | handles R4_F11,R4_F30,R4_F45,R4_F64,R4_F81,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}], "raw_request_by_agent": {"0": {"request_messages": [{"role": "system", "content": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may also appear as System.Single in CLR. Do not add the 'f' suffix to numbers — the environment handles it automatically.)\nThen output:\nA force of 2.5 may be a moderate value for the first attempt\n{\"name\":\"PushForward\",\"arguments\":{\"forceScale\":2.5}}\n\nExample 2: If the tool documentation lists: ExecutePlan(string plan) : void // function comment\nThen output:\nNeed to avoid obstacle; combine turns then advance.\n{\"name\":\"ExecutePlan\",\"arguments\":{\"plan\":\"L2,U3,R1\"}}\n\nExample 3: If the tool documentation lists: MoveRight(int steps) : void // function comment\nThen output:\nTarget is to the right; take a small step.\n{\"name\":\"MoveRight\",\"arguments\":{\"steps\":3}}\n\nExample 4: If the tool documentation lists: SetConfig(bool wrap, float speed) : void // function comment\nThen output:\nEnable wrap and slow down for stability.\n{\"name\":\"SetConfig\",\"arguments\":{\"wrap\":true,\"speed\":0.5}}"}, {"role": "user", "content": [{"type": "text", "text": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, camera role ordering, and handle permutation; the reset observation contains all induction evidence, with no cross-round exploration or shared history. Valid synchronous edit/view/Undo calls return a resolved frame early.\r\n\r\n\r\nAvailable tools:\r\n- BevelEdgeGroup\n kind: method\n docstring: Bevel the complete perimeter represented by one current marked edge-group ID.\n signature: BevelEdgeGroup(string groupId, float amount) : bool\n return_type: bool\n access: call\n arguments:\r\n - groupId (string) [required]: Current R_EG* group ID.\n - amount (float) [required]: Bevel amount in [0.04,0.20].\n- ConnectFace\n kind: method\n docstring: Connect one current marked face and publish the new center-face revision.\n signature: ConnectFace(string faceId) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n- ExtrudeFace\n kind: method\n docstring: Extrude one current face along its local normal. No parameter is silently clamped.\n signature: ExtrudeFace(string faceId, float distance, float capScale) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distance (float) [required]: Signed distance in [-0.60,0.60], abs>=0.05.\n - capScale (float) [required]: Cap scale in [0.55,1.35].\n- SetFaceMaterial\n kind: method\n docstring: Assign a visible material slot to one current face.\n signature: SetFaceMaterial(string faceId, int slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - slot (int) [required]: Material slot 0, 1, or 2.\n- SetQueryView\n kind: method\n docstring: Change only the query camera. This reveals occluded query surfaces but no hidden rule or score.\n signature: SetQueryView(string slot) : bool\n return_type: bool\n access: call\n arguments:\r\n - slot (string) [required]: FRONT_ISO, RIGHT_ISO, or TOP_ISO.\n- SubmitTransformation\n kind: method\n docstring: Freeze the current committed query, continuously score it once, and end the round. Low quality still terminates.\n signature: SubmitTransformation() : bool\n return_type: bool\n access: call\n- TranslateFace\n kind: method\n docstring: Translate one current face along its local normal while preserving the original interface side.\n signature: TranslateFace(string faceId, float distanceAlongNormal) : bool\n return_type: bool\n access: call\n arguments:\r\n - faceId (string) [required]: Current R_F* face ID.\n - distanceAlongNormal (float) [required]: Signed local-normal distance in [-0.50,0.50], abs>=0.05.\n- UndoLastEdit\n kind: method\n docstring: Restore the complete previous committed mesh snapshot and publish fresh IDs.\n signature: UndoLastEdit() : bool\n return_type: bool\n access: call\r\n\n\nPlay budget for this task:\n- You can play up to 1 game round(s).\n- In each game round, you can take up to 12 operation step(s).\n- Across the whole task, you can take at most 12 operation step(s). Earlier successful game rounds are recorded, but the rollout continues until the final game round. Only the score from the final game round is used as the final evaluation score.\n\nCurrent observation (step_msg):\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Initial view · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48698 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Initial view · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "Inspect the query from another angle to identify the magenta-marked face and cyan-marked perimeter.\n{\"name\":\"SetQueryView\",\"arguments\":{\"slot\":\"RIGHT_ISO\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nVIEW - query changed to RIGHT_ISO; examples and hidden truth are unchanged\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=48898 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The cyan selector marks the complete perimeter group, so apply its bevel first; the example chamfers appear moderate.\n{\"name\":\"BevelEdgeGroup\",\"arguments\":{\"groupId\":\"R0_EG40\",\"amount\":0.12}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - beveled R0_EG40 by 0.12 | rev R1 | mesh C613C5EE | handles R1_F19,R1_F23,R1_F26,R1_F29,R1_F54,R1_EG74\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50290 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The magenta-marked central face is R1_F23. Connect it to create the inset center face used for the subsequent local feature.\n{\"name\":\"ConnectFace\",\"arguments\":{\"faceId\":\"R1_F23\"}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - connected R1_F23 | rev R2 | mesh CA5B4C43 | handles R2_F17,R2_F44,R2_F45,R2_F47,R2_F54,R2_EG51\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=50646 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The new connected center is R2_F44. The examples show a reduced cap projecting outward from this center, so extrude it with a moderate positive distance and a smaller cap.\n{\"name\":\"ExtrudeFace\",\"arguments\":{\"faceId\":\"R2_F44\",\"distance\":0.35,\"capScale\":0.7}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - extruded R2_F44 by 0.35 scale 0.70 | rev R3 | mesh 45B794B6 | handles R3_F45,R3_F63,R3_F64,R3_F65,R3_F75,R3_EG21\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=55466 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}, {"role": "assistant", "content": "The extrusion cap is the magenta-following face R3_F63. The examples give this newly created feature its contrasting third material region.\n{\"name\":\"SetFaceMaterial\",\"arguments\":{\"faceId\":\"R3_F63\",\"slot\":2}}"}, {"role": "user", "content": [{"type": "text", "text": "Step result: reward=0.0, done=False\n\nEnvironment response (step_msg):\nStep context:\nACCEPTED - set R3_F63 material slot 2 | rev R4 | mesh 9B3E05A0 | handles R4_F11,R4_F30,R4_F45,R4_F64,R4_F81,R4_EG18\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=52774 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 2 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=32150 chars]"}}, {"type": "text", "text": "Image: [Current attempt · After action · Camera 3 · Frame 1/1]"}, {"type": "image_url", "image_url": {"url": "[omitted image data URL payload; length=31006 chars]"}}]}]}}, "raw_response_by_agent": {"0": {"assistant_raw": "The selected perimeter bevel, connected-and-reduced outward extrusion, and contrasting cap material have been applied; submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "action_extracted": "{\"name\":\"SubmitTransformation\",\"arguments\":{}}", "usage": {"last": {"cache_write_input_tokens": 0, "cached_input_tokens": 27904, "input_tokens": 36416, "output_tokens": 171, "reasoning_output_tokens": 120, "total_tokens": 36587}, "model_context_window": 258400, "total": {"cache_write_input_tokens": 0, "cached_input_tokens": 82816, "input_tokens": 121066, "output_tokens": 2882, "reasoning_output_tokens": 2507, "total_tokens": 123948}}}}, "step_messages": [{"unity_id": 0, "step_msg": "['\\nSUBMITTED - immutable mesh 9B3E05A0 | Q 0.175 | geometry=0.000 ruleTopology=0.000 material=0.497 protected=0.500 scoreBudget=1.000 effect=0.000 raw=0.174503 caps=0.40 final=0.174503\\n', '\\r\\n[task prompt]\\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\\n\\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\\n\\nConnectFace creates a new current center face; ExtrudeFace acts along that face\\'s local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\\n\\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, ...[truncated]", "history_attempt_count": 0, "history_attempt_step_counts": [], "history_episode_count": 0, "history_step_counts": [], "message_count": 14, "message_roles": ["system", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user", "assistant", "user"], "messages": [{"role": "system", "text_preview": "You are a task execution assistant skilled at summarizing both virtual and real-world dynamics, and interacting with the environment.\n\n- Your goal: based on the tool documentation provided by the current task, select an available tool and output a tool call to interact with the world and complete the task.\n\nInput Information (what the environment provides to you)\n- At the start, the environment will give you a brief task description, typically accompanied by an initial visual observation.\n- If this is not the first step, you may also receive information from the previous n steps (for i in 1..n),\n including: prev_step_i_obs, prev_step_i_action, prev_step_i_reward, prev_step_i_done, which respectively represent\n the observation received, the action taken, the reward earned, and whether the attempt ended at that step.\n- For some tasks, you may also receive step execution history from past attempts (an \"attempt\" is one complete interaction round with the environment, from start to done), to help you understand the environment's rules.\n- If the previous step's action had invalid parameters (e.g. type errors, illegal arguments, or other issues), the environment will provide error information in the current step as part of the state.\n\nOutput Format:\n- First, output your reasoning inside a block:\n THINK_CONTENT\n- Then output your chosen tool call:\n {\"name\":\"TOOL_NAME\",\"arguments\":{...}}\n- Do not output anything other than ... and ....\n- Do not output any code. Tool names must come from the task's tool documentation. Arguments must strictly follow the TaskApi parameter format (e.g. string format, enum value ranges, action string rules, etc.).\n- Use JSON numbers directly for numeric values, and JSON true/false for booleans. Do not add C# `f` suffix to floats.\n\nExamples\n\nExample 1:\nIf the tool documentation lists: PushForward(float forceScale) : void // function comment\n(Note: C# float may ...[truncated]"}, {"role": "user", "text_preview": "[task prompt]\nInfer one local mesh-edit rule from the complete EXAMPLE A and EXAMPLE B before-to-after pairs, then transfer that rule to the editable QUERY. Examples and query deliberately differ in topology, orientation, local face valence, palette, glyph assignment, and opaque handle permutation. Use the magenta face-center glyph and cyan edge-group glyph as semantic selectors; colors and raw handle numbers are not the rule. Each feed is an independent resolved decision image. SetQueryView changes only the query camera and reveals no hidden answer.\n\nThis is a func task. Use the separately documented functions. Current public IDs begin with the shown revision R. Every accepted geometry/material mutation and every accepted Undo publishes a monotonically newer revision and retires all older face and edge-group IDs. A rejected call and a view call do not change the revision, committed mesh, collider, or Undo snapshots. Parameters must be finite and inside their documented ranges; values are never silently clamped.\n\nConnectFace creates a new current center face; ExtrudeFace acts along that face's local normal with the requested signed distance and cap scale; TranslateFace transfers a face along its local normal; BevelEdgeGroup acts on the complete marked-face perimeter group; SetFaceMaterial assigns one of the three visible material slots. The examples may imply a composition and operation order. Undo restores the complete prior committed mesh and publishes fresh IDs.\n\nOnly SubmitTransformation scores and ends the game round. It freezes an immutable committed snapshot and returns continuous quality Q in [0,1]. Target-region geometry, hidden silhouettes, feature anchors, rule/topology invariants, material regions, score budget, and preservation of every nonselected original surface all matter. Any explicit low-quality or empty submit still ends normally. Seed deterministically changes rule family and parameters, topologies, rotations, palette, glyph, came...[truncated]"}, {"role": "assistant", "text_preview": "The selected perimeter bevel, connected-and-reduced outward extrusion, and contrasting cap material have been applied; submit the committed transformation.\n{\"name\":\"SubmitTransformation\",\"arguments\":{}}"}, {"role": "user", "text_preview": "Step result: reward=0.17450284957885742, done=True\n\nEnvironment response (step_msg):\nStep context:\nSUBMITTED - immutable mesh 9B3E05A0 | Q 0.175 | geometry=0.000 ruleTopology=0.000 material=0.497 protected=0.500 scoreBudget=1.000 effect=0.000 raw=0.174503 caps=0.40 final=0.174503\n\n\r\n\r\nReset context:\nSeed 10. Two complete examples and the editable query are visible. Query view changes reveal only occluded query surfaces; rule, palette, glyph, topology, rotation, and handle permutations are deterministic and independent.\n\nImage: [Current attempt · After action · Camera 1 · Frame 1/1]\nImage: [Current attempt · After action · Camera 2 · Frame 1/1]\nImage: [Current attempt · After action · Camera 3 · Frame 1/1]\n[image_parts=3]"}]}]}], "trajectory": {"saved": true, "trajectory_id": "traj-29f88020533a", "output_path": "Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl", "prefix_attempt_count": 1, "target_attempt_index": 2}, "job_id": "Task168_MeshEditRuleInduction3D-seed-0010::codex-gpt-5.6-terra-medium", "job_index": 9, "case_index": 9, "model_index": 0, "worker_index": 5, "parallel_slot_index": 5, "parallel_eval": true} diff --git a/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl b/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl deleted file mode 100644 index 9b651490ba664b1e56d6def964bbb5aad23a566b..0000000000000000000000000000000000000000 --- a/artifacts/records/0100-0199/0168-Task168_MeshEditRuleInduction3D/codex_gpt-5.6-terra_medium/Task168_MeshEditRuleInduction3D_seeds1_10_trajectories.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bf2052428860eda441e33f64e34c9eb5d236b892e8c214cefd95824833669973 -size 13794868 diff --git a/manifest.json b/manifest.json index 5236a7541e112cb11db6959a37d90dd6b6929af3..80494984a897fff99bfba922c9becb22f0279105 100644 --- a/manifest.json +++ b/manifest.json @@ -1,7 +1,7 @@ { "counts": { "env_artifacts": 10, - "record_artifacts": 422, + "record_artifacts": 412, "task_artifacts": 568 }, "created_at": "2026-09-14T12:15:11+00:00", diff --git a/registry/records.jsonl b/registry/records.jsonl index d98cf30f21048a8ea5aba08e3457999a3242ea21..833589fde7dfffd40cee2815b2a83a6b7a841b9c 100644 --- a/registry/records.jsonl +++ b/registry/records.jsonl @@ -284,8 +284,6 @@ {"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": true, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0198-AmbiguityAwareTableDialogue/codex_gpt-5.6-terra_medium/AmbiguityAwareTableDialogue_seeds1_10_trajectories.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "trajectories", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "266bb5f41b202f9deab17c3897305f0393f4337cdcf90e57817f429703b14f0c", "size_bytes": 450562, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/AmbiguityAwareTableDialogue_seeds1_10_trajectories.jsonl", "task_id": 198, "task_name": "AmbiguityAwareTableDialogue", "wrapper_commit": "19f771d"} {"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": false, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0141-ArticulatedVisibilityPlanner3D/codex_gpt-5.6-terra_medium/ArticulatedVisibilityPlanner3D_seeds1_10_results.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "results", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "b4e77699fffa54eeb77f6420df97fc73d53be47e1ab19358290ef47ae688dd36", "size_bytes": 1276827, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/ArticulatedVisibilityPlanner3D_seeds1_10_results.jsonl", "task_id": 141, "task_name": "ArticulatedVisibilityPlanner3D", "wrapper_commit": "19f771d"} {"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": true, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0141-ArticulatedVisibilityPlanner3D/codex_gpt-5.6-terra_medium/ArticulatedVisibilityPlanner3D_seeds1_10_trajectories.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "trajectories", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "b63ced64e82d9249fe2f818309177ae32099fa0063b219357d7dd42adcaced1f", "size_bytes": 8828619, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/ArticulatedVisibilityPlanner3D_seeds1_10_trajectories.jsonl", "task_id": 141, "task_name": "ArticulatedVisibilityPlanner3D", "wrapper_commit": "19f771d"} -{"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": false, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_results.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "results", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "6f4f1238d3a42b5b3a3a86d3272522ed777df881a0bef64672561da8b22604df", "size_bytes": 1197132, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/AsymmetricObserverCommand3D_seeds1_10_results.jsonl", "task_id": 155, "task_name": "AsymmetricObserverCommand3D", "wrapper_commit": "19f771d"} -{"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": true, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0155-AsymmetricObserverCommand3D/codex_gpt-5.6-terra_medium/AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "trajectories", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "202e2b0aec7b7dc471a87043060fef6f30fc57b950d4049789eeac8590008bef", "size_bytes": 14026578, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/AsymmetricObserverCommand3D_seeds1_10_trajectories.jsonl", "task_id": 155, "task_name": "AsymmetricObserverCommand3D", "wrapper_commit": "19f771d"} {"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": false, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0142-AsynchronousIncidentTimeline3D/codex_gpt-5.6-terra_medium/AsynchronousIncidentTimeline3D_seeds1_10_results.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "results", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "7eeb6c2076798a813486629043867ca3eb4d61d9d957954cb84ea35ef086d210", "size_bytes": 767031, "source_path": "AgentArk/tmp/task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908/AsynchronousIncidentTimeline3D_seeds1_10_results.jsonl", "task_id": 142, "task_name": "AsynchronousIncidentTimeline3D", "wrapper_commit": "19f771d"} {"codex_context_mode": "lean", "format": "jsonl", "image_inclusive": true, "kind": "record", "model": "gpt-5.6-terra", "model_folder": "codex_gpt-5.6-terra_medium", "outer_commit": "c296c91", "path": "artifacts/records/0100-0199/0142-AsynchronousIncidentTimeline3D/codex_gpt-5.6-terra_medium/AsynchronousIncidentTimeline3D_seeds1_10_trajectories.jsonl", "provider": "codex", "reasoning_effort": "medium", "record_kind": "trajectories", "record_set": "task138-200-codex-gpt-5.6-terra-medium-seeds1-10-20260908", "records": 10, "repo_id": "P90-RushB/AgentArk", "seed_range": [1, 10], "sha256": "8e09de85f38c139f4ad060d5c1ff63d498d90857fc25ac2e23bcf766cd9cbbd8", "size_bytes": 7569462, "source_path": 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